Publications 2024

@article{akter_estimating_2024,
	title = {Estimating soil moisture from environmental gamma radiation monitoring data},
	volume = {23},
	issn = {1539-1663, 1539-1663},
	url = {https://acsess.onlinelibrary.wiley.com/doi/10.1002/vzj2.20384},
	doi = {10.1002/vzj2.20384},
	abstract = {Abstract
            Soil moisture (SM) information is invaluable for a wide range of applications, including weather forecasting, hydrological and land surface modeling, and agricultural production. However, there is still a lack of sensing information that adequately represents root‐zone SM for longer periods and larger spatial scales. One option for root‐zone SM observation is terrestrial gamma radiation (TGR), as it is inversely related to SM. Hence, the near real‐time data of more than 5000 environmental gamma radiation (EGR) monitoring stations archived by the EUropean Radiological Data Exchange Platform (EURDEP) is a potential source to develop a root‐zone SM product for Europe without extra investments in SM sensors. This study aims to investigate to what extent the EURDEP data can be used for SM estimation. For this, two EGR monitoring stations were equipped with in situ SM sensors to measure reference SM. The terrestrial component of EGR was extracted after eliminating the contributions of rain washout and secondary cosmic radiation, and used to obtain a functional relationship with SM. We predicted the weekly volumetric SM with a root mean square error of 7\%–9\% from TGR measurements. Nevertheless, we believe that this technique, due to its greater penetration depth and long data legacy, can provide useful data complementary to satellite‐based remote sensing techniques to estimate root‐zone SM at the continental scale.
          , 
            Core Ideas
            
              
                
                  An extensive early warning monitoring network for environmental gamma radiation (EGR) is maintained in Europe.
                
                
                  Since soil moisture influences EGR, this database could be used to derive continental soil moisture products.
                
                
                  To test this, two monitoring stations in Germany were selected and equipped with reference soil moisture sensors.
                
                
                  From the terrestrial component of EGR, soil moisture was determined with an error of 7–9 vol.\%.
                
              
            
          , 
            Plain Language Summary
            
              Information on the temporal dynamics of SM across a large area is vital for many sectors. An extensive network for monitoring EGR detectors that has been operated across Europe after the Chernobyl nuclear accident is a potential source for deriving continental‐scale SM information without additional costs. We investigated how accurately SM can be estimated from the data of two of such detectors. The results showed that weekly SM estimates with an accuracy of 0.07–0.09 cm
              3
              cm
              −3
              are feasible after adequate data processing accounting for other factors affecting EGR. We also discussed possible sources that affected the accuracy of the SM estimates and provided directions for further research. Despite the current limitations, EGR data show potential for estimating SM across Europe.},
	language = {en},
	number = {6},
	urldate = {2024-11-21},
	journal = {Vadose Zone Journal},
	author = {Akter, Sonia and Huisman, Johan Alexander and Bogena, Heye Reemt},
	month = nov,
	year = {2024},
	pages = {e20384},
}

Abstract Soil moisture (SM) information is invaluable for a wide range of applications, including weather forecasting, hydrological and land surface modeling, and agricultural production. However, there is still a lack of sensing information that adequately represents root‐zone SM for longer periods and larger spatial scales. One option for root‐zone SM observation is terrestrial gamma radiation (TGR), as it is inversely related to SM. Hence, the near real‐time data of more than 5000 environmental gamma radiation (EGR) monitoring stations archived by the EUropean Radiological Data Exchange Platform (EURDEP) is a potential source to develop a root‐zone SM product for Europe without extra investments in SM sensors. This study aims to investigate to what extent the EURDEP data can be used for SM estimation. For this, two EGR monitoring stations were equipped with in situ SM sensors to measure reference SM. The terrestrial component of EGR was extracted after eliminating the contributions of rain washout and secondary cosmic radiation, and used to obtain a functional relationship with SM. We predicted the weekly volumetric SM with a root mean square error of 7%–9% from TGR measurements. Nevertheless, we believe that this technique, due to its greater penetration depth and long data legacy, can provide useful data complementary to satellite‐based remote sensing techniques to estimate root‐zone SM at the continental scale. , Core Ideas An extensive early warning monitoring network for environmental gamma radiation (EGR) is maintained in Europe. Since soil moisture influences EGR, this database could be used to derive continental soil moisture products. To test this, two monitoring stations in Germany were selected and equipped with reference soil moisture sensors. From the terrestrial component of EGR, soil moisture was determined with an error of 7–9 vol.%. , Plain Language Summary Information on the temporal dynamics of SM across a large area is vital for many sectors. An extensive network for monitoring EGR detectors that has been operated across Europe after the Chernobyl nuclear accident is a potential source for deriving continental‐scale SM information without additional costs. We investigated how accurately SM can be estimated from the data of two of such detectors. The results showed that weekly SM estimates with an accuracy of 0.07–0.09 cm 3 cm −3 are feasible after adequate data processing accounting for other factors affecting EGR. We also discussed possible sources that affected the accuracy of the SM estimates and provided directions for further research. Despite the current limitations, EGR data show potential for estimating SM across Europe.

@article{amelung_architecture_2024,
	title = {Architecture of soil microaggregates: {Advanced} methodologies to explore properties and functions},
	volume = {187},
	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},
	number = {1},
	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 = feb,
	year = {2024},
	pages = {17--50},
}

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{barrios_evapotranspiration_2024,
	title = {Evapotranspiration and surface energy fluxes across {Europe}, {Africa} and {Eastern} {South} {America} throughout the operational life of the {Meteosat} second generation satellite},
	issn = {2049-6060, 2049-6060},
	url = {https://rmets.onlinelibrary.wiley.com/doi/10.1002/gdj3.235},
	doi = {10.1002/gdj3.235},
	abstract = {Abstract
            The exchange of energy and water fluxes between the Earth's surface and the atmosphere is crucial to a series of processes that impact human life. Noteworthy examples are agriculture yields, water availability, intensity and extent of droughts and the ability of ecosystems to provide services to society. The relevance of these processes has motivated the Satellite Application Facility on Land Surface Analysis (LSA SAF) programme to set up an operational framework to estimate—among other variables—evapotranspiration (ET) and surface energy fluxes (SEF) on the basis of observations by the Meteosat Second Generation (MSG) satellite. The LSA SAF programme has recently launched the reprocessing of the ET and SEF datasets on the basis of the most recent version of the algorithm and homogenous forcing datasets. This article features the resulting ET/SEF dataset, a Data Record that encompasses the period from the start of the operational life of the MSG satellite (2004) till 2020 and covers the field of view of the MSG satellite (i.e. Europe, Africa and Eastern South America). Details on the algorithm and the datasets driving the ET/SEF estimates are also provided as well as a quality assessment.},
	language = {en},
	urldate = {2025-02-14},
	journal = {Geoscience Data Journal},
	author = {Barrios, J. M. and Arboleda, A. and Dutra, E. and Trigo, I. and Gellens‐Meulenberghs, F.},
	month = jan,
	year = {2024},
	pages = {gdj3.235},
}

Abstract The exchange of energy and water fluxes between the Earth's surface and the atmosphere is crucial to a series of processes that impact human life. Noteworthy examples are agriculture yields, water availability, intensity and extent of droughts and the ability of ecosystems to provide services to society. The relevance of these processes has motivated the Satellite Application Facility on Land Surface Analysis (LSA SAF) programme to set up an operational framework to estimate—among other variables—evapotranspiration (ET) and surface energy fluxes (SEF) on the basis of observations by the Meteosat Second Generation (MSG) satellite. The LSA SAF programme has recently launched the reprocessing of the ET and SEF datasets on the basis of the most recent version of the algorithm and homogenous forcing datasets. This article features the resulting ET/SEF dataset, a Data Record that encompasses the period from the start of the operational life of the MSG satellite (2004) till 2020 and covers the field of view of the MSG satellite (i.e. Europe, Africa and Eastern South America). Details on the algorithm and the datasets driving the ET/SEF estimates are also provided as well as a quality assessment.

