Publications 2023

@article{altdorff_toward_2023,
	title = {Toward {Large}‐{Scale} {Soil} {Moisture} {Monitoring} {Using} {Rail}‐{Based} {Cosmic} {Ray} {Neutron} {Sensing}},
	volume = {59},
	issn = {0043-1397, 1944-7973},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR033514},
	doi = {10.1029/2022WR033514},
	abstract = {Abstract 
            Cosmic ray neutron sensing (CRNS) has become a promising method for soil water content (SWC) monitoring. Stationary CRNS offers hectare‐scale average SWC measurements at fixed locations maintenance‐free and continuous in time, while car‐borne CRNS roving can reveal spatial SWC patterns at medium scales, but only on certain survey days. The novel concept of a permanent mobile CRNS system on rails promises to combine the advantages of both methods, while its technical implementation, data processing and interpretation raised a new level of complexity. This study introduced a fully automatic CRNS rail‐borne system as the first of its kind, installed within the locomotive of a cargo train. Data recorded from September 2021 to July 2022 along an ∼9 km railway segment were analyzed, as repeatedly used by the train, supported by local SWC measurements (soil samples and dielectric methods), car‐borne and stationary CRNS. The results revealed consistent spatial SWC patterns and temporary variation along the track at a daily resolution. The observed variability was mostly related to surface features, seasonal dynamics and different responses of the railway segments to wetting and drying periods, while some variations were related to measurement uncertainties. The achieved medium scale of SWC mapping could support large scale hydrological modeling and detection of environmental risks, such as droughts and wildfires. Hence, rail‐borne CRNS has the chance to become a central tool of continuous SWC monitoring for larger scales (≤10‐km), with the additional benefit of providing root‐zone soil moisture, potentially even in sub‐daily resolution. 
          ,  
            Key Points 
             
               
                 
                  The first rail‐borne Cosmic ray neutron sensing system for automatic and continuous soil water content monitoring at the hectare scale is presented 
                 
                 
                  The system provided almost uninterrupted data from September 2021 to July 2022 along a 9 km railway track in the Harz low mountains, Germany 
                 
                 
                  Results showed spatial pattern, related to surface features, seasonal change, and individual responses of railway parts to wetting and drying},
	language = {en},
	number = {3},
	urldate = {2024-05-16},
	journal = {Water Resources Research},
	author = {Altdorff, Daniel and Oswald, Sascha E. and Zacharias, Steffen and Zengerle, Carmen and Dietrich, Peter and Mollenhauer, Hannes and Attinger, Sabine and Schrön, Martin},
	month = mar,
	year = {2023},
	pages = {e2022WR033514},
}

Abstract Cosmic ray neutron sensing (CRNS) has become a promising method for soil water content (SWC) monitoring. Stationary CRNS offers hectare‐scale average SWC measurements at fixed locations maintenance‐free and continuous in time, while car‐borne CRNS roving can reveal spatial SWC patterns at medium scales, but only on certain survey days. The novel concept of a permanent mobile CRNS system on rails promises to combine the advantages of both methods, while its technical implementation, data processing and interpretation raised a new level of complexity. This study introduced a fully automatic CRNS rail‐borne system as the first of its kind, installed within the locomotive of a cargo train. Data recorded from September 2021 to July 2022 along an ∼9 km railway segment were analyzed, as repeatedly used by the train, supported by local SWC measurements (soil samples and dielectric methods), car‐borne and stationary CRNS. The results revealed consistent spatial SWC patterns and temporary variation along the track at a daily resolution. The observed variability was mostly related to surface features, seasonal dynamics and different responses of the railway segments to wetting and drying periods, while some variations were related to measurement uncertainties. The achieved medium scale of SWC mapping could support large scale hydrological modeling and detection of environmental risks, such as droughts and wildfires. Hence, rail‐borne CRNS has the chance to become a central tool of continuous SWC monitoring for larger scales (≤10‐km), with the additional benefit of providing root‐zone soil moisture, potentially even in sub‐daily resolution. , Key Points The first rail‐borne Cosmic ray neutron sensing system for automatic and continuous soil water content monitoring at the hectare scale is presented The system provided almost uninterrupted data from September 2021 to July 2022 along a 9 km railway track in the Harz low mountains, Germany Results showed spatial pattern, related to surface features, seasonal change, and individual responses of railway parts to wetting and drying

@article{amelung_architecture_2023,
	title = {Architecture of soil microaggregates: {Advanced} methodologies to explore properties and functions},
	issn = {1436-8730, 1522-2624},
	shorttitle = {Architecture of soil microaggregates},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/jpln.202300149},
	doi = {10.1002/jpln.202300149},
	abstract = {Abstract
            The functions of soils are intimately linked to their three‐dimensional pore space and the associated biogeochemical interfaces, mirrored in the complex structure that developed during pedogenesis. Under stress overload, soil disintegrates into smaller compound structures, conventionally named aggregates. Microaggregates ({\textless}250 µm) are recognized as the most stable soil structural units. They are built of mineral, organic, and biotic materials, provide habitats for a vast diversity of microorganisms, and are closely involved in the cycling of matter and energy. However, exploring the architecture of soil microaggregates and their linkage to soil functions remains a challenging but demanding scientific endeavor. With the advent of complementary spectromicroscopic and tomographic techniques, we can now assess and visualize the size, composition, and porosity of microaggregates and the spatial arrangement of their interior building units. Their combinations with advanced experimental pedology, multi‐isotope labeling experiments, and computational approaches pave the way to investigate microaggregate turnover and stability, explore their role in element cycling, and unravel the intricate linkage between structure and function. However, spectromicroscopic techniques operate at different scales and resolutions, and have specific requirements for sample preparation and microaggregate isolation; hence, special attention must be paid to both the separation of microaggregates in a reproducible manner and the synopsis of the geography of information that originates from the diverse complementary instrumental techniques. The latter calls for further development of strategies for synlocation and synscaling beyond the present state of correlative analysis. Here, we present examples of recent scientific progress and review both options and challenges of the joint application of cutting‐edge techniques to achieve a sophisticated picture of the properties and functions of soil microaggregates.},
	language = {en},
	urldate = {2024-11-14},
	journal = {Journal of Plant Nutrition and Soil Science},
	author = {Amelung, Wulf and Tang, Ni and Siebers, Nina and Aehnelt, Michaela and Eusterhues, Karin and Felde, Vincent J. M. N. L. and Guggenberger, Georg and Kaiser, Klaus and Kögel‐Knabner, Ingrid and Klumpp, Erwin and Knief, Claudia and Kruse, Jens and Lehndorff, Eva and Mikutta, Robert and Peth, Stephan and Ray, Nadja and Prechtel, Alexander and Ritschel, Thomas and Schweizer, Steffen A. and Woche, Susanne K. and Wu, Bei and Totsche, Kai U.},
	month = sep,
	year = {2023},
	pages = {jpln.202300149},
}

Abstract The functions of soils are intimately linked to their three‐dimensional pore space and the associated biogeochemical interfaces, mirrored in the complex structure that developed during pedogenesis. Under stress overload, soil disintegrates into smaller compound structures, conventionally named aggregates. Microaggregates (\textless250 µm) are recognized as the most stable soil structural units. They are built of mineral, organic, and biotic materials, provide habitats for a vast diversity of microorganisms, and are closely involved in the cycling of matter and energy. However, exploring the architecture of soil microaggregates and their linkage to soil functions remains a challenging but demanding scientific endeavor. With the advent of complementary spectromicroscopic and tomographic techniques, we can now assess and visualize the size, composition, and porosity of microaggregates and the spatial arrangement of their interior building units. Their combinations with advanced experimental pedology, multi‐isotope labeling experiments, and computational approaches pave the way to investigate microaggregate turnover and stability, explore their role in element cycling, and unravel the intricate linkage between structure and function. However, spectromicroscopic techniques operate at different scales and resolutions, and have specific requirements for sample preparation and microaggregate isolation; hence, special attention must be paid to both the separation of microaggregates in a reproducible manner and the synopsis of the geography of information that originates from the diverse complementary instrumental techniques. The latter calls for further development of strategies for synlocation and synscaling beyond the present state of correlative analysis. Here, we present examples of recent scientific progress and review both options and challenges of the joint application of cutting‐edge techniques to achieve a sophisticated picture of the properties and functions of soil microaggregates.

@article{andrade-linares_climate_2023,
	title = {Climate change and management intensity alter spatial distribution and abundance of {P} mineralizing bacteria and arbuscular mycorrhizal fungi in mountainous grassland soils},
	volume = {186},
	issn = {00380717},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0038071723002377},
	doi = {10.1016/j.soilbio.2023.109175},
	language = {en},
	urldate = {2024-05-16},
	journal = {Soil Biology and Biochemistry},
	author = {Andrade-Linares, Diana Rocío and Schwerdtner, Ulrike and Schulz, Stefanie and Dannenmann, Michael and Spohn, Marie and Baum, Christel and Gasche, Rainer and Wiesmeier, Martin and Garcia-Franco, Noelia and Schloter, Michael},
	month = nov,
	year = {2023},
	pages = {109175},
}

@article{arora_building_2023,
	title = {Building {Cross}-{Site} and {Cross}-{Network} collaborations in critical zone science},
	volume = {618},
	issn = {00221694},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0022169423001907},
	doi = {10.1016/j.jhydrol.2023.129248},
	language = {en},
	urldate = {2024-05-16},
	journal = {Journal of Hydrology},
	author = {Arora, Bhavna and Kuppel, Sylvain and Wellen, Christopher and Oswald, Claire and Groh, Jannis and Payandi-Rolland, Dahédrey and Stegen, James and Coffinet, Sarah},
	month = mar,
	year = {2023},
	pages = {129248},
}

@article{asam_avhrr_2023,
	title = {{AVHRR} {NDVI} {Compositing} {Method} {Comparison} and {Generation} of {Multi}-{Decadal} {Time} {Series}—{A} {TIMELINE} {Thematic} {Processor}},
	volume = {15},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2072-4292},
	url = {https://www.mdpi.com/2072-4292/15/6/1631},
	doi = {10.3390/rs15061631},
	abstract = {Remote sensing image composites are crucial for a wide range of remote sensing applications, such as multi-decadal time series analysis. The Advanced Very High Resolution Radiometer (AVHRR) instrument has provided daily data since the early 1980s at a spatial resolution of 1 km, allowing analyses of climate change-related environmental processes. For monitoring vegetation conditions, the Normalized Difference Vegetation Index (NDVI) is the most widely used metric. However, to actually enable such analyses, a consistent NDVI time series over the AVHRR time-span needs to be created. In this context, the aim of this study is to thoroughly assess the effect of different compositing procedures on AVHRR NDVI composites, as no standard procedure has been established. Thirteen different compositing methods have been implemented; daily, decadal, and monthly composites over Europe and Northern Africa have been calculated for the year 2007, and the resulting data sets have been thoroughly evaluated according to six criteria. The median approach was selected as the best-performing compositing algorithm considering all the investigated aspects. However, the combination of the NDVI value and viewing and illumination angles as the criteria for the best-pixel selection proved to be a promising approach, too. The generated NDVI time series, currently ranging from 1981–2018, shows a consistent behavior and close agreement to the standard MODIS NDVI product. The conducted analyses demonstrate the strong influence of compositing procedures on the resulting AVHRR NDVI composites.},
	language = {en},
	number = {6},
	urldate = {2024-05-16},
	journal = {Remote Sensing},
	author = {Asam, Sarah and Eisfelder, Christina and Hirner, Andreas and Reiners, Philipp and Holzwarth, Stefanie and Bachmann, Martin},
	month = mar,
	year = {2023},
	pages = {1631},
}

Remote sensing image composites are crucial for a wide range of remote sensing applications, such as multi-decadal time series analysis. The Advanced Very High Resolution Radiometer (AVHRR) instrument has provided daily data since the early 1980s at a spatial resolution of 1 km, allowing analyses of climate change-related environmental processes. For monitoring vegetation conditions, the Normalized Difference Vegetation Index (NDVI) is the most widely used metric. However, to actually enable such analyses, a consistent NDVI time series over the AVHRR time-span needs to be created. In this context, the aim of this study is to thoroughly assess the effect of different compositing procedures on AVHRR NDVI composites, as no standard procedure has been established. Thirteen different compositing methods have been implemented; daily, decadal, and monthly composites over Europe and Northern Africa have been calculated for the year 2007, and the resulting data sets have been thoroughly evaluated according to six criteria. The median approach was selected as the best-performing compositing algorithm considering all the investigated aspects. However, the combination of the NDVI value and viewing and illumination angles as the criteria for the best-pixel selection proved to be a promising approach, too. The generated NDVI time series, currently ranging from 1981–2018, shows a consistent behavior and close agreement to the standard MODIS NDVI product. The conducted analyses demonstrate the strong influence of compositing procedures on the resulting AVHRR NDVI composites.

@article{bacour_assimilation_2023,
	title = {Assimilation of multiple datasets results in large differences in regional- to global-scale {NEE} and {GPP} budgets simulated by a terrestrial biosphere model},
	volume = {20},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1726-4189},
	url = {https://bg.copernicus.org/articles/20/1089/2023/},
	doi = {10.5194/bg-20-1089-2023},
	abstract = {Abstract. In spite of the importance of land ecosystems in offsetting carbon dioxide
emissions released by anthropogenic activities into the atmosphere, the
spatiotemporal dynamics of terrestrial carbon fluxes remain largely
uncertain at regional to global scales. Over the past decade, data
assimilation (DA) techniques have grown in importance for improving these
fluxes simulated by terrestrial biosphere models (TBMs), by optimizing model
parameter values while also pinpointing possible parameterization
deficiencies. Although the joint assimilation of multiple data streams is
expected to constrain a wider range of model processes, their actual
benefits in terms of reduction in model uncertainty are still
under-researched, also given the technical challenges. In this study, we
investigated with a consistent DA framework and the ORCHIDEE-LMDz
TBM–atmosphere model how the assimilation of different combinations of data
streams may result in different regional to global carbon budgets. To do so,
we performed comprehensive DA experiments where three datasets (in situ measurements
of net carbon exchange and latent heat fluxes, spaceborne estimates of the
normalized difference vegetation index, and atmospheric CO2
concentration data measured at stations) were assimilated alone or
simultaneously. We thus evaluated their complementarity and usefulness to
constrain net and gross C land fluxes. We found that a major challenge in
improving the spatial distribution of the land C sinks and sources with
atmospheric CO2 data relates to the correction of the soil carbon
imbalance.},
	language = {en},
	number = {6},
	urldate = {2024-11-14},
	journal = {Biogeosciences},
	author = {Bacour, Cédric and MacBean, Natasha and Chevallier, Frédéric and Léonard, Sébastien and Koffi, Ernest N. and Peylin, Philippe},
	month = mar,
	year = {2023},
	pages = {1089--1111},
}

Abstract. In spite of the importance of land ecosystems in offsetting carbon dioxide emissions released by anthropogenic activities into the atmosphere, the spatiotemporal dynamics of terrestrial carbon fluxes remain largely uncertain at regional to global scales. Over the past decade, data assimilation (DA) techniques have grown in importance for improving these fluxes simulated by terrestrial biosphere models (TBMs), by optimizing model parameter values while also pinpointing possible parameterization deficiencies. Although the joint assimilation of multiple data streams is expected to constrain a wider range of model processes, their actual benefits in terms of reduction in model uncertainty are still under-researched, also given the technical challenges. In this study, we investigated with a consistent DA framework and the ORCHIDEE-LMDz TBM–atmosphere model how the assimilation of different combinations of data streams may result in different regional to global carbon budgets. To do so, we performed comprehensive DA experiments where three datasets (in situ measurements of net carbon exchange and latent heat fluxes, spaceborne estimates of the normalized difference vegetation index, and atmospheric CO2 concentration data measured at stations) were assimilated alone or simultaneously. We thus evaluated their complementarity and usefulness to constrain net and gross C land fluxes. We found that a major challenge in improving the spatial distribution of the land C sinks and sources with atmospheric CO2 data relates to the correction of the soil carbon imbalance.

@article{barouillet_investigating_2023,
	title = {Investigating the effects of anthropogenic stressors on lake biota using sedimentary {\textless}span style="font-variant:small-caps;"{\textgreater}{DNA}{\textless}/span{\textgreater}},
	volume = {68},
	issn = {0046-5070, 1365-2427},
	shorttitle = {Investigating the effects of anthropogenic stressors on lake biota using sedimentary {\textless}span style="font-variant},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/fwb.14027},
	doi = {10.1111/fwb.14027},
	abstract = {Abstract
            
              
                
                  
                    Analyses of sedimentary DNA (
                    sed
                    DNA) have increased exponentially over the last decade and hold great potential to study the effects of anthropogenic stressors on lake biota over time.
                  
                
                
                  
                    Herein, we synthesise the literature that has applied a
                    sed
                    DNA approach to track historical changes in lake biodiversity in response to anthropogenic impacts, with an emphasis on the past
                    c.
                    200 years.
                  
                
                
                  We identified the following research themes that are of particular relevance: (1) eutrophication and climate change as key drivers of limnetic communities; (2) increasing homogenisation of limnetic communities across large spatial scales; and (3) the dynamics and effects of invasive species as traced in lake sediment archives.
                
                
                  
                    Altogether, this review highlights the potential of
                    sed
                    DNA to draw a more comprehensive picture of the response of lake biota to anthropogenic stressors, opening up new avenues in the field of paleoecology by unrevealing a hidden historical biodiversity, building new paleo‐indicators, and reflecting either taxonomic or functional attributes.
                  
                
                
                  
                    Broadly,
                    sed
                    DNA analyses provide new perspectives that can inform ecosystem management, conservation, and restoration by offering an approach to measure ecological integrity and vulnerability, as well as ecosystem functioning.},
	language = {en},
	number = {11},
	urldate = {2024-11-14},
	journal = {Freshwater Biology},
	author = {Barouillet, Cécilia and Monchamp, Marie‐Eve and Bertilsson, Stefan and Brasell, Katie and Domaizon, Isabelle and Epp, Laura S. and Ibrahim, Anan and Mejbel, Hebah and Nwosu, Ebuka Canisius and Pearman, John K. and Picard, Maïlys and Thomson‐Laing, Georgia and Tsugeki, Narumi and Von Eggers, Jordan and Gregory‐Eaves, Irene and Pick, Frances and Wood, Susanna A. and Capo, Eric},
	month = nov,
	year = {2023},
	pages = {1799--1817},
}

Abstract Analyses of sedimentary DNA ( sed DNA) have increased exponentially over the last decade and hold great potential to study the effects of anthropogenic stressors on lake biota over time. Herein, we synthesise the literature that has applied a sed DNA approach to track historical changes in lake biodiversity in response to anthropogenic impacts, with an emphasis on the past c. 200 years. We identified the following research themes that are of particular relevance: (1) eutrophication and climate change as key drivers of limnetic communities; (2) increasing homogenisation of limnetic communities across large spatial scales; and (3) the dynamics and effects of invasive species as traced in lake sediment archives. Altogether, this review highlights the potential of sed DNA to draw a more comprehensive picture of the response of lake biota to anthropogenic stressors, opening up new avenues in the field of paleoecology by unrevealing a hidden historical biodiversity, building new paleo‐indicators, and reflecting either taxonomic or functional attributes. Broadly, sed DNA analyses provide new perspectives that can inform ecosystem management, conservation, and restoration by offering an approach to measure ecological integrity and vulnerability, as well as ecosystem functioning.

@article{batchu_deep_2023,
	title = {A {Deep} {Learning} {Data} {Fusion} {Model} {Using} {Sentinel}-1/2, {SoilGrids}, {SMAP}, and {GLDAS} for {Soil} {Moisture} {Retrieval}},
	volume = {24},
	copyright = {http://www.ametsoc.org/PUBSReuseLicenses},
	issn = {1525-755X, 1525-7541},
	url = {https://journals.ametsoc.org/view/journals/hydr/24/10/JHM-D-22-0118.1.xml},
	doi = {10.1175/JHM-D-22-0118.1},
	abstract = {Abstract
            
              We develop a deep learning–based convolutional-regression model that estimates the volumetric soil moisture content in the top ∼5 cm of soil. Input predictors include Sentinel-1 (active radar) and Sentinel-2 (multispectral imagery), as well as geophysical variables from SoilGrids and modeled soil moisture fields from SMAP and GLDAS. The model was trained and evaluated on data from ∼1000 in situ sensors globally over the period 2015–21 and obtained an average per-sensor correlation of 0.707 and ubRMSE of 0.055 m
              3
              m
              −3
              , and it can be used to produce a soil moisture map at a nominal 320-m resolution. These results are benchmarked against 14 other soil moisture evaluation research works at different locations, and an ablation study was used to identify important predictors.
            
            
              Significance Statement
              Soil moisture is a key variable in various agriculture and water management systems. Accurate and high-resolution estimates of soil moisture have multiple downstream benefits such as reduced water wastage by better understanding and managing the consumption of water, utilizing smarter irrigation methods and effective canal water management. We develop a deep learning–based model that estimates the volumetric soil moisture content in the top ∼5 cm of soil at a nominal 320-m resolution. Our results demonstrate that machine learning is a useful tool for fusing different modalities with ease, while producing high-resolution models that are not location specific. Future work could explore the possibility of using temporal input sources to further improve model performance.},
	number = {10},
	urldate = {2024-11-14},
	journal = {Journal of Hydrometeorology},
	author = {Batchu, Vishal and Nearing, Grey and Gulshan, Varun},
	month = oct,
	year = {2023},
	pages = {1789--1823},
}

Abstract We develop a deep learning–based convolutional-regression model that estimates the volumetric soil moisture content in the top ∼5 cm of soil. Input predictors include Sentinel-1 (active radar) and Sentinel-2 (multispectral imagery), as well as geophysical variables from SoilGrids and modeled soil moisture fields from SMAP and GLDAS. The model was trained and evaluated on data from ∼1000 in situ sensors globally over the period 2015–21 and obtained an average per-sensor correlation of 0.707 and ubRMSE of 0.055 m 3 m −3 , and it can be used to produce a soil moisture map at a nominal 320-m resolution. These results are benchmarked against 14 other soil moisture evaluation research works at different locations, and an ablation study was used to identify important predictors. Significance Statement Soil moisture is a key variable in various agriculture and water management systems. Accurate and high-resolution estimates of soil moisture have multiple downstream benefits such as reduced water wastage by better understanding and managing the consumption of water, utilizing smarter irrigation methods and effective canal water management. We develop a deep learning–based model that estimates the volumetric soil moisture content in the top ∼5 cm of soil at a nominal 320-m resolution. Our results demonstrate that machine learning is a useful tool for fusing different modalities with ease, while producing high-resolution models that are not location specific. Future work could explore the possibility of using temporal input sources to further improve model performance.

@article{blettner_transboundary_2023,
	title = {Transboundary {Rainfall} {Estimation} {Using} {Commercial} {Microwave} {Links}},
	volume = {10},
	issn = {2333-5084, 2333-5084},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023EA002869},
	doi = {10.1029/2023EA002869},
	abstract = {Abstract
            Unlike actual rainfall, the spatial extent of rainfall maps is often determined by administrative and political boundaries. Similarly, data from commercial microwave links (CMLs) is usually acquired on a national basis and exchange among countries is limited. Up to now, this has prohibited the generation of transboundary CML‐based rainfall maps despite the great extension of networks across the world. We present CML based transboundary rainfall maps for the first time, using independent CML data sets from Germany and the Czech Republic. We show that straightforward algorithms used for quality control strongly reduce anomalies in the results. We find that, after quality control, CML‐based rainfall maps can be generated via joint and consistent processing, and that these maps allow to seamlessly visualize rainfall events traversing the German‐Czech border. This demonstrates that quality control represents a crucial step for large‐scale (e.g., continental) CML‐based rainfall estimation.
          , 
            Plain Language Summary
            Rainfall maps are usually based on gauge observations on the ground or radar. They are crucial for predicting or reconstructing flooding events. Commercial microwave links are special kinds of rainfall sensors. Their actual purpose is the signal propagation within a cellular network. However, since the signal is attenuated when it rains, they can also be exploited for rainfall estimation. To estimate rainfall from the observed attenuation requires careful data processing. Algorithms for that are usually adjusted to national data sets with their specific characteristics. In this study, we use, for the first time, two independent data sets of commercial microwave links (from Germany and the Czech Republic) with the aim of generating transboundary rainfall maps. As the data sets vary in many respects, several algorithms need to be adjusted and extended to allow processing them consistently. We show that it is possible to create meaningful rainfall maps of rain events that traverse the border between Germany and the Czech Republic. This can be considered a major step toward seamless rainfall maps on even larger, that is, continental scale.
          , 
            Key Points
            
              
                
                  Transboundary rainfall maps based on independent networks of commercial microwave links (CMLs) are generated for the first time
                
                
                  German and Czech data sets of CMLs differ significantly with respect to network characteristics
                
                
                  Quality control is important for heterogeneous data of CMLs},
	language = {en},
	number = {8},
	urldate = {2024-11-14},
	journal = {Earth and Space Science},
	author = {Blettner, Nico and Fencl, Martin and Bareš, Vojtěch and Kunstmann, Harald and Chwala, Christian},
	month = aug,
	year = {2023},
	pages = {e2023EA002869},
}

Abstract Unlike actual rainfall, the spatial extent of rainfall maps is often determined by administrative and political boundaries. Similarly, data from commercial microwave links (CMLs) is usually acquired on a national basis and exchange among countries is limited. Up to now, this has prohibited the generation of transboundary CML‐based rainfall maps despite the great extension of networks across the world. We present CML based transboundary rainfall maps for the first time, using independent CML data sets from Germany and the Czech Republic. We show that straightforward algorithms used for quality control strongly reduce anomalies in the results. We find that, after quality control, CML‐based rainfall maps can be generated via joint and consistent processing, and that these maps allow to seamlessly visualize rainfall events traversing the German‐Czech border. This demonstrates that quality control represents a crucial step for large‐scale (e.g., continental) CML‐based rainfall estimation. , Plain Language Summary Rainfall maps are usually based on gauge observations on the ground or radar. They are crucial for predicting or reconstructing flooding events. Commercial microwave links are special kinds of rainfall sensors. Their actual purpose is the signal propagation within a cellular network. However, since the signal is attenuated when it rains, they can also be exploited for rainfall estimation. To estimate rainfall from the observed attenuation requires careful data processing. Algorithms for that are usually adjusted to national data sets with their specific characteristics. In this study, we use, for the first time, two independent data sets of commercial microwave links (from Germany and the Czech Republic) with the aim of generating transboundary rainfall maps. As the data sets vary in many respects, several algorithms need to be adjusted and extended to allow processing them consistently. We show that it is possible to create meaningful rainfall maps of rain events that traverse the border between Germany and the Czech Republic. This can be considered a major step toward seamless rainfall maps on even larger, that is, continental scale. , Key Points Transboundary rainfall maps based on independent networks of commercial microwave links (CMLs) are generated for the first time German and Czech data sets of CMLs differ significantly with respect to network characteristics Quality control is important for heterogeneous data of CMLs

@article{bloomfield_environmental_2023,
	title = {Environmental controls on the light use efficiency of terrestrial gross primary production},
	volume = {29},
	issn = {1354-1013, 1365-2486},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/gcb.16511},
	doi = {10.1111/gcb.16511},
	abstract = {Abstract
            
              Gross primary production (GPP) by terrestrial ecosystems is a key quantity in the global carbon cycle. The instantaneous controls of leaf‐level photosynthesis are well established, but there is still no consensus on the mechanisms by which canopy‐level GPP depends on spatial and temporal variation in the environment. The standard model of photosynthesis provides a robust mechanistic representation for C
              3
              species; however, additional assumptions are required to “scale up” from leaf to canopy. As a consequence, competing models make inconsistent predictions about how GPP will respond to continuing environmental change. This problem is addressed here by means of an empirical analysis of the light use efficiency (LUE) of GPP inferred from eddy covariance carbon dioxide flux measurements, in situ measurements of photosynthetically active radiation (PAR), and remotely sensed estimates of the fraction of PAR (fAPAR) absorbed by the vegetation canopy. Focusing on LUE allows potential drivers of GPP to be separated from its overriding dependence on light. GPP data from over 100 sites, collated over 20 years and located in a range of biomes and climate zones, were extracted from the FLUXNET2015 database and combined with remotely sensed fAPAR data to estimate daily LUE. Daytime air temperature, vapor pressure deficit, diffuse fraction of solar radiation, and soil moisture were shown to be salient predictors of LUE in a generalized linear mixed‐effects model. The same model design was fitted to site‐based LUE estimates generated by 16 terrestrial ecosystem models. The published models showed wide variation in the shape, the strength, and even the sign of the environmental effects on modeled LUE. These findings highlight important model deficiencies and suggest a need to progress beyond simple “goodness of fit” comparisons of inferred and predicted carbon fluxes toward an approach focused on the functional responses of the underlying dependencies.},
	language = {en},
	number = {4},
	urldate = {2024-11-14},
	journal = {Global Change Biology},
	author = {Bloomfield, Keith J. and Stocker, Benjamin D. and Keenan, Trevor F. and Prentice, I. Colin},
	month = feb,
	year = {2023},
	pages = {1037--1053},
}

Abstract Gross primary production (GPP) by terrestrial ecosystems is a key quantity in the global carbon cycle. The instantaneous controls of leaf‐level photosynthesis are well established, but there is still no consensus on the mechanisms by which canopy‐level GPP depends on spatial and temporal variation in the environment. The standard model of photosynthesis provides a robust mechanistic representation for C 3 species; however, additional assumptions are required to “scale up” from leaf to canopy. As a consequence, competing models make inconsistent predictions about how GPP will respond to continuing environmental change. This problem is addressed here by means of an empirical analysis of the light use efficiency (LUE) of GPP inferred from eddy covariance carbon dioxide flux measurements, in situ measurements of photosynthetically active radiation (PAR), and remotely sensed estimates of the fraction of PAR (fAPAR) absorbed by the vegetation canopy. Focusing on LUE allows potential drivers of GPP to be separated from its overriding dependence on light. GPP data from over 100 sites, collated over 20 years and located in a range of biomes and climate zones, were extracted from the FLUXNET2015 database and combined with remotely sensed fAPAR data to estimate daily LUE. Daytime air temperature, vapor pressure deficit, diffuse fraction of solar radiation, and soil moisture were shown to be salient predictors of LUE in a generalized linear mixed‐effects model. The same model design was fitted to site‐based LUE estimates generated by 16 terrestrial ecosystem models. The published models showed wide variation in the shape, the strength, and even the sign of the environmental effects on modeled LUE. These findings highlight important model deficiencies and suggest a need to progress beyond simple “goodness of fit” comparisons of inferred and predicted carbon fluxes toward an approach focused on the functional responses of the underlying dependencies.

@article{bloomfield_towards_2023,
	title = {Towards a {General} {Monitoring} {System} for {Terrestrial} {Primary} {Production}: {A} {Test} {Spanning} the {European} {Drought} of 2018},
	volume = {15},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2072-4292},
	shorttitle = {Towards a {General} {Monitoring} {System} for {Terrestrial} {Primary} {Production}},
	url = {https://www.mdpi.com/2072-4292/15/6/1693},
	doi = {10.3390/rs15061693},
	abstract = {(1) Land surface models require inputs of temperature and moisture variables to generate predictions of gross primary production (GPP). Differences between leaf and air temperature vary temporally and spatially and may be especially pronounced under conditions of low soil moisture availability. The Sentinel-3 satellite mission offers estimates of the land surface temperature (LST), which for vegetated pixels can be adopted as the canopy temperature. Could remotely sensed estimates of LST offer a parsimonious input to models by combining information on leaf temperature and hydration? (2) Using a light use efficiency model that requires only a handful of input variables, we generated GPP simulations for comparison with eddy-covariance inferred estimates available from flux sites within the Integrated Carbon Observation System. Remotely sensed LST and greenness data were input from Sentinel-3. Gridded air temperature data were obtained from the European Centre for Medium-Range Weather Forecasts. We chose the years 2018–2019 to exploit the natural experiment of a pronounced European drought. (3) Simulated GPP showed good agreement with flux-derived estimates. During dry conditions, simulations forced with LST performed better than those with air temperature for shrubland, grassland and savanna sites. (4) This study advances the prospect for a global GPP monitoring system that will rely primarily on remotely sensed inputs.},
	language = {en},
	number = {6},
	urldate = {2024-11-14},
	journal = {Remote Sensing},
	author = {Bloomfield, Keith J. and Van Hoolst, Roel and Balzarolo, Manuela and Janssens, Ivan A. and Vicca, Sara and Ghent, Darren and Prentice, I. Colin},
	month = mar,
	year = {2023},
	pages = {1693},
}

(1) Land surface models require inputs of temperature and moisture variables to generate predictions of gross primary production (GPP). Differences between leaf and air temperature vary temporally and spatially and may be especially pronounced under conditions of low soil moisture availability. The Sentinel-3 satellite mission offers estimates of the land surface temperature (LST), which for vegetated pixels can be adopted as the canopy temperature. Could remotely sensed estimates of LST offer a parsimonious input to models by combining information on leaf temperature and hydration? (2) Using a light use efficiency model that requires only a handful of input variables, we generated GPP simulations for comparison with eddy-covariance inferred estimates available from flux sites within the Integrated Carbon Observation System. Remotely sensed LST and greenness data were input from Sentinel-3. Gridded air temperature data were obtained from the European Centre for Medium-Range Weather Forecasts. We chose the years 2018–2019 to exploit the natural experiment of a pronounced European drought. (3) Simulated GPP showed good agreement with flux-derived estimates. During dry conditions, simulations forced with LST performed better than those with air temperature for shrubland, grassland and savanna sites. (4) This study advances the prospect for a global GPP monitoring system that will rely primarily on remotely sensed inputs.

@article{blume_was_2023,
	title = {Was haben {Baumkronen} mit dem {Grundwasser} zu tun?},
	copyright = {Creative Commons Attribution Share Alike 4.0 International},
	url = {https://gfzpublic.gfz-potsdam.de/pubman/item/item_5022446},
	doi = {10.48440/GFZ.SYSERDE.13.01.1},
	abstract = {Der Kronendurchlass, d. h. der Anteil des Niederschlags, der durch das Kronendach des Waldes dringt, wird stark von der Art des Niederschlags und den Eigenschaften des Waldbestands beeinflusst. Das komplexe Zusammenspiel dieser Faktoren inklusive ihrer jahreszeitlichen Veränderungen zu entschlüsseln, ist eine große wissenschaftliche Herausforderung und nur mit langjährigem Monitoring in verschiedenen Waldbeständen möglich. Das Langzeit-Umweltobservatorium TERENO Nord-Ost zur Erforschung der regionalen Auswirkungen des Globalen Wandels liefert hierfür ideale Voraussetzungen.},
	language = {de},
	urldate = {2024-11-14},
	author = {Blume, Theresa and Schneider, Lisa and Güntner, Andreas and Morgner, Markus and Wummel, Jörg},
	year = {2023},
	note = {Artwork Size: 6 pages, 2 MB
Medium: application/pdf
Publisher: Deutsches GeoForschungsZentrum GFZ},
	pages = {6 pages, 2 MB},
}

Der Kronendurchlass, d. h. der Anteil des Niederschlags, der durch das Kronendach des Waldes dringt, wird stark von der Art des Niederschlags und den Eigenschaften des Waldbestands beeinflusst. Das komplexe Zusammenspiel dieser Faktoren inklusive ihrer jahreszeitlichen Veränderungen zu entschlüsseln, ist eine große wissenschaftliche Herausforderung und nur mit langjährigem Monitoring in verschiedenen Waldbeständen möglich. Das Langzeit-Umweltobservatorium TERENO Nord-Ost zur Erforschung der regionalen Auswirkungen des Globalen Wandels liefert hierfür ideale Voraussetzungen.

@article{boas_seasonal_2023,
	title = {Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system ({SEAS5}) long-range meteorological forecasts in a land surface modelling approach},
	volume = {27},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1607-7938},
	url = {https://hess.copernicus.org/articles/27/3143/2023/},
	doi = {10.5194/hess-27-3143-2023},
	abstract = {Abstract. Long-range weather forecasts provide predictions of atmospheric, ocean and land surface conditions that can potentially be used in land surface and hydrological models to predict the water and energy status of the land surface or in crop growth models to predict yield for water resources or agricultural planning. However, the coarse spatial and temporal resolutions of available forecast products have hindered their widespread use in such modelling applications, which usually require high-resolution input data. In this study, we applied sub-seasonal (up to 4 months) and seasonal (7 months) weather forecasts from the latest European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system (SEAS5) in a land surface modelling approach using the Community Land Model version 5.0 (CLM5). Simulations were conducted for 2017–2020 forced with sub-seasonal and seasonal weather forecasts over two different domains with contrasting climate and cropping conditions: the German state of North Rhine-Westphalia (DE-NRW) and the Australian state of Victoria (AUS-VIC). We found that, after pre-processing of the forecast products (i.e. temporal downscaling of precipitation and incoming short-wave radiation), the simulations forced with seasonal and sub-seasonal forecasts were able to provide a model output that was very close to the reference simulation results forced by reanalysis data (the mean annual crop yield showed maximum differences of 0.28 and 0.36 t ha−1 for AUS-VIC and DE-NRW respectively). Differences between seasonal and sub-seasonal experiments were insignificant. The forecast experiments were able to satisfactorily capture recorded inter-annual variations of crop yield. In addition, they also reproduced the generally higher inter-annual differences in crop yield across the AUS-VIC domain (approximately 50 \% inter-annual differences in recorded yields and up to 17 \% inter-annual differences in simulated yields) compared to the DE-NRW domain (approximately 15 \% inter-annual differences in recorded yields and up to 5 \% in simulated yields). The
high- and low-yield seasons (2020 and 2018) among the 4 simulated years
were clearly reproduced in the forecast simulation results. Furthermore,
sub-seasonal and seasonal simulations reflected the early harvest in the
drought year of 2018 in the DE-NRW domain. However, simulated inter-annual yield variability was lower in all simulations compared to the
official statistics. While general soil moisture trends, such as the
European drought in 2018, were captured by the seasonal experiments, we
found systematic overestimations and underestimations in both the forecast and reference simulations compared to the Soil Moisture Active Passive Level-3 soil moisture product (SMAP L3) and the Soil Moisture Climate Change Initiative Combined dataset from the European Space Agency (ESA CCI). These observed biases of soil moisture and the low inter-annual differences in simulated crop yield indicate the need to improve the
representation of these variables in CLM5 to increase the model sensitivity
to drought stress and other crop stressors.},
	language = {en},
	number = {16},
	urldate = {2024-11-14},
	journal = {Hydrology and Earth System Sciences},
	author = {Boas, Theresa and Bogena, Heye Reemt and Ryu, Dongryeol and Vereecken, Harry and Western, Andrew and Hendricks Franssen, Harrie-Jan},
	month = aug,
	year = {2023},
	pages = {3143--3167},
}

Abstract. Long-range weather forecasts provide predictions of atmospheric, ocean and land surface conditions that can potentially be used in land surface and hydrological models to predict the water and energy status of the land surface or in crop growth models to predict yield for water resources or agricultural planning. However, the coarse spatial and temporal resolutions of available forecast products have hindered their widespread use in such modelling applications, which usually require high-resolution input data. In this study, we applied sub-seasonal (up to 4 months) and seasonal (7 months) weather forecasts from the latest European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system (SEAS5) in a land surface modelling approach using the Community Land Model version 5.0 (CLM5). Simulations were conducted for 2017–2020 forced with sub-seasonal and seasonal weather forecasts over two different domains with contrasting climate and cropping conditions: the German state of North Rhine-Westphalia (DE-NRW) and the Australian state of Victoria (AUS-VIC). We found that, after pre-processing of the forecast products (i.e. temporal downscaling of precipitation and incoming short-wave radiation), the simulations forced with seasonal and sub-seasonal forecasts were able to provide a model output that was very close to the reference simulation results forced by reanalysis data (the mean annual crop yield showed maximum differences of 0.28 and 0.36 t ha−1 for AUS-VIC and DE-NRW respectively). Differences between seasonal and sub-seasonal experiments were insignificant. The forecast experiments were able to satisfactorily capture recorded inter-annual variations of crop yield. In addition, they also reproduced the generally higher inter-annual differences in crop yield across the AUS-VIC domain (approximately 50 % inter-annual differences in recorded yields and up to 17 % inter-annual differences in simulated yields) compared to the DE-NRW domain (approximately 15 % inter-annual differences in recorded yields and up to 5 % in simulated yields). The high- and low-yield seasons (2020 and 2018) among the 4 simulated years were clearly reproduced in the forecast simulation results. Furthermore, sub-seasonal and seasonal simulations reflected the early harvest in the drought year of 2018 in the DE-NRW domain. However, simulated inter-annual yield variability was lower in all simulations compared to the official statistics. While general soil moisture trends, such as the European drought in 2018, were captured by the seasonal experiments, we found systematic overestimations and underestimations in both the forecast and reference simulations compared to the Soil Moisture Active Passive Level-3 soil moisture product (SMAP L3) and the Soil Moisture Climate Change Initiative Combined dataset from the European Space Agency (ESA CCI). These observed biases of soil moisture and the low inter-annual differences in simulated crop yield indicate the need to improve the representation of these variables in CLM5 to increase the model sensitivity to drought stress and other crop stressors.

@article{bollinger_influence_2023,
	title = {The influence of season, hunting mode, and habitat specialization on riparian spiders as key predators in the aquatic-terrestrial linkage},
	volume = {13},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-023-50420-w},
	doi = {10.1038/s41598-023-50420-w},
	abstract = {Abstract
            Freshwater ecosystems subsidize riparian zones with high-quality nutrients via the emergence of aquatic insects. Spiders are dominant consumers of these insect subsidies. However, little is known about the variation of aquatic insect consumption across spiders of different hunting modes, habitat specializations, seasons, and systems. To explore this, we assembled a large stable isotope dataset (n {\textgreater} 1000) of aquatic versus terrestrial sources and six spider species over four points in time adjacent to a lotic and a lentic system. The spiders represent three hunting modes each consisting of a wetland specialist and a habitat generalist. We expected that specialists would feed more on aquatic prey than their generalist counterparts. Mixing models showed that spiders’ diet consisted of 17–99\% of aquatic sources, with no clear effect of habitat specialization. Averaged over the whole study period, web builders (WB) showed the highest proportions (78\%) followed by ground hunters (GH, 42\%) and vegetation hunters (VH, 31\%). Consumption of aquatic prey was highest in June and August, which is most pronounced in GH and WBs, with the latter feeding almost entirely on aquatic sources during this period. Additionally, the elevated importance of high-quality lipids from aquatic origin during fall is indicated by elemental analyses pointing to an accumulation of lipids in October, which represent critical energy reserves during winter. Consequently, this study underlines the importance of aquatic prey irrespective of the habitat specialization of spiders. Furthermore, it suggests that energy flows vary substantially between spider hunting modes and seasons.},
	language = {en},
	number = {1},
	urldate = {2024-11-14},
	journal = {Scientific Reports},
	author = {Bollinger, Eric and Zubrod, Jochen P. and Englert, Dominic and Graf, Nadin and Weisner, Oliver and Kolb, Sebastian and Schäfer, Ralf B. and Entling, Martin H. and Schulz, Ralf},
	month = dec,
	year = {2023},
	pages = {22950},
}

Abstract Freshwater ecosystems subsidize riparian zones with high-quality nutrients via the emergence of aquatic insects. Spiders are dominant consumers of these insect subsidies. However, little is known about the variation of aquatic insect consumption across spiders of different hunting modes, habitat specializations, seasons, and systems. To explore this, we assembled a large stable isotope dataset (n \textgreater 1000) of aquatic versus terrestrial sources and six spider species over four points in time adjacent to a lotic and a lentic system. The spiders represent three hunting modes each consisting of a wetland specialist and a habitat generalist. We expected that specialists would feed more on aquatic prey than their generalist counterparts. Mixing models showed that spiders’ diet consisted of 17–99% of aquatic sources, with no clear effect of habitat specialization. Averaged over the whole study period, web builders (WB) showed the highest proportions (78%) followed by ground hunters (GH, 42%) and vegetation hunters (VH, 31%). Consumption of aquatic prey was highest in June and August, which is most pronounced in GH and WBs, with the latter feeding almost entirely on aquatic sources during this period. Additionally, the elevated importance of high-quality lipids from aquatic origin during fall is indicated by elemental analyses pointing to an accumulation of lipids in October, which represent critical energy reserves during winter. Consequently, this study underlines the importance of aquatic prey irrespective of the habitat specialization of spiders. Furthermore, it suggests that energy flows vary substantially between spider hunting modes and seasons.

@article{bonet-garcia_environmental_2023,
	title = {Environmental research infrastructures are not (yet) ready to address ecosystem conservation challenge},
	volume = {18},
	issn = {1748-9326},
	url = {https://iopscience.iop.org/article/10.1088/1748-9326/acec01},
	doi = {10.1088/1748-9326/acec01},
	abstract = {Abstract
            Research infrastructures (RIs) are tools intended to be a fundamental pillar in producing knowledge regarding the functioning of Earth’s vital systems. However, it is unclear to what extent these instruments can help to deal with global biodiversity challenges. This paper presents the first assessment of the alignment between the services provided by environmental RIs, and the knowledge requested to address three specific Global Challenges concerning biodiversity loss at a global level: threatened species, alien species and ecosystem conservation. We characterized the specific needs and Subchallenges behind each Global Challenge. We also collected the services provided by 44 relevant environmental RIs in a standardized form. Then, we assessed to what extent those services are useful to address the challenges’ needs. Our results show that RIs, as a whole, are better suited to respond to species-related challenges than to challenges involving whole ecosystems. Nevertheless, the overlap among challenges’ needs is quite significant. Nearly half of the identified needs are shared between the ‘threatened species’ and the ‘ecosystem conservation’ challenges. Most of the assessed RIs work with multiple Earth System’s compartments at the same time (e.g. terrestrial + marine, terrestrial + freshwater, etc). Regarding the spatial extent of the studied RIs, most of the ecosystem-based RIs focus on the country scale, while most of the RIs specialized in species-related challenges work at a global scale. Considering the needs required to address the studied challenges, we have found that the RIs assessed in this study do not cover several of them. These gaps comprise complex data combinations that the studied RIs do not provide. Most of these gaps can be attributed to the ‘ecosystem conservation’ challenge. We consider that RIs were generally built to support pure basic research, which hampers their contribution to combat biodiversity loss. Because of the urgency to address global biodiversity challenges, we suggest adding new functionalities to make RIs work as problem-oriented facilities.},
	number = {9},
	urldate = {2024-11-14},
	journal = {Environmental Research Letters},
	author = {Bonet-García, Francisco J and Pando, Francisco and Suárez-Muñoz, María and Cabello, Javier},
	month = sep,
	year = {2023},
	pages = {093002},
}

Abstract Research infrastructures (RIs) are tools intended to be a fundamental pillar in producing knowledge regarding the functioning of Earth’s vital systems. However, it is unclear to what extent these instruments can help to deal with global biodiversity challenges. This paper presents the first assessment of the alignment between the services provided by environmental RIs, and the knowledge requested to address three specific Global Challenges concerning biodiversity loss at a global level: threatened species, alien species and ecosystem conservation. We characterized the specific needs and Subchallenges behind each Global Challenge. We also collected the services provided by 44 relevant environmental RIs in a standardized form. Then, we assessed to what extent those services are useful to address the challenges’ needs. Our results show that RIs, as a whole, are better suited to respond to species-related challenges than to challenges involving whole ecosystems. Nevertheless, the overlap among challenges’ needs is quite significant. Nearly half of the identified needs are shared between the ‘threatened species’ and the ‘ecosystem conservation’ challenges. Most of the assessed RIs work with multiple Earth System’s compartments at the same time (e.g. terrestrial + marine, terrestrial + freshwater, etc). Regarding the spatial extent of the studied RIs, most of the ecosystem-based RIs focus on the country scale, while most of the RIs specialized in species-related challenges work at a global scale. Considering the needs required to address the studied challenges, we have found that the RIs assessed in this study do not cover several of them. These gaps comprise complex data combinations that the studied RIs do not provide. Most of these gaps can be attributed to the ‘ecosystem conservation’ challenge. We consider that RIs were generally built to support pure basic research, which hampers their contribution to combat biodiversity loss. Because of the urgency to address global biodiversity challenges, we suggest adding new functionalities to make RIs work as problem-oriented facilities.

@article{borriero_uncertainty_2023,
	title = {Uncertainty in water transit time estimation with {StorAge} {Selection} functions and tracer data interpolation},
	volume = {27},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1607-7938},
	url = {https://hess.copernicus.org/articles/27/2989/2023/},
	doi = {10.5194/hess-27-2989-2023},
	abstract = {Abstract. Transit time distributions (TTDs) of streamflow are useful descriptors for understanding flow and solute transport in catchments. Catchment-scale TTDs can be modeled using tracer data (e.g. oxygen isotopes, such as δ18O) in inflow and outflows by employing StorAge Selection (SAS) functions.
However, tracer data are often sparse in space and time, so they need to be interpolated to increase their spatiotemporal resolution. Moreover, SAS functions can be parameterized with different forms, but there is no general agreement on which one should be used. Both of these aspects induce uncertainty in the simulated TTDs, and the individual uncertainty sources as well as their combined effect have not been fully investigated.
This study provides a comprehensive analysis of the TTD uncertainty resulting from 12 model setups obtained by combining different interpolation schemes for δ18O in precipitation and distinct SAS functions.
For each model setup, we found behavioral solutions with satisfactory model performance for in-stream δ18O (KGE {\textgreater} 0.55, where KGE refers to the Kling–Gupta efficiency). Differences in KGE values were statistically significant, thereby showing the relevance of the chosen setup for simulating TTDs.
We found a large uncertainty in the simulated TTDs, represented by a large range of variability in the 95 \% confidence interval of the median transit time, varying at the most by between 259 and 1009 d across all tested setups. Uncertainty in TTDs was mainly associated with the temporal interpolation of δ18O in precipitation, the choice between time-variant and time-invariant SAS functions, flow conditions, and the use of nonspatially interpolated δ18O in precipitation.
We discuss the implications of these results for the SAS framework, uncertainty characterization in TTD-based models, and the influence of the uncertainty for water quality and quantity studies.},
	language = {en},
	number = {15},
	urldate = {2024-11-14},
	journal = {Hydrology and Earth System Sciences},
	author = {Borriero, Arianna and Kumar, Rohini and Nguyen, Tam V. and Fleckenstein, Jan H. and Lutz, Stefanie R.},
	month = aug,
	year = {2023},
	pages = {2989--3004},
}

Abstract. Transit time distributions (TTDs) of streamflow are useful descriptors for understanding flow and solute transport in catchments. Catchment-scale TTDs can be modeled using tracer data (e.g. oxygen isotopes, such as δ18O) in inflow and outflows by employing StorAge Selection (SAS) functions. However, tracer data are often sparse in space and time, so they need to be interpolated to increase their spatiotemporal resolution. Moreover, SAS functions can be parameterized with different forms, but there is no general agreement on which one should be used. Both of these aspects induce uncertainty in the simulated TTDs, and the individual uncertainty sources as well as their combined effect have not been fully investigated. This study provides a comprehensive analysis of the TTD uncertainty resulting from 12 model setups obtained by combining different interpolation schemes for δ18O in precipitation and distinct SAS functions. For each model setup, we found behavioral solutions with satisfactory model performance for in-stream δ18O (KGE \textgreater 0.55, where KGE refers to the Kling–Gupta efficiency). Differences in KGE values were statistically significant, thereby showing the relevance of the chosen setup for simulating TTDs. We found a large uncertainty in the simulated TTDs, represented by a large range of variability in the 95 % confidence interval of the median transit time, varying at the most by between 259 and 1009 d across all tested setups. Uncertainty in TTDs was mainly associated with the temporal interpolation of δ18O in precipitation, the choice between time-variant and time-invariant SAS functions, flow conditions, and the use of nonspatially interpolated δ18O in precipitation. We discuss the implications of these results for the SAS framework, uncertainty characterization in TTD-based models, and the influence of the uncertainty for water quality and quantity studies.

@article{borrmann_mispel_2023,
	title = {{MISPEL}: {A} {Multi}-{Crop} {Spectral} {Library} for {Statistical} {Crop} {Trait} {Retrieval} and {Agricultural} {Monitoring}},
	volume = {15},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2072-4292},
	shorttitle = {{MISPEL}},
	url = {https://www.mdpi.com/2072-4292/15/14/3664},
	doi = {10.3390/rs15143664},
	abstract = {Spatiotemporally accurate estimates of crop traits are essential for both scientific modeling and practical decision making in sustainable agricultural management. Besides efficient and concise methods to derive these traits, site- and crop-specific reference data are needed to develop and validate retrieval methods. To address this shortcoming, this study first includes the establishment of ’MISPEL’, a comprehensive spectral library (SpecLib) containing hyperspectral measurements and reference data for six key traits of ten widely grown crops. Secondly, crop-specific statistical leaf area index (LAI) models for winter wheat are developed based on a hyperspectral (MISPELFR) and a simulated Sentinel-2 (MISPELS2) SpecLib applying four nonparametric methods. Finally, an independent Sentinel-2 model evaluation at the DEMMIN test site in Germany is conducted, including a comparison with the commonly used SNAP-LAI product. To date, MISPEL comprises a set of 1411 spectra of ten crops and more than 6800 associated reference measurements. Cross-validations of winter wheat LAI models revealed that Elastic-net generalized linear model (GLMNET) and Gaussian process (GP) regressions outperformed partial least squares (PLS) and random forest (RF) regressions, showing RSQ values up to 0.86 and a minimal NRMSE of 0.21 using MISPELFR. GLMNET and GP models based on MISPELS2 further outperformed SNAP-based LAI estimates derived for the external validation site. Thus, it is concluded that the presented SpecLib ’MISPEL’ and applied methodology have a very high potential for deriving diverse crop traits of multiple crops in view of most recent and future multi-, super-, and hyperspectral satellite missions.},
	language = {en},
	number = {14},
	urldate = {2024-11-14},
	journal = {Remote Sensing},
	author = {Borrmann, Peter and Brandt, Patric and Gerighausen, Heike},
	month = jul,
	year = {2023},
	pages = {3664},
}

Spatiotemporally accurate estimates of crop traits are essential for both scientific modeling and practical decision making in sustainable agricultural management. Besides efficient and concise methods to derive these traits, site- and crop-specific reference data are needed to develop and validate retrieval methods. To address this shortcoming, this study first includes the establishment of ’MISPEL’, a comprehensive spectral library (SpecLib) containing hyperspectral measurements and reference data for six key traits of ten widely grown crops. Secondly, crop-specific statistical leaf area index (LAI) models for winter wheat are developed based on a hyperspectral (MISPELFR) and a simulated Sentinel-2 (MISPELS2) SpecLib applying four nonparametric methods. Finally, an independent Sentinel-2 model evaluation at the DEMMIN test site in Germany is conducted, including a comparison with the commonly used SNAP-LAI product. To date, MISPEL comprises a set of 1411 spectra of ten crops and more than 6800 associated reference measurements. Cross-validations of winter wheat LAI models revealed that Elastic-net generalized linear model (GLMNET) and Gaussian process (GP) regressions outperformed partial least squares (PLS) and random forest (RF) regressions, showing RSQ values up to 0.86 and a minimal NRMSE of 0.21 using MISPELFR. GLMNET and GP models based on MISPELS2 further outperformed SNAP-based LAI estimates derived for the external validation site. Thus, it is concluded that the presented SpecLib ’MISPEL’ and applied methodology have a very high potential for deriving diverse crop traits of multiple crops in view of most recent and future multi-, super-, and hyperspectral satellite missions.

@article{borsdorf_comparison_2023,
	title = {Comparison of {Seasonal} and {Diurnal} {Concentration} {Profiles} of {BVOCs} in {Coniferous} and {Deciduous} {Forests}},
	volume = {14},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2073-4433},
	url = {https://www.mdpi.com/2073-4433/14/9/1347},
	doi = {10.3390/atmos14091347},
	abstract = {Ambient atmospheric concentrations of isoprene and monoterpenes were measured at two forest sites, one deciduous and one coniferous, over the year 2022. Both sites in a regional area were sampled monthly between April and September. The samples were taken using sorbent tubes and analyzed with thermal desorption–gas chromatography–mass spectrometry. The highest concentrations were determined in August at both sites. While isoprene is the most abundant compound at the deciduous forest with an average concentration of 5.59 µg m−3 in August, α-pinene and β-pinene dominate throughout the year at the coniferous forest with the highest concentrations also in August (3.44 µg m−3 and 1.51 µg m−3). Because other monoterpenes (camphene, sabinene, 3-carene, p-cymene and limonene) are also emitted in significant amounts, the total concentration measured in the coniferous forest is higher (7.96 µg m−3 in August) in comparison to the deciduous forest (6.08 µg m−3). Regarding the detected monoterpenes in the deciduous forest, sabinene is the dominant monoterpene in addition to α-pinene and is sometimes present in higher (July) or equal (August) concentrations. The seasonal and diurnal concentrations of all monoterpenes correlate very well with each other at both sites. An exception is sabinene with a diurnal concentration profile similar to isoprene.},
	language = {en},
	number = {9},
	urldate = {2024-11-14},
	journal = {Atmosphere},
	author = {Borsdorf, Helko and Bentele, Maja and Müller, Michael and Rebmann, Corinna and Mayer, Thomas},
	month = aug,
	year = {2023},
	pages = {1347},
}

Ambient atmospheric concentrations of isoprene and monoterpenes were measured at two forest sites, one deciduous and one coniferous, over the year 2022. Both sites in a regional area were sampled monthly between April and September. The samples were taken using sorbent tubes and analyzed with thermal desorption–gas chromatography–mass spectrometry. The highest concentrations were determined in August at both sites. While isoprene is the most abundant compound at the deciduous forest with an average concentration of 5.59 µg m−3 in August, α-pinene and β-pinene dominate throughout the year at the coniferous forest with the highest concentrations also in August (3.44 µg m−3 and 1.51 µg m−3). Because other monoterpenes (camphene, sabinene, 3-carene, p-cymene and limonene) are also emitted in significant amounts, the total concentration measured in the coniferous forest is higher (7.96 µg m−3 in August) in comparison to the deciduous forest (6.08 µg m−3). Regarding the detected monoterpenes in the deciduous forest, sabinene is the dominant monoterpene in addition to α-pinene and is sometimes present in higher (July) or equal (August) concentrations. The seasonal and diurnal concentrations of all monoterpenes correlate very well with each other at both sites. An exception is sabinene with a diurnal concentration profile similar to isoprene.

@article{bottero_impact_2023,
	title = {Impact of landscape configuration and composition on pollinator communities across different {European} biogeographic regions},
	volume = {11},
	issn = {2296-701X},
	url = {https://www.frontiersin.org/articles/10.3389/fevo.2023.1128228/full},
	doi = {10.3389/fevo.2023.1128228},
	abstract = {Introduction
              Heterogeneity in composition and spatial configuration of landscape elements support diversity and abundance of flower-visiting insects, but this is likely dependent on taxonomic group, spatial scale, weather and climatic conditions, and is particularly impacted by agricultural intensification. Here, we analyzed the impacts of both aspects of landscape heterogeneity and the role of climatic and weather conditions on pollinating insect communities in two economically important mass-flowering crops across Europe.
            
            
              Methods
              Using a standardized approach, we collected data on the abundance of five insect groups (honey bees, bumble bees, other bees, hover flies and butterflies) in eight oilseed rape and eight apple orchard sites (in crops and adjacent crop margins), across eight European countries (128 sites in total) encompassing four biogeographic regions, and quantified habitat heterogeneity by calculating relevant landscape metrics for composition (proportion and diversity of land-use types) and configuration (the aggregation and isolation of land-use patches).
            
            
              Results
              We found that flower-visiting insects responded to landscape and climate parameters in taxon- and crop-specific ways. For example, landscape diversity was positively correlated with honey bee and solitary bee abundance in oilseed rape fields, and hover fly abundance in apple orchards. In apple sites, the total abundance of all pollinators, and particularly bumble bees and solitary bees, decreased with an increasing proportion of orchards in the surrounding landscape. In oilseed rape sites, less-intensively managed habitats (i.e., woodland, grassland, meadows, and hedgerows) positively influenced all pollinators, particularly bumble bees and butterflies. Additionally, our data showed that daily and annual temperature, as well as annual precipitation and precipitation seasonality, affects the abundance of flower-visiting insects, although, again, these impacts appeared to be taxon- or crop-specific.
            
            
              Discussion
              Thus, in the context of global change, our findings emphasize the importance of understanding the role of taxon-specific responses to both changes in land use and climate, to ensure continued delivery of pollination services to pollinator-dependent crops.},
	urldate = {2024-11-14},
	journal = {Frontiers in Ecology and Evolution},
	author = {Bottero, Irene and Dominik, Christophe and Schweiger, Olivier and Albrecht, Matthias and Attridge, Eleanor and Brown, Mark J. F. and Cini, Elena and Costa, Cecilia and De La Rúa, Pilar and De Miranda, Joachim R. and Di Prisco, Gennaro and Dzul Uuh, Daniel and Hodge, Simon and Ivarsson, Kjell and Knauer, Anina C. and Klein, Alexandra-Maria and Mänd, Marika and Martínez-López, Vicente and Medrzycki, Piotr and Pereira-Peixoto, Helena and Potts, Simon and Raimets, Risto and Rundlöf, Maj and Schwarz, Janine M. and Senapathi, Deepa and Tamburini, Giovanni and Talaván, Estefanía Tobajas and Stout, Jane C.},
	month = may,
	year = {2023},
	pages = {1128228},
}

Introduction Heterogeneity in composition and spatial configuration of landscape elements support diversity and abundance of flower-visiting insects, but this is likely dependent on taxonomic group, spatial scale, weather and climatic conditions, and is particularly impacted by agricultural intensification. Here, we analyzed the impacts of both aspects of landscape heterogeneity and the role of climatic and weather conditions on pollinating insect communities in two economically important mass-flowering crops across Europe. Methods Using a standardized approach, we collected data on the abundance of five insect groups (honey bees, bumble bees, other bees, hover flies and butterflies) in eight oilseed rape and eight apple orchard sites (in crops and adjacent crop margins), across eight European countries (128 sites in total) encompassing four biogeographic regions, and quantified habitat heterogeneity by calculating relevant landscape metrics for composition (proportion and diversity of land-use types) and configuration (the aggregation and isolation of land-use patches). Results We found that flower-visiting insects responded to landscape and climate parameters in taxon- and crop-specific ways. For example, landscape diversity was positively correlated with honey bee and solitary bee abundance in oilseed rape fields, and hover fly abundance in apple orchards. In apple sites, the total abundance of all pollinators, and particularly bumble bees and solitary bees, decreased with an increasing proportion of orchards in the surrounding landscape. In oilseed rape sites, less-intensively managed habitats (i.e., woodland, grassland, meadows, and hedgerows) positively influenced all pollinators, particularly bumble bees and butterflies. Additionally, our data showed that daily and annual temperature, as well as annual precipitation and precipitation seasonality, affects the abundance of flower-visiting insects, although, again, these impacts appeared to be taxon- or crop-specific. Discussion Thus, in the context of global change, our findings emphasize the importance of understanding the role of taxon-specific responses to both changes in land use and climate, to ensure continued delivery of pollination services to pollinator-dependent crops.

@article{brogi_monitoring_2023,
	title = {Monitoring {Irrigation} in {Small} {Orchards} with {Cosmic}-{Ray} {Neutron} {Sensors}},
	volume = {23},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1424-8220},
	url = {https://www.mdpi.com/1424-8220/23/5/2378},
	doi = {10.3390/s23052378},
	abstract = {Due to their unique characteristics, cosmic-ray neutron sensors (CRNSs) have potential in monitoring and informing irrigation management, and thus optimising the use of water resources in agriculture. However, practical methods to monitor small, irrigated fields with CRNSs are currently not available and the challenges of targeting areas smaller than the CRNS sensing volume are mostly unaddressed. In this study, CRNSs are used to continuously monitor soil moisture (SM) dynamics in two irrigated apple orchards (Agia, Greece) of {\textasciitilde}1.2 ha. The CRNS-derived SM was compared to a reference SM obtained by weighting a dense sensor network. In the 2021 irrigation period, CRNSs could only capture the timing of irrigation events, and an ad hoc calibration resulted in improvements only in the hours before irrigation (RMSE between 0.020 and 0.035). In 2022, a correction based on neutron transport simulations, and on SM measurements from a non-irrigated location, was tested. In the nearby irrigated field, the proposed correction improved the CRNS-derived SM (from 0.052 to 0.031 RMSE) and, most importantly, allowed for monitoring the magnitude of SM dynamics that are due to irrigation. The results are a step forward in using CRNSs as a decision support system in irrigation management.},
	language = {en},
	number = {5},
	urldate = {2024-11-14},
	journal = {Sensors},
	author = {Brogi, Cosimo and Pisinaras, Vassilios and Köhli, Markus and Dombrowski, Olga and Hendricks Franssen, Harrie-Jan and Babakos, Konstantinos and Chatzi, Anna and Panagopoulos, Andreas and Bogena, Heye Reemt},
	month = feb,
	year = {2023},
	pages = {2378},
}

Due to their unique characteristics, cosmic-ray neutron sensors (CRNSs) have potential in monitoring and informing irrigation management, and thus optimising the use of water resources in agriculture. However, practical methods to monitor small, irrigated fields with CRNSs are currently not available and the challenges of targeting areas smaller than the CRNS sensing volume are mostly unaddressed. In this study, CRNSs are used to continuously monitor soil moisture (SM) dynamics in two irrigated apple orchards (Agia, Greece) of ~1.2 ha. The CRNS-derived SM was compared to a reference SM obtained by weighting a dense sensor network. In the 2021 irrigation period, CRNSs could only capture the timing of irrigation events, and an ad hoc calibration resulted in improvements only in the hours before irrigation (RMSE between 0.020 and 0.035). In 2022, a correction based on neutron transport simulations, and on SM measurements from a non-irrigated location, was tested. In the nearby irrigated field, the proposed correction improved the CRNS-derived SM (from 0.052 to 0.031 RMSE) and, most importantly, allowed for monitoring the magnitude of SM dynamics that are due to irrigation. The results are a step forward in using CRNSs as a decision support system in irrigation management.