@inproceedings{bayat_can_2024,
	address = {Athens, Greece},
	title = {Can {We} {Trust} {Geostationary} {SEVIRI}-{MSG} {Evapotranspiration} {Products} {Across} {Europe}: {Six}-{Dimensional} {Accuracy} {Assessment}},
	copyright = {https://doi.org/10.15223/policy-029},
	isbn = {9798350360325},
	shorttitle = {Can {We} {Trust} {Geostationary} {SEVIRI}-{MSG} {Evapotranspiration} {Products} {Across} {Europe}},
	url = {https://ieeexplore.ieee.org/document/10642052/},
	doi = {10.1109/IGARSS53475.2024.10642052},
	urldate = {2024-11-21},
	booktitle = {{IGARSS} 2024 - 2024 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium}},
	publisher = {IEEE},
	author = {Bayat, Bagher and Raj, Rahul and Graf, Alexander and Vereecken, Harry and Montzka, Carsten},
	month = jul,
	year = {2024},
	pages = {6188--6192},
}

@article{bayat_comprehensive_2024,
	title = {Comprehensive accuracy assessment of long-term geostationary {SEVIRI}-{MSG} evapotranspiration estimates across {Europe}},
	volume = {301},
	issn = {00344257},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0034425723004261},
	doi = {10.1016/j.rse.2023.113875},
	language = {en},
	urldate = {2024-11-21},
	journal = {Remote Sensing of Environment},
	author = {Bayat, Bagher and Raj, Rahul and Graf, Alexander and Vereecken, Harry and Montzka, Carsten},
	month = feb,
	year = {2024},
	pages = {113875},
}

@article{bazzi_assimilating_2024,
	title = {Assimilating {Sentinel}-2 data in a modified vegetation photosynthesis and respiration model ({VPRM}) to improve the simulation of croplands {CO2} fluxes in {Europe}},
	volume = {127},
	issn = {15698432},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1569843224000207},
	doi = {10.1016/j.jag.2024.103666},
	language = {en},
	urldate = {2025-02-14},
	journal = {International Journal of Applied Earth Observation and Geoinformation},
	author = {Bazzi, Hassan and Ciais, Philippe and Abbessi, Ezzeddine and Makowski, David and Santaren, Diego and Ceschia, Eric and Brut, Aurore and Tallec, Tiphaine and Buchmann, Nina and Maier, Regine and Acosta, Manuel and Loubet, Benjamin and Buysse, Pauline and Léonard, Joël and Bornet, Frédéric and Fayad, Ibrahim and Lian, Jinghui and Baghdadi, Nicolas and Segura Barrero, Ricard and Brümmer, Christian and Schmidt, Marius and Heinesch, Bernard and Mauder, Matthias and Gruenwald, Thomas},
	month = mar,
	year = {2024},
	pages = {103666},
}

@article{bogena_metrology-assisted_2024,
	title = {Metrology-{Assisted} {Production} in {Agriculture} and {Forestry}},
	volume = {24},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1424-8220},
	url = {https://www.mdpi.com/1424-8220/24/23/7542},
	doi = {10.3390/s24237542},
	abstract = {According to the Food and Agriculture Organization of the United Nations, climate change will negatively affect food security and increase pressure on freshwater resources [...]},
	language = {en},
	number = {23},
	urldate = {2025-01-07},
	journal = {Sensors},
	author = {Bogena, H. R. and Brogi, C. and Hübner, C. and Panagopoulos, A.},
	month = nov,
	year = {2024},
	pages = {7542},
}

According to the Food and Agriculture Organization of the United Nations, climate change will negatively affect food security and increase pressure on freshwater resources [...]

@article{borchers_comprehensive_2024,
	title = {A {Comprehensive} {Assessment} of {Carbon} {Dioxide} {Removal} {Options} for {Germany}},
	volume = {12},
	issn = {2328-4277, 2328-4277},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023EF003986},
	doi = {10.1029/2023EF003986},
	abstract = {Abstract
            
              To reach their net‐zero targets, countries will have to compensate hard‐to‐abate CO
              2
              emissions through carbon dioxide removal (CDR). Yet, current assessments rarely include socio‐cultural or institutional aspects or fail to contextualize CDR options for implementation. Here we present a context‐specific feasibility assessment of CDR options for the example of Germany. We assess 14 CDR options, including three chemical carbon capture options, six options for bioenergy combined with carbon capture and storage (BECCS), and five options that aim to increase ecosystem carbon uptake. The assessment addresses technological, economic, environmental, institutional, social‐cultural and systemic considerations using a traffic‐light system to evaluate implementation opportunities and hurdles. We find that in Germany CDR options like cover crops or seagrass restoration currently face comparably low implementation hurdles in terms of technological, economic, or environmental feasibility and low institutional or social opposition but show comparably small CO
              2
              removal potentials. In contrast, some BECCS options that show high CDR potentials face significant techno‐economic, societal and institutional hurdles when it comes to the geological storage of CO
              2
              . While a combination of CDR options is likely required to meet the net‐zero target in Germany, the current climate protection law includes a limited set of options. Our analysis aims to provide comprehensive information on CDR hurdles and possibilities for Germany for use in further research on CDR options, climate, and energy scenario development, as well as an effective decision support basis for various actors.
            
          , 
            Plain Language Summary
            
              Countries aiming to achieve net‐zero emissions will have to remove the remaining carbon dioxide from the atmosphere through carbon dioxide removal (CDR). However, current assessments of CDR options rarely consider socio‐cultural or institutional aspects or set the CDR options in the specific context of their implementation. In this study, researchers conducted the first context‐specific feasibility assessment of CDR options in Germany, considering six dimensions, including technological, economic, environmental, institutional, and social‐cultural aspects. The study assessed 14 CDR options, including chemical carbon capture options, bioenergy combined with carbon capture and storage, and options to increase ecosystem carbon uptake. The study found that CDR options like cover crops or seagrass restoration face low implementation hurdles but have small CO
              2
              removal potentials, while options like woody‐biomass combustion or mixed‐feedstock biogas production have high CDR potentials but face large economic and institutional hurdles. The analysis aims to provide comprehensive information on CDR options for use in further research and as an effective decision support basis for a range of actors.
            
          , 
            Key Points
            
              
                
                  More context‐specific assessments of carbon dioxide removal (CDR) options are needed to guide national net‐zero decision making
                
                
                  
                    Ecosystem‐based CDR options with comparably low implementation hurdles in Germany show relatively small CO
                    2
                    removal potentials
                  
                
                
                  High CDR potential options in Germany face high institutional, technological and societal hurdles linked in many ways to geological storage},
	language = {en},
	number = {5},
	urldate = {2024-11-21},
	journal = {Earth's Future},
	author = {Borchers, Malgorzata and Förster, Johannes and Thrän, Daniela and Beck, Silke and Thoni, Terese and Korte, Klaas and Gawel, Erik and Markus, Till and Schaller, Romina and Rhoden, Imke and Chi, Yaxuan and Dahmen, Nicolaus and Dittmeyer, Roland and Dolch, Tobias and Dold, Christian and Herbst, Michael and Heß, Dominik and Kalhori, Aram and Koop‐Jakobsen, Ketil and Li, Zhan and Oschlies, Andreas and Reusch, Thorsten B. H. and Sachs, Torsten and Schmidt‐Hattenberger, Cornelia and Stevenson, Angela and Wu, Jiajun and Yeates, Christopher and Mengis, Nadine},
	month = may,
	year = {2024},
	pages = {e2023EF003986},
}

Abstract To reach their net‐zero targets, countries will have to compensate hard‐to‐abate CO 2 emissions through carbon dioxide removal (CDR). Yet, current assessments rarely include socio‐cultural or institutional aspects or fail to contextualize CDR options for implementation. Here we present a context‐specific feasibility assessment of CDR options for the example of Germany. We assess 14 CDR options, including three chemical carbon capture options, six options for bioenergy combined with carbon capture and storage (BECCS), and five options that aim to increase ecosystem carbon uptake. The assessment addresses technological, economic, environmental, institutional, social‐cultural and systemic considerations using a traffic‐light system to evaluate implementation opportunities and hurdles. We find that in Germany CDR options like cover crops or seagrass restoration currently face comparably low implementation hurdles in terms of technological, economic, or environmental feasibility and low institutional or social opposition but show comparably small CO 2 removal potentials. In contrast, some BECCS options that show high CDR potentials face significant techno‐economic, societal and institutional hurdles when it comes to the geological storage of CO 2 . While a combination of CDR options is likely required to meet the net‐zero target in Germany, the current climate protection law includes a limited set of options. Our analysis aims to provide comprehensive information on CDR hurdles and possibilities for Germany for use in further research on CDR options, climate, and energy scenario development, as well as an effective decision support basis for various actors. , Plain Language Summary Countries aiming to achieve net‐zero emissions will have to remove the remaining carbon dioxide from the atmosphere through carbon dioxide removal (CDR). However, current assessments of CDR options rarely consider socio‐cultural or institutional aspects or set the CDR options in the specific context of their implementation. In this study, researchers conducted the first context‐specific feasibility assessment of CDR options in Germany, considering six dimensions, including technological, economic, environmental, institutional, and social‐cultural aspects. The study assessed 14 CDR options, including chemical carbon capture options, bioenergy combined with carbon capture and storage, and options to increase ecosystem carbon uptake. The study found that CDR options like cover crops or seagrass restoration face low implementation hurdles but have small CO 2 removal potentials, while options like woody‐biomass combustion or mixed‐feedstock biogas production have high CDR potentials but face large economic and institutional hurdles. The analysis aims to provide comprehensive information on CDR options for use in further research and as an effective decision support basis for a range of actors. , Key Points More context‐specific assessments of carbon dioxide removal (CDR) options are needed to guide national net‐zero decision making Ecosystem‐based CDR options with comparably low implementation hurdles in Germany show relatively small CO 2 removal potentials High CDR potential options in Germany face high institutional, technological and societal hurdles linked in many ways to geological storage

@article{borriero_value_2024,
	title = {The value of instream stable water isotope and nitrate concentration data for calibrating a travel time‐based water quality model},
	volume = {38},
	issn = {0885-6087, 1099-1085},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.15154},
	doi = {10.1002/hyp.15154},
	abstract = {Abstract
            
              Transit time‐based water quality models using StorAge Selection (SAS) functions are crucial for nitrate (NO
              3
              −
              ) management. However, relying solely on instream NO
              3
              −
              concentration for model calibration can result in poor parameter identifiability. This is due to the interaction, or correlation, between transport parameters, such as SAS function parameters, and denitrification rate, which challenges accurate parameters identification and description of catchment‐scale hydrological processes. To tackle this issue, we conducted three Monte‐Carlo experiments for a German mesoscale catchment by calibrating a SAS‐based model with daily instream NO
              3
              −
              concentrations (Experiment 1), monthly instream stable water isotopes (e.g. δ
              18
              O) (Experiment 2) and both datasets (Experiment 3). Our findings revealed comparable ranges of SAS transport parameters and median water transit times (TT
              50
              ) across the experiments. This suggests that, despite their distinct reactive or conservative nature, and sampling strategies, the NO
              3
              −
              and δ
              18
              O time series offer similar information for calibration. However, the absolute values of transport parameters and TT
              50
              time series, as well as the degree of parameter interaction differed. Experiment 1 showed greater interaction between certain transport parameters and denitrification rate, leading to greater equifinality. Conversely, Experiment 3 yielded reduced parameters interaction, which enhanced transport parameters identifiability and decreased uncertainty in TT
              50
              time series. Hence, even a modest effort to incorporate only monthly δ
              18
              O values in model calibration for highly frequent NO
              3
              −
              , improved the description of hydrological transport. This study showcased the value of combining NO
              3
              −
              and δ
              18
              O model results to improve transport parameter identifiability and model robustness, which ultimately enhances NO
              3
              −
              management strategies.},
	language = {en},
	number = {5},
	urldate = {2024-11-21},
	journal = {Hydrological Processes},
	author = {Borriero, A. and Musolff, A. and Kumar, R. and Fleckenstein, J. H. and Lutz, S. R. and Nguyen, T. V.},
	month = may,
	year = {2024},
	pages = {e15154},
}

Abstract Transit time‐based water quality models using StorAge Selection (SAS) functions are crucial for nitrate (NO 3 − ) management. However, relying solely on instream NO 3 − concentration for model calibration can result in poor parameter identifiability. This is due to the interaction, or correlation, between transport parameters, such as SAS function parameters, and denitrification rate, which challenges accurate parameters identification and description of catchment‐scale hydrological processes. To tackle this issue, we conducted three Monte‐Carlo experiments for a German mesoscale catchment by calibrating a SAS‐based model with daily instream NO 3 − concentrations (Experiment 1), monthly instream stable water isotopes (e.g. δ 18 O) (Experiment 2) and both datasets (Experiment 3). Our findings revealed comparable ranges of SAS transport parameters and median water transit times (TT 50 ) across the experiments. This suggests that, despite their distinct reactive or conservative nature, and sampling strategies, the NO 3 − and δ 18 O time series offer similar information for calibration. However, the absolute values of transport parameters and TT 50 time series, as well as the degree of parameter interaction differed. Experiment 1 showed greater interaction between certain transport parameters and denitrification rate, leading to greater equifinality. Conversely, Experiment 3 yielded reduced parameters interaction, which enhanced transport parameters identifiability and decreased uncertainty in TT 50 time series. Hence, even a modest effort to incorporate only monthly δ 18 O values in model calibration for highly frequent NO 3 − , improved the description of hydrological transport. This study showcased the value of combining NO 3 − and δ 18 O model results to improve transport parameter identifiability and model robustness, which ultimately enhances NO 3 − management strategies.

@article{bulut_estimation_2024,
	title = {Estimation of surface soil moisture from {Sentinel}-1 synthetic aperture radar imagery using machine learning method},
	volume = {36},
	issn = {23529385},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S2352938524002337},
	doi = {10.1016/j.rsase.2024.101369},
	language = {en},
	urldate = {2025-02-13},
	journal = {Remote Sensing Applications: Society and Environment},
	author = {Bulut, Ünal and Mohammadi, Babak and Duan, Zheng},
	month = nov,
	year = {2024},
	pages = {101369},
}

@misc{cao_parameterization_2024,
	title = {Parameterization of an {Ecosystem} {Light}-{Use}-{Efficiency} {Model} for {Predicting} {Global} {Gpp} {Using} {Modis} and {Fluxnet}},
	url = {https://www.ssrn.com/abstract=4932740},
	doi = {10.2139/ssrn.4932740},
	urldate = {2024-11-21},
	publisher = {SSRN},
	author = {Cao, Chunyan and Ma, Xuanlong and Yang, Wei and Yan, Kai and Liu, Feng and Wang, Yuanyuan and Huete, Alfredo},
	year = {2024},
}

@article{chen_analysis_2024,
	title = {Analysis of {Spatiotemporal} {Distribution} of {Evaporation} {Fractions} of {Different} {Vegetation} {Types} {Based} on {FLUXNET} {Site}},
	volume = {21},
	copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
	issn = {1545-598X, 1558-0571},
	url = {https://ieeexplore.ieee.org/document/10371332/},
	doi = {10.1109/LGRS.2023.3345894},
	urldate = {2024-11-14},
	journal = {IEEE Geoscience and Remote Sensing Letters},
	author = {Chen, Lijuan and Chen, Haishan and Wang, Ren and Wei, Geng},
	year = {2024},
	pages = {1--5},
}

@article{chen_20012022_2024,
	title = {A 2001–2022 global gross primary productivity dataset using an ensemble model based on the random forest method},
	volume = {21},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1726-4189},
	url = {https://bg.copernicus.org/articles/21/4285/2024/},
	doi = {10.5194/bg-21-4285-2024},
	abstract = {Abstract. Advancements in remote sensing technology have significantly contributed to the improvement of models for estimating terrestrial gross primary productivity (GPP). However, discrepancies in the spatial distribution and interannual variability within GPP datasets pose challenges to a comprehensive understanding of the terrestrial carbon cycle. In contrast to previous models that rely on remote sensing and environmental variables, we developed an ensemble model based on the random forest method (denoted ERF model). This model used GPP outputs from established models: Eddy Covariance Light Use Efficiency (EC-LUE), GPP estimate model based on Kernel Normalized Difference Vegetation Index (GPP-kNDVI), GPP estimate model based on Near-Infrared Reflectance of Vegetation (GPP-NIRv), Revised-EC-LUE, Vegetation Photosynthesis Model (VPM), and GPP estimate model based on the Moderate Resolution Imaging Spectroradiometer (MODIS). These outputs were used as inputs to estimate GPP. The ERF model demonstrated superior performance, explaining 85.1 \% of the monthly GPP variations at 170 sites and surpassing the performance of selected GPP estimate models (67.7 \%–77.5 \%) and an independent random forest model using remote sensing and environmental variables (81.5 \%). Additionally, the ERF model improved accuracy across each month and with various subranges, mitigating the issue of “high-value underestimation and low-value overestimation” in GPP estimates. Over the period from 2001 to 2022, the global GPP estimated by the ERF model was 132.7 PgC yr−1, with an increasing trend of 0.42 PgC yr−2, which is comparable to or slightly better than the accuracy of other mainstream GPP datasets in terms of validation results of GPP observations independent of FLUXNET (i.e., ChinaFLUX). Importantly, for a growing number of GPP datasets, our study provides a way to integrate these GPP datasets, which may lead to a more reliable estimate of global GPP.},
	language = {en},
	number = {19},
	urldate = {2024-11-21},
	journal = {Biogeosciences},
	author = {Chen, Xin and Chen, Tiexi and Li, Xiaodong and Chai, Yuanfang and Zhou, Shengjie and Guo, Renjie and Dai, Jie},
	month = oct,
	year = {2024},
	pages = {4285--4300},
}

Abstract. Advancements in remote sensing technology have significantly contributed to the improvement of models for estimating terrestrial gross primary productivity (GPP). However, discrepancies in the spatial distribution and interannual variability within GPP datasets pose challenges to a comprehensive understanding of the terrestrial carbon cycle. In contrast to previous models that rely on remote sensing and environmental variables, we developed an ensemble model based on the random forest method (denoted ERF model). This model used GPP outputs from established models: Eddy Covariance Light Use Efficiency (EC-LUE), GPP estimate model based on Kernel Normalized Difference Vegetation Index (GPP-kNDVI), GPP estimate model based on Near-Infrared Reflectance of Vegetation (GPP-NIRv), Revised-EC-LUE, Vegetation Photosynthesis Model (VPM), and GPP estimate model based on the Moderate Resolution Imaging Spectroradiometer (MODIS). These outputs were used as inputs to estimate GPP. The ERF model demonstrated superior performance, explaining 85.1 % of the monthly GPP variations at 170 sites and surpassing the performance of selected GPP estimate models (67.7 %–77.5 %) and an independent random forest model using remote sensing and environmental variables (81.5 %). Additionally, the ERF model improved accuracy across each month and with various subranges, mitigating the issue of “high-value underestimation and low-value overestimation” in GPP estimates. Over the period from 2001 to 2022, the global GPP estimated by the ERF model was 132.7 PgC yr−1, with an increasing trend of 0.42 PgC yr−2, which is comparable to or slightly better than the accuracy of other mainstream GPP datasets in terms of validation results of GPP observations independent of FLUXNET (i.e., ChinaFLUX). Importantly, for a growing number of GPP datasets, our study provides a way to integrate these GPP datasets, which may lead to a more reliable estimate of global GPP.