@incollection{brogi_soil_2023,
	title = {Soil processes in the hydrologic cycle},
	copyright = {https://www.elsevier.com/tdm/userlicense/1.0/},
	isbn = {978-0-323-95133-3},
	url = {https://linkinghub.elsevier.com/retrieve/pii/B9780128229743000793},
	language = {en},
	urldate = {2024-11-14},
	booktitle = {Encyclopedia of {Soils} in the {Environment}},
	publisher = {Elsevier},
	author = {Brogi, Cosimo and Vereecken, Harry and Bogena, Heye Reemt and Brocca, Luca},
	year = {2023},
	doi = {10.1016/B978-0-12-822974-3.00079-3},
	pages = {469--481},
}

@article{boker_added_2023,
	title = {Added value of an atmospheric circulation pattern‐based statistical downscaling approach for daily precipitation distributions in complex terrain},
	volume = {43},
	issn = {0899-8418, 1097-0088},
	url = {https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.8136},
	doi = {10.1002/joc.8136},
	abstract = {Abstract
            Reliable prediction of heavy precipitation events causing floods in a world of changing climate is crucial for the development of appropriate adaption strategies. Many attempts to provide such predictions have already been conducted but there is still much potential for improvement left. This is particularly true for statistical downscaling of heavy precipitation due to changes present in the corresponding atmospheric drivers. In this study, a circulation pattern (CP) conditional downscaling to the station level is proposed which considers occurring frequency changes of CPs. Following a strict circulation‐to‐environment approach we use atmospheric predictors to derive CPs. Subsequently, precipitation observations are used to derive CP conditional cumulative distribution functions (CDFs) of daily precipitation. Raw precipitation time series are sampled from these CDFs. Bias correction is applied to the sampled time series with quantile mapping (QM) and parametric transfer functions (PTFs) as methods being tested. The added value of this CP conditional downscaling approach is evaluated against the corresponding common non‐CP conditional approach. The performance evaluation is conducted by using Kling–Gupta Efficiency (KGE), root mean squared error (RMSE), and mean absolute error (MAE) metrics. In both cases the applied bias correction is identical. Potential added value can therefore only be attributed to the CP conditioning. It can be shown that the proposed CP conditional downscaling approach is capable of yielding more reliable and accurate downscaled daily precipitation time series in comparison to a non‐CP conditional approach. This can be seen in particular for the extreme parts of the distribution. Above the 95th percentile, an average performance gain of +0.24 and a maximum gain of +0.6 in terms of KGE is observed. These findings support the assumption of conserving and utilizing atmospheric information through CPs can be beneficial for more reliable statistical precipitation downscaling. Due to the availability of these atmospheric predictors in climate model output, the presented method is potentially suitable for downscaling precipitation projections.},
	language = {en},
	number = {11},
	urldate = {2024-11-14},
	journal = {International Journal of Climatology},
	author = {Böker, Brian and Laux, Patrick and Olschewski, Patrick and Kunstmann, Harald},
	month = sep,
	year = {2023},
	pages = {5130--5153},
}

Abstract Reliable prediction of heavy precipitation events causing floods in a world of changing climate is crucial for the development of appropriate adaption strategies. Many attempts to provide such predictions have already been conducted but there is still much potential for improvement left. This is particularly true for statistical downscaling of heavy precipitation due to changes present in the corresponding atmospheric drivers. In this study, a circulation pattern (CP) conditional downscaling to the station level is proposed which considers occurring frequency changes of CPs. Following a strict circulation‐to‐environment approach we use atmospheric predictors to derive CPs. Subsequently, precipitation observations are used to derive CP conditional cumulative distribution functions (CDFs) of daily precipitation. Raw precipitation time series are sampled from these CDFs. Bias correction is applied to the sampled time series with quantile mapping (QM) and parametric transfer functions (PTFs) as methods being tested. The added value of this CP conditional downscaling approach is evaluated against the corresponding common non‐CP conditional approach. The performance evaluation is conducted by using Kling–Gupta Efficiency (KGE), root mean squared error (RMSE), and mean absolute error (MAE) metrics. In both cases the applied bias correction is identical. Potential added value can therefore only be attributed to the CP conditioning. It can be shown that the proposed CP conditional downscaling approach is capable of yielding more reliable and accurate downscaled daily precipitation time series in comparison to a non‐CP conditional approach. This can be seen in particular for the extreme parts of the distribution. Above the 95th percentile, an average performance gain of +0.24 and a maximum gain of +0.6 in terms of KGE is observed. These findings support the assumption of conserving and utilizing atmospheric information through CPs can be beneficial for more reliable statistical precipitation downscaling. Due to the availability of these atmospheric predictors in climate model output, the presented method is potentially suitable for downscaling precipitation projections.

@inproceedings{chabrillat_monitoring_2023,
	address = {Pasadena, CA, USA},
	title = {Monitoring {Soil} {Properties} {Using} {EnMAP} {Spaceborne} {Imaging} {Spectroscopy} {Mission}},
	copyright = {https://doi.org/10.15223/policy-029},
	isbn = {9798350320107},
	url = {https://ieeexplore.ieee.org/document/10282165/},
	doi = {10.1109/IGARSS52108.2023.10282165},
	urldate = {2024-11-14},
	booktitle = {{IGARSS} 2023 - 2023 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium}},
	publisher = {IEEE},
	author = {Chabrillat, Sabine and Milewski, Robert and Ward, Kathrin and Foerster, Saskia and Guillaso, Stephane and Loy, Christopher and Ben-Dor, Eyal and Tziolas, Nikos and Schmid, Thomas and Van Wesemael, Bas and Demattê, José A. M.},
	month = jul,
	year = {2023},
	pages = {1130--1133},
}

@article{chang_observational_2023,
	title = {Observational constraints reduce model spread but not uncertainty in global wetland methane emission estimates},
	volume = {29},
	issn = {1354-1013, 1365-2486},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/gcb.16755},
	doi = {10.1111/gcb.16755},
	abstract = {Abstract
            
              The recent rise in atmospheric methane (CH
              4
              ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH
              4
              source, estimates of global wetland CH
              4
              emissions vary widely among approaches taken by bottom‐up (BU) process‐based biogeochemical models and top‐down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi‐model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH
              4
              emission estimates and model performance. We find that using better‐performing models identified by observational constraints reduces the spread of wetland CH
              4
              emission estimates by 62\% and 39\% for BU‐ and TD‐based approaches, respectively. However, global BU and TD CH
              4
              emission estimate discrepancies increased by about 15\% (from 31 to 36 TgCH
              4
              year
              −1
              ) when the top 20\% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter‐site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH
              4
              models to move beyond static benchmarking and focus on evaluating site‐specific and ecosystem‐specific variabilities inferred from observations.},
	language = {en},
	number = {15},
	urldate = {2024-11-14},
	journal = {Global Change Biology},
	author = {Chang, Kuang‐Yu and Riley, William J. and Collier, Nathan and McNicol, Gavin and Fluet‐Chouinard, Etienne and Knox, Sara H. and Delwiche, Kyle B. and Jackson, Robert B. and Poulter, Benjamin and Saunois, Marielle and Chandra, Naveen and Gedney, Nicola and Ishizawa, Misa and Ito, Akihiko and Joos, Fortunat and Kleinen, Thomas and Maggi, Federico and McNorton, Joe and Melton, Joe R. and Miller, Paul and Niwa, Yosuke and Pasut, Chiara and Patra, Prabir K. and Peng, Changhui and Peng, Sushi and Segers, Arjo and Tian, Hanqin and Tsuruta, Aki and Yao, Yuanzhi and Yin, Yi and Zhang, Wenxin and Zhang, Zhen and Zhu, Qing and Zhu, Qiuan and Zhuang, Qianlai},
	month = aug,
	year = {2023},
	pages = {4298--4312},
}

Abstract The recent rise in atmospheric methane (CH 4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH 4 source, estimates of global wetland CH 4 emissions vary widely among approaches taken by bottom‐up (BU) process‐based biogeochemical models and top‐down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi‐model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH 4 emission estimates and model performance. We find that using better‐performing models identified by observational constraints reduces the spread of wetland CH 4 emission estimates by 62% and 39% for BU‐ and TD‐based approaches, respectively. However, global BU and TD CH 4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH 4 year −1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter‐site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH 4 models to move beyond static benchmarking and focus on evaluating site‐specific and ecosystem‐specific variabilities inferred from observations.

@article{chen_estimation_2023,
	title = {Estimation of global land surface evapotranspiration and its trend using a surface energy balance constrained deep learning model},
	volume = {627},
	issn = {00221694},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0022169423011666},
	doi = {10.1016/j.jhydrol.2023.130224},
	language = {en},
	urldate = {2024-11-14},
	journal = {Journal of Hydrology},
	author = {Chen, Han and Ghani Razaqpur, A. and Wei, Yizhao and Huang, Jinhui Jeanne and Li, Han and McBean, Edward},
	month = dec,
	year = {2023},
	pages = {130224},
}

@article{chen_radar-based_2023,
	title = {A {Radar}-{Based} {Quantitative} {Precipitation} {Estimation} {Algorithm} to {Overcome} the {Impact} of {Vertical} {Gradients} of {Warm}-{Rain} {Precipitation}: {The} {Flood} in {Western} {Germany} on 14 {July} 2021},
	volume = {24},
	copyright = {http://www.ametsoc.org/PUBSReuseLicenses},
	issn = {1525-755X, 1525-7541},
	shorttitle = {A {Radar}-{Based} {Quantitative} {Precipitation} {Estimation} {Algorithm} to {Overcome} the {Impact} of {Vertical} {Gradients} of {Warm}-{Rain} {Precipitation}},
	url = {https://journals.ametsoc.org/view/journals/hydr/24/3/JHM-D-22-0111.1.xml},
	doi = {10.1175/JHM-D-22-0111.1},
	abstract = {Abstract
            
              The demand of accurate, near-real-time radar-based quantitative precipitation estimation (QPE), which is key to feed hydrological models and enable reliable flash flood predictions, was highlighted again by the disastrous floods following after an intense stratiform precipitation field passing western Germany on 14 July 2021. Three state-of-the-art rainfall algorithms based on reflectivity
              Z
              , specific differential phase
              K
              DP
              , and specific attenuation
              A
              were applied to observations of four polarimetric C-band radars operated by the German Meteorological Service [DWD (Deutscher Wetterdienst)]. Due to the large vertical gradients of precipitation below the melting layer suggesting warm-rain processes, all QPE products significantly underestimate surface precipitation. We propose two mitigation approaches: (i) vertical profile (VP) corrections for
              Z
              and
              K
              DP
              and (ii) gap filling using observations of a local X-band radar, JuXPol. We also derive rainfall retrievals from vertically pointing Micro Rain Radar (MRR) profiles, which indirectly take precipitation gradients in the lower few hundreds of meters into account. When evaluated with DWD rain gauge measurements, those retrievals result in pronounced improvements, especially for the
              A
              -based retrieval partly due to its lower sensitivity to the variability of raindrop size distributions. The VP correction further improves QPE by reducing the normalized root-mean-square error by 23\% and the normalized mean bias by 20\%. With the use of gap-filling JuXPol data, the
              A
              -based retrieval gives the lowest errors followed by the
              Z
              -based retrievals in combination with VP corrections. The presented algorithms demonstrate the increased value of radar-based QPE application for warm-rain events and related potential flash flooding warnings.},
	number = {3},
	urldate = {2024-11-14},
	journal = {Journal of Hydrometeorology},
	author = {Chen, Ju-Yu and Reinoso-Rondinel, Ricardo and Trömel, Silke and Simmer, Clemens and Ryzhkov, Alexander},
	month = mar,
	year = {2023},
	pages = {521--536},
}

Abstract The demand of accurate, near-real-time radar-based quantitative precipitation estimation (QPE), which is key to feed hydrological models and enable reliable flash flood predictions, was highlighted again by the disastrous floods following after an intense stratiform precipitation field passing western Germany on 14 July 2021. Three state-of-the-art rainfall algorithms based on reflectivity Z , specific differential phase K DP , and specific attenuation A were applied to observations of four polarimetric C-band radars operated by the German Meteorological Service [DWD (Deutscher Wetterdienst)]. Due to the large vertical gradients of precipitation below the melting layer suggesting warm-rain processes, all QPE products significantly underestimate surface precipitation. We propose two mitigation approaches: (i) vertical profile (VP) corrections for Z and K DP and (ii) gap filling using observations of a local X-band radar, JuXPol. We also derive rainfall retrievals from vertically pointing Micro Rain Radar (MRR) profiles, which indirectly take precipitation gradients in the lower few hundreds of meters into account. When evaluated with DWD rain gauge measurements, those retrievals result in pronounced improvements, especially for the A -based retrieval partly due to its lower sensitivity to the variability of raindrop size distributions. The VP correction further improves QPE by reducing the normalized root-mean-square error by 23% and the normalized mean bias by 20%. With the use of gap-filling JuXPol data, the A -based retrieval gives the lowest errors followed by the Z -based retrievals in combination with VP corrections. The presented algorithms demonstrate the increased value of radar-based QPE application for warm-rain events and related potential flash flooding warnings.

@article{chen_nirp_2023,
	title = {{NIRP} as a remote sensing proxy for measuring gross primary production across different biomes and climate zones: {Performance} and limitations},
	volume = {122},
	issn = {15698432},
	shorttitle = {{NIRP} as a remote sensing proxy for measuring gross primary production across different biomes and climate zones},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1569843223002613},
	doi = {10.1016/j.jag.2023.103437},
	language = {en},
	urldate = {2024-11-14},
	journal = {International Journal of Applied Earth Observation and Geoinformation},
	author = {Chen, Siyuan and Sui, Lichun and Liu, Liangyun and Liu, Xinjie and Li, Jonathan and Huang, Lingxiao and Li, Xing and Qian, Xiaojin},
	month = aug,
	year = {2023},
	pages = {103437},
}

@article{chen_evidence_2023,
	title = {Evidence for widespread thermal optimality of ecosystem respiration},
	volume = {7},
	issn = {2397-334X},
	url = {https://www.nature.com/articles/s41559-023-02121-w},
	doi = {10.1038/s41559-023-02121-w},
	language = {en},
	number = {9},
	urldate = {2024-11-14},
	journal = {Nature Ecology \& Evolution},
	author = {Chen, Weinan and Wang, Song and Wang, Jinsong and Xia, Jianyang and Luo, Yiqi and Yu, Guirui and Niu, Shuli},
	month = jul,
	year = {2023},
	pages = {1379--1387},
}

@article{conradt_cross-sectoral_2023,
	title = {Cross-sectoral impacts of the 2018–2019 {Central} {European} drought and climate resilience in the {German} part of the {Elbe} {River} basin},
	volume = {23},
	issn = {1436-3798, 1436-378X},
	url = {https://link.springer.com/10.1007/s10113-023-02032-3},
	doi = {10.1007/s10113-023-02032-3},
	abstract = {Abstract
            
              The 2018–2019 Central European drought was probably the most extreme in Germany since the early sixteenth century. We assess the multiple consequences of the drought for natural systems, the economy and human health in the German part of the Elbe River basin, an area of 97,175 km
              2
              including the cities of Berlin and Hamburg and contributing about 18\% to the German GDP. We employ meteorological, hydrological and socio-economic data to build a comprehensive picture of the drought severity, its multiple effects and cross-sectoral consequences in the basin. Time series of different drought indices illustrate the severity of the 2018–2019 drought and how it progressed from meteorological water deficits via soil water depletion towards low groundwater levels and river runoff, and losses in vegetation productivity. The event resulted in severe production losses in agriculture (minus 20–40\% for staple crops) and forestry (especially through forced logging of damaged wood: 25.1 million tons in 2018–2020 compared to only 3.4 million tons in 2015–2017), while other economic sectors remained largely unaffected. However, there is no guarantee that this socio-economic stability will be sustained in future drought events; this is discussed in the light of 2022, another dry year holding the potential for a compound crisis. Given the increased probability for more intense and long-lasting droughts in most parts of Europe, this example of actual cross-sectoral drought impacts will be relevant for drought awareness and preparation planning in other regions.},
	language = {en},
	number = {1},
	urldate = {2024-11-14},
	journal = {Regional Environmental Change},
	author = {Conradt, Tobias and Engelhardt, Henry and Menz, Christoph and Vicente-Serrano, Sergio M. and Farizo, Begoña Alvarez and Peña-Angulo, Dhais and Domínguez-Castro, Fernando and Eklundh, Lars and Jin, Hongxiao and Boincean, Boris and Murphy, Conor and López-Moreno, J. Ignacio},
	month = mar,
	year = {2023},
	pages = {32},
}

Abstract The 2018–2019 Central European drought was probably the most extreme in Germany since the early sixteenth century. We assess the multiple consequences of the drought for natural systems, the economy and human health in the German part of the Elbe River basin, an area of 97,175 km 2 including the cities of Berlin and Hamburg and contributing about 18% to the German GDP. We employ meteorological, hydrological and socio-economic data to build a comprehensive picture of the drought severity, its multiple effects and cross-sectoral consequences in the basin. Time series of different drought indices illustrate the severity of the 2018–2019 drought and how it progressed from meteorological water deficits via soil water depletion towards low groundwater levels and river runoff, and losses in vegetation productivity. The event resulted in severe production losses in agriculture (minus 20–40% for staple crops) and forestry (especially through forced logging of damaged wood: 25.1 million tons in 2018–2020 compared to only 3.4 million tons in 2015–2017), while other economic sectors remained largely unaffected. However, there is no guarantee that this socio-economic stability will be sustained in future drought events; this is discussed in the light of 2022, another dry year holding the potential for a compound crisis. Given the increased probability for more intense and long-lasting droughts in most parts of Europe, this example of actual cross-sectoral drought impacts will be relevant for drought awareness and preparation planning in other regions.

@article{crago_what_2023,
	title = {What is the {Priestley}–{Taylor} wet-surface evaporation parameter? {Testing} four hypotheses},
	volume = {27},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1607-7938},
	shorttitle = {What is the {Priestley}–{Taylor} wet-surface evaporation parameter?},
	url = {https://hess.copernicus.org/articles/27/3205/2023/},
	doi = {10.5194/hess-27-3205-2023},
	abstract = {Abstract. This study compares four different hypotheses regarding the nature
of the Priestley–Taylor parameter α. They are as follows:
 α is a universal constant. The Bowen ratio (H/LE, where H is the sensible heat flux, and LE is
the latent heat flux) for equilibrium (i.e., saturated air column near the
surface) evaporation is a constant times the Bowen ratio at minimal
advection (Andreas et al., 2013). Minimal advection over a wet surface corresponds to a particular relative humidity value. α is a constant fraction of the difference from the minimum value of 1 to the maximum value of α proposed by Priestley and Taylor (1972).
Formulas for α are developed for the last three hypotheses. Weather,
radiation, and surface energy flux data from 171 FLUXNET eddy covariance
stations were used. The condition LEref/LEp{\textgreater} 0.90 was
taken as the criterion for nearly saturated conditions (where LEref is
the reference, and LEp is the apparent potential evaporation rate from the equation by Penman, 1948). Daily and monthly average data from the sites were
obtained. All formulations for α include one model parameter which
is optimized such that the root mean square error of the target variable was
minimized. For each model, separate optimizations were done for predictions
of the target variables α, wet-surface evaporation (α
multiplied by equilibrium evaporation rate) and actual evaporation (the
latter using a highly successful version of the complementary relationship
of evaporation). Overall, the second and fourth hypotheses received the best
support from the data.},
	language = {en},
	number = {17},
	urldate = {2024-11-14},
	journal = {Hydrology and Earth System Sciences},
	author = {Crago, Richard D. and Szilagyi, Jozsef and Qualls, Russell J.},
	month = sep,
	year = {2023},
	pages = {3205--3220},
}

Abstract. This study compares four different hypotheses regarding the nature of the Priestley–Taylor parameter α. They are as follows: α is a universal constant. The Bowen ratio (H/LE, where H is the sensible heat flux, and LE is the latent heat flux) for equilibrium (i.e., saturated air column near the surface) evaporation is a constant times the Bowen ratio at minimal advection (Andreas et al., 2013). Minimal advection over a wet surface corresponds to a particular relative humidity value. α is a constant fraction of the difference from the minimum value of 1 to the maximum value of α proposed by Priestley and Taylor (1972). Formulas for α are developed for the last three hypotheses. Weather, radiation, and surface energy flux data from 171 FLUXNET eddy covariance stations were used. The condition LEref/LEp\textgreater 0.90 was taken as the criterion for nearly saturated conditions (where LEref is the reference, and LEp is the apparent potential evaporation rate from the equation by Penman, 1948). Daily and monthly average data from the sites were obtained. All formulations for α include one model parameter which is optimized such that the root mean square error of the target variable was minimized. For each model, separate optimizations were done for predictions of the target variables α, wet-surface evaporation (α multiplied by equilibrium evaporation rate) and actual evaporation (the latter using a highly successful version of the complementary relationship of evaporation). Overall, the second and fourth hypotheses received the best support from the data.

@article{dadi_oxygen_2023,
	title = {Oxygen {Dependent} {Temperature} {Regulation} of {Benthic} {Fluxes} in {Reservoirs}},
	volume = {37},
	issn = {0886-6236, 1944-9224},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022GB007647},
	doi = {10.1029/2022GB007647},
	abstract = {Abstract
            
              Temperature and dissolved oxygen concentration are critical factors affecting the exchange of solutes between sediment and water; both factors will be affected by warming of lakes and thereby influence water quality. Temperature and oxygen responses of single solute fluxes are well known; however, not much is known about the interaction of temperature and oxygen in regulating the balance of different fluxes in the benthic environment. We analyzed benthic flux (mobilization and immobilization) data of various solutes (dissolved organic carbon (DOC), CH
              4
              , NO
              3
              −
              ‐N, NH
              4
              +
              ‐N, SRP, SO
              4
              −
              , Fe, Mn, and O
              2
              ) collected from laboratory incubations of 142 sediment cores from 5 different reservoirs incubated under varying in situ temperature and oxygen conditions. Oxygen was the primary driver of benthic fluxes, while temperature and total organic content were secondary. Temperature effects on benthic fluxes were stronger under anoxic conditions which imply that warming will substantially increase the benthic fluxes if the sediment surface becomes anoxic. The varying temperature response of processes underlying the studied fluxes will result in a shift of their relative importance in the benthic environment, especially in shallow lakes that are more vulnerable to warming. For example, more anoxic conditions will shift the equilibrium between net sulfate reduction and methane release toward the latter. We also predict that physical effects of warming leading to hypolimnetic oxygen depletion, that is, stronger stratification and longer hypolimnetic confinement will increase the benthic mobilization of phosphorus, DOC, and methane into water and immobilization of sulfate by the sediments even in deep lakes.
            
          , 
            Plain Language Summary
            Temperature and dissolved oxygen concentration control the release of undesirable components buried in lake or reservoir sediments, that is, nutrients, metals, and organic matter, which can cause water quality problems. We investigated the effects of rising temperature and levels of oxygen on the release of undesirable components by performing experiments using sediments and water from five different reservoirs. The sediments with a layer of water on top were incubated under different in situ temperature (low and high) and oxygen conditions (with and without). Our results show that the absence of oxygen was the main cause of the release of nutrients and metals. When there was no oxygen in the sediment and water, nutrients and metals were released from the sediment into the water and this effect increased when temperature was high. There is higher possibility that phosphorus, dissolved organic carbon, and methane will be released from sediments in some reservoirs as a result of global warming.
          , 
            Key Points
            
              
                
                  Solute fluxes from benthic lake sediments varied in response to temperature, with oxygen fluxes responding most strongly
                
                
                  Temperature effects on the magnitude of benthic fluxes were stronger under anoxic than oxic conditions
                
                
                  Direct temperature effects on reservoir water quality will be small compared to indirect effects through anoxia facilitation},
	language = {en},
	number = {4},
	urldate = {2024-11-14},
	journal = {Global Biogeochemical Cycles},
	author = {Dadi, Tallent and Friese, Kurt and Wendt‐Potthoff, Katrin and Marcé, Rafael and Koschorreck, Matthias},
	month = apr,
	year = {2023},
	pages = {e2022GB007647},
}

Abstract Temperature and dissolved oxygen concentration are critical factors affecting the exchange of solutes between sediment and water; both factors will be affected by warming of lakes and thereby influence water quality. Temperature and oxygen responses of single solute fluxes are well known; however, not much is known about the interaction of temperature and oxygen in regulating the balance of different fluxes in the benthic environment. We analyzed benthic flux (mobilization and immobilization) data of various solutes (dissolved organic carbon (DOC), CH 4 , NO 3 − ‐N, NH 4 + ‐N, SRP, SO 4 − , Fe, Mn, and O 2 ) collected from laboratory incubations of 142 sediment cores from 5 different reservoirs incubated under varying in situ temperature and oxygen conditions. Oxygen was the primary driver of benthic fluxes, while temperature and total organic content were secondary. Temperature effects on benthic fluxes were stronger under anoxic conditions which imply that warming will substantially increase the benthic fluxes if the sediment surface becomes anoxic. The varying temperature response of processes underlying the studied fluxes will result in a shift of their relative importance in the benthic environment, especially in shallow lakes that are more vulnerable to warming. For example, more anoxic conditions will shift the equilibrium between net sulfate reduction and methane release toward the latter. We also predict that physical effects of warming leading to hypolimnetic oxygen depletion, that is, stronger stratification and longer hypolimnetic confinement will increase the benthic mobilization of phosphorus, DOC, and methane into water and immobilization of sulfate by the sediments even in deep lakes. , Plain Language Summary Temperature and dissolved oxygen concentration control the release of undesirable components buried in lake or reservoir sediments, that is, nutrients, metals, and organic matter, which can cause water quality problems. We investigated the effects of rising temperature and levels of oxygen on the release of undesirable components by performing experiments using sediments and water from five different reservoirs. The sediments with a layer of water on top were incubated under different in situ temperature (low and high) and oxygen conditions (with and without). Our results show that the absence of oxygen was the main cause of the release of nutrients and metals. When there was no oxygen in the sediment and water, nutrients and metals were released from the sediment into the water and this effect increased when temperature was high. There is higher possibility that phosphorus, dissolved organic carbon, and methane will be released from sediments in some reservoirs as a result of global warming. , Key Points Solute fluxes from benthic lake sediments varied in response to temperature, with oxygen fluxes responding most strongly Temperature effects on the magnitude of benthic fluxes were stronger under anoxic than oxic conditions Direct temperature effects on reservoir water quality will be small compared to indirect effects through anoxia facilitation

@article{de_pue_temporal_2023,
	title = {Temporal variability of observed and simulated gross primary productivity, modulated by vegetation state and hydrometeorological drivers},
	volume = {20},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1726-4189},
	url = {https://bg.copernicus.org/articles/20/4795/2023/},
	doi = {10.5194/bg-20-4795-2023},
	abstract = {Abstract. The gross primary production (GPP) of the terrestrial biosphere is a key source of variability in the global carbon cycle. It is modulated by hydrometeorological drivers (i.e. short-wave radiation, air temperature, vapour pressure deficit and soil moisture) and the vegetation state (i.e. canopy greenness, leaf area index) at instantaneous to interannual timescales. In this study, we set out to evaluate the ability of GPP models to capture this variability. Eleven models were considered, which rely purely on remote sensing data (RS-driven), meteorological data (meteo-driven, e.g. dynamic global vegetation models; DGVMs) or a combination of both (hybrid, e.g. light-use efficiency, LUE, models). They were evaluated using in situ observations at 61 eddy covariance sites, covering a broad range of herbaceous and forest biomes. The results illustrated how the determinant of temporal variability shifts from meteorological variables at sub-seasonal timescales to biophysical variables at seasonal and interannual timescales. RS-driven models lacked the sensitivity to the dominant drivers at short timescales (i.e. short-wave radiation and vapour pressure deficit) and failed to capture the decoupling of photosynthesis and canopy greenness (e.g. in evergreen forests). Conversely, meteo-driven models accurately captured the variability across timescales, despite the challenges in the prognostic simulation of the vegetation state. The largest errors were found in water-limited sites, where the accuracy of the soil moisture dynamics determines the quality of the GPP estimates. In arid herbaceous sites, canopy greenness and photosynthesis were more tightly coupled, resulting in improved results with RS-driven models. Hybrid models capitalized on the combination of RS observations and meteorological information. LUE models were among the most accurate models to monitor GPP across all biomes, despite their simple architecture. Overall, we conclude that the combination of meteorological drivers and remote sensing observations is required to yield an accurate reproduction of the spatio-temporal variability of GPP. To further advance the performance of DGVMs, improvements in the soil moisture dynamics and vegetation evolution are needed.},
	language = {en},
	number = {23},
	urldate = {2024-11-14},
	journal = {Biogeosciences},
	author = {De Pue, Jan and Wieneke, Sebastian and Bastos, Ana and Barrios, José Miguel and Liu, Liyang and Ciais, Philippe and Arboleda, Alirio and Hamdi, Rafiq and Maleki, Maral and Maignan, Fabienne and Gellens-Meulenberghs, Françoise and Janssens, Ivan and Balzarolo, Manuela},
	month = dec,
	year = {2023},
	pages = {4795--4818},
}

Abstract. The gross primary production (GPP) of the terrestrial biosphere is a key source of variability in the global carbon cycle. It is modulated by hydrometeorological drivers (i.e. short-wave radiation, air temperature, vapour pressure deficit and soil moisture) and the vegetation state (i.e. canopy greenness, leaf area index) at instantaneous to interannual timescales. In this study, we set out to evaluate the ability of GPP models to capture this variability. Eleven models were considered, which rely purely on remote sensing data (RS-driven), meteorological data (meteo-driven, e.g. dynamic global vegetation models; DGVMs) or a combination of both (hybrid, e.g. light-use efficiency, LUE, models). They were evaluated using in situ observations at 61 eddy covariance sites, covering a broad range of herbaceous and forest biomes. The results illustrated how the determinant of temporal variability shifts from meteorological variables at sub-seasonal timescales to biophysical variables at seasonal and interannual timescales. RS-driven models lacked the sensitivity to the dominant drivers at short timescales (i.e. short-wave radiation and vapour pressure deficit) and failed to capture the decoupling of photosynthesis and canopy greenness (e.g. in evergreen forests). Conversely, meteo-driven models accurately captured the variability across timescales, despite the challenges in the prognostic simulation of the vegetation state. The largest errors were found in water-limited sites, where the accuracy of the soil moisture dynamics determines the quality of the GPP estimates. In arid herbaceous sites, canopy greenness and photosynthesis were more tightly coupled, resulting in improved results with RS-driven models. Hybrid models capitalized on the combination of RS observations and meteorological information. LUE models were among the most accurate models to monitor GPP across all biomes, despite their simple architecture. Overall, we conclude that the combination of meteorological drivers and remote sensing observations is required to yield an accurate reproduction of the spatio-temporal variability of GPP. To further advance the performance of DGVMs, improvements in the soil moisture dynamics and vegetation evolution are needed.

@article{dega_probabilistic_2023,
	title = {Probabilistic prediction by means of the propagation of response variable uncertainty through a {Monte} {Carlo} approach in regression random forest: {Application} to soil moisture regionalization},
	volume = {11},
	issn = {2296-665X},
	shorttitle = {Probabilistic prediction by means of the propagation of response variable uncertainty through a {Monte} {Carlo} approach in regression random forest},
	url = {https://www.frontiersin.org/articles/10.3389/fenvs.2023.1009191/full},
	doi = {10.3389/fenvs.2023.1009191},
	abstract = {Probabilistic predictions aim to produce a prediction interval with probabilities associated with each possible outcome instead of a single value for each outcome. In multiple regression problems, this can be achieved by propagating the known uncertainties in data of the response variables through a Monte Carlo approach. This paper presents an analysis of the impact of the training response variable uncertainty on the prediction uncertainties with the help of a comparison with probabilistic prediction obtained with quantile regression random forest. The result is an uncertainty quantification of the impact on the prediction. The approach is illustrated with the example of the probabilistic regionalization of soil moisture derived from cosmic-ray neutron sensing measurements, providing a regional-scale soil moisture map with data uncertainty quantification covering the Selke river catchment, eastern Germany.},
	urldate = {2024-11-14},
	journal = {Frontiers in Environmental Science},
	author = {Dega, Ségolène and Dietrich, Peter and Schrön, Martin and Paasche, Hendrik},
	month = jan,
	year = {2023},
	pages = {1009191},
}

Probabilistic predictions aim to produce a prediction interval with probabilities associated with each possible outcome instead of a single value for each outcome. In multiple regression problems, this can be achieved by propagating the known uncertainties in data of the response variables through a Monte Carlo approach. This paper presents an analysis of the impact of the training response variable uncertainty on the prediction uncertainties with the help of a comparison with probabilistic prediction obtained with quantile regression random forest. The result is an uncertainty quantification of the impact on the prediction. The approach is illustrated with the example of the probabilistic regionalization of soil moisture derived from cosmic-ray neutron sensing measurements, providing a regional-scale soil moisture map with data uncertainty quantification covering the Selke river catchment, eastern Germany.

@article{denager_point-scale_2023,
	title = {Point-scale multi-objective calibration of the {Community} {Land} {Model} (version 5.0) using in situ observations of water and energy fluxes and variables},
	volume = {27},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1607-7938},
	url = {https://hess.copernicus.org/articles/27/2827/2023/},
	doi = {10.5194/hess-27-2827-2023},
	abstract = {Abstract. This study evaluates water and energy fluxes and variables in combination with parameter optimization of version 5 of the state-of-the-art Community Land Model (CLM5) land surface model, using 6 years of hourly
observations of latent heat flux, sensible heat flux, groundwater recharge,
soil moisture and soil temperature from an agricultural observatory in
Denmark. The results show that multi-objective calibration in combination
with truncated singular value decomposition and Tikhonov regularization is a powerful method to improve the current practice of using lookup tables to define parameter values in land surface models. Using measurements of
turbulent fluxes as the target variable, parameter optimization is capable
of matching simulations and observations of latent heat, especially during
the summer period, whereas simulated sensible heat is clearly biased. Of the
30 parameters considered, the soil texture, monthly leaf area index (LAI) in summer, stomatal
conductance and root distribution have the highest influence on the
local-scale simulation results. The results from this study contribute to
improvements of the model characterization of water and energy fluxes. This work highlights the importance of performing parameter calibration using
observations of hydrologic and energy fluxes and variables to obtain the optimal parameter values for a land surface model.},
	language = {en},
	number = {14},
	urldate = {2024-11-14},
	journal = {Hydrology and Earth System Sciences},
	author = {Denager, Tanja and Sonnenborg, Torben O. and Looms, Majken C. and Bogena, Heye and Jensen, Karsten H.},
	month = jul,
	year = {2023},
	pages = {2827--2845},
}

Abstract. This study evaluates water and energy fluxes and variables in combination with parameter optimization of version 5 of the state-of-the-art Community Land Model (CLM5) land surface model, using 6 years of hourly observations of latent heat flux, sensible heat flux, groundwater recharge, soil moisture and soil temperature from an agricultural observatory in Denmark. The results show that multi-objective calibration in combination with truncated singular value decomposition and Tikhonov regularization is a powerful method to improve the current practice of using lookup tables to define parameter values in land surface models. Using measurements of turbulent fluxes as the target variable, parameter optimization is capable of matching simulations and observations of latent heat, especially during the summer period, whereas simulated sensible heat is clearly biased. Of the 30 parameters considered, the soil texture, monthly leaf area index (LAI) in summer, stomatal conductance and root distribution have the highest influence on the local-scale simulation results. The results from this study contribute to improvements of the model characterization of water and energy fluxes. This work highlights the importance of performing parameter calibration using observations of hydrologic and energy fluxes and variables to obtain the optimal parameter values for a land surface model.

@article{deseano_diaz_response_2023,
	title = {Response of a grassland species to dry environmental conditions from water stable isotopic monitoring: no evident shift in root water uptake to wetter soil layers},
	volume = {482},
	issn = {0032-079X, 1573-5036},
	shorttitle = {Response of a grassland species to dry environmental conditions from water stable isotopic monitoring},
	url = {https://link.springer.com/10.1007/s11104-022-05703-y},
	doi = {10.1007/s11104-022-05703-y},
	abstract = {Abstract
            
              Aims
              
                We aimed at assessing the influence of above- and below-ground environmental conditions over the performance of
                Centaurea jacea
                L., a drought-resistant grassland forb species.
              
            
            
              Methods
              
                Transpiration rate, CO
                2
                assimilation rate, leaf water potential, instantaneous and intrinsic water use efficiency, temperature, relative humidity, vapor pressure deficit and soil water content in one plant and root length density in four plants, all grown in custom-made columns, were monitored daily for 87 days in the lab. The soil water isotopic composition in eleven depths was recorded daily in a non-destructive manner. The isotopic composition of plant transpiration was inferred from gas chamber measurements. Vertical isotopic gradients in the soil column were created by adding labeled water. Daily root water uptake (RWU) profiles were computed using the multi-source mixing model Stable Isotope Analysis in R (Parnell et al. PLoS ONE 5(3):1–5, 2010).
              
            
            
              Results
              
                RWU occurred mainly in soil layer 0–15 cm, ranging from 79 to 44\%, even when water was more easily available in deeper layers. In wet soil, the transpiration rate was driven mainly by vapor pressure deficit and light intensity. Once soil water content was less than 0.12 cm
                3
                cm
                − 3
                , the computed canopy conductance declined, which restricted leaf gas exchange. Leaf water potential dropped steeply to around − 3 MPa after soil water content was below 0.10 cm
                3
                cm
                − 3
                .
              
            
            
              Conclusion
              Our comprehensive data set contributes to a better understanding of the effects of drought on a grassland species and the limits of its acclimation in dry conditions.},
	language = {en},
	number = {1-2},
	urldate = {2024-11-14},
	journal = {Plant and Soil},
	author = {Deseano Diaz, Paulina Alejandra and Van Dusschoten, Dagmar and Kübert, Angelika and Brüggemann, Nicolas and Javaux, Mathieu and Merz, Steffen and Vanderborght, Jan and Vereecken, Harry and Dubbert, Maren and Rothfuss, Youri},
	month = jan,
	year = {2023},
	pages = {491--512},
}

Abstract Aims We aimed at assessing the influence of above- and below-ground environmental conditions over the performance of Centaurea jacea L., a drought-resistant grassland forb species. Methods Transpiration rate, CO 2 assimilation rate, leaf water potential, instantaneous and intrinsic water use efficiency, temperature, relative humidity, vapor pressure deficit and soil water content in one plant and root length density in four plants, all grown in custom-made columns, were monitored daily for 87 days in the lab. The soil water isotopic composition in eleven depths was recorded daily in a non-destructive manner. The isotopic composition of plant transpiration was inferred from gas chamber measurements. Vertical isotopic gradients in the soil column were created by adding labeled water. Daily root water uptake (RWU) profiles were computed using the multi-source mixing model Stable Isotope Analysis in R (Parnell et al. PLoS ONE 5(3):1–5, 2010). Results RWU occurred mainly in soil layer 0–15 cm, ranging from 79 to 44%, even when water was more easily available in deeper layers. In wet soil, the transpiration rate was driven mainly by vapor pressure deficit and light intensity. Once soil water content was less than 0.12 cm 3 cm − 3 , the computed canopy conductance declined, which restricted leaf gas exchange. Leaf water potential dropped steeply to around − 3 MPa after soil water content was below 0.10 cm 3 cm − 3 . Conclusion Our comprehensive data set contributes to a better understanding of the effects of drought on a grassland species and the limits of its acclimation in dry conditions.

@article{determann_nutrient_2023,
	title = {Nutrient retention in a small reservoir under changed variability of inflow nutrient concentration},
	volume = {13},
	issn = {2044-2041, 2044-205X},
	url = {https://www.tandfonline.com/doi/full/10.1080/20442041.2024.2305105},
	doi = {10.1080/20442041.2024.2305105},
	language = {en},
	number = {4},
	urldate = {2024-11-14},
	journal = {Inland Waters},
	author = {Determann, Maria and Musolff, Andreas and Frassl, Marieke A. and Rinke, Karsten and Shatwell, Tom},
	month = oct,
	year = {2023},
	pages = {560--575},
}

@misc{dombrowski_land_2023,
	title = {Land surface modeling as a tool to explore sustainable irrigation practices in {Mediterranean} fruit orchards},
	url = {https://essopenarchive.org/users/709172/articles/693525-land-surface-modeling-as-a-tool-to-explore-sustainable-irrigation-practices-in-mediterranean-fruit-orchards?commit=aa17a2787bc7795b5c72a83e4c174be511b408d0},
	doi = {10.22541/essoar.170365318.84320452/v1},
	urldate = {2024-11-14},
	publisher = {Preprints},
	author = {Dombrowski, Olga and Brogi, Cosimo and Franssen, Harrie-Jan Hendricks and Pisinaras, Vassilios and Panagopoulos, Andreas and Swenson, Sean and Bogena, Heye},
	month = dec,
	year = {2023},
}

@article{donahue_deep_2023,
	title = {Deep learning estimation of northern hemisphere soil freeze-thaw dynamics using satellite multi-frequency microwave brightness temperature observations},
	volume = {6},
	issn = {2624-909X},
	url = {https://www.frontiersin.org/articles/10.3389/fdata.2023.1243559/full},
	doi = {10.3389/fdata.2023.1243559},
	abstract = {Satellite microwave sensors are well suited for monitoring landscape freeze-thaw (FT) transitions owing to the strong brightness temperature (TB) or backscatter response to changes in liquid water abundance between predominantly frozen and thawed conditions. The FT retrieval is also a sensitive climate indicator with strong biophysical importance. However, retrieval algorithms can have difficulty distinguishing the FT status of soils from that of overlying features such as snow and vegetation, while variable land conditions can also degrade performance. Here, we applied a deep learning model using a multilayer convolutional neural network driven by AMSR2 and SMAP TB records, and trained on surface ({\textasciitilde}0–5 cm depth) soil temperature FT observations. Soil FT states were classified for the local morning (6 a.m.) and evening (6 p.m.) conditions corresponding to SMAP descending and ascending orbital overpasses, mapped to a 9 km polar grid spanning a five-year (2016–2020) record and Northern Hemisphere domain. Continuous variable estimates of the probability of frozen or thawed conditions were derived using a model cost function optimized against FT observational training data. Model results derived using combined multi-frequency (1.4, 18.7, 36.5 GHz) TBs produced the highest soil FT accuracy over other models derived using only single sensor or single frequency TB inputs. Moreover, SMAP L-band (1.4 GHz) TBs provided enhanced soil FT information and performance gain over model results derived using only AMSR2 TB inputs. The resulting soil FT classification showed favorable and consistent performance against soil FT observations from ERA5 reanalysis (mean percent accuracy, MPA: 92.7\%) and
              in situ
              weather stations (MPA: 91.0\%). The soil FT accuracy was generally consistent between morning and afternoon predictions and across different land covers and seasons. The model also showed better FT accuracy than ERA5 against regional weather station measurements (91.0\% vs. 86.1\% MPA). However, model confidence was lower in complex terrain where FT spatial heterogeneity was likely beneath the effective model grain size. Our results provide a high level of precision in mapping soil FT dynamics to improve understanding of complex seasonal transitions and their influence on ecological processes and climate feedbacks, with the potential to inform Earth system model predictions.},
	urldate = {2025-02-14},
	journal = {Frontiers in Big Data},
	author = {Donahue, Kellen and Kimball, John S. and Du, Jinyang and Bunt, Fredrick and Colliander, Andreas and Moghaddam, Mahta and Johnson, Jesse and Kim, Youngwook and Rawlins, Michael A.},
	month = nov,
	year = {2023},
	pages = {1243559},
}

Satellite microwave sensors are well suited for monitoring landscape freeze-thaw (FT) transitions owing to the strong brightness temperature (TB) or backscatter response to changes in liquid water abundance between predominantly frozen and thawed conditions. The FT retrieval is also a sensitive climate indicator with strong biophysical importance. However, retrieval algorithms can have difficulty distinguishing the FT status of soils from that of overlying features such as snow and vegetation, while variable land conditions can also degrade performance. Here, we applied a deep learning model using a multilayer convolutional neural network driven by AMSR2 and SMAP TB records, and trained on surface (~0–5 cm depth) soil temperature FT observations. Soil FT states were classified for the local morning (6 a.m.) and evening (6 p.m.) conditions corresponding to SMAP descending and ascending orbital overpasses, mapped to a 9 km polar grid spanning a five-year (2016–2020) record and Northern Hemisphere domain. Continuous variable estimates of the probability of frozen or thawed conditions were derived using a model cost function optimized against FT observational training data. Model results derived using combined multi-frequency (1.4, 18.7, 36.5 GHz) TBs produced the highest soil FT accuracy over other models derived using only single sensor or single frequency TB inputs. Moreover, SMAP L-band (1.4 GHz) TBs provided enhanced soil FT information and performance gain over model results derived using only AMSR2 TB inputs. The resulting soil FT classification showed favorable and consistent performance against soil FT observations from ERA5 reanalysis (mean percent accuracy, MPA: 92.7%) and in situ weather stations (MPA: 91.0%). The soil FT accuracy was generally consistent between morning and afternoon predictions and across different land covers and seasons. The model also showed better FT accuracy than ERA5 against regional weather station measurements (91.0% vs. 86.1% MPA). However, model confidence was lower in complex terrain where FT spatial heterogeneity was likely beneath the effective model grain size. Our results provide a high level of precision in mapping soil FT dynamics to improve understanding of complex seasonal transitions and their influence on ecological processes and climate feedbacks, with the potential to inform Earth system model predictions.

@article{dong_enhancing_2023,
	title = {Enhancing {GNSS}-{R} {Soil} {Moisture} {Accuracy} with {Vegetation} and {Roughness} {Correction}},
	volume = {14},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2073-4433},
	url = {https://www.mdpi.com/2073-4433/14/3/509},
	doi = {10.3390/atmos14030509},
	abstract = {Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) has been proven to be a cost-effective and efficient tool for monitoring the Earth’s surface soil moisture (SSM) with unparalleled spatial and temporal resolution. However, the accuracy and reliability of GNSS-R SSM estimation are affected by surface vegetation and roughness. In this study, the sensitivity of delay Doppler map (DDM)-derived effective reflectivity to SSM is analyzed and validated. The individual effective reflectivity is projected onto the 36 km × 36 km Equal-Area Scalable Earth-Grid 2.0 (EASE-Grid2) to form the observation image, which is used to construct a global GNSS-R SSM retrieval model with the SMAP SSM serving as the reference value. In order to improve the accuracy of retrieved SSM from CYGNSS, the effective reflectivity is corrected using vegetation opacity and roughness coefficient parameters from SMAP products. Additionally, the impacts of vegetation and roughness on the estimated SSM were comprehensively evaluated. The results demonstrate that the accuracy of SSM retrieved by GNSS-R is improved with correcting vegetation over different types of vegetation-covered areas. The retrieval algorithm achieves an accuracy of 0.046 cm3cm−3, resulting in a mean improvement of 4.4\%. Validation of the retrieval algorithm through in situ measurements confirms its stable.},
	language = {en},
	number = {3},
	urldate = {2024-11-14},
	journal = {Atmosphere},
	author = {Dong, Zhounan and Jin, Shuanggen and Chen, Guodong and Wang, Peng},
	month = mar,
	year = {2023},
	pages = {509},
}

Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) has been proven to be a cost-effective and efficient tool for monitoring the Earth’s surface soil moisture (SSM) with unparalleled spatial and temporal resolution. However, the accuracy and reliability of GNSS-R SSM estimation are affected by surface vegetation and roughness. In this study, the sensitivity of delay Doppler map (DDM)-derived effective reflectivity to SSM is analyzed and validated. The individual effective reflectivity is projected onto the 36 km × 36 km Equal-Area Scalable Earth-Grid 2.0 (EASE-Grid2) to form the observation image, which is used to construct a global GNSS-R SSM retrieval model with the SMAP SSM serving as the reference value. In order to improve the accuracy of retrieved SSM from CYGNSS, the effective reflectivity is corrected using vegetation opacity and roughness coefficient parameters from SMAP products. Additionally, the impacts of vegetation and roughness on the estimated SSM were comprehensively evaluated. The results demonstrate that the accuracy of SSM retrieved by GNSS-R is improved with correcting vegetation over different types of vegetation-covered areas. The retrieval algorithm achieves an accuracy of 0.046 cm3cm−3, resulting in a mean improvement of 4.4%. Validation of the retrieval algorithm through in situ measurements confirms its stable.

@article{du_assessment_2023,
	title = {Assessment of {Surface} {Fractional} {Water} {Impacts} on {SMAP} {Soil} {Moisture} {Retrieval}},
	volume = {16},
	copyright = {https://creativecommons.org/licenses/by/4.0/legalcode},
	issn = {1939-1404, 2151-1535},
	url = {https://ieeexplore.ieee.org/document/10136690/},
	doi = {10.1109/JSTARS.2023.3278686},
	urldate = {2024-11-14},
	journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	author = {Du, Jinyang and Kimball, John S. and Chan, Steven K. and Chaubell, Mario Julian and Bindlish, Rajat and Dunbar, R. Scott and Colliander, Andreas},
	year = {2023},
	pages = {4871--4881},
}

@article{fang_vegetation_2023,
	title = {Vegetation photosynthetic phenology dataset in northern terrestrial ecosystems},
	volume = {10},
	issn = {2052-4463},
	url = {https://www.nature.com/articles/s41597-023-02224-w},
	doi = {10.1038/s41597-023-02224-w},
	abstract = {Abstract
            Vegetation phenology can profoundly modulate the climate-biosphere interactions and thus plays a crucial role in regulating the terrestrial carbon cycle and the climate. However, most previous phenology studies rely on traditional vegetation indices, which are inadequate to characterize the seasonal activity of photosynthesis. Here, we generated an annual vegetation photosynthetic phenology dataset with a spatial resolution of 0.05 degrees from 2001 to 2020, using the latest gross primary productivity product based on solar-induced chlorophyll fluorescence (GOSIF-GPP). We combined smoothing splines with multiple change-point detection to retrieve the phenology metrics: start of the growing season (SOS), end of the growing season (EOS), and length of growing season (LOS) for terrestrial ecosystems above 30° N latitude (Northern Biomes). Our phenology product can be used to validate and develop phenology or carbon cycle models and monitor the climate change impacts on terrestrial ecosystems.},
	language = {en},
	number = {1},
	urldate = {2024-11-14},
	journal = {Scientific Data},
	author = {Fang, Jing and Li, Xing and Xiao, Jingfeng and Yan, Xiaodong and Li, Bolun and Liu, Feng},
	month = may,
	year = {2023},
	pages = {300},
}

Abstract Vegetation phenology can profoundly modulate the climate-biosphere interactions and thus plays a crucial role in regulating the terrestrial carbon cycle and the climate. However, most previous phenology studies rely on traditional vegetation indices, which are inadequate to characterize the seasonal activity of photosynthesis. Here, we generated an annual vegetation photosynthetic phenology dataset with a spatial resolution of 0.05 degrees from 2001 to 2020, using the latest gross primary productivity product based on solar-induced chlorophyll fluorescence (GOSIF-GPP). We combined smoothing splines with multiple change-point detection to retrieve the phenology metrics: start of the growing season (SOS), end of the growing season (EOS), and length of growing season (LOS) for terrestrial ecosystems above 30° N latitude (Northern Biomes). Our phenology product can be used to validate and develop phenology or carbon cycle models and monitor the climate change impacts on terrestrial ecosystems.

@misc{fatima_improved_2023,
	title = {Improved representation of soil moisture simulations through incorporation of cosmic-ray neutron count measurements in a large-scale hydrologic model},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	url = {https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1548/},
	doi = {10.5194/egusphere-2023-1548},
	abstract = {Abstract. Profound knowledge of soil moisture and its variability plays a crucial role in hydrological modeling to support agricultural management, flood and drought monitoring and forecasting, and groundwater recharge estimation. Cosmic-ray neutron sensing (CRNS) have been recognized as a promising tool for soil moisture monitoring due to their hectare-scale footprint and decimeter-scale measurement depth. Different approaches exists that could be the basis for incorporating CRNS data into distributed hydrologic models, but largely still need to be implemented, thoroughly compared, and tested across different soil and vegetation types. This study establishes a framework to accommodate neutron count measurements and assess the accuracy of soil water content simulated by the mesoscale Hydrological Model (mHM) for the first time. It covers CRNS observations across different vegetation types in Germany ranging from agricultural areas to forest. We include two different approaches to estimate CRNS neutron counts in mHM based on the simulated soil moisture: a method based on the Desilets equation and another one based on the Cosmic-ray Soil Moisture Interaction Code (COSMIC). Within the Desilets approach, we further test two different averaging methods for the vertically layered soil moisture, namely uniform vs. non-uniform weighting scheme depending on the CRNS penetrating depth. A Monte Carlos simulation with Latin hypercube sampling approach (with N = 100,000) is employed to explore and constrain the (behavioral) mHM parameterizations against observed CRNS neutron counts. Overall, the three methods perform well with Kling-Gupta efficiency {\textgreater} 0.8 and percent bias {\textless} 1 \% across the majority of investigated sites. We find that the non-uniform weighting scheme in the Desilets method provide the most reliable performance, whereas the more commonly used approach with uniformly weighted average soil moisture overestimates the observed CRNS neutron counts. We then also demonstrate the usefulness of incorporating CRNS measurements into mHM for the simulations of both soil moisture and evapotranspiration and add a broader discussion on the potential and guidelines of incorporating CRNS measurements in large-scale hydrological and land surface models.},
	urldate = {2024-11-14},
	publisher = {Catchment hydrology/Modelling approaches},
	author = {Fatima, Eshrat and Kumar, Rohini and Attinger, Sabine and Kaluza, Maren and Rakovec, Oldrich and Rebmann, Corinna and Rosolem, Rafael and Oswald, Sascha and Samaniego, Luis and Zacharias, Steffen and Schrön, Martin},
	month = jul,
	year = {2023},
}

Abstract. Profound knowledge of soil moisture and its variability plays a crucial role in hydrological modeling to support agricultural management, flood and drought monitoring and forecasting, and groundwater recharge estimation. Cosmic-ray neutron sensing (CRNS) have been recognized as a promising tool for soil moisture monitoring due to their hectare-scale footprint and decimeter-scale measurement depth. Different approaches exists that could be the basis for incorporating CRNS data into distributed hydrologic models, but largely still need to be implemented, thoroughly compared, and tested across different soil and vegetation types. This study establishes a framework to accommodate neutron count measurements and assess the accuracy of soil water content simulated by the mesoscale Hydrological Model (mHM) for the first time. It covers CRNS observations across different vegetation types in Germany ranging from agricultural areas to forest. We include two different approaches to estimate CRNS neutron counts in mHM based on the simulated soil moisture: a method based on the Desilets equation and another one based on the Cosmic-ray Soil Moisture Interaction Code (COSMIC). Within the Desilets approach, we further test two different averaging methods for the vertically layered soil moisture, namely uniform vs. non-uniform weighting scheme depending on the CRNS penetrating depth. A Monte Carlos simulation with Latin hypercube sampling approach (with N = 100,000) is employed to explore and constrain the (behavioral) mHM parameterizations against observed CRNS neutron counts. Overall, the three methods perform well with Kling-Gupta efficiency \textgreater 0.8 and percent bias \textless 1 % across the majority of investigated sites. We find that the non-uniform weighting scheme in the Desilets method provide the most reliable performance, whereas the more commonly used approach with uniformly weighted average soil moisture overestimates the observed CRNS neutron counts. We then also demonstrate the usefulness of incorporating CRNS measurements into mHM for the simulations of both soil moisture and evapotranspiration and add a broader discussion on the potential and guidelines of incorporating CRNS measurements in large-scale hydrological and land surface models.

@article{feng_comprehensive_2023,
	title = {Comprehensive assessment of global atmospheric downward longwave radiation in the state-of-the-art reanalysis using satellite and flux tower observations},
	volume = {60},
	issn = {0930-7575, 1432-0894},
	url = {https://link.springer.com/10.1007/s00382-022-06366-2},
	doi = {10.1007/s00382-022-06366-2},
	language = {en},
	number = {5-6},
	urldate = {2024-11-14},
	journal = {Climate Dynamics},
	author = {Feng, Chunjie and Zhang, Xiaotong and Xu, Jiawen and Yang, Shuyue and Guan, Shikang and Jia, Kun and Yao, Yunjun},
	month = mar,
	year = {2023},
	pages = {1495--1521},
}

@inproceedings{fluhrer_estimating_2023,
	address = {Pasadena, CA, USA},
	title = {Estimating {Soil} {Moisture} {Profiles} by {Combining} {P}-{Band} {SAR} with {Hydrological} {Modeling}},
	copyright = {https://doi.org/10.15223/policy-029},
	isbn = {9798350320107},
	url = {https://ieeexplore.ieee.org/document/10282246/},
	doi = {10.1109/IGARSS52108.2023.10282246},
	urldate = {2024-11-14},
	booktitle = {{IGARSS} 2023 - 2023 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium}},
	publisher = {IEEE},
	author = {Fluhrer, Anke and Jagdhuber, Thomas and Montzka, Carsten and Schumacher, Maike and Alemohammad, Hamed and Tabatabaeenejad, Alireza and Kunstmann, Harald and Entekhabi, Dara},
	month = jul,
	year = {2023},
	pages = {2846--2849},
}

@article{freund_european_2023,
	title = {European tree-ring isotopes indicate unusual recent hydroclimate},
	volume = {4},
	issn = {2662-4435},
	url = {https://www.nature.com/articles/s43247-022-00648-7},
	doi = {10.1038/s43247-022-00648-7},
	abstract = {Abstract
            In recent decades, Europe has experienced more frequent flood and drought events. However, little is known about the long-term, spatiotemporal hydroclimatic changes across Europe. Here we present a climate field reconstruction spanning the entire European continent based on tree-ring stable isotopes. A pronounced seasonal consistency in climate response across Europe leads to a unique, well-verified spatial field reconstruction of European summer hydroclimate back to AD 1600. We find three distinct phases of European hydroclimate variability as possible fingerprints of solar activity (coinciding with the Maunder Minimum and the end of the Little Ice Age) and pronounced decadal variability superimposed by a long-term drying trend from the mid-20th century. We show that the recent European summer drought (2015–2018) is highly unusual in a multi-century context and unprecedented for large parts of central and western Europe. The reconstruction provides further evidence of European summer droughts potentially being influenced by anthropogenic warming and draws attention to regional differences.},
	language = {en},
	number = {1},
	urldate = {2024-11-15},
	journal = {Communications Earth \& Environment},
	author = {Freund, Mandy B. and Helle, Gerhard and Balting, Daniel F. and Ballis, Natasha and Schleser, Gerhard H. and Cubasch, Ulrich},
	month = feb,
	year = {2023},
	pages = {26},
}

Abstract In recent decades, Europe has experienced more frequent flood and drought events. However, little is known about the long-term, spatiotemporal hydroclimatic changes across Europe. Here we present a climate field reconstruction spanning the entire European continent based on tree-ring stable isotopes. A pronounced seasonal consistency in climate response across Europe leads to a unique, well-verified spatial field reconstruction of European summer hydroclimate back to AD 1600. We find three distinct phases of European hydroclimate variability as possible fingerprints of solar activity (coinciding with the Maunder Minimum and the end of the Little Ice Age) and pronounced decadal variability superimposed by a long-term drying trend from the mid-20th century. We show that the recent European summer drought (2015–2018) is highly unusual in a multi-century context and unprecedented for large parts of central and western Europe. The reconstruction provides further evidence of European summer droughts potentially being influenced by anthropogenic warming and draws attention to regional differences.

@article{frindte_spatial_2023,
	title = {Spatial patterns of prokaryotic communities in kettle hole soils follow soil horizonation},
	volume = {185},
	issn = {09291393},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0929139322004127},
	doi = {10.1016/j.apsoil.2022.104796},
	language = {en},
	urldate = {2024-11-14},
	journal = {Applied Soil Ecology},
	author = {Frindte, Katharina and Kolb, Steffen and Sommer, Michael and Augustin, Jürgen and Knief, Claudia},
	month = may,
	year = {2023},
	pages = {104796},
}

@article{fu_temperature_2023,
	title = {Temperature {Thresholds} for {Carbon} {Flux} {Variation} and {Warming}‐{Induced} {Changes}},
	volume = {128},
	issn = {2169-897X, 2169-8996},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JD039747},
	doi = {10.1029/2023JD039747},
	abstract = {Abstract
            
              The response of net ecosystem exchange (NEE) to environmental changes is strongly nonlinear, characterized with threshold behaviors that are not well understood. Here, we investigated the threshold behaviors in the relationship between NEE and surface air temperature based on FLUXNET2015 observations, Community Land Model simulations, and Coupled Model Intercomparison Project Phase 6 (CMIP6) model outputs. An air temperature threshold of 16.4°C, corresponding to the maximum carbon sink (
              T
              sink
              ), was identified for all 205 FLUXNET2015 sites combined. For deciduous broadleaf and mixed forests, wetlands and wheat‐barley croplands, and rice‐maize‐soybean croplands, we identified a carbon‐source threshold (
              T
              source
              ) of 6.8, 5.0, and 18.0°C, respectively, beyond which the ecosystem becomes less of a carbon source. Five cold climate types mainly encompassing these plant functional types showed a clear carbon‐source
              T
              source
              of 12.2°C. Six CMIP6 models project a threshold temperature increase of 1.0–2.8°C by the 2090s, which results primarily from a shift of the optimum temperature for gross primary production. Not accounting for the warming‐induced threshold changes may lead to an estimated time of the average summer air temperature passing
              T
              sink
              that is earlier by 4.5–6.7 and 6.4–12.2 years at low (15°N–15°S) and high (≥60°S or ≥ 60°N) latitudes, respectively.
            
          , 
            Plain Language Summary
            Net ecosystem exchange (NEE) reflects the ecosystem's role as a carbon source or sink. Previous studies on NEE indicated a temperature threshold for the size of ecosystem carbon sink. It remains unknown whether a similar threshold for carbon source might exist, how the thresholds for both carbon source and sink may vary with climate and ecosystem types, and how they may change with global warming. The present study fills these gaps. Here we extract patterns of temperature thresholds for different climate and ecosystem types, and project a 1.0–2.8°C increase of threshold temperature by the end of the century. This increase has to be properly accounted for when studying the terrestrial carbon‐climate interactions.
          , 
            Key Points
            
              
                
                  An air temperature threshold of 16.4°C, corresponding to the maximum carbon sink, was identified for all 205 FLUXNET2015 sites combined
                
                
                  Deciduous broadleaf forests, mixed forests, and croplands showed a temperature threshold corresponding to a sudden change of carbon source
                
                
                  Coupled Model Intercomparison Project Phase 6 models project a 1.0–2.8°C increase of threshold temperature by the end of the century},
	language = {en},
	number = {21},
	urldate = {2024-11-14},
	journal = {Journal of Geophysical Research: Atmospheres},
	author = {Fu, Congsheng and Wang, Guiling and Yang, Yuting and Wu, Huawu and Wu, Haohao and Zhang, Haixia and Xia, Ye},
	month = nov,
	year = {2023},
	pages = {e2023JD039747},
}

Abstract The response of net ecosystem exchange (NEE) to environmental changes is strongly nonlinear, characterized with threshold behaviors that are not well understood. Here, we investigated the threshold behaviors in the relationship between NEE and surface air temperature based on FLUXNET2015 observations, Community Land Model simulations, and Coupled Model Intercomparison Project Phase 6 (CMIP6) model outputs. An air temperature threshold of 16.4°C, corresponding to the maximum carbon sink ( T sink ), was identified for all 205 FLUXNET2015 sites combined. For deciduous broadleaf and mixed forests, wetlands and wheat‐barley croplands, and rice‐maize‐soybean croplands, we identified a carbon‐source threshold ( T source ) of 6.8, 5.0, and 18.0°C, respectively, beyond which the ecosystem becomes less of a carbon source. Five cold climate types mainly encompassing these plant functional types showed a clear carbon‐source T source of 12.2°C. Six CMIP6 models project a threshold temperature increase of 1.0–2.8°C by the 2090s, which results primarily from a shift of the optimum temperature for gross primary production. Not accounting for the warming‐induced threshold changes may lead to an estimated time of the average summer air temperature passing T sink that is earlier by 4.5–6.7 and 6.4–12.2 years at low (15°N–15°S) and high (≥60°S or ≥ 60°N) latitudes, respectively. , Plain Language Summary Net ecosystem exchange (NEE) reflects the ecosystem's role as a carbon source or sink. Previous studies on NEE indicated a temperature threshold for the size of ecosystem carbon sink. It remains unknown whether a similar threshold for carbon source might exist, how the thresholds for both carbon source and sink may vary with climate and ecosystem types, and how they may change with global warming. The present study fills these gaps. Here we extract patterns of temperature thresholds for different climate and ecosystem types, and project a 1.0–2.8°C increase of threshold temperature by the end of the century. This increase has to be properly accounted for when studying the terrestrial carbon‐climate interactions. , Key Points An air temperature threshold of 16.4°C, corresponding to the maximum carbon sink, was identified for all 205 FLUXNET2015 sites combined Deciduous broadleaf forests, mixed forests, and croplands showed a temperature threshold corresponding to a sudden change of carbon source Coupled Model Intercomparison Project Phase 6 models project a 1.0–2.8°C increase of threshold temperature by the end of the century

@article{gachibu_wangari_identifying_2023,
	title = {Identifying landscape hot and cold spots of soil greenhouse gas fluxes by combining field measurements and remote sensing data},
	volume = {20},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1726-4189},
	url = {https://bg.copernicus.org/articles/20/5029/2023/},
	doi = {10.5194/bg-20-5029-2023},
	abstract = {Abstract. Upscaling chamber measurements of soil greenhouse gas (GHG) fluxes from point scale to landscape scale remain challenging due to the high variability in the fluxes in space and time. This study measured GHG fluxes and soil parameters at selected point locations (n=268), thereby implementing a stratified sampling approach on a mixed-land-use landscape (∼5.8 km2). Based on these field-based measurements and remotely sensed data on landscape and vegetation properties, we used random forest (RF) models to predict GHG fluxes at a landscape scale (1 m resolution) in summer and autumn. The RF models, combining field-measured soil parameters and remotely sensed data, outperformed those with field-measured predictors or remotely sensed data alone. Available satellite data products from Sentinel-2 on vegetation cover and water content played a more significant role than those attributes derived from a digital elevation model, possibly due to their ability to capture both spatial and seasonal changes in the ecosystem parameters within the landscape. Similar seasonal patterns of higher soil/ecosystem respiration (SR/ER–CO2) and nitrous oxide (N2O) fluxes in summer and higher methane (CH4) uptake in autumn were observed in both the measured and predicted landscape fluxes. Based on the upscaled fluxes, we also assessed the contribution of hot spots to the total landscape fluxes. The identified emission hot spots occupied a small landscape area (7 \% to 16 \%) but accounted for up to 42 \% of the landscape GHG fluxes. Our study showed that combining remotely sensed data with chamber measurements and soil properties is a promising approach for identifying spatial patterns and hot spots of GHG fluxes across heterogeneous landscapes. Such information may be used to inform targeted mitigation strategies at the landscape scale.},
	language = {en},
	number = {24},
	urldate = {2024-11-15},
	journal = {Biogeosciences},
	author = {Gachibu Wangari, Elizabeth and Mwangada Mwanake, Ricky and Houska, Tobias and Kraus, David and Gettel, Gretchen Maria and Kiese, Ralf and Breuer, Lutz and Butterbach-Bahl, Klaus},
	month = dec,
	year = {2023},
	pages = {5029--5067},
}

Abstract. Upscaling chamber measurements of soil greenhouse gas (GHG) fluxes from point scale to landscape scale remain challenging due to the high variability in the fluxes in space and time. This study measured GHG fluxes and soil parameters at selected point locations (n=268), thereby implementing a stratified sampling approach on a mixed-land-use landscape (∼5.8 km2). Based on these field-based measurements and remotely sensed data on landscape and vegetation properties, we used random forest (RF) models to predict GHG fluxes at a landscape scale (1 m resolution) in summer and autumn. The RF models, combining field-measured soil parameters and remotely sensed data, outperformed those with field-measured predictors or remotely sensed data alone. Available satellite data products from Sentinel-2 on vegetation cover and water content played a more significant role than those attributes derived from a digital elevation model, possibly due to their ability to capture both spatial and seasonal changes in the ecosystem parameters within the landscape. Similar seasonal patterns of higher soil/ecosystem respiration (SR/ER–CO2) and nitrous oxide (N2O) fluxes in summer and higher methane (CH4) uptake in autumn were observed in both the measured and predicted landscape fluxes. Based on the upscaled fluxes, we also assessed the contribution of hot spots to the total landscape fluxes. The identified emission hot spots occupied a small landscape area (7 % to 16 %) but accounted for up to 42 % of the landscape GHG fluxes. Our study showed that combining remotely sensed data with chamber measurements and soil properties is a promising approach for identifying spatial patterns and hot spots of GHG fluxes across heterogeneous landscapes. Such information may be used to inform targeted mitigation strategies at the landscape scale.