@article{chinta_machine_2024,
	title = {Machine {Learning} {Driven} {Sensitivity} {Analysis} of {E3SM} {Land} {Model} {Parameters} for {Wetland} {Methane} {Emissions}},
	volume = {16},
	issn = {1942-2466, 1942-2466},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023MS004115},
	doi = {10.1029/2023MS004115},
	abstract = {Abstract
            
              Methane (CH
              4
              ) is globally the second most critical greenhouse gas after carbon dioxide, contributing to 16\%–25\% of the observed atmospheric warming. Wetlands are the primary natural source of methane emissions globally. However, wetland methane emission estimates from biogeochemistry models contain considerable uncertainty. One of the main sources of this uncertainty arises from the numerous uncertain model parameters within various physical, biological, and chemical processes that influence methane production, oxidation, and transport. Sensitivity Analysis (SA) can help identify critical parameters for methane emission and achieve reduced biases and uncertainties in future projections. This study performs SA for 19 selected parameters responsible for critical biogeochemical processes in the methane module of the Energy Exascale Earth System Model (E3SM) land model (ELM). The impact of these parameters on various CH
              4
              fluxes is examined at 14 FLUXNET‐ CH
              4
              sites with diverse vegetation types. Given the extensive number of model simulations needed for global variance‐based SA, we employ a machine learning (ML) algorithm to emulate the complex behavior of ELM methane biogeochemistry. We found that parameters linked to CH
              4
              production and diffusion generally present the highest sensitivities despite apparent seasonal variation. Comparing simulated emissions from perturbed parameter sets against FLUXNET‐CH
              4
              observations revealed that better performances can be achieved at each site compared to the default parameter values. This presents a scope for further improving simulated emissions using parameter calibration with advanced optimization techniques.
            
          , 
            Plain Language Summary
            Methane is a critical greenhouse gas, and wetlands are the largest natural source of it. Accurately predicting methane emissions from wetlands is key to tackling climate change. But these predictions, made through computer models, are seldom spot‐on. Why? Because there are many factors in the models that lead to uncertain predictions. A major source of this uncertainty arises from the empirical model parameters. Just as tuning a radio dial ensures clear reception, models need properly adjusted parameters for accurate predictions. A sensitivity analysis was performed to determine which parameters are most crucial for accurate predictions. Instead of running the complex numerical model every time, machine learning was employed to create a faster and simpler version. Using this approach, five parameters were pinpointed as particularly sensitive, significantly impacting the predictions. The comparison of model‐predicted methane emissions with real‐world measurements showed that the model performed well in some cases but needed tweaking in others. Refining these sensitive parameters with more real‐world observations could make better predictions in the future.
          , 
            Key Points
            
              
                
                  Identified five key sensitive parameters for methane emissions using the Sobol sensitivity analysis method
                
                
                  Parameters linked to production and diffusion present the highest sensitivities despite apparent seasonal variation
                
                
                  Fourteen out of nineteen model parameters exert negligible influence on methane emissions},
	language = {en},
	number = {7},
	urldate = {2024-11-21},
	journal = {Journal of Advances in Modeling Earth Systems},
	author = {Chinta, Sandeep and Gao, Xiang and Zhu, Qing},
	month = jul,
	year = {2024},
	pages = {e2023MS004115},
}

Abstract Methane (CH 4 ) is globally the second most critical greenhouse gas after carbon dioxide, contributing to 16%–25% of the observed atmospheric warming. Wetlands are the primary natural source of methane emissions globally. However, wetland methane emission estimates from biogeochemistry models contain considerable uncertainty. One of the main sources of this uncertainty arises from the numerous uncertain model parameters within various physical, biological, and chemical processes that influence methane production, oxidation, and transport. Sensitivity Analysis (SA) can help identify critical parameters for methane emission and achieve reduced biases and uncertainties in future projections. This study performs SA for 19 selected parameters responsible for critical biogeochemical processes in the methane module of the Energy Exascale Earth System Model (E3SM) land model (ELM). The impact of these parameters on various CH 4 fluxes is examined at 14 FLUXNET‐ CH 4 sites with diverse vegetation types. Given the extensive number of model simulations needed for global variance‐based SA, we employ a machine learning (ML) algorithm to emulate the complex behavior of ELM methane biogeochemistry. We found that parameters linked to CH 4 production and diffusion generally present the highest sensitivities despite apparent seasonal variation. Comparing simulated emissions from perturbed parameter sets against FLUXNET‐CH 4 observations revealed that better performances can be achieved at each site compared to the default parameter values. This presents a scope for further improving simulated emissions using parameter calibration with advanced optimization techniques. , Plain Language Summary Methane is a critical greenhouse gas, and wetlands are the largest natural source of it. Accurately predicting methane emissions from wetlands is key to tackling climate change. But these predictions, made through computer models, are seldom spot‐on. Why? Because there are many factors in the models that lead to uncertain predictions. A major source of this uncertainty arises from the empirical model parameters. Just as tuning a radio dial ensures clear reception, models need properly adjusted parameters for accurate predictions. A sensitivity analysis was performed to determine which parameters are most crucial for accurate predictions. Instead of running the complex numerical model every time, machine learning was employed to create a faster and simpler version. Using this approach, five parameters were pinpointed as particularly sensitive, significantly impacting the predictions. The comparison of model‐predicted methane emissions with real‐world measurements showed that the model performed well in some cases but needed tweaking in others. Refining these sensitive parameters with more real‐world observations could make better predictions in the future. , Key Points Identified five key sensitive parameters for methane emissions using the Sobol sensitivity analysis method Parameters linked to production and diffusion present the highest sensitivities despite apparent seasonal variation Fourteen out of nineteen model parameters exert negligible influence on methane emissions

@article{cui_modis-based_2024,
	title = {{MODIS}-based modeling of evapotranspiration from woody vegetation supported by root-zone water storage},
	volume = {303},
	issn = {00344257},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0034425724000117},
	doi = {10.1016/j.rse.2024.114000},
	language = {en},
	urldate = {2024-11-26},
	journal = {Remote Sensing of Environment},
	author = {Cui, Guotao and Guo, Weichao and Goulden, Michael and Bales, Roger},
	month = mar,
	year = {2024},
	pages = {114000},
}

@article{dannenmann_fates_2024,
	title = {Fates of slurry-nitrogen applied to mountain grasslands: the importance of dinitrogen emissions versus plant {N} uptake},
	issn = {0178-2762, 1432-0789},
	shorttitle = {Fates of slurry-nitrogen applied to mountain grasslands},
	url = {https://link.springer.com/10.1007/s00374-024-01826-9},
	doi = {10.1007/s00374-024-01826-9},
	abstract = {Abstract
            
              Intensive fertilization of grasslands with cattle slurry can cause high environmental nitrogen (N) losses in form of ammonia (NH
              3
              ), nitrous oxide (N
              2
              O), and nitrate (NO
              3
              −
              ) leaching. Still, knowledge on short-term fertilizer N partitioning between plants and dinitrogen (N
              2
              ) emissions is lacking. Therefore, we applied highly
              15
              N-enriched cattle slurry (97 kg N ha
              −1
              ) to pre-alpine grassland field mesocosms. We traced the slurry
              15
              N in the plant-soil system and to denitrification losses (N
              2
              , N
              2
              O) over 29 days in high temporal resolution. Gaseous ammonia (NH
              3
              ), N
              2
              as well N
              2
              O losses at about 20 kg N ha
              −1
              were observed only within the first 3 days after fertilization and were dominated by NH
              3
              . Nitrous oxide emissions (0.1 kg N ha
              −1
              ) were negligible, while N
              2
              emissions accounted for 3 kg of fertilizer N ha
              −1
              . The relatively low denitrification losses can be explained by the rapid plant uptake of fertilizer N, particularly from 0–4 cm depth, with plant N uptake exceeding denitrification N losses by an order of magnitude already after 3 days. After 17 days, total aboveground plant N uptake reached 100 kg N ha
              −1
              , with 33\% of N derived from the applied N fertilizer. Half of the fertilizer N was found in above and belowground biomass, while at about 25\% was recovered in the soil and 25\% was lost, mainly in form of gaseous emissions, with minor N leaching. Overall, this study shows that plant N uptake plays a dominant role in controlling denitrification losses at high N application rates in pre-alpine grassland soils.},
	language = {en},
	urldate = {2024-11-26},
	journal = {Biology and Fertility of Soils},
	author = {Dannenmann, Michael and Yankelzon, Irina and Wähling, Svenja and Ramm, Elisabeth and Schreiber, Mirella and Ostler, Ulrike and Schlingmann, Marcus and Stange, Claus Florian and Kiese, Ralf and Butterbach-Bahl, Klaus and Friedl, Johannes and Scheer, Clemens},
	month = may,
	year = {2024},
}

Abstract Intensive fertilization of grasslands with cattle slurry can cause high environmental nitrogen (N) losses in form of ammonia (NH 3 ), nitrous oxide (N 2 O), and nitrate (NO 3 − ) leaching. Still, knowledge on short-term fertilizer N partitioning between plants and dinitrogen (N 2 ) emissions is lacking. Therefore, we applied highly 15 N-enriched cattle slurry (97 kg N ha −1 ) to pre-alpine grassland field mesocosms. We traced the slurry 15 N in the plant-soil system and to denitrification losses (N 2 , N 2 O) over 29 days in high temporal resolution. Gaseous ammonia (NH 3 ), N 2 as well N 2 O losses at about 20 kg N ha −1 were observed only within the first 3 days after fertilization and were dominated by NH 3 . Nitrous oxide emissions (0.1 kg N ha −1 ) were negligible, while N 2 emissions accounted for 3 kg of fertilizer N ha −1 . The relatively low denitrification losses can be explained by the rapid plant uptake of fertilizer N, particularly from 0–4 cm depth, with plant N uptake exceeding denitrification N losses by an order of magnitude already after 3 days. After 17 days, total aboveground plant N uptake reached 100 kg N ha −1 , with 33% of N derived from the applied N fertilizer. Half of the fertilizer N was found in above and belowground biomass, while at about 25% was recovered in the soil and 25% was lost, mainly in form of gaseous emissions, with minor N leaching. Overall, this study shows that plant N uptake plays a dominant role in controlling denitrification losses at high N application rates in pre-alpine grassland soils.

@article{daras_mass-change_2024,
	title = {Mass-change {And} {Geosciences} {International} {Constellation} ({MAGIC}) expected impact on science and applications},
	volume = {236},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {0956-540X, 1365-246X},
	url = {https://academic.oup.com/gji/article/236/3/1288/7473715},
	doi = {10.1093/gji/ggad472},
	abstract = {SUMMARY
            The joint ESA/NASA Mass-change And Geosciences International Constellation (MAGIC) has the objective to extend time-series from previous gravity missions, including an improvement of accuracy and spatio-temporal resolution. The long-term monitoring of Earth’s gravity field carries information on mass change induced by water cycle, climate change and mass transport processes between atmosphere, cryosphere, oceans and solid Earth. MAGIC will be composed of two satellite pairs flying in different orbit planes. The NASA/DLR-led first pair (P1) is expected to be in a near-polar orbit around 500 km of altitude; while the second ESA-led pair (P2) is expected to be in an inclined orbit of 65°–70° at approximately 400 km altitude. The ESA-led pair P2 Next Generation Gravity Mission shall be launched after P1 in a staggered manner to form the MAGIC constellation. The addition of an inclined pair shall lead to reduction of temporal aliasing effects and consequently of reliance on de-aliasing models and post-processing. The main novelty of the MAGIC constellation is the delivery of mass-change products at higher spatial resolution, temporal (i.e. subweekly) resolution, shorter latency and higher accuracy than the Gravity Recovery and Climate Experiment (GRACE) and Gravity Recovery and Climate Experiment Follow-On (GRACE-FO). This will pave the way to new science applications and operational services. In this paper, an overview of various fields of science and service applications for hydrology, cryosphere, oceanography, solid Earth, climate change and geodesy is provided. These thematic fields and newly enabled applications and services were analysed in the frame of the initial ESA Science Support activities for MAGIC. The analyses of MAGIC scenarios for different application areas in the field of geosciences confirmed that the double-pair configuration will significantly enlarge the number of observable mass-change phenomena by resolving smaller spatial scales with an uncertainty that satisfies evolved user requirements expressed by international bodies such as IUGG. The required uncertainty levels of dedicated thematic fields met by MAGIC unfiltered Level-2 products will benefit hydrological applications by recovering more than 90 per cent of the major river basins worldwide at 260 km spatial resolution, cryosphere applications by enabling mass change signal separation in the interior of Greenland from those in the coastal zones and by resolving small-scale mass variability in challenging regions such as the Antarctic Peninsula, oceanography applications by monitoring meridional overturning circulation changes on timescales of years and decades, climate applications by detecting amplitude and phase changes of Terrestrial Water Storage after 30 yr in 64 and 56 per cent of the global land areas and solid Earth applications by lowering the Earthquake detection threshold from magnitude 8.8 to magnitude 7.4 with spatial resolution increased to 333 km.},
	language = {en},
	number = {3},
	urldate = {2025-01-13},
	journal = {Geophysical Journal International},
	author = {Daras, I and March, G and Pail, R and Hughes, C W and Braitenberg, C and Güntner, A and Eicker, A and Wouters, B and Heller-Kaikov, B and Pivetta, T and Pastorutti, A},
	month = jan,
	year = {2024},
	pages = {1288--1308},
}

SUMMARY The joint ESA/NASA Mass-change And Geosciences International Constellation (MAGIC) has the objective to extend time-series from previous gravity missions, including an improvement of accuracy and spatio-temporal resolution. The long-term monitoring of Earth’s gravity field carries information on mass change induced by water cycle, climate change and mass transport processes between atmosphere, cryosphere, oceans and solid Earth. MAGIC will be composed of two satellite pairs flying in different orbit planes. The NASA/DLR-led first pair (P1) is expected to be in a near-polar orbit around 500 km of altitude; while the second ESA-led pair (P2) is expected to be in an inclined orbit of 65°–70° at approximately 400 km altitude. The ESA-led pair P2 Next Generation Gravity Mission shall be launched after P1 in a staggered manner to form the MAGIC constellation. The addition of an inclined pair shall lead to reduction of temporal aliasing effects and consequently of reliance on de-aliasing models and post-processing. The main novelty of the MAGIC constellation is the delivery of mass-change products at higher spatial resolution, temporal (i.e. subweekly) resolution, shorter latency and higher accuracy than the Gravity Recovery and Climate Experiment (GRACE) and Gravity Recovery and Climate Experiment Follow-On (GRACE-FO). This will pave the way to new science applications and operational services. In this paper, an overview of various fields of science and service applications for hydrology, cryosphere, oceanography, solid Earth, climate change and geodesy is provided. These thematic fields and newly enabled applications and services were analysed in the frame of the initial ESA Science Support activities for MAGIC. The analyses of MAGIC scenarios for different application areas in the field of geosciences confirmed that the double-pair configuration will significantly enlarge the number of observable mass-change phenomena by resolving smaller spatial scales with an uncertainty that satisfies evolved user requirements expressed by international bodies such as IUGG. The required uncertainty levels of dedicated thematic fields met by MAGIC unfiltered Level-2 products will benefit hydrological applications by recovering more than 90 per cent of the major river basins worldwide at 260 km spatial resolution, cryosphere applications by enabling mass change signal separation in the interior of Greenland from those in the coastal zones and by resolving small-scale mass variability in challenging regions such as the Antarctic Peninsula, oceanography applications by monitoring meridional overturning circulation changes on timescales of years and decades, climate applications by detecting amplitude and phase changes of Terrestrial Water Storage after 30 yr in 64 and 56 per cent of the global land areas and solid Earth applications by lowering the Earthquake detection threshold from magnitude 8.8 to magnitude 7.4 with spatial resolution increased to 333 km.

@article{de_roos_assimilation_2024,
	title = {Assimilation of {Sentinel}‐1 {Backscatter} to {Update} {AquaCrop} {Estimates} of {Soil} {Moisture} and {Crop} {Biomass}},
	volume = {129},
	issn = {2169-8953, 2169-8961},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024JG008231},
	doi = {10.1029/2024JG008231},
	abstract = {Abstract
            
              
              This study assesses the potential of regional microwave backscatter data assimilation (DA) in AquaCrop for the first time, using NASA's Land Information System. The objective is to assess whether the assimilation setup can improve surface soil moisture (SSM) and crop biomass estimates. SSM and crop biomass simulations from AquaCrop were updated using Sentinel‐1 synthetic aperture radar observations, over three regions in Europe in two separate DA experiments. The first experiment concerned updating SSM using VV‐polarized backscatter and the corrections were propagated via the model to the biomass. In the second experiment, the DA setup was extended by also updating the biomass with VH‐polarized backscatter. SSM was evaluated with local in situ data and with downscaled Soil Moisture Active Passive (SMAP) retrievals for all cropland grid cells, whereas crop biomass was compared to SMAP vegetation optical depth and the Copernicus dry matter productivity. The assimilation showed mixed results for root mean square error and Pearson's correlation, with slight overall improvements in the (anomaly) correlations of updated SSM relative to independent in situ and satellite data. By contrast, the biomass estimates obtained with backscatter DA did not agree better with reference data sets. Overall, the SSM evaluation showed that there is potential in using Sentinel‐1 backscatter for assimilation in AquaCrop, but the present setup was not able to improve crop biomass estimates. Our study reveals how the complex interaction between SSM, crop biomass and backscatter affect the impact and performance of DA, offering insight into ways to optimize DA for crop growth estimation.
            
          , 
            Plain Language Summary
            
              
              This study evaluates if using observations from a microwave satellite, Sentinel‐1 (S1) can improve model simulations of a crop model AquaCrop, specifically for both soil moisture and crop biomass, over different regions in Europe. For each day in which S1 observations were available over the region, the modeled soil moisture and biomass were “updated” based on these observations, which over time is expected to reduce the model error and uncertainty. This iterative process is called data assimilation (DA) and was executed in a model framework called NASA's Land Information System. Two DA experiments were held. In the first DA experiment, only soil moisture was updated by S1 observations, but the changes in soil moisture were expected to also affect the biomass simulations compared to no DA model runs. In the second DA experiment, both the soil moisture and biomass were updated with S1 data. When comparing the results with independent data sets, the assimilation showed mixed results. The soil moisture showed slight improvements after DA, but the biomass estimates did not improve. Given the complexity of S1 data over agricultural areas, more research is required to optimally perform DA before this setup is able to improve crop growth estimation.
            