@misc{ghaffar_towards_2023,
	title = {Towards a data-effective calibration of a fully distributed catchment water quality model},
	url = {https://essopenarchive.org/users/699981/articles/687063-towards-a-data-effective-calibration-of-a-fully-distributed-catchment-water-quality-model?commit=a81118a0e128f537829537f78bb8bd136754ba69},
	doi = {10.22541/essoar.170067059.92088535/v1},
	abstract = {Distributed hydrological water quality models are increasingly being
used to manage natural resources at the catchment scale but there are no
calibration guidelines for selecting the most useful gauging stations.
In this study, we investigated the influence of calibration schemes on
the spatiotemporal performance of a fully distributed process-based
hydrological water quality model (mHM-Nitrate) for discharge and nitrate
simulations at Bode catchment in central Germany. We used a single- and
two multi-site calibration schemes where the two multi-site schemes
varied in number of gauging stations but each subcatchment represented
different dominant land uses of the catchment. To extract a set of
behavioral parameters for each calibration scheme, we chose a sequential
multi-criteria method with 300.000 iterations.
          For discharge (Q), model performance was similar among the three schemes
(NSE varied from 0.88 to 0.92). However, for nitrate concentration, the
multi-site schemes performed better than the single site scheme. This
improvement may be attributed to that multi-site schemes incorporated a
broader range of data, including low Q and NO3- values, thus provided a
better representation of within-catchment diversity. Conversely, adding
more gauging stations in the multi-site approaches did not lead to
further improvements in catchment representation but showed wider 95\%
uncertainty boundaries. Thus, adding observations that contained similar
information on catchment characteristics did not seem to improve model
performance and increased uncertainty. These results highlight the
importance of strategically selecting gauging stations that reflect the
full range of catchment heterogeneity rather than seeking to maximize
station number, to optimize parameter calibration.},
	urldate = {2024-11-14},
	publisher = {Preprints},
	author = {Ghaffar, Salman and Zhou, Xiangqian and Jomaa, Seifeddine and Yang, Xiaoqiang and Meon, Günter and Rode, Michael},
	month = nov,
	year = {2023},
}

Distributed hydrological water quality models are increasingly being used to manage natural resources at the catchment scale but there are no calibration guidelines for selecting the most useful gauging stations. In this study, we investigated the influence of calibration schemes on the spatiotemporal performance of a fully distributed process-based hydrological water quality model (mHM-Nitrate) for discharge and nitrate simulations at Bode catchment in central Germany. We used a single- and two multi-site calibration schemes where the two multi-site schemes varied in number of gauging stations but each subcatchment represented different dominant land uses of the catchment. To extract a set of behavioral parameters for each calibration scheme, we chose a sequential multi-criteria method with 300.000 iterations. For discharge (Q), model performance was similar among the three schemes (NSE varied from 0.88 to 0.92). However, for nitrate concentration, the multi-site schemes performed better than the single site scheme. This improvement may be attributed to that multi-site schemes incorporated a broader range of data, including low Q and NO3- values, thus provided a better representation of within-catchment diversity. Conversely, adding more gauging stations in the multi-site approaches did not lead to further improvements in catchment representation but showed wider 95% uncertainty boundaries. Thus, adding observations that contained similar information on catchment characteristics did not seem to improve model performance and increased uncertainty. These results highlight the importance of strategically selecting gauging stations that reflect the full range of catchment heterogeneity rather than seeking to maximize station number, to optimize parameter calibration.

@article{ghausi_radiative_2023,
	title = {Radiative controls by clouds and thermodynamics shape surface temperatures and turbulent fluxes over land},
	volume = {120},
	issn = {0027-8424, 1091-6490},
	url = {https://pnas.org/doi/10.1073/pnas.2220400120},
	doi = {10.1073/pnas.2220400120},
	abstract = {Land surface temperatures (LSTs) are strongly shaped by radiation but are modulated by turbulent fluxes and hydrologic cycling as the presence of water vapor in the atmosphere (clouds) and at the surface (evaporation) affects temperatures across regions. Here, we used a thermodynamic systems framework forced with independent observations to show that the climatological variations in LSTs across dry and humid regions are mainly mediated through radiative effects. We first show that the turbulent fluxes of sensible and latent heat are constrained by thermodynamics and the local radiative conditions. This constraint arises from the ability of radiative heating at the surface to perform work to maintain turbulent fluxes and sustain vertical mixing within the convective boundary layer. This implies that reduced evaporative cooling in dry regions is then compensated for by an increased sensible heat flux and buoyancy, which is consistent with observations. We show that the mean temperature variation across dry and humid regions is mainly controlled by clouds that reduce surface heating by solar radiation. Using satellite observations for cloudy and clear-sky conditions, we show that clouds cool the land surface over humid regions by up to 7 K, while in arid regions, this effect is absent due to the lack of clouds. We conclude that radiation and thermodynamic limits are the primary controls on LSTs and turbulent flux exchange which leads to an emergent simplicity in the observed climatological patterns within the complex climate system.},
	language = {en},
	number = {29},
	urldate = {2024-11-14},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Ghausi, Sarosh Alam and Tian, Yinglin and Zehe, Erwin and Kleidon, Axel},
	month = jul,
	year = {2023},
	pages = {e2220400120},
}

Land surface temperatures (LSTs) are strongly shaped by radiation but are modulated by turbulent fluxes and hydrologic cycling as the presence of water vapor in the atmosphere (clouds) and at the surface (evaporation) affects temperatures across regions. Here, we used a thermodynamic systems framework forced with independent observations to show that the climatological variations in LSTs across dry and humid regions are mainly mediated through radiative effects. We first show that the turbulent fluxes of sensible and latent heat are constrained by thermodynamics and the local radiative conditions. This constraint arises from the ability of radiative heating at the surface to perform work to maintain turbulent fluxes and sustain vertical mixing within the convective boundary layer. This implies that reduced evaporative cooling in dry regions is then compensated for by an increased sensible heat flux and buoyancy, which is consistent with observations. We show that the mean temperature variation across dry and humid regions is mainly controlled by clouds that reduce surface heating by solar radiation. Using satellite observations for cloudy and clear-sky conditions, we show that clouds cool the land surface over humid regions by up to 7 K, while in arid regions, this effect is absent due to the lack of clouds. We conclude that radiation and thermodynamic limits are the primary controls on LSTs and turbulent flux exchange which leads to an emergent simplicity in the observed climatological patterns within the complex climate system.

@article{giardina_diagnosing_2023,
	title = {Diagnosing evapotranspiration responses to water deficit across biomes using deep learning},
	volume = {240},
	issn = {0028-646X, 1469-8137},
	url = {https://nph.onlinelibrary.wiley.com/doi/10.1111/nph.19197},
	doi = {10.1111/nph.19197},
	abstract = {Summary
            
              
                
                  Accounting for water limitation is key to determining vegetation sensitivity to drought. Quantifying water limitation effects on evapotranspiration (ET) is challenged by the heterogeneity of vegetation types, climate zones and vertically along the rooting zone.
                
                
                  Here, we train deep neural networks using flux measurements to study ET responses to progressing drought conditions. We determine a water stress factor (fET) that isolates ET reductions from effects of atmospheric aridity and other covarying drivers. We regress fET against the cumulative water deficit, which reveals the control of whole‐column moisture availability.
                
                
                  We find a variety of ET responses to water stress. Responses range from rapid declines of fET to 10\% of its water‐unlimited rate at several savannah and grassland sites, to mild fET reductions in most forests, despite substantial water deficits. Most sensitive responses are found at the most arid and warm sites.
                
                
                  A combination of regulation of stomatal and hydraulic conductance and access to belowground water reservoirs, whether in groundwater or deep soil moisture, could explain the different behaviors observed across sites. This variety of responses is not captured by a standard land surface model, likely reflecting simplifications in its representation of belowground water storage.},
	language = {en},
	number = {3},
	urldate = {2024-11-14},
	journal = {New Phytologist},
	author = {Giardina, Francesco and Gentine, Pierre and Konings, Alexandra G. and Seneviratne, Sonia I. and Stocker, Benjamin D.},
	month = nov,
	year = {2023},
	pages = {968--983},
}

Summary Accounting for water limitation is key to determining vegetation sensitivity to drought. Quantifying water limitation effects on evapotranspiration (ET) is challenged by the heterogeneity of vegetation types, climate zones and vertically along the rooting zone. Here, we train deep neural networks using flux measurements to study ET responses to progressing drought conditions. We determine a water stress factor (fET) that isolates ET reductions from effects of atmospheric aridity and other covarying drivers. We regress fET against the cumulative water deficit, which reveals the control of whole‐column moisture availability. We find a variety of ET responses to water stress. Responses range from rapid declines of fET to 10% of its water‐unlimited rate at several savannah and grassland sites, to mild fET reductions in most forests, despite substantial water deficits. Most sensitive responses are found at the most arid and warm sites. A combination of regulation of stomatal and hydraulic conductance and access to belowground water reservoirs, whether in groundwater or deep soil moisture, could explain the different behaviors observed across sites. This variety of responses is not captured by a standard land surface model, likely reflecting simplifications in its representation of belowground water storage.

@article{giraud_cplantbox_2023,
	title = {{CPlantBox}: a fully coupled modelling platform for the water and carbon fluxes in the soil–plant–atmosphere continuum},
	volume = {5},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2517-5025},
	shorttitle = {{CPlantBox}},
	url = {https://academic.oup.com/insilicoplants/article/doi/10.1093/insilicoplants/diad009/7224987},
	doi = {10.1093/insilicoplants/diad009},
	abstract = {Abstract
            A plant’s development is strongly linked to the water and carbon flows in the soil–plant–atmosphere continuum. Expected climate shifts will alter the water and carbon cycles and will affect plant phenotypes. Comprehensive models that simulate mechanistically and dynamically the feedback loops between a plant’s three-dimensional development and the water and carbon flows are useful tools to evaluate the sustainability of genotype–environment–management combinations which do not yet exist. In this study, we present the latest version of the open-source three-dimensional Functional–Structural Plant Model CPlantBox with PiafMunch and DuMu\$\{\}{\textasciicircum}\{{\textbackslash}text\{x\}\}\$ coupling. This new implementation can be used to study the interactions between known or hypothetical processes at the plant scale. We simulated semi-mechanistically the development of generic C3 monocots from 10 to 25 days after sowing and undergoing an atmospheric dry spell of 1 week (no precipitation). We compared the results for dry spells starting on different days (Day 11 or 18) against a wetter and colder baseline scenario. Compared with the baseline, the dry spells led to a lower instantaneous water-use efficiency. Moreover, the temperature-induced increased enzymatic activity led to a higher maintenance respiration which diminished the amount of sucrose available for growth. Both of these effects were stronger for the later dry spell compared with the early dry spell. We could thus use CPlantBox to simulate diverging emerging processes (like carbon partitioning) defining the plants’ phenotypic plasticity response to their environment. The model remains to be validated against independent observations of the soil–plant–atmosphere continuum.},
	language = {en},
	number = {2},
	urldate = {2024-11-14},
	journal = {in silico Plants},
	author = {Giraud, Mona and Gall, Samuel Le and Harings, Moritz and Javaux, Mathieu and Leitner, Daniel and Meunier, Félicien and Rothfuss, Youri and Van Dusschoten, Dagmar and Vanderborght, Jan and Vereecken, Harry and Lobet, Guillaume and Schnepf, Andrea},
	editor = {Amy, Marshall-Colon},
	month = jul,
	year = {2023},
	pages = {diad009},
}

Abstract A plant’s development is strongly linked to the water and carbon flows in the soil–plant–atmosphere continuum. Expected climate shifts will alter the water and carbon cycles and will affect plant phenotypes. Comprehensive models that simulate mechanistically and dynamically the feedback loops between a plant’s three-dimensional development and the water and carbon flows are useful tools to evaluate the sustainability of genotype–environment–management combinations which do not yet exist. In this study, we present the latest version of the open-source three-dimensional Functional–Structural Plant Model CPlantBox with PiafMunch and DuMu$\{\}{\textasciicircum}\{{\}text\{x\}\}$ coupling. This new implementation can be used to study the interactions between known or hypothetical processes at the plant scale. We simulated semi-mechanistically the development of generic C3 monocots from 10 to 25 days after sowing and undergoing an atmospheric dry spell of 1 week (no precipitation). We compared the results for dry spells starting on different days (Day 11 or 18) against a wetter and colder baseline scenario. Compared with the baseline, the dry spells led to a lower instantaneous water-use efficiency. Moreover, the temperature-induced increased enzymatic activity led to a higher maintenance respiration which diminished the amount of sucrose available for growth. Both of these effects were stronger for the later dry spell compared with the early dry spell. We could thus use CPlantBox to simulate diverging emerging processes (like carbon partitioning) defining the plants’ phenotypic plasticity response to their environment. The model remains to be validated against independent observations of the soil–plant–atmosphere continuum.

@article{golub_diel_2023,
	title = {Diel, seasonal, and inter-annual variation in carbon dioxide effluxes from lakes and reservoirs},
	volume = {18},
	issn = {1748-9326},
	url = {https://iopscience.iop.org/article/10.1088/1748-9326/acb834},
	doi = {10.1088/1748-9326/acb834},
	abstract = {Abstract
            
              Accounting for temporal changes in carbon dioxide (CO
              2
              ) effluxes from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of flux measurements of CO
              2
              based on the eddy covariance method from 13 lakes and reservoirs in the Northern Hemisphere, and quantify dynamics at multiple temporal scales. We found pronounced sub-annual variability in CO
              2
              flux at all sites. By accounting for diel variation, only 11\% of site-months were net daily sinks of CO
              2
              . Annual CO
              2
              emissions had an average of 25\% (range 3\%–58\%) interannual variation. Similar to studies on streams, nighttime emissions regularly exceeded daytime emissions. Biophysical regulations of CO
              2
              flux variability were delineated through mutual information analysis. Sample analysis of CO
              2
              fluxes indicate the importance of continuous measurements. Better characterization of short- and long-term variability is necessary to understand and improve detection of temporal changes of CO
              2
              fluxes in response to natural and anthropogenic drivers. Our results indicate that existing global lake carbon budgets relying primarily on daytime measurements yield underestimates of net emissions.},
	number = {3},
	urldate = {2024-11-14},
	journal = {Environmental Research Letters},
	author = {Golub, Malgorzata and Koupaei-Abyazani, Nikaan and Vesala, Timo and Mammarella, Ivan and Ojala, Anne and Bohrer, Gil and Weyhenmeyer, Gesa A and Blanken, Peter D and Eugster, Werner and Koebsch, Franziska and Chen, Jiquan and Czajkowski, Kevin and Deshmukh, Chandrashekhar and Guérin, Frederic and Heiskanen, Jouni and Humphreys, Elyn and Jonsson, Anders and Karlsson, Jan and Kling, George and Lee, Xuhui and Liu, Heping and Lohila, Annalea and Lundin, Erik and Morin, Tim and Podgrajsek, Eva and Provenzale, Maria and Rutgersson, Anna and Sachs, Torsten and Sahlée, Erik and Serça, Dominique and Shao, Changliang and Spence, Christopher and Strachan, Ian B and Xiao, Wei and Desai, Ankur R},
	month = mar,
	year = {2023},
	pages = {034046},
}

Abstract Accounting for temporal changes in carbon dioxide (CO 2 ) effluxes from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of flux measurements of CO 2 based on the eddy covariance method from 13 lakes and reservoirs in the Northern Hemisphere, and quantify dynamics at multiple temporal scales. We found pronounced sub-annual variability in CO 2 flux at all sites. By accounting for diel variation, only 11% of site-months were net daily sinks of CO 2 . Annual CO 2 emissions had an average of 25% (range 3%–58%) interannual variation. Similar to studies on streams, nighttime emissions regularly exceeded daytime emissions. Biophysical regulations of CO 2 flux variability were delineated through mutual information analysis. Sample analysis of CO 2 fluxes indicate the importance of continuous measurements. Better characterization of short- and long-term variability is necessary to understand and improve detection of temporal changes of CO 2 fluxes in response to natural and anthropogenic drivers. Our results indicate that existing global lake carbon budgets relying primarily on daytime measurements yield underestimates of net emissions.

@article{graf_joint_2023,
	title = {Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects},
	volume = {4},
	issn = {2662-4435},
	url = {https://www.nature.com/articles/s43247-023-00958-4},
	doi = {10.1038/s43247-023-00958-4},
	abstract = {Abstract
            Both carbon dioxide uptake and albedo of the land surface affect global climate. However, climate change mitigation by increasing carbon uptake can cause a warming trade-off by decreasing albedo, with most research focusing on afforestation and its interaction with snow. Here, we present carbon uptake and albedo observations from 176 globally distributed flux stations. We demonstrate a gradual decline in maximum achievable annual albedo as carbon uptake increases, even within subgroups of non-forest and snow-free ecosystems. Based on a paired-site permutation approach, we quantify the likely impact of land use on carbon uptake and albedo. Shifting to the maximum attainable carbon uptake at each site would likely cause moderate net global warming for the first approximately 20 years, followed by a strong cooling effect. A balanced policy co-optimizing carbon uptake and albedo is possible that avoids warming on any timescale, but results in a weaker long-term cooling effect.},
	language = {en},
	number = {1},
	urldate = {2024-11-14},
	journal = {Communications Earth \& Environment},
	author = {Graf, Alexander and Wohlfahrt, Georg and Aranda-Barranco, Sergio and Arriga, Nicola and Brümmer, Christian and Ceschia, Eric and Ciais, Philippe and Desai, Ankur R. and Di Lonardo, Sara and Gharun, Mana and Grünwald, Thomas and Hörtnagl, Lukas and Kasak, Kuno and Klosterhalfen, Anne and Knohl, Alexander and Kowalska, Natalia and Leuchner, Michael and Lindroth, Anders and Mauder, Matthias and Migliavacca, Mirco and Morel, Alexandra C. and Pfennig, Andreas and Poorter, Hendrik and Terán, Christian Poppe and Reitz, Oliver and Rebmann, Corinna and Sanchez-Azofeifa, Arturo and Schmidt, Marius and Šigut, Ladislav and Tomelleri, Enrico and Yu, Ke and Varlagin, Andrej and Vereecken, Harry},
	month = aug,
	year = {2023},
	pages = {298},
}

Abstract Both carbon dioxide uptake and albedo of the land surface affect global climate. However, climate change mitigation by increasing carbon uptake can cause a warming trade-off by decreasing albedo, with most research focusing on afforestation and its interaction with snow. Here, we present carbon uptake and albedo observations from 176 globally distributed flux stations. We demonstrate a gradual decline in maximum achievable annual albedo as carbon uptake increases, even within subgroups of non-forest and snow-free ecosystems. Based on a paired-site permutation approach, we quantify the likely impact of land use on carbon uptake and albedo. Shifting to the maximum attainable carbon uptake at each site would likely cause moderate net global warming for the first approximately 20 years, followed by a strong cooling effect. A balanced policy co-optimizing carbon uptake and albedo is possible that avoids warming on any timescale, but results in a weaker long-term cooling effect.

@article{guo_estimating_2023,
	title = {Estimating {Global} {GPP} {From} the {Plant} {Functional} {Type} {Perspective} {Using} a {Machine} {Learning} {Approach}},
	volume = {128},
	issn = {2169-8953, 2169-8961},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022JG007100},
	doi = {10.1029/2022JG007100},
	abstract = {Abstract
            
              The long‐term monitoring of gross primary production (GPP) is crucial to the assessment of the carbon cycle of terrestrial ecosystems. In this study, a well‐known machine learning model (random forest, RF) is established to reconstruct the global GPP data set named ECGC\_GPP. The model distinguished nine functional plant types, including C3 and C4 crops, using eddy fluxes, meteorological variables, and leaf area index (LAI) as training data of RF model. Based on ERA5\_Land and the corrected GEOV2 data, global monthly GPP data set at a 0.05° resolution from 1999 to 2019 was estimated. The results showed that the RF model could explain 74.81\% of the monthly variation of GPP in the testing data set, of which the average contribution of LAI reached 41.73\%. The average annual and standard deviation of GPP during 1999–2019 were 117.14 ± 1.51 Pg C yr
              −1
              , with an upward trend of 0.21 Pg C yr
              −2
              (
              p
               {\textless} 0.01). By using the plant functional type classification, the underestimation of cropland is improved. Therefore, ECGC\_GPP provides reasonable global spatial pattern and long‐term trend of annual GPP.
            
          , 
            Plain Language Summary
            Accurate estimation of gross primary production (GPP) is critical for understanding the terrestrial ecosystem carbon cycle. There are a variety of GPP data sets based on different methods, but huge differences validated by the GPP measured values of flux observation towers still exist. At present, a large amount of GPP measured data provides us with the opportunity to use machine learning models to estimate global GPP. This paper presents a new global GPP data set (ECGC\_GPP) with 0.05° and monthly scales from 1999 to 2019. This GPP data set is based on random forest model and driven by remote sensing data from GEOV2 and climate data from ERA5\_Land. In ECGC\_GPP, site‐level training models are constructed based on plant functional types (especially C3 and C4 crops) to improve accuracy. All these improvements are aimed at improving the lack of interannual fluctuations and the underestimation of cropland in current machine learning data set.
          , 
            Key Points
            
              
                
                  The accuracy of gross primary production (GPP) estimation can be improved by distinguishing plant functional types, especially for C3 and C4 crops
                
                
                  Significant increasing trend is found in this random forest‐based data set
                
                
                  Leaf area index plays a leading role in both the average state and long‐term trend of GPP},
	language = {en},
	number = {4},
	urldate = {2024-11-15},
	journal = {Journal of Geophysical Research: Biogeosciences},
	author = {Guo, Renjie and Chen, Tiexi and Chen, Xin and Yuan, Wenping and Liu, Shuci and He, Bin and Li, Lin and Wang, Shengzhen and Hu, Ting and Yan, Qingyun and Wei, Xueqiong and Dai, Jie},
	month = apr,
	year = {2023},
	pages = {e2022JG007100},
}

Abstract The long‐term monitoring of gross primary production (GPP) is crucial to the assessment of the carbon cycle of terrestrial ecosystems. In this study, a well‐known machine learning model (random forest, RF) is established to reconstruct the global GPP data set named ECGC_GPP. The model distinguished nine functional plant types, including C3 and C4 crops, using eddy fluxes, meteorological variables, and leaf area index (LAI) as training data of RF model. Based on ERA5_Land and the corrected GEOV2 data, global monthly GPP data set at a 0.05° resolution from 1999 to 2019 was estimated. The results showed that the RF model could explain 74.81% of the monthly variation of GPP in the testing data set, of which the average contribution of LAI reached 41.73%. The average annual and standard deviation of GPP during 1999–2019 were 117.14 ± 1.51 Pg C yr −1 , with an upward trend of 0.21 Pg C yr −2 ( p  \textless 0.01). By using the plant functional type classification, the underestimation of cropland is improved. Therefore, ECGC_GPP provides reasonable global spatial pattern and long‐term trend of annual GPP. , Plain Language Summary Accurate estimation of gross primary production (GPP) is critical for understanding the terrestrial ecosystem carbon cycle. There are a variety of GPP data sets based on different methods, but huge differences validated by the GPP measured values of flux observation towers still exist. At present, a large amount of GPP measured data provides us with the opportunity to use machine learning models to estimate global GPP. This paper presents a new global GPP data set (ECGC_GPP) with 0.05° and monthly scales from 1999 to 2019. This GPP data set is based on random forest model and driven by remote sensing data from GEOV2 and climate data from ERA5_Land. In ECGC_GPP, site‐level training models are constructed based on plant functional types (especially C3 and C4 crops) to improve accuracy. All these improvements are aimed at improving the lack of interannual fluctuations and the underestimation of cropland in current machine learning data set. , Key Points The accuracy of gross primary production (GPP) estimation can be improved by distinguishing plant functional types, especially for C3 and C4 crops Significant increasing trend is found in this random forest‐based data set Leaf area index plays a leading role in both the average state and long‐term trend of GPP

@article{gupta_analytical_2023,
	title = {Analytical framework based on thermodynamics to estimate spatially distributed surface energy fluxes from remotely sensed radiations},
	volume = {295},
	issn = {00344257},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0034425723002109},
	doi = {10.1016/j.rse.2023.113659},
	language = {en},
	urldate = {2024-11-15},
	journal = {Remote Sensing of Environment},
	author = {Gupta, Mayank and Wild, Martin and Ghosh, Subimal},
	month = sep,
	year = {2023},
	pages = {113659},
}

@article{guseva_bulk_2023,
	title = {Bulk {Transfer} {Coefficients} {Estimated} {From} {Eddy}‐{Covariance} {Measurements} {Over} {Lakes} and {Reservoirs}},
	volume = {128},
	issn = {2169-897X, 2169-8996},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022JD037219},
	doi = {10.1029/2022JD037219},
	abstract = {Abstract
            
              The drag coefficient, Stanton number and Dalton number are of particular importance for estimating the surface turbulent fluxes of momentum, heat and water vapor using bulk parameterization. Although these bulk transfer coefficients have been extensively studied over the past several decades in marine and large‐lake environments, there are no studies analyzing their variability for smaller lakes. Here, we evaluated these coefficients through directly measured surface fluxes using the eddy‐covariance technique over more than 30 lakes and reservoirs of different sizes and depths. Our analysis showed that the transfer coefficients (adjusted to neutral atmospheric stability) were generally within the range reported in previous studies for large lakes and oceans. All transfer coefficients exhibit a substantial increase at low wind speeds ({\textless}3 m s
              −1
              ), which was found to be associated with the presence of gusts and capillary waves (except Dalton number). Stanton number was found to be on average a factor of 1.3 higher than Dalton number, likely affecting the Bowen ratio method. At high wind speeds, the transfer coefficients remained relatively constant at values of 1.6·10
              −3
              , 1.4·10
              −3
              , 1.0·10
              −3
              , respectively. We found that the variability of the transfer coefficients among the lakes could be associated with lake surface area. In flux parameterizations at lake surfaces, it is recommended to consider variations in the drag coefficient and Stanton number due to wind gustiness and capillary wave roughness while Dalton number could be considered as constant at all wind speeds.
            
          , 
            Plain Language Summary
            In our study, we investigate the bulk transfer coefficients, which are of particular importance for estimation the turbulent fluxes of momentum, heat and water vapor in the atmospheric surface layer, above lakes and reservoirs. The incorrect representation of the surface fluxes above inland waters can potentially lead to errors in weather and climate prediction models. For the first time we made this synthesis using a compiled data set consisting of existing eddy‐covariance flux measurements over 23 lakes and 8 reservoirs. Our results revealed substantial increase of the transfer coefficients at low wind speeds, which is often not taken into account in models. The observed increase in the drag coefficient (momentum transfer coefficient) and Stanton number (heat transfer coefficient) could be associated with the presence of wind gusts and capillary waves. In flux parameterizations at lake surface, it is recommended to consider them for accurate flux representation. Although the bulk transfer coefficients were relatively constant at high wind speeds, we found that the Stanton number systematically exceeds the Dalton number (water vapor transfer coefficient), despite the fact they are typically considered to be equal. This difference may affect the Bowen ratio method and result in biased estimates of lake evaporation.
          , 
            Key Points
            
              
                
                  Bulk transfer coefficients exhibit a substantial increase at low wind speed
                
                
                  The increase is explained by wind gustiness and capillary wave roughness
                
                
                  At higher wind speed, drag coefficient and Stanton number decrease with lake surface area},
	language = {en},
	number = {2},
	urldate = {2024-11-15},
	journal = {Journal of Geophysical Research: Atmospheres},
	author = {Guseva, S. and Armani, F. and Desai, A. R. and Dias, N. L. and Friborg, T. and Iwata, H. and Jansen, J. and Lükő, G. and Mammarella, I. and Repina, I. and Rutgersson, A. and Sachs, T. and Scholz, K. and Spank, U. and Stepanenko, V. and Torma, P. and Vesala, T. and Lorke, A.},
	month = jan,
	year = {2023},
	pages = {e2022JD037219},
}

Abstract The drag coefficient, Stanton number and Dalton number are of particular importance for estimating the surface turbulent fluxes of momentum, heat and water vapor using bulk parameterization. Although these bulk transfer coefficients have been extensively studied over the past several decades in marine and large‐lake environments, there are no studies analyzing their variability for smaller lakes. Here, we evaluated these coefficients through directly measured surface fluxes using the eddy‐covariance technique over more than 30 lakes and reservoirs of different sizes and depths. Our analysis showed that the transfer coefficients (adjusted to neutral atmospheric stability) were generally within the range reported in previous studies for large lakes and oceans. All transfer coefficients exhibit a substantial increase at low wind speeds (\textless3 m s −1 ), which was found to be associated with the presence of gusts and capillary waves (except Dalton number). Stanton number was found to be on average a factor of 1.3 higher than Dalton number, likely affecting the Bowen ratio method. At high wind speeds, the transfer coefficients remained relatively constant at values of 1.6·10 −3 , 1.4·10 −3 , 1.0·10 −3 , respectively. We found that the variability of the transfer coefficients among the lakes could be associated with lake surface area. In flux parameterizations at lake surfaces, it is recommended to consider variations in the drag coefficient and Stanton number due to wind gustiness and capillary wave roughness while Dalton number could be considered as constant at all wind speeds. , Plain Language Summary In our study, we investigate the bulk transfer coefficients, which are of particular importance for estimation the turbulent fluxes of momentum, heat and water vapor in the atmospheric surface layer, above lakes and reservoirs. The incorrect representation of the surface fluxes above inland waters can potentially lead to errors in weather and climate prediction models. For the first time we made this synthesis using a compiled data set consisting of existing eddy‐covariance flux measurements over 23 lakes and 8 reservoirs. Our results revealed substantial increase of the transfer coefficients at low wind speeds, which is often not taken into account in models. The observed increase in the drag coefficient (momentum transfer coefficient) and Stanton number (heat transfer coefficient) could be associated with the presence of wind gusts and capillary waves. In flux parameterizations at lake surface, it is recommended to consider them for accurate flux representation. Although the bulk transfer coefficients were relatively constant at high wind speeds, we found that the Stanton number systematically exceeds the Dalton number (water vapor transfer coefficient), despite the fact they are typically considered to be equal. This difference may affect the Bowen ratio method and result in biased estimates of lake evaporation. , Key Points Bulk transfer coefficients exhibit a substantial increase at low wind speed The increase is explained by wind gustiness and capillary wave roughness At higher wind speed, drag coefficient and Stanton number decrease with lake surface area

@article{haenelt_antibiotic_2023,
	title = {Antibiotic resistance indicator genes in biofilm and planktonic microbial communities after wastewater discharge},
	volume = {14},
	issn = {1664-302X},
	url = {https://www.frontiersin.org/articles/10.3389/fmicb.2023.1252870/full},
	doi = {10.3389/fmicb.2023.1252870},
	abstract = {The spread of bacteria with antibiotic resistance genes (ARGs) in aquatic ecosystems is of growing concern as this can pose a risk of transmission to humans and animals. While the impact of wastewater treatment plant (WWTP) effluent on ARG abundance in surface waters has been studied extensively, less is known about the fate of ARGs in biofilms. The proximity and dense growth of microorganisms in combination with the accumulation of higher antibiotic concentrations in biofilms might render biofilms a reservoir for ARGs. Seasonal parameters such as water temperature, precipitation, and antibiotic concentrations should be considered as well, as they may further influence the fate of ARGs in aquatic ecosystems. Here we investigated the effect of WWTP effluent on the abundance of the sulfonamide resistance genes
              sul1
              and
              sul2
              , and the integrase gene
              intI1
              in biofilm and surface water compartments of a river in Germany with a gradient of anthropogenic impact using quantitative PCR. Furthermore, we analyzed the bacterial community structure in both compartments via 16S rRNA gene amplicon sequencing, following the river downstream. Additionally, conventional water parameters and sulfonamide concentrations were measured, and seasonal aspects were considered by comparing the fate of ARGs and bacterial community diversity in the surface water compartment between the summer and winter season. Our results show that biofilm compartments near the WWTP had a higher relative abundance of ARGs (up to 4.7\%) than surface waters (\<2.8\%). Sulfonamide resistance genes were more persistent further downstream (\>10 km) of the WWTP in the hot and dry summer season than in winter. This finding is likely a consequence of the higher proportion of wastewater and thus wastewater-derived microorganisms in the river during summer periods. We observed distinct bacterial communities and ARG abundance between the biofilm and surface water compartment, but even greater variations when considering seasonal and spatiotemporal parameters. This underscores the need to consider seasonal aspects when studying the fate of ARGs in aquatic ecosystems.},
	urldate = {2024-11-15},
	journal = {Frontiers in Microbiology},
	author = {Haenelt, Sarah and Richnow, Hans-Hermann and Müller, Jochen A. and Musat, Niculina},
	month = sep,
	year = {2023},
	pages = {1252870},
}

The spread of bacteria with antibiotic resistance genes (ARGs) in aquatic ecosystems is of growing concern as this can pose a risk of transmission to humans and animals. While the impact of wastewater treatment plant (WWTP) effluent on ARG abundance in surface waters has been studied extensively, less is known about the fate of ARGs in biofilms. The proximity and dense growth of microorganisms in combination with the accumulation of higher antibiotic concentrations in biofilms might render biofilms a reservoir for ARGs. Seasonal parameters such as water temperature, precipitation, and antibiotic concentrations should be considered as well, as they may further influence the fate of ARGs in aquatic ecosystems. Here we investigated the effect of WWTP effluent on the abundance of the sulfonamide resistance genes sul1 and sul2 , and the integrase gene intI1 in biofilm and surface water compartments of a river in Germany with a gradient of anthropogenic impact using quantitative PCR. Furthermore, we analyzed the bacterial community structure in both compartments via 16S rRNA gene amplicon sequencing, following the river downstream. Additionally, conventional water parameters and sulfonamide concentrations were measured, and seasonal aspects were considered by comparing the fate of ARGs and bacterial community diversity in the surface water compartment between the summer and winter season. Our results show that biofilm compartments near the WWTP had a higher relative abundance of ARGs (up to 4.7%) than surface waters (<2.8%). Sulfonamide resistance genes were more persistent further downstream (>10 km) of the WWTP in the hot and dry summer season than in winter. This finding is likely a consequence of the higher proportion of wastewater and thus wastewater-derived microorganisms in the river during summer periods. We observed distinct bacterial communities and ARG abundance between the biofilm and surface water compartment, but even greater variations when considering seasonal and spatiotemporal parameters. This underscores the need to consider seasonal aspects when studying the fate of ARGs in aquatic ecosystems.

@article{haenelt_fate_2023,
	title = {The fate of sulfonamide resistance genes and anthropogenic pollution marker {intI1} after discharge of wastewater into a pristine river stream},
	volume = {14},
	issn = {1664-302X},
	url = {https://www.frontiersin.org/articles/10.3389/fmicb.2023.1058350/full},
	doi = {10.3389/fmicb.2023.1058350},
	abstract = {Introduction
              
                Currently there are sparse regulations regarding the discharge of antibiotics from wastewater treatment plants (WWTP) into river systems, making surface waters a latent reservoir for antibiotics and antibiotic resistance genes (ARGs). To better understand factors that influence the fate of ARGs in the environment and to foster surveillance of antibiotic resistance spreading in such habitats, several indicator genes have been proposed, including the integrase gene
                intI1
                and the sulfonamide resistance genes
                sul1
                and
                sul2
                .
              
            
            
              Methods
              
                Here we used quantitative PCR and long-read nanopore sequencing to monitor the abundance of these indicator genes and ARGs present as class 1 integron gene cassettes in a river system from pristine source to WWTP-impacted water. ARG abundance was compared with the dynamics of the microbial communities determined
                via
                16S rRNA gene amplicon sequencing, conventional water parameters and the concentration of sulfamethoxazole (SMX), sulfamethazine (SMZ) and sulfadiazine (SDZ).
              
            
            
              Results
              
                Our results show that WWTP effluent was the principal source of all three sulfonamides with highest concentrations for SMX (median 8.6 ng/l), and of the indicator genes
                sul1
                ,
                sul2
                and
                intI1
                with median relative abundance to 16S rRNA gene of 0.55, 0.77 and 0.65\%, respectively. Downstream from the WWTP, water quality improved constantly, including lower sulfonamide concentrations, decreasing abundances of
                sul1
                and
                sul2
                and lower numbers and diversity of ARGs in the class 1 integron. The riverine microbial community partially recovered after receiving WWTP effluent, which was consolidated by a microbiome recovery model. Surprisingly, the relative abundance of
                intI1
                increased 3-fold over 13 km of the river stretch, suggesting an internal gene multiplication.
              
            
            
              Discussion
              We found no evidence that low amounts of sulfonamides in the aquatic environment stimulate the maintenance or even spread of corresponding ARGs. Nevertheless, class 1 integrons carrying various ARGs were still present 13 km downstream from the WWTP. Therefore, limiting the release of ARG-harboring microorganisms may be more crucial for restricting the environmental spread of antimicrobial resistance than attenuating ng/L concentrations of antibiotics.},
	urldate = {2024-11-15},
	journal = {Frontiers in Microbiology},
	author = {Haenelt, Sarah and Wang, Gangan and Kasmanas, Jonas Coelho and Musat, Florin and Richnow, Hans Hermann and Da Rocha, Ulisses Nunes and Müller, Jochen A. and Musat, Niculina},
	month = jan,
	year = {2023},
	pages = {1058350},
}

Introduction Currently there are sparse regulations regarding the discharge of antibiotics from wastewater treatment plants (WWTP) into river systems, making surface waters a latent reservoir for antibiotics and antibiotic resistance genes (ARGs). To better understand factors that influence the fate of ARGs in the environment and to foster surveillance of antibiotic resistance spreading in such habitats, several indicator genes have been proposed, including the integrase gene intI1 and the sulfonamide resistance genes sul1 and sul2 . Methods Here we used quantitative PCR and long-read nanopore sequencing to monitor the abundance of these indicator genes and ARGs present as class 1 integron gene cassettes in a river system from pristine source to WWTP-impacted water. ARG abundance was compared with the dynamics of the microbial communities determined via 16S rRNA gene amplicon sequencing, conventional water parameters and the concentration of sulfamethoxazole (SMX), sulfamethazine (SMZ) and sulfadiazine (SDZ). Results Our results show that WWTP effluent was the principal source of all three sulfonamides with highest concentrations for SMX (median 8.6 ng/l), and of the indicator genes sul1 , sul2 and intI1 with median relative abundance to 16S rRNA gene of 0.55, 0.77 and 0.65%, respectively. Downstream from the WWTP, water quality improved constantly, including lower sulfonamide concentrations, decreasing abundances of sul1 and sul2 and lower numbers and diversity of ARGs in the class 1 integron. The riverine microbial community partially recovered after receiving WWTP effluent, which was consolidated by a microbiome recovery model. Surprisingly, the relative abundance of intI1 increased 3-fold over 13 km of the river stretch, suggesting an internal gene multiplication. Discussion We found no evidence that low amounts of sulfonamides in the aquatic environment stimulate the maintenance or even spread of corresponding ARGs. Nevertheless, class 1 integrons carrying various ARGs were still present 13 km downstream from the WWTP. Therefore, limiting the release of ARG-harboring microorganisms may be more crucial for restricting the environmental spread of antimicrobial resistance than attenuating ng/L concentrations of antibiotics.

@article{han_ensemble_2023,
	title = {Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale},
	volume = {16},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1991-9603},
	url = {https://gmd.copernicus.org/articles/16/5825/2023/},
	doi = {10.5194/gmd-16-5825-2023},
	abstract = {Abstract. Accurate information on surface soil moisture (SSM) content at a global scale under different climatic conditions is important for hydrological and climatological applications. Machine-learning-based systematic integration of in situ hydrological measurements, complex environmental and climate data, and satellite observation facilitate the generation of reliable data products to monitor and analyse the exchange of water, energy, and carbon in the Earth system at a proper space–time resolution. This study investigates the estimation of daily SSM using 8 optimised machine learning (ML) algorithms and 10 ensemble models (constructed via model bootstrap aggregating techniques and five-fold cross-validation). The algorithmic implementations were trained and tested using International Soil Moisture Network (ISMN) data collected from 1722 stations distributed across the world. The result showed that the K-neighbours Regressor (KNR) had the lowest root-mean-square error (0.0379 cm3 cm−3) on the “test\_random” set (for testing the performance of randomly split data during training), the Random Forest Regressor (RFR) had the lowest RMSE (0.0599 cm3 cm−3) on the “test\_temporal” set (for testing the performance on the period that was not used in training), and AdaBoost (AB) had the lowest RMSE (0.0786 cm3 cm−3) on the “test\_independent-stations” set (for testing the performance on the stations that were not used in training). Independent evaluation on novel stations across different climate zones was conducted. For the optimised ML algorithms, the median RMSE values were below 0.1 cm3 cm−3. GradientBoosting (GB), Multi-layer Perceptron Regressor (MLPR), Stochastic Gradient Descent Regressor (SGDR), and RFR achieved a median r score of 0.6 in 12, 11, 9, and 9 climate zones, respectively, out of 15 climate zones. The performance of ensemble models improved significantly, with the median RMSE value below 0.075 cm3 cm−3 for all climate zones. All voting regressors achieved r scores of above 0.6 in 13 climate zones; BSh (hot semi-arid climate) and BWh (hot desert climate) were the exceptions because of the sparse distribution of training stations. The metric evaluation showed that ensemble models can improve the performance of single ML algorithms and achieve more stable results. Based on the results computed for three different test sets, the ensemble model with KNR, RFR and Extreme Gradient Boosting (XB) performed the best. Overall, our investigation shows that ensemble machine learning algorithms have a greater capability with respect to predicting SSM compared with the optimised or base ML algorithms; this indicates their huge potential applicability in estimating water cycle budgets, managing irrigation, and predicting crop yields.},
	language = {en},
	number = {20},
	urldate = {2025-02-14},
	journal = {Geoscientific Model Development},
	author = {Han, Qianqian and Zeng, Yijian and Zhang, Lijie and Cira, Calimanut-Ionut and Prikaziuk, Egor and Duan, Ting and Wang, Chao and Szabó, Brigitta and Manfreda, Salvatore and Zhuang, Ruodan and Su, Bob},
	month = oct,
	year = {2023},
	pages = {5825--5845},
}

Abstract. Accurate information on surface soil moisture (SSM) content at a global scale under different climatic conditions is important for hydrological and climatological applications. Machine-learning-based systematic integration of in situ hydrological measurements, complex environmental and climate data, and satellite observation facilitate the generation of reliable data products to monitor and analyse the exchange of water, energy, and carbon in the Earth system at a proper space–time resolution. This study investigates the estimation of daily SSM using 8 optimised machine learning (ML) algorithms and 10 ensemble models (constructed via model bootstrap aggregating techniques and five-fold cross-validation). The algorithmic implementations were trained and tested using International Soil Moisture Network (ISMN) data collected from 1722 stations distributed across the world. The result showed that the K-neighbours Regressor (KNR) had the lowest root-mean-square error (0.0379 cm3 cm−3) on the “test_random” set (for testing the performance of randomly split data during training), the Random Forest Regressor (RFR) had the lowest RMSE (0.0599 cm3 cm−3) on the “test_temporal” set (for testing the performance on the period that was not used in training), and AdaBoost (AB) had the lowest RMSE (0.0786 cm3 cm−3) on the “test_independent-stations” set (for testing the performance on the stations that were not used in training). Independent evaluation on novel stations across different climate zones was conducted. For the optimised ML algorithms, the median RMSE values were below 0.1 cm3 cm−3. GradientBoosting (GB), Multi-layer Perceptron Regressor (MLPR), Stochastic Gradient Descent Regressor (SGDR), and RFR achieved a median r score of 0.6 in 12, 11, 9, and 9 climate zones, respectively, out of 15 climate zones. The performance of ensemble models improved significantly, with the median RMSE value below 0.075 cm3 cm−3 for all climate zones. All voting regressors achieved r scores of above 0.6 in 13 climate zones; BSh (hot semi-arid climate) and BWh (hot desert climate) were the exceptions because of the sparse distribution of training stations. The metric evaluation showed that ensemble models can improve the performance of single ML algorithms and achieve more stable results. Based on the results computed for three different test sets, the ensemble model with KNR, RFR and Extreme Gradient Boosting (XB) performed the best. Overall, our investigation shows that ensemble machine learning algorithms have a greater capability with respect to predicting SSM compared with the optimised or base ML algorithms; this indicates their huge potential applicability in estimating water cycle budgets, managing irrigation, and predicting crop yields.

@article{han_global_2023,
	title = {Global long term daily 1 km surface soil moisture dataset with physics informed machine learning},
	volume = {10},
	issn = {2052-4463},
	url = {https://www.nature.com/articles/s41597-023-02011-7},
	doi = {10.1038/s41597-023-02011-7},
	abstract = {Abstract
            
              Although soil moisture is a key factor of hydrologic and climate applications, global continuous high resolution soil moisture datasets are still limited. Here we use physics-informed machine learning to generate a global, long-term, spatially continuous high resolution dataset of surface soil moisture, using International Soil Moisture Network (ISMN), remote sensing and meteorological data, guided with the knowledge of physical processes impacting soil moisture dynamics. Global Surface Soil Moisture (GSSM1 km) provides surface soil moisture (0–5 cm) at 1 km spatial and daily temporal resolution over the period 2000–2020. The performance of the GSSM1 km dataset is evaluated with testing and validation datasets, and via inter-comparisons with existing soil moisture products. The root mean square error of GSSM1 km in testing set is 0.05 cm
              3
              /cm
              3
              , and correlation coefficient is 0.9. In terms of the feature importance, Antecedent Precipitation Evaporation Index (APEI) is the most important significant predictor among 18 predictors, followed by evaporation and longitude. GSSM1 km product can support the investigation of large-scale climate extremes and long-term trend analysis.},
	language = {en},
	number = {1},
	urldate = {2024-11-15},
	journal = {Scientific Data},
	author = {Han, Qianqian and Zeng, Yijian and Zhang, Lijie and Wang, Chao and Prikaziuk, Egor and Niu, Zhenguo and Su, Bob},
	month = feb,
	year = {2023},
	pages = {101},
}

Abstract Although soil moisture is a key factor of hydrologic and climate applications, global continuous high resolution soil moisture datasets are still limited. Here we use physics-informed machine learning to generate a global, long-term, spatially continuous high resolution dataset of surface soil moisture, using International Soil Moisture Network (ISMN), remote sensing and meteorological data, guided with the knowledge of physical processes impacting soil moisture dynamics. Global Surface Soil Moisture (GSSM1 km) provides surface soil moisture (0–5 cm) at 1 km spatial and daily temporal resolution over the period 2000–2020. The performance of the GSSM1 km dataset is evaluated with testing and validation datasets, and via inter-comparisons with existing soil moisture products. The root mean square error of GSSM1 km in testing set is 0.05 cm 3 /cm 3 , and correlation coefficient is 0.9. In terms of the feature importance, Antecedent Precipitation Evaporation Index (APEI) is the most important significant predictor among 18 predictors, followed by evaporation and longitude. GSSM1 km product can support the investigation of large-scale climate extremes and long-term trend analysis.

@article{harley_riverine_2023,
	title = {Riverine {Dissolved} {Inorganic} {Carbon} {Export} {From} the {Southeast} {Alaskan} {Drainage} {Basin} {With} {Implications} for {Coastal} {Ocean} {Processes}},
	volume = {128},
	issn = {2169-8953, 2169-8961},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JG007609},
	doi = {10.1029/2023JG007609},
	abstract = {Abstract
            
              Dissolved inorganic carbon (DIC) represents an important but poorly constrained form of lateral carbon flux to the oceans. With high precipitation rates, large glaciers, and dense temperate rainforest, Southeast Alaska plays a critical role in the transport of carbon to the Gulf of Alaska (GOA). Previous estimates of DIC flux across the Southeast Alaska Drainage Basin (SEAKDB) are poorly constrained in space and time. Our goal was to incorporate recent measurements of DIC concentrations with previous measurements from the U.S. Geological Survey in order to model the spatial and temporal patterns of riverine DIC transport from SEAK to the GOA. We aggregated DIC concentration measurements from 1957 to 2020 and associated measurements of mean daily discharge. We then constructed load estimation models to generate concentration predictions across 24 watersheds. By spatially matching measurements of DIC with SEAKDB watersheds, we extrapolated concentration predictions across 2,455 watersheds encompassing approximately 190,000 km
              2
              . Models were aggregated according to two factors, the presence of karst and the discharge regime. Finally, monthly flux predictions were generated for each watershed using predicted concentrations and runoff estimates from the Distributed Climate Water Balance Model. Mean annual DIC flux from the SEAKDB was 2.36 Tg C with an average yield of 12.52 g C m
              −2
              . Both karst presence and flow regimes modified DIC flux and speciation across coastal marine areas. The high resolution of DIC flux estimates will provide useful inputs for describing seasonal C dynamics, and further refines our understanding of C budgets in the Pacific temperate rainforest and the surrounding marine environment.
            
          , 
            Plain Language Summary
            Understanding how carbon moves through ecosystems is critical in a changing climate. Dissolved carbon in aquatic environments plays a critical role in driving large‐scale processes such as ocean acidification, which represents a threat to many marine ecosystems. Despite the importance of understanding and accounting for carbon as it moves through the environment, the transfer of dissolved inorganic carbon (DIC) (such as carbon dioxide) from the terrestrial environment to the marine environment is often overlooked. Streams and rivers transfer carbon from land to ocean and represent a significant source of carbon to the marine environment, especially in areas that have large amounts of freshwater discharge such as Southeast Alaska. In this study, we created a model which generates predictions for how much DIC is entering the marine environment of Southeast Alaska. For each of 2,455 watersheds identified in this region we calculated monthly flux estimates which we grouped into large marine zones. Our overall flux estimate agrees well with previous estimates, but here our model provides more highly resolved spatial and temporal flux values which reveals seasonal and geographic patterns of DIC transfer from rivers to the marine environment.
          , 
            Key Points
            
              
                
                  Lateral flux of dissolved inorganic carbon (DIC) is not well resolved in time and space in Southeast Alaska
                
                
                  We present robust models for DIC flux on a watershed basis that are spatially and temporally explicit
                
                
                  Calculated DIC flux from Southeast Alaska watersheds is heavily influenced by streamflow regime and karst presence},
	language = {en},
	number = {10},
	urldate = {2024-11-17},
	journal = {Journal of Geophysical Research: Biogeosciences},
	author = {Harley, John R. and Biles, Frances E. and Brooks, Mariela K. and Fellman, Jason and Hood, Eran and D’Amore, David V.},
	month = oct,
	year = {2023},
	pages = {e2023JG007609},
}

Abstract Dissolved inorganic carbon (DIC) represents an important but poorly constrained form of lateral carbon flux to the oceans. With high precipitation rates, large glaciers, and dense temperate rainforest, Southeast Alaska plays a critical role in the transport of carbon to the Gulf of Alaska (GOA). Previous estimates of DIC flux across the Southeast Alaska Drainage Basin (SEAKDB) are poorly constrained in space and time. Our goal was to incorporate recent measurements of DIC concentrations with previous measurements from the U.S. Geological Survey in order to model the spatial and temporal patterns of riverine DIC transport from SEAK to the GOA. We aggregated DIC concentration measurements from 1957 to 2020 and associated measurements of mean daily discharge. We then constructed load estimation models to generate concentration predictions across 24 watersheds. By spatially matching measurements of DIC with SEAKDB watersheds, we extrapolated concentration predictions across 2,455 watersheds encompassing approximately 190,000 km 2 . Models were aggregated according to two factors, the presence of karst and the discharge regime. Finally, monthly flux predictions were generated for each watershed using predicted concentrations and runoff estimates from the Distributed Climate Water Balance Model. Mean annual DIC flux from the SEAKDB was 2.36 Tg C with an average yield of 12.52 g C m −2 . Both karst presence and flow regimes modified DIC flux and speciation across coastal marine areas. The high resolution of DIC flux estimates will provide useful inputs for describing seasonal C dynamics, and further refines our understanding of C budgets in the Pacific temperate rainforest and the surrounding marine environment. , Plain Language Summary Understanding how carbon moves through ecosystems is critical in a changing climate. Dissolved carbon in aquatic environments plays a critical role in driving large‐scale processes such as ocean acidification, which represents a threat to many marine ecosystems. Despite the importance of understanding and accounting for carbon as it moves through the environment, the transfer of dissolved inorganic carbon (DIC) (such as carbon dioxide) from the terrestrial environment to the marine environment is often overlooked. Streams and rivers transfer carbon from land to ocean and represent a significant source of carbon to the marine environment, especially in areas that have large amounts of freshwater discharge such as Southeast Alaska. In this study, we created a model which generates predictions for how much DIC is entering the marine environment of Southeast Alaska. For each of 2,455 watersheds identified in this region we calculated monthly flux estimates which we grouped into large marine zones. Our overall flux estimate agrees well with previous estimates, but here our model provides more highly resolved spatial and temporal flux values which reveals seasonal and geographic patterns of DIC transfer from rivers to the marine environment. , Key Points Lateral flux of dissolved inorganic carbon (DIC) is not well resolved in time and space in Southeast Alaska We present robust models for DIC flux on a watershed basis that are spatially and temporally explicit Calculated DIC flux from Southeast Alaska watersheds is heavily influenced by streamflow regime and karst presence

@article{hascoet_learning_2023,
	title = {Learning {Global} {Evapotranspiration} {Dataset} {Corrections} from a {Water} {Cycle} {Closure} {Supervision}},
	volume = {16},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2072-4292},
	url = {https://www.mdpi.com/2072-4292/16/1/170},
	doi = {10.3390/rs16010170},
	abstract = {Evapotranspiration (E) is one of the most uncertain components of the global water cycle (WC). Improving global E estimates is necessary to improve our understanding of climate and its impact on available surface water resources. This work presents a methodology for deriving monthly corrections to global E datasets at 0.25∘ resolution. A principled approach is proposed to firstly use indirect information from the other water components to correct E estimates at the catchment level, and secondly to extend this sparse catchment-level information to global pixel-level corrections using machine learning (ML). Several E satellite products are available, each with its own errors (both random and systematic). Four such global E datasets are used to validate the proposed approach and highlight its ability to extract seasonal and regional systematic biases. The resulting E corrections are shown to accurately generalize WC closure constraints to unseen catchments. With an average deviation of 14\% from the original E datasets, the proposed method achieves up to 20\% WC residual reduction on the most favorable dataset.},
	language = {en},
	number = {1},
	urldate = {2024-11-15},
	journal = {Remote Sensing},
	author = {Hascoet, Tristan and Pellet, Victor and Aires, Filipe and Takiguchi, Tetsuya},
	month = dec,
	year = {2023},
	pages = {170},
}

Evapotranspiration (E) is one of the most uncertain components of the global water cycle (WC). Improving global E estimates is necessary to improve our understanding of climate and its impact on available surface water resources. This work presents a methodology for deriving monthly corrections to global E datasets at 0.25∘ resolution. A principled approach is proposed to firstly use indirect information from the other water components to correct E estimates at the catchment level, and secondly to extend this sparse catchment-level information to global pixel-level corrections using machine learning (ML). Several E satellite products are available, each with its own errors (both random and systematic). Four such global E datasets are used to validate the proposed approach and highlight its ability to extract seasonal and regional systematic biases. The resulting E corrections are shown to accurately generalize WC closure constraints to unseen catchments. With an average deviation of 14% from the original E datasets, the proposed method achieves up to 20% WC residual reduction on the most favorable dataset.

@article{haubrock_long-term_2023,
	title = {Long-term trends in abundances of non-native species across biomes, realms, and taxonomic groups in {Europe}},
	volume = {884},
	issn = {00489697},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0048969723024294},
	doi = {10.1016/j.scitotenv.2023.163808},
	language = {en},
	urldate = {2024-11-15},
	journal = {Science of The Total Environment},
	author = {Haubrock, Phillip J. and Pilotto, Francesca and Soto, Ismael and Kühn, Ingolf and Verreycken, Hugo and Seebens, Hanno and Cuthbert, Ross N. and Haase, Peter},
	month = aug,
	year = {2023},
	pages = {163808},
}

@article{he_stateart_2023,
	title = {Do {State}‐{Of}‐{The}‐{Art} {Atmospheric} {CO}$_{\textrm{2}}$ {Inverse} {Models} {Capture} {Drought} {Impacts} on the {European} {Land} {Carbon} {Uptake}?},
	volume = {15},
	issn = {1942-2466, 1942-2466},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022MS003150},
	doi = {10.1029/2022MS003150},
	abstract = {Abstract
            
              The European land carbon uptake has been heavily impacted by several recent severe droughts, yet quantitative estimates of carbon uptake anomalies are uncertain. Atmospheric CO
              2
              inverse models (AIMs) provide observation‐based estimates of the large‐scale carbon flux dynamics, but how well they capture drought impacts on the terrestrial carbon uptake is poorly known. Here we assessed the capacity of state‐of‐the‐art AIMs in monitoring drought impacts on the European carbon uptake over 2001–2015 using observations of environmental variability and vegetation function and made comparisons with bottom‐up estimates of carbon uptake anomalies. We found that global inversions with only limited surface CO
              2
              observations give divergent estimates of drought impacts. Regional inversions assimilating denser CO
              2
              observations over Europe demonstrated some improved consistency, with all inversions capturing a reduction in carbon uptake during the 2012 drought. However, they failed to capture the reduction caused by the 2015 drought. Finally, we found that a set of inversions that assimilated satellite XCO
              2
              or assimilated environmental variables plus surface CO
              2
              observations better captured carbon uptake anomalies induced by both the 2012 and 2015 droughts. In addition, the recent Orbiting Carbon Observatory—2 XCO
              2
              inversions showed good potential in capturing drought impacts, with better performances for larger‐scale droughts like the 2018 drought. These results suggest that surface CO
              2
              observations may still be too sparse to fully capture the impact of drought on the carbon cycle at subcontinental scales over Europe, and satellite XCO
              2
              and ancillary environmental data can be used to improve observational constraints in atmospheric inversion systems.
            
          , 
            Plain Language Summary
            
              Atmospheric CO
              2
              inverse models (AIMs) are useful tools for quantifying the response of large‐scale carbon uptake to climate extremes, but their capacity for monitoring drought impacts, particularly at regional scales, is not fully explored. In this study, we assessed the capacity of state‐of‐the‐art AIMs for monitoring drought impacts on the European land carbon uptake over 2001–2015 using a large array of observational and model data sets. We found: (a) global inversions with only limited surface CO
              2
              observations face a great challenge in monitoring drought impacts on the European carbon uptake; (b) Regional inversions assimilated denser CO
              2
              observations over Europe, for the EUROCOM project, demonstrated some improved consistency but are still deficient, showing divergent estimates in interannual variability of carbon uptake for most years; and (c) A set of inversion systems that assimilated satellite XCO
              2
              or assimilated environmental variables plus surface CO
              2
              observations better captured annual and seasonal anomalies caused by droughts. Our study demonstrates that surface CO
              2
              observations may still be too sparse to fully capture the impact of drought on the carbon cycle at subcontinental scales over Europe, whereby satellite XCO
              2
              and ancillary environmental data can offer observational constraints for improving the estimates.
            
          , 
            Key Points
            
              
                
                  
                    Global inversions with only limited surface CO
                    2
                    observations give divergent estimates of drought impacts on the European carbon uptake
                  
                
                
                  
                    Regional inversions assimilating denser CO
                    2
                    observations over Europe demonstrate some improved consistency but are still deficient
                  
                
                
                  
                    The inversions assimilating satellite XCO
                    2
                    or environmental variables in addition to surface CO
                    2
                    largely improve the estimates},
	language = {en},
	number = {6},
	urldate = {2024-11-15},
	journal = {Journal of Advances in Modeling Earth Systems},
	author = {He, Wei and Jiang, Fei and Ju, Weimin and Byrne, Brendan and Xiao, Jingfeng and Nguyen, Ngoc Tu and Wu, Mousong and Wang, Songhan and Wang, Jun and Rödenbeck, Christian and Li, Xing and Scholze, Marko and Monteil, Guillaume and Wang, Hengmao and Zhou, Yanlian and He, Qiaoning and Chen, Jing M.},
	month = jun,
	year = {2023},
	pages = {e2022MS003150},
}

Abstract The European land carbon uptake has been heavily impacted by several recent severe droughts, yet quantitative estimates of carbon uptake anomalies are uncertain. Atmospheric CO 2 inverse models (AIMs) provide observation‐based estimates of the large‐scale carbon flux dynamics, but how well they capture drought impacts on the terrestrial carbon uptake is poorly known. Here we assessed the capacity of state‐of‐the‐art AIMs in monitoring drought impacts on the European carbon uptake over 2001–2015 using observations of environmental variability and vegetation function and made comparisons with bottom‐up estimates of carbon uptake anomalies. We found that global inversions with only limited surface CO 2 observations give divergent estimates of drought impacts. Regional inversions assimilating denser CO 2 observations over Europe demonstrated some improved consistency, with all inversions capturing a reduction in carbon uptake during the 2012 drought. However, they failed to capture the reduction caused by the 2015 drought. Finally, we found that a set of inversions that assimilated satellite XCO 2 or assimilated environmental variables plus surface CO 2 observations better captured carbon uptake anomalies induced by both the 2012 and 2015 droughts. In addition, the recent Orbiting Carbon Observatory—2 XCO 2 inversions showed good potential in capturing drought impacts, with better performances for larger‐scale droughts like the 2018 drought. These results suggest that surface CO 2 observations may still be too sparse to fully capture the impact of drought on the carbon cycle at subcontinental scales over Europe, and satellite XCO 2 and ancillary environmental data can be used to improve observational constraints in atmospheric inversion systems. , Plain Language Summary Atmospheric CO 2 inverse models (AIMs) are useful tools for quantifying the response of large‐scale carbon uptake to climate extremes, but their capacity for monitoring drought impacts, particularly at regional scales, is not fully explored. In this study, we assessed the capacity of state‐of‐the‐art AIMs for monitoring drought impacts on the European land carbon uptake over 2001–2015 using a large array of observational and model data sets. We found: (a) global inversions with only limited surface CO 2 observations face a great challenge in monitoring drought impacts on the European carbon uptake; (b) Regional inversions assimilated denser CO 2 observations over Europe, for the EUROCOM project, demonstrated some improved consistency but are still deficient, showing divergent estimates in interannual variability of carbon uptake for most years; and (c) A set of inversion systems that assimilated satellite XCO 2 or assimilated environmental variables plus surface CO 2 observations better captured annual and seasonal anomalies caused by droughts. Our study demonstrates that surface CO 2 observations may still be too sparse to fully capture the impact of drought on the carbon cycle at subcontinental scales over Europe, whereby satellite XCO 2 and ancillary environmental data can offer observational constraints for improving the estimates. , Key Points Global inversions with only limited surface CO 2 observations give divergent estimates of drought impacts on the European carbon uptake Regional inversions assimilating denser CO 2 observations over Europe demonstrate some improved consistency but are still deficient The inversions assimilating satellite XCO 2 or environmental variables in addition to surface CO 2 largely improve the estimates

@misc{helle_stable_2023,
	title = {Stable oxygen isotope ratios of tree-ring cellulose from oak ({Quercus} robur) at {Lake} {Tiefer} {See}, {Mecklenburg} {Lake} {District}, {Northeastern} {Germany}},
	copyright = {Creative Commons Attribution 4.0 International},
	url = {https://dataservices.gfz-potsdam.de/tereno-new/showshort.php?id=6670569a-fa4a-11ed-95b8-f851ad6d1e4b},
	doi = {10.5880/TERENO.TRSI.2023.002},
	abstract = {An annually resolved chronologies of oxygen isotopes from five living oak (Quercus robur) trees have been measured from tree ring cellulose covering up to the last 180 years (1836CE – 2020CE). This tree-ring stable isotope data set was established within the ‘Terrestrial Environmental Observatories’ (TERENO) of the Helmholtz Association. The site “Lake Tiefer See” is subject to the TERENO monitoring activities at the Northeast German Lowland Observatory coordinated by the GFZ German Research Centre for Geosciences in Potsdam. The data set comprises the δ18O records with respect to the international VSMOW standard. Lake Tiefer See (53°350 N, 12°320 E) is located 90 km NNW of Berlin in the morainic terrain of the NE-German Polish Basin. It is part of in the N–S trending Klocksin Lake Chain. The sampled trees are growing at the southern shore of the lake. Fifteen co-dominant Quercus robur tree individuals were cored at about 1.3m above ground from two opposite positions using an increment corer of 5 mm diameter (Suunto, Finland or Mora, Sweden).},
	urldate = {2024-11-15},
	publisher = {GFZ Data Services},
	author = {Helle, Gerhard and Brauer, Achim and Heinrich, Ingo},
	collaborator = {Helle, Gerhard and Brauer, Achim and Heinrich, Ingo and Hilbich, Michelle and Pechipaykoska, Ivana and Schürheck, Lucas},
	year = {2023},
	keywords = {18O/16O, EARTH SCIENCE \> CLIMATE INDICATORS \> PALEOCLIMATE INDICATORS, EARTH SCIENCE \> CLIMATE INDICATORS \> PALEOCLIMATE INDICATORS \> BIOLOGICAL RECORDS \> TREE RINGS, EARTH SCIENCE \> CLIMATE INDICATORS \> PALEOCLIMATE INDICATORS \> BIOLOGICAL RECORDS \> TREE RINGS \> ISOTOPIC ANALYSIS, EARTH SCIENCE \> CLIMATE INDICATORS \> PALEOCLIMATE INDICATORS \> BIOLOGICAL RECORDS \> TREE RINGS \> ISOTOPIC ANALYSIS \> CARBON ISOTOPE, EARTH SCIENCE \> CLIMATE INDICATORS \> PALEOCLIMATE INDICATORS \> PALEOCLIMATE RECONSTRUCTIONS, EARTH SCIENCE \> PALEOCLIMATE \> LAND RECORDS \> TREE RINGS, Lake Tiefer See, Mecklenburg lake district, Northeastern Germany, Quercus robur, TERENO, TERENO Nordost, TERENO Northeast, TERrestrial ENvironmental Observatories, cellulose, chronology, d18O, latewood, oak, stable oxygen isotopes, time series, tree rings},
}

An annually resolved chronologies of oxygen isotopes from five living oak (Quercus robur) trees have been measured from tree ring cellulose covering up to the last 180 years (1836CE – 2020CE). This tree-ring stable isotope data set was established within the ‘Terrestrial Environmental Observatories’ (TERENO) of the Helmholtz Association. The site “Lake Tiefer See” is subject to the TERENO monitoring activities at the Northeast German Lowland Observatory coordinated by the GFZ German Research Centre for Geosciences in Potsdam. The data set comprises the δ18O records with respect to the international VSMOW standard. Lake Tiefer See (53°350 N, 12°320 E) is located 90 km NNW of Berlin in the morainic terrain of the NE-German Polish Basin. It is part of in the N–S trending Klocksin Lake Chain. The sampled trees are growing at the southern shore of the lake. Fifteen co-dominant Quercus robur tree individuals were cored at about 1.3m above ground from two opposite positions using an increment corer of 5 mm diameter (Suunto, Finland or Mora, Sweden).