          , 
            Key Points
            
              
                
                  First assessment of Sentinel‐1 backscatter data assimilation in a crop model integrated into NASA's Land Information System
                
                
                  Co‐ and cross‐polarization backscatter observations were used to update regional AquaCrop soil moisture and biomass estimates, respectively
                
                
                  Sentinel‐1 data assimilation resulted in improved soil moisture estimates, but further research is needed for optimal vegetation updating},
	language = {en},
	number = {10},
	urldate = {2024-11-26},
	journal = {Journal of Geophysical Research: Biogeosciences},
	author = {De Roos, Shannon and Bechtold, Michel and Busschaert, Louise and Lievens, Hans and De Lannoy, Gabrielle J. M.},
	month = oct,
	year = {2024},
	pages = {e2024JG008231},
}

Abstract This study assesses the potential of regional microwave backscatter data assimilation (DA) in AquaCrop for the first time, using NASA's Land Information System. The objective is to assess whether the assimilation setup can improve surface soil moisture (SSM) and crop biomass estimates. SSM and crop biomass simulations from AquaCrop were updated using Sentinel‐1 synthetic aperture radar observations, over three regions in Europe in two separate DA experiments. The first experiment concerned updating SSM using VV‐polarized backscatter and the corrections were propagated via the model to the biomass. In the second experiment, the DA setup was extended by also updating the biomass with VH‐polarized backscatter. SSM was evaluated with local in situ data and with downscaled Soil Moisture Active Passive (SMAP) retrievals for all cropland grid cells, whereas crop biomass was compared to SMAP vegetation optical depth and the Copernicus dry matter productivity. The assimilation showed mixed results for root mean square error and Pearson's correlation, with slight overall improvements in the (anomaly) correlations of updated SSM relative to independent in situ and satellite data. By contrast, the biomass estimates obtained with backscatter DA did not agree better with reference data sets. Overall, the SSM evaluation showed that there is potential in using Sentinel‐1 backscatter for assimilation in AquaCrop, but the present setup was not able to improve crop biomass estimates. Our study reveals how the complex interaction between SSM, crop biomass and backscatter affect the impact and performance of DA, offering insight into ways to optimize DA for crop growth estimation. , Plain Language Summary This study evaluates if using observations from a microwave satellite, Sentinel‐1 (S1) can improve model simulations of a crop model AquaCrop, specifically for both soil moisture and crop biomass, over different regions in Europe. For each day in which S1 observations were available over the region, the modeled soil moisture and biomass were “updated” based on these observations, which over time is expected to reduce the model error and uncertainty. This iterative process is called data assimilation (DA) and was executed in a model framework called NASA's Land Information System. Two DA experiments were held. In the first DA experiment, only soil moisture was updated by S1 observations, but the changes in soil moisture were expected to also affect the biomass simulations compared to no DA model runs. In the second DA experiment, both the soil moisture and biomass were updated with S1 data. When comparing the results with independent data sets, the assimilation showed mixed results. The soil moisture showed slight improvements after DA, but the biomass estimates did not improve. Given the complexity of S1 data over agricultural areas, more research is required to optimally perform DA before this setup is able to improve crop growth estimation. , Key Points First assessment of Sentinel‐1 backscatter data assimilation in a crop model integrated into NASA's Land Information System Co‐ and cross‐polarization backscatter observations were used to update regional AquaCrop soil moisture and biomass estimates, respectively Sentinel‐1 data assimilation resulted in improved soil moisture estimates, but further research is needed for optimal vegetation updating

@article{dinh_impacts_2024,
	title = {Impacts of land-use change on biospheric carbon: an oriented benchmark using the {ORCHIDEE} land surface model},
	volume = {17},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1991-9603},
	shorttitle = {Impacts of land-use change on biospheric carbon},
	url = {https://gmd.copernicus.org/articles/17/6725/2024/},
	doi = {10.5194/gmd-17-6725-2024},
	abstract = {Abstract. Land-use change (LUC) impacts biospheric carbon, encompassing biomass carbon and soil organic carbon (SOC). Despite the use of dynamic global vegetation models (DGVMs) in estimating the anthropogenic perturbation of biospheric carbon stocks, critical evaluations of model performance concerning LUC impacts are scarce. Here, we present a systematic evaluation of the performance of the DGVM Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) in reproducing observed LUC impacts on biospheric carbon stocks over Europe. First, we compare model predictions with observation-based gridded estimates of net and gross primary productivity (NPP and GPP), biomass growth patterns, and SOC stocks. Second, we evaluate the predicted response of soil carbon stocks to LUC based on data from forest inventories, paired plots, chronosequences, and repeated sampling designs. Third, we use interpretable machine learning to identify factors contributing to discrepancies between simulations and observations, including drivers and processes not resolved in ORCHIDEE (e.g. erosion, soil fertility). Results indicate agreement between the model and observed spatial patterns and temporal trends, such as the increase in biomass with age, when simulating biosphere carbon stocks. The direction of the SOC responses to LUC generally aligns between simulated and observed data. However, the model underestimates carbon gains for cropland-to-grassland conversions and carbon losses for grassland-to-cropland and forest-to-cropland conversions. These discrepancies are attributed to bias arising from soil erosion rate, which is not fully captured in ORCHIDEE. Our study provides an oriented benchmark for assessing the DGVMs against observations and explores their potential in studying the impact of LUCs on SOC stocks.},
	language = {en},
	number = {17},
	urldate = {2024-11-26},
	journal = {Geoscientific Model Development},
	author = {Dinh, Thi Lan Anh and Goll, Daniel and Ciais, Philippe and Lauerwald, Ronny},
	month = sep,
	year = {2024},
	pages = {6725--6744},
}

Abstract. Land-use change (LUC) impacts biospheric carbon, encompassing biomass carbon and soil organic carbon (SOC). Despite the use of dynamic global vegetation models (DGVMs) in estimating the anthropogenic perturbation of biospheric carbon stocks, critical evaluations of model performance concerning LUC impacts are scarce. Here, we present a systematic evaluation of the performance of the DGVM Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) in reproducing observed LUC impacts on biospheric carbon stocks over Europe. First, we compare model predictions with observation-based gridded estimates of net and gross primary productivity (NPP and GPP), biomass growth patterns, and SOC stocks. Second, we evaluate the predicted response of soil carbon stocks to LUC based on data from forest inventories, paired plots, chronosequences, and repeated sampling designs. Third, we use interpretable machine learning to identify factors contributing to discrepancies between simulations and observations, including drivers and processes not resolved in ORCHIDEE (e.g. erosion, soil fertility). Results indicate agreement between the model and observed spatial patterns and temporal trends, such as the increase in biomass with age, when simulating biosphere carbon stocks. The direction of the SOC responses to LUC generally aligns between simulated and observed data. However, the model underestimates carbon gains for cropland-to-grassland conversions and carbon losses for grassland-to-cropland and forest-to-cropland conversions. These discrepancies are attributed to bias arising from soil erosion rate, which is not fully captured in ORCHIDEE. Our study provides an oriented benchmark for assessing the DGVMs against observations and explores their potential in studying the impact of LUCs on SOC stocks.

@article{do_novel_2024,
	title = {A novel validation of satellite soil moisture using {SM2RAIN}-derived rainfall estimates},
	volume = {5},
	issn = {2673-6187},
	url = {https://www.frontiersin.org/articles/10.3389/frsen.2024.1474088/full},
	doi = {10.3389/frsen.2024.1474088},
	abstract = {Despite the importance of soil moisture (SM) in various applications and the need to validate satellite SM products, the current
              in situ
              SM network is still inadequate, even for developed country such as the United States. Recently, SM2RAIN (Soil Moisture to Rain) algorithm has prominently emerged as a bottom-up approach to derive rainfall data from SM. In this study, we evaluated whether SM2RAIN algorithm and rain gauges, which are more abundant and readily available than
              in situ
              SM, can be used to validate satellite-based SMAP SM estimates. Since errors in SMAP SM propagate to SMAP-derived rainfall, the skills of SM2RAIN might be able to provide insights on the accuracy of SMAP SM observations. While the correlation between SM2RAIN skills and SMAP SM skills was found to be statistically significant, the strength of the correlation varied among different climate zones and annual rainfall classes. Specifically, weaker correlations were observed in arid and lower rainfall regions (median R value of 0.12), while stronger correlations were found in temperate and higher rainfall regions (median R value of 0.54). In term of over/under-estimation tendencies, 56\% of the stations had the same tendencies (SM2RAIN skills and satellite SM skills both have positive or negative PBIAS value).},
	urldate = {2025-02-13},
	journal = {Frontiers in Remote Sensing},
	author = {Do, Son K. and Tran, Thanh-Nhan-Duc and Le, Manh-Hung and Bolten, John and Lakshmi, Venkataraman},
	month = nov,
	year = {2024},
	pages = {1474088},
}

Despite the importance of soil moisture (SM) in various applications and the need to validate satellite SM products, the current in situ SM network is still inadequate, even for developed country such as the United States. Recently, SM2RAIN (Soil Moisture to Rain) algorithm has prominently emerged as a bottom-up approach to derive rainfall data from SM. In this study, we evaluated whether SM2RAIN algorithm and rain gauges, which are more abundant and readily available than in situ SM, can be used to validate satellite-based SMAP SM estimates. Since errors in SMAP SM propagate to SMAP-derived rainfall, the skills of SM2RAIN might be able to provide insights on the accuracy of SMAP SM observations. While the correlation between SM2RAIN skills and SMAP SM skills was found to be statistically significant, the strength of the correlation varied among different climate zones and annual rainfall classes. Specifically, weaker correlations were observed in arid and lower rainfall regions (median R value of 0.12), while stronger correlations were found in temperate and higher rainfall regions (median R value of 0.54). In term of over/under-estimation tendencies, 56% of the stations had the same tendencies (SM2RAIN skills and satellite SM skills both have positive or negative PBIAS value).

@article{dombrowski_land_2024,
	title = {Land {Surface} {Modeling} as a {Tool} to {Explore} {Sustainable} {Irrigation} {Practices} in {Mediterranean} {Fruit} {Orchards}},
	volume = {60},
	issn = {0043-1397, 1944-7973},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023WR036139},
	doi = {10.1029/2023WR036139},
	abstract = {Abstract
            Irrigation strongly influences land‐atmosphere processes from regional to global scale. Therefore, an accurate representation of irrigation is crucial to understand these interactions and address water resources issues. While irrigation schemes are increasingly integrated into land surface models, their evaluation and further development remains challenging due to data limitations. This study assessed the representation of field‐scale irrigation using the Community Land Model version 5 (CLM5) through comparison of observed and simulated soil moisture, transpiration and crop yield. Irrigation was simulated by (a) adjusting the current irrigation routine and (b) by implementing a novel irrigation data stream that allows to directly use observed irrigation amounts and schedules. In a following step, the effect of different irrigation scenarios at the regional scale was simulated by using this novel data stream. At the plot scale, the novel irrigation data stream performed better in representing observed SM dynamics compared to the current irrigation routine. Nonetheless, simplifications in crop and irrigation representation and uncertainty in the relation between water stress and yield currently limit the ability of CLM5 for field‐scale irrigation scheduling. Still, the simulations revealed valuable insights into model performance that can inform and improve the modeling beyond the field scale. At regional scale, the simulations identified irrigation priorities and potential water savings. Furthermore, application of LSMs such as CLM5 can help to study the effects of irrigation beyond water availability, for example, on energy fluxes and climate, thus providing a powerful tool to assess the broader implications of irrigation at larger scale.
          , 
            Plain Language Summary
            Irrigation impacts how land and atmosphere interact, both locally and globally. Therefore, it is important to understand the effects of irrigation practices and improve how water resources are managed. Advanced models such as land surface models now include irrigation. However, developing these models is difficult due to limited data. This study used the Community Land Model version 5 (CLM5) to compare observed and simulated soil moisture, plant water use, and crop yield. Two methods were used: an updated irrigation routine and a new data stream that uses actual irrigation amounts and schedules. The new data stream more accurately represented soil moisture. Simplifications in how the model handles crops and irrigation, and uncertainty about the link between water stress and yield, limit CLM5's effectiveness for precise irrigation planning. Still, the simulations provided valuable insights into the model's performance. At a regional level, the simulations highlighted key areas for irrigation and potential water savings. Models like CLM5 can help study the effects of irrigation on water availability, energy fluxes, and climate, making them useful tools for improving water management and allocation.
          , 
            Key Points
            
              
                
                  The CLM5 irrigation routine is tested at different scales and enhanced with the option to prescribe irrigation amounts and schedules
                
                
                  Soil moisture dynamics were simulated well but model simplifications limit the ability of CLM5 for field‐scale irrigation scheduling
                
                
                  Regional simulations using different irrigation scenarios identified priorities and water savings for improved irrigation management},
	language = {en},
	number = {7},
	urldate = {2024-11-26},
	journal = {Water Resources Research},
	author = {Dombrowski, O. and Brogi, C. and Hendricks Franssen, H.‐J. and Pisinaras, V. and Panagopoulos, A. and Swenson, S. and Bogena, H.},
	month = jul,
	year = {2024},
	pages = {e2023WR036139},
}

Abstract Irrigation strongly influences land‐atmosphere processes from regional to global scale. Therefore, an accurate representation of irrigation is crucial to understand these interactions and address water resources issues. While irrigation schemes are increasingly integrated into land surface models, their evaluation and further development remains challenging due to data limitations. This study assessed the representation of field‐scale irrigation using the Community Land Model version 5 (CLM5) through comparison of observed and simulated soil moisture, transpiration and crop yield. Irrigation was simulated by (a) adjusting the current irrigation routine and (b) by implementing a novel irrigation data stream that allows to directly use observed irrigation amounts and schedules. In a following step, the effect of different irrigation scenarios at the regional scale was simulated by using this novel data stream. At the plot scale, the novel irrigation data stream performed better in representing observed SM dynamics compared to the current irrigation routine. Nonetheless, simplifications in crop and irrigation representation and uncertainty in the relation between water stress and yield currently limit the ability of CLM5 for field‐scale irrigation scheduling. Still, the simulations revealed valuable insights into model performance that can inform and improve the modeling beyond the field scale. At regional scale, the simulations identified irrigation priorities and potential water savings. Furthermore, application of LSMs such as CLM5 can help to study the effects of irrigation beyond water availability, for example, on energy fluxes and climate, thus providing a powerful tool to assess the broader implications of irrigation at larger scale. , Plain Language Summary Irrigation impacts how land and atmosphere interact, both locally and globally. Therefore, it is important to understand the effects of irrigation practices and improve how water resources are managed. Advanced models such as land surface models now include irrigation. However, developing these models is difficult due to limited data. This study used the Community Land Model version 5 (CLM5) to compare observed and simulated soil moisture, plant water use, and crop yield. Two methods were used: an updated irrigation routine and a new data stream that uses actual irrigation amounts and schedules. The new data stream more accurately represented soil moisture. Simplifications in how the model handles crops and irrigation, and uncertainty about the link between water stress and yield, limit CLM5's effectiveness for precise irrigation planning. Still, the simulations provided valuable insights into the model's performance. At a regional level, the simulations highlighted key areas for irrigation and potential water savings. Models like CLM5 can help study the effects of irrigation on water availability, energy fluxes, and climate, making them useful tools for improving water management and allocation. , Key Points The CLM5 irrigation routine is tested at different scales and enhanced with the option to prescribe irrigation amounts and schedules Soil moisture dynamics were simulated well but model simplifications limit the ability of CLM5 for field‐scale irrigation scheduling Regional simulations using different irrigation scenarios identified priorities and water savings for improved irrigation management

@article{droppers_multi-model_2024,
	title = {Multi-model hydrological reference dataset over continental {Europe} and an {African} basin},
	volume = {11},
	issn = {2052-4463},
	url = {https://www.nature.com/articles/s41597-024-03825-9},
	doi = {10.1038/s41597-024-03825-9},
	language = {en},
	number = {1},
	urldate = {2025-02-13},
	journal = {Scientific Data},
	author = {Droppers, Bram and Rakovec, Oldrich and Avila, Leandro and Azimi, Shima and Cortés-Torres, Nicolás and De León Pérez, David and Imhoff, Ruben and Francés, Félix and Kollet, Stefan and Rigon, Riccardo and Weerts, Albrecht and Samaniego, Luis},
	month = sep,
	year = {2024},
	pages = {1009},
}

@article{duarte_review_2024,
	title = {A {Review} on {Soil} {Moisture} {Dynamics} {Monitoring} in {Semi}-{Arid} {Ecosystems}: {Methods}, {Techniques}, and {Tools} {Applied} at {Different} {Scales}},
	volume = {14},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2076-3417},
	shorttitle = {A {Review} on {Soil} {Moisture} {Dynamics} {Monitoring} in {Semi}-{Arid} {Ecosystems}},
	url = {https://www.mdpi.com/2076-3417/14/17/7677},
	doi = {10.3390/app14177677},
	abstract = {Soil moisture (SM) plays a crucial role in land–atmosphere interaction systems, directly influencing evapotranspiration, photosynthesis, and the water dynamics of the soil surface. Invariably, SM is negatively impacted by disturbances such as fires, which are becoming more frequent across semi-arid ecosystems. Different ecological restoration activities have been implemented to mitigate the impacts of disturbance that, when left untreated, can worsen the effects of recurrent droughts and accelerate desertification and land degradation processes. To measure and monitor the dynamics of SM, advanced techniques and tools have been developed that integrate remote sensing and in situ measurement. This review encompasses various themes on the application of remote sensing for measuring and monitoring SM dynamics in semi-arid ecosystems at different scales. We focused our analysis on the western United States region and thus have developed a review on the following topics: (a) the different data sources (e.g., satellite, unmanned aerial vehicles), (b) approaches to measure field-based SM, and (c) algorithms and techniques to model SM at different scales. We summarize these topics by emphasizing repeatable approaches for the transparent estimation of this variable, identifying current data gaps, and highlighting future trends to fulfill the expanding demand for SM monitoring strategies.},
	language = {en},
	number = {17},
	urldate = {2025-02-13},
	journal = {Applied Sciences},
	author = {Duarte, Efrain and Hernandez, Alexander},
	month = aug,
	year = {2024},
	pages = {7677},
}

Soil moisture (SM) plays a crucial role in land–atmosphere interaction systems, directly influencing evapotranspiration, photosynthesis, and the water dynamics of the soil surface. Invariably, SM is negatively impacted by disturbances such as fires, which are becoming more frequent across semi-arid ecosystems. Different ecological restoration activities have been implemented to mitigate the impacts of disturbance that, when left untreated, can worsen the effects of recurrent droughts and accelerate desertification and land degradation processes. To measure and monitor the dynamics of SM, advanced techniques and tools have been developed that integrate remote sensing and in situ measurement. This review encompasses various themes on the application of remote sensing for measuring and monitoring SM dynamics in semi-arid ecosystems at different scales. We focused our analysis on the western United States region and thus have developed a review on the following topics: (a) the different data sources (e.g., satellite, unmanned aerial vehicles), (b) approaches to measure field-based SM, and (c) algorithms and techniques to model SM at different scales. We summarize these topics by emphasizing repeatable approaches for the transparent estimation of this variable, identifying current data gaps, and highlighting future trends to fulfill the expanding demand for SM monitoring strategies.