@misc{helle_stable_2023,
	title = {Stable carbon isotope ratios of tree-ring cellulose from oak ({Quercus} robur) at {Lake} {Tiefer} {See}, {Mecklenburg} {Lake} {District}, {Northeastern} {Germany}},
	copyright = {Creative Commons Attribution 4.0 International},
	url = {https://dataservices.gfz-potsdam.de/tereno-new/showshort.php?id=7fe28391-fa4a-11ed-95b8-f851ad6d1e4b},
	doi = {10.5880/TERENO.TRSI.2023.001},
	abstract = {An annually resolved chronologies of carbon isotopes from five living oak (Quercus robur) trees have been measured from tree ring cellulose covering up to the last 180 years (1836CE – 2020CE). This tree-ring stable isotope data set was established within the ‘Terrestrial Environmental Observatories’ (TERENO) of the Helmholtz Association. The site “Lake Tiefer See” is subject to the TERENO monitoring activities at the Northeast German Lowland Observatory coordinated by the GFZ German Research Centre for Geosciences in Potsdam. The data set comprises the δ13C records with respect to the international VPDB standard. Lake Tiefer See (53°350 N, 12°320 E) is located 90 km NNW of Berlin in the morainic terrain of the NE-German Polish Basin. It is part of in the N–S trending Klocksin Lake Chain. The sampled trees are growing at the southern shore of the lake. Fifteen co-dominant Quercus robur tree individuals were cored at about 1.3m above ground from two opposite positions using an increment corer of 5 mm diameter (Suunto, Finland or Mora, Sweden).},
	urldate = {2024-11-15},
	publisher = {GFZ Data Services},
	author = {Helle, Gerhard and Brauer, Achim and Heinrich, Ingo},
	collaborator = {Helle, Gerhard and Brauer, Achim and Heinrich, Ingo and Hilbich, Michelle and Pechipaykoska, Ivana and Schürheck, Lucas},
	year = {2023},
	keywords = {13C/12C, EARTH SCIENCE \> CLIMATE INDICATORS \> PALEOCLIMATE INDICATORS, EARTH SCIENCE \> CLIMATE INDICATORS \> PALEOCLIMATE INDICATORS \> BIOLOGICAL RECORDS \> TREE RINGS, EARTH SCIENCE \> CLIMATE INDICATORS \> PALEOCLIMATE INDICATORS \> BIOLOGICAL RECORDS \> TREE RINGS \> ISOTOPIC ANALYSIS, EARTH SCIENCE \> CLIMATE INDICATORS \> PALEOCLIMATE INDICATORS \> BIOLOGICAL RECORDS \> TREE RINGS \> ISOTOPIC ANALYSIS \> CARBON ISOTOPE, EARTH SCIENCE \> CLIMATE INDICATORS \> PALEOCLIMATE INDICATORS \> PALEOCLIMATE RECONSTRUCTIONS, EARTH SCIENCE \> PALEOCLIMATE \> LAND RECORDS \> TREE RINGS, Lake Tiefer See, Mecklenburg lake district, Northeastern Germany, Quercus robur, TERENO, TERENO Nordost, TERENO Northeast, TERrestrial ENvironmental Observatories, Tree rings, cellulose, chronology, d13C, latewood, oak, stable carbon isotopes, time series},
}

An annually resolved chronologies of carbon isotopes from five living oak (Quercus robur) trees have been measured from tree ring cellulose covering up to the last 180 years (1836CE – 2020CE). This tree-ring stable isotope data set was established within the ‘Terrestrial Environmental Observatories’ (TERENO) of the Helmholtz Association. The site “Lake Tiefer See” is subject to the TERENO monitoring activities at the Northeast German Lowland Observatory coordinated by the GFZ German Research Centre for Geosciences in Potsdam. The data set comprises the δ13C records with respect to the international VPDB standard. Lake Tiefer See (53°350 N, 12°320 E) is located 90 km NNW of Berlin in the morainic terrain of the NE-German Polish Basin. It is part of in the N–S trending Klocksin Lake Chain. The sampled trees are growing at the southern shore of the lake. Fifteen co-dominant Quercus robur tree individuals were cored at about 1.3m above ground from two opposite positions using an increment corer of 5 mm diameter (Suunto, Finland or Mora, Sweden).

@article{hermans_advancing_2023,
	title = {Advancing measurements and representations of subsurface heterogeneity and dynamic processes: towards {4D} hydrogeology},
	volume = {27},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1607-7938},
	shorttitle = {Advancing measurements and representations of subsurface heterogeneity and dynamic processes},
	url = {https://hess.copernicus.org/articles/27/255/2023/},
	doi = {10.5194/hess-27-255-2023},
	abstract = {Abstract. Essentially all hydrogeological processes are strongly influenced by the subsurface spatial heterogeneity and the temporal variation of environmental conditions, hydraulic properties, and solute concentrations. This spatial and temporal variability generally leads to effective behaviors and emerging phenomena that cannot be predicted from conventional approaches based on homogeneous assumptions and models. However, it is not always clear when, why, how, and at what scale the 4D (3D + time) nature of the subsurface needs to be considered in hydrogeological monitoring, modeling, and applications. In this paper, we discuss the interest and potential for the monitoring and characterization of spatial and temporal variability, including 4D imaging, in a series of hydrogeological processes: (1) groundwater fluxes, (2) solute transport and reaction, (3) vadose zone dynamics, and (4) surface–subsurface water interactions. We first identify the main challenges related to the coupling of spatial and temporal fluctuations for these processes. We then highlight recent innovations that have led to significant breakthroughs in high-resolution space–time imaging and modeling the characterization, monitoring, and modeling of these spatial and temporal fluctuations. We finally propose a classification of processes and applications at different scales according to their need and potential for high-resolution space–time imaging. We thus advocate a more systematic characterization of the dynamic and 3D nature of the subsurface for a series of critical processes and emerging applications. This calls for the validation of 4D imaging techniques at highly instrumented observatories and the harmonization of open databases to share hydrogeological data sets in their 4D components.},
	language = {en},
	number = {1},
	urldate = {2024-11-15},
	journal = {Hydrology and Earth System Sciences},
	author = {Hermans, Thomas and Goderniaux, Pascal and Jougnot, Damien and Fleckenstein, Jan H. and Brunner, Philip and Nguyen, Frédéric and Linde, Niklas and Huisman, Johan Alexander and Bour, Olivier and Lopez Alvis, Jorge and Hoffmann, Richard and Palacios, Andrea and Cooke, Anne-Karin and Pardo-Álvarez, Álvaro and Blazevic, Lara and Pouladi, Behzad and Haruzi, Peleg and Fernandez Visentini, Alejandro and Nogueira, Guilherme E. H. and Tirado-Conde, Joel and Looms, Majken C. and Kenshilikova, Meruyert and Davy, Philippe and Le Borgne, Tanguy},
	month = jan,
	year = {2023},
	pages = {255--287},
}

Abstract. Essentially all hydrogeological processes are strongly influenced by the subsurface spatial heterogeneity and the temporal variation of environmental conditions, hydraulic properties, and solute concentrations. This spatial and temporal variability generally leads to effective behaviors and emerging phenomena that cannot be predicted from conventional approaches based on homogeneous assumptions and models. However, it is not always clear when, why, how, and at what scale the 4D (3D + time) nature of the subsurface needs to be considered in hydrogeological monitoring, modeling, and applications. In this paper, we discuss the interest and potential for the monitoring and characterization of spatial and temporal variability, including 4D imaging, in a series of hydrogeological processes: (1) groundwater fluxes, (2) solute transport and reaction, (3) vadose zone dynamics, and (4) surface–subsurface water interactions. We first identify the main challenges related to the coupling of spatial and temporal fluctuations for these processes. We then highlight recent innovations that have led to significant breakthroughs in high-resolution space–time imaging and modeling the characterization, monitoring, and modeling of these spatial and temporal fluctuations. We finally propose a classification of processes and applications at different scales according to their need and potential for high-resolution space–time imaging. We thus advocate a more systematic characterization of the dynamic and 3D nature of the subsurface for a series of critical processes and emerging applications. This calls for the validation of 4D imaging techniques at highly instrumented observatories and the harmonization of open databases to share hydrogeological data sets in their 4D components.

@article{heyvaert_impact_2023,
	title = {Impact of {Design} {Factors} for {ESA} {CCI} {Satellite} {Soil} {Moisture} {Data} {Assimilation} over {Europe}},
	volume = {24},
	copyright = {http://creativecommons.org/licenses/by/4.0/},
	issn = {1525-755X, 1525-7541},
	url = {https://journals.ametsoc.org/view/journals/hydr/24/7/JHM-D-22-0141.1.xml},
	doi = {10.1175/JHM-D-22-0141.1},
	abstract = {Abstract
            In this study, soil moisture retrievals of the combined active–passive ESA Climate Change Initiative (CCI) soil moisture product are assimilated into the Noah-MP land surface model over Europe using a one-dimensional ensemble Kalman filter and an 18-yr study period. The performance of the data assimilation (DA) system is evaluated by comparing it with a model-only experiment (at in situ sites) and by assessing statistics of innovations and increments as DA diagnostics (over the entire domain). For both assessments, we explore the impact of three design choices, resulting in the following insights. 1) The magnitude of the assumed observation errors strongly affects the skill improvements evaluated against in situ stations and internal diagnostics. 2) Choosing between climatological or monthly cumulative distribution function matching as the observation bias correction method only has a marginal effect on the in situ skill of the DA system. However, the internal diagnostics suggest a more robust system parameterization if the observations are rescaled monthly. 3) The choice of atmospheric reanalysis dataset to force the land surface model affects the model-only skill and the DA skill improvements. The model-only skill is higher with input from the MERRA-2 than with input from the ERA5 reanalysis, resulting in larger DA skill improvements for the latter. Additionally, we show that the added value of the DA strongly depends on the quality of the satellite retrievals and land cover, with the most substantial soil moisture skill improvements occurring over croplands and skill degradation occurring over densely forested areas.},
	number = {7},
	urldate = {2024-11-15},
	journal = {Journal of Hydrometeorology},
	author = {Heyvaert, Zdenko and Scherrer, Samuel and Bechtold, Michel and Gruber, Alexander and Dorigo, Wouter and Kumar, Sujay and De Lannoy, Gabriëlle},
	month = jul,
	year = {2023},
	pages = {1193--1208},
}

Abstract In this study, soil moisture retrievals of the combined active–passive ESA Climate Change Initiative (CCI) soil moisture product are assimilated into the Noah-MP land surface model over Europe using a one-dimensional ensemble Kalman filter and an 18-yr study period. The performance of the data assimilation (DA) system is evaluated by comparing it with a model-only experiment (at in situ sites) and by assessing statistics of innovations and increments as DA diagnostics (over the entire domain). For both assessments, we explore the impact of three design choices, resulting in the following insights. 1) The magnitude of the assumed observation errors strongly affects the skill improvements evaluated against in situ stations and internal diagnostics. 2) Choosing between climatological or monthly cumulative distribution function matching as the observation bias correction method only has a marginal effect on the in situ skill of the DA system. However, the internal diagnostics suggest a more robust system parameterization if the observations are rescaled monthly. 3) The choice of atmospheric reanalysis dataset to force the land surface model affects the model-only skill and the DA skill improvements. The model-only skill is higher with input from the MERRA-2 than with input from the ERA5 reanalysis, resulting in larger DA skill improvements for the latter. Additionally, we show that the added value of the DA strongly depends on the quality of the satellite retrievals and land cover, with the most substantial soil moisture skill improvements occurring over croplands and skill degradation occurring over densely forested areas.

@article{hu_twenty-year_2023,
	title = {A twenty-year dataset of soil moisture and vegetation optical depth from {AMSR}-{E}/2 measurements using the multi-channel collaborative algorithm},
	volume = {292},
	issn = {00344257},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0034425723001463},
	doi = {10.1016/j.rse.2023.113595},
	language = {en},
	urldate = {2024-11-15},
	journal = {Remote Sensing of Environment},
	author = {Hu, Lu and Zhao, Tianjie and Ju, Weimin and Peng, Zhiqing and Shi, Jiancheng and Rodríguez-Fernández, Nemesio J. and Wigneron, Jean-Pierre and Cosh, Michael H. and Yang, Kun and Lu, Hui and Yao, Panpan},
	month = jul,
	year = {2023},
	pages = {113595},
}

@article{hu_evaluating_2023,
	title = {Evaluating {European} {ECOSTRESS} {Hub} {Evapotranspiration} {Products} {Across} a {Range} of {Soil}‐{Atmospheric} {Aridity} and {Biomes} {Over} {Europe}},
	volume = {59},
	issn = {0043-1397, 1944-7973},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR034132},
	doi = {10.1029/2022WR034132},
	abstract = {Abstract
            
              The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is a scientific mission that collects high spatio‐temporal resolution (∼70 m, 1–5 days average revisit time) thermal images since its launch on 29 June 2018. As a predecessor of future missions, one of the main objectives of ECOSTRESS is to retrieve and understand the spatio‐temporal variations in terrestrial evapotranspiration (ET) and its responses to soil water availability and atmospheric aridity. In the European ECOSTRESS Hub (EEH), by taking advantage of land surface temperature (LST) retrievals, we generated ECOSTRESS ET products over Europe and Africa using three models with different structures and parameterization schemes, namely Surface Energy Balance System (SEBS) and Two Source Energy Balance (TSEB) parametric models, as well as the non‐parametric Surface Temperature Initiated Closure (STIC) model. A comprehensive evaluation of the EEH ET products was conducted with respect to flux measurements from 19 eddy covariance sites in Europe over six different biomes with diverse aridity levels. Results revealed comparable performances of STIC and SEBS (RMSE of ∼70 W m
              −2
              ). However, the relatively complex TSEB model produced a higher RMSE of ∼90 W m
              −2
              . Comparison between STIC ET estimates and the operational ECOSTRESS ET product from NASA PT‐JPL model showed a larger RMSE (around 50 W m
              −2
              higher) for the PT‐JPL ET estimates. Substantial overestimation ({\textgreater}80 W m
              −2
              ) in PT‐JPL ET estimates was evident over shrublands and savannas, presumably due to weak constraint of LST in the model. Overall, the EEH supports ET retrieval for the future high‐resolution thermal missions.
            
          , 
            Key Points
            
              
                
                  Evapotranspiration products over Europe and Africa were generated using three models (Surface Temperature Initiated Closure, Surface Energy Balance System, and Two Source Energy Balance) in the European ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) Hub
                
                
                  Comparison at 19 eddy covariance sites revealed noteworthy model divergence with increasing aridity and vegetation sparseness
                
                
                  A substantial overestimation of the official NASA ECOSTRESS PT‐JPL evapotranspiration product was found under high water limitations},
	language = {en},
	number = {8},
	urldate = {2024-11-15},
	journal = {Water Resources Research},
	author = {Hu, Tian and Mallick, Kaniska and Hitzelberger, Patrik and Didry, Yoanne and Boulet, Gilles and Szantoi, Zoltan and Koetz, Benjamin and Alonso, Itziar and Pascolini‐Campbell, Madeleine and Halverson, Gregory and Cawse‐Nicholson, Kerry and Hulley, Glynn C. and Hook, Simon and Bhattarai, Nishan and Olioso, Albert and Roujean, Jean‐Louis and Gamet, Philippe and Su, Bob},
	month = aug,
	year = {2023},
	pages = {e2022WR034132},
}

Abstract The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is a scientific mission that collects high spatio‐temporal resolution (∼70 m, 1–5 days average revisit time) thermal images since its launch on 29 June 2018. As a predecessor of future missions, one of the main objectives of ECOSTRESS is to retrieve and understand the spatio‐temporal variations in terrestrial evapotranspiration (ET) and its responses to soil water availability and atmospheric aridity. In the European ECOSTRESS Hub (EEH), by taking advantage of land surface temperature (LST) retrievals, we generated ECOSTRESS ET products over Europe and Africa using three models with different structures and parameterization schemes, namely Surface Energy Balance System (SEBS) and Two Source Energy Balance (TSEB) parametric models, as well as the non‐parametric Surface Temperature Initiated Closure (STIC) model. A comprehensive evaluation of the EEH ET products was conducted with respect to flux measurements from 19 eddy covariance sites in Europe over six different biomes with diverse aridity levels. Results revealed comparable performances of STIC and SEBS (RMSE of ∼70 W m −2 ). However, the relatively complex TSEB model produced a higher RMSE of ∼90 W m −2 . Comparison between STIC ET estimates and the operational ECOSTRESS ET product from NASA PT‐JPL model showed a larger RMSE (around 50 W m −2 higher) for the PT‐JPL ET estimates. Substantial overestimation (\textgreater80 W m −2 ) in PT‐JPL ET estimates was evident over shrublands and savannas, presumably due to weak constraint of LST in the model. Overall, the EEH supports ET retrieval for the future high‐resolution thermal missions. , Key Points Evapotranspiration products over Europe and Africa were generated using three models (Surface Temperature Initiated Closure, Surface Energy Balance System, and Two Source Energy Balance) in the European ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) Hub Comparison at 19 eddy covariance sites revealed noteworthy model divergence with increasing aridity and vegetation sparseness A substantial overestimation of the official NASA ECOSTRESS PT‐JPL evapotranspiration product was found under high water limitations

@article{hu_parameter_2023,
	title = {Parameter variability across different timescales in the energy balance-based model and its effect on evapotranspiration estimation},
	volume = {871},
	issn = {00489697},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S004896972300534X},
	doi = {10.1016/j.scitotenv.2023.161919},
	language = {en},
	urldate = {2024-11-15},
	journal = {Science of The Total Environment},
	author = {Hu, Xiaolong and Shi, Liangsheng and Lian, Xie and Bian, Jiang},
	month = may,
	year = {2023},
	pages = {161919},
}

@article{hoye_modern_2023,
	title = {Modern {Approaches} to the {Monitoring} of {Biоdiversity} ({MAMBO})},
	volume = {9},
	issn = {2367-7163},
	url = {https://riojournal.com/article/116951/},
	doi = {10.3897/rio.9.e116951},
	abstract = {EU policies, such as the EU biodiversity strategy 2030 and the Birds and Habitats Directives, demand unbiased, integrated and regularly updated biodiversity and ecosystem service data. However, efforts to monitor wildlife and other species groups are spatially and temporally fragmented, taxonomically biased, and lack integration in Europe. To bridge this gap, the MAMBO project will develop, test and implement enabling tools for monitoring conservation status and ecological requirements of species and habitats for which knowledge gaps still exist. MAMBO brings together the technical expertise of computer science, remote sensing, social science expertise on human-technology interactions, environmental economy, and citizen science, with the biological expertise on species, ecology, and conservation biology. MAMBO is built around stakeholder engagement and knowledge exchange (WP1) and the integration of new technology with existing research infrastructures (WP2). MAMBO will develop, test, and demonstrate new tools for monitoring species (WP3) and habitats (WP4) in a co-design process to create novel standards for species and habitat monitoring across the EU and beyond. MAMBO will work with stakeholders to identify user and policy needs for biodiversity monitoring and investigate the requirements for setting up a virtual lab to automate workflow deployment and efficient computing of the vast data streams (from on the ground sensors, and remote sensing) required to improve monitoring activities across Europe (WP4). Together with stakeholders, MAMBO will assess these new tools at demonstration sites distributed across Europe (WP5) to identify bottlenecks, analyze the cost-effectiveness of different tools, integrate data streams and upscale results (WP6). This will feed into the co-design of future, improved and more cost-effective monitoring schemes for species and habitats using novel technologies (WP7), and thus lead to a better management of protected sites and species.},
	urldate = {2024-11-15},
	journal = {Research Ideas and Outcomes},
	author = {Høye, Toke and August, Tom and Balzan, Mario V and Biesmeijer, Koos and Bonnet, Pierre and Breeze, Tom and Dominik, Christophe and Gerard, France and Joly, Alexis and Kalkman, Vincent and Kissling, W. Daniel and Metodiev, Teodor and Moeslund, Jesper and Potts, Simon and Roy, David and Schweiger, Oliver and Senapathi, Deepa and Settele, Josef and Stoev, Pavel and Stowell, Dan},
	month = dec,
	year = {2023},
	pages = {e116951},
}

EU policies, such as the EU biodiversity strategy 2030 and the Birds and Habitats Directives, demand unbiased, integrated and regularly updated biodiversity and ecosystem service data. However, efforts to monitor wildlife and other species groups are spatially and temporally fragmented, taxonomically biased, and lack integration in Europe. To bridge this gap, the MAMBO project will develop, test and implement enabling tools for monitoring conservation status and ecological requirements of species and habitats for which knowledge gaps still exist. MAMBO brings together the technical expertise of computer science, remote sensing, social science expertise on human-technology interactions, environmental economy, and citizen science, with the biological expertise on species, ecology, and conservation biology. MAMBO is built around stakeholder engagement and knowledge exchange (WP1) and the integration of new technology with existing research infrastructures (WP2). MAMBO will develop, test, and demonstrate new tools for monitoring species (WP3) and habitats (WP4) in a co-design process to create novel standards for species and habitat monitoring across the EU and beyond. MAMBO will work with stakeholders to identify user and policy needs for biodiversity monitoring and investigate the requirements for setting up a virtual lab to automate workflow deployment and efficient computing of the vast data streams (from on the ground sensors, and remote sensing) required to improve monitoring activities across Europe (WP4). Together with stakeholders, MAMBO will assess these new tools at demonstration sites distributed across Europe (WP5) to identify bottlenecks, analyze the cost-effectiveness of different tools, integrate data streams and upscale results (WP6). This will feed into the co-design of future, improved and more cost-effective monitoring schemes for species and habitats using novel technologies (WP7), and thus lead to a better management of protected sites and species.

@article{iannino_resource-dependent_2023,
	title = {Resource-dependent foraging behaviour of grazers enhances effects of nutrient enrichment on algal biomass},
	volume = {201},
	issn = {0029-8549, 1432-1939},
	url = {https://link.springer.com/10.1007/s00442-022-05308-3},
	doi = {10.1007/s00442-022-05308-3},
	abstract = {Abstract
            
              Both the quantity and nutritional quality of food resources can strongly influence the foraging movements of herbivores, which in turn determine the strength of top-down control on primary producer biomass. Nutrient enrichment can alter the biomass and nutritional quality of primary producers, but the consequences for the foraging of herbivores and hence for top-down control are still poorly understood. In this study, we combined a two-factorial experiment (two nutrient levels × grazing by the freshwater gastropod
              Ancylus fluviatilis
              ) with video analyses tracking grazers’ movements to investigate nutrient enrichment effects on spatial ranges of grazing activity and algal biomass removal. Natural stream biofilms were grown in phosphorus-enriched (P+) and phosphorus-poor flumes (P−) for two weeks before
              A. fluviatilis
              were added to the flumes and allowed to graze on biofilm for an additional 2 weeks. Total periphyton biomass was enhanced by P+ and reduced by grazer presence. However, the total grazer effect depended on the nutrient level: at the end of the experiment, on average 95\% of algal cover were removed by grazing in the P− flumes versus 26\% in the P+ flumes. Fast movements of
              A. fluviatilis
              were detected significantly more often in the P− treatment, whereas grazers were detected resting more often in the P+ treatment. Our results demonstrate that nutrient enrichment can increase primary producer biomass both directly and indirectly by limiting the foraging ranges of herbivores. The resulting feedback loop between reduced grazing activity and increased plant biomass might in turn exacerbate eutrophication effects on habitat structure.},
	language = {en},
	number = {2},
	urldate = {2024-11-15},
	journal = {Oecologia},
	author = {Iannino, Alessandra and Fink, Patrick and Vosshage, Alexander Tim Ludwig and Weitere, Markus},
	month = feb,
	year = {2023},
	pages = {479--488},
}

Abstract Both the quantity and nutritional quality of food resources can strongly influence the foraging movements of herbivores, which in turn determine the strength of top-down control on primary producer biomass. Nutrient enrichment can alter the biomass and nutritional quality of primary producers, but the consequences for the foraging of herbivores and hence for top-down control are still poorly understood. In this study, we combined a two-factorial experiment (two nutrient levels × grazing by the freshwater gastropod Ancylus fluviatilis ) with video analyses tracking grazers’ movements to investigate nutrient enrichment effects on spatial ranges of grazing activity and algal biomass removal. Natural stream biofilms were grown in phosphorus-enriched (P+) and phosphorus-poor flumes (P−) for two weeks before A. fluviatilis were added to the flumes and allowed to graze on biofilm for an additional 2 weeks. Total periphyton biomass was enhanced by P+ and reduced by grazer presence. However, the total grazer effect depended on the nutrient level: at the end of the experiment, on average 95% of algal cover were removed by grazing in the P− flumes versus 26% in the P+ flumes. Fast movements of A. fluviatilis were detected significantly more often in the P− treatment, whereas grazers were detected resting more often in the P+ treatment. Our results demonstrate that nutrient enrichment can increase primary producer biomass both directly and indirectly by limiting the foraging ranges of herbivores. The resulting feedback loop between reduced grazing activity and increased plant biomass might in turn exacerbate eutrophication effects on habitat structure.

@inproceedings{jagdhuber_potential_2023,
	address = {Pasadena, CA, USA},
	title = {On the {Potential} of {Active} and {Passive} {Microwave} {Remote} {Sensing} for {Tracking} {Seasonal} {Dynamics} of {Evapotranspiration}},
	copyright = {https://doi.org/10.15223/policy-029},
	isbn = {9798350320107},
	url = {https://ieeexplore.ieee.org/document/10283234/},
	doi = {10.1109/IGARSS52108.2023.10283234},
	urldate = {2024-11-15},
	booktitle = {{IGARSS} 2023 - 2023 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium}},
	publisher = {IEEE},
	author = {Jagdhuber, T. and Fluhrer, A. and Chaparro, D. and Dubois, C. and Hellwig, F. M. and Bayat, B. and Montzka, C. and Baur, M. J. and Ramati, M. and Kübert, A. and Mueller, M. M. and Schellenberg, K. and Boehm, M. and Jonard, F. and Steele-Dunne, S. and Piles, M. and Entekhabi, D.},
	month = jul,
	year = {2023},
	pages = {2610--2613},
}

@misc{kalhori_long-term_2023,
	title = {Long-term flux measurements suggest dynamic emission factors are needed for rewetted peatlands},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	url = {https://www.researchsquare.com/article/rs-3241711/v1},
	doi = {10.21203/rs.3.rs-3241711/v1},
	abstract = {Abstract
          Rewetting drained peatlands is recognized as a leading and effective natural solution to curb greenhouse gas (GHG) emissions. However, rewetting creates novel ecosystems whose emission behavior is not well captured by the currently used emission factors (EFs). These EFs are static and do not capture the temporal dynamics of GHG emissions. Hence, they often do not reflect the true emission reduction potential after rewetting. Here, we provide long-term data showing a mismatch between actual emissions and default EFs and revealing the temporal patterns of annual CO2 and CH4 fluxes in a rewetted peatland site in northeastern Germany. We show that site-level annual emissions of CO2 and CH4 approach the IPCC default EFs and those suggested for the German national inventory report only between 13 to 16 years after rewetting. Over the entire study period, we observed a source-to-sink transition of annual CO2 fluxes with a decreasing trend of -0.36 t CO2-C ha-1 yr-1, and a decrease in annual CH4 emissions of -23.6 kg CH4 ha-1 yr-1. Our results indicate that EFs should represent the temporally dynamic nature of peatlands post rewetting and consider the effects of site characteristics to better estimate associated GHG budgets.},
	urldate = {2024-11-15},
	publisher = {In Review},
	author = {Kalhori, Aram and Wille, Christian and Gottschalk, Pia and Li, Zhan and Hashemi, Joshua and Kemper, Karl and Sachs, Torsten},
	month = sep,
	year = {2023},
}

Abstract Rewetting drained peatlands is recognized as a leading and effective natural solution to curb greenhouse gas (GHG) emissions. However, rewetting creates novel ecosystems whose emission behavior is not well captured by the currently used emission factors (EFs). These EFs are static and do not capture the temporal dynamics of GHG emissions. Hence, they often do not reflect the true emission reduction potential after rewetting. Here, we provide long-term data showing a mismatch between actual emissions and default EFs and revealing the temporal patterns of annual CO2 and CH4 fluxes in a rewetted peatland site in northeastern Germany. We show that site-level annual emissions of CO2 and CH4 approach the IPCC default EFs and those suggested for the German national inventory report only between 13 to 16 years after rewetting. Over the entire study period, we observed a source-to-sink transition of annual CO2 fluxes with a decreasing trend of -0.36 t CO2-C ha-1 yr-1, and a decrease in annual CH4 emissions of -23.6 kg CH4 ha-1 yr-1. Our results indicate that EFs should represent the temporally dynamic nature of peatlands post rewetting and consider the effects of site characteristics to better estimate associated GHG budgets.

@article{keller_catchment_2023,
	title = {Catchment scale spatial distribution of soil enzyme activities in a mountainous {German} coniferous forest},
	volume = {177},
	issn = {00380717},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S003807172200342X},
	doi = {10.1016/j.soilbio.2022.108885},
	language = {en},
	urldate = {2024-11-15},
	journal = {Soil Biology and Biochemistry},
	author = {Keller, Nora and Bol, Roland and Herre, Michael and Marschner, Bernd and Heinze, Stefanie},
	month = feb,
	year = {2023},
	pages = {108885},
}

@article{klesse_legacy_2023,
	title = {Legacy effects in radial tree growth are rarely significant after accounting for biological memory},
	volume = {111},
	issn = {0022-0477, 1365-2745},
	url = {https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2745.14045},
	doi = {10.1111/1365-2745.14045},
	abstract = {Abstract
            
              
                
                  Drought legacies in radial tree growth are an important feature of variability in biomass accumulation and are widely used to characterize forest resilience to climate change. Defined as a deviation from normal growth, the statistical significance of legacy effects depends on the definition of “normal”—expected growth under average conditions—which has not received sufficient scrutiny.
                
                
                  We re‐examined legacy effect analyses using the International Tree‐Ring Data Bank (ITRDB) and then produced synthetic tree‐ring data to disentangle four key variables influencing the magnitude of legacy effects. We hypothesized that legacy effects (i) are mainly influenced by the auto‐correlation of the radial growth time series (phi), (ii) depend on climate‐growth cross‐correlation (rho), (iii) are directly proportional to the inherent variability of the growth time series (standard deviation, SD), and (iv) scale with the chosen extreme event threshold.
                
                
                  Using a data simulation approach, we were able to reproduce observed lag patterns, demonstrating that legacy effects are a direct outcome of ubiquitous biological memory. We found that stronger legacy effects for conifers compared to angiosperms is a consequence of their higher auto‐correlation, and that the detectability of legacy effects following rare drought events at individual sites is compromised by strong background stochasticity.
                
                
                  
                    Synthesis
                    . We propose two pathways forward to improve the assessment and interpretation of legacy effects: First, we highlight the need to account for auto‐correlated residuals of climate‐growth regression models a posteriori, thereby retrospectively adjusting expectations for “normal” growth variability. Alternatively, we recommend including lagged climate variables in regression models a priori. By doing so, the magnitude of detected legacy effects is greatly reduced and biological memory is directly attributed to antecedent climatic drivers. We argue that future analyses should focus on understanding the functional reasons for how and why key statistical parameters describing this biological memory differ across species and sites. These two pathways should also stimulate improved process‐based representation of vegetation carbon dynamics in mechanistic models.},
	language = {en},
	number = {6},
	urldate = {2024-11-15},
	journal = {Journal of Ecology},
	author = {Klesse, Stefan and Babst, Flurin and Evans, Margaret E. K. and Hurley, Alexander and Pappas, Christoforos and Peters, Richard L.},
	month = jun,
	year = {2023},
	pages = {1188--1202},
}

Abstract Drought legacies in radial tree growth are an important feature of variability in biomass accumulation and are widely used to characterize forest resilience to climate change. Defined as a deviation from normal growth, the statistical significance of legacy effects depends on the definition of “normal”—expected growth under average conditions—which has not received sufficient scrutiny. We re‐examined legacy effect analyses using the International Tree‐Ring Data Bank (ITRDB) and then produced synthetic tree‐ring data to disentangle four key variables influencing the magnitude of legacy effects. We hypothesized that legacy effects (i) are mainly influenced by the auto‐correlation of the radial growth time series (phi), (ii) depend on climate‐growth cross‐correlation (rho), (iii) are directly proportional to the inherent variability of the growth time series (standard deviation, SD), and (iv) scale with the chosen extreme event threshold. Using a data simulation approach, we were able to reproduce observed lag patterns, demonstrating that legacy effects are a direct outcome of ubiquitous biological memory. We found that stronger legacy effects for conifers compared to angiosperms is a consequence of their higher auto‐correlation, and that the detectability of legacy effects following rare drought events at individual sites is compromised by strong background stochasticity. Synthesis . We propose two pathways forward to improve the assessment and interpretation of legacy effects: First, we highlight the need to account for auto‐correlated residuals of climate‐growth regression models a posteriori, thereby retrospectively adjusting expectations for “normal” growth variability. Alternatively, we recommend including lagged climate variables in regression models a priori. By doing so, the magnitude of detected legacy effects is greatly reduced and biological memory is directly attributed to antecedent climatic drivers. We argue that future analyses should focus on understanding the functional reasons for how and why key statistical parameters describing this biological memory differ across species and sites. These two pathways should also stimulate improved process‐based representation of vegetation carbon dynamics in mechanistic models.

@article{kong_synergistic_2023,
	title = {Synergistic {Effects} of {Warming} and {Internal} {Nutrient} {Loading} {Interfere} with the {Long}-{Term} {Stability} of {Lake} {Restoration} and {Induce} {Sudden} {Re}-eutrophication},
	volume = {57},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {0013-936X, 1520-5851},
	url = {https://pubs.acs.org/doi/10.1021/acs.est.2c07181},
	doi = {10.1021/acs.est.2c07181},
	language = {en},
	number = {9},
	urldate = {2024-11-15},
	journal = {Environmental Science \& Technology},
	author = {Kong, Xiangzhen and Determann, Maria and Andersen, Tobias Kuhlmann and Barbosa, Carolina Cerqueira and Dadi, Tallent and Janssen, Annette B.G. and Paule-Mercado, Ma. Cristina and Pujoni, Diego Guimarães Florencio and Schultze, Martin and Rinke, Karsten},
	month = mar,
	year = {2023},
	pages = {4003--4013},
}

@article{krevh_soilwater_2023,
	title = {Soil–{Water} {Dynamics} {Investigation} at {Agricultural} {Hillslope} with {High}-{Precision} {Weighing} {Lysimeters} and {Soil}–{Water} {Collection} {Systems}},
	volume = {15},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2073-4441},
	url = {https://www.mdpi.com/2073-4441/15/13/2398},
	doi = {10.3390/w15132398},
	abstract = {A quantitative understanding of actual evapotranspiration (ETa) and soil–water dynamics in a hillslope agroecosystem is vital for sustainable water resource management and soil conservation; however, the complexity of processes and conditions involving lateral subsurface flow (LSF) can be a limiting factor in the full comprehension of hillslope soil–water dynamics. The research was carried out at SUPREHILL CZO located on a hillslope agroecosystem (vineyard) over a period of two years (2021–2022) by combining soil characterization and field hydrological measurements, including weighing lysimeters, sensor measurements, and LSF collection system measurements. Lysimeters were placed on the hilltop and the footslope, both having a dynamic controlled bottom boundary, which corresponded to field pressure head measurements, to mimic field soil–water dynamics. Water balance components between the two positions on the slope were compared with the goal of identifying differences that might reveal hydrologically driven differences due to LSF paths across the hillslope. The usually considered limitations of these lysimeters, or the borders preventing LSF through the domain, acted as an aid within this installation setup, as the lack of LSF was compensated for through the pumping system at the footslope. The findings from lysimeters were compared with LSF collection system measurements. Weighing lysimeter data indicated that LSF controlled ETa rates. The results suggest that the onset of LSF contributes to the spatial crop productivity distribution in hillslopes. The present approach may be useful for investigating the impact of LSF on water balance components for similar hillslope sites and crops or other soil surface covers.},
	language = {en},
	number = {13},
	urldate = {2024-11-15},
	journal = {Water},
	author = {Krevh, Vedran and Groh, Jannis and Filipović, Lana and Gerke, Horst H. and Defterdarović, Jasmina and Thompson, Sally and Sraka, Mario and Bogunović, Igor and Kovač, Zoran and Robinson, Nathan and Baumgartl, Thomas and Filipović, Vilim},
	month = jun,
	year = {2023},
	pages = {2398},
}

A quantitative understanding of actual evapotranspiration (ETa) and soil–water dynamics in a hillslope agroecosystem is vital for sustainable water resource management and soil conservation; however, the complexity of processes and conditions involving lateral subsurface flow (LSF) can be a limiting factor in the full comprehension of hillslope soil–water dynamics. The research was carried out at SUPREHILL CZO located on a hillslope agroecosystem (vineyard) over a period of two years (2021–2022) by combining soil characterization and field hydrological measurements, including weighing lysimeters, sensor measurements, and LSF collection system measurements. Lysimeters were placed on the hilltop and the footslope, both having a dynamic controlled bottom boundary, which corresponded to field pressure head measurements, to mimic field soil–water dynamics. Water balance components between the two positions on the slope were compared with the goal of identifying differences that might reveal hydrologically driven differences due to LSF paths across the hillslope. The usually considered limitations of these lysimeters, or the borders preventing LSF through the domain, acted as an aid within this installation setup, as the lack of LSF was compensated for through the pumping system at the footslope. The findings from lysimeters were compared with LSF collection system measurements. Weighing lysimeter data indicated that LSF controlled ETa rates. The results suggest that the onset of LSF contributes to the spatial crop productivity distribution in hillslopes. The present approach may be useful for investigating the impact of LSF on water balance components for similar hillslope sites and crops or other soil surface covers.

@article{kohli_uranos_2023,
	title = {{URANOS} v1.0 – the {Ultra} {Rapid} {Adaptable} {Neutron}-{Only} {Simulation} for {Environmental} {Research}},
	volume = {16},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1991-9603},
	url = {https://gmd.copernicus.org/articles/16/449/2023/},
	doi = {10.5194/gmd-16-449-2023},
	abstract = {Abstract. The understanding of neutron transport by Monte Carlo simulations led to major advancements towards precise interpretation of measurements. URANOS (Ultra Rapid Neutron-Only Simulation) is a free software package which has been developed in the last few years in cooperation with particle physics and environmental sciences, specifically for the purposes of cosmic-ray neutron sensing (CRNS). Its versatile user interface and input/output scheme tailored for CRNS applications offers hydrologists straightforward access to model individual scenarios and to directly perform advanced neutron transport calculations. The geometry can be modeled layer-wise, whereas in each layer a voxel geometry is extruded using a two-dimensional map from pixel images representing predefined materials and allowing for the construction of objects on the basis of pixel graphics without a three-dimensional editor. It furthermore features predefined cosmic-ray neutron spectra and detector configurations and also allows for a replication of important site characteristics of study areas – from a small pond to the catchment scale. The simulation thereby gives precise answers to questions like from which location do neutrons originate? How do they propagate to the sensor? What is the neutron's response to certain environmental changes? In recent years, URANOS has been successfully employed by a number of studies, for example, to calculate the cosmic-ray neutron footprint, signals in complex geometries like mobile applications on roads, urban environments and snow patterns.},
	language = {en},
	number = {2},
	urldate = {2024-11-15},
	journal = {Geoscientific Model Development},
	author = {Köhli, Markus and Schrön, Martin and Zacharias, Steffen and Schmidt, Ulrich},
	month = jan,
	year = {2023},
	pages = {449--477},
}

Abstract. The understanding of neutron transport by Monte Carlo simulations led to major advancements towards precise interpretation of measurements. URANOS (Ultra Rapid Neutron-Only Simulation) is a free software package which has been developed in the last few years in cooperation with particle physics and environmental sciences, specifically for the purposes of cosmic-ray neutron sensing (CRNS). Its versatile user interface and input/output scheme tailored for CRNS applications offers hydrologists straightforward access to model individual scenarios and to directly perform advanced neutron transport calculations. The geometry can be modeled layer-wise, whereas in each layer a voxel geometry is extruded using a two-dimensional map from pixel images representing predefined materials and allowing for the construction of objects on the basis of pixel graphics without a three-dimensional editor. It furthermore features predefined cosmic-ray neutron spectra and detector configurations and also allows for a replication of important site characteristics of study areas – from a small pond to the catchment scale. The simulation thereby gives precise answers to questions like from which location do neutrons originate? How do they propagate to the sensor? What is the neutron's response to certain environmental changes? In recent years, URANOS has been successfully employed by a number of studies, for example, to calculate the cosmic-ray neutron footprint, signals in complex geometries like mobile applications on roads, urban environments and snow patterns.

@article{kunzel_wrainfo_2023,
	title = {{WRaINfo}: {An} {Open} {Source} {Library} for {Weather} {Radar} {INformation} for {FURUNO} {Weather} {Radars} {Based} on {Wradlib}},
	volume = {11},
	issn = {2049-9647},
	shorttitle = {{WRaINfo}},
	url = {http://openresearchsoftware.metajnl.com/articles/10.5334/jors.453/},
	doi = {10.5334/jors.453},
	language = {en},
	urldate = {2024-11-15},
	journal = {Journal of Open Research Software},
	author = {Künzel, Alice and Mühlbauer, Kai and Neelmeijer, Julia and Spengler, Daniel},
	month = oct,
	year = {2023},
	pages = {9},
}

@article{laux_robustness_2023,
	title = {The {Robustness} of the {Derived} {Design} {Life} {Levels} of {Heavy} {Precipitation} {Events} in the {Pre}-{Alpine} {Oberland} {Region} of {Southern} {Germany}},
	volume = {14},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2073-4433},
	url = {https://www.mdpi.com/2073-4433/14/9/1384},
	doi = {10.3390/atmos14091384},
	abstract = {Extreme value analysis (EVA) is well-established to derive hydrometeorological design values for infrastructures that have to withstand extreme events. Since there is concern about increased extremes with higher hazard potential under climate change, alterations of EVA are introduced for which statistical properties are no longer assumed to be constant but vary over time. In this study, both stationary and non-stationary EVA models are used to derive design life levels (DLLs) of daily precipitation in the pre-alpine Oberland region of Southern Germany, an orographically complex region characterized by heavy precipitation events and climate change. As EVA is fraught with uncertainties, it is crucial to quantify its methodological impacts: two theoretical distributions (i.e., Generalized Extreme Value (GEV) and Generalized Pareto (GP) distribution), four different parameter estimation techniques (i.e., Maximum Likelihood Estimation (MLE), L-moments, Generalized Maximum Likelihood Estimation (GMLE), and Bayesian estimation method) are evaluated and compared. The study reveals large methodological uncertainties. Discrepancies due to the parameter estimation methods may reach up to 45\% of the mean absolute value, while differences between stationary and non-stationary models are of the same magnitude (differences in DLLs up to 40\%). For the end of this century in the Oberland region, there is no robust tendency towards increased extremes found.},
	language = {en},
	number = {9},
	urldate = {2024-11-15},
	journal = {Atmosphere},
	author = {Laux, Patrick and Weber, Elena and Feldmann, David and Kunstmann, Harald},
	month = sep,
	year = {2023},
	pages = {1384},
}

Extreme value analysis (EVA) is well-established to derive hydrometeorological design values for infrastructures that have to withstand extreme events. Since there is concern about increased extremes with higher hazard potential under climate change, alterations of EVA are introduced for which statistical properties are no longer assumed to be constant but vary over time. In this study, both stationary and non-stationary EVA models are used to derive design life levels (DLLs) of daily precipitation in the pre-alpine Oberland region of Southern Germany, an orographically complex region characterized by heavy precipitation events and climate change. As EVA is fraught with uncertainties, it is crucial to quantify its methodological impacts: two theoretical distributions (i.e., Generalized Extreme Value (GEV) and Generalized Pareto (GP) distribution), four different parameter estimation techniques (i.e., Maximum Likelihood Estimation (MLE), L-moments, Generalized Maximum Likelihood Estimation (GMLE), and Bayesian estimation method) are evaluated and compared. The study reveals large methodological uncertainties. Discrepancies due to the parameter estimation methods may reach up to 45% of the mean absolute value, while differences between stationary and non-stationary models are of the same magnitude (differences in DLLs up to 40%). For the end of this century in the Oberland region, there is no robust tendency towards increased extremes found.

@article{leng_metabolism_2023,
	title = {Metabolism and carbonate buffering drive seasonal dynamics of {CO2} emissions from two {German} reservoirs},
	volume = {242},
	issn = {00431354},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0043135423007388},
	doi = {10.1016/j.watres.2023.120302},
	language = {en},
	urldate = {2024-11-15},
	journal = {Water Research},
	author = {Leng, Peifang and Koschorreck, Matthias},
	month = aug,
	year = {2023},
	pages = {120302},
}

@article{li_bessv20_2023,
	title = {{BESSv2}.0: {A} satellite-based and coupled-process model for quantifying long-term global land–atmosphere fluxes},
	volume = {295},
	issn = {00344257},
	shorttitle = {{BESSv2}.0},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S003442572300247X},
	doi = {10.1016/j.rse.2023.113696},
	language = {en},
	urldate = {2024-11-15},
	journal = {Remote Sensing of Environment},
	author = {Li, Bolun and Ryu, Youngryel and Jiang, Chongya and Dechant, Benjamin and Liu, Jiangong and Yan, Yulin and Li, Xing},
	month = sep,
	year = {2023},
	pages = {113696},
}

@misc{li_camele_2023,
	title = {{CAMELE}: {Collocation}-{Analyzed} {Multi}-source {Ensembled} {Land} {Evapotranspiration} {Data}},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	shorttitle = {{CAMELE}},
	url = {https://essd.copernicus.org/preprints/essd-2023-226/essd-2023-226.pdf},
	doi = {10.5194/essd-2023-226},
	abstract = {Abstract. Land evapotranspiration (ET) plays a crucial role in Earth's water-carbon cycle, and accurately estimating global land ET is vital for advancing our understanding of land-atmosphere interactions. Despite the development of numerous ET products in recent decades, widely used products still possess inherent uncertainties arising from using different forcing inputs and imperfect model parameterizations. Furthermore, the lack of sufficient global in-situ observations makes direct evaluation of ET products impractical, impeding their utilization and assimilation. Therefore, establishing a reliable global benchmark dataset and exploring evaluation methodologies for ET products is paramount. This study aims to address these challenges by (1) proposing a collocation-based method that considers non-zero error cross-correlation for merging multi-source data and (2) employing this merging method to generate a long-term daily global ET product at resolutions of 0.1° (2000–2020) and 0.25° (1980–2022), incorporating inputs from ERA5L, FluxCom, PMLv2, GLDAS, and GLEAM. The resulting product is the Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data (CAMELE). CAMELE exhibits excellent performance across various vegetation coverage types, as validated against in-situ observations. The evaluation process yielded Pearson correlation coefficients (R) of 0.63 and 0.65, root-mean-square-errors (RMSE) of 0.81 and 0.73 mm/d, unbiased root-mean-square-errors (ubRMSE) of 1.20 and 1.04 mm/d, mean absolute errors (MAE) of 0.81 and 0.73 mm/d, and Kling-Gupta efficiency (KGE) of 0.60 and 0.65 on average over resolutions of 0.1° and 0.25°, respectively.},
	urldate = {2024-11-15},
	publisher = {ESSD – Land/Hydrology},
	author = {Li, Changming and Liu, Ziwei and Yang, Wencong and Tu, Zhuoyi and Han, Juntai and Li, Sien and Yang, Hanbo},
	month = jul,
	year = {2023},
}

Abstract. Land evapotranspiration (ET) plays a crucial role in Earth's water-carbon cycle, and accurately estimating global land ET is vital for advancing our understanding of land-atmosphere interactions. Despite the development of numerous ET products in recent decades, widely used products still possess inherent uncertainties arising from using different forcing inputs and imperfect model parameterizations. Furthermore, the lack of sufficient global in-situ observations makes direct evaluation of ET products impractical, impeding their utilization and assimilation. Therefore, establishing a reliable global benchmark dataset and exploring evaluation methodologies for ET products is paramount. This study aims to address these challenges by (1) proposing a collocation-based method that considers non-zero error cross-correlation for merging multi-source data and (2) employing this merging method to generate a long-term daily global ET product at resolutions of 0.1° (2000–2020) and 0.25° (1980–2022), incorporating inputs from ERA5L, FluxCom, PMLv2, GLDAS, and GLEAM. The resulting product is the Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data (CAMELE). CAMELE exhibits excellent performance across various vegetation coverage types, as validated against in-situ observations. The evaluation process yielded Pearson correlation coefficients (R) of 0.63 and 0.65, root-mean-square-errors (RMSE) of 0.81 and 0.73 mm/d, unbiased root-mean-square-errors (ubRMSE) of 1.20 and 1.04 mm/d, mean absolute errors (MAE) of 0.81 and 0.73 mm/d, and Kling-Gupta efficiency (KGE) of 0.60 and 0.65 on average over resolutions of 0.1° and 0.25°, respectively.

@article{li_water_2023,
	title = {Water table depth assimilation in integrated terrestrial system models at the larger catchment scale},
	volume = {5},
	issn = {2624-9375},
	url = {https://www.frontiersin.org/articles/10.3389/frwa.2023.1150999/full},
	doi = {10.3389/frwa.2023.1150999},
	abstract = {As an important source of water for human beings, groundwater plays a significant role in human production and life. However, different sources of uncertainty may lead to unsatisfactory simulations of groundwater hydrodynamics with hydrological models. The goal of this study is to investigate the impact of assimilating groundwater data into the Terrestrial System Modeling Platform (TSMP) for improving hydrological modeling in a real-world case. Daily groundwater table depth (WTD) measurements from the year 2018 for the Rur catchment in Germany were assimilated by the Localized Ensemble Kalman Filter (LEnKF) into TSMP. The LEnKF is used with a localization radius so that the assimilated measurements only update model states in a limited radius around the measurements, in order to avoid unphysical updates related to spurious correlations. Due to the mismatch between groundwater measurements and the coarse model resolution (500 m), the measurements need careful screening before data assimilation (DA). Based on the spatial autocorrelation of the WTD deduced from the measurements, three different filter localization radii (2.5, 5, and 10 km) were evaluated for assimilation. The bias in the simulated water table and the root mean square error (RMSE) are reduced after DA, compared with runs without DA [i.e., open loop (OL) runs]. The best results at the assimilated locations are obtained for a localization radius of 10 km, with an 81\% reduction of RMSE at the measurement locations, and slightly smaller RMSE reductions for the 5 and 2.5 km radius. The validation with independent WTD data showed the best results for a localization radius of 10 km, but groundwater table characterization could only be improved for sites \<2.5 km from measurement locations. In case of a localization radius of 10 km the RMSE-reduction was 30\% for those nearby sites. Simulated soil moisture was validated against soil moisture measured by cosmic-ray neutron sensors (CRNS), but no RMSE reduction was observed for DA-runs compared to OL-run. However, in both cases, the correlation between measured and simulated soil moisture content was high (between 0.70 and 0.89, except for the Wuestebach site).},
	urldate = {2024-11-15},
	journal = {Frontiers in Water},
	author = {Li, Fang and Kurtz, Wolfgang and Hung, Ching Pui and Vereecken, Harry and Hendricks Franssen, Harrie-Jan},
	month = mar,
	year = {2023},
	pages = {1150999},
}

As an important source of water for human beings, groundwater plays a significant role in human production and life. However, different sources of uncertainty may lead to unsatisfactory simulations of groundwater hydrodynamics with hydrological models. The goal of this study is to investigate the impact of assimilating groundwater data into the Terrestrial System Modeling Platform (TSMP) for improving hydrological modeling in a real-world case. Daily groundwater table depth (WTD) measurements from the year 2018 for the Rur catchment in Germany were assimilated by the Localized Ensemble Kalman Filter (LEnKF) into TSMP. The LEnKF is used with a localization radius so that the assimilated measurements only update model states in a limited radius around the measurements, in order to avoid unphysical updates related to spurious correlations. Due to the mismatch between groundwater measurements and the coarse model resolution (500 m), the measurements need careful screening before data assimilation (DA). Based on the spatial autocorrelation of the WTD deduced from the measurements, three different filter localization radii (2.5, 5, and 10 km) were evaluated for assimilation. The bias in the simulated water table and the root mean square error (RMSE) are reduced after DA, compared with runs without DA [i.e., open loop (OL) runs]. The best results at the assimilated locations are obtained for a localization radius of 10 km, with an 81% reduction of RMSE at the measurement locations, and slightly smaller RMSE reductions for the 5 and 2.5 km radius. The validation with independent WTD data showed the best results for a localization radius of 10 km, but groundwater table characterization could only be improved for sites <2.5 km from measurement locations. In case of a localization radius of 10 km the RMSE-reduction was 30% for those nearby sites. Simulated soil moisture was validated against soil moisture measured by cosmic-ray neutron sensors (CRNS), but no RMSE reduction was observed for DA-runs compared to OL-run. However, in both cases, the correlation between measured and simulated soil moisture content was high (between 0.70 and 0.89, except for the Wuestebach site).

@article{lin_underestimated_2023,
	title = {Underestimated {Interannual} {Variability} of {Terrestrial} {Vegetation} {Production} by {Terrestrial} {Ecosystem} {Models}},
	volume = {37},
	issn = {0886-6236, 1944-9224},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023GB007696},
	doi = {10.1029/2023GB007696},
	abstract = {Abstract
            Vegetation gross primary production (GPP) is the largest terrestrial carbon flux and plays an important role in regulating the carbon sink. Current terrestrial ecosystem models (TEMs) are indispensable tools for evaluating and predicting GPP. However, to which degree the TEMs can capture the interannual variability (IAV) of GPP remains unclear. With large data sets of remote sensing, in situ observations, and predictions of TEMs at a global scale, this study found that the current TEMs substantially underestimate the GPP IAV in comparison to observations at global flux towers. Our results also showed the larger underestimations of IAV in GPP at nonforest ecosystem types than forest types, especially in arid and semiarid grassland and shrubland. One cause of the underestimation is that the IAV in GPP predicted by models is strongly dependent on canopy structure, that is, leaf area index (LAI), and the models underestimate the changes of canopy physiology responding to climate change. On the other hand, the simulated interannual variations of LAI are much less than the observed. Our results highlight the importance of improving TEMs by precisely characterizing the contribution of canopy physiological changes on the IAV in GPP and of clarifying the reason for the underestimated IAV in LAI. With these efforts, it may be possible to accurately predict the IAV in GPP and the stability of the global carbon sink in the context of global climate change.
          , 
            Key Points
            
              
                
                  Current terrestrial ecosystem models (TEMs) substantially underestimate the interannual variability (IAV) of gross primary production (GPP) in comparison to observations at global flux sites
                
                
                  The IAV of GPP in TEMs is strongly depended on leaf area index (LAI), which is one of the causes for the underestimation of IAV in GPP and the simulated IAV in LAI from TEMs is much less than the observation
                
                
                  Precisely characterizing the contribution of vegetation physiological changes may improve the performance of predicting IAV in GPP from TEMs},
	language = {en},
	number = {4},
	urldate = {2024-11-15},
	journal = {Global Biogeochemical Cycles},
	author = {Lin, Shangrong and Hu, Zhongmin and Wang, Yingping and Chen, Xiuzhi and He, Bin and Song, Zhaoliang and Sun, Shaobo and Wu, Chaoyang and Zheng, Yi and Xia, Xiaosheng and Liu, Liyang and Tang, Jing and Sun, Qing and Joos, Fortunat and Yuan, Wenping},
	month = apr,
	year = {2023},
	pages = {e2023GB007696},
}

Abstract Vegetation gross primary production (GPP) is the largest terrestrial carbon flux and plays an important role in regulating the carbon sink. Current terrestrial ecosystem models (TEMs) are indispensable tools for evaluating and predicting GPP. However, to which degree the TEMs can capture the interannual variability (IAV) of GPP remains unclear. With large data sets of remote sensing, in situ observations, and predictions of TEMs at a global scale, this study found that the current TEMs substantially underestimate the GPP IAV in comparison to observations at global flux towers. Our results also showed the larger underestimations of IAV in GPP at nonforest ecosystem types than forest types, especially in arid and semiarid grassland and shrubland. One cause of the underestimation is that the IAV in GPP predicted by models is strongly dependent on canopy structure, that is, leaf area index (LAI), and the models underestimate the changes of canopy physiology responding to climate change. On the other hand, the simulated interannual variations of LAI are much less than the observed. Our results highlight the importance of improving TEMs by precisely characterizing the contribution of canopy physiological changes on the IAV in GPP and of clarifying the reason for the underestimated IAV in LAI. With these efforts, it may be possible to accurately predict the IAV in GPP and the stability of the global carbon sink in the context of global climate change. , Key Points Current terrestrial ecosystem models (TEMs) substantially underestimate the interannual variability (IAV) of gross primary production (GPP) in comparison to observations at global flux sites The IAV of GPP in TEMs is strongly depended on leaf area index (LAI), which is one of the causes for the underestimation of IAV in GPP and the simulated IAV in LAI from TEMs is much less than the observation Precisely characterizing the contribution of vegetation physiological changes may improve the performance of predicting IAV in GPP from TEMs

@article{liu_improved_2023,
	title = {Improved {Vegetation} {Photosynthetic} {Phenology} {Monitoring} in the {Northern} {Ecosystems} {Using} {Total} {Canopy} {Solar}‐{Induced} {Chlorophyll} {Fluorescence} {Derived} {From} {TROPOMI}},
	volume = {128},
	issn = {2169-8953, 2169-8961},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022JG007369},
	doi = {10.1029/2022JG007369},
	abstract = {Abstract
            Solar‐Induced chlorophyll Fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI) with substantially improved spatiotemporal resolutions provides a new potential to improve satellite‐based phenology monitoring. The performance of TROPOMI SIF for tracking vegetation photosynthetic phenology, and how it compares to conventional vegetation indices (VIs)‐based approaches, however, have not been adequately assessed. Total canopy SIF, as a better proxy of Gross Primary Productivity (GPP) than original directional SIF, is a new SIF to estimate phenology while its performance has not been investigated. This study assesses the capability of TROPOMI SIF before and after canopy correction for phenology monitoring and improves our understanding of these questions. Benchmarked by tower‐based GPP, TROPOMI SIF generally performed better than VIs, especially for capturing the End Of Season (EOS) of vegetation photosynthetic activity at deciduous broadleaf forest (DBF), evergreen forest (ENF), and croplands (CRO) sites, but not for Start Of Season (SOS). This suggested that the advantage of SIF over VIs depended on phenological metrics. The total canopy SIF emission obtained through canopy correction generally performed better than the original SIF retrievals, especially in estimating the EOS of forest sites (DBF, MF, ENF), but soil correction did not further improve the accuracy of phenological monitoring. When comparing SIF‐ and VI‐based phenological metrics over northern terrestrial ecosystems, SIF showed earlier senescence date widely, while the differences in onset date were region dependent. These results indicate the necessity of canopy correction to convert directional SIF to canopy total SIF when using satellite SIF products to estimate phenological metrics.
          , 
            Plain Language Summary
            Phenology is an important ecological indicator of terrestrial carbon cycle, and satellite‐based remote sensing provides an effective approach to estimating phenological metrics over large scales. However, phenology monitoring using vegetation indices represents canopy “greenness” which is not fully synchronized with photosynthetic activity. Solar‐Induced chlorophyll Fluorescence (SIF) has great potential in phenology monitoring but is limited by the coarse spatiotemporal resolution. The emergence of TROPOspheric Monitoring Instrument (TROPOMI), with spatial resolution up to 7 km × 3.5 km and daily revisit, has brought new opportunities to SIF‐based phenology monitoring. This study demonstrated the advantages of TROPOMI SIF, especially at the total canopy level for phenology monitoring which had potential for improving large‐scale mapping of phenological characterizations. Specifically, total canopy SIF had much better accuracy than the original SIF observations, but soil correction did not have further improvement. This can provide a valuable reference for the application of TROPOMI SIF to monitor vegetation phenology.
          , 
            Key Points
            
              
                
                  Total canopy SIF emission (SIFtotal) from TROPOspheric Monitoring Instrument (TROPOMI) more accurately tracked the Gross Primary Productivity (GPP) trajectory than original TROPOMI SIF
                
                
                  
                    SIFtotal from TROPOMI outperformed MODIS EVI and NIR
                    V
                    in phenology monitoring using GPP as benchmark
                  
                
                
                  Soil correction did not further improve the performance of SIFtotal in phenology monitoring},
	language = {en},
	number = {6},
	urldate = {2024-11-15},
	journal = {Journal of Geophysical Research: Biogeosciences},
	author = {Liu, Haoran and Liu, Junzhi and Yin, Yueqiang and Walther, Sophia and Ma, Xuanlong and Zhang, Zhaoying and Chen, Yuhan},
	month = jun,
	year = {2023},
	pages = {e2022JG007369},
}

Abstract Solar‐Induced chlorophyll Fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI) with substantially improved spatiotemporal resolutions provides a new potential to improve satellite‐based phenology monitoring. The performance of TROPOMI SIF for tracking vegetation photosynthetic phenology, and how it compares to conventional vegetation indices (VIs)‐based approaches, however, have not been adequately assessed. Total canopy SIF, as a better proxy of Gross Primary Productivity (GPP) than original directional SIF, is a new SIF to estimate phenology while its performance has not been investigated. This study assesses the capability of TROPOMI SIF before and after canopy correction for phenology monitoring and improves our understanding of these questions. Benchmarked by tower‐based GPP, TROPOMI SIF generally performed better than VIs, especially for capturing the End Of Season (EOS) of vegetation photosynthetic activity at deciduous broadleaf forest (DBF), evergreen forest (ENF), and croplands (CRO) sites, but not for Start Of Season (SOS). This suggested that the advantage of SIF over VIs depended on phenological metrics. The total canopy SIF emission obtained through canopy correction generally performed better than the original SIF retrievals, especially in estimating the EOS of forest sites (DBF, MF, ENF), but soil correction did not further improve the accuracy of phenological monitoring. When comparing SIF‐ and VI‐based phenological metrics over northern terrestrial ecosystems, SIF showed earlier senescence date widely, while the differences in onset date were region dependent. These results indicate the necessity of canopy correction to convert directional SIF to canopy total SIF when using satellite SIF products to estimate phenological metrics. , Plain Language Summary Phenology is an important ecological indicator of terrestrial carbon cycle, and satellite‐based remote sensing provides an effective approach to estimating phenological metrics over large scales. However, phenology monitoring using vegetation indices represents canopy “greenness” which is not fully synchronized with photosynthetic activity. Solar‐Induced chlorophyll Fluorescence (SIF) has great potential in phenology monitoring but is limited by the coarse spatiotemporal resolution. The emergence of TROPOspheric Monitoring Instrument (TROPOMI), with spatial resolution up to 7 km × 3.5 km and daily revisit, has brought new opportunities to SIF‐based phenology monitoring. This study demonstrated the advantages of TROPOMI SIF, especially at the total canopy level for phenology monitoring which had potential for improving large‐scale mapping of phenological characterizations. Specifically, total canopy SIF had much better accuracy than the original SIF observations, but soil correction did not have further improvement. This can provide a valuable reference for the application of TROPOMI SIF to monitor vegetation phenology. , Key Points Total canopy SIF emission (SIFtotal) from TROPOspheric Monitoring Instrument (TROPOMI) more accurately tracked the Gross Primary Productivity (GPP) trajectory than original TROPOMI SIF SIFtotal from TROPOMI outperformed MODIS EVI and NIR V in phenology monitoring using GPP as benchmark Soil correction did not further improve the performance of SIFtotal in phenology monitoring

@article{liu_intercomparison_2023,
	title = {Intercomparison and evaluation of ten global {ET} products at site and basin scales},
	volume = {617},
	issn = {00221694},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0022169422014573},
	doi = {10.1016/j.jhydrol.2022.128887},
	language = {en},
	urldate = {2024-11-15},
	journal = {Journal of Hydrology},
	author = {Liu, Huiyuan and Xin, Xiaozhou and Su, Zhongbo and Zeng, Yijian and Lian, Ting and Li, Li and Yu, Shanshan and Zhang, Hailong},
	month = feb,
	year = {2023},
	pages = {128887},
}

@article{liu_evaluating_2023,
	title = {Evaluating a global soil moisture dataset from a multitask model ({GSM3} v1.0) with potential applications for crop threats},
	volume = {16},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1991-9603},
	url = {https://gmd.copernicus.org/articles/16/1553/2023/},
	doi = {10.5194/gmd-16-1553-2023},
	abstract = {Abstract. Climate change threatens our ability to grow food for an ever-increasing population. There is a
need for high-quality soil moisture predictions in under-monitored regions
like Africa. However, it is unclear if soil moisture processes are globally
similar enough to allow our models trained on available in situ data to
maintain accuracy in unmonitored regions. We present a multitask long
short-term memory (LSTM) model that learns simultaneously from global
satellite-based data and in situ soil moisture data. This model is evaluated in
both random spatial holdout mode and continental holdout mode (trained on
some continents, tested on a different one). The model compared favorably to
current land surface models, satellite products, and a candidate machine
learning model, reaching a global median correlation of 0.792 for the random
spatial holdout test. It behaved surprisingly well in Africa and Australia,
showing high correlation even when we excluded their sites from the training
set, but it performed relatively poorly in Alaska where rapid changes are
occurring. In all but one continent (Asia), the multitask model in the
worst-case scenario test performed better than the soil moisture active
passive (SMAP) 9 km product. Factorial analysis has shown that the LSTM model's
accuracy varies with terrain aspect, resulting in lower performance for dry
and south-facing slopes or wet and north-facing slopes. This knowledge
helps us apply the model while understanding its limitations. This model is
being integrated into an operational agricultural assistance application
which currently provides information to 13 million African farmers.},
	language = {en},
	number = {5},
	urldate = {2024-11-15},
	journal = {Geoscientific Model Development},
	author = {Liu, Jiangtao and Hughes, David and Rahmani, Farshid and Lawson, Kathryn and Shen, Chaopeng},
	month = mar,
	year = {2023},
	pages = {1553--1567},
}

Abstract. Climate change threatens our ability to grow food for an ever-increasing population. There is a need for high-quality soil moisture predictions in under-monitored regions like Africa. However, it is unclear if soil moisture processes are globally similar enough to allow our models trained on available in situ data to maintain accuracy in unmonitored regions. We present a multitask long short-term memory (LSTM) model that learns simultaneously from global satellite-based data and in situ soil moisture data. This model is evaluated in both random spatial holdout mode and continental holdout mode (trained on some continents, tested on a different one). The model compared favorably to current land surface models, satellite products, and a candidate machine learning model, reaching a global median correlation of 0.792 for the random spatial holdout test. It behaved surprisingly well in Africa and Australia, showing high correlation even when we excluded their sites from the training set, but it performed relatively poorly in Alaska where rapid changes are occurring. In all but one continent (Asia), the multitask model in the worst-case scenario test performed better than the soil moisture active passive (SMAP) 9 km product. Factorial analysis has shown that the LSTM model's accuracy varies with terrain aspect, resulting in lower performance for dry and south-facing slopes or wet and north-facing slopes. This knowledge helps us apply the model while understanding its limitations. This model is being integrated into an operational agricultural assistance application which currently provides information to 13 million African farmers.

@article{liu_contrasting_2023,
	title = {Contrasting responses of peak vegetation growth to asymmetric warming: {Evidences} from {FLUXNET} and satellite observations},
	volume = {29},
	issn = {1354-1013, 1365-2486},
	shorttitle = {Contrasting responses of peak vegetation growth to asymmetric warming},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/gcb.16592},
	doi = {10.1111/gcb.16592},
	abstract = {Abstract
            
              The peak growth of plant in summer is an important indicator of the capacity of terrestrial ecosystem productivity, and ongoing studies have shown its responses to climate warming as represented in the mean temperature. However, the impacts from the asymmetrical warming, that is, different rates in the changes of daytime (
              T
              max
              ) and nighttime (
              T
              min
              ) warming were mostly ignored. Using 60 flux sites (674 site‐year in total) measurements and satellite observations from two independent satellite platforms (Global Inventory Monitoring and Modeling Studies [1982–2015]; MODIS [2000–2020]) over the Northern Hemisphere (≥30°N), here we show that the peak growth, as represented by both flux‐based maximum primary productivity and the maximum greenness indices (maximum normalized difference vegetation index and enhanced vegetation index), responded oppositely to daytime and nighttime warming.  (peak growth showed negative responses to
              T
              max
              , but positive responses to
              T
              min
              ) dominated in most ecosystems and climate types, especially in water‐limited ecosystems, while  (peak growth showed positive responses to
              T
              max
              , but negative responses to
              T
              min
              ) was primarily observed in high latitude regions. These contrasting responses could be explained by the strong association between asymmetric warming and water conditions, including soil moisture, evapotranspiration/potential evapotranspiration, and the vapor pressure deficit. Our results are therefore important to the understanding of the responses of peak growth to climate change, and consequently a better representation of asymmetrical warming in future ecosystem models by differentiating the contributions between daytime and nighttime warming.},
	language = {en},
	number = {8},
	urldate = {2024-11-15},
	journal = {Global Change Biology},
	author = {Liu, Ying and Wu, Chaoyang and Wang, Xiaoyue and Zhang, Yao},
	month = apr,
	year = {2023},
	pages = {2363--2379},
}

Abstract The peak growth of plant in summer is an important indicator of the capacity of terrestrial ecosystem productivity, and ongoing studies have shown its responses to climate warming as represented in the mean temperature. However, the impacts from the asymmetrical warming, that is, different rates in the changes of daytime ( T max ) and nighttime ( T min ) warming were mostly ignored. Using 60 flux sites (674 site‐year in total) measurements and satellite observations from two independent satellite platforms (Global Inventory Monitoring and Modeling Studies [1982–2015]; MODIS [2000–2020]) over the Northern Hemisphere (≥30°N), here we show that the peak growth, as represented by both flux‐based maximum primary productivity and the maximum greenness indices (maximum normalized difference vegetation index and enhanced vegetation index), responded oppositely to daytime and nighttime warming. (peak growth showed negative responses to T max , but positive responses to T min ) dominated in most ecosystems and climate types, especially in water‐limited ecosystems, while (peak growth showed positive responses to T max , but negative responses to T min ) was primarily observed in high latitude regions. These contrasting responses could be explained by the strong association between asymmetric warming and water conditions, including soil moisture, evapotranspiration/potential evapotranspiration, and the vapor pressure deficit. Our results are therefore important to the understanding of the responses of peak growth to climate change, and consequently a better representation of asymmetrical warming in future ecosystem models by differentiating the contributions between daytime and nighttime warming.

@article{lu_ecosystem_2023,
	title = {Ecosystem {Evapotranspiration} {Partitioning} and {Its} {Spatial}–{Temporal} {Variation} {Based} on {Eddy} {Covariance} {Observation} and {Machine} {Learning} {Method}},
	volume = {15},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2072-4292},
	url = {https://www.mdpi.com/2072-4292/15/19/4831},
	doi = {10.3390/rs15194831},
	abstract = {Partitioning evapotranspiration (ET) into vegetation transpiration (T) and soil evaporation (E) is challenging, but it is key to improving the understanding of plant water use and changes in terrestrial ecosystems. Considering that the transpiration of vegetation at night is minimal and can be negligible, we established a machine learning model (i.e., extreme gradient boosting algorithm (XGBoost)) for soil evaporation estimation based on night-time evapotranspiration observation data from eddy covariance towers, remote sensing data, and meteorological reanalysis data. Daytime T was consequently calculated as the difference between the total evapotranspiration and predicted daytime soil evaporation. The soil evaporation estimation model was validated based on the remaining night-time ET data (i.e., model test dataset), the non-growing season ET data of the natural ecosystem, and ET data during the fallow periods of croplands. The validation results showed that XGBoost had a better performance in E estimation, with the average overall accuracy of NSE 0.657, R 0.806, and RMSE 11.344 W/m2. The average annual T/ET of the examined ten ecosystems was 0.50 ± 0.08, with the highest value in deciduous broadleaf forests (0.68 ± 0.11), followed by mixed forests (0.61 ± 0.04), and the lowest in croplands (0.40 ± 0.08). We further examined the impact of the leaf area index (LAI) and vapor pressure deficit (VPD) on the variation in T/ET. Overall, at the interannual scale, LAI contributed 28\% to the T/ET variation, while VPD had a small (5\%) influence. On a seasonal scale, LAI also exerted a stronger impact (1{\textasciitilde}90\%) on T/ET compared to VPD (1{\textasciitilde}77\%). Our study suggests that the XGBoost machine learning model has good performance in ET partitioning, and this method is mainly data-driven without prior knowledge, which may provide a simple and valuable method in global ET partitioning and T/ET estimation.},
	language = {en},
	number = {19},
	urldate = {2024-11-15},
	journal = {Remote Sensing},
	author = {Lu, Linjun and Zhang, Danwen and Zhang, Jie and Zhang, Jiahua and Zhang, Sha and Bai, Yun and Yang, Shanshan},
	month = oct,
	year = {2023},
	pages = {4831},
}

Partitioning evapotranspiration (ET) into vegetation transpiration (T) and soil evaporation (E) is challenging, but it is key to improving the understanding of plant water use and changes in terrestrial ecosystems. Considering that the transpiration of vegetation at night is minimal and can be negligible, we established a machine learning model (i.e., extreme gradient boosting algorithm (XGBoost)) for soil evaporation estimation based on night-time evapotranspiration observation data from eddy covariance towers, remote sensing data, and meteorological reanalysis data. Daytime T was consequently calculated as the difference between the total evapotranspiration and predicted daytime soil evaporation. The soil evaporation estimation model was validated based on the remaining night-time ET data (i.e., model test dataset), the non-growing season ET data of the natural ecosystem, and ET data during the fallow periods of croplands. The validation results showed that XGBoost had a better performance in E estimation, with the average overall accuracy of NSE 0.657, R 0.806, and RMSE 11.344 W/m2. The average annual T/ET of the examined ten ecosystems was 0.50 ± 0.08, with the highest value in deciduous broadleaf forests (0.68 ± 0.11), followed by mixed forests (0.61 ± 0.04), and the lowest in croplands (0.40 ± 0.08). We further examined the impact of the leaf area index (LAI) and vapor pressure deficit (VPD) on the variation in T/ET. Overall, at the interannual scale, LAI contributed 28% to the T/ET variation, while VPD had a small (5%) influence. On a seasonal scale, LAI also exerted a stronger impact (1~90%) on T/ET compared to VPD (1~77%). Our study suggests that the XGBoost machine learning model has good performance in ET partitioning, and this method is mainly data-driven without prior knowledge, which may provide a simple and valuable method in global ET partitioning and T/ET estimation.

@article{larm_multi-year_2023,
	title = {Multi-year belowground data of minirhizotron facilities in {Selhausen}},
	volume = {10},
	issn = {2052-4463},
	url = {https://www.nature.com/articles/s41597-023-02570-9},
	doi = {10.1038/s41597-023-02570-9},
	abstract = {Abstract
            The production of crops secure the human food supply, but climate change is bringing new challenges. Dynamic plant growth and corresponding environmental data are required to uncover phenotypic crop responses to the changing environment. There are many datasets on above-ground organs of crops, but roots and the surrounding soil are rarely the subject of longer term studies. Here, we present what we believe to be the first comprehensive collection of root and soil data, obtained at two minirhizotron facilities located close together that have the same local climate but differ in soil type. Both facilities have 7m-long horizontal tubes at several depths that were used for crosshole ground-penetrating radar and minirhizotron camera systems. Soil sensors provide observations at a high temporal and spatial resolution. The ongoing measurements cover five years of maize and wheat trials, including drought stress treatments and crop mixtures. We make the processed data available for use in investigating the processes within the soil–plant continuum and the root images to develop and compare image analysis methods.},
	language = {en},
	number = {1},
	urldate = {2024-11-15},
	journal = {Scientific Data},
	author = {Lärm, Lena and Bauer, Felix Maximilian and Hermes, Normen and Van Der Kruk, Jan and Vereecken, Harry and Vanderborght, Jan and Nguyen, Thuy Huu and Lopez, Gina and Seidel, Sabine Julia and Ewert, Frank and Schnepf, Andrea and Klotzsche, Anja},
	month = oct,
	year = {2023},
	pages = {672},
}

Abstract The production of crops secure the human food supply, but climate change is bringing new challenges. Dynamic plant growth and corresponding environmental data are required to uncover phenotypic crop responses to the changing environment. There are many datasets on above-ground organs of crops, but roots and the surrounding soil are rarely the subject of longer term studies. Here, we present what we believe to be the first comprehensive collection of root and soil data, obtained at two minirhizotron facilities located close together that have the same local climate but differ in soil type. Both facilities have 7m-long horizontal tubes at several depths that were used for crosshole ground-penetrating radar and minirhizotron camera systems. Soil sensors provide observations at a high temporal and spatial resolution. The ongoing measurements cover five years of maize and wheat trials, including drought stress treatments and crop mixtures. We make the processed data available for use in investigating the processes within the soil–plant continuum and the root images to develop and compare image analysis methods.

@inproceedings{ma_soil_2023,
	address = {Pasadena, CA, USA},
	title = {Soil {Moisture} {Retrieval} from the {Integration} of {SMAP} and {ASCAT} {Using} {Machine} {Learning} {Approach}},
	copyright = {https://doi.org/10.15223/policy-029},
	isbn = {9798350320107},
	url = {https://ieeexplore.ieee.org/document/10283186/},
	doi = {10.1109/IGARSS52108.2023.10283186},
	urldate = {2024-11-15},
	booktitle = {{IGARSS} 2023 - 2023 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium}},
	publisher = {IEEE},
	author = {Ma, Hongliang and Zeng, Jiangyuan and Chen, Nengcheng and Zhang, Xiang and Li, Xiaojun and Wigneron, Jean-Pierre},
	month = jul,
	year = {2023},
	pages = {3190--3193},
}

@article{mazzariello_intercomparison_2023,
	title = {Intercomparison of recent microwave satellite soil moisture products on {European} ecoregions},
	volume = {626},
	issn = {00221694},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0022169423012532},
	doi = {10.1016/j.jhydrol.2023.130311},
	language = {en},
	urldate = {2024-11-15},
	journal = {Journal of Hydrology},
	author = {Mazzariello, A. and Albano, R. and Lacava, T. and Manfreda, S. and Sole, A.},
	month = nov,
	year = {2023},
	pages = {130311},
}

@article{mcnicol_upscaling_2023,
	title = {Upscaling {Wetland} {Methane} {Emissions} {From} the {FLUXNET}‐{CH4} {Eddy} {Covariance} {Network} ({UpCH4} v1.0): {Model} {Development}, {Network} {Assessment}, and {Budget} {Comparison}},
	volume = {4},
	issn = {2576-604X, 2576-604X},
	shorttitle = {Upscaling {Wetland} {Methane} {Emissions} {From} the {FLUXNET}‐{CH4} {Eddy} {Covariance} {Network} ({UpCH4} v1.0)},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023AV000956},
	doi = {10.1029/2023AV000956},
	abstract = {Abstract
            
              Wetlands are responsible for 20\%–31\% of global methane (CH
              4
              ) emissions and account for a large source of uncertainty in the global CH
              4
              budget. Data‐driven upscaling of CH
              4
              fluxes from eddy covariance measurements can provide new and independent bottom‐up estimates of wetland CH
              4
              emissions. Here, we develop a six‐predictor random forest upscaling model (UpCH4), trained on 119 site‐years of eddy covariance CH
              4
              flux data from 43 freshwater wetland sites in the FLUXNET‐CH4 Community Product. Network patterns in site‐level annual means and mean seasonal cycles of CH
              4
              fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash‐Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH
              4
              emissions of 146 ± 43 TgCH
              4
               y
              −1
              for 2001–2018 which agrees closely with current bottom‐up land surface models (102–181 TgCH
              4
               y
              −1
              ) and overlaps with top‐down atmospheric inversion models (155–200 TgCH
              4
               y
              −1
              ). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH
              4
              fluxes has the potential to produce realistic extra‐tropical wetland CH
              4
              emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid‐to‐arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC (
              https://doi.org/10.3334/ORNLDAAC/2253
              ).
            
          , 
            Plain Language Summary
            Wetlands account for a large share of global methane emissions to the atmosphere, but current estimates vary widely in magnitude (∼30\% uncertainty on annual global emissions) and spatial distribution, with diverging predictions for tropical rice growing (e.g., Bengal basin), rainforest (e.g., Amazon basin), and floodplain savannah (e.g., Sudd) regions. Wetland methane model estimates could be improved by increased use of land surface methane flux data. Upscaling approaches use flux data collected across globally distributed measurement networks in a machine learning framework to extrapolate fluxes in space and time. Here, we train and evaluate a methane upscaling model (UpCH4) and use it to generate monthly, globally gridded wetland methane emissions estimates for 2001–2018. The UpCH4 model uses only six predictor variables among which temperature is dominant. Global annual methane emissions estimates and associated uncertainty ranges from upscaling fall within state‐of‐the‐art model ensemble estimates from the Global Carbon Project (GCP) methane budget. In some tropical regions, the spatial pattern of UpCH4 emissions diverged from GCP predictions, however, inclusion of flux measurements from additional ground‐based sites, together with refined maps of tropical wetlands extent, could reduce these prediction uncertainties.
          , 
            Key Points
            
              
                
                  
                    Random forest models trained on FLUXNET‐CH4 methane fluxes reproduced spatiotemporal patterns in extra‐tropical wetlands (
                    R
                    2
                    : 0.59–0.64)
                  
                
                
                  
                    Globally upscaled annual wetland methane emissions (146 TgCH
                    4
                     y
                    −1
                    ) overlapped with land surface and inversion model ensemble estimates
                  
                
                
                  Humid/monsoon tropics dominate upscaled wetland methane emissions (∼68\%) and uncertainties (∼78\%) due to limited FLUXNET‐CH4 site coverage},
	language = {en},
	number = {5},
	urldate = {2024-11-15},
	journal = {AGU Advances},
	author = {McNicol, Gavin and Fluet‐Chouinard, Etienne and Ouyang, Zutao and Knox, Sara and Zhang, Zhen and Aalto, Tuula and Bansal, Sheel and Chang, Kuang‐Yu and Chen, Min and Delwiche, Kyle and Feron, Sarah and Goeckede, Mathias and Liu, Jinxun and Malhotra, Avni and Melton, Joe R. and Riley, William and Vargas, Rodrigo and Yuan, Kunxiaojia and Ying, Qing and Zhu, Qing and Alekseychik, Pavel and Aurela, Mika and Billesbach, David P. and Campbell, David I. and Chen, Jiquan and Chu, Housen and Desai, Ankur R. and Euskirchen, Eugenie and Goodrich, Jordan and Griffis, Timothy and Helbig, Manuel and Hirano, Takashi and Iwata, Hiroki and Jurasinski, Gerald and King, John and Koebsch, Franziska and Kolka, Randall and Krauss, Ken and Lohila, Annalea and Mammarella, Ivan and Nilson, Mats and Noormets, Asko and Oechel, Walter and Peichl, Matthias and Sachs, Torsten and Sakabe, Ayaka and Schulze, Christopher and Sonnentag, Oliver and Sullivan, Ryan C. and Tuittila, Eeva‐Stiina and Ueyama, Masahito and Vesala, Timo and Ward, Eric and Wille, Christian and Wong, Guan Xhuan and Zona, Donatella and Windham‐Myers, Lisamarie and Poulter, Benjamin and Jackson, Robert B.},
	month = oct,
	year = {2023},
	pages = {e2023AV000956},
}

Abstract Wetlands are responsible for 20%–31% of global methane (CH 4 ) emissions and account for a large source of uncertainty in the global CH 4 budget. Data‐driven upscaling of CH 4 fluxes from eddy covariance measurements can provide new and independent bottom‐up estimates of wetland CH 4 emissions. Here, we develop a six‐predictor random forest upscaling model (UpCH4), trained on 119 site‐years of eddy covariance CH 4 flux data from 43 freshwater wetland sites in the FLUXNET‐CH4 Community Product. Network patterns in site‐level annual means and mean seasonal cycles of CH 4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash‐Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH 4 emissions of 146 ± 43 TgCH 4  y −1 for 2001–2018 which agrees closely with current bottom‐up land surface models (102–181 TgCH 4  y −1 ) and overlaps with top‐down atmospheric inversion models (155–200 TgCH 4  y −1 ). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH 4 fluxes has the potential to produce realistic extra‐tropical wetland CH 4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid‐to‐arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC ( https://doi.org/10.3334/ORNLDAAC/2253 ). , Plain Language Summary Wetlands account for a large share of global methane emissions to the atmosphere, but current estimates vary widely in magnitude (∼30% uncertainty on annual global emissions) and spatial distribution, with diverging predictions for tropical rice growing (e.g., Bengal basin), rainforest (e.g., Amazon basin), and floodplain savannah (e.g., Sudd) regions. Wetland methane model estimates could be improved by increased use of land surface methane flux data. Upscaling approaches use flux data collected across globally distributed measurement networks in a machine learning framework to extrapolate fluxes in space and time. Here, we train and evaluate a methane upscaling model (UpCH4) and use it to generate monthly, globally gridded wetland methane emissions estimates for 2001–2018. The UpCH4 model uses only six predictor variables among which temperature is dominant. Global annual methane emissions estimates and associated uncertainty ranges from upscaling fall within state‐of‐the‐art model ensemble estimates from the Global Carbon Project (GCP) methane budget. In some tropical regions, the spatial pattern of UpCH4 emissions diverged from GCP predictions, however, inclusion of flux measurements from additional ground‐based sites, together with refined maps of tropical wetlands extent, could reduce these prediction uncertainties. , Key Points Random forest models trained on FLUXNET‐CH4 methane fluxes reproduced spatiotemporal patterns in extra‐tropical wetlands ( R 2 : 0.59–0.64) Globally upscaled annual wetland methane emissions (146 TgCH 4  y −1 ) overlapped with land surface and inversion model ensemble estimates Humid/monsoon tropics dominate upscaled wetland methane emissions (∼68%) and uncertainties (∼78%) due to limited FLUXNET‐CH4 site coverage

@inproceedings{mengen_extended_2023,
	address = {Pasadena, CA, USA},
	title = {Extended {Alpha} {Approximation} {Method} for the {Retrieval} of {Soil} {Moisture} {Under} {Dynamic} {Vegetation} by {Multi}-{Incidence} {Angle} {Sentinel}-1},
	copyright = {https://doi.org/10.15223/policy-029},
	isbn = {9798350320107},
	url = {https://ieeexplore.ieee.org/document/10282711/},
	doi = {10.1109/IGARSS52108.2023.10282711},
	urldate = {2024-11-15},
	booktitle = {{IGARSS} 2023 - 2023 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium}},
	publisher = {IEEE},
	author = {Mengen, David and Balenzano, Anna and Jagdhuber, Thomas and Mattia, Francesco and Vereecken, Harry and Montzka, Carsten},
	month = jul,
	year = {2023},
	pages = {2649--2652},
}

@article{mengen_high_2023,
	title = {High {Spatial} and {Temporal} {Soil} {Moisture} {Retrieval} in {Agricultural} {Areas} {Using} {Multi}-{Orbit} and {Vegetation} {Adapted} {Sentinel}-1 {SAR} {Time} {Series}},
	volume = {15},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2072-4292},
	url = {https://www.mdpi.com/2072-4292/15/9/2282},
	doi = {10.3390/rs15092282},
	abstract = {The retrieval of soil moisture information with spatially and temporally high resolution from Synthetic Aperture Radar (SAR) observations is still a challenge. By using multi-orbit Sentinel-1 C-band time series, we present a novel approach for estimating volumetric soil moisture content for agricultural areas with a temporal resolution of one to two days, based on a short-term change detection method. By applying an incidence angle normalization and a Fourier Series transformation, the effect of varying incidence angles on the backscattering signal could be reduced. As the C-band co-polarized backscattering signal is prone to vegetational changes, it is used in this study for the vegetational correction of its related backscatter ratios. The retrieving algorithm was implemented in a cloud-processing environment, enabling a potential global and scalable application. Validated against eight in-situ cosmic ray neutron probe stations across the Rur catchment (Germany) as well as six capacitance stations at the Apulian Tavoliere (Italy) site for the years 2018 to 2020, the method achieves a correlation coefficient of R of 0.63 with an unbiased Root Mean Square Error of 0.063 m3/m3.},
	language = {en},
	number = {9},
	urldate = {2024-11-15},
	journal = {Remote Sensing},
	author = {Mengen, David and Jagdhuber, Thomas and Balenzano, Anna and Mattia, Francesco and Vereecken, Harry and Montzka, Carsten},
	month = apr,
	year = {2023},
	pages = {2282},
}

The retrieval of soil moisture information with spatially and temporally high resolution from Synthetic Aperture Radar (SAR) observations is still a challenge. By using multi-orbit Sentinel-1 C-band time series, we present a novel approach for estimating volumetric soil moisture content for agricultural areas with a temporal resolution of one to two days, based on a short-term change detection method. By applying an incidence angle normalization and a Fourier Series transformation, the effect of varying incidence angles on the backscattering signal could be reduced. As the C-band co-polarized backscattering signal is prone to vegetational changes, it is used in this study for the vegetational correction of its related backscatter ratios. The retrieving algorithm was implemented in a cloud-processing environment, enabling a potential global and scalable application. Validated against eight in-situ cosmic ray neutron probe stations across the Rur catchment (Germany) as well as six capacitance stations at the Apulian Tavoliere (Italy) site for the years 2018 to 2020, the method achieves a correlation coefficient of R of 0.63 with an unbiased Root Mean Square Error of 0.063 m3/m3.

@article{mi_optimizing_2023,
	title = {Optimizing selective withdrawal strategies to mitigate hypoxia under water-level reduction in {Germany}'s largest drinking water reservoir},
	issn = {10010742},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1001074223002760},
	doi = {10.1016/j.jes.2023.06.025},
	language = {en},
	urldate = {2024-05-16},
	journal = {Journal of Environmental Sciences},
	author = {Mi, Chenxi and Rinke, Karsten and Shatwell, Tom},
	month = jun,
	year = {2023},
	pages = {S1001074223002760},
}

@article{mi_cascading_2023,
	title = {Cascading climate effects in deep reservoirs: {Full} assessment of physical and biogeochemical dynamics under ensemble climate projections and ways towards adaptation},
	issn = {0044-7447, 1654-7209},
	shorttitle = {Cascading climate effects in deep reservoirs},
	url = {https://link.springer.com/10.1007/s13280-023-01950-0},
	doi = {10.1007/s13280-023-01950-0},
	abstract = {Abstract
            
              We coupled twenty-first century climate projections with a well-established water quality model to depict future ecological changes of Rappbode Reservoir, Germany. Our results document a chain of climate-driven effects propagating through the aquatic ecosystem and interfering with drinking water supply: intense climate warming (RCP8.5 scenario) will firstly trigger a strong increase in water temperatures, in turn leading to metalimnetic hypoxia, accelerating sediment nutrient release and finally boosting blooms of the cyanobacterium
              Planktothrix rubescens
              . Such adverse water quality developments will be suppressed under RCP2.6 and 6.0 indicating that mitigation of climate change is improving water security. Our results also suggested surface withdrawal can be an effective adaptation strategy to make the reservoir ecosystem more resilient to climate warming. The identified consequences from climate warming and adaptation strategies are relevant to many deep waters in the temperate zone, and the conclusion should provide important guidances for stakeholders to confront potential climate changes.},
	language = {en},
	urldate = {2024-11-15},
	journal = {Ambio},
	author = {Mi, Chenxi and Shatwell, Tom and Kong, Xiangzhen and Rinke, Karsten},
	month = nov,
	year = {2023},
}

Abstract We coupled twenty-first century climate projections with a well-established water quality model to depict future ecological changes of Rappbode Reservoir, Germany. Our results document a chain of climate-driven effects propagating through the aquatic ecosystem and interfering with drinking water supply: intense climate warming (RCP8.5 scenario) will firstly trigger a strong increase in water temperatures, in turn leading to metalimnetic hypoxia, accelerating sediment nutrient release and finally boosting blooms of the cyanobacterium Planktothrix rubescens . Such adverse water quality developments will be suppressed under RCP2.6 and 6.0 indicating that mitigation of climate change is improving water security. Our results also suggested surface withdrawal can be an effective adaptation strategy to make the reservoir ecosystem more resilient to climate warming. The identified consequences from climate warming and adaptation strategies are relevant to many deep waters in the temperate zone, and the conclusion should provide important guidances for stakeholders to confront potential climate changes.

@article{min_characterizing_2023,
	title = {Characterizing the accuracy of satellite-based products to detect soil moisture at the global scale},
	volume = {432},
	issn = {00167061},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0016706123000654},
	doi = {10.1016/j.geoderma.2023.116388},
	language = {en},
	urldate = {2024-11-15},
	journal = {Geoderma},
	author = {Min, Xiaoxiao and Li, Danlu and Shangguan, YuLin and Tian, Shuo and Shi, Zhou},
	month = apr,
	year = {2023},
	pages = {116388},
}

@article{mollenhauer_ground_2023,
	title = {Ground {Truth} {Validation} of {Sentinel}-2 {Data} {Using} {Mobile} {Wireless} {Ad} {Hoc} {Sensor} {Networks} ({MWSN}) in {Vegetation} {Stands}},
	volume = {15},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2072-4292},
	url = {https://www.mdpi.com/2072-4292/15/19/4663},
	doi = {10.3390/rs15194663},
	abstract = {Satellite-based remote sensing (RS) data are increasingly used to map and monitor local, regional, and global environmental phenomena and processes. Although the availability of RS data has improved significantly, especially in recent years, operational applications to derive value-added information products are still limited by close-range validation and verification deficits. This is mainly due to the gap between standardized and sufficiently available close-range and RS data in type, quality, and quantity. However, to ensure the best possible linkage of close-range and RS data, it makes sense to simultaneously record close-range data in addition to the availability of environmental models. This critical gap is filled by the presented mobile wireless ad hoc sensor network (MWSN) concept, which records sufficient close-range data automatically and in a standardized way, even at local and regional levels. This paper presents a field study conducted as part of the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN), focusing on the information gained with respect to estimating the vegetation state with the help of multispectral data by simultaneous observation of an MWSN during a Sentinel-2A (S2A) overflight. Based on a cross-calibration of the two systems, a comparable spectral characteristic of the data sets could be achieved. Building upon this, an analysis of the data regarding the influence of solar altitude, test side topography and land cover, and sub-pixel heterogeneity was accomplished. In particular, variations due to spatial heterogeneity and dynamics in the diurnal cycle show to what extent such complementary measurement systems can improve the data from RS products concerning the vegetation type and atmospheric conditions.},
	language = {en},
	number = {19},
	urldate = {2024-11-15},
	journal = {Remote Sensing},
	author = {Mollenhauer, Hannes and Borg, Erik and Pflug, Bringfried and Fichtelmann, Bernd and Dahms, Thorsten and Lorenz, Sebastian and Mollenhauer, Olaf and Lausch, Angela and Bumberger, Jan and Dietrich, Peter},
	month = sep,
	year = {2023},
	pages = {4663},
}

Satellite-based remote sensing (RS) data are increasingly used to map and monitor local, regional, and global environmental phenomena and processes. Although the availability of RS data has improved significantly, especially in recent years, operational applications to derive value-added information products are still limited by close-range validation and verification deficits. This is mainly due to the gap between standardized and sufficiently available close-range and RS data in type, quality, and quantity. However, to ensure the best possible linkage of close-range and RS data, it makes sense to simultaneously record close-range data in addition to the availability of environmental models. This critical gap is filled by the presented mobile wireless ad hoc sensor network (MWSN) concept, which records sufficient close-range data automatically and in a standardized way, even at local and regional levels. This paper presents a field study conducted as part of the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN), focusing on the information gained with respect to estimating the vegetation state with the help of multispectral data by simultaneous observation of an MWSN during a Sentinel-2A (S2A) overflight. Based on a cross-calibration of the two systems, a comparable spectral characteristic of the data sets could be achieved. Building upon this, an analysis of the data regarding the influence of solar altitude, test side topography and land cover, and sub-pixel heterogeneity was accomplished. In particular, variations due to spatial heterogeneity and dynamics in the diurnal cycle show to what extent such complementary measurement systems can improve the data from RS products concerning the vegetation type and atmospheric conditions.

@article{montzka_sensitivity_2023,
	title = {Sensitivity of {LiDAR} {Parameters} to {Aboveground} {Biomass} in {Winter} {Spelt}},
	volume = {7},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2504-446X},
	url = {https://www.mdpi.com/2504-446X/7/2/121},
	doi = {10.3390/drones7020121},
	abstract = {Information about the current biomass state of crops is important to evaluate whether the growth conditions are adequate in terms of water and nutrient supply to determine if there is need to react to diseases and to predict the expected yield. Passive optical Unmanned Aerial Vehicle (UAV)-based sensors such as RGB or multispectral cameras are able to sense the canopy surface and record, e.g., chlorophyll-related plant characteristics, which are often indirectly correlated to aboveground biomass. However, direct measurements of the plant structure can be provided by LiDAR systems. In this study, different LiDAR-based parameters are evaluated according to their relationship to aboveground fresh and dry biomass (AGB) for a winter spelt experimental field in Dahmsdorf, Brandenburg, Germany. The parameters crop height, gap fraction, and LiDAR intensity are analyzed according to their individual correlation with AGB, and also a multiparameter analysis using the Ordinary Least Squares Regression (OLS) is performed. Results indicate high absolute correlations of AGB with gap fraction and crop height (−0.82 and 0.77 for wet and −0.70 and 0.66 for dry AGB, respectively), whereas intensity needs further calibration or processing before it can be adequately used to estimate AGB (−0.27 and 0.22 for wet and dry AGB, respectively). An important outcome of this study is that the combined utilization of all LiDAR parameters via an OLS analysis results in less accurate AGB estimation than with gap fraction or crop height alone. Moreover, future AGB states in June and July were able to be estimated from May LiDAR parameters with high accuracy, indicating stable spatial patterns in crop characteristics over time.},
	language = {en},
	number = {2},
	urldate = {2024-11-15},
	journal = {Drones},
	author = {Montzka, Carsten and Donat, Marco and Raj, Rahul and Welter, Philipp and Bates, Jordan Steven},
	month = feb,
	year = {2023},
	pages = {121},
}

Information about the current biomass state of crops is important to evaluate whether the growth conditions are adequate in terms of water and nutrient supply to determine if there is need to react to diseases and to predict the expected yield. Passive optical Unmanned Aerial Vehicle (UAV)-based sensors such as RGB or multispectral cameras are able to sense the canopy surface and record, e.g., chlorophyll-related plant characteristics, which are often indirectly correlated to aboveground biomass. However, direct measurements of the plant structure can be provided by LiDAR systems. In this study, different LiDAR-based parameters are evaluated according to their relationship to aboveground fresh and dry biomass (AGB) for a winter spelt experimental field in Dahmsdorf, Brandenburg, Germany. The parameters crop height, gap fraction, and LiDAR intensity are analyzed according to their individual correlation with AGB, and also a multiparameter analysis using the Ordinary Least Squares Regression (OLS) is performed. Results indicate high absolute correlations of AGB with gap fraction and crop height (−0.82 and 0.77 for wet and −0.70 and 0.66 for dry AGB, respectively), whereas intensity needs further calibration or processing before it can be adequately used to estimate AGB (−0.27 and 0.22 for wet and dry AGB, respectively). An important outcome of this study is that the combined utilization of all LiDAR parameters via an OLS analysis results in less accurate AGB estimation than with gap fraction or crop height alone. Moreover, future AGB states in June and July were able to be estimated from May LiDAR parameters with high accuracy, indicating stable spatial patterns in crop characteristics over time.

@article{mwanake_interactive_2023,
	title = {Interactive effects of catchment mean water residence time and agricultural area on water physico-chemical variables and {GHG} saturations in headwater streams},
	volume = {5},
	issn = {2624-9375},
	url = {https://www.frontiersin.org/articles/10.3389/frwa.2023.1220544/full},
	doi = {10.3389/frwa.2023.1220544},
	abstract = {Greenhouse gas emissions from headwater streams are linked to multiple sources influenced by terrestrial land use and hydrology, yet partitioning these sources at catchment scales remains highly unexplored. To address this gap, we sampled year-long stable water isotopes (δ
              18
              O and δ
              2
              H) from 17 headwater streams differing in catchment agricultural areas. We calculated mean residence times (MRT) and young water fractions (YWF) based on the seasonality of δ
              18
              O signals and linked these hydrological measures to catchment characteristics, mean annual water physico-chemical variables, and GHG \% saturations. The MRT and the YWF ranged from 0.25 to 4.77 years and 3 to 53\%, respectively. The MRT of stream water was significantly negatively correlated with stream slope (r
              2
              = 0.58) but showed no relationship with the catchment area. Streams in agriculture-dominated catchments were annual hotspots of GHG oversaturation, which we attributed to precipitation-driven terrestrial inputs of dissolved GHGs for streams with shorter MRTs and nutrients and GHG inflows from groundwater for streams with longer MRTs. Based on our findings, future research should also consider water mean residence time estimates as indicators of integrated hydrological processes linking discharge and land use effects on annual GHG dynamics in headwater streams.},
	urldate = {2024-11-15},
	journal = {Frontiers in Water},
	author = {Mwanake, Ricky Mwangada and Gettel, Gretchen Maria and Wangari, Elizabeth Gachibu and Butterbach-Bahl, Klaus and Kiese, Ralf},
	month = jul,
	year = {2023},
	pages = {1220544},
}

Greenhouse gas emissions from headwater streams are linked to multiple sources influenced by terrestrial land use and hydrology, yet partitioning these sources at catchment scales remains highly unexplored. To address this gap, we sampled year-long stable water isotopes (δ 18 O and δ 2 H) from 17 headwater streams differing in catchment agricultural areas. We calculated mean residence times (MRT) and young water fractions (YWF) based on the seasonality of δ 18 O signals and linked these hydrological measures to catchment characteristics, mean annual water physico-chemical variables, and GHG % saturations. The MRT and the YWF ranged from 0.25 to 4.77 years and 3 to 53%, respectively. The MRT of stream water was significantly negatively correlated with stream slope (r 2 = 0.58) but showed no relationship with the catchment area. Streams in agriculture-dominated catchments were annual hotspots of GHG oversaturation, which we attributed to precipitation-driven terrestrial inputs of dissolved GHGs for streams with shorter MRTs and nutrients and GHG inflows from groundwater for streams with longer MRTs. Based on our findings, future research should also consider water mean residence time estimates as indicators of integrated hydrological processes linking discharge and land use effects on annual GHG dynamics in headwater streams.

@article{mwanake_anthropogenic_2023,
	title = {Anthropogenic activities significantly increase annual greenhouse gas ({GHG}) fluxes from temperate headwater streams in {Germany}},
	volume = {20},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1726-4189},
	url = {https://bg.copernicus.org/articles/20/3395/2023/},
	doi = {10.5194/bg-20-3395-2023},
	abstract = {Abstract. Anthropogenic activities increase the contributions of inland waters to
global greenhouse gas (GHG; CO2, CH4, and N2O) budgets, yet
the mechanisms driving these increases are still not well constrained. In
this study, we quantified year-long GHG concentrations, fluxes, and water
physico-chemical variables from 28 sites contrasted by land use across five
headwater catchments in Germany. Based on linear mixed-effects models, we
showed that land use was more significant than seasonality in controlling
the intra-annual variability of the GHGs. Streams in agriculture-dominated
catchments or with wastewater inflows had up to 10 times higher daily
CO2, CH4, and N2O emissions and were also more temporally
variable (CV {\textgreater} 55 \%) than forested streams. Our findings also
suggested that nutrient, labile carbon, and dissolved GHG inputs from the
agricultural and settlement areas may have supported these hotspots and
hot-moments of fluvial GHG emissions. Overall, the annual emission from
anthropogenic-influenced streams in CO2 equivalents was up to 20 times
higher (∼ 71 kg CO2 m−2 yr−1) than from
natural streams (∼ 3 kg CO2 m−2 yr−1), with
CO2 accounting for up to 81 \% of these annual emissions, while
N2O and CH4 accounted for up to 18 \% and 7 \%, respectively. The
positive influence of anthropogenic activities on fluvial GHG emissions also
resulted in a breakdown of the expected declining trends of fluvial GHG
emissions with stream size. Therefore, future studies should focus on
anthropogenically perturbed streams, as their GHG emissions are much more
variable in space and time and can potentially introduce the largest
uncertainties to fluvial GHG estimates.},
	language = {en},
	number = {16},
	urldate = {2024-11-15},
	journal = {Biogeosciences},
	author = {Mwanake, Ricky Mwangada and Gettel, Gretchen Maria and Wangari, Elizabeth Gachibu and Glaser, Clarissa and Houska, Tobias and Breuer, Lutz and Butterbach-Bahl, Klaus and Kiese, Ralf},
	month = aug,
	year = {2023},
	pages = {3395--3422},
}

Abstract. Anthropogenic activities increase the contributions of inland waters to global greenhouse gas (GHG; CO2, CH4, and N2O) budgets, yet the mechanisms driving these increases are still not well constrained. In this study, we quantified year-long GHG concentrations, fluxes, and water physico-chemical variables from 28 sites contrasted by land use across five headwater catchments in Germany. Based on linear mixed-effects models, we showed that land use was more significant than seasonality in controlling the intra-annual variability of the GHGs. Streams in agriculture-dominated catchments or with wastewater inflows had up to 10 times higher daily CO2, CH4, and N2O emissions and were also more temporally variable (CV \textgreater 55 %) than forested streams. Our findings also suggested that nutrient, labile carbon, and dissolved GHG inputs from the agricultural and settlement areas may have supported these hotspots and hot-moments of fluvial GHG emissions. Overall, the annual emission from anthropogenic-influenced streams in CO2 equivalents was up to 20 times higher (∼ 71 kg CO2 m−2 yr−1) than from natural streams (∼ 3 kg CO2 m−2 yr−1), with CO2 accounting for up to 81 % of these annual emissions, while N2O and CH4 accounted for up to 18 % and 7 %, respectively. The positive influence of anthropogenic activities on fluvial GHG emissions also resulted in a breakdown of the expected declining trends of fluvial GHG emissions with stream size. Therefore, future studies should focus on anthropogenically perturbed streams, as their GHG emissions are much more variable in space and time and can potentially introduce the largest uncertainties to fluvial GHG estimates.

@article{nathaniel_metaflux_2023,
	title = {{MetaFlux}: {Meta}-learning global carbon fluxes from sparse spatiotemporal observations},
	volume = {10},
	issn = {2052-4463},
	shorttitle = {{MetaFlux}},
	url = {https://www.nature.com/articles/s41597-023-02349-y},
	doi = {10.1038/s41597-023-02349-y},
	abstract = {Abstract
            
              We provide a global, long-term carbon flux dataset of gross primary production and ecosystem respiration generated using meta-learning, called
              MetaFlux
              . The idea behind meta-learning stems from the need to learn efficiently given sparse data by learning how to learn broad features across tasks to better infer other poorly sampled ones. Using meta-trained ensemble of deep models, we generate global carbon products on daily and monthly timescales at a 0.25-degree spatial resolution from 2001 to 2021, through a combination of reanalysis and remote-sensing products. Site-level validation finds that MetaFlux ensembles have lower validation error by 5–7\% compared to their non-meta-trained counterparts. In addition, they are more robust to extreme observations, with 4–24\% lower errors. We also checked for seasonality, interannual variability, and correlation to solar-induced fluorescence of the upscaled product and found that MetaFlux outperformed other machine-learning based carbon product, especially in the tropics and semi-arids by 10–40\%. Overall, MetaFlux can be used to study a wide range of biogeochemical processes.},
	language = {en},
	number = {1},
	urldate = {2024-11-15},
	journal = {Scientific Data},
	author = {Nathaniel, Juan and Liu, Jiangong and Gentine, Pierre},
	month = jul,
	year = {2023},
	pages = {440},
}

Abstract We provide a global, long-term carbon flux dataset of gross primary production and ecosystem respiration generated using meta-learning, called MetaFlux . The idea behind meta-learning stems from the need to learn efficiently given sparse data by learning how to learn broad features across tasks to better infer other poorly sampled ones. Using meta-trained ensemble of deep models, we generate global carbon products on daily and monthly timescales at a 0.25-degree spatial resolution from 2001 to 2021, through a combination of reanalysis and remote-sensing products. Site-level validation finds that MetaFlux ensembles have lower validation error by 5–7% compared to their non-meta-trained counterparts. In addition, they are more robust to extreme observations, with 4–24% lower errors. We also checked for seasonality, interannual variability, and correlation to solar-induced fluorescence of the upscaled product and found that MetaFlux outperformed other machine-learning based carbon product, especially in the tropics and semi-arids by 10–40%. Overall, MetaFlux can be used to study a wide range of biogeochemical processes.

@article{naz_continental-scale_2023,
	title = {Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, {ParFlow}-{CLM} (v3.6.0), over {Europe}},
	volume = {16},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1991-9603},
	url = {https://gmd.copernicus.org/articles/16/1617/2023/},
	doi = {10.5194/gmd-16-1617-2023},
	abstract = {Abstract. High-resolution large-scale predictions of hydrologic states and fluxes are important for many multi-scale applications, including water resource management. However, many of the existing global- to continental-scale hydrological models are applied at coarse resolution and neglect more complex processes such as lateral surface and groundwater flow, thereby not capturing smaller-scale hydrologic processes. Applications of high-resolution and physically based integrated hydrological models are often limited to watershed scales, neglecting the mesoscale climate effects on the water cycle. We implemented an integrated, physically based coupled land surface groundwater model, ParFlow-CLM version 3.6.0, over a pan-European model domain at 0.0275∘ (∼3 km) resolution. The model simulates a three-dimensional variably saturated groundwater-flow-solving Richards equation and overland flow with a two-dimensional kinematic wave approximation, which is fully integrated with land surface exchange processes. A comprehensive evaluation of multiple hydrologic variables including discharge, surface soil moisture (SM), evapotranspiration (ET), snow water equivalent (SWE), total water storage (TWS), and water table depth (WTD) resulting from a 10-year (1997–2006) model simulation was performed using in situ and remote sensing (RS) observations. Overall, the uncalibrated ParFlow-CLM model showed good agreement in simulating river discharge for 176 gauging stations across Europe (average Spearman's rank correlation (R) of 0.77). At the local scale, ParFlow-CLM model performed well for ET (R{\textgreater}0.94) against eddy covariance observations but showed relatively large differences for SM and WTD (median R values of 0.7 and 0.50, respectively) when compared with soil moisture networks and groundwater-monitoring-well data. However, model performance varied between hydroclimate regions, with the best agreement to RS datasets being shown in semi-arid and arid regions for most variables. Conversely, the largest differences between modeled and RS datasets (e.g., for SM, SWE, and TWS) are shown in humid and cold regions. Our findings highlight the importance of including multiple variables using both local-scale and large-scale RS datasets in model evaluations for a better understanding of physically based fully distributed hydrologic model performance and uncertainties in water and energy fluxes over continental scales and across different hydroclimate regions. The large-scale, high-resolution setup also forms a basis for future studies and provides an evaluation reference for climate change impact projections and a climatology for hydrological forecasting considering the effects of lateral surface and groundwater flows.},
	language = {en},
	number = {6},
	urldate = {2024-11-15},
	journal = {Geoscientific Model Development},
	author = {Naz, Bibi S. and Sharples, Wendy and Ma, Yueling and Goergen, Klaus and Kollet, Stefan},
	month = mar,
	year = {2023},
	pages = {1617--1639},
}

Abstract. High-resolution large-scale predictions of hydrologic states and fluxes are important for many multi-scale applications, including water resource management. However, many of the existing global- to continental-scale hydrological models are applied at coarse resolution and neglect more complex processes such as lateral surface and groundwater flow, thereby not capturing smaller-scale hydrologic processes. Applications of high-resolution and physically based integrated hydrological models are often limited to watershed scales, neglecting the mesoscale climate effects on the water cycle. We implemented an integrated, physically based coupled land surface groundwater model, ParFlow-CLM version 3.6.0, over a pan-European model domain at 0.0275∘ (∼3 km) resolution. The model simulates a three-dimensional variably saturated groundwater-flow-solving Richards equation and overland flow with a two-dimensional kinematic wave approximation, which is fully integrated with land surface exchange processes. A comprehensive evaluation of multiple hydrologic variables including discharge, surface soil moisture (SM), evapotranspiration (ET), snow water equivalent (SWE), total water storage (TWS), and water table depth (WTD) resulting from a 10-year (1997–2006) model simulation was performed using in situ and remote sensing (RS) observations. Overall, the uncalibrated ParFlow-CLM model showed good agreement in simulating river discharge for 176 gauging stations across Europe (average Spearman's rank correlation (R) of 0.77). At the local scale, ParFlow-CLM model performed well for ET (R\textgreater0.94) against eddy covariance observations but showed relatively large differences for SM and WTD (median R values of 0.7 and 0.50, respectively) when compared with soil moisture networks and groundwater-monitoring-well data. However, model performance varied between hydroclimate regions, with the best agreement to RS datasets being shown in semi-arid and arid regions for most variables. Conversely, the largest differences between modeled and RS datasets (e.g., for SM, SWE, and TWS) are shown in humid and cold regions. Our findings highlight the importance of including multiple variables using both local-scale and large-scale RS datasets in model evaluations for a better understanding of physically based fully distributed hydrologic model performance and uncertainties in water and energy fluxes over continental scales and across different hydroclimate regions. The large-scale, high-resolution setup also forms a basis for future studies and provides an evaluation reference for climate change impact projections and a climatology for hydrological forecasting considering the effects of lateral surface and groundwater flows.

@misc{nguyen_technical_2023,
	title = {Technical {Note}: {Revisiting} the {Procedure} for {Quantification} of the {Young} {Water} {Fraction} {Based} on {Seasonal} {Tracer} {Cycles}},
	shorttitle = {Technical {Note}},
	url = {https://essopenarchive.org/users/420245/articles/651944-technical-note-revisiting-the-procedure-for-quantification-of-the-young-water-fraction-based-on-seasonal-tracer-cycles?commit=8659f13fc8251751907321bdf3d88e803979cb1e},
	doi = {10.22541/essoar.168889846.63034096/v1},
	abstract = {The transit time (TT) of streamflow encapsulates information about how
catchments store and release water and solutes of different ages. The
young water fraction (Fyw), the fraction of streamflow that is younger
than a certain age (normally 2–3 months), has been increasingly used as
an alternative metric to the commonly used mean TT (mTT). In the
commonly used (‘traditional’) procedure presented by Kirchner (2016),
the age threshold (τyw) of Fyw separating young from old water is not
pre-defined and differs from catchment to catchment depending on the
shape of the (gamma) transit time distribution. However, it can be
argued that it is important to use the same pre-defined τyw for
inter-catchment comparison of Fyw. In this study, we propose an
alternative (‘proposed’) procedure for the estimation of Fyw with any
pre-defined τyw. This allows us to also compare the effects of data
sampling frequencies on the results of Fyw estimation using the same
τyw. We applied the traditional and proposed procedures using daily
oxygen isotope (δ18O) data in the Alp and Erlenbach catchments,
Switzerland. We found that our proposed and the traditional procedure
can give very different Fyw values. With the proposed procedure, the
estimated Fyw significantly increases when the sampling frequency
changes from sub-monthly to monthly time steps. Overall, our study
highlights the importance of the selection of τyw and the sampling
frequency in Fyw estimation, which should be given more attention.},
	urldate = {2024-11-15},
	publisher = {Preprints},
	author = {Nguyen, Tam Van and Kumar, Rohini and Heidbuchel, Ingo and Borriero, Arianna and Fleckenstein, Jan},
	month = jul,
	year = {2023},
}

The transit time (TT) of streamflow encapsulates information about how catchments store and release water and solutes of different ages. The young water fraction (Fyw), the fraction of streamflow that is younger than a certain age (normally 2–3 months), has been increasingly used as an alternative metric to the commonly used mean TT (mTT). In the commonly used (‘traditional’) procedure presented by Kirchner (2016), the age threshold (τyw) of Fyw separating young from old water is not pre-defined and differs from catchment to catchment depending on the shape of the (gamma) transit time distribution. However, it can be argued that it is important to use the same pre-defined τyw for inter-catchment comparison of Fyw. In this study, we propose an alternative (‘proposed’) procedure for the estimation of Fyw with any pre-defined τyw. This allows us to also compare the effects of data sampling frequencies on the results of Fyw estimation using the same τyw. We applied the traditional and proposed procedures using daily oxygen isotope (δ18O) data in the Alp and Erlenbach catchments, Switzerland. We found that our proposed and the traditional procedure can give very different Fyw values. With the proposed procedure, the estimated Fyw significantly increases when the sampling frequency changes from sub-monthly to monthly time steps. Overall, our study highlights the importance of the selection of τyw and the sampling frequency in Fyw estimation, which should be given more attention.

@article{nieberding_evaluation_2023,
	title = {Evaluation of {Three} {Soil} {Moisture} {Profile} {Sensors} {Using} {Laboratory} and {Field} {Experiments}},
	volume = {23},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1424-8220},
	url = {https://www.mdpi.com/1424-8220/23/14/6581},
	doi = {10.3390/s23146581},
	abstract = {Soil moisture profile sensors (SMPSs) have a high potential for climate-smart agriculture due to their easy handling and ability to perform simultaneous measurements at different depths. To date, an accurate and easy-to-use method for the evaluation of long SMPSs is not available. In this study, we developed laboratory and field experiments to evaluate three different SMPSs (SoilVUE10, Drill\&Drop, and SMT500) in terms of measurement accuracy, sensor-to-sensor variability, and temperature stability. The laboratory experiment features a temperature-controlled lysimeter to evaluate intra-sensor variability and temperature stability of SMPSs. The field experiment features a water level-controlled sandbox and reference TDR measurements to evaluate the soil water measurement accuracy of the SMPS. In both experiments, a well-characterized fine sand was used as measurement medium to ensure homogeneous dielectric properties in the measurement domain of the sensors. The laboratory experiments with the lysimeter showed that the Drill\&Drop sensor has the highest temperature sensitivity with a decrease of 0.014 m3 m−3 per 10 °C, but at the same time showed the lowest intra- and inter-sensor variability. The field experiment with the sandbox showed that all three SMPSs have a similar performance (average RMSE ≈ 0.023 m3 m−3) with higher uncertainties at intermediate soil moisture contents. The presented combination of laboratory and field tests were found to be well suited to evaluate the performance of SMPSs and will be used to test additional SMPSs in the future.},
	language = {en},
	number = {14},
	urldate = {2024-11-15},
	journal = {Sensors},
	author = {Nieberding, Felix and Huisman, Johan Alexander and Huebner, Christof and Schilling, Bernd and Weuthen, Ansgar and Bogena, Heye Reemt},
	month = jul,
	year = {2023},
	pages = {6581},
}

Soil moisture profile sensors (SMPSs) have a high potential for climate-smart agriculture due to their easy handling and ability to perform simultaneous measurements at different depths. To date, an accurate and easy-to-use method for the evaluation of long SMPSs is not available. In this study, we developed laboratory and field experiments to evaluate three different SMPSs (SoilVUE10, Drill&Drop, and SMT500) in terms of measurement accuracy, sensor-to-sensor variability, and temperature stability. The laboratory experiment features a temperature-controlled lysimeter to evaluate intra-sensor variability and temperature stability of SMPSs. The field experiment features a water level-controlled sandbox and reference TDR measurements to evaluate the soil water measurement accuracy of the SMPS. In both experiments, a well-characterized fine sand was used as measurement medium to ensure homogeneous dielectric properties in the measurement domain of the sensors. The laboratory experiments with the lysimeter showed that the Drill&Drop sensor has the highest temperature sensitivity with a decrease of 0.014 m3 m−3 per 10 °C, but at the same time showed the lowest intra- and inter-sensor variability. The field experiment with the sandbox showed that all three SMPSs have a similar performance (average RMSE ≈ 0.023 m3 m−3) with higher uncertainties at intermediate soil moisture contents. The presented combination of laboratory and field tests were found to be well suited to evaluate the performance of SMPSs and will be used to test additional SMPSs in the future.

@article{nwosu_early_2023,
	title = {Early human impact on lake cyanobacteria revealed by a {Holocene} record of sedimentary ancient {DNA}},
	volume = {6},
	issn = {2399-3642},
	url = {https://www.nature.com/articles/s42003-023-04430-z},
	doi = {10.1038/s42003-023-04430-z},
	abstract = {Abstract
            
              Sedimentary DNA-based studies revealed the effects of human activity on lake cyanobacteria communities over the last centuries, yet we continue to lack information over longer timescales. Here, we apply high-resolution molecular analyses on sedimentary ancient DNA to reconstruct the history of cyanobacteria throughout the Holocene in a lake in north-eastern Germany. We find a substantial increase in cyanobacteria abundance coinciding with deforestation during the early Bronze Age around 4000 years ago, suggesting increased nutrient supply to the lake by local communities settling on the lakeshore. The next substantial human-driven increase in cyanobacteria abundance occurred only about a century ago due to intensified agricultural fertilisation which caused the dominance of potentially toxic taxa (e.g.,
              Aphanizomenon
              ). Our study provides evidence that humans began to locally impact lake ecology much earlier than previously assumed. Consequently, managing aquatic systems today requires awareness of the legacy of human influence dating back potentially several millennia.},
	language = {en},
	number = {1},
	urldate = {2024-11-15},
	journal = {Communications Biology},
	author = {Nwosu, Ebuka Canisius and Brauer, Achim and Monchamp, Marie-Eve and Pinkerneil, Sylvia and Bartholomäus, Alexander and Theuerkauf, Martin and Schmidt, Jens-Peter and Stoof-Leichsenring, Kathleen R. and Wietelmann, Theresa and Kaiser, Jerome and Wagner, Dirk and Liebner, Susanne},
	month = jan,
	year = {2023},
	pages = {72},
}

Abstract Sedimentary DNA-based studies revealed the effects of human activity on lake cyanobacteria communities over the last centuries, yet we continue to lack information over longer timescales. Here, we apply high-resolution molecular analyses on sedimentary ancient DNA to reconstruct the history of cyanobacteria throughout the Holocene in a lake in north-eastern Germany. We find a substantial increase in cyanobacteria abundance coinciding with deforestation during the early Bronze Age around 4000 years ago, suggesting increased nutrient supply to the lake by local communities settling on the lakeshore. The next substantial human-driven increase in cyanobacteria abundance occurred only about a century ago due to intensified agricultural fertilisation which caused the dominance of potentially toxic taxa (e.g., Aphanizomenon ). Our study provides evidence that humans began to locally impact lake ecology much earlier than previously assumed. Consequently, managing aquatic systems today requires awareness of the legacy of human influence dating back potentially several millennia.

@article{orlowski_challenges_2023,
	title = {Challenges in studying water fluxes within the soil-plant-atmosphere continuum: {A} tracer-based perspective on pathways to progress},
	volume = {881},
	issn = {00489697},
	shorttitle = {Challenges in studying water fluxes within the soil-plant-atmosphere continuum},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0048969723021290},
	doi = {10.1016/j.scitotenv.2023.163510},
	language = {en},
	urldate = {2024-11-15},
	journal = {Science of The Total Environment},
	author = {Orlowski, Natalie and Rinderer, Michael and Dubbert, Maren and Ceperley, Natalie and Hrachowitz, Markus and Gessler, Arthur and Rothfuss, Youri and Sprenger, Matthias and Heidbüchel, Ingo and Kübert, Angelika and Beyer, Matthias and Zuecco, Giulia and McCarter, Colin},
	month = jul,
	year = {2023},
	pages = {163510},
}

@article{paasche_quantification_2023,
	title = {Quantification of data‐related uncertainty of spatially dense soil moisture patterns on the small catchment scale estimated using unsupervised multiple regression},
	volume = {22},
	issn = {1539-1663, 1539-1663},
	url = {https://acsess.onlinelibrary.wiley.com/doi/10.1002/vzj2.20258},
	doi = {10.1002/vzj2.20258},
	abstract = {Abstract
            Multiple regression analysis is a valuable method to reduce information gaps in a sparse soil moisture data set by fusing its information content with those of densely mapped data sets. Regression analysis utilizing uncertain data results in an indeterminate regression model and indeterminate soil moisture predictions when applying the regression model. We employ an unsupervised multiple regression approaches, taking optimally located sparse soil moisture measurements directly as coefficients in a linear regression model. We propagate data uncertainties into our probabilistic soil moisture estimation results by embedding the regression in a Monte Carlo approach. The computed uncertainty defines the quantitative limit for information retrieval from the resultant ensemble of soil moisture maps. This raises doubts on the true presence of some prominent channel‐like features of increased soil moisture that are clearly visible in a previously and deterministically derived soil moisture map ignoring the presence of data uncertainty. The approach followed in this work is computationally simple and could be applied routinely to databases of similar size. Insufficient uncertainty communication by the data provider became the biggest obstacle in our efforts and led us to the insight that the geoscientific community may need to revise their standards with regard to uncertainty communication related to measured and processed data.
          , 
            Core Ideas
            
              
                
                  Data uncertainty propagation through regression by means of a Monte Carlo approach.
                
                
                  Unsupervised nonlinear regression and its dependency on optimal sparse sampling.
                
                
                  Uncertainty communication for proper information retrieval.},
	language = {en},
	number = {4},
	urldate = {2024-11-15},
	journal = {Vadose Zone Journal},
	author = {Paasche, Hendrik and Schröter, Ingmar},
	month = jul,
	year = {2023},
	pages = {e20258},
}

Abstract Multiple regression analysis is a valuable method to reduce information gaps in a sparse soil moisture data set by fusing its information content with those of densely mapped data sets. Regression analysis utilizing uncertain data results in an indeterminate regression model and indeterminate soil moisture predictions when applying the regression model. We employ an unsupervised multiple regression approaches, taking optimally located sparse soil moisture measurements directly as coefficients in a linear regression model. We propagate data uncertainties into our probabilistic soil moisture estimation results by embedding the regression in a Monte Carlo approach. The computed uncertainty defines the quantitative limit for information retrieval from the resultant ensemble of soil moisture maps. This raises doubts on the true presence of some prominent channel‐like features of increased soil moisture that are clearly visible in a previously and deterministically derived soil moisture map ignoring the presence of data uncertainty. The approach followed in this work is computationally simple and could be applied routinely to databases of similar size. Insufficient uncertainty communication by the data provider became the biggest obstacle in our efforts and led us to the insight that the geoscientific community may need to revise their standards with regard to uncertainty communication related to measured and processed data. , Core Ideas Data uncertainty propagation through regression by means of a Monte Carlo approach. Unsupervised nonlinear regression and its dependency on optimal sparse sampling. Uncertainty communication for proper information retrieval.

@article{panwar_methodological_2023,
	title = {Methodological challenges and new perspectives of shifting vegetation phenology in eddy covariance data},
	volume = {13},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-023-41048-x},
	doi = {10.1038/s41598-023-41048-x},
	abstract = {Abstract
            While numerous studies report shifts in vegetation phenology, in this regard eddy covariance (EC) data, despite its continuous high-frequency observations, still requires further exploration. Furthermore, there is no general consensus on optimal methodologies for data smoothing and extracting phenological transition dates (PTDs). Here, we revisit existing methodologies and present new prospects to investigate phenological changes in gross primary productivity (GPP) from EC measurements. First, we present a smoothing technique of GPP time series through the derivative of its smoothed annual cumulative sum. Second, we calculate PTDs and their trends from a commonly used threshold method that identifies days with a fixed percentage of the annual maximum GPP. A systematic analysis is performed for various thresholds ranging from 0.1 to 0.7. Lastly, we examine the relation of PTDs trends to trends in GPP across the years on a weekly basis. Results from 47 EC sites with long time series ({\textgreater} 10 years) show that advancing trends in start of season (SOS) are strongest at lower thresholds but for the end of season (EOS) at higher thresholds. Moreover, the trends are variable at different thresholds for individual vegetation types and individual sites, outlining reasonable concerns on using a single threshold value. Relationship of trends in PTDs and weekly GPP reveal association of advanced SOS and delayed EOS to increase in immediate primary productivity, but not to the trends in overall seasonal productivity. Drawing on these analyses, we emphasise on abstaining from subjective choices and investigating relationship of PTDs trend to finer temporal trends of GPP. Our study examines existing methodological challenges and presents approaches that optimize the use of EC data in identifying vegetation phenological changes and their relation to carbon uptake.},
	language = {en},
	number = {1},
	urldate = {2024-11-15},
	journal = {Scientific Reports},
	author = {Panwar, Annu and Migliavacca, Mirco and Nelson, Jacob A. and Cortés, José and Bastos, Ana and Forkel, Matthias and Winkler, Alexander J.},
	month = aug,
	year = {2023},
	pages = {13885},
}

Abstract While numerous studies report shifts in vegetation phenology, in this regard eddy covariance (EC) data, despite its continuous high-frequency observations, still requires further exploration. Furthermore, there is no general consensus on optimal methodologies for data smoothing and extracting phenological transition dates (PTDs). Here, we revisit existing methodologies and present new prospects to investigate phenological changes in gross primary productivity (GPP) from EC measurements. First, we present a smoothing technique of GPP time series through the derivative of its smoothed annual cumulative sum. Second, we calculate PTDs and their trends from a commonly used threshold method that identifies days with a fixed percentage of the annual maximum GPP. A systematic analysis is performed for various thresholds ranging from 0.1 to 0.7. Lastly, we examine the relation of PTDs trends to trends in GPP across the years on a weekly basis. Results from 47 EC sites with long time series (\textgreater 10 years) show that advancing trends in start of season (SOS) are strongest at lower thresholds but for the end of season (EOS) at higher thresholds. Moreover, the trends are variable at different thresholds for individual vegetation types and individual sites, outlining reasonable concerns on using a single threshold value. Relationship of trends in PTDs and weekly GPP reveal association of advanced SOS and delayed EOS to increase in immediate primary productivity, but not to the trends in overall seasonal productivity. Drawing on these analyses, we emphasise on abstaining from subjective choices and investigating relationship of PTDs trend to finer temporal trends of GPP. Our study examines existing methodological challenges and presents approaches that optimize the use of EC data in identifying vegetation phenological changes and their relation to carbon uptake.

@misc{pasik_uncertainty_2023,
	title = {Uncertainty estimation for a new exponential filter-based long-term root-zone soil moisture dataset from {C3S} surface observations},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	url = {https://egusphere.copernicus.org/preprints/2023/egusphere-2023-47/},
	doi = {10.5194/egusphere-2023-47},
	abstract = {Abstract. Soil moisture is a key variable in monitoring climate and an important component of the hydrological, carbon, and energy cycles. Satellite products ameliorate the sparsity of field measurements but are inherently limited to observing the near-surface layer, while water available in the unobserved root zone controls critical processes like plant water uptake and evapotranspiration. A variety of approaches exists for modelling root-zone soil moisture (RZSM), including approximating it from surface layer observations. While the number of available RZSM datasets is growing, they usually do not contain estimates of their uncertainty. In this paper we derive a long-term RZSM dataset (2002–2020) from the Copernicus Climate Change Service (C3S) surface soil moisture (SSM) COMBINED product via the exponential filter (EF) method. We identify the optimal value of the method’s model parameter T , which controls the level of smoothing and delaying applied to the surface observations, by maximizing the correlation of RZSM estimates with field measurements from the International Soil Moisture Network (ISMN). Optimized T-parameter values were calculated for four soil depth layers (0–10 cm, 10–40 cm, 40–100 cm, and 100–200 cm) and used to calculate a global RZSM dataset. The quality of this dataset is then globally evaluated against RZSM estimates of the ERA5-Land reanalysis. Results of the product comparison show satisfactory skill in all four layers with median Pearson correlation ranging from 0.54 in the topmost to 0.28 in the deepest soil layer. Temporally-dynamic product uncertainties for each of the RZSM product layers are estimated by applying standard uncertainty propagation to SSM input data and by estimating structural uncertainties of the EF method from ISMN ground reference measurements taken at the surface and in varying depths. Uncertainty estimates were found to exhibit both realistic absolute magnitudes as well as temporal variations. The product described here is, to our best knowledge, the first global, long-term, uncertainty-characterized, and purely observation-based product for RZSM estimates up to 2 m depth.},
	urldate = {2024-11-15},
	publisher = {Hydrology},
	author = {Pasik, Adam and Gruber, Alexander and Preimesberger, Wolfgang and De Santis, Domenico and Dorigo, Wouter},
	month = mar,
	year = {2023},
}

Abstract. Soil moisture is a key variable in monitoring climate and an important component of the hydrological, carbon, and energy cycles. Satellite products ameliorate the sparsity of field measurements but are inherently limited to observing the near-surface layer, while water available in the unobserved root zone controls critical processes like plant water uptake and evapotranspiration. A variety of approaches exists for modelling root-zone soil moisture (RZSM), including approximating it from surface layer observations. While the number of available RZSM datasets is growing, they usually do not contain estimates of their uncertainty. In this paper we derive a long-term RZSM dataset (2002–2020) from the Copernicus Climate Change Service (C3S) surface soil moisture (SSM) COMBINED product via the exponential filter (EF) method. We identify the optimal value of the method’s model parameter T , which controls the level of smoothing and delaying applied to the surface observations, by maximizing the correlation of RZSM estimates with field measurements from the International Soil Moisture Network (ISMN). Optimized T-parameter values were calculated for four soil depth layers (0–10 cm, 10–40 cm, 40–100 cm, and 100–200 cm) and used to calculate a global RZSM dataset. The quality of this dataset is then globally evaluated against RZSM estimates of the ERA5-Land reanalysis. Results of the product comparison show satisfactory skill in all four layers with median Pearson correlation ranging from 0.54 in the topmost to 0.28 in the deepest soil layer. Temporally-dynamic product uncertainties for each of the RZSM product layers are estimated by applying standard uncertainty propagation to SSM input data and by estimating structural uncertainties of the EF method from ISMN ground reference measurements taken at the surface and in varying depths. Uncertainty estimates were found to exhibit both realistic absolute magnitudes as well as temporal variations. The product described here is, to our best knowledge, the first global, long-term, uncertainty-characterized, and purely observation-based product for RZSM estimates up to 2 m depth.

@article{pasqualini_effects_2023,
	title = {Effects of incomplete sampling on macroinvertebrate secondary production estimates in a forested headwater stream},
	volume = {850},
	issn = {0018-8158, 1573-5117},
	url = {https://link.springer.com/10.1007/s10750-023-05238-y},
	doi = {10.1007/s10750-023-05238-y},
	abstract = {Abstract
            Estimates of secondary production depend on the efficiency of sampling methods in capturing abundances and body lengths of the entire macroinvertebrate community. The efficiency of common sampling methods in fulfilling these criteria is poorly understood. We compared the effects of a Surber sampler (250 µm mesh size) and a Freeze corer in capturing abundance, biomass, and secondary production of macroinvertebrates in a forested headwater stream. We then examined how the use of nets with different mesh sizes could affect estimates of secondary production. Macroinvertebrate abundance was three times lower, and biomass was three times higher with the Surber than with the Freeze corer. Neither method captured the entire length distribution, and incomplete sampling of body lengths and abundance resulted in underestimating total secondary production by 48\% (Surber) and 49\% (Freeze corer). We estimated that reducing the mesh size from 250 to 100 µm would reduce the underestimation of production from {\textasciitilde} 48 to {\textasciitilde} 12\% due to the inclusion of smaller individuals. Our results improve the efficiency of common sampling methods, allowing a reliable quantification of the role of macroinvertebrates in stream ecosystem functioning.},
	language = {en},
	number = {14},
	urldate = {2024-11-15},
	journal = {Hydrobiologia},
	author = {Pasqualini, Julia and Majdi, Nabil and Brauns, Mario},
	month = aug,
	year = {2023},
	pages = {3113--3124},
}

Abstract Estimates of secondary production depend on the efficiency of sampling methods in capturing abundances and body lengths of the entire macroinvertebrate community. The efficiency of common sampling methods in fulfilling these criteria is poorly understood. We compared the effects of a Surber sampler (250 µm mesh size) and a Freeze corer in capturing abundance, biomass, and secondary production of macroinvertebrates in a forested headwater stream. We then examined how the use of nets with different mesh sizes could affect estimates of secondary production. Macroinvertebrate abundance was three times lower, and biomass was three times higher with the Surber than with the Freeze corer. Neither method captured the entire length distribution, and incomplete sampling of body lengths and abundance resulted in underestimating total secondary production by 48% (Surber) and 49% (Freeze corer). We estimated that reducing the mesh size from 250 to 100 µm would reduce the underestimation of production from ~ 48 to ~ 12% due to the inclusion of smaller individuals. Our results improve the efficiency of common sampling methods, allowing a reliable quantification of the role of macroinvertebrates in stream ecosystem functioning.