@misc{ehrhardt_effects_2024,
	title = {Effects of changes in climatic conditions on soil water storage patterns},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	url = {https://egusphere.copernicus.org/preprints/2024/egusphere-2024-118/},
	doi = {10.5194/egusphere-2024-118},
	abstract = {Abstract. The soil water storage (SWS) defines crop productivity of a soil and varies under differing climatic conditions. Pattern identification and quantification of these variations remains difficult due to the non-linear behaviour of SWS changes over time. We hypothesize that these patterns can be revealed by applying wavelet analysis to an eight-year time series of SWS, precipitation (P) and actual evapotranspiration (ETa) in similar soils of lysimeters in a colder and drier location and a warmer and wetter location within Germany. Correlations between SWS, P and ETa at these sites might reveal the influence of altered climatic conditions but also from subsequent wet and dry years on SWS changes. We found that wet and dry years exerted influence on SWS changes by leading to faster or slower response times of SWS changes to precipitation in respect to normal years. Extreme precipitation events were visible in SWS and P wavelet spectra. Time shifts in correlations between ETa and SWS became smaller at the wetter and warmer site over time in comparison to the cooler and drier site where they stayed constant. This could be attributed to an earlier onset of the vegetation period over the years and thus to an earlier ETa peak every year and reflects the direct impact of changing climate on soil water budget parameters. Long-term observations ({\textgreater}30 years) might reveal similar time shifts for a drier climate. Analysis of the SWS capacity could provide information on how different climatic conditions affect the long-term storage behaviour of soils.},
	urldate = {2024-11-26},
	publisher = {Vadose Zone Hydrology/Instruments and observation techniques},
	author = {Ehrhardt, Annelie and Groh, Jannis S. and Gerke, Horst H.},
	month = feb,
	year = {2024},
}

Abstract. The soil water storage (SWS) defines crop productivity of a soil and varies under differing climatic conditions. Pattern identification and quantification of these variations remains difficult due to the non-linear behaviour of SWS changes over time. We hypothesize that these patterns can be revealed by applying wavelet analysis to an eight-year time series of SWS, precipitation (P) and actual evapotranspiration (ETa) in similar soils of lysimeters in a colder and drier location and a warmer and wetter location within Germany. Correlations between SWS, P and ETa at these sites might reveal the influence of altered climatic conditions but also from subsequent wet and dry years on SWS changes. We found that wet and dry years exerted influence on SWS changes by leading to faster or slower response times of SWS changes to precipitation in respect to normal years. Extreme precipitation events were visible in SWS and P wavelet spectra. Time shifts in correlations between ETa and SWS became smaller at the wetter and warmer site over time in comparison to the cooler and drier site where they stayed constant. This could be attributed to an earlier onset of the vegetation period over the years and thus to an earlier ETa peak every year and reflects the direct impact of changing climate on soil water budget parameters. Long-term observations (\textgreater30 years) might reveal similar time shifts for a drier climate. Analysis of the SWS capacity could provide information on how different climatic conditions affect the long-term storage behaviour of soils.

@article{fairbairn_evaluation_2024,
	title = {Evaluation of an {Adaptive} {Soil} {Moisture} {Bias} {Correction} {Approach} in the {ECMWF} {Land} {Data} {Assimilation} {System}},
	volume = {16},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2072-4292},
	url = {https://www.mdpi.com/2072-4292/16/3/493},
	doi = {10.3390/rs16030493},
	abstract = {Satellite-derived soil moisture (SM) observations are widely assimilated in global land data assimilation systems. These systems typically assume zero-mean errors in the land surface model and observations. In practice, systematic differences (biases) exist between the observed and modelled SM. Commonly, the observed SM biases are removed by rescaling techniques or via a machine learning approach. However, these methods do not account for non-stationary biases, which can result from issues with the satellite retrieval algorithms or changes in the land surface model. Therefore, we test a novel application of adaptive SM bias correction (BC) in the European Centre for Medium Range Weather Forecasts (ECMWF) land data assimilation system. A two-stage filter is formulated to dynamically correct biases from satellite-derived active ASCAT C-band and passive L-band SMOS surface SM observations. This complements the operational seasonal rescaling of the ASCAT observations and the SMOS neural network retrieval while allowing the assimilation to correct subseasonal-scale errors. Experiments are performed on the ECMWF stand-alone surface analysis, which is a simplified version of the integrated forecasting system. Over a 3 year test period, the adaptive BC reduces the seasonal-scale (observation−forecast) departures by up to 20\% (30\%) for the ASCAT (SMOS). The adaptive BC leads to (1) slight improvements in the SM analysis performance and (2) moderate but statistically significant reductions in the 1–5 day relative humidity forecast errors in the boundary layer of the Northern Hemisphere midlatitudes. Future work will test the adaptive SM BC in the full integrated forecasting system.},
	language = {en},
	number = {3},
	urldate = {2024-11-26},
	journal = {Remote Sensing},
	author = {Fairbairn, David and De Rosnay, Patricia and Weston, Peter},
	month = jan,
	year = {2024},
	pages = {493},
}

Satellite-derived soil moisture (SM) observations are widely assimilated in global land data assimilation systems. These systems typically assume zero-mean errors in the land surface model and observations. In practice, systematic differences (biases) exist between the observed and modelled SM. Commonly, the observed SM biases are removed by rescaling techniques or via a machine learning approach. However, these methods do not account for non-stationary biases, which can result from issues with the satellite retrieval algorithms or changes in the land surface model. Therefore, we test a novel application of adaptive SM bias correction (BC) in the European Centre for Medium Range Weather Forecasts (ECMWF) land data assimilation system. A two-stage filter is formulated to dynamically correct biases from satellite-derived active ASCAT C-band and passive L-band SMOS surface SM observations. This complements the operational seasonal rescaling of the ASCAT observations and the SMOS neural network retrieval while allowing the assimilation to correct subseasonal-scale errors. Experiments are performed on the ECMWF stand-alone surface analysis, which is a simplified version of the integrated forecasting system. Over a 3 year test period, the adaptive BC reduces the seasonal-scale (observation−forecast) departures by up to 20% (30%) for the ASCAT (SMOS). The adaptive BC leads to (1) slight improvements in the SM analysis performance and (2) moderate but statistically significant reductions in the 1–5 day relative humidity forecast errors in the boundary layer of the Northern Hemisphere midlatitudes. Future work will test the adaptive SM BC in the full integrated forecasting system.

@article{feng_satellite-based_2024,
	title = {Satellite-based re-examination of global soil moisture variation},
	issn = {02731177},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0273117724012584},
	doi = {10.1016/j.asr.2024.12.030},
	language = {en},
	urldate = {2025-02-13},
	journal = {Advances in Space Research},
	author = {Feng, Huihui and Wang, Shu and Li, Shijie and Wang, Wei and Li, Jingya and Gu, Runxi and Huang, Jixian},
	month = dec,
	year = {2024},
	pages = {S0273117724012584},
}

@misc{francke_virtual_2024,
	title = {Virtual joint field campaign: a framework of synthetic landscapes to assess multiscale measurement methods of water storage},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	shorttitle = {Virtual joint field campaign},
	url = {https://gmd.copernicus.org/preprints/gmd-2024-106/},
	doi = {10.5194/gmd-2024-106},
	abstract = {Abstract. The major challenge of multiscale measurement methods beyond the point scale is their complex interpretation in the light of landscape heterogeneity. For example, methods like cosmic-ray neutron sensing, remote sensing, or hydrogravimetry are all able to provide an integral value on the water storage, representative for their individual measurement volume. A rigorous assessment of their performance is often hindered by the lack of knowledge about the truth at their corresponding scale, given the high complexity and detail of natural landscapes. In this study we suggest a synthetic virtual landscape that allows for an exact definition of all variables of interest and, consequently, constitutes the so-called "virtual truth" free of knowledge gaps. Such a landscape can be explored in various "virtual field campaigns" using "virtual sensors" that mimic the response and characteristics of actual devices. We use dedicated physically-based models to simulate the signal a sensor would receive. These model outputs termed "virtual observations" can be explored and also allow the reconstruction of water storage, which can then readily be compared to the "virtual truth". Insights from this comparison could help to better understand real measurements and their uncertainties, and to challenge accepted knowledge about signal processing and data interpretation. The "Virtual Joint Field Campaign" is an open collaborative framework for constructing such landscapes. It comprises data and methods to create and combine different compartments of the landscape (e.g. atmosphere, soil, vegetation). The present study demonstrates virtual observations with Cosmic Ray Neutron Sensing, Hydrogravimetry, and Remote Sensing in three exemplary landscapes. It enables unprecedented opportunities for the systematic assessment of the sensor’s strengths and weaknesses and even their combined use.},
	urldate = {2024-11-26},
	publisher = {Hydrology},
	author = {Francke, Till and Brogi, Cosimo