@misc{paulus_interpretability_2023,
	title = {Interpretability of negative latent heat fluxes from {Eddy} {Covariance} measurements during dry conditions},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	url = {https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2556/},
	doi = {10.5194/egusphere-2023-2556},
	abstract = {Abstract. It is known from arid and semi-arid ecosystems that atmospheric water vapor is directly adsorbed by the soil matrix during the night. Soil water vapor adsorption was typically neglected and only recently got attention because of improvements in measurement techniques. One technique rarely explored is eddy covariance (EC). EC nighttime measurements are usually discarded, but soil water vapor adsorption may be detectable as downwards-directed EC latent heat (λE) flux measurements under dry conditions. We propose a classification method to exclude conditions of dew and fog when λE derived from EC is not trustworthy due to stable atmospheric conditions. We compare downwards-directed λE fluxes from EC with measurements from weighable lysimeters for four years in a Mediterranean Savannah ecosystem and three years in a temperate agricultural site. Our aim is to assess if overnight water inputs from soil water vapor adsorption differ between ecosystems and how well they are detectable by EC.  At the Mediterranean site, the lysimeters measured soil water vapor adsorption each summer whereas at the temperate site soil water vapor adsorption was much rarer, and measured predominantly under extreme drought. In 30 \% of nights in the four-year measurement period at the Mediterranean site, the EC technique detected downward-directed λE fluxes of which 88.8 \% were confirmed to be soil water vapor adsorption by at least one lysimeter. At the temperate site, downward-directed λE fluxes were only recorded during 15 \% of the nights, with only 36.8 \% of half-hours matching simultaneous lysimeter measurement of soil water vapor adsorption. Although this relationship slightly improved to 60\% under bare soil conditions and extreme droughts, this underlines that soil water vapor adsorption is likely a much more relevant process in arid ecosystems compared to temperate ones and that the EC method was able to capture this difference. The comparisons of the magnitudes between the two methods revealed a substantial underestimation of soil water vapor adsorption with EC. This underestimation was, however, on par with the underestimation in evaporation. Based on a random forest-based feature selection we found the mismatch between the techniques being dominantly related to the site's inherent spatiotemporal variations in soil conditions, namely soil water status, and soil (surface) temperature.  We further demonstrate that although the water flux is very small with mean values of 0.04 or 0.06 mm per night depending on either EC or lysimeter detection it can be a substantial fraction of the diel soil water balance under dry conditions. Although the two instruments substantially differ with regard to the evaporative fraction with 64\% and 25\% for the lysimeter and EC methods, they are in either case substantial. Given the usefulness of EC for detecting soil water vapor adsorption as demonstrated here, there is potential for investigating adsorption in more climate regions at longer timescales thanks to the greater abundance of EC measurements compared to lysimeter observations.},
	urldate = {2024-11-15},
	publisher = {Biogeochemistry: Air - Land Exchange},
	author = {Paulus, Sinikka J. and Orth, Rene and Lee, Sung-Ching and Hildebrandt, Anke and Jung, Martin and Nelson, Jacob A. and El-Madany, Tarek S. and Carrara, Arnaud and Moreno, Gerardo and Mauder, Matthias and Groh, Jannis and Graf, Alexander and Reichstein, Markus and Migliavacca, Mirco},
	month = nov,
	year = {2023},
}

Abstract. It is known from arid and semi-arid ecosystems that atmospheric water vapor is directly adsorbed by the soil matrix during the night. Soil water vapor adsorption was typically neglected and only recently got attention because of improvements in measurement techniques. One technique rarely explored is eddy covariance (EC). EC nighttime measurements are usually discarded, but soil water vapor adsorption may be detectable as downwards-directed EC latent heat (λE) flux measurements under dry conditions. We propose a classification method to exclude conditions of dew and fog when λE derived from EC is not trustworthy due to stable atmospheric conditions. We compare downwards-directed λE fluxes from EC with measurements from weighable lysimeters for four years in a Mediterranean Savannah ecosystem and three years in a temperate agricultural site. Our aim is to assess if overnight water inputs from soil water vapor adsorption differ between ecosystems and how well they are detectable by EC.  At the Mediterranean site, the lysimeters measured soil water vapor adsorption each summer whereas at the temperate site soil water vapor adsorption was much rarer, and measured predominantly under extreme drought. In 30 % of nights in the four-year measurement period at the Mediterranean site, the EC technique detected downward-directed λE fluxes of which 88.8 % were confirmed to be soil water vapor adsorption by at least one lysimeter. At the temperate site, downward-directed λE fluxes were only recorded during 15 % of the nights, with only 36.8 % of half-hours matching simultaneous lysimeter measurement of soil water vapor adsorption. Although this relationship slightly improved to 60% under bare soil conditions and extreme droughts, this underlines that soil water vapor adsorption is likely a much more relevant process in arid ecosystems compared to temperate ones and that the EC method was able to capture this difference. The comparisons of the magnitudes between the two methods revealed a substantial underestimation of soil water vapor adsorption with EC. This underestimation was, however, on par with the underestimation in evaporation. Based on a random forest-based feature selection we found the mismatch between the techniques being dominantly related to the site's inherent spatiotemporal variations in soil conditions, namely soil water status, and soil (surface) temperature.  We further demonstrate that although the water flux is very small with mean values of 0.04 or 0.06 mm per night depending on either EC or lysimeter detection it can be a substantial fraction of the diel soil water balance under dry conditions. Although the two instruments substantially differ with regard to the evaporative fraction with 64% and 25% for the lysimeter and EC methods, they are in either case substantial. Given the usefulness of EC for detecting soil water vapor adsorption as demonstrated here, there is potential for investigating adsorption in more climate regions at longer timescales thanks to the greater abundance of EC measurements compared to lysimeter observations.

@article{peng_rfi-suppressed_2023,
	title = {An {RFI}-suppressed {SMOS} {L}-band multi-angular brightness temperature dataset spanning over a decade (since 2010)},
	volume = {10},
	issn = {2052-4463},
	url = {https://www.nature.com/articles/s41597-023-02499-z},
	doi = {10.1038/s41597-023-02499-z},
	abstract = {Abstract
            
              The Soil Moisture Ocean Salinity (SMOS) was the first mission providing L-band multi-angular brightness temperature (TB) at the global scale. However, radio frequency interferences (RFI) and aliasing effects degrade, when present SMOS TBs, and thus affect the retrieval of land parameters. To alleviate this, a refined SMOS multi-angular TB dataset was generated based on a two-step regression approach. This approach smooths the TBs and reconstructs data at the incidence angle with large TB uncertainties. Compared with Centre Aval de Traitement des Données SMOS (CATDS) TB product, this dataset shows a better relationship with the Soil Moisture Active Passive (SMAP) TB and enhanced correlation with
              in-situ
              measured soil moisture. This RFI-suppressed SMOS TB dataset, spanning more than a decade (since 2010), is expected to provide opportunities for better retrieval of land parameters and scientific applications.},
	language = {en},
	number = {1},
	urldate = {2024-11-15},
	journal = {Scientific Data},
	author = {Peng, Zhiqing and Zhao, Tianjie and Shi, Jiancheng and Kerr, Yann H. and Rodríguez-Fernández, Nemesio J. and Yao, Panpan and Che, Tao},
	month = sep,
	year = {2023},
	pages = {599},
}

Abstract The Soil Moisture Ocean Salinity (SMOS) was the first mission providing L-band multi-angular brightness temperature (TB) at the global scale. However, radio frequency interferences (RFI) and aliasing effects degrade, when present SMOS TBs, and thus affect the retrieval of land parameters. To alleviate this, a refined SMOS multi-angular TB dataset was generated based on a two-step regression approach. This approach smooths the TBs and reconstructs data at the incidence angle with large TB uncertainties. Compared with Centre Aval de Traitement des Données SMOS (CATDS) TB product, this dataset shows a better relationship with the Soil Moisture Active Passive (SMAP) TB and enhanced correlation with in-situ measured soil moisture. This RFI-suppressed SMOS TB dataset, spanning more than a decade (since 2010), is expected to provide opportunities for better retrieval of land parameters and scientific applications.

@article{pohl_long-term_2023,
	title = {Long-term daily hydrometeorological drought indices, soil moisture, and evapotranspiration for {ICOS} sites},
	volume = {10},
	issn = {2052-4463},
	url = {https://www.nature.com/articles/s41597-023-02192-1},
	doi = {10.1038/s41597-023-02192-1},
	abstract = {Abstract
            Eddy covariance sites are ideally suited for the study of extreme events on ecosystems as they allow the exchange of trace gases and energy fluxes between ecosystems and the lower atmosphere to be directly measured on a continuous basis. However, standardized definitions of hydroclimatic extremes are needed to render studies of extreme events comparable across sites. This requires longer datasets than are available from on-site measurements in order to capture the full range of climatic variability. We present a dataset of drought indices based on precipitation (Standardized Precipitation Index, SPI), atmospheric water balance (Standardized Precipitation Evapotranspiration Index, SPEI), and soil moisture (Standardized Soil Moisture Index, SSMI) for 101 ecosystem sites from the Integrated Carbon Observation System (ICOS) with daily temporal resolution from 1950 to 2021. Additionally, we provide simulated soil moisture and evapotranspiration for each site from the Mesoscale Hydrological Model (mHM). These could be utilised for gap-filling or long-term research, among other applications. We validate our data set with measurements from ICOS and discuss potential research avenues.},
	language = {en},
	number = {1},
	urldate = {2024-11-15},
	journal = {Scientific Data},
	author = {Pohl, Felix and Rakovec, Oldrich and Rebmann, Corinna and Hildebrandt, Anke and Boeing, Friedrich and Hermanns, Floris and Attinger, Sabine and Samaniego, Luis and Kumar, Rohini},
	month = may,
	year = {2023},
	pages = {281},
}

Abstract Eddy covariance sites are ideally suited for the study of extreme events on ecosystems as they allow the exchange of trace gases and energy fluxes between ecosystems and the lower atmosphere to be directly measured on a continuous basis. However, standardized definitions of hydroclimatic extremes are needed to render studies of extreme events comparable across sites. This requires longer datasets than are available from on-site measurements in order to capture the full range of climatic variability. We present a dataset of drought indices based on precipitation (Standardized Precipitation Index, SPI), atmospheric water balance (Standardized Precipitation Evapotranspiration Index, SPEI), and soil moisture (Standardized Soil Moisture Index, SSMI) for 101 ecosystem sites from the Integrated Carbon Observation System (ICOS) with daily temporal resolution from 1950 to 2021. Additionally, we provide simulated soil moisture and evapotranspiration for each site from the Mesoscale Hydrological Model (mHM). These could be utilised for gap-filling or long-term research, among other applications. We validate our data set with measurements from ICOS and discuss potential research avenues.

@article{pohl_observational_2023,
	title = {Observational evidence of legacy effects of the 2018 drought on a mixed deciduous forest in {Germany}},
	volume = {13},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-023-38087-9},
	doi = {10.1038/s41598-023-38087-9},
	abstract = {Abstract
            
              Forests play a major role in the global carbon cycle, and droughts have been shown to explain much of the interannual variability in the terrestrial carbon sink capacity. The quantification of drought legacy effects on ecosystem carbon fluxes is a challenging task, and research on the ecosystem scale remains sparse. In this study we investigate the delayed response of an extreme drought event on the carbon cycle in the mixed deciduous forest site ’Hohes Holz’ (DE-HoH) located in Central Germany, using the measurements taken between 2015 and 2020. Our analysis demonstrates that the extreme drought and heat event in 2018 had strong legacy effects on the carbon cycle in 2019, but not in 2020. On an annual basis, net ecosystem productivity was
              
                
                  \$\${\textbackslash}sim 16{\textbackslash},{\textbackslash}\%\$\$
                  
                    
                      ∼
                      16
                      
                      \%
                    
                  
                
              
              higher in 2018 (
              
                
                  \$\${\textbackslash}sim 424{\textbackslash},\{{\textbackslash}hbox \{g\}\_\{{\textbackslash}text \{C\}\}\}{\textbackslash}hbox \{m\}{\textasciicircum}\{-2\}\$\$
                  
                    
                      ∼
                      424
                      
                      
                        g
                        C
                      
                      
                        m
                        
                          -
                          2
                        
                      
                    
                  
                
              
              ) and
              
                
                  \$\${\textbackslash}sim 25{\textbackslash},{\textbackslash}\%\$\$
                  
                    
                      ∼
                      25
                      
                      \%
                    
                  
                
              
              lower in 2019 (
              
                
                  \$\${\textbackslash}sim 274{\textbackslash},\{{\textbackslash}hbox \{g\}\_\{{\textbackslash}text \{C\}\}\}{\textbackslash}hbox \{m\}{\textasciicircum}\{-2\}\$\$
                  
                    
                      ∼
                      274
                      
                      
                        g
                        C
                      
                      
                        m
                        
                          -
                          2
                        
                      
                    
                  
                
              
              ) compared to pre-drought years (
              
                
                  \$\${\textbackslash}sim 367{\textbackslash},\{{\textbackslash}hbox \{g\}\_\{{\textbackslash}text \{C\}\}\}{\textbackslash}hbox \{m\}{\textasciicircum}\{-2\}\$\$
                  
                    
                      ∼
                      367
                      
                      
                        g
                        C
                      
                      
                        m
                        
                          -
                          2
                        
                      
                    
                  
                
              
              ). Using spline regression, we show that while current hydrometeorological conditions can explain forest productivity in 2020, they do not fully explain the decrease in productivity in 2019. Including long-term drought information in the statistical model reduces overestimation error of productivity in 2019 by nearly
              
                
                  \$\$50{\textbackslash},{\textbackslash}\%\$\$
                  
                    
                      50
                      
                      \%
                    
                  
                
              
              . We also found that short-term drought events have positive impacts on the carbon cycle at the beginning of the vegetation season, but negative impacts in later summer, while long-term drought events have generally negative impacts throughout the growing season. Overall, our findings highlight the importance of considering the diverse and complex impacts of extreme events on ecosystem fluxes, including the timing, temporal scale, and magnitude of the events, and the need to use consistent definitions of drought to clearly convey immediate and delayed responses.},
	language = {en},
	number = {1},
	urldate = {2024-11-15},
	journal = {Scientific Reports},
	author = {Pohl, Felix and Werban, Ulrike and Kumar, Rohini and Hildebrandt, Anke and Rebmann, Corinna},
	month = jul,
	year = {2023},
	pages = {10863},
}

Abstract Forests play a major role in the global carbon cycle, and droughts have been shown to explain much of the interannual variability in the terrestrial carbon sink capacity. The quantification of drought legacy effects on ecosystem carbon fluxes is a challenging task, and research on the ecosystem scale remains sparse. In this study we investigate the delayed response of an extreme drought event on the carbon cycle in the mixed deciduous forest site ’Hohes Holz’ (DE-HoH) located in Central Germany, using the measurements taken between 2015 and 2020. Our analysis demonstrates that the extreme drought and heat event in 2018 had strong legacy effects on the carbon cycle in 2019, but not in 2020. On an annual basis, net ecosystem productivity was $$\sim 16\,\%$$ ∼ 16 % higher in 2018 ( $$\sim 424\,\\hbox \g\_\\text \C\\\\hbox \m\\textasciicircum\-2\$$ ∼ 424 g C m - 2 ) and$$ \sim 25\,\% $$∼ 25 % lower in 2019 ($$ \sim 274\,\\hbox \g\_\\text \C\\\\hbox \m\\textasciicircum\-2\ $$∼ 274 g C m - 2 ) compared to pre-drought years ($$ \sim 367\,\\hbox \g\_\\text \C\\\\hbox \m\\textasciicircum\-2\ $$∼ 367 g C m - 2 ). Using spline regression, we show that while current hydrometeorological conditions can explain forest productivity in 2020, they do not fully explain the decrease in productivity in 2019. Including long-term drought information in the statistical model reduces overestimation error of productivity in 2019 by nearly$$ 50\,\%$$ 50 % . We also found that short-term drought events have positive impacts on the carbon cycle at the beginning of the vegetation season, but negative impacts in later summer, while long-term drought events have generally negative impacts throughout the growing season. Overall, our findings highlight the importance of considering the diverse and complex impacts of extreme events on ecosystem fluxes, including the timing, temporal scale, and magnitude of the events, and the need to use consistent definitions of drought to clearly convey immediate and delayed responses.

@article{poppe_teran_rising_2023,
	title = {Rising water-use efficiency in {European} grasslands is driven by increased primary production},
	volume = {4},
	issn = {2662-4435},
	url = {https://www.nature.com/articles/s43247-023-00757-x},
	doi = {10.1038/s43247-023-00757-x},
	abstract = {Abstract
            Water-use efficiency is the amount of carbon assimilated per water used by an ecosystem and a key indicator of ecosystem functioning, but its variability in response to climate change and droughts is not thoroughly understood. Here, we investigated trends, drought response and drivers of three water-use efficiency indices from 1995–2018 in Europe with remote sensing data that considered long-term environmental effects. We show that inherent water-use efficiency decreased by −4.2\% in Central Europe, exhibiting threatened ecosystem functioning. In European grasslands it increased by +24.2\%, by regulated transpiration and increased carbon assimilation. Further, we highlight modulation of water-use efficiency drought response by hydro-climate and the importance of adaptive canopy conductance on ecosystem function. Our results imply that decoupling carbon assimilation from canopy conductance and efficient water management strategies could make the difference between threatened and well-coping ecosystems with ongoing climate change, and provide important insights for land surface model development.},
	language = {en},
	number = {1},
	urldate = {2024-11-15},
	journal = {Communications Earth \& Environment},
	author = {Poppe Terán, Christian and Naz, Bibi S. and Graf, Alexander and Qu, Yuquan and Hendricks Franssen, Harrie-Jan and Baatz, Roland and Ciais, Phillipe and Vereecken, Harry},
	month = mar,
	year = {2023},
	pages = {95},
}

Abstract Water-use efficiency is the amount of carbon assimilated per water used by an ecosystem and a key indicator of ecosystem functioning, but its variability in response to climate change and droughts is not thoroughly understood. Here, we investigated trends, drought response and drivers of three water-use efficiency indices from 1995–2018 in Europe with remote sensing data that considered long-term environmental effects. We show that inherent water-use efficiency decreased by −4.2% in Central Europe, exhibiting threatened ecosystem functioning. In European grasslands it increased by +24.2%, by regulated transpiration and increased carbon assimilation. Further, we highlight modulation of water-use efficiency drought response by hydro-climate and the importance of adaptive canopy conductance on ecosystem function. Our results imply that decoupling carbon assimilation from canopy conductance and efficient water management strategies could make the difference between threatened and well-coping ecosystems with ongoing climate change, and provide important insights for land surface model development.

@article{prikaziuk_simulation_2023,
	title = {Simulation of ecosystem fluxes with the {SCOPE} model: {Sensitivity} to parametrization and evaluation with flux tower observations},
	volume = {284},
	issn = {00344257},
	shorttitle = {Simulation of ecosystem fluxes with the {SCOPE} model},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0034425722004308},
	doi = {10.1016/j.rse.2022.113324},
	language = {en},
	urldate = {2024-11-15},
	journal = {Remote Sensing of Environment},
	author = {Prikaziuk, Egor and Migliavacca, Mirco and Su, Zhongbo (Bob) and Van Der Tol, Christiaan},
	month = jan,
	year = {2023},
	pages = {113324},
}

@article{qian_high_2023,
	title = {High uncertainty of evapotranspiration products under extreme climatic conditions},
	volume = {626},
	issn = {00221694},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S002216942301274X},
	doi = {10.1016/j.jhydrol.2023.130332},
	language = {en},
	urldate = {2024-11-15},
	journal = {Journal of Hydrology},
	author = {Qian, Long and Zhang, Zhitao and Wu, Lifeng and Fan, Shaoshuai and Yu, Xingjiao and Liu, Xiaogang and Ba, Yalan and Ma, Haijiao and Wang, Yicheng},
	month = nov,
	year = {2023},
	pages = {130332},
}

@misc{radtke_fractions_2023,
	title = {Fractions of {Different} {Young} {Water} {Ages} are {Sensitive} to {Discharge} and {Land} {Use} - an {Integrated} {Analysis} of {Water} {Age} {Metrics} under {Varying} {Hydrological} {Conditions} for {Contrasting} {Sub}-{Catchments} in {Central} {Germany}},
	url = {https://essopenarchive.org/users/645893/articles/658105-fractions-of-different-young-water-ages-are-sensitive-to-discharge-and-land-use-an-integrated-analysis-of-water-age-metrics-under-varying-hydrological-conditions-for-contrasting-sub-catchments-in-central-germany?commit=0bfd065780aaeaa5510bb12c1bead9ec64b2f58d},
	doi = {10.22541/essoar.169143883.33463468/v1},
	abstract = {With ongoing climate change and more frequent high flows and droughts,
it becomes inevitable to understand potentially altered catchment
processes under changing climatic conditions. Water age metrics such as
median transit times and young water fractions are useful variables to
understand the process dynamics of catchments and the release of solutes
to the streams. This study, based on extensive high-frequency stable
isotope data, unravels the changing contribution of different water ages
to stream water in six heterogeneous catchments, located in the Harz
mountains and the adjacent northern lowlands in Central Germany.
Fractions of water up to 7 days old (Fyw7), comparable with water from
recent precipitation events, and fractions of water up to 60 days old
(Fyw60) were simulated by the tran-SAS model. As Fyw7 and Fyw60 were
sensitive to discharge, an integrated analysis of high and low flows was
conducted. This revealed an increasing contribution of young water for
increasing discharge, with larger contributions of young water during
wet spells compared to dry spells. Considering the seasons, young water
fractions increased in summer and autumn, which indicates higher
contributions of young water after prolonged dry conditions. Moreover,
the relationship between catchment characteristics and the water age
metrics revealed an increasing amount of young water with increasing
agricultural area, while the amount of young water decreased with
increasing grassland proportion. By combining transit time modelling
with high-frequency isotopic signatures in contrasting sub-catchments in
Central Germany, our study extends the understanding of hydrological
processes under high and low flow conditions.},
	urldate = {2024-11-15},
	publisher = {Preprints},
	author = {Radtke, Christina Franziska and Lutz, Stefanie and Müller, Christin and Merz, Ralf and Kumar, Rohini and Knöller, Kay},
	month = aug,
	year = {2023},
}

With ongoing climate change and more frequent high flows and droughts, it becomes inevitable to understand potentially altered catchment processes under changing climatic conditions. Water age metrics such as median transit times and young water fractions are useful variables to understand the process dynamics of catchments and the release of solutes to the streams. This study, based on extensive high-frequency stable isotope data, unravels the changing contribution of different water ages to stream water in six heterogeneous catchments, located in the Harz mountains and the adjacent northern lowlands in Central Germany. Fractions of water up to 7 days old (Fyw7), comparable with water from recent precipitation events, and fractions of water up to 60 days old (Fyw60) were simulated by the tran-SAS model. As Fyw7 and Fyw60 were sensitive to discharge, an integrated analysis of high and low flows was conducted. This revealed an increasing contribution of young water for increasing discharge, with larger contributions of young water during wet spells compared to dry spells. Considering the seasons, young water fractions increased in summer and autumn, which indicates higher contributions of young water after prolonged dry conditions. Moreover, the relationship between catchment characteristics and the water age metrics revealed an increasing amount of young water with increasing agricultural area, while the amount of young water decreased with increasing grassland proportion. By combining transit time modelling with high-frequency isotopic signatures in contrasting sub-catchments in Central Germany, our study extends the understanding of hydrological processes under high and low flow conditions.

@article{rahmati_continuous_2023,
	title = {Continuous increase in evaporative demand shortened the growing season of {European} ecosystems in the last decade},
	volume = {4},
	issn = {2662-4435},
	url = {https://www.nature.com/articles/s43247-023-00890-7},
	doi = {10.1038/s43247-023-00890-7},
	abstract = {Abstract
            Despite previous reports on European growing seasons lengthening due to global warming, evidence shows that this trend has been reversing in the past decade due to increased transpiration needs. To asses this, we used an innovative method along with space-based observations to determine the timing of greening and dormancy and then to determine existing trends of them and causes. Early greening still occurs, albeit at slower rates than before. However, a recent (2011–2020) shift in the timing of dormancy has caused the season length to decrease back to 1980s levels. This shortening of season length is attributed primarily to higher atmospheric water demand in summer that suppresses transpiration even for soil moisture levels as of previous years. Transpiration suppression implies that vegetation is unable to meet the high transpiration needs. Our results have implications for future management of European ecosystems (e.g., net carbon balance and water and energy exchange with atmosphere) in a warmer world.},
	language = {en},
	number = {1},
	urldate = {2024-11-15},
	journal = {Communications Earth \& Environment},
	author = {Rahmati, Mehdi and Graf, Alexander and Poppe Terán, Christian and Amelung, Wulf and Dorigo, Wouter and Franssen, Harrie-Jan Hendricks and Montzka, Carsten and Or, Dani and Sprenger, Matthias and Vanderborght, Jan and Verhoest, Niko E. C. and Vereecken, Harry},
	month = jul,
	year = {2023},
	pages = {236},
}

Abstract Despite previous reports on European growing seasons lengthening due to global warming, evidence shows that this trend has been reversing in the past decade due to increased transpiration needs. To asses this, we used an innovative method along with space-based observations to determine the timing of greening and dormancy and then to determine existing trends of them and causes. Early greening still occurs, albeit at slower rates than before. However, a recent (2011–2020) shift in the timing of dormancy has caused the season length to decrease back to 1980s levels. This shortening of season length is attributed primarily to higher atmospheric water demand in summer that suppresses transpiration even for soil moisture levels as of previous years. Transpiration suppression implies that vegetation is unable to meet the high transpiration needs. Our results have implications for future management of European ecosystems (e.g., net carbon balance and water and energy exchange with atmosphere) in a warmer world.

@article{rahmati_soil_2023,
	title = {Soil is a living archive of the {Earth} system},
	volume = {4},
	issn = {2662-138X},
	url = {https://www.nature.com/articles/s43017-023-00454-5},
	doi = {10.1038/s43017-023-00454-5},
	language = {en},
	number = {7},
	urldate = {2024-11-15},
	journal = {Nature Reviews Earth \& Environment},
	author = {Rahmati, Mehdi and Or, Dani and Amelung, Wulf and Bauke, Sara L. and Bol, Roland and Hendricks Franssen, Harrie-Jan and Montzka, Carsten and Vanderborght, Jan and Vereecken, Harry},
	month = jun,
	year = {2023},
	pages = {421--423},
}

@article{ramsauer_global_2023,
	title = {Global {Soil} {Moisture} {Estimation} based on {GPM} {IMERG} {Data} using a {Site} {Specific} {Adjusted} {Antecedent} {Precipitation} {Index}},
	volume = {44},
	issn = {0143-1161, 1366-5901},
	url = {https://www.tandfonline.com/doi/full/10.1080/01431161.2022.2162351},
	doi = {10.1080/01431161.2022.2162351},
	language = {en},
	number = {2},
	urldate = {2024-11-15},
	journal = {International Journal of Remote Sensing},
	author = {Ramsauer, Thomas and Marzahn, Philip},
	month = jan,
	year = {2023},
	pages = {542--566},
}

@article{rasche_change_2023,
	title = {A change in perspective: downhole cosmic-ray neutron sensing for the estimation of soil moisture},
	volume = {27},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1607-7938},
	shorttitle = {A change in perspective},
	url = {https://hess.copernicus.org/articles/27/3059/2023/},
	doi = {10.5194/hess-27-3059-2023},
	abstract = {Abstract. Above-ground cosmic-ray neutron sensing (CRNS) allows for the non-invasive estimation of the field-scale soil moisture content in the upper
decimetres of the soil. However, large parts of the deeper vadose zone remain outside of its observational window. Retrieving soil moisture
information from these deeper layers requires extrapolation, modelling or other methods, all of which come with methodological challenges. Against
this background, we investigate CRNS for downhole soil moisture measurements in deeper layers of the vadose zone. To render calibration with in situ
soil moisture measurements unnecessary, we rescaled neutron intensities observed below the terrain surface with intensities measured above a waterbody. An experimental set-up with a CRNS sensor deployed at different depths of up to 10 m below the surface in a groundwater observation well
combined with particle transport simulations revealed the response of downhole thermal neutron intensities to changes in the soil moisture content at
the depth of the downhole neutron detector as well as in the layers above it. The simulation results suggest that the sensitive measurement radius
of several decimetres, which depends on soil moisture and soil bulk density, exceeds that of a standard active neutron probe (which is only about
30 cm). We derived transfer functions to estimate downhole neutron signals from soil moisture information, and we describe approaches for
using these transfer functions in an inverse way to derive soil moisture from the observed neutron signals. The in situ neutron and soil moisture
observations confirm the applicability of these functions and prove the concept of passive downhole soil moisture estimation, even at larger depths,
using cosmic-ray neutron sensing.},
	language = {en},
	number = {16},
	urldate = {2024-11-15},
	journal = {Hydrology and Earth System Sciences},
	author = {Rasche, Daniel and Weimar, Jannis and Schrön, Martin and Köhli, Markus and Morgner, Markus and Güntner, Andreas and Blume, Theresa},
	month = aug,
	year = {2023},
	pages = {3059--3082},
}

Abstract. Above-ground cosmic-ray neutron sensing (CRNS) allows for the non-invasive estimation of the field-scale soil moisture content in the upper decimetres of the soil. However, large parts of the deeper vadose zone remain outside of its observational window. Retrieving soil moisture information from these deeper layers requires extrapolation, modelling or other methods, all of which come with methodological challenges. Against this background, we investigate CRNS for downhole soil moisture measurements in deeper layers of the vadose zone. To render calibration with in situ soil moisture measurements unnecessary, we rescaled neutron intensities observed below the terrain surface with intensities measured above a waterbody. An experimental set-up with a CRNS sensor deployed at different depths of up to 10 m below the surface in a groundwater observation well combined with particle transport simulations revealed the response of downhole thermal neutron intensities to changes in the soil moisture content at the depth of the downhole neutron detector as well as in the layers above it. The simulation results suggest that the sensitive measurement radius of several decimetres, which depends on soil moisture and soil bulk density, exceeds that of a standard active neutron probe (which is only about 30 cm). We derived transfer functions to estimate downhole neutron signals from soil moisture information, and we describe approaches for using these transfer functions in an inverse way to derive soil moisture from the observed neutron signals. The in situ neutron and soil moisture observations confirm the applicability of these functions and prove the concept of passive downhole soil moisture estimation, even at larger depths, using cosmic-ray neutron sensing.

@article{reitz_environmental_2023,
	title = {Environmental {Drivers} of {Gross} {Primary} {Productivity} and {Light} {Use} {Efficiency} of a {Temperate} {Spruce} {Forest}},
	volume = {128},
	issn = {2169-8953, 2169-8961},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022JG007197},
	doi = {10.1029/2022JG007197},
	abstract = {Abstract
            Various environmental variables drive gross primary productivity (GPP) and light use efficiency (LUE) of forest ecosystems. However, due to their intertwined nature and the complexity of measuring absorbed photosynthetically active radiation (APAR) of forest canopies, the assessment of LUE and the importance of its environmental drivers are difficult. Here, we present a unique combination of measurements during the 2021 growing season including eddy covariance derived GPP, sap flow, Sentinel‐2 derived canopy chlorophyll content and in situ measured APAR. The importance of environmental variables for GPP models is quantified with state‐of‐the‐art machine learning techniques. A special focus is put on photosynthesis‐limiting conditions, which are identified by a comparison of GPP and sap flow hysteretic responses to Vapor pressure deficit (VPD) and APAR. Results demonstrate that (a) LUE of the canopy's green part was on average 4.0\% ± 2.3\%, (b) canopy chlorophyll content as a seasonal variable for photosynthetic capacity was important for GPP predictions, and (c) on days with high VPD, tree‐scale sap flow and ecosystem‐scale GPP both shift to a clockwise hysteretic response to APAR. We demonstrate that the onset of such a clockwise hysteretic pattern of sap flow to APAR is a good indicator of stomatal closure related to water‐limiting conditions at the ecosystem‐scale.
          , 
            Plain Language Summary
            The efficiency by which a forest uses sunlight to perform photosynthesis is an important feature for climate and ecosystem modeling. However, the light that is actually captured by forests and is useable for photosynthesis is difficult to assess. Here, we show a sophisticated approach to estimate the light use efficiency of a spruce forest in Germany and analyze environmental influences on it and on photosynthesis. Our results indicate that about 4\% of the light useable for photosynthesis was actually used by the forest during the 2021 growing season and that seasonal variations of chlorophyll in the canopy are a good indicator for carbon capture.
          , 
            Key Points
            
              
                
                  A seasonal variable such as canopy chlorophyll content was useful to predict gross primary productivity with machine learning models
                
                
                  A clockwise hysteretic pattern of sap flow to radiation is a good indicator of water‐related stomatal closure
                
                
                  The light use efficiency of green parts of a spruce forest was 4.0\% with a standard deviation of 2.3\% during the 2021 growing season},
	language = {en},
	number = {2},
	urldate = {2024-11-15},
	journal = {Journal of Geophysical Research: Biogeosciences},
	author = {Reitz, O. and Bogena, H. and Neuwirth, B. and Sanchez‐Azofeifa, A. and Graf, A. and Bates, J. and Leuchner, M.},
	month = feb,
	year = {2023},
	pages = {e2022JG007197},
}

Abstract Various environmental variables drive gross primary productivity (GPP) and light use efficiency (LUE) of forest ecosystems. However, due to their intertwined nature and the complexity of measuring absorbed photosynthetically active radiation (APAR) of forest canopies, the assessment of LUE and the importance of its environmental drivers are difficult. Here, we present a unique combination of measurements during the 2021 growing season including eddy covariance derived GPP, sap flow, Sentinel‐2 derived canopy chlorophyll content and in situ measured APAR. The importance of environmental variables for GPP models is quantified with state‐of‐the‐art machine learning techniques. A special focus is put on photosynthesis‐limiting conditions, which are identified by a comparison of GPP and sap flow hysteretic responses to Vapor pressure deficit (VPD) and APAR. Results demonstrate that (a) LUE of the canopy's green part was on average 4.0% ± 2.3%, (b) canopy chlorophyll content as a seasonal variable for photosynthetic capacity was important for GPP predictions, and (c) on days with high VPD, tree‐scale sap flow and ecosystem‐scale GPP both shift to a clockwise hysteretic response to APAR. We demonstrate that the onset of such a clockwise hysteretic pattern of sap flow to APAR is a good indicator of stomatal closure related to water‐limiting conditions at the ecosystem‐scale. , Plain Language Summary The efficiency by which a forest uses sunlight to perform photosynthesis is an important feature for climate and ecosystem modeling. However, the light that is actually captured by forests and is useable for photosynthesis is difficult to assess. Here, we show a sophisticated approach to estimate the light use efficiency of a spruce forest in Germany and analyze environmental influences on it and on photosynthesis. Our results indicate that about 4% of the light useable for photosynthesis was actually used by the forest during the 2021 growing season and that seasonal variations of chlorophyll in the canopy are a good indicator for carbon capture. , Key Points A seasonal variable such as canopy chlorophyll content was useful to predict gross primary productivity with machine learning models A clockwise hysteretic pattern of sap flow to radiation is a good indicator of water‐related stomatal closure The light use efficiency of green parts of a spruce forest was 4.0% with a standard deviation of 2.3% during the 2021 growing season

@article{rode_seasonal_2023,
	title = {Seasonal variation and release of soluble reactive phosphorus in an agricultural upland headwater in central {Germany}},
	volume = {27},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1607-7938},
	url = {https://hess.copernicus.org/articles/27/1261/2023/},
	doi = {10.5194/hess-27-1261-2023},
	abstract = {Abstract. Soluble reactive phosphorus (SRP) concentrations in
agricultural headwaters can display pronounced seasonal variability at low
flow, often with the highest concentrations occurring in summer. These SRP
concentrations often exceed eutrophication levels, but their main sources,
spatial distribution, and temporal dynamics are often unknown. The purpose
of this study is therefore to differentiate between potential SRP losses and
releases from soil drainage, anoxic riparian wetlands, and stream sediments
in an agricultural headwater catchment. To identify the dominant SRP sources,
we carried out three longitudinal stream sampling campaigns for SRP
concentrations and fluxes. We used salt dilution tests and natural
222Rn to determine water fluxes in different sections of the stream,
and we sampled for SRP, Fe, and 14C dissolved organic carbon (DOC) to examine possible redox-mediated
mobilization from riparian wetlands and stream sediments. The results
indicate that a single short section in the upper headwater reach was
responsible for most of the SRP fluxes to the stream. Analysis of samples
taken under summer low-flow conditions revealed that the stream water SRP
concentrations, the fraction of SRP within total dissolved P (TDP), and DOC radiocarbon ages matched those in the groundwater
entering the gaining section. Pore water from the stream sediment showed
evidence of reductive mobilization of SRP, but the exchange fluxes were
probably too small to contribute substantially to SRP stream concentrations.
We also found no evidence that shallow flow paths from riparian wetlands
contributed to the observed SRP loads in the stream. Combined, the results of
this campaign and previous monitoring suggest that groundwater is the main
long-term contributor of SRP at low flow, and agricultural phosphorus is
largely buffered in the soil zone. We argue that the seasonal variation of
SRP concentrations was mainly caused by variations in the proportion of
groundwater present in the streamflow, which was highest during summer low-flow periods. Accurate knowledge of the various input pathways is important
for choosing effective management measures in a given catchment, as it is
also possible that observations of seasonal SRP dilution patterns stem from
increased mobilization in riparian zones or from point sources.},
	language = {en},
	number = {6},
	urldate = {2024-11-15},
	journal = {Hydrology and Earth System Sciences},
	author = {Rode, Michael and Tittel, Jörg and Reinstorf, Frido and Schubert, Michael and Knöller, Kay and Gilfedder, Benjamin and Merensky-Pöhlein, Florian and Musolff, Andreas},
	month = mar,
	year = {2023},
	pages = {1261--1277},
}

Abstract. Soluble reactive phosphorus (SRP) concentrations in agricultural headwaters can display pronounced seasonal variability at low flow, often with the highest concentrations occurring in summer. These SRP concentrations often exceed eutrophication levels, but their main sources, spatial distribution, and temporal dynamics are often unknown. The purpose of this study is therefore to differentiate between potential SRP losses and releases from soil drainage, anoxic riparian wetlands, and stream sediments in an agricultural headwater catchment. To identify the dominant SRP sources, we carried out three longitudinal stream sampling campaigns for SRP concentrations and fluxes. We used salt dilution tests and natural 222Rn to determine water fluxes in different sections of the stream, and we sampled for SRP, Fe, and 14C dissolved organic carbon (DOC) to examine possible redox-mediated mobilization from riparian wetlands and stream sediments. The results indicate that a single short section in the upper headwater reach was responsible for most of the SRP fluxes to the stream. Analysis of samples taken under summer low-flow conditions revealed that the stream water SRP concentrations, the fraction of SRP within total dissolved P (TDP), and DOC radiocarbon ages matched those in the groundwater entering the gaining section. Pore water from the stream sediment showed evidence of reductive mobilization of SRP, but the exchange fluxes were probably too small to contribute substantially to SRP stream concentrations. We also found no evidence that shallow flow paths from riparian wetlands contributed to the observed SRP loads in the stream. Combined, the results of this campaign and previous monitoring suggest that groundwater is the main long-term contributor of SRP at low flow, and agricultural phosphorus is largely buffered in the soil zone. We argue that the seasonal variation of SRP concentrations was mainly caused by variations in the proportion of groundwater present in the streamflow, which was highest during summer low-flow periods. Accurate knowledge of the various input pathways is important for choosing effective management measures in a given catchment, as it is also possible that observations of seasonal SRP dilution patterns stem from increased mobilization in riparian zones or from point sources.

@article{scheiffele_comment_2023,
	title = {Comment on ‘{Examining} the variation of soil moisture from cosmic-ray neutron probes footprint: experimental results from a {COSMOS}-{UK} site’ by {Howells}, {O}.{D}., {Petropoulos}, {G}.{P}., et al., {Environ} {Earth} {Sci} 82, 41 (2023)},
	volume = {82},
	issn = {1866-6280, 1866-6299},
	shorttitle = {Comment on ‘{Examining} the variation of soil moisture from cosmic-ray neutron probes footprint},
	url = {https://link.springer.com/10.1007/s12665-023-11186-6},
	doi = {10.1007/s12665-023-11186-6},
	language = {en},
	number = {20},
	urldate = {2024-11-15},
	journal = {Environmental Earth Sciences},
	author = {Scheiffele, Lena M. and Schrön, Martin and Köhli, Markus and Dimitrova-Petrova, Katya and Altdorff, Daniel and Franz, Trenton and Rosolem, Rafael and Evans, Jonathan and Blake, James and Bogena, Heye and McJannet, David and Baroni, Gabriele and Desilets, Darin and Oswald, Sascha E.},
	month = oct,
	year = {2023},
	pages = {474},
}

@article{scherrer_bias-blind_2023,
	title = {Bias-blind and bias-aware assimilation of leaf area index into the {Noah}-{MP} land surface model over {Europe}},
	volume = {27},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1607-7938},
	url = {https://hess.copernicus.org/articles/27/4087/2023/},
	doi = {10.5194/hess-27-4087-2023},
	abstract = {Abstract. Data assimilation (DA) of remotely sensed leaf area index (LAI) can help to improve land surface model estimates of energy, water, and carbon variables. So far, most studies have used bias-blind LAI DA approaches, i.e. without correcting for biases between model forecasts and observations. This might hamper the performance of the DA algorithms in the case of large biases in observations or simulations or both. We perform bias-blind and bias-aware DA of Copernicus Global Land Service LAI into the Noah-MP land surface model forced by the ERA5 reanalysis over Europe in the 2002–2019 period, and we evaluate how the choice of bias correction affects estimates of gross primary productivity (GPP), evapotranspiration (ET), runoff, and soil moisture. In areas with a large LAI bias, the bias-blind LAI DA leads to a reduced bias between observed and modelled LAI, an improved agreement of GPP, ET, and runoff estimates with independent products, but a worse agreement of soil moisture estimates with the European Space Agency Climate Change Initiative (ESA CCI) soil moisture product. While comparisons to in situ soil moisture in areas with weak bias indicate an improvement of the representation of soil moisture climatology, bias-blind LAI DA can lead to unrealistic shifts in soil moisture climatology in areas with strong bias. For example, when the assimilated LAI data in irrigated areas are much higher than those simulated without any irrigation activated, LAI will be increased and soil moisture will be depleted. Furthermore, the bias-blind LAI DA produces a pronounced sawtooth pattern due to model drift between DA updates, because each update pushes the Noah-MP leaf model to an unstable state. This model drift also propagates to short-term estimates of GPP and ET and to internal DA diagnostics that indicate a suboptimal DA system performance. The bias-aware approaches based on a priori rescaling of LAI observations to the model climatology avoid the negative effects of the bias-blind assimilation. They retain the improvements in GPP anomalies from the bias-blind DA but forego improvements in the root mean square deviations (RMSDs) of GPP, ET, and runoff. As an alternative to rescaling, we discuss the implications of our results for model calibration or joint parameter and state update DA, which has the potential to combine bias reduction with optimal DA system performance.},
	language = {en},
	number = {22},
	urldate = {2024-11-15},
	journal = {Hydrology and Earth System Sciences},
	author = {Scherrer, Samuel and De Lannoy, Gabriëlle and Heyvaert, Zdenko and Bechtold, Michel and Albergel, Clement and El-Madany, Tarek S. and Dorigo, Wouter},
	month = nov,
	year = {2023},
	pages = {4087--4114},
}

Abstract. Data assimilation (DA) of remotely sensed leaf area index (LAI) can help to improve land surface model estimates of energy, water, and carbon variables. So far, most studies have used bias-blind LAI DA approaches, i.e. without correcting for biases between model forecasts and observations. This might hamper the performance of the DA algorithms in the case of large biases in observations or simulations or both. We perform bias-blind and bias-aware DA of Copernicus Global Land Service LAI into the Noah-MP land surface model forced by the ERA5 reanalysis over Europe in the 2002–2019 period, and we evaluate how the choice of bias correction affects estimates of gross primary productivity (GPP), evapotranspiration (ET), runoff, and soil moisture. In areas with a large LAI bias, the bias-blind LAI DA leads to a reduced bias between observed and modelled LAI, an improved agreement of GPP, ET, and runoff estimates with independent products, but a worse agreement of soil moisture estimates with the European Space Agency Climate Change Initiative (ESA CCI) soil moisture product. While comparisons to in situ soil moisture in areas with weak bias indicate an improvement of the representation of soil moisture climatology, bias-blind LAI DA can lead to unrealistic shifts in soil moisture climatology in areas with strong bias. For example, when the assimilated LAI data in irrigated areas are much higher than those simulated without any irrigation activated, LAI will be increased and soil moisture will be depleted. Furthermore, the bias-blind LAI DA produces a pronounced sawtooth pattern due to model drift between DA updates, because each update pushes the Noah-MP leaf model to an unstable state. This model drift also propagates to short-term estimates of GPP and ET and to internal DA diagnostics that indicate a suboptimal DA system performance. The bias-aware approaches based on a priori rescaling of LAI observations to the model climatology avoid the negative effects of the bias-blind assimilation. They retain the improvements in GPP anomalies from the bias-blind DA but forego improvements in the root mean square deviations (RMSDs) of GPP, ET, and runoff. As an alternative to rescaling, we discuss the implications of our results for model calibration or joint parameter and state update DA, which has the potential to combine bias reduction with optimal DA system performance.

@article{schmidt_system_2023,
	title = {System for automated {Quality} {Control} ({SaQC}) to enable traceable and reproducible data streams in environmental science},
	volume = {169},
	issn = {13648152},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1364815223001950},
	doi = {10.1016/j.envsoft.2023.105809},
	language = {en},
	urldate = {2024-11-15},
	journal = {Environmental Modelling \& Software},
	author = {Schmidt, Lennart and Schäfer, David and Geller, Juliane and Lünenschloss, Peter and Palm, Bert and Rinke, Karsten and Rebmann, Corinna and Rode, Michael and Bumberger, Jan},
	month = nov,
	year = {2023},
	pages = {105809},
}

@article{schmidt_potential_2023,
	title = {Potential of {Optical} {Spaceborne} {Sensors} for the {Differentiation} of {Plastics} in the {Environment}},
	volume = {15},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2072-4292},
	url = {https://www.mdpi.com/2072-4292/15/8/2020},
	doi = {10.3390/rs15082020},
	abstract = {Plastics are part of our everyday life, as they are versatile materials and can be produced inexpensively. Approximately 10 Gt of plastics have been produced to date, of which the majority have been accumulated in landfills or have been spread into the terrestrial and aquatic environment in an uncontrolled way. Once in the environment, plastic litter—in its large form or degraded into microplastics—causes several harms to a variety of species. Thus, the detection of plastic waste is a pressing research question in remote sensing. The majority of studies have used Sentinel-2 or WorldView-3 data and empirically explore the usefulness of the given spectral channels for the detection of plastic litter in the environment. On the other hand, laboratory infrared spectroscopy is an established technique for the differentiation of plastic types based on their type-specific absorption bands; the potential of hyperspectral remote sensing for mapping plastics in the environment has not yet been fully explored. In this study, reflectance spectra of the five most commonly used plastic types were used for spectral sensor simulations of ten selected multispectral and hyperspectral sensors. Their signals were classified in order to differentiate between the plastic types as would be measured in nature and to investigate sensor-specific spectral configurations neglecting spatial resolution limitations. Here, we show that most multispectral sensors are not able to differentiate between plastic types, while hyperspectral sensors are. To resolve absorption bands of plastics with multispectral sensors, the number, position, and width of the SWIR channels are decisive for a good classification of plastics. As ASTER and WorldView-3 had/have narrow SWIR channels that match with diagnostic absorption bands of plastics, they yielded outstanding results. Central wavelengths at 1141, 1217, 1697, and 1716 nm, in combination with narrow bandwidths of 10–20 nm, have the highest capability for plastic differentiation.},
	language = {en},
	number = {8},
	urldate = {2024-11-15},
	journal = {Remote Sensing},
	author = {Schmidt, Toni and Kuester, Theres and Smith, Taylor and Bochow, Mathias},
	month = apr,
	year = {2023},
	pages = {2020},
}

Plastics are part of our everyday life, as they are versatile materials and can be produced inexpensively. Approximately 10 Gt of plastics have been produced to date, of which the majority have been accumulated in landfills or have been spread into the terrestrial and aquatic environment in an uncontrolled way. Once in the environment, plastic litter—in its large form or degraded into microplastics—causes several harms to a variety of species. Thus, the detection of plastic waste is a pressing research question in remote sensing. The majority of studies have used Sentinel-2 or WorldView-3 data and empirically explore the usefulness of the given spectral channels for the detection of plastic litter in the environment. On the other hand, laboratory infrared spectroscopy is an established technique for the differentiation of plastic types based on their type-specific absorption bands; the potential of hyperspectral remote sensing for mapping plastics in the environment has not yet been fully explored. In this study, reflectance spectra of the five most commonly used plastic types were used for spectral sensor simulations of ten selected multispectral and hyperspectral sensors. Their signals were classified in order to differentiate between the plastic types as would be measured in nature and to investigate sensor-specific spectral configurations neglecting spatial resolution limitations. Here, we show that most multispectral sensors are not able to differentiate between plastic types, while hyperspectral sensors are. To resolve absorption bands of plastics with multispectral sensors, the number, position, and width of the SWIR channels are decisive for a good classification of plastics. As ASTER and WorldView-3 had/have narrow SWIR channels that match with diagnostic absorption bands of plastics, they yielded outstanding results. Central wavelengths at 1141, 1217, 1697, and 1716 nm, in combination with narrow bandwidths of 10–20 nm, have the highest capability for plastic differentiation.

@article{schnepper_evaluation_2023,
	title = {Evaluation of precipitation measurement methods using data from a precision lysimeter network},
	volume = {27},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1607-7938},
	url = {https://hess.copernicus.org/articles/27/3265/2023/},
	doi = {10.5194/hess-27-3265-2023},
	abstract = {Abstract. Accurate precipitation data are essential for assessing the water balance of ecosystems. Methods for point precipitation determination are influenced by wind, precipitation type and intensity and/or technical issues. High-precision weighable lysimeters provide precipitation measurements at ground level that are less affected by wind disturbances and are assumed to be relatively close to actual precipitation. The problem in previous studies was that the biases in precipitation data introduced by different precipitation measurement methods were not comprehensively compared with and quantified on the basis of those obtained by lysimeters in different regions in Germany. The aim was to quantify measurement errors in standard precipitation gauges
as compared to the lysimeter reference and to analyze the effect of
precipitation correction algorithms on the gauge data quality. Both correction methods rely on empirical constants to account for known external influences on the measurements, following a generic and a site-specific approach. Reference precipitation data were obtained from high-precision weighable lysimeters of the TERrestrial ENvironmental Observatories (TERENO)-SOILCan lysimeter network. Gauge types included tipping bucket gauges (TBs), weighable gauges (WGs), acoustic sensors (ASs) and optical laser disdrometers (LDs). From 2015-2018, data were collected at three locations in Germany, and 1 h aggregated values for precipitation above a threshold of 0.1 mm h−1 were compared. The results show that all investigated measurement methods underestimated
the precipitation amounts relative to the lysimeter references for long-term
precipitation totals with catch ratios (CRs) of between 33 \%–92 \%. Data from ASs had overall biases of −0.25 to −0.07 mm h−1, while data from WGs and LDs showed the lowest measurement bias (−0.14 to −0.06 mm h−1 and −0.01 to −0.02 mm h−1). Two TBs showed systematic deviations with biases of −0.69 to −0.61 mm h−1, while other TBs were in the previously reported range with biases of −0.2 mm h−1. The site-specific and generic correction schemes reduced the hourly measurement bias by 0.13 and 0.08 mm h−1 for the TBs and by 0.09 and 0.07 mm h−1 for the WGs and increased long-term CRs by 14 \% and 9 \% and by 10 \% and 11 \%, respectively. It could be shown that the lysimeter reference operated with minor uncertainties in long-term measurements under different site and weather
conditions. The results indicate that considerable precipitation measurement
errors can occur even at well-maintained and professionally operated stations equipped with standard precipitation gauges. This generally leads
to an underestimation of the actual precipitation amounts. The results
suggest that the application of relatively simple correction schemes, manual
or automated data quality checks, instrument calibrations, and/or an adequate
choice of observation period can help improve the data quality of
gauge-based measurements for water balance calculations, ecosystem modeling, water management, assessment of agricultural irrigation needs, or
radar-based precipitation analyses.},
	language = {en},
	number = {17},
	urldate = {2024-11-15},
	journal = {Hydrology and Earth System Sciences},
	author = {Schnepper, Tobias and Groh, Jannis and Gerke, Horst H. and Reichert, Barbara and Pütz, Thomas},
	month = sep,
	year = {2023},
	pages = {3265--3292},
}

Abstract. Accurate precipitation data are essential for assessing the water balance of ecosystems. Methods for point precipitation determination are influenced by wind, precipitation type and intensity and/or technical issues. High-precision weighable lysimeters provide precipitation measurements at ground level that are less affected by wind disturbances and are assumed to be relatively close to actual precipitation. The problem in previous studies was that the biases in precipitation data introduced by different precipitation measurement methods were not comprehensively compared with and quantified on the basis of those obtained by lysimeters in different regions in Germany. The aim was to quantify measurement errors in standard precipitation gauges as compared to the lysimeter reference and to analyze the effect of precipitation correction algorithms on the gauge data quality. Both correction methods rely on empirical constants to account for known external influences on the measurements, following a generic and a site-specific approach. Reference precipitation data were obtained from high-precision weighable lysimeters of the TERrestrial ENvironmental Observatories (TERENO)-SOILCan lysimeter network. Gauge types included tipping bucket gauges (TBs), weighable gauges (WGs), acoustic sensors (ASs) and optical laser disdrometers (LDs). From 2015-2018, data were collected at three locations in Germany, and 1 h aggregated values for precipitation above a threshold of 0.1 mm h−1 were compared. The results show that all investigated measurement methods underestimated the precipitation amounts relative to the lysimeter references for long-term precipitation totals with catch ratios (CRs) of between 33 %–92 %. Data from ASs had overall biases of −0.25 to −0.07 mm h−1, while data from WGs and LDs showed the lowest measurement bias (−0.14 to −0.06 mm h−1 and −0.01 to −0.02 mm h−1). Two TBs showed systematic deviations with biases of −0.69 to −0.61 mm h−1, while other TBs were in the previously reported range with biases of −0.2 mm h−1. The site-specific and generic correction schemes reduced the hourly measurement bias by 0.13 and 0.08 mm h−1 for the TBs and by 0.09 and 0.07 mm h−1 for the WGs and increased long-term CRs by 14 % and 9 % and by 10 % and 11 %, respectively. It could be shown that the lysimeter reference operated with minor uncertainties in long-term measurements under different site and weather conditions. The results indicate that considerable precipitation measurement errors can occur even at well-maintained and professionally operated stations equipped with standard precipitation gauges. This generally leads to an underestimation of the actual precipitation amounts. The results suggest that the application of relatively simple correction schemes, manual or automated data quality checks, instrument calibrations, and/or an adequate choice of observation period can help improve the data quality of gauge-based measurements for water balance calculations, ecosystem modeling, water management, assessment of agricultural irrigation needs, or radar-based precipitation analyses.

@article{schreiber_impacts_2023,
	title = {Impacts of slurry acidification and injection on fertilizer nitrogen fates in grassland},
	volume = {125},
	issn = {1385-1314, 1573-0867},
	url = {https://link.springer.com/10.1007/s10705-022-10239-9},
	doi = {10.1007/s10705-022-10239-9},
	abstract = {Abstract
            
              Low nitrogen (N) use efficiency of broadcast slurry application leads to nutrient losses, air and water pollution, greenhouse gas emissions and—in particular in a warming climate—to soil N mining. Here we test the alternative slurry acidification and injection techniques for their mitigation potential compared to broadcast spreading in montane grassland. We determined (1) the fate of
              15
              N labelled slurry in the plant-soil-microbe system and soil-atmosphere exchange of greenhouse gases over one fertilization/harvest cycle and (2) assessed the longer-term contribution of fertilizer
              15
              N to soil organic N formation by the end of the growing season. The isotope tracing approach was combined with a space for time climate change experiment. Simulated climate change increased productivity, ecosystem respiration, and net methane uptake irrespective of management, but the generally low N
              2
              O fluxes remained unchanged. Compared to the broadcast spreading, slurry acidification showed lowest N losses, thus increased productivity and fertilizer N use efficiency (38\%
              15
              N recovery in plant aboveground plant biomass). In contrast, slurry injection showed highest total fertilizer N losses, but increased fertilization-induced soil organic N formation by 9–12 kg N ha
              −1
              season
              −1
              . Slurry management effects on N
              2
              O and CH
              4
              fluxes remained negligible. In sum, our study shows that the tested alternative slurry application techniques can increase N use efficiency and/or promote soil organic N formation from applied fertilizer to a remarkable extent. However, this is still not sufficient to prevent soil N mining mostly resulting from large plant N exports that even exceed total fertilizer N inputs.},
	language = {en},
	number = {2},
	urldate = {2024-11-15},
	journal = {Nutrient Cycling in Agroecosystems},
	author = {Schreiber, Mirella and Bazaios, Elpida and Ströbel, Barbara and Wolf, Benjamin and Ostler, Ulrike and Gasche, Rainer and Schlingmann, Marcus and Kiese, Ralf and Dannenmann, Michael},
	month = mar,
	year = {2023},
	pages = {171--186},
}

Abstract Low nitrogen (N) use efficiency of broadcast slurry application leads to nutrient losses, air and water pollution, greenhouse gas emissions and—in particular in a warming climate—to soil N mining. Here we test the alternative slurry acidification and injection techniques for their mitigation potential compared to broadcast spreading in montane grassland. We determined (1) the fate of 15 N labelled slurry in the plant-soil-microbe system and soil-atmosphere exchange of greenhouse gases over one fertilization/harvest cycle and (2) assessed the longer-term contribution of fertilizer 15 N to soil organic N formation by the end of the growing season. The isotope tracing approach was combined with a space for time climate change experiment. Simulated climate change increased productivity, ecosystem respiration, and net methane uptake irrespective of management, but the generally low N 2 O fluxes remained unchanged. Compared to the broadcast spreading, slurry acidification showed lowest N losses, thus increased productivity and fertilizer N use efficiency (38% 15 N recovery in plant aboveground plant biomass). In contrast, slurry injection showed highest total fertilizer N losses, but increased fertilization-induced soil organic N formation by 9–12 kg N ha −1 season −1 . Slurry management effects on N 2 O and CH 4 fluxes remained negligible. In sum, our study shows that the tested alternative slurry application techniques can increase N use efficiency and/or promote soil organic N formation from applied fertilizer to a remarkable extent. However, this is still not sufficient to prevent soil N mining mostly resulting from large plant N exports that even exceed total fertilizer N inputs.

@article{schreiner_standard_2023,
	title = {Standard {Versus} {Natural}: {Assessing} the {Impact} of {Environmental} {Variables} on {Organic} {Matter} {Decomposition} in {Streams} {Using} {Three} {Substrates}},
	volume = {42},
	issn = {0730-7268, 1552-8618},
	shorttitle = {Standard {Versus} {Natural}},
	url = {https://setac.onlinelibrary.wiley.com/doi/10.1002/etc.5577},
	doi = {10.1002/etc.5577},
	abstract = {Abstract
            
              The decomposition of allochthonous organic matter, such as leaves, is a crucial ecosystem process in low‐order streams. Microbial communities, including fungi and bacteria, colonize allochthonous organic material, break up large molecules, and increase the nutritional value for macroinvertebrates. Environmental variables are known to affect microbial as well as macroinvertebrate communities and alter their ability to decompose organic matter. Studying the relationship between environmental variables and decomposition has mainly been realized using leaves, with the drawbacks of differing substrate composition and consequently between‐study variability. To overcome these drawbacks, artificial substrates have been developed, serving as standardizable surrogates. In the present study, we compared microbial and total decomposition of leaves with the standardized substrates of decotabs and, only for microbial decomposition, of cotton strips, across 70 stream sites in a Germany‐wide study. Furthermore, we identified the most influential environmental variables for the decomposition of each substrate from a range of 26 variables, including pesticide toxicity, concentrations of nutrients, and trace elements, using stability selection. The microbial as well as total decomposition of the standardized substrates (i.e., cotton strips and decotabs) were weak or not associated with that of the natural substrate (i.e., leaves,
              r
              ² {\textless} 0.01 to
              r
              ² = 0.04). The decomposition of the two standardized substrates, however, showed a moderate association (
              r
              ² = 0.21), which is probably driven by their similar composition, with both being made of cellulose. Different environmental variables were identified as the most influential for each of the substrates and the directions of these relationships contrasted between the substrates. Our results imply that these standardized substrates are unsuitable surrogates when investigating the decomposition of allochthonous organic matter in streams.
              Environ Toxicol Chem
              2023;42:2007–2018. © 2023 The Authors.
              Environmental Toxicology and Chemistry
              published by Wiley Periodicals LLC on behalf of SETAC.},
	language = {en},
	number = {9},
	urldate = {2024-11-15},
	journal = {Environmental Toxicology and Chemistry},
	author = {Schreiner, Verena C. and Liebmann, Liana and Feckler, Alexander and Liess, Matthias and Link, Moritz and Schneeweiss, Anke and Truchy, Amélie and Von Tümpling, Wolf and Vormeier, Philipp and Weisner, Oliver and Schäfer, Ralf B. and Bundschuh, Mirco},
	month = sep,
	year = {2023},
	pages = {2007--2018},
}

Abstract The decomposition of allochthonous organic matter, such as leaves, is a crucial ecosystem process in low‐order streams. Microbial communities, including fungi and bacteria, colonize allochthonous organic material, break up large molecules, and increase the nutritional value for macroinvertebrates. Environmental variables are known to affect microbial as well as macroinvertebrate communities and alter their ability to decompose organic matter. Studying the relationship between environmental variables and decomposition has mainly been realized using leaves, with the drawbacks of differing substrate composition and consequently between‐study variability. To overcome these drawbacks, artificial substrates have been developed, serving as standardizable surrogates. In the present study, we compared microbial and total decomposition of leaves with the standardized substrates of decotabs and, only for microbial decomposition, of cotton strips, across 70 stream sites in a Germany‐wide study. Furthermore, we identified the most influential environmental variables for the decomposition of each substrate from a range of 26 variables, including pesticide toxicity, concentrations of nutrients, and trace elements, using stability selection. The microbial as well as total decomposition of the standardized substrates (i.e., cotton strips and decotabs) were weak or not associated with that of the natural substrate (i.e., leaves, r ² \textless 0.01 to r ² = 0.04). The decomposition of the two standardized substrates, however, showed a moderate association ( r ² = 0.21), which is probably driven by their similar composition, with both being made of cellulose. Different environmental variables were identified as the most influential for each of the substrates and the directions of these relationships contrasted between the substrates. Our results imply that these standardized substrates are unsuitable surrogates when investigating the decomposition of allochthonous organic matter in streams. Environ Toxicol Chem 2023;42:2007–2018. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

@article{schron_signal_2023,
	title = {Signal contribution of distant areas to cosmic-ray neutron sensors – implications for footprint and sensitivity},
	volume = {27},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1607-7938},
	url = {https://hess.copernicus.org/articles/27/723/2023/},
	doi = {10.5194/hess-27-723-2023},
	abstract = {Abstract. This paper presents a new theoretical approach to estimate the contribution of distant areas to the measurement signal of cosmic-ray neutron detectors for snow and soil moisture monitoring. The algorithm is based on the local neutron production and the transport mechanism, given by the neutron–moisture relationship and the radial intensity function, respectively. The purely analytical approach has been validated with physics-based neutron transport simulations for heterogeneous soil moisture patterns, exemplary landscape features, and remote fields at a distance. We found that the method provides good approximations of simulated signal contributions in patchy soils with typical deviations of less than 1 \%. Moreover, implications of this concept have been investigated for the neutron–moisture relationship, where the signal contribution of an area has the potential to explain deviating shapes of this curve that are often reported in the literature. Finally, the method has been used to develop a new practical footprint definition to express whether or not a distant area's soil moisture change is actually detectable in terms of measurement precision. The presented concepts answer long-lasting questions about the influence of distant landscape structures in the integral footprint of the sensor without the need for computationally expensive simulations. The new insights are highly relevant to support signal interpretation, data harmonization, and sensor calibration and will be particularly useful for sensors positioned in complex terrain or on agriculturally managed sites.},
	language = {en},
	number = {3},
	urldate = {2024-11-15},
	journal = {Hydrology and Earth System Sciences},
	author = {Schrön, Martin and Köhli, Markus and Zacharias, Steffen},
	month = feb,
	year = {2023},
	pages = {723--738},
}

Abstract. This paper presents a new theoretical approach to estimate the contribution of distant areas to the measurement signal of cosmic-ray neutron detectors for snow and soil moisture monitoring. The algorithm is based on the local neutron production and the transport mechanism, given by the neutron–moisture relationship and the radial intensity function, respectively. The purely analytical approach has been validated with physics-based neutron transport simulations for heterogeneous soil moisture patterns, exemplary landscape features, and remote fields at a distance. We found that the method provides good approximations of simulated signal contributions in patchy soils with typical deviations of less than 1 %. Moreover, implications of this concept have been investigated for the neutron–moisture relationship, where the signal contribution of an area has the potential to explain deviating shapes of this curve that are often reported in the literature. Finally, the method has been used to develop a new practical footprint definition to express whether or not a distant area's soil moisture change is actually detectable in terms of measurement precision. The presented concepts answer long-lasting questions about the influence of distant landscape structures in the integral footprint of the sensor without the need for computationally expensive simulations. The new insights are highly relevant to support signal interpretation, data harmonization, and sensor calibration and will be particularly useful for sensors positioned in complex terrain or on agriculturally managed sites.

@misc{schron_buoy-based_2023,
	title = {Buoy-based detection of low-energy cosmic-ray neutrons to monitor the influence of atmospheric, geomagnetic, and heliospheric effects},
	url = {https://www.authorea.com/users/76472/articles/695074-buoy-based-detection-of-low-energy-cosmic-ray-neutrons-to-monitor-the-influence-of-atmospheric-geomagnetic-and-heliospheric-effects?commit=40122804b18211f8e16402b9c5949d53490b8b5e},
	doi = {10.22541/au.170319441.16528907/v1},
	abstract = {Cosmic radiation on Earth responds to heliospheric, geomagnetic,
atmospheric, and lithospheric changes. In order to use its signal for
soil hydrological monitoring, the signal of thermal and epithermal
neutron detectors needs to be corrected for external influencing
factors. However, theories about the neutron response to soil water, air
pressure, air humidity, and incoming cosmic radiation are still under
debate. To challenge these theories, we isolated the neutron response
from almost any terrestrial changes by operating bare and moderated
neutron detectors in a buoy on a lake in Germany from July 15 to
December 02, 2014. We found that the count rate over water has been
better predicted by a recent theory compared to the traditional
approach. We further found strong linear correlation parameters to air
pressure and air humidity for epithermal neutrons, while thermal
neutrons responded differently. Correction for incoming radiation proved
to be necessary for both thermal and epithermal neutrons, for which we
tested different neutron monitors and correction methods. Here, the
conventional approach worked best with the Jungfraujoch monitor in
Switzerland, while the approach from a recent study was able to
adequately rescale data from more remote neutron monitors. However, no
approach was able to sufficiently remove the signal from a major Forbush
decrease event, to which thermal and epithermal neutrons showed a
comparatively strong response. The buoy detector experiment provided a
unique dataset for empirical testing of traditional and new theories on
CRNS. It could serve as a local alternative to reference data from
remote neutron monitors.},
	urldate = {2024-11-15},
	publisher = {Preprints},
	author = {Schrön, Martin and Rasche, Daniel and Weimar, Jannis and Köhli, Markus Otto and Herbst, Konstantin and Boehrer, Bertram and Hertle, Lasse and Kögler, Simon and Zacharias, Steffen},
	month = dec,
	year = {2023},
}

Cosmic radiation on Earth responds to heliospheric, geomagnetic, atmospheric, and lithospheric changes. In order to use its signal for soil hydrological monitoring, the signal of thermal and epithermal neutron detectors needs to be corrected for external influencing factors. However, theories about the neutron response to soil water, air pressure, air humidity, and incoming cosmic radiation are still under debate. To challenge these theories, we isolated the neutron response from almost any terrestrial changes by operating bare and moderated neutron detectors in a buoy on a lake in Germany from July 15 to December 02, 2014. We found that the count rate over water has been better predicted by a recent theory compared to the traditional approach. We further found strong linear correlation parameters to air pressure and air humidity for epithermal neutrons, while thermal neutrons responded differently. Correction for incoming radiation proved to be necessary for both thermal and epithermal neutrons, for which we tested different neutron monitors and correction methods. Here, the conventional approach worked best with the Jungfraujoch monitor in Switzerland, while the approach from a recent study was able to adequately rescale data from more remote neutron monitors. However, no approach was able to sufficiently remove the signal from a major Forbush decrease event, to which thermal and epithermal neutrons showed a comparatively strong response. The buoy detector experiment provided a unique dataset for empirical testing of traditional and new theories on CRNS. It could serve as a local alternative to reference data from remote neutron monitors.

@inproceedings{schucknecht_estimating_2023,
	address = {Munich},
	title = {Estimating {Aboveground} {Biomass} and {Nitrogen} {Concentration} in {Grasslands} with {Multispectral} and {Hyperspectral} {Satellite} {Data}},
	isbn = {978-1-957171-24-1},
	url = {https://opg.optica.org/abstract.cfm?URI=HMISE-2023-HM1C.2},
	doi = {10.1364/HMISE.2023.HM1C.2},
	abstract = {Spatial information on grassland biomass and nitrogen concentration are important for precision agriculture. We compare machine learning with hybrid models to estimate both parameters with Sentinel-2 data, and test hybrid models with hyperspectral EnMAP data.},
	language = {en},
	urldate = {2024-11-15},
	booktitle = {Optica {Sensing} {Congress} 2023 ({AIS}, {FTS}, {HISE}, {Sensors}, {ES})},
	publisher = {Optica Publishing Group},
	author = {Schucknecht, Anne and Reinermann, Sophie and Kiese, Ralf},
	year = {2023},
	pages = {HM1C.2},
}

Spatial information on grassland biomass and nitrogen concentration are important for precision agriculture. We compare machine learning with hybrid models to estimate both parameters with Sentinel-2 data, and test hybrid models with hyperspectral EnMAP data.

@article{shangguan_1_2023,
	title = {A 1 km {Global} {Carbon} {Flux} {Dataset} {Using} {In} {Situ} {Measurements} and {Deep} {Learning}},
	volume = {14},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1999-4907},
	url = {https://www.mdpi.com/1999-4907/14/5/913},
	doi = {10.3390/f14050913},
	abstract = {Global carbon fluxes describe the carbon exchange between land and atmosphere. However, already available global carbon fluxes datasets have not been adjusted by the available site data and deep learning tools. In this work, a global carbon fluxes dataset (named as GCFD) of gross primary productivity (GPP), terrestrial ecosystem respiration (RECO), and net ecosystem exchange (NEE) has been developed via a deep learning based convolutional neural network (CNN) model. The dataset has a spatial resolution of 1 km at three time steps per month from January 1999 to June 2020. Flux measurements were used as a training target while remote sensing of vegetation conditions and meteorological data were used as predictors. The results showed that CNN could outperform other commonly used machine learning methods such as random forest (RF) and artificial neural network (ANN) by leading to satisfactory performance with R2 values of the validation stage as 0.82, 0.72 and 0.62 for GPP, RECO, and NEE modelling, respectively. Thus, CNN trained using reanalysis meteorological data and remote sensing data was chosen to produce the global dataset. GCFD showed higher accuracy and more spatial details than some other global carbon flux datasets with reasonable spatial pattern and temporal variation. GCFD is also in accordance with vegetation conditions detected by remote sensing. Owing to the obtained results, GCFD can be a useful reference for various meteorological and ecological analyses and modelling, especially when high resolution carbon flux maps are required.},
	language = {en},
	number = {5},
	urldate = {2024-11-15},
	journal = {Forests},
	author = {Shangguan, Wei and Xiong, Zili and Nourani, Vahid and Li, Qingliang and Lu, Xingjie and Li, Lu and Huang, Feini and Zhang, Ye and Sun, Wenye and Dai, Yongjiu},
	month = apr,
	year = {2023},
	pages = {913},
}

Global carbon fluxes describe the carbon exchange between land and atmosphere. However, already available global carbon fluxes datasets have not been adjusted by the available site data and deep learning tools. In this work, a global carbon fluxes dataset (named as GCFD) of gross primary productivity (GPP), terrestrial ecosystem respiration (RECO), and net ecosystem exchange (NEE) has been developed via a deep learning based convolutional neural network (CNN) model. The dataset has a spatial resolution of 1 km at three time steps per month from January 1999 to June 2020. Flux measurements were used as a training target while remote sensing of vegetation conditions and meteorological data were used as predictors. The results showed that CNN could outperform other commonly used machine learning methods such as random forest (RF) and artificial neural network (ANN) by leading to satisfactory performance with R2 values of the validation stage as 0.82, 0.72 and 0.62 for GPP, RECO, and NEE modelling, respectively. Thus, CNN trained using reanalysis meteorological data and remote sensing data was chosen to produce the global dataset. GCFD showed higher accuracy and more spatial details than some other global carbon flux datasets with reasonable spatial pattern and temporal variation. GCFD is also in accordance with vegetation conditions detected by remote sensing. Owing to the obtained results, GCFD can be a useful reference for various meteorological and ecological analyses and modelling, especially when high resolution carbon flux maps are required.

@article{shukla_assessing_2023,
	title = {Assessing {Impacts} of {Land} {Use} and {Land} {Cover} ({LULC}) {Change} on {Stream} {Flow} and {Runoff} in {Rur} {Basin}, {Germany}},
	volume = {15},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2071-1050},
	url = {https://www.mdpi.com/2071-1050/15/12/9811},
	doi = {10.3390/su15129811},
	abstract = {Understanding the impact of land use/land cover (LULC) change on hydrology is the key to sustainable water resource management. In this study, we used the Soil and Water Assessment Tool (SWAT) to evaluate the impact of LULC change on the runoff in the Rur basin, Germany. The SWAT model was calibrated against the observed data of stream flow and runoff at three sites (Stah, Linnich, and Monschau) between 2000 and 2010 and validated between 2011 and 2015. The performance of the hydrological model was assessed by using statistical parameters such as the coefficient of determination (R2), p-value, r-value, and percentage bias (PBAIS). Our analysis reveals that the average R2 values for model calibration and validation were 0.68 and 0.67 (n = 3), respectively. The impacts of three change scenarios on stream runoff were assessed by replacing the partial forest with urban settlements, agricultural land, and grasslands compared to the 2006 LULC map. The SWAT model captured, overall, the spatio-temporal patterns and effects of LULC change on the stream runoffs despite the heterogeneous runoff responses related to the variable impacts of the different LULC. The results show that LULC change from deciduous forest to urban settlements, agricultural land, or grasslands increased the overall basin runoff by 43\%, 14\%, and 4\%, respectively.},
	language = {en},
	number = {12},
	urldate = {2024-11-15},
	journal = {Sustainability},
	author = {Shukla, Saurabh and Meshesha, Tesfa Worku and Sen, Indra S. and Bol, Roland and Bogena, Heye and Wang, Junye},
	month = jun,
	year = {2023},
	pages = {9811},
}

Understanding the impact of land use/land cover (LULC) change on hydrology is the key to sustainable water resource management. In this study, we used the Soil and Water Assessment Tool (SWAT) to evaluate the impact of LULC change on the runoff in the Rur basin, Germany. The SWAT model was calibrated against the observed data of stream flow and runoff at three sites (Stah, Linnich, and Monschau) between 2000 and 2010 and validated between 2011 and 2015. The performance of the hydrological model was assessed by using statistical parameters such as the coefficient of determination (R2), p-value, r-value, and percentage bias (PBAIS). Our analysis reveals that the average R2 values for model calibration and validation were 0.68 and 0.67 (n = 3), respectively. The impacts of three change scenarios on stream runoff were assessed by replacing the partial forest with urban settlements, agricultural land, and grasslands compared to the 2006 LULC map. The SWAT model captured, overall, the spatio-temporal patterns and effects of LULC change on the stream runoffs despite the heterogeneous runoff responses related to the variable impacts of the different LULC. The results show that LULC change from deciduous forest to urban settlements, agricultural land, or grasslands increased the overall basin runoff by 43%, 14%, and 4%, respectively.

@article{skulovich_long-term_2023,
	title = {A {Long}-term {Consistent} {Artificial} {Intelligence} and {Remote} {Sensing}-based {Soil} {Moisture} {Dataset}},
	volume = {10},
	issn = {2052-4463},
	url = {https://www.nature.com/articles/s41597-023-02053-x},
	doi = {10.1038/s41597-023-02053-x},
	abstract = {Abstract
            
              The Consistent Artificial Intelligence (AI)-based Soil Moisture (CASM) dataset is a global, consistent, and long-term, remote sensing soil moisture (SM) dataset created using machine learning. It is based on the NASA Soil Moisture Active Passive (SMAP) satellite mission SM data and is aimed at extrapolating SMAP-like quality SM back in time using previous satellite microwave platforms. CASM represents SM in the top soil layer, and it is defined on a global 25 km EASE-2 grid and for 2002–2020 with a 3-day temporal resolution. The seasonal cycle is removed for the neural network training to ensure its skill is targeted at predicting SM extremes. CASM comparison to 367 global
              in-situ
              SM monitoring sites shows a SMAP-like median correlation of 0.66. Additionally, the SM product uncertainty was assessed, and both aleatoric and epistemic uncertainties were estimated and included in the dataset. CASM dataset can be used to study a wide range of hydrological, carbon cycle, and energy processes since only a consistent long-term dataset allows assessing changes in water availability and water stress.},
	language = {en},
	number = {1},
	urldate = {2024-11-15},
	journal = {Scientific Data},
	author = {Skulovich, Olya and Gentine, Pierre},
	month = mar,
	year = {2023},
	pages = {154},
}

Abstract The Consistent Artificial Intelligence (AI)-based Soil Moisture (CASM) dataset is a global, consistent, and long-term, remote sensing soil moisture (SM) dataset created using machine learning. It is based on the NASA Soil Moisture Active Passive (SMAP) satellite mission SM data and is aimed at extrapolating SMAP-like quality SM back in time using previous satellite microwave platforms. CASM represents SM in the top soil layer, and it is defined on a global 25 km EASE-2 grid and for 2002–2020 with a 3-day temporal resolution. The seasonal cycle is removed for the neural network training to ensure its skill is targeted at predicting SM extremes. CASM comparison to 367 global in-situ SM monitoring sites shows a SMAP-like median correlation of 0.66. Additionally, the SM product uncertainty was assessed, and both aleatoric and epistemic uncertainties were estimated and included in the dataset. CASM dataset can be used to study a wide range of hydrological, carbon cycle, and energy processes since only a consistent long-term dataset allows assessing changes in water availability and water stress.

@article{sloan_robust_2023,
	title = {Robust inference of ecosystem soil water stress from eddy covariance data},
	volume = {343},
	issn = {01681923},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0168192323004343},
	doi = {10.1016/j.agrformet.2023.109744},
	language = {en},
	urldate = {2024-11-15},
	journal = {Agricultural and Forest Meteorology},
	author = {Sloan, Brandon P. and Feng, Xue},
	month = dec,
	year = {2023},
	pages = {109744},
}

@article{sobaga_assessment_2023,
	title = {Assessment of the interactions between soil–biosphere–atmosphere ({ISBA}) land surface model soil hydrology, using four closed-form soil water relationships and several lysimeters},
	volume = {27},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1607-7938},
	url = {https://hess.copernicus.org/articles/27/2437/2023/},
	doi = {10.5194/hess-27-2437-2023},
	abstract = {Abstract. Soil water drainage is the main source of groundwater recharge and river flow. It is therefore a key process for water resource management. In this study, we evaluate the soil hydrology and the soil water drainage, simulated by the interactions between soil–biosphere–atmosphere (ISBA) land surface model currently used for hydrological applications from the watershed scale to the global scale, where parameters are generally not calibrated. This evaluation is done using seven lysimeters from two long-term model approach sites measuring hourly water dynamics between 2009 and 2019 in northeastern France. These 2 m depth lysimeters are filled with different soil types and are either maintained as bare soil or covered with vegetation. Four closed-form equations describing soil water retention and hydraulic conductivity functions are tested, namely the commonly used equations from Brooks and Corey (1966) and van Genuchten (1980), a combination of the van Genuchten (1980) soil water retention function with the Brooks and Corey (1966) unsaturated hydraulic conductivity function, and, for the very first time in a land surface model (LSM), a modified version of the van Genuchten (1980) equations, with a new hydraulic conductivity curve proposed by Iden et al. (2015). The results indicate good performance by ISBA with the different closure equations in terms of soil volumetric water content and water mass. The drained flow at the bottom of the lysimeter is well simulated, using Brooks and Corey (1966), while some weaknesses appear with van Genuchten (1980) due to the abrupt shape near the saturation of its hydraulic conductivity function. The mixed form or the new van Genuchten (1980) hydraulic conductivity function from Iden et al. (2015) allows the solving of this problem and even improves the simulation of the drainage dynamic, especially for intense drainage events. The study also highlights the importance of the vertical heterogeneity of the soil hydrodynamic parameters to correctly simulate the drainage dynamic, in addition to the primary influence of the parameters characterizing the shape of the soil water retention function.},
	language = {en},
	number = {13},
	urldate = {2024-11-15},
	journal = {Hydrology and Earth System Sciences},
	author = {Sobaga, Antoine and Decharme, Bertrand and Habets, Florence and Delire, Christine and Enjelvin, Noële and Redon, Paul-Olivier and Faure-Catteloin, Pierre and Le Moigne, Patrick},
	month = jul,
	year = {2023},
	pages = {2437--2461},
}

Abstract. Soil water drainage is the main source of groundwater recharge and river flow. It is therefore a key process for water resource management. In this study, we evaluate the soil hydrology and the soil water drainage, simulated by the interactions between soil–biosphere–atmosphere (ISBA) land surface model currently used for hydrological applications from the watershed scale to the global scale, where parameters are generally not calibrated. This evaluation is done using seven lysimeters from two long-term model approach sites measuring hourly water dynamics between 2009 and 2019 in northeastern France. These 2 m depth lysimeters are filled with different soil types and are either maintained as bare soil or covered with vegetation. Four closed-form equations describing soil water retention and hydraulic conductivity functions are tested, namely the commonly used equations from Brooks and Corey (1966) and van Genuchten (1980), a combination of the van Genuchten (1980) soil water retention function with the Brooks and Corey (1966) unsaturated hydraulic conductivity function, and, for the very first time in a land surface model (LSM), a modified version of the van Genuchten (1980) equations, with a new hydraulic conductivity curve proposed by Iden et al. (2015). The results indicate good performance by ISBA with the different closure equations in terms of soil volumetric water content and water mass. The drained flow at the bottom of the lysimeter is well simulated, using Brooks and Corey (1966), while some weaknesses appear with van Genuchten (1980) due to the abrupt shape near the saturation of its hydraulic conductivity function. The mixed form or the new van Genuchten (1980) hydraulic conductivity function from Iden et al. (2015) allows the solving of this problem and even improves the simulation of the drainage dynamic, especially for intense drainage events. The study also highlights the importance of the vertical heterogeneity of the soil hydrodynamic parameters to correctly simulate the drainage dynamic, in addition to the primary influence of the parameters characterizing the shape of the soil water retention function.

@article{spath_landatmosphere_2023,
	title = {The land–atmosphere feedback observatory: a new observational approach for characterizing land–atmosphere feedback},
	volume = {12},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2193-0864},
	shorttitle = {The land–atmosphere feedback observatory},
	url = {https://gi.copernicus.org/articles/12/25/2023/},
	doi = {10.5194/gi-12-25-2023},
	abstract = {Abstract. Important topics in land–atmosphere (L–A) feedback research are water and energy balances and heterogeneities of fluxes at the land surface and in the atmospheric boundary layer (ABL). To target these questions, the Land–Atmosphere Feedback Observatory (LAFO) has been installed in southwestern Germany. The instrumentation allows comprehensive and high-resolution measurements from the bedrock to the lower free troposphere. Grouped into three components, atmosphere, soil and land surface, and vegetation, the LAFO observation strategy aims for simultaneous measurements in all three compartments. For this purpose the LAFO sensor synergy contains lidar systems to measure the atmospheric key variables of humidity, temperature and wind. At the land surface, eddy covariance stations are operated to record the energy distribution of radiation, sensible, latent and ground heat fluxes. Together with a water and temperature sensor network, the soil water content and temperature are monitored in the agricultural investigation area. As for vegetation, crop height, leaf area index and phenological growth stage values are registered. The observations in LAFO are organized into operational measurements and
intensive observation periods (IOPs). Operational measurements aim for long
time series datasets to investigate statistics, and we present as an example the correlation between mixing layer height and surface fluxes. The potential of IOPs is demonstrated with a 24 h case study using dynamic and thermodynamic profiles with lidar and a surface layer observation that uses the scanning differential absorption lidar to relate atmospheric humidity patterns to soil water structures. Both IOPs and long-term observations will provide new insight into exchange
processes and their statistics for improving the representation of L–A feedbacks in climate and numerical weather prediction models. The lidar component in particular will support the investigation of coupling to the
atmosphere.},
	language = {en},
	number = {1},
	urldate = {2024-11-15},
	journal = {Geoscientific Instrumentation, Methods and Data Systems},
	author = {Späth, Florian and Rajtschan, Verena and Weber, Tobias K. D. and Morandage, Shehan and Lange, Diego and Abbas, Syed Saqlain and Behrendt, Andreas and Ingwersen, Joachim and Streck, Thilo and Wulfmeyer, Volker},
	month = jan,
	year = {2023},
	pages = {25--44},
}

Abstract. Important topics in land–atmosphere (L–A) feedback research are water and energy balances and heterogeneities of fluxes at the land surface and in the atmospheric boundary layer (ABL). To target these questions, the Land–Atmosphere Feedback Observatory (LAFO) has been installed in southwestern Germany. The instrumentation allows comprehensive and high-resolution measurements from the bedrock to the lower free troposphere. Grouped into three components, atmosphere, soil and land surface, and vegetation, the LAFO observation strategy aims for simultaneous measurements in all three compartments. For this purpose the LAFO sensor synergy contains lidar systems to measure the atmospheric key variables of humidity, temperature and wind. At the land surface, eddy covariance stations are operated to record the energy distribution of radiation, sensible, latent and ground heat fluxes. Together with a water and temperature sensor network, the soil water content and temperature are monitored in the agricultural investigation area. As for vegetation, crop height, leaf area index and phenological growth stage values are registered. The observations in LAFO are organized into operational measurements and intensive observation periods (IOPs). Operational measurements aim for long time series datasets to investigate statistics, and we present as an example the correlation between mixing layer height and surface fluxes. The potential of IOPs is demonstrated with a 24 h case study using dynamic and thermodynamic profiles with lidar and a surface layer observation that uses the scanning differential absorption lidar to relate atmospheric humidity patterns to soil water structures. Both IOPs and long-term observations will provide new insight into exchange processes and their statistics for improving the representation of L–A feedbacks in climate and numerical weather prediction models. The lidar component in particular will support the investigation of coupling to the atmosphere.

@article{sanchez-zapero_global_2023,
	title = {Global estimates of surface albedo from {Sentinel}-3 {OLCI} and {SLSTR} data for {Copernicus} {Climate} {Change} {Service}: {Algorithm} and preliminary validation},
	volume = {287},
	issn = {00344257},
	shorttitle = {Global estimates of surface albedo from {Sentinel}-3 {OLCI} and {SLSTR} data for {Copernicus} {Climate} {Change} {Service}},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0034425723000111},
	doi = {10.1016/j.rse.2023.113460},
	language = {en},
	urldate = {2024-11-15},
	journal = {Remote Sensing of Environment},
	author = {Sánchez-Zapero, Jorge and Camacho, Fernando and Martínez-Sánchez, Enrique and Gorroño, Javier and León-Tavares, Jonathan and Benhadj, Iskander and Toté, Carolien and Swinnen, Else and Muñoz-Sabater, Joaquín},
	month = mar,
	year = {2023},
	pages = {113460},
}

@article{sanchez-zapero_surface_2023,
	title = {Surface {ALbedo} {VALidation} ({SALVAL}) {Platform}: {Towards} {CEOS} {LPV} {Validation} {Stage} 4—{Application} to {Three} {Global} {Albedo} {Climate} {Data} {Records}},
	volume = {15},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2072-4292},
	shorttitle = {Surface {ALbedo} {VALidation} ({SALVAL}) {Platform}},
	url = {https://www.mdpi.com/2072-4292/15/4/1081},
	doi = {10.3390/rs15041081},
	abstract = {The Surface ALbedo VALidation (SALVAL) online platform is designed to allow producers of satellite-based albedo products to move to operational validation systems. The SALVAL tool integrates long-term satellite products, global in situ datasets, and community-agreed-upon validation protocols into an online and interactive platform. The SALVAL tool, available on the ESA Cal/Val portal, was developed by EOLAB under the framework outlined by the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) Land Product Validation (LPV) subgroup, and provides transparency, consistency, and traceability to the validation process. In this demonstration, three satellite-based albedo climate data records from different operational services were validated and intercompared using the SALVAL platform: (1) the Climate Change Service (C3S) multi-sensor product, (2) the NASA MODIS MCD43A3 product (C6.1) and (3) Beijing Normal University’s Global LAnd Surface Satellites (GLASS) version 4 products. This work demonstrates that the three satellite albedo datasets enable long-term reliable and consistent retrievals at the global scale, with some discrepancies between them associated with the retrieval processing chain. The three satellite albedo products show similar uncertainties (RMSD = 0.03) when comparing the best quality retrievals with ground measurements. The SALVAL platform has proven to be a useful tool to validate and intercompare albedo datasets, allowing them to reach stage 4 of the CEOS LPV validation hierarchy.},
	language = {en},
	number = {4},
	urldate = {2024-11-15},
	journal = {Remote Sensing},
	author = {Sánchez-Zapero, Jorge and Martínez-Sánchez, Enrique and Camacho, Fernando and Wang, Zhuosen and Carrer, Dominique and Schaaf, Crystal and García-Haro, Francisco Javier and Nickeson, Jaime and Cosh, Michael},
	month = feb,
	year = {2023},
	pages = {1081},
}

The Surface ALbedo VALidation (SALVAL) online platform is designed to allow producers of satellite-based albedo products to move to operational validation systems. The SALVAL tool integrates long-term satellite products, global in situ datasets, and community-agreed-upon validation protocols into an online and interactive platform. The SALVAL tool, available on the ESA Cal/Val portal, was developed by EOLAB under the framework outlined by the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) Land Product Validation (LPV) subgroup, and provides transparency, consistency, and traceability to the validation process. In this demonstration, three satellite-based albedo climate data records from different operational services were validated and intercompared using the SALVAL platform: (1) the Climate Change Service (C3S) multi-sensor product, (2) the NASA MODIS MCD43A3 product (C6.1) and (3) Beijing Normal University’s Global LAnd Surface Satellites (GLASS) version 4 products. This work demonstrates that the three satellite albedo datasets enable long-term reliable and consistent retrievals at the global scale, with some discrepancies between them associated with the retrieval processing chain. The three satellite albedo products show similar uncertainties (RMSD = 0.03) when comparing the best quality retrievals with ground measurements. The SALVAL platform has proven to be a useful tool to validate and intercompare albedo datasets, allowing them to reach stage 4 of the CEOS LPV validation hierarchy.

@article{tang_detection_2023,
	title = {Detection and attribution of an anomaly in terrestrial photosynthesis in {Europe} during the {COVID}-19 lockdown},
	volume = {903},
	issn = {00489697},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0048969723047745},
	doi = {10.1016/j.scitotenv.2023.166149},
	language = {en},
	urldate = {2024-11-15},
	journal = {Science of The Total Environment},
	author = {Tang, Angela Che Ing and Flechard, Christophe R. and Arriga, Nicola and Papale, Dario and Stoy, Paul C. and Buchmann, Nina and Cuntz, Matthias and Douros, John and Fares, Silvano and Knohl, Alexander and Šigut, Ladislav and Simioni, Guillaume and Timmermans, Renske and Grünwald, Thomas and Ibrom, Andreas and Loubet, Benjamin and Mammarella, Ivan and Belelli Marchesini, Luca and Nilsson, Mats and Peichl, Matthias and Rebmann, Corinna and Schmidt, Marius and Bernhofer, Christian and Berveiller, Daniel and Cremonese, Edoardo and El-Madany, Tarek S. and Gharun, Mana and Gianelle, Damiano and Hörtnagl, Lukas and Roland, Marilyn and Varlagin, Andrej and Fu, Zheng and Heinesch, Bernard and Janssens, Ivan and Kowalska, Natalia and Dušek, Jiří and Gerosa, Giacomo and Mölder, Meelis and Tuittila, Eeva-Stiina and Loustau, Denis},
	month = dec,
	year = {2023},
	pages = {166149},
}

@article{tiede_new_2023,
	title = {New {Insights} {Into} the {Dynamics} of {Wet} {Antenna} {Attenuation} {Based} on {In} {Situ} {Estimations} {Provided} by the {Dedicated} {Field} {Experiment} {ATTRRA2}},
	volume = {20},
	copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
	issn = {1545-598X, 1558-0571},
	url = {https://ieeexplore.ieee.org/document/10268068/},
	doi = {10.1109/LGRS.2023.3320755},
	urldate = {2024-11-15},
	journal = {IEEE Geoscience and Remote Sensing Letters},
	author = {Tiede, Jonas and Chwala, Christian and Siart, Uwe},
	year = {2023},
	pages = {1--5},
}

@article{tumajer_dendrometers_2023,
	title = {Dendrometers challenge the ‘moon wood concept’ by elucidating the absence of lunar cycles in tree stem radius oscillation},
	volume = {13},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-023-47013-y},
	doi = {10.1038/s41598-023-47013-y},
	abstract = {Abstract
            Wood is a sustainable natural resource and an important global commodity. According to the ‘moon wood theory’, the properties of wood, including its growth and water content, are believed to oscillate with the lunar cycle. Despite contradicting our current understanding of plant functioning, this theory is commonly exploited for marketing wooden products. To examine the moon wood theory, we applied a wavelet power transformation to series of 2,000,000 hourly stem radius records from dendrometers. We separated the influence of 74 consecutive lunar cycles and meteorological conditions on the stem variation of 62 trees and six species. We show that the dynamics of stem radius consist of overlapping oscillations with periods of 1 day, 6 months, and 1 year. These oscillations in stem dimensions were tightly coupled to oscillations in the series of air temperature and vapour pressure deficit. By contrast, we revealed no imprint of the lunar cycle on the stem radius variation of any species. We call for scepticism towards the moon wood theory, at least as far as the stem water content and radial growth are concerned. We foresee that similar studies employing robust scientific approaches will be increasingly needed in the future to cope with misleading concepts.},
	language = {en},
	number = {1},
	urldate = {2024-11-15},
	journal = {Scientific Reports},
	author = {Tumajer, Jan and Braun, Sabine and Burger, Andreas and Scharnweber, Tobias and Smiljanic, Marko and Walthert, Lorenz and Zweifel, Roman and Wilmking, Martin},
	month = nov,
	year = {2023},
	pages = {19904},
}

Abstract Wood is a sustainable natural resource and an important global commodity. According to the ‘moon wood theory’, the properties of wood, including its growth and water content, are believed to oscillate with the lunar cycle. Despite contradicting our current understanding of plant functioning, this theory is commonly exploited for marketing wooden products. To examine the moon wood theory, we applied a wavelet power transformation to series of 2,000,000 hourly stem radius records from dendrometers. We separated the influence of 74 consecutive lunar cycles and meteorological conditions on the stem variation of 62 trees and six species. We show that the dynamics of stem radius consist of overlapping oscillations with periods of 1 day, 6 months, and 1 year. These oscillations in stem dimensions were tightly coupled to oscillations in the series of air temperature and vapour pressure deficit. By contrast, we revealed no imprint of the lunar cycle on the stem radius variation of any species. We call for scepticism towards the moon wood theory, at least as far as the stem water content and radial growth are concerned. We foresee that similar studies employing robust scientific approaches will be increasingly needed in the future to cope with misleading concepts.

@article{ueyama_modeled_2023,
	title = {Modeled production, oxidation, and transport processes of wetland methane emissions in temperate, boreal, and {Arctic} regions},
	volume = {29},
	issn = {1354-1013, 1365-2486},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/gcb.16594},
	doi = {10.1111/gcb.16594},
	abstract = {Abstract
            
              Wetlands are the largest natural source of methane (CH
              4
              ) to the atmosphere. The eddy covariance method provides robust measurements of net ecosystem exchange of CH
              4
              , but interpreting its spatiotemporal variations is challenging due to the co‐occurrence of CH
              4
              production, oxidation, and transport dynamics. Here, we estimate these three processes using a data‐model fusion approach across 25 wetlands in temperate, boreal, and Arctic regions. Our data‐constrained model—iPEACE—reasonably reproduced CH
              4
              emissions at 19 of the 25 sites with normalized root mean square error of 0.59, correlation coefficient of 0.82, and normalized standard deviation of 0.87. Among the three processes, CH
              4
              production appeared to be the most important process, followed by oxidation in explaining inter‐site variations in CH
              4
              emissions. Based on a sensitivity analysis, CH
              4
              emissions were generally more sensitive to decreased water table than to increased gross primary productivity or soil temperature. For periods with leaf area index (LAI) of ≥20\% of its annual peak, plant‐mediated transport appeared to be the major pathway for CH
              4
              transport. Contributions from ebullition and diffusion were relatively high during low LAI ({\textless}20\%) periods. The lag time between CH
              4
              production and CH
              4
              emissions tended to be short in fen sites (3 ± 2 days) and long in bog sites (13 ± 10 days). Based on a principal component analysis, we found that parameters for CH
              4
              production, plant‐mediated transport, and diffusion through water explained 77\% of the variance in the parameters across the 19 sites, highlighting the importance of these parameters for predicting wetland CH
              4
              emissions across biomes. These processes and associated parameters for CH
              4
              emissions among and within the wetlands provide useful insights for interpreting observed net CH
              4
              fluxes, estimating sensitivities to biophysical variables, and modeling global CH
              4
              fluxes.},
	language = {en},
	number = {8},
	urldate = {2024-11-15},
	journal = {Global Change Biology},
	author = {Ueyama, Masahito and Knox, Sara H. and Delwiche, Kyle B. and Bansal, Sheel and Riley, William J. and Baldocchi, Dennis and Hirano, Takashi and McNicol, Gavin and Schafer, Karina and Windham‐Myers, Lisamarie and Poulter, Benjamin and Jackson, Robert B. and Chang, Kuang‐Yu and Chen, Jiquen and Chu, Housen and Desai, Ankur R. and Gogo, Sébastien and Iwata, Hiroki and Kang, Minseok and Mammarella, Ivan and Peichl, Matthias and Sonnentag, Oliver and Tuittila, Eeva‐Stiina and Ryu, Youngryel and Euskirchen, Eugénie S. and Göckede, Mathias and Jacotot, Adrien and Nilsson, Mats B. and Sachs, Torsten},
	month = apr,
	year = {2023},
	pages = {2313--2334},
}

Abstract Wetlands are the largest natural source of methane (CH 4 ) to the atmosphere. The eddy covariance method provides robust measurements of net ecosystem exchange of CH 4 , but interpreting its spatiotemporal variations is challenging due to the co‐occurrence of CH 4 production, oxidation, and transport dynamics. Here, we estimate these three processes using a data‐model fusion approach across 25 wetlands in temperate, boreal, and Arctic regions. Our data‐constrained model—iPEACE—reasonably reproduced CH 4 emissions at 19 of the 25 sites with normalized root mean square error of 0.59, correlation coefficient of 0.82, and normalized standard deviation of 0.87. Among the three processes, CH 4 production appeared to be the most important process, followed by oxidation in explaining inter‐site variations in CH 4 emissions. Based on a sensitivity analysis, CH 4 emissions were generally more sensitive to decreased water table than to increased gross primary productivity or soil temperature. For periods with leaf area index (LAI) of ≥20% of its annual peak, plant‐mediated transport appeared to be the major pathway for CH 4 transport. Contributions from ebullition and diffusion were relatively high during low LAI (\textless20%) periods. The lag time between CH 4 production and CH 4 emissions tended to be short in fen sites (3 ± 2 days) and long in bog sites (13 ± 10 days). Based on a principal component analysis, we found that parameters for CH 4 production, plant‐mediated transport, and diffusion through water explained 77% of the variance in the parameters across the 19 sites, highlighting the importance of these parameters for predicting wetland CH 4 emissions across biomes. These processes and associated parameters for CH 4 emissions among and within the wetlands provide useful insights for interpreting observed net CH 4 fluxes, estimating sensitivities to biophysical variables, and modeling global CH 4 fluxes.

@article{van_der_woude_near-real-time_2023,
	title = {Near-real-time {CO}$_{\textrm{2}}$ fluxes from {CarbonTracker} {Europe} for high-resolution atmospheric modeling},
	volume = {15},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1866-3516},
	url = {https://essd.copernicus.org/articles/15/579/2023/},
	doi = {10.5194/essd-15-579-2023},
	abstract = {Abstract. We present the CarbonTracker Europe High-Resolution (CTE-HR) system that estimates carbon dioxide (CO2) exchange over Europe at high resolution (0.1 × 0.2∘) and in near real time (about 2 months' latency). It includes a dynamic anthropogenic emission model, which uses easily available statistics on economic activity, energy use, and weather to generate anthropogenic emissions with dynamic time profiles at high spatial and temporal resolution (0.1×0.2∘, hourly). Hourly net ecosystem productivity (NEP) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEP is downscaled to 0.1×0.2∘ using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. Ocean CO2 fluxes are included in our product, based on Jena CarboScope ocean CO2 fluxes, which are downscaled using wind speed and temperature. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe. We assess the skill of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 European drought, (b) to capture the reduction of anthropogenic emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ECMWF-IFS, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city center of Amsterdam (the Netherlands). We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and anthropogenic emissions (COVID-19 pandemic in 2020). After applying transport of emitted CO2, the CTE-HR fluxes have lower median root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor person's inversion). RMSEs are close to those of the reanalysis with the CTE data assimilation system. This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE. We furthermore compare CO2 concentration observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different emission sectors in summer and winter. Interestingly, in periods where synoptic-scale transport variability dominates CO2 concentration variations, the CTE-HR fluxes perform similarly to low-resolution fluxes (5–10× coarsened). The remaining 10 \% of the simulated CO2 mole fraction differs by {\textgreater}2 ppm between the low-resolution and high-resolution flux representation and is clearly associated with coherent structures (“plumes”) originating from emission hotspots such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely populated region like the Amsterdam city center, our modeled fluxes underestimate the magnitude of measured eddy covariance fluxes but capture their substantial diurnal variations in summertime and wintertime well. We conclude that our product is a promising tool for modeling the European carbon budget at a high resolution in near real time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near-real-time monitoring and modeling, for example, as an a priori flux product in a CO2 data assimilation system. The data are available at https://doi.org/10.18160/20Z1-AYJ2 (van der Woude, 2022a).},
	language = {en},
	number = {2},
	urldate = {2024-11-15},
	journal = {Earth System Science Data},
	author = {Van Der Woude, Auke M. and De Kok, Remco and Smith, Naomi and Luijkx, Ingrid T. and Botía, Santiago and Karstens, Ute and Kooijmans, Linda M. J. and Koren, Gerbrand and Meijer, Harro A. J. and Steeneveld, Gert-Jan and Storm, Ida and Super, Ingrid and Scheeren, Hubertus A. and Vermeulen, Alex and Peters, Wouter},
	month = feb,
	year = {2023},
	pages = {579--605},
}

Abstract. We present the CarbonTracker Europe High-Resolution (CTE-HR) system that estimates carbon dioxide (CO2) exchange over Europe at high resolution (0.1 × 0.2∘) and in near real time (about 2 months' latency). It includes a dynamic anthropogenic emission model, which uses easily available statistics on economic activity, energy use, and weather to generate anthropogenic emissions with dynamic time profiles at high spatial and temporal resolution (0.1×0.2∘, hourly). Hourly net ecosystem productivity (NEP) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEP is downscaled to 0.1×0.2∘ using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. Ocean CO2 fluxes are included in our product, based on Jena CarboScope ocean CO2 fluxes, which are downscaled using wind speed and temperature. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe. We assess the skill of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 European drought, (b) to capture the reduction of anthropogenic emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ECMWF-IFS, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city center of Amsterdam (the Netherlands). We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and anthropogenic emissions (COVID-19 pandemic in 2020). After applying transport of emitted CO2, the CTE-HR fluxes have lower median root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor person's inversion). RMSEs are close to those of the reanalysis with the CTE data assimilation system. This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE. We furthermore compare CO2 concentration observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different emission sectors in summer and winter. Interestingly, in periods where synoptic-scale transport variability dominates CO2 concentration variations, the CTE-HR fluxes perform similarly to low-resolution fluxes (5–10× coarsened). The remaining 10 % of the simulated CO2 mole fraction differs by \textgreater2 ppm between the low-resolution and high-resolution flux representation and is clearly associated with coherent structures (“plumes”) originating from emission hotspots such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely populated region like the Amsterdam city center, our modeled fluxes underestimate the magnitude of measured eddy covariance fluxes but capture their substantial diurnal variations in summertime and wintertime well. We conclude that our product is a promising tool for modeling the European carbon budget at a high resolution in near real time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near-real-time monitoring and modeling, for example, as an a priori flux product in a CO2 data assimilation system. The data are available at https://doi.org/10.18160/20Z1-AYJ2 (van der Woude, 2022a).

@article{vekuri_widely-used_2023,
	title = {A widely-used eddy covariance gap-filling method creates systematic bias in carbon balance estimates},
	volume = {13},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-023-28827-2},
	doi = {10.1038/s41598-023-28827-2},
	abstract = {Abstract
            
              Climate change mitigation requires, besides reductions in greenhouse gas emissions, actions to increase carbon sinks in terrestrial ecosystems. A key measurement method for quantifying such sinks and calibrating models is the eddy covariance technique, but it requires imputation, or gap-filling, of missing data for determination of annual carbon balances of ecosystems. Previous comparisons of gap-filling methods have concluded that commonly used methods, such as marginal distribution sampling (MDS), do not have a significant impact on the carbon balance estimate. By analyzing an extensive, global data set, we show that MDS causes significant carbon balance errors for northern (latitude
              
                
                  \$\${\textgreater}60{\textasciicircum}{\textbackslash}circ\$\$
                  
                    
                      {\textgreater}
                      
                        60
                        ∘
                      
                    
                  
                
              
              ) sites. MDS systematically overestimates the carbon dioxide (CO
              
                
                  \$\$\_2\$\$
                  
                    
                      
                      2
                    
                  
                
              
              ) emissions of carbon sources and underestimates the CO
              
                
                  \$\$\_2\$\$
                  
                    
                      
                      2
                    
                  
                
              
              sequestration of carbon sinks. We also reveal reasons for these biases and show how a machine learning method called extreme gradient boosting or a modified implementation of MDS can be used to substantially reduce the northern site bias.},
	language = {en},
	number = {1},
	urldate = {2024-11-15},
	journal = {Scientific Reports},
	author = {Vekuri, Henriikka and Tuovinen, Juha-Pekka and Kulmala, Liisa and Papale, Dario and Kolari, Pasi and Aurela, Mika and Laurila, Tuomas and Liski, Jari and Lohila, Annalea},
	month = jan,
	year = {2023},
	pages = {1720},
}

Abstract Climate change mitigation requires, besides reductions in greenhouse gas emissions, actions to increase carbon sinks in terrestrial ecosystems. A key measurement method for quantifying such sinks and calibrating models is the eddy covariance technique, but it requires imputation, or gap-filling, of missing data for determination of annual carbon balances of ecosystems. Previous comparisons of gap-filling methods have concluded that commonly used methods, such as marginal distribution sampling (MDS), do not have a significant impact on the carbon balance estimate. By analyzing an extensive, global data set, we show that MDS causes significant carbon balance errors for northern (latitude $$\textgreater60\textasciicircum\circ$$ \textgreater 60 ∘ ) sites. MDS systematically overestimates the carbon dioxide (CO $$_2$$ 2 ) emissions of carbon sources and underestimates the CO $$_2$$ 2 sequestration of carbon sinks. We also reveal reasons for these biases and show how a machine learning method called extreme gradient boosting or a modified implementation of MDS can be used to substantially reduce the northern site bias.

@article{von_gonner_citizen_2023,
	title = {Citizen science for assessing pesticide impacts in agricultural streams},
	volume = {857},
	issn = {00489697},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0048969722067079},
	doi = {10.1016/j.scitotenv.2022.159607},
	language = {en},
	urldate = {2024-11-15},
	journal = {Science of The Total Environment},
	author = {Von Gönner, Julia and Bowler, Diana E. and Gröning, Jonas and Klauer, Anna-Katharina and Liess, Matthias and Neuer, Lilian and Bonn, Aletta},
	month = jan,
	year = {2023},
	pages = {159607},
}

@article{vormeier_width_2023,
	title = {Width of vegetated buffer strips to protect aquatic life from pesticide effects},
	volume = {231},
	issn = {00431354},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0043135423000623},
	doi = {10.1016/j.watres.2023.119627},
	language = {en},
	urldate = {2024-11-15},
	journal = {Water Research},
	author = {Vormeier, Philipp and Liebmann, Liana and Weisner, Oliver and Liess, Matthias},
	month = mar,
	year = {2023},
	pages = {119627},
}

@article{vormeier_corrigendum_2023,
	title = {Corrigendum to “{Temporal} scales of pesticide exposure and risks in {German} small streams” [{Sci}. {Total} {Environ}. (2023) 871/162105]},
	volume = {877},
	issn = {00489697},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0048969723013773},
	doi = {10.1016/j.scitotenv.2023.162761},
	language = {en},
	urldate = {2024-11-15},
	journal = {Science of The Total Environment},
	author = {Vormeier, Philipp and Schreiner, Verena C. and Liebmann, Liana and Link, Moritz and Schäfer, Ralf B. and Schneeweiss, Anke and Weisner, Oliver and Liess, Matthias},
	month = jun,
	year = {2023},
	pages = {162761},
}

@article{vormeier_temporal_2023,
	title = {Temporal scales of pesticide exposure and risks in {German} small streams},
	volume = {871},
	issn = {00489697},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0048969723007210},
	doi = {10.1016/j.scitotenv.2023.162105},
	language = {en},
	urldate = {2024-11-15},
	journal = {Science of The Total Environment},
	author = {Vormeier, Philipp and Schreiner, Verena C. and Liebmann, Liana and Link, Moritz and Schäfer, Ralf B. and Schneeweiss, Anke and Weisner, Oliver and Liess, Matthias},
	month = may,
	year = {2023},
	pages = {162105},
}

@article{wachholz_stoichiometry_2023,
	title = {Stoichiometry on the edge—humans induce strong imbalances of reactive {C}:{N}:{P} ratios in streams},
	volume = {18},
	issn = {1748-9326},
	shorttitle = {Stoichiometry on the edge—humans induce strong imbalances of reactive {C}},
	url = {https://iopscience.iop.org/article/10.1088/1748-9326/acc3b1},
	doi = {10.1088/1748-9326/acc3b1},
	abstract = {Abstract
            Anthropogenic nutrient inputs led to severe degradation of surface water resources, affecting aquatic ecosystem health and functioning. Ecosystem functions such as nutrient cycling and ecosystem metabolism are not only affected by the over-abundance of a single macronutrient but also by the stoichiometry of the reactive molecular forms of dissolved organic carbon (rOC), nitrogen (rN), and phosphorus (rP). So far, studies mainly considered only single macronutrients or used stoichiometric ratios such as N:P or C:N independent from each other. We argue that a mutual assessment of reactive nutrient ratios rOC:rN:rP relative to organismic demands enables us to refine the definition of nutrient depletion versus excess and to understand their linkages to catchment-internal biogeochemical and hydrological processes. Here we show that the majority (94\%) of the studied 574 German catchments show a depletion or co-depletion in rOC and rP, illustrating the ubiquity of excess N in anthropogenically influenced landscapes. We found an emerging spatial pattern of depletion classes linked to the interplay of agricultural sources and subsurface denitrification for rN and topographic controls of rOC. We classified catchments into stoichio-static and stochio-dynamic catchments based on their degree of intra-annual variability of rOC:rN:rP ratios. Stoichio-static catchments (36\% of all catchments) tend to have higher rN median concentrations, lower temporal rN variability and generally low rOC medians. Our results demonstrate the severe extent of imbalances in rOC:rN:rP ratios in German rivers due to human activities. This likely affects the inland-water nutrient retention efficiency, their level of eutrophication, and their role in the global carbon cycle. Thus, it calls for a more holistic catchment and aquatic ecosystem management integrating rOC:rN:rP stoichiometry as a fundamental principle.},
	number = {4},
	urldate = {2024-11-15},
	journal = {Environmental Research Letters},
	author = {Wachholz, Alexander and Dehaspe, Joni and Ebeling, Pia and Kumar, Rohini and Musolff, Andreas and Saavedra, Felipe and Winter, Carolin and Yang, Soohyun and Graeber, Daniel},
	month = apr,
	year = {2023},
	pages = {044016},
}

Abstract Anthropogenic nutrient inputs led to severe degradation of surface water resources, affecting aquatic ecosystem health and functioning. Ecosystem functions such as nutrient cycling and ecosystem metabolism are not only affected by the over-abundance of a single macronutrient but also by the stoichiometry of the reactive molecular forms of dissolved organic carbon (rOC), nitrogen (rN), and phosphorus (rP). So far, studies mainly considered only single macronutrients or used stoichiometric ratios such as N:P or C:N independent from each other. We argue that a mutual assessment of reactive nutrient ratios rOC:rN:rP relative to organismic demands enables us to refine the definition of nutrient depletion versus excess and to understand their linkages to catchment-internal biogeochemical and hydrological processes. Here we show that the majority (94%) of the studied 574 German catchments show a depletion or co-depletion in rOC and rP, illustrating the ubiquity of excess N in anthropogenically influenced landscapes. We found an emerging spatial pattern of depletion classes linked to the interplay of agricultural sources and subsurface denitrification for rN and topographic controls of rOC. We classified catchments into stoichio-static and stochio-dynamic catchments based on their degree of intra-annual variability of rOC:rN:rP ratios. Stoichio-static catchments (36% of all catchments) tend to have higher rN median concentrations, lower temporal rN variability and generally low rOC medians. Our results demonstrate the severe extent of imbalances in rOC:rN:rP ratios in German rivers due to human activities. This likely affects the inland-water nutrient retention efficiency, their level of eutrophication, and their role in the global carbon cycle. Thus, it calls for a more holistic catchment and aquatic ecosystem management integrating rOC:rN:rP stoichiometry as a fundamental principle.

@misc{wagner_improved_2023,
	title = {Improved rain event detection in {Commercial} {Microwave} {Link} time series via combination with {MSG} {SEVIRI} data},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	url = {https://amt.copernicus.org/preprints/amt-2023-175/amt-2023-175.pdf},
	doi = {10.5194/amt-2023-175},
	abstract = {Abstract. The most reliable areal precipitation estimation is usually generated via combinations of different measurements and devices by merging their individual advantages. Path-averaged rain rate can be derived from Commercial Microwave Links (CML), where attenuation of the emitted radiation is strongly related with rainfall rate. CMLs can be combined with data from other rainfall measurements or used individually. They are available almost worldwide and often represent the only opportunity of ground-based measurement in data scarce regions. Deriving rainfall estimates from CML data requires extensive data processing, though. The separation of the attenuation time series in rainy and dry periods (rain event detection) is the most important step in this processing and largely determines the quality of the resulting rainfall estimates. In this study, we investigate the suitability of Meteosat Second Generation Spinning Enhanced Visible and InfraRed Imager (MSG SEVIRI) satellite data as an auxiliary-data-based (ADB) rain event detection method. We compare this method with two time-series-based (TSB) rain event detection methods. The investigation uses data from 3901 CMLs in Germany for four months in summer 2021 and is carried out for the two SEVIRI-derived products PC and PC-Ph. We analyse all rain event detection methods for different precipitation intensity, differences between day and night, as well as their influence on the performance of rainfall estimates from individual CMLs. The radar product RADKLIM-YW is used for validation. The results show that both SEVIRI products are promising candidates for ADB rainfall detection methods and led to at least equivalent results as the TSB methods. The main uncertainty of all methods was found for light rain. Slightly better results were obtained during the day than at night, which is caused by dew formation on CML antennas and the reduced availability of SEVIRI channels at night. In general, the ADB methods lead to improvements for CMLs performing comparatively weakly using TSB methods. Based on these results, combinations of ADB and TSB methods were developed by emphasizing their specific advantages. Compared to basic and advanced TSB methods, these combinations were able to improve the Matthews Correlation Coefficient of the rain event detection from 0.53 (0.57 resp.) to 0.62 during the day and from 0.47 (0.55 resp.) to 0.6 during the night. Our results show that utilising MSG SEVIRI data in CML data processing significantly increases the quality of the rain event detection step, in particular for CMLs which are challenging to process with TSB methods.},
	urldate = {2024-11-15},
	publisher = {Others (Wind, Precipitation, Temperature, etc.)/Remote Sensing/Data Processing and Information Retrieval},
	author = {Wagner, Andreas and Chwala, Christian and Graf, Maximilian and Polz, Julius and Lliso, Llorenç and Lahuerta, José Alberto and Kunstmann, Harald},
	month = oct,
	year = {2023},
}

Abstract. The most reliable areal precipitation estimation is usually generated via combinations of different measurements and devices by merging their individual advantages. Path-averaged rain rate can be derived from Commercial Microwave Links (CML), where attenuation of the emitted radiation is strongly related with rainfall rate. CMLs can be combined with data from other rainfall measurements or used individually. They are available almost worldwide and often represent the only opportunity of ground-based measurement in data scarce regions. Deriving rainfall estimates from CML data requires extensive data processing, though. The separation of the attenuation time series in rainy and dry periods (rain event detection) is the most important step in this processing and largely determines the quality of the resulting rainfall estimates. In this study, we investigate the suitability of Meteosat Second Generation Spinning Enhanced Visible and InfraRed Imager (MSG SEVIRI) satellite data as an auxiliary-data-based (ADB) rain event detection method. We compare this method with two time-series-based (TSB) rain event detection methods. The investigation uses data from 3901 CMLs in Germany for four months in summer 2021 and is carried out for the two SEVIRI-derived products PC and PC-Ph. We analyse all rain event detection methods for different precipitation intensity, differences between day and night, as well as their influence on the performance of rainfall estimates from individual CMLs. The radar product RADKLIM-YW is used for validation. The results show that both SEVIRI products are promising candidates for ADB rainfall detection methods and led to at least equivalent results as the TSB methods. The main uncertainty of all methods was found for light rain. Slightly better results were obtained during the day than at night, which is caused by dew formation on CML antennas and the reduced availability of SEVIRI channels at night. In general, the ADB methods lead to improvements for CMLs performing comparatively weakly using TSB methods. Based on these results, combinations of ADB and TSB methods were developed by emphasizing their specific advantages. Compared to basic and advanced TSB methods, these combinations were able to improve the Matthews Correlation Coefficient of the rain event detection from 0.53 (0.57 resp.) to 0.62 during the day and from 0.47 (0.55 resp.) to 0.6 during the night. Our results show that utilising MSG SEVIRI data in CML data processing significantly increases the quality of the rain event detection step, in particular for CMLs which are challenging to process with TSB methods.

@misc{wagner_global_2023,
	title = {Global {Scale} {Mapping} of {Subsurface} {Scattering} {Signals} {Impacting} {ASCAT} {Soil} {Moisture} {Retrievals}},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	url = {https://www.techrxiv.org/doi/full/10.36227/techrxiv.24013890.v1},
	doi = {10.36227/techrxiv.24013890.v1},
	abstract = {{\textless}p{\textgreater}Microwave pulses can penetrate several centimeters to decimeters into dry soils. As a result, active microwave sensors are sensitive to discontinuities in the soil profile caused by the presence of stones, rocks or distinct soil layers. Such subsurface scattering effects can disturb the retrieval of soil moisture, vegetation and other land surface properties from active microwave measurements. In this study we mapped subsurface scatterers impacting C-band backscatter measurements acquired by the Advanced Scatterometer (ASCAT) on a global scale. Users of ASCAT soil moisture data distributed by the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management are recommended to use the subsurface scattering masks generated within this study.{\textless}/p{\textgreater}},
	urldate = {2024-11-15},
	author = {Wagner, Wolfgang and Lindorfer, Roland and Hahn, Sebastian and Kim, Hyingglok and Vreugdenhil, Mariette and Gruber, Alexander and Fischer, Milan and Trnka, Miroslav},
	month = aug,
	year = {2023},
}

\textlessp\textgreaterMicrowave pulses can penetrate several centimeters to decimeters into dry soils. As a result, active microwave sensors are sensitive to discontinuities in the soil profile caused by the presence of stones, rocks or distinct soil layers. Such subsurface scattering effects can disturb the retrieval of soil moisture, vegetation and other land surface properties from active microwave measurements. In this study we mapped subsurface scatterers impacting C-band backscatter measurements acquired by the Advanced Scatterometer (ASCAT) on a global scale. Users of ASCAT soil moisture data distributed by the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management are recommended to use the subsurface scattering masks generated within this study.\textless/p\textgreater

@article{wang_flow_2023,
	title = {Flow paths and wetness conditions explain spatiotemporal variation of nitrogen retention for a temperate, humid catchment},
	volume = {625},
	issn = {00221694},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0022169423009666},
	doi = {10.1016/j.jhydrol.2023.130024},
	language = {en},
	urldate = {2024-11-15},
	journal = {Journal of Hydrology},
	author = {Wang, Qiaoyu and Yang, Jie and Heidbüchel, Ingo and Yu, Xuan and Lu, Chunhui},
	month = oct,
	year = {2023},
	pages = {130024},
}

@article{wei_nitrogen_2023,
	title = {Nitrogen immobilization caused by chemical formation of black- and amide-{N} in soil},
	volume = {429},
	issn = {00167061},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S001670612200581X},
	doi = {10.1016/j.geoderma.2022.116274},
	language = {en},
	urldate = {2024-11-15},
	journal = {Geoderma},
	author = {Wei, Jing and Knicker, Heike and Zhou, Zheyan and Eckhardt, Kai-Uwe and Leinweber, Peter and Wissel, Holger and Yuan, Wenping and Brüggemann, Nicolas},
	month = jan,
	year = {2023},
	pages = {116274},
}

@article{weilandt_early_2023,
	title = {Early {Crop} {Classification} via {Multi}-{Modal} {Satellite} {Data} {Fusion} and {Temporal} {Attention}},
	volume = {15},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2072-4292},
	url = {https://www.mdpi.com/2072-4292/15/3/799},
	doi = {10.3390/rs15030799},
	abstract = {In this article, we propose a deep learning-based algorithm for the classification of crop types from Sentinel-1 and Sentinel-2 time series data which is based on the celebrated transformer architecture. Crucially, we enable our algorithm to do early classification, i.e., predict crop types at arbitrary time points early in the year with a single trained model (progressive intra-season classification). Such early season predictions are of practical relevance for instance for yield forecasts or the modeling of agricultural water balances, therefore being important for the public as well as the private sector. Furthermore, we improve the mechanism of combining different data sources for the prediction task, allowing for both optical and radar data as inputs (multi-modal data fusion) without the need for temporal interpolation. We can demonstrate the effectiveness of our approach on an extensive data set from three federal states of Germany reaching an average F1 score of 0.92 using data of a complete growing season to predict the eight most important crop types and an F1 score above 0.8 when doing early classification at least one month before harvest time. In carefully chosen experiments, we can show that our model generalizes well in time and space.},
	language = {en},
	number = {3},
	urldate = {2024-11-15},
	journal = {Remote Sensing},
	author = {Weilandt, Frank and Behling, Robert and Goncalves, Romulo and Madadi, Arash and Richter, Lorenz and Sanona, Tiago and Spengler, Daniel and Welsch, Jona},
	month = jan,
	year = {2023},
	pages = {799},
}

In this article, we propose a deep learning-based algorithm for the classification of crop types from Sentinel-1 and Sentinel-2 time series data which is based on the celebrated transformer architecture. Crucially, we enable our algorithm to do early classification, i.e., predict crop types at arbitrary time points early in the year with a single trained model (progressive intra-season classification). Such early season predictions are of practical relevance for instance for yield forecasts or the modeling of agricultural water balances, therefore being important for the public as well as the private sector. Furthermore, we improve the mechanism of combining different data sources for the prediction task, allowing for both optical and radar data as inputs (multi-modal data fusion) without the need for temporal interpolation. We can demonstrate the effectiveness of our approach on an extensive data set from three federal states of Germany reaching an average F1 score of 0.92 using data of a complete growing season to predict the eight most important crop types and an F1 score above 0.8 when doing early classification at least one month before harvest time. In carefully chosen experiments, we can show that our model generalizes well in time and space.

@misc{westermann_does_2023,
	title = {Does dynamically modelled leaf area improve predictions of land surface water and carbon fluxes? – {Insights} into dynamic vegetation modules},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	shorttitle = {Does dynamically modelled leaf area improve predictions of land surface water and carbon fluxes?},
	url = {https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2101/},
	doi = {10.5194/egusphere-2023-2101},
	abstract = {Abstract. Land-surface models represent exchange processes between soil and atmosphere via the surface by coupling water, energy and carbon fluxes. As it strongly mediates the link between these cycles and, vegetation is an important component of land-surface models. In doing so, some of these models include modules for vegetation dynamics which allow adaptation of vegetation biomass, especially leaf area index, to environmental conditions. Here, we conducted a model-data comparison to investigate whether and how vegetation dynamics in the models improves the representation of vegetation processes and related surface fluxes in two specific models ECLand and Noah-MP in contrast to using prescribed values from look-up tables or satellite-based products. We compare model results with stations from the FLUXNET 2015 dataset covering a range in climate and vegetation types, the MODIS leaf area product, and use more detailed information from the TERENO site “Hohes Holz". With the current implementation, switching vegetation dynamics on did not enhance representativeness of e.g. leaf area index and net ecosystem exchange in ECLand, while Noah-MP improved it only for some sites. The representation of energy fluxes and soil moisture was almost unaffected for both models. Interestingly, for both models, the performance regarding vegetation- and hydrology-related variables was unrelated, such that the weak performance regarding e.g. leaf area index did not detoriate the performance regarding e.g. latent heat flux. One reason, we showed here, might be that implemented ecosystem processes diverge from the observations in their seasonal patterns and variability. Noah-MP includes a seasonal hysteresis of the relationship between leaf area index and gross primary production that cannot be found in observations. The same relationship is represented by a strong linear response in ECLand which substantially underestimates the variability seen in observations. For both, water and carbon fluxes, the current implemented modules for vegetation dynamics in these two models yielded no better model performance compared to runs with static vegetation and prescribed leaf-area climatology.},
	urldate = {2024-11-15},
	publisher = {Biogeochemistry: Modelling, Terrestrial},
	author = {Westermann, Sven Armin and Hildebrandt, Anke and Bousetta, Souhail and Thober, Stephan},
	month = oct,
	year = {2023},
}

Abstract. Land-surface models represent exchange processes between soil and atmosphere via the surface by coupling water, energy and carbon fluxes. As it strongly mediates the link between these cycles and, vegetation is an important component of land-surface models. In doing so, some of these models include modules for vegetation dynamics which allow adaptation of vegetation biomass, especially leaf area index, to environmental conditions. Here, we conducted a model-data comparison to investigate whether and how vegetation dynamics in the models improves the representation of vegetation processes and related surface fluxes in two specific models ECLand and Noah-MP in contrast to using prescribed values from look-up tables or satellite-based products. We compare model results with stations from the FLUXNET 2015 dataset covering a range in climate and vegetation types, the MODIS leaf area product, and use more detailed information from the TERENO site “Hohes Holz". With the current implementation, switching vegetation dynamics on did not enhance representativeness of e.g. leaf area index and net ecosystem exchange in ECLand, while Noah-MP improved it only for some sites. The representation of energy fluxes and soil moisture was almost unaffected for both models. Interestingly, for both models, the performance regarding vegetation- and hydrology-related variables was unrelated, such that the weak performance regarding e.g. leaf area index did not detoriate the performance regarding e.g. latent heat flux. One reason, we showed here, might be that implemented ecosystem processes diverge from the observations in their seasonal patterns and variability. Noah-MP includes a seasonal hysteresis of the relationship between leaf area index and gross primary production that cannot be found in observations. The same relationship is represented by a strong linear response in ECLand which substantially underestimates the variability seen in observations. For both, water and carbon fluxes, the current implemented modules for vegetation dynamics in these two models yielded no better model performance compared to runs with static vegetation and prescribed leaf-area climatology.

@article{wieser_first_2023,
	title = {First implementation of a new cross-disciplinary observation strategy for heavy precipitation events from formation to flooding},
	volume = {82},
	issn = {1866-6280, 1866-6299},
	url = {https://link.springer.com/10.1007/s12665-023-11050-7},
	doi = {10.1007/s12665-023-11050-7},
	abstract = {Abstract
            
              Heavy Precipitation Events (HPE) are the result of enormous quantities of water vapor being transported to a limited area. HPE rainfall rates and volumes cannot be fully stored on and below the land surface, often leading to floods with short forecast lead times that may cause damage to humans, properties, and infrastructure. Toward an improved scientific understanding of the entire process chain from HPE formation to flooding at the catchment scale, we propose an elaborated event-triggered observation concept. It combines flexible mobile observing systems out of the fields of meteorology, hydrology and geophysics with stationary networks to capture atmospheric transport processes, heterogeneous precipitation patterns, land surface and subsurface storage processes, and runoff dynamics. As part of the Helmholtz Research Infrastructure MOSES (Modular Observation Solutions for Earth Systems), the effectiveness of our observation strategy is illustrated by its initial implementation in the Mueglitz river basin (210 km
              2
              ), a headwater catchment of the Elbe in the Eastern Ore Mountains with historical and recent extreme flood events. Punctual radiosonde observations combined with continuous microwave radiometer measurements and back trajectory calculations deliver information about the moisture sources, and initiation and development of HPE. X-band radar observations calibrated by ground-based disdrometers and rain gauges deliver precipitation information with high spatial resolution. Runoff measurements in small sub-catchments complement the discharge time series of the operational network of gauging stations. Closing the catchment water balance at the HPE scale, however, is still challenging. While evapotranspiration is of less importance when studying short-term convective HPE, information on the spatial distribution and on temporal variations of soil moisture and total water storage by stationary and roving cosmic ray measurements and by hybrid terrestrial gravimetry offer prospects for improved quantification of the storage term of the water balance equation. Overall, the cross-disciplinary observation strategy presented here opens up new ways toward an integrative and scale-bridging understanding of event dynamics.},
	language = {en},
	number = {17},
	urldate = {2024-11-15},
	journal = {Environmental Earth Sciences},
	author = {Wieser, Andreas and Güntner, Andreas and Dietrich, Peter and Handwerker, Jan and Khordakova, Dina and Ködel, Uta and Kohler, Martin and Mollenhauer, Hannes and Mühr, Bernhard and Nixdorf, Erik and Reich, Marvin and Rolf, Christian and Schrön, Martin and Schütze, Claudia and Weber, Ute},
	month = sep,
	year = {2023},
	pages = {406},
}

Abstract Heavy Precipitation Events (HPE) are the result of enormous quantities of water vapor being transported to a limited area. HPE rainfall rates and volumes cannot be fully stored on and below the land surface, often leading to floods with short forecast lead times that may cause damage to humans, properties, and infrastructure. Toward an improved scientific understanding of the entire process chain from HPE formation to flooding at the catchment scale, we propose an elaborated event-triggered observation concept. It combines flexible mobile observing systems out of the fields of meteorology, hydrology and geophysics with stationary networks to capture atmospheric transport processes, heterogeneous precipitation patterns, land surface and subsurface storage processes, and runoff dynamics. As part of the Helmholtz Research Infrastructure MOSES (Modular Observation Solutions for Earth Systems), the effectiveness of our observation strategy is illustrated by its initial implementation in the Mueglitz river basin (210 km 2 ), a headwater catchment of the Elbe in the Eastern Ore Mountains with historical and recent extreme flood events. Punctual radiosonde observations combined with continuous microwave radiometer measurements and back trajectory calculations deliver information about the moisture sources, and initiation and development of HPE. X-band radar observations calibrated by ground-based disdrometers and rain gauges deliver precipitation information with high spatial resolution. Runoff measurements in small sub-catchments complement the discharge time series of the operational network of gauging stations. Closing the catchment water balance at the HPE scale, however, is still challenging. While evapotranspiration is of less importance when studying short-term convective HPE, information on the spatial distribution and on temporal variations of soil moisture and total water storage by stationary and roving cosmic ray measurements and by hybrid terrestrial gravimetry offer prospects for improved quantification of the storage term of the water balance equation. Overall, the cross-disciplinary observation strategy presented here opens up new ways toward an integrative and scale-bridging understanding of event dynamics.

@article{winter_droughts_2023,
	title = {Droughts can reduce the nitrogen retention capacity of catchments},
	volume = {27},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1607-7938},
	url = {https://hess.copernicus.org/articles/27/303/2023/},
	doi = {10.5194/hess-27-303-2023},
	abstract = {Abstract. In 2018–2019, Central Europe experienced an unprecedented 2-year drought with severe impacts on society and ecosystems. In this study, we analyzed the impact of this drought on water quality by comparing long-term (1997–2017) nitrate export with 2018–2019 export in a heterogeneous mesoscale catchment. We combined data-driven analysis with process-based modeling to analyze nitrogen retention and the underlying mechanisms in the soils and during subsurface transport. We found a drought-induced shift in concentration–discharge relationships, reflecting exceptionally low riverine nitrate concentrations during dry periods and exceptionally high concentrations during subsequent wet periods. Nitrate loads were up to 73 \% higher compared to the long-term load–discharge relationship. Model simulations confirmed that this increase was driven by decreased denitrification and plant uptake and subsequent flushing of accumulated nitrogen during rewetting. Fast transit times ({\textless}2 months) during wet periods in the upstream sub-catchments enabled a fast water quality response to drought. In contrast, longer transit times downstream ({\textgreater}20 years) inhibited a fast response but potentially contribute to a long-term drought legacy. Overall, our study reveals that severe droughts, which are predicted to become more frequent across Europe, can reduce the nitrogen retention capacity of catchments, thereby intensifying nitrate pollution and threatening water quality.},
	language = {en},
	number = {1},
	urldate = {2024-11-15},
	journal = {Hydrology and Earth System Sciences},
	author = {Winter, Carolin and Nguyen, Tam V. and Musolff, Andreas and Lutz, Stefanie R. and Rode, Michael and Kumar, Rohini and Fleckenstein, Jan H.},
	month = jan,
	year = {2023},
	pages = {303--318},
}

Abstract. In 2018–2019, Central Europe experienced an unprecedented 2-year drought with severe impacts on society and ecosystems. In this study, we analyzed the impact of this drought on water quality by comparing long-term (1997–2017) nitrate export with 2018–2019 export in a heterogeneous mesoscale catchment. We combined data-driven analysis with process-based modeling to analyze nitrogen retention and the underlying mechanisms in the soils and during subsurface transport. We found a drought-induced shift in concentration–discharge relationships, reflecting exceptionally low riverine nitrate concentrations during dry periods and exceptionally high concentrations during subsequent wet periods. Nitrate loads were up to 73 % higher compared to the long-term load–discharge relationship. Model simulations confirmed that this increase was driven by decreased denitrification and plant uptake and subsequent flushing of accumulated nitrogen during rewetting. Fast transit times (\textless2 months) during wet periods in the upstream sub-catchments enabled a fast water quality response to drought. In contrast, longer transit times downstream (\textgreater20 years) inhibited a fast response but potentially contribute to a long-term drought legacy. Overall, our study reveals that severe droughts, which are predicted to become more frequent across Europe, can reduce the nitrogen retention capacity of catchments, thereby intensifying nitrate pollution and threatening water quality.

@article{wu_global-scale_2023,
	title = {A global-scale intercomparison of {Triple} {Collocation} {Analysis}- and ground-based soil moisture time-variant errors derived from different rescaling techniques},
	volume = {285},
	issn = {00344257},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S003442572200493X},
	doi = {10.1016/j.rse.2022.113387},
	language = {en},
	urldate = {2024-11-15},
	journal = {Remote Sensing of Environment},
	author = {Wu, Kai and Ryu, Dongryeol and Wagner, Wolfgang and Hu, Zhongmin},
	month = feb,
	year = {2023},
	pages = {113387},
}

@article{xi_evaluating_2023,
	title = {Evaluating the {Effects} of {Precipitation} and {Evapotranspiration} on {Soil} {Moisture} {Variability} {Within} {CMIP5} {Using} {SMAP} and {ERA5} {Data}},
	volume = {59},
	issn = {0043-1397, 1944-7973},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR034225},
	doi = {10.1029/2022WR034225},
	abstract = {Abstract
            
              The effects of precipitation (Pr) and evapotranspiration (ET) on surface soil moisture (SSM) play an essential role in the land‐atmosphere system. Here we evaluate multimodel differences of these effects within the Coupled Model Intercomparison Project Phase 5 (CMIP5) compared to Soil Moisture Active Passive (SMAP) products and ECMWF Reanalysis v5 (ERA5) as references in a frequency domain. The variability of SSM, Pr, and ET within three frequency bands (1/7 ∼ 1/30 days
              −1
              , 1/30 ∼ 1/90 days
              −1
              , and 1/90 ∼ 1/365 days
              −1
              ) after normalization is quantified using Fourier transform. We analyze the impact of ET and Pr on SSM variability based on a transfer function assuming that these variables form a linear time‐invariant (LTI) system. For the total effects of ET and Pr on SSM variability, the CMIP5 estimations are smaller than the reference data in the two higher frequency bands and are larger than the reference data in the lowest frequency band. Besides, the effects on SSM by Pr and ET are found to be different across the three frequency bands. In each frequency band, the variability of the factor that dominates SSM (i.e., Pr or ET) from CMIP5 is smaller than that from the references. This study identifies the spatiotemporal distribution of differences between CMIP5 models and references (SMAP and ERA5) in simulating ET and Pr effects on SSM within three frequency bands. This study provides insightful information on how soil moisture variability is affected by varying precipitation and evapotranspiration at different time scales within Earth System Models.
            
          , 
            Plain Language Summary
            Climate is influenced by the interactions between the land surface and atmosphere boundary, and soil moisture is a key component of these physical processes. Precipitation and evapotranspiration, as two major variables involved in these interactions, have been largely regarded as essential processes affecting soil moisture dynamics. However, Earth System Models have large uncertainties in simulating these effects. This study compares the average performance of 14 Earth System Models in capturing the effects of precipitation and evapotranspiration on surface soil moisture variability. We find that (a) soil moisture is mainly affected by precipitation at weekly to seasonal time scales and by evapotranspiration at seasonal to annual time scales; (b) compared to two largely used reference data, the total effects of precipitation and evapotranspiration on soil moisture is smaller at weekly to seasonal time scales and are larger at seasonal to annual time scale; and (c) spatially, models tend to simulate less variability of precipitation or evapotranspiration as a major control on surface soil moisture.
          , 
            Key Points
            
              
                
                  The effects of precipitation and evapotranspiration on soil moisture variability can be analyzed in a frequency domain
                
                
                  Precipitation dominates weekly to seasonal variability and evapotranspiration dominates seasonal to annual variability of soil moisture
                
                
                  Earth System Models shall be improved in simulating soil moisture temporal variabilities},
	language = {en},
	number = {5},
	urldate = {2024-11-15},
	journal = {Water Resources Research},
	author = {Xi, Xuan and Zhuang, Qianlai and Kim, Seungbum and Gentine, Pierre},
	month = may,
	year = {2023},
	pages = {e2022WR034225},
}

Abstract The effects of precipitation (Pr) and evapotranspiration (ET) on surface soil moisture (SSM) play an essential role in the land‐atmosphere system. Here we evaluate multimodel differences of these effects within the Coupled Model Intercomparison Project Phase 5 (CMIP5) compared to Soil Moisture Active Passive (SMAP) products and ECMWF Reanalysis v5 (ERA5) as references in a frequency domain. The variability of SSM, Pr, and ET within three frequency bands (1/7 ∼ 1/30 days −1 , 1/30 ∼ 1/90 days −1 , and 1/90 ∼ 1/365 days −1 ) after normalization is quantified using Fourier transform. We analyze the impact of ET and Pr on SSM variability based on a transfer function assuming that these variables form a linear time‐invariant (LTI) system. For the total effects of ET and Pr on SSM variability, the CMIP5 estimations are smaller than the reference data in the two higher frequency bands and are larger than the reference data in the lowest frequency band. Besides, the effects on SSM by Pr and ET are found to be different across the three frequency bands. In each frequency band, the variability of the factor that dominates SSM (i.e., Pr or ET) from CMIP5 is smaller than that from the references. This study identifies the spatiotemporal distribution of differences between CMIP5 models and references (SMAP and ERA5) in simulating ET and Pr effects on SSM within three frequency bands. This study provides insightful information on how soil moisture variability is affected by varying precipitation and evapotranspiration at different time scales within Earth System Models. , Plain Language Summary Climate is influenced by the interactions between the land surface and atmosphere boundary, and soil moisture is a key component of these physical processes. Precipitation and evapotranspiration, as two major variables involved in these interactions, have been largely regarded as essential processes affecting soil moisture dynamics. However, Earth System Models have large uncertainties in simulating these effects. This study compares the average performance of 14 Earth System Models in capturing the effects of precipitation and evapotranspiration on surface soil moisture variability. We find that (a) soil moisture is mainly affected by precipitation at weekly to seasonal time scales and by evapotranspiration at seasonal to annual time scales; (b) compared to two largely used reference data, the total effects of precipitation and evapotranspiration on soil moisture is smaller at weekly to seasonal time scales and are larger at seasonal to annual time scale; and (c) spatially, models tend to simulate less variability of precipitation or evapotranspiration as a major control on surface soil moisture. , Key Points The effects of precipitation and evapotranspiration on soil moisture variability can be analyzed in a frequency domain Precipitation dominates weekly to seasonal variability and evapotranspiration dominates seasonal to annual variability of soil moisture Earth System Models shall be improved in simulating soil moisture temporal variabilities

@misc{xiangzhen_kong_synergistic_2023,
	title = {Synergistic effects of warming and internal nutrient loading interfere with the long-term stability of lake restoration and induce sudden re-eutrophication},
	copyright = {Creative Commons Attribution 4.0 International, Open Access},
	url = {https://zenodo.org/record/7580961},
	doi = {10.5281/ZENODO.7580961},
	abstract = {{\textless}strong{\textgreater}This repository contains the dataset linked to the following publication:{\textless}/strong{\textgreater} {\textless}strong{\textgreater}Article title: {\textless}/strong{\textgreater}Synergistic effects of warming and internal nutrient loading interfere with the long-term stability of lake restoration and induce sudden re-eutrophication {\textless}strong{\textgreater}Journal: {\textless}/strong{\textgreater}{\textless}em{\textgreater}Environmental Science \& Technology{\textless}/em{\textgreater} {\textless}strong{\textgreater}DOI{\textless}/strong{\textgreater}: 10.1021/acs.est.2c07181 {\textless}strong{\textgreater}Abstract:{\textless}/strong{\textgreater} Phosphorus (P) precipitation is among the most effective treatments to mitigate lake eutrophication. However, after a period of high effectiveness, studies have shown possible re-eutrophication and the return of harmful algal blooms. While such abrupt ecological changes were attributed to the internal P loading, the role of lake warming and its potential synergistic effects with internal loading, thus far, has been understudied. Here, in a eutrophic lake in central Germany, we quantified the driving mechanisms of the abrupt re-eutrophication and cyanobacterial blooms in 2016 (30 years after the first P precipitation). A process-based lake ecosystem model (GOTM-WET) was established using a high-frequency monitoring dataset covering contrasting trophic states. Model analyses suggested that the internal P release accounted for 68\% of the cyanobacterial biomass proliferation, while lake warming contributed to 32\%, including direct effects via promoting growth (18\%) and synergistic effects via intensifying internal P loading (14\%). The model further showed that the synergy was attributed to prolonged lake hypolimnion warming and oxygen depletion. Our study unravels the substantial role of lake warming in promoting cyanobacterial blooms in re-eutrophicated lakes. The warming effects on cyanobacteria via promoting internal loading need more attention in lake management, particularly for urban lakes. {\textless}strong{\textgreater}SYNOPSIS: {\textless}/strong{\textgreater}Warming synergistically promotes re-eutrophication with internal nutrient loading and exacerbates cyanobacterial blooms in urban lakes 30 years after phosphorus mitigation. {\textless}strong{\textgreater}Data description {\textless}/strong{\textgreater}by Xiangzhen Kong (xzkong@niglas.ac.cn), 2023-02-20 ---Wet chemical analysis on water samples taken at five depths (0.5, 2.5, 5.0, 7.0 and 9.0 m) from the deepest point in the lake (BA1) at biweekly intervals from 2018.5-2021.8. File name: BAB\_BA1\_TN\_mgL.obs (total nitrogen concentration) BAB\_BA1\_NH4\_mgL.obs (ammonium nitrogen concentration) BAB\_BA1\_NO3\_mgL.obs (nitrate nitrogen concentration) BAB\_BA1\_TP\_mgL.obs (total phosphorus concentration) BAB\_BA1\_SRP\_mgL.obs (Soluble reactive phosphorus concentration) BAB\_BA1\_DP\_mgL.obs (dissolved P concentration) BAB\_BA1\_DOC\_mgL.obs (Dissolved organic carbon concentration) BAB\_BA1\_Si\_mgL.obs (dissolved silicon concentration) BAB\_BA1\_Chla\_HPLC\_DIN\_mgL.obs (Chl-a concentration) ---CTD probe profile data from the deepest point in the lake (BA1) from 2017.8 to 2021.8 at biweekly basis with approximately 0.1 m vertical resolution File name: t\_prof\_file\_barleber\_ctm644.obs (water temperature) oxy\_prof\_file\_barleber\_ctm644 (Dissolved oxygen) turb\_prof\_file\_barleber\_ctm644.obs (Turbidity) chla\_prof\_file\_barleber\_ctm644.obs (Chl-a concentration) ---BBE probe profile data from the deepest point in the lake (BA1) from 2017.8 to 2021.8 at biweekly basis with approximately 0.1 m vertical resolution File name: totalChla\_prof\_file\_barleber\_FP2101.obs (Chl-a concentration) bluegreen\_prof\_file\_barleber\_FP2101.obs (Blue-green algae Chl-a concentration) green\_prof\_file\_barleber\_FP2101.obs (Green algae Chl-a concentration) diatom\_prof\_file\_barleber\_FP2101.obs (Diatom Chl-a concentration)},
	urldate = {2024-11-15},
	publisher = {Zenodo},
	author = {{Xiangzhen Kong} and Determann, Maria and Andersen, Tobias Kuhlmann and Barbosa, Carolina Cerqueira and {Tallent Dadi} and Janssen, Annette B.G. and {Ma. Cristina Paule-Mercado} and Pujoni, Diego Guimarães Florencio and Schultze, Martin and Rinke, Karsten},
	month = feb,
	year = {2023},
	keywords = {GOTM-WET, climate change, cyanobacterial blooms, eutrophicatoin, internal loading, phosphorus precipitation, urban lake},
}

\textlessstrong\textgreaterThis repository contains the dataset linked to the following publication:\textless/strong\textgreater \textlessstrong\textgreaterArticle title: \textless/strong\textgreaterSynergistic effects of warming and internal nutrient loading interfere with the long-term stability of lake restoration and induce sudden re-eutrophication \textlessstrong\textgreaterJournal: \textless/strong\textgreater\textlessem\textgreaterEnvironmental Science & Technology\textless/em\textgreater \textlessstrong\textgreaterDOI\textless/strong\textgreater: 10.1021/acs.est.2c07181 \textlessstrong\textgreaterAbstract:\textless/strong\textgreater Phosphorus (P) precipitation is among the most effective treatments to mitigate lake eutrophication. However, after a period of high effectiveness, studies have shown possible re-eutrophication and the return of harmful algal blooms. While such abrupt ecological changes were attributed to the internal P loading, the role of lake warming and its potential synergistic effects with internal loading, thus far, has been understudied. Here, in a eutrophic lake in central Germany, we quantified the driving mechanisms of the abrupt re-eutrophication and cyanobacterial blooms in 2016 (30 years after the first P precipitation). A process-based lake ecosystem model (GOTM-WET) was established using a high-frequency monitoring dataset covering contrasting trophic states. Model analyses suggested that the internal P release accounted for 68% of the cyanobacterial biomass proliferation, while lake warming contributed to 32%, including direct effects via promoting growth (18%) and synergistic effects via intensifying internal P loading (14%). The model further showed that the synergy was attributed to prolonged lake hypolimnion warming and oxygen depletion. Our study unravels the substantial role of lake warming in promoting cyanobacterial blooms in re-eutrophicated lakes. The warming effects on cyanobacteria via promoting internal loading need more attention in lake management, particularly for urban lakes. \textlessstrong\textgreaterSYNOPSIS: \textless/strong\textgreaterWarming synergistically promotes re-eutrophication with internal nutrient loading and exacerbates cyanobacterial blooms in urban lakes 30 years after phosphorus mitigation. \textlessstrong\textgreaterData description \textless/strong\textgreaterby Xiangzhen Kong (xzkong@niglas.ac.cn), 2023-02-20 —Wet chemical analysis on water samples taken at five depths (0.5, 2.5, 5.0, 7.0 and 9.0 m) from the deepest point in the lake (BA1) at biweekly intervals from 2018.5-2021.8. File name: BAB_BA1_TN_mgL.obs (total nitrogen concentration) BAB_BA1_NH4_mgL.obs (ammonium nitrogen concentration) BAB_BA1_NO3_mgL.obs (nitrate nitrogen concentration) BAB_BA1_TP_mgL.obs (total phosphorus concentration) BAB_BA1_SRP_mgL.obs (Soluble reactive phosphorus concentration) BAB_BA1_DP_mgL.obs (dissolved P concentration) BAB_BA1_DOC_mgL.obs (Dissolved organic carbon concentration) BAB_BA1_Si_mgL.obs (dissolved silicon concentration) BAB_BA1_Chla_HPLC_DIN_mgL.obs (Chl-a concentration) —CTD probe profile data from the deepest point in the lake (BA1) from 2017.8 to 2021.8 at biweekly basis with approximately 0.1 m vertical resolution File name: t_prof_file_barleber_ctm644.obs (water temperature) oxy_prof_file_barleber_ctm644 (Dissolved oxygen) turb_prof_file_barleber_ctm644.obs (Turbidity) chla_prof_file_barleber_ctm644.obs (Chl-a concentration) —BBE probe profile data from the deepest point in the lake (BA1) from 2017.8 to 2021.8 at biweekly basis with approximately 0.1 m vertical resolution File name: totalChla_prof_file_barleber_FP2101.obs (Chl-a concentration) bluegreen_prof_file_barleber_FP2101.obs (Blue-green algae Chl-a concentration) green_prof_file_barleber_FP2101.obs (Green algae Chl-a concentration) diatom_prof_file_barleber_FP2101.obs (Diatom Chl-a concentration)

@article{xie_monitoring_2023,
	title = {Monitoring of carbon-water fluxes at {Eurasian} meteorological stations using random forest and remote sensing},
	volume = {10},
	issn = {2052-4463},
	url = {https://www.nature.com/articles/s41597-023-02473-9},
	doi = {10.1038/s41597-023-02473-9},
	abstract = {Abstract
            
              Simulating the carbon-water fluxes at more widely distributed meteorological stations based on the sparsely and unevenly distributed eddy covariance flux stations is needed to accurately understand the carbon-water cycle of terrestrial ecosystems. We established a new framework consisting of machine learning, determination coefficient (R
              2
              ), Euclidean distance, and remote sensing (RS), to simulate the daily net ecosystem carbon dioxide exchange (NEE) and water flux (WF) of the Eurasian meteorological stations using a random forest model or/and RS. The daily NEE and WF datasets with RS-based information (NEE-RS and WF-RS) for 3774 and 4427 meteorological stations during 2002–2020 were produced, respectively. And the daily NEE and WF datasets without RS-based information (NEE-WRS and WF-WRS) for 4667 and 6763 meteorological stations during 1983–2018 were generated, respectively. For each meteorological station, the carbon-water fluxes meet accuracy requirements and have quasi-observational properties. These four carbon-water flux datasets have great potential to improve the assessments of the ecosystem carbon-water dynamics.},
	language = {en},
	number = {1},
	urldate = {2024-11-15},
	journal = {Scientific Data},
	author = {Xie, Mingjuan and Ma, Xiaofei and Wang, Yuangang and Li, Chaofan and Shi, Haiyang and Yuan, Xiuliang and Hellwich, Olaf and Chen, Chunbo and Zhang, Wenqiang and Zhang, Chen and Ling, Qing and Gao, Ruixiang and Zhang, Yu and Ochege, Friday Uchenna and Frankl, Amaury and De Maeyer, Philippe and Buchmann, Nina and Feigenwinter, Iris and Olesen, Jørgen E. and Juszczak, Radoslaw and Jacotot, Adrien and Korrensalo, Aino and Pitacco, Andrea and Varlagin, Andrej and Shekhar, Ankit and Lohila, Annalea and Carrara, Arnaud and Brut, Aurore and Kruijt, Bart and Loubet, Benjamin and Heinesch, Bernard and Chojnicki, Bogdan and Helfter, Carole and Vincke, Caroline and Shao, Changliang and Bernhofer, Christian and Brümmer, Christian and Wille, Christian and Tuittila, Eeva-Stiina and Nemitz, Eiko and Meggio, Franco and Dong, Gang and Lanigan, Gary and Niedrist, Georg and Wohlfahrt, Georg and Zhou, Guoyi and Goded, Ignacio and Gruenwald, Thomas and Olejnik, Janusz and Jansen, Joachim and Neirynck, Johan and Tuovinen, Juha-Pekka and Zhang, Junhui and Klumpp, Katja and Pilegaard, Kim and Šigut, Ladislav and Klemedtsson, Leif and Tezza, Luca and Hörtnagl, Lukas and Urbaniak, Marek and Roland, Marilyn and Schmidt, Marius and Sutton, Mark A. and Hehn, Markus and Saunders, Matthew and Mauder, Matthias and Aurela, Mika and Korkiakoski, Mika and Du, Mingyuan and Vendrame, Nadia and Kowalska, Natalia and Leahy, Paul G. and Alekseychik, Pavel and Shi, Peili and Weslien, Per and Chen, Shiping and Fares, Silvano and Friborg, Thomas and Tallec, Tiphaine and Kato, Tomomichi and Sachs, Torsten and Maximov, Trofim and Di Cella, Umberto Morra and Moderow, Uta and Li, Yingnian and He, Yongtao and Kosugi, Yoshiko and Luo, Geping},
	month = sep,
	year = {2023},
	pages = {587},
}

Abstract Simulating the carbon-water fluxes at more widely distributed meteorological stations based on the sparsely and unevenly distributed eddy covariance flux stations is needed to accurately understand the carbon-water cycle of terrestrial ecosystems. We established a new framework consisting of machine learning, determination coefficient (R 2 ), Euclidean distance, and remote sensing (RS), to simulate the daily net ecosystem carbon dioxide exchange (NEE) and water flux (WF) of the Eurasian meteorological stations using a random forest model or/and RS. The daily NEE and WF datasets with RS-based information (NEE-RS and WF-RS) for 3774 and 4427 meteorological stations during 2002–2020 were produced, respectively. And the daily NEE and WF datasets without RS-based information (NEE-WRS and WF-WRS) for 4667 and 6763 meteorological stations during 1983–2018 were generated, respectively. For each meteorological station, the carbon-water fluxes meet accuracy requirements and have quasi-observational properties. These four carbon-water flux datasets have great potential to improve the assessments of the ecosystem carbon-water dynamics.

@article{xiong_assessing_2023,
	title = {Assessing the {Reliability} of {Global} {Carbon} {Flux} {Dataset} {Compared} to {Existing} {Datasets} and {Their} {Spatiotemporal} {Characteristics}},
	volume = {11},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2225-1154},
	url = {https://www.mdpi.com/2225-1154/11/10/205},
	doi = {10.3390/cli11100205},
	abstract = {Land carbon fluxes play a critical role in ecosystems, and acquiring a comprehensive global database of carbon fluxes is essential for understanding the Earth’s carbon cycle. The primary methods of obtaining the spatial distribution of land carbon fluxes include utilizing machine learning models based on in situ measurements, estimating through satellite remote sensing, and simulating ecosystem models. Recently, an innovative machine learning product known as the Global Carbon Flux Dataset (GCFD) has been released. In this study, we assessed the reliability of the GCFD by comparing it with existing data products, including two machine learning products (FLUXCOM and NIES (National Institute for Environmental Studies)), two ecosystem model products (TRENDY and EC-LUE (eddy covariance–light use efficiency model)), and one remote sensing product (Global Land Surface Satellite), on both site and global scales. Our findings indicate that, in terms of average absolute difference, the spatial distribution of the GCFD is most similar to the NIES product, albeit with slightly larger discrepancies compared to the other two types of products. When using site observations as the benchmark, gross primary production (GPP), respiration of ecosystem (RECO), and net ecosystem exchange of machine learning products exhibit higher R2 (ranging from 0.57 to 0.85, 0.53–0.79, and 0.31–0.70, respectively) compared to model products and remote sensing products. Furthermore, we analyzed the spatial and temporal distribution characteristics of carbon fluxes in various regions. The results demonstrate an upward trend in both GPP and RECO over the past two decades, while NEE exhibits an opposite trend. This trend is particularly pronounced in tropical regions, where higher GPP is observed in tropical, subtropical, and oceanic climate zones. Additionally, two remote sensing variables that influence changes in carbon fluxes, i.e., fraction absorbed photosynthetically active radiation and leaf area index, exhibit relatively consistent spatial and temporal characteristics. Overall, our study can provide valuable insights into different types of carbon flux products and contribute to understanding the general features of global carbon fluxes.},
	language = {en},
	number = {10},
	urldate = {2024-11-15},
	journal = {Climate},
	author = {Xiong, Zili and Shangguan, Wei and Nourani, Vahid and Li, Qingliang and Lu, Xingjie and Li, Lu and Huang, Feini and Zhang, Ye and Sun, Wenye and Yuan, Hua and Li, Xueyan},
	month = oct,
	year = {2023},
	pages = {205},
}

Land carbon fluxes play a critical role in ecosystems, and acquiring a comprehensive global database of carbon fluxes is essential for understanding the Earth’s carbon cycle. The primary methods of obtaining the spatial distribution of land carbon fluxes include utilizing machine learning models based on in situ measurements, estimating through satellite remote sensing, and simulating ecosystem models. Recently, an innovative machine learning product known as the Global Carbon Flux Dataset (GCFD) has been released. In this study, we assessed the reliability of the GCFD by comparing it with existing data products, including two machine learning products (FLUXCOM and NIES (National Institute for Environmental Studies)), two ecosystem model products (TRENDY and EC-LUE (eddy covariance–light use efficiency model)), and one remote sensing product (Global Land Surface Satellite), on both site and global scales. Our findings indicate that, in terms of average absolute difference, the spatial distribution of the GCFD is most similar to the NIES product, albeit with slightly larger discrepancies compared to the other two types of products. When using site observations as the benchmark, gross primary production (GPP), respiration of ecosystem (RECO), and net ecosystem exchange of machine learning products exhibit higher R2 (ranging from 0.57 to 0.85, 0.53–0.79, and 0.31–0.70, respectively) compared to model products and remote sensing products. Furthermore, we analyzed the spatial and temporal distribution characteristics of carbon fluxes in various regions. The results demonstrate an upward trend in both GPP and RECO over the past two decades, while NEE exhibits an opposite trend. This trend is particularly pronounced in tropical regions, where higher GPP is observed in tropical, subtropical, and oceanic climate zones. Additionally, two remote sensing variables that influence changes in carbon fluxes, i.e., fraction absorbed photosynthetically active radiation and leaf area index, exhibit relatively consistent spatial and temporal characteristics. Overall, our study can provide valuable insights into different types of carbon flux products and contribute to understanding the general features of global carbon fluxes.

@article{yang_reconstruction_2023,
	title = {Reconstruction of a spatially seamless, daily {SMAP} ({SSD}\_SMAP) surface soil moisture dataset from 2015 to 2021},
	volume = {621},
	issn = {00221694},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0022169423005218},
	doi = {10.1016/j.jhydrol.2023.129579},
	language = {en},
	urldate = {2024-11-15},
	journal = {Journal of Hydrology},
	author = {Yang, Haoxuan and Wang, Qunming},
	month = jun,
	year = {2023},
	pages = {129579},
}

@article{yang_upscaling_2023,
	title = {Upscaling {Tracer}‐{Aided} {Ecohydrological} {Modeling} to {Larger} {Catchments}: {Implications} for {Process} {Representation} and {Heterogeneity} in {Landscape} {Organization}},
	volume = {59},
	issn = {0043-1397, 1944-7973},
	shorttitle = {Upscaling {Tracer}‐{Aided} {Ecohydrological} {Modeling} to {Larger} {Catchments}},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR033033},
	doi = {10.1029/2022WR033033},
	abstract = {Abstract
            
              Stable isotopes of water are ideal tracers to integrate into process‐based models, advancing ecohydrological understanding. Current tracer‐aided ecohydrological modeling is mostly conducted in relatively small‐scale catchments, due to limited tracer data availability and often highly damped stream isotope signals in larger catchments ({\textgreater}100 km
              2
              ). Recent model developments have prioritized better spatial representation, offering new potential for advancing upscaling in tracer‐aided modeling. Here, we adapted the fully distributed EcH
              2
              O‐iso model to the Selke catchment (456 km
              2
              , Germany), incorporating monthly sampled isotopes from seven sites between 2012 and 2017. Parameter sensitivity analysis indicated that the information content of isotope data was generally complementary to discharge and more sensitive to runoff partitioning, soil water and energy dynamics. Multi‐criteria calibrations revealed that inclusion of isotopes could significantly improve discharge performance during validations and isotope simulations, resulting in more reasonable estimates of the seasonality of stream water ages. However, capturing isotopic signals of highly non‐linear near‐surface processes remained challenging for the upscaled model, but still allowed for plausible simulation of water ages reflecting non‐stationarity in transport and mixing. The detailed modeling also helped unravel spatio‐temporally varying patterns of water storage‐flux‐age interactions and their interplay under severe drought conditions. Embracing the upscaling challenges, this study demonstrated that even coarsely sampled isotope data can be of value in aiding ecohydrological modeling and consequent process representation in larger catchments. The derived innovative insights into ecohydrological functioning at scales commensurate with management decision making, are of particular importance for guiding science‐based measures for tackling environmental changes.
            
          , 
            Key Points
            
              
                
                  
                    Process‐based tracer‐aided ecohydrological modeling is upscaled to {\textgreater}100 km
                    2
                    catchments using stable water isotopes
                  
                
                
                  Isotopes benefit large‐scale modeling in substantially improving model robustness and reliability of water age estimates
                
                
                  Larger‐scale water partitioning and drought responses are controlled by heterogeneity in catchment organization},
	language = {en},
	number = {3},
	urldate = {2024-11-15},
	journal = {Water Resources Research},
	author = {Yang, Xiaoqiang and Tetzlaff, Doerthe and Müller, Christin and Knöller, Kay and Borchardt, Dietrich and Soulsby, Chris},
	month = mar,
	year = {2023},
	pages = {e2022WR033033},
}

Abstract Stable isotopes of water are ideal tracers to integrate into process‐based models, advancing ecohydrological understanding. Current tracer‐aided ecohydrological modeling is mostly conducted in relatively small‐scale catchments, due to limited tracer data availability and often highly damped stream isotope signals in larger catchments (\textgreater100 km 2 ). Recent model developments have prioritized better spatial representation, offering new potential for advancing upscaling in tracer‐aided modeling. Here, we adapted the fully distributed EcH 2 O‐iso model to the Selke catchment (456 km 2 , Germany), incorporating monthly sampled isotopes from seven sites between 2012 and 2017. Parameter sensitivity analysis indicated that the information content of isotope data was generally complementary to discharge and more sensitive to runoff partitioning, soil water and energy dynamics. Multi‐criteria calibrations revealed that inclusion of isotopes could significantly improve discharge performance during validations and isotope simulations, resulting in more reasonable estimates of the seasonality of stream water ages. However, capturing isotopic signals of highly non‐linear near‐surface processes remained challenging for the upscaled model, but still allowed for plausible simulation of water ages reflecting non‐stationarity in transport and mixing. The detailed modeling also helped unravel spatio‐temporally varying patterns of water storage‐flux‐age interactions and their interplay under severe drought conditions. Embracing the upscaling challenges, this study demonstrated that even coarsely sampled isotope data can be of value in aiding ecohydrological modeling and consequent process representation in larger catchments. The derived innovative insights into ecohydrological functioning at scales commensurate with management decision making, are of particular importance for guiding science‐based measures for tackling environmental changes. , Key Points Process‐based tracer‐aided ecohydrological modeling is upscaled to \textgreater100 km 2 catchments using stable water isotopes Isotopes benefit large‐scale modeling in substantially improving model robustness and reliability of water age estimates Larger‐scale water partitioning and drought responses are controlled by heterogeneity in catchment organization

@article{yang_large-stream_2023,
	title = {Large-stream nitrate retention patterns shift during droughts: {Seasonal} to sub-daily insights from high-frequency data-model fusion},
	volume = {243},
	issn = {00431354},
	shorttitle = {Large-stream nitrate retention patterns shift during droughts},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0043135423007832},
	doi = {10.1016/j.watres.2023.120347},
	language = {en},
	urldate = {2024-11-15},
	journal = {Water Research},
	author = {Yang, Xiaoqiang and Zhang, Xiaolin and Graeber, Daniel and Hensley, Robert and Jarvie, Helen and Lorke, Andreas and Borchardt, Dietrich and Li, Qiongfang and Rode, Michael},
	month = sep,
	year = {2023},
	pages = {120347},
}

@inproceedings{yang_toward_2023,
	address = {Pasadena, CA, USA},
	title = {Toward {Field} {Level} {Drought} and {Irrigation} {Monitoring} {Using} {Machine} {Learning} {Based} {High}-{Resolution} {Soil} {Moisture} ({ML}-{HRSM}) {Data}},
	copyright = {https://doi.org/10.15223/policy-029},
	isbn = {9798350320107},
	url = {https://ieeexplore.ieee.org/document/10283282/},
	doi = {10.1109/IGARSS52108.2023.10283282},
	urldate = {2024-11-15},
	booktitle = {{IGARSS} 2023 - 2023 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium}},
	publisher = {IEEE},
	author = {Yang, Zhengwei and Huang, Jingyi and Zhang, Zhou},
	month = jul,
	year = {2023},
	pages = {3570--3573},
}

@article{yi_assessment_2023,
	title = {Assessment of five {SMAP} soil moisture products using {ISMN} ground-based measurements over varied environmental conditions},
	volume = {619},
	issn = {00221694},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0022169423002676},
	doi = {10.1016/j.jhydrol.2023.129325},
	language = {en},
	urldate = {2024-11-15},
	journal = {Journal of Hydrology},
	author = {Yi, Chuanxiang and Li, Xiaojun and Zeng, Jiangyuan and Fan, Lei and Xie, Zhiqing and Gao, Lun and Xing, Zanpin and Ma, Hongliang and Boudah, Antoine and Zhou, Hongwei and Zhou, Wenjun and Sheng, Ye and Dong, Tianxiang and Wigneron, Jean-Pierre},
	month = apr,
	year = {2023},
	pages = {129325},
}

@article{zhang_improved_2023,
	title = {An improved light use efficiency model by considering canopy nitrogen concentrations and multiple environmental factors},
	volume = {332},
	issn = {01681923},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0168192323000539},
	doi = {10.1016/j.agrformet.2023.109359},
	language = {en},
	urldate = {2024-11-15},
	journal = {Agricultural and Forest Meteorology},
	author = {Zhang, Helin and Bai, Jia and Sun, Rui and Wang, Yan and Xiao, Zhiqiang and Song, Bowen},
	month = apr,
	year = {2023},
	pages = {109359},
}

@article{zhang_effect_2023,
	title = {The effect of relative humidity on eddy covariance latent heat flux measurements and its implication for partitioning into transpiration and evaporation},
	volume = {330},
	issn = {01681923},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0168192322004919},
	doi = {10.1016/j.agrformet.2022.109305},
	language = {en},
	urldate = {2024-11-15},
	journal = {Agricultural and Forest Meteorology},
	author = {Zhang, Weijie and Jung, Martin and Migliavacca, Mirco and Poyatos, Rafael and Miralles, Diego G. and El-Madany, Tarek S. and Galvagno, Marta and Carrara, Arnaud and Arriga, Nicola and Ibrom, Andreas and Mammarella, Ivan and Papale, Dario and Cleverly, Jamie R. and Liddell, Michael and Wohlfahrt, Georg and Markwitz, Christian and Mauder, Matthias and Paul-Limoges, Eugenie and Schmidt, Marius and Wolf, Sebastian and Brümmer, Christian and Arain, M. Altaf and Fares, Silvano and Kato, Tomomichi and Ardö, Jonas and Oechel, Walter and Hanson, Chad and Korkiakoski, Mika and Biraud, Sébastien and Steinbrecher, Rainer and Billesbach, Dave and Montagnani, Leonardo and Woodgate, William and Shao, Changliang and Carvalhais, Nuno and Reichstein, Markus and Nelson, Jacob A.},
	month = mar,
	year = {2023},
	pages = {109305},
}

@article{zhang_soil_2023,
	title = {Soil {Moisture} and {Atmospheric} {Aridity} {Impact} {Spatio}‐{Temporal} {Changes} in {Evapotranspiration} at a {Global} {Scale}},
	volume = {128},
	issn = {2169-897X, 2169-8996},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022JD038046},
	doi = {10.1029/2022JD038046},
	abstract = {Abstract
            
              Evapotranspiration (ET) constitutes the water exchange from land to the atmosphere, which in turn modulates precipitation and soil moisture (SM). Multiple lines of evidence document complex feedbacks between changes in ET and temperature, atmospheric CO
              2
              and vegetation greening. However, the existing analyses on global changes in ET do not account for the direct effects of SM supply and atmospheric water demand, expressed by vapor pressure deficit (VPD), while considering multiple environmental variables. Here we evaluated the performance of ET products using 140 flux towers included in the FLUXNET database. All ET products show reasonable performance, with an overall correlation higher than 0.7 and better performance at a higher latitude. From analysis of the ensemble mean of annual ET, we show insignificant (
              P
               = 0.06) trends in global ET during 1982–2020 and a significantly (
              P
               {\textless} 0.01) increasing trend during 2002–2020. Changes in GLEAM ET generally exert a positive response to changes in SM and a negative response to changes in VPD. Yet, these effects are not globally consistent and are largely determined by changes in vegetation transpiration. Using our finding as a benchmark, Earth System Models mostly reproduce the positive response of ET to SM with less coupling strength, while showing negative effects of VPD on ET with stronger coupling strength. Our study highlights that concurrent soil drying and atmospheric aridity could intensify water exchanges and the importance of realistically representing SM‐VPD‐ET interactions in models for accurate predictions of the hydrological cycle.
            
          , 
            Plain Language Summary
            Evapotranspiration (ET) links the water exchange between land and the atmosphere, and thereby plays an important role in regulating precipitation and soil moisture (SM). In this study, using FLUXNET in situ observations we demonstrate the high performance of different ET products (GLEAM, ERA5, GLDAS, and MERRA2) which are widely used to characterize long‐term changes in global ET. Decreases in SM and increases in vapor pressure deficit (VPD) are expected to lead to decreases in ET worldwide. Using our observations as a benchmark, most of Earth System Models participating in the Coupled Model Intercomparison Project Phase 6 underestimate the positive response of ET to SM, while overestimating the negative effects of VPD on ET. This study provides a comprehensive understanding of the impacts of SM and VPD on global changes in ET.
          , 
            Key Points
            
              
                
                  Insignificant trends in global evapotranspiration (ET) over the last four decades, but significantly increasing trends in ET during recent two decades
                
                
                  ET generally exerts a positive response to soil moisture (SM) and a negative response to vapor pressure deficit (VPD), but effects are not globally consistent
                
                
                  Earth system models underestimate the response of ET to SM while overestimating the response of ET to VPD},
	language = {en},
	number = {8},
	urldate = {2024-11-15},
	journal = {Journal of Geophysical Research: Atmospheres},
	author = {Zhang, Wenmin and Koch, Julian and Wei, Fangli and Zeng, Zhenzhong and Fang, Zhongxiang and Fensholt, Rasmus},
	month = apr,
	year = {2023},
	pages = {e2022JD038046},
}

Abstract Evapotranspiration (ET) constitutes the water exchange from land to the atmosphere, which in turn modulates precipitation and soil moisture (SM). Multiple lines of evidence document complex feedbacks between changes in ET and temperature, atmospheric CO 2 and vegetation greening. However, the existing analyses on global changes in ET do not account for the direct effects of SM supply and atmospheric water demand, expressed by vapor pressure deficit (VPD), while considering multiple environmental variables. Here we evaluated the performance of ET products using 140 flux towers included in the FLUXNET database. All ET products show reasonable performance, with an overall correlation higher than 0.7 and better performance at a higher latitude. From analysis of the ensemble mean of annual ET, we show insignificant ( P  = 0.06) trends in global ET during 1982–2020 and a significantly ( P  \textless 0.01) increasing trend during 2002–2020. Changes in GLEAM ET generally exert a positive response to changes in SM and a negative response to changes in VPD. Yet, these effects are not globally consistent and are largely determined by changes in vegetation transpiration. Using our finding as a benchmark, Earth System Models mostly reproduce the positive response of ET to SM with less coupling strength, while showing negative effects of VPD on ET with stronger coupling strength. Our study highlights that concurrent soil drying and atmospheric aridity could intensify water exchanges and the importance of realistically representing SM‐VPD‐ET interactions in models for accurate predictions of the hydrological cycle. , Plain Language Summary Evapotranspiration (ET) links the water exchange between land and the atmosphere, and thereby plays an important role in regulating precipitation and soil moisture (SM). In this study, using FLUXNET in situ observations we demonstrate the high performance of different ET products (GLEAM, ERA5, GLDAS, and MERRA2) which are widely used to characterize long‐term changes in global ET. Decreases in SM and increases in vapor pressure deficit (VPD) are expected to lead to decreases in ET worldwide. Using our observations as a benchmark, most of Earth System Models participating in the Coupled Model Intercomparison Project Phase 6 underestimate the positive response of ET to SM, while overestimating the negative effects of VPD on ET. This study provides a comprehensive understanding of the impacts of SM and VPD on global changes in ET. , Key Points Insignificant trends in global evapotranspiration (ET) over the last four decades, but significantly increasing trends in ET during recent two decades ET generally exerts a positive response to soil moisture (SM) and a negative response to vapor pressure deficit (VPD), but effects are not globally consistent Earth system models underestimate the response of ET to SM while overestimating the response of ET to VPD

@article{zhang_new_2023,
	title = {New data‐driven method for estimation of net ecosystem carbon exchange at meteorological stations effectively increases the global carbon flux data},
	volume = {14},
	issn = {2041-210X, 2041-210X},
	url = {https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.14188},
	doi = {10.1111/2041-210X.14188},
	abstract = {Abstract
            
              
                
                  The eddy covariance (EC) flux stations have great limitations in the evaluation of the global net ecosystem carbon exchange (NEE) and in the uncertainty reduction due to their sparse and uneven distribution and spatial representation. If the EC stations are linked with widely distributed meteorological stations using machine learning (ML) and remote sensing, it will play a big role in effectively improving the accuracy of the global NEE assessment and reducing uncertainty.
                
                
                  
                    In this study, we developed a framework for estimating NEE at meteorological stations. We first optimized the hyperparameters and input variables of the ML model based on the optimization method called an adaptive genetic algorithm. Then, we developed 566 random forest (RF)‐based NEE estimation models by the strategy of spatial leave‐out‐one cross‐validation. We innovatively established the Euclidean distance‐based accuracy projection algorithm of the
                    R
                    square (
                    R
                    2
                    ), which could test the accuracy of each model to estimate the NEE of the specific flux at the weather station. Only the model with the highest
                    R
                    2
                    was selected from the models with a prediction accuracy of
                    R
                    2
                     {\textgreater} 0.5 for the specific meteorological stations to estimate its NEE.
                  
                
                
                  
                    4674 out of 10,289 weather stations around the world might match at least one of the 566 NEE estimation models with a projected accuracy of
                    R
                    2
                     {\textgreater} 0.5. The NEE estimation models we screened for the meteorological stations showed a reliable performance and a higher accuracy than the former studies. The NEE values of the most (96.9\%) screened meteorological stations around the world are negative (carbon sink) and most (65.3\%) of those showed an increasing trend in the mean annual NEE (carbon sink).
                  
                
                
                  
                    The NEE dataset produced at the meteorological stations could be used as a supplement to the EC observations and quasi‐observation data to assess the NEE products of the global grid. The NEE dataset is publicly available via the figshare with
                    https://doi.org/10.6084/m9.figshare.20485563.v1
                    .},
	language = {en},
	number = {9},
	urldate = {2024-11-15},
	journal = {Methods in Ecology and Evolution},
	author = {Zhang, Wenqiang and Luo, Geping and Yuan, Xiuliang and Li, Chaofan and Xie, Mingjuan and Wang, Yuangang and Ma, Xiaofei and Shi, Haiyang and Hamdi, Rafiq and Hellwich, Olaf and Ma, Xiumei and Termonia, Piet and De Maeyer, Philippe},
	month = sep,
	year = {2023},
	pages = {2449--2463},
}

Abstract The eddy covariance (EC) flux stations have great limitations in the evaluation of the global net ecosystem carbon exchange (NEE) and in the uncertainty reduction due to their sparse and uneven distribution and spatial representation. If the EC stations are linked with widely distributed meteorological stations using machine learning (ML) and remote sensing, it will play a big role in effectively improving the accuracy of the global NEE assessment and reducing uncertainty. In this study, we developed a framework for estimating NEE at meteorological stations. We first optimized the hyperparameters and input variables of the ML model based on the optimization method called an adaptive genetic algorithm. Then, we developed 566 random forest (RF)‐based NEE estimation models by the strategy of spatial leave‐out‐one cross‐validation. We innovatively established the Euclidean distance‐based accuracy projection algorithm of the R square ( R 2 ), which could test the accuracy of each model to estimate the NEE of the specific flux at the weather station. Only the model with the highest R 2 was selected from the models with a prediction accuracy of R 2  \textgreater 0.5 for the specific meteorological stations to estimate its NEE. 4674 out of 10,289 weather stations around the world might match at least one of the 566 NEE estimation models with a projected accuracy of R 2  \textgreater 0.5. The NEE estimation models we screened for the meteorological stations showed a reliable performance and a higher accuracy than the former studies. The NEE values of the most (96.9%) screened meteorological stations around the world are negative (carbon sink) and most (65.3%) of those showed an increasing trend in the mean annual NEE (carbon sink). The NEE dataset produced at the meteorological stations could be used as a supplement to the EC observations and quasi‐observation data to assess the NEE products of the global grid. The NEE dataset is publicly available via the figshare with https://doi.org/10.6084/m9.figshare.20485563.v1 .

@article{zhang_disentangling_2023,
	title = {Disentangling {In}‐{Stream} {Nitrate} {Uptake} {Pathways} {Based} on {Two}‐{Station} {High}‐{Frequency} {Monitoring} in {High}‐{Order} {Streams}},
	volume = {59},
	issn = {0043-1397, 1944-7973},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR032329},
	doi = {10.1029/2022WR032329},
	abstract = {Abstract
            
              In‐stream nitrate (NO
              3
              −
              ) uptake in rivers involves complex autotrophic and heterotrophic pathways, which often vary spatiotemporally due to biotic and abiotic drivers. High‐frequency monitoring of NO
              3
              −
              mass balance between upstream and downstream measurement sites can quantitatively disentangle multi‐path NO
              3
              −
              uptake dynamics at the reach scale. However, this approach remains limited to a few river types and has not been fully explored for higher‐order streams with varying hydro‐morphological and biogeochemical conditions. We conducted two‐station 15‐min monitoring in five high‐order stream reaches in central Germany, calculating the NO
              3
              −
              ‐N mass balance and whole‐stream metabolism based on time series of NO
              3
              −
              ‐N and dissolved oxygen, respectively. With thorough considerations of lateral inputs, the calculated net NO
              3
              −
              ‐N uptake rates (
              
              ) differed substantially among campaigns (ranging from −151.1 to 357.6 mg N m
              2
              d
              −1
              , with cases of negative values representing net NO
              3
              −
              ‐N release), and exhibited higher
              
              during the post‐wet season than during the dry season. Subtracting autotrophic assimilation (
              
              , stoichiometrically coupled to stream metabolism) from
              
              ,
              
              represented the net balance of heterotrophic NO
              3
              −
              ‐N uptake (
              
               {\textgreater} 0, the dominance of denitrification and heterotrophic assimilation) and NO
              3
              −
              ‐N release (
              
               {\textless} 0, the dominance of nitrification/mineralization). This rarely reported uptake pathway contributed substantially to
              
              patterns, especially during post‐wet seasons; moreover, it appeared to exhibit various diel patterns, and for
              
              {\textgreater} 0, diel minima occurred during the daytime. These findings advance our understanding of complex reach‐scale N‐retention processes and can help develop future modeling concepts at the river‐network scale.
            
          , 
            Key Points
            
              
                
                  Two‐station monitoring disentangles nitrate uptake pathways and their temporal dynamics in heterogeneous high‐order streams
                
                
                  Net nitrate uptake exhibits high variation, seasonally and across reach conditions, with cases of consistent net release
                
                
                  Heterotrophic nitrate uptake and release were higher during post‐wet seasons and exhibited various diel patterns},
	language = {en},
	number = {3},
	urldate = {2024-11-15},
	journal = {Water Resources Research},
	author = {Zhang, Xiaolin and Yang, Xiaoqiang and Hensley, Robert and Lorke, Andreas and Rode, Michael},
	month = mar,
	year = {2023},
	pages = {e2022WR032329},
}

Abstract In‐stream nitrate (NO 3 − ) uptake in rivers involves complex autotrophic and heterotrophic pathways, which often vary spatiotemporally due to biotic and abiotic drivers. High‐frequency monitoring of NO 3 − mass balance between upstream and downstream measurement sites can quantitatively disentangle multi‐path NO 3 − uptake dynamics at the reach scale. However, this approach remains limited to a few river types and has not been fully explored for higher‐order streams with varying hydro‐morphological and biogeochemical conditions. We conducted two‐station 15‐min monitoring in five high‐order stream reaches in central Germany, calculating the NO 3 − ‐N mass balance and whole‐stream metabolism based on time series of NO 3 − ‐N and dissolved oxygen, respectively. With thorough considerations of lateral inputs, the calculated net NO 3 − ‐N uptake rates ( ) differed substantially among campaigns (ranging from −151.1 to 357.6 mg N m 2 d −1 , with cases of negative values representing net NO 3 − ‐N release), and exhibited higher during the post‐wet season than during the dry season. Subtracting autotrophic assimilation ( , stoichiometrically coupled to stream metabolism) from , represented the net balance of heterotrophic NO 3 − ‐N uptake (  \textgreater 0, the dominance of denitrification and heterotrophic assimilation) and NO 3 − ‐N release (  \textless 0, the dominance of nitrification/mineralization). This rarely reported uptake pathway contributed substantially to patterns, especially during post‐wet seasons; moreover, it appeared to exhibit various diel patterns, and for \textgreater 0, diel minima occurred during the daytime. These findings advance our understanding of complex reach‐scale N‐retention processes and can help develop future modeling concepts at the river‐network scale. , Key Points Two‐station monitoring disentangles nitrate uptake pathways and their temporal dynamics in heterogeneous high‐order streams Net nitrate uptake exhibits high variation, seasonally and across reach conditions, with cases of consistent net release Heterotrophic nitrate uptake and release were higher during post‐wet seasons and exhibited various diel patterns

@article{zhang_generation_2023,
	title = {Generation of global 1 km daily soil moisture product from 2000 to 2020 using ensemble learning},
	volume = {15},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1866-3516},
	url = {https://essd.copernicus.org/articles/15/2055/2023/},
	doi = {10.5194/essd-15-2055-2023},
	abstract = {Abstract. Motivated by the lack of long-term global soil moisture products
with both high spatial and temporal resolutions, a global 1 km daily
spatiotemporally continuous soil moisture product (GLASS SM) was generated
from 2000 to 2020 using an ensemble learning model (eXtreme Gradient
Boosting – XGBoost). The model was developed by integrating multiple
datasets, including albedo, land surface temperature, and leaf area index
products from the Global Land Surface Satellite (GLASS) product suite, as
well as the European reanalysis (ERA5-Land) soil moisture product, in situ
soil moisture dataset from the International Soil Moisture Network (ISMN),
and auxiliary datasets (Multi-Error-Removed Improved-Terrain (MERIT) DEM and
Global gridded soil information (SoilGrids)). Given the relatively large-scale differences between point-scale
in situ measurements and other datasets, the triple collocation (TC) method
was adopted to select the representative soil moisture stations and their
measurements for creating the training samples. To fully evaluate the model
performance, three validation strategies were explored: random,
site independent, and year independent. Results showed that although the
XGBoost model achieved the highest accuracy on the random test samples, it
was clearly a result of model overfitting. Meanwhile, training the model
with representative stations selected by the TC method could considerably
improve its performance for site- or year-independent test samples. The
overall validation accuracy of the model trained using representative
stations on the site-independent test samples, which was least likely to be
overfitted, was a correlation coefficient (R) of 0.715 and root mean square
error (RMSE) of 0.079 m3 m−3. Moreover, compared to the model
developed without station filtering, the validation accuracies of the model
trained with representative stations improved significantly for most stations,
with the median R and unbiased RMSE (ubRMSE) of the model for each station
increasing from 0.64 to 0.74 and decreasing from 0.055 to 0.052 m3 m−3, respectively. Further validation of the GLASS SM product across
four independent soil moisture networks revealed its ability to capture the
temporal dynamics of measured soil moisture (R=0.69–0.89; ubRMSE =
0.033–0.048 m3 m−3). Lastly, the intercomparison between the
GLASS SM product and two global microwave soil moisture datasets – the 1 km
Soil Moisture Active Passive/Sentinel-1 L2 Radiometer/Radar soil moisture
product and the European Space Agency Climate Change Initiative combined
soil moisture product at 0.25∘ – indicated that the derived
product maintained a more complete spatial coverage and exhibited high
spatiotemporal consistency with those two soil moisture products. The annual
average GLASS SM dataset from 2000 to 2020 can be freely downloaded from
https://doi.org/10.5281/zenodo.7172664 (Zhang et al., 2022a),
and the complete product at daily scale is available at
http://glass.umd.edu/soil\_moisture/ (last access: 12 May 2023).},
	language = {en},
	number = {5},
	urldate = {2024-11-15},
	journal = {Earth System Science Data},
	author = {Zhang, Yufang and Liang, Shunlin and Ma, Han and He, Tao and Wang, Qian and Li, Bing and Xu, Jianglei and Zhang, Guodong and Liu, Xiaobang and Xiong, Changhao},
	month = may,
	year = {2023},
	pages = {2055--2079},
}

Abstract. Motivated by the lack of long-term global soil moisture products with both high spatial and temporal resolutions, a global 1 km daily spatiotemporally continuous soil moisture product (GLASS SM) was generated from 2000 to 2020 using an ensemble learning model (eXtreme Gradient Boosting – XGBoost). The model was developed by integrating multiple datasets, including albedo, land surface temperature, and leaf area index products from the Global Land Surface Satellite (GLASS) product suite, as well as the European reanalysis (ERA5-Land) soil moisture product, in situ soil moisture dataset from the International Soil Moisture Network (ISMN), and auxiliary datasets (Multi-Error-Removed Improved-Terrain (MERIT) DEM and Global gridded soil information (SoilGrids)). Given the relatively large-scale differences between point-scale in situ measurements and other datasets, the triple collocation (TC) method was adopted to select the representative soil moisture stations and their measurements for creating the training samples. To fully evaluate the model performance, three validation strategies were explored: random, site independent, and year independent. Results showed that although the XGBoost model achieved the highest accuracy on the random test samples, it was clearly a result of model overfitting. Meanwhile, training the model with representative stations selected by the TC method could considerably improve its performance for site- or year-independent test samples. The overall validation accuracy of the model trained using representative stations on the site-independent test samples, which was least likely to be overfitted, was a correlation coefficient (R) of 0.715 and root mean square error (RMSE) of 0.079 m3 m−3. Moreover, compared to the model developed without station filtering, the validation accuracies of the model trained with representative stations improved significantly for most stations, with the median R and unbiased RMSE (ubRMSE) of the model for each station increasing from 0.64 to 0.74 and decreasing from 0.055 to 0.052 m3 m−3, respectively. Further validation of the GLASS SM product across four independent soil moisture networks revealed its ability to capture the temporal dynamics of measured soil moisture (R=0.69–0.89; ubRMSE = 0.033–0.048 m3 m−3). Lastly, the intercomparison between the GLASS SM product and two global microwave soil moisture datasets – the 1 km Soil Moisture Active Passive/Sentinel-1 L2 Radiometer/Radar soil moisture product and the European Space Agency Climate Change Initiative combined soil moisture product at 0.25∘ – indicated that the derived product maintained a more complete spatial coverage and exhibited high spatiotemporal consistency with those two soil moisture products. The annual average GLASS SM dataset from 2000 to 2020 can be freely downloaded from https://doi.org/10.5281/zenodo.7172664 (Zhang et al., 2022a), and the complete product at daily scale is available at http://glass.umd.edu/soil_moisture/ (last access: 12 May 2023).

@article{zheng_21-year_2023,
	title = {A 21-year dataset (2000–2020) of gap-free global daily surface soil moisture at 1-km grid resolution},
	volume = {10},
	issn = {2052-4463},
	url = {https://www.nature.com/articles/s41597-023-01991-w},
	doi = {10.1038/s41597-023-01991-w},
	abstract = {Abstract
            
              Global soil moisture estimates from current satellite missions are suffering from inherent discontinuous observations and coarse spatial resolution, which limit applications especially at the fine spatial scale. This study developed a dataset of global gap-free surface soil moisture (SSM) at daily 1-km resolution from 2000 to 2020. This is achieved based on the European Space Agency - Climate Change Initiative (ESA-CCI) SSM combined product at 0.25° resolution. Firstly, an operational gap-filling method was developed to fill the missing data in the ESA-CCI SSM product using SSM of the ERA5 reanalysis dataset. Random Forest algorithm was then adopted to disaggregate the coarse-resolution SSM to 1-km, with the help of International Soil Moisture Network
              in-situ
              observations and other optical remote sensing datasets. The generated 1-km SSM product had good accuracy, with a high correlation coefficent (0.89) and a low unbiased Root Mean Square Error (0.045 m
              3
              /m
              3
              ) by cross-validation. To the best of our knowledge, this is currently the only long-term global gap-free 1-km soil moisture dataset by far.},
	language = {en},
	number = {1},
	urldate = {2024-11-15},
	journal = {Scientific Data},
	author = {Zheng, Chaolei and Jia, Li and Zhao, Tianjie},
	month = mar,
	year = {2023},
	pages = {139},
}

Abstract Global soil moisture estimates from current satellite missions are suffering from inherent discontinuous observations and coarse spatial resolution, which limit applications especially at the fine spatial scale. This study developed a dataset of global gap-free surface soil moisture (SSM) at daily 1-km resolution from 2000 to 2020. This is achieved based on the European Space Agency - Climate Change Initiative (ESA-CCI) SSM combined product at 0.25° resolution. Firstly, an operational gap-filling method was developed to fill the missing data in the ESA-CCI SSM product using SSM of the ERA5 reanalysis dataset. Random Forest algorithm was then adopted to disaggregate the coarse-resolution SSM to 1-km, with the help of International Soil Moisture Network in-situ observations and other optical remote sensing datasets. The generated 1-km SSM product had good accuracy, with a high correlation coefficent (0.89) and a low unbiased Root Mean Square Error (0.045 m 3 /m 3 ) by cross-validation. To the best of our knowledge, this is currently the only long-term global gap-free 1-km soil moisture dataset by far.

@misc{zheng_evaluation_2023,
	title = {Evaluation of reanalysis soil moisture products using {Cosmic} {Ray} {Neutron} {Sensor} observations across the globe},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	url = {https://hess.copernicus.org/preprints/hess-2023-224/hess-2023-224.pdf},
	doi = {10.5194/hess-2023-224},
	abstract = {Abstract. Accurate soil moisture information is vital for flood and drought predictions, crop growth and agricultural water management. Reanalysis soil moisture products with multi-decadal temporal coverage are gradually becoming a good alternative for providing global soil moisture data in various applications compared to in-situ measurements and satellite products. Much effort has been devoted to evaluating the performance of soil moisture products, yet the scale discrepancy between point measurements and grid cell soil moisture products limits the assessment quality. As the land surface and hydrological modelling community evolve towards the next generation of (sub)kilometer resolution models, Cosmic Ray Neutron Sensors (CRNS) that provide estimates of root-zone soil moisture at the field scale ({\textasciitilde}250 m radius from the sensor and up to 0.7 m deep), may consequently be more suitable for soil moisture product evaluation as they cover a relatively larger footprint, when compared to traditional methods. In this study, we perform a comprehensive evaluation of seven widely-used reanalysis soil moisture products (ERA5-Land, CFSv2, MERRA2, JRA55, GLDAS-Noah, CRA40 and GLEAM datasets) against 135 CRNS sites from the UK, Europe, USA and Australia. We evaluate the products using six metrics capturing different aspects of soil moisture dynamics. Results show that all reanalysis products exhibit good temporal correlation with the measurements, with the median of temporal correlation coefficient (R) values spanning from 0.69 to 0.79, though large deviations are found at sites with seasonally varying vegetation cover. Poor performance is observed across products for soil moisture anomalies timeseries, with R values varying from 0.49 to 0.70. The performance of reanalysis products differs greatly across regions, climate, land covers and topographic conditions. In general, all products tend to overestimate in arid climates and underestimate in humid regions as well as grassland. Most reanalysis products perform poorly in steep terrain. Relatively low temporal correlation and high Bias are detected in some sites from west of the UK, which might be associated with relatively low bulk density and high soil organic carbon. Overall, ERA5-Land, CFSv2, CRA40, GLEAM exhibit superior performance compared to MERRA2, GLDAS-Noah and JRA55. We recommend ERA5-Land and CFSv2 should be used in humid climates, whereas CRA40 and GLEAM perform better in arid regions. GLEAM is more effective in shrubland regions. Our findings also provide insights on directions for improvement of soil moisture products for product developers.},
	urldate = {2024-11-15},
	publisher = {Global hydrology/Instruments and observation techniques},
	author = {Zheng, Yanchen and Coxon, Gemma and Woods, Ross and Power, Daniel and Rico-Ramirez, Miguel Angel and McJannet, David and Rosolem, Rafael and Li, Jianzhu and Feng, Ping},
	month = oct,
	year = {2023},
}

Abstract. Accurate soil moisture information is vital for flood and drought predictions, crop growth and agricultural water management. Reanalysis soil moisture products with multi-decadal temporal coverage are gradually becoming a good alternative for providing global soil moisture data in various applications compared to in-situ measurements and satellite products. Much effort has been devoted to evaluating the performance of soil moisture products, yet the scale discrepancy between point measurements and grid cell soil moisture products limits the assessment quality. As the land surface and hydrological modelling community evolve towards the next generation of (sub)kilometer resolution models, Cosmic Ray Neutron Sensors (CRNS) that provide estimates of root-zone soil moisture at the field scale (~250 m radius from the sensor and up to 0.7 m deep), may consequently be more suitable for soil moisture product evaluation as they cover a relatively larger footprint, when compared to traditional methods. In this study, we perform a comprehensive evaluation of seven widely-used reanalysis soil moisture products (ERA5-Land, CFSv2, MERRA2, JRA55, GLDAS-Noah, CRA40 and GLEAM datasets) against 135 CRNS sites from the UK, Europe, USA and Australia. We evaluate the products using six metrics capturing different aspects of soil moisture dynamics. Results show that all reanalysis products exhibit good temporal correlation with the measurements, with the median of temporal correlation coefficient (R) values spanning from 0.69 to 0.79, though large deviations are found at sites with seasonally varying vegetation cover. Poor performance is observed across products for soil moisture anomalies timeseries, with R values varying from 0.49 to 0.70. The performance of reanalysis products differs greatly across regions, climate, land covers and topographic conditions. In general, all products tend to overestimate in arid climates and underestimate in humid regions as well as grassland. Most reanalysis products perform poorly in steep terrain. Relatively low temporal correlation and high Bias are detected in some sites from west of the UK, which might be associated with relatively low bulk density and high soil organic carbon. Overall, ERA5-Land, CFSv2, CRA40, GLEAM exhibit superior performance compared to MERRA2, GLDAS-Noah and JRA55. We recommend ERA5-Land and CFSv2 should be used in humid climates, whereas CRA40 and GLEAM perform better in arid regions. GLEAM is more effective in shrubland regions. Our findings also provide insights on directions for improvement of soil moisture products for product developers.

@article{zhou_stream_2023,
	title = {Stream restoration can reduce nitrate levels in agricultural landscapes},
	volume = {896},
	issn = {00489697},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0048969723035349},
	doi = {10.1016/j.scitotenv.2023.164911},
	language = {en},
	urldate = {2024-11-15},
	journal = {Science of The Total Environment},
	author = {Zhou, Xiangqian and Jomaa, Seifeddine and Yang, Xiaoqiang and Merz, Ralf and Wang, Yanping and Rode, Michael},
	month = oct,
	year = {2023},
	pages = {164911},
}

@article{zhou_prognostic_2023,
	title = {A prognostic vegetation phenology model to predict seasonal maximum and time series of global leaf area index using climate variables},
	volume = {342},
	issn = {01681923},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S016819232300429X},
	doi = {10.1016/j.agrformet.2023.109739},
	language = {en},
	urldate = {2024-11-15},
	journal = {Agricultural and Forest Meteorology},
	author = {Zhou, Xuewen and Xin, Qinchuan and Zhang, Shulei and Delzon, Sylvain and Dai, Yongjiu},
	month = nov,
	year = {2023},
	pages = {109739},
}

@article{zhou_long-term_2023,
	title = {Long-term effects of rewetting and drought on {GPP} in a temperate peatland based on satellite remote sensing data},
	volume = {882},
	issn = {00489697},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0048969723020144},
	doi = {10.1016/j.scitotenv.2023.163395},
	language = {en},
	urldate = {2024-11-15},
	journal = {Science of The Total Environment},
	author = {Zhou, Yinying and Sachs, Torsten and Li, Zhan and Pang, Yuwen and Xu, Junfeng and Kalhori, Aram and Wille, Christian and Peng, Xiaoxue and Fu, Xianhao and Wu, Yanfei and Wu, Lin},
	month = jul,
	year = {2023},
	pages = {163395},
}

@article{zhu_end--end_2023,
	title = {An end-to-end satellite-based {GPP} estimation model devoid of meteorological and land cover data},
	volume = {331},
	issn = {01681923},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S016819232300031X},
	doi = {10.1016/j.agrformet.2023.109337},
	language = {en},
	urldate = {2024-11-15},
	journal = {Agricultural and Forest Meteorology},
	author = {Zhu, Wenquan and Zhao, Cenliang and Xie, Zhiying},
	month = mar,
	year = {2023},
	pages = {109337},
}

@article{zohaib_impact_2023,
	title = {Impact of {Vegetation} {Gradient} and {Land} {Cover} {Conditions} on {Soil} {Moisture} {Retrievals} {From} {Different} {Frequencies} and {Acquisition} {Times} of {AMSR2}},
	volume = {61},
	copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
	issn = {0196-2892, 1558-0644},
	url = {https://ieeexplore.ieee.org/document/10092811/},
	doi = {10.1109/TGRS.2023.3264505},
	urldate = {2024-11-15},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	author = {Zohaib, Muhammad and Kim, Hyunglok and Lakshmi, Venkataraman},
	year = {2023},
	pages = {1--14},
}

@article{zweifel_networking_2023,
	title = {Networking the forest infrastructure towards near real-time monitoring – {A} white paper},
	volume = {872},
	issn = {00489697},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0048969723007830},
	doi = {10.1016/j.scitotenv.2023.162167},
	language = {en},
	urldate = {2024-11-15},
	journal = {Science of The Total Environment},
	author = {Zweifel, Roman and Pappas, Christoforos and Peters, Richard L. and Babst, Flurin and Balanzategui, Daniel and Basler, David and Bastos, Ana and Beloiu, Mirela and Buchmann, Nina and Bose, Arun K. and Braun, Sabine and Damm, Alexander and D'Odorico, Petra and Eitel, Jan U.H. and Etzold, Sophia and Fonti, Patrick and Rouholahnejad Freund, Elham and Gessler, Arthur and Haeni, Matthias and Hoch, Günter and Kahmen, Ansgar and Körner, Christian and Krejza, Jan and Krumm, Frank and Leuchner, Michael and Leuschner, Christoph and Lukovic, Mirko and Martínez-Vilalta, Jordi and Matula, Radim and Meesenburg, Henning and Meir, Patrick and Plichta, Roman and Poyatos, Rafael and Rohner, Brigitte and Ruehr, Nadine and Salomón, Roberto L. and Scharnweber, Tobias and Schaub, Marcus and Steger, David N. and Steppe, Kathy and Still, Christopher and Stojanović, Marko and Trotsiuk, Volodymyr and Vitasse, Yann and Von Arx, Georg and Wilmking, Martin and Zahnd, Cedric and Sterck, Frank},
	month = may,
	year = {2023},
	pages = {162167},
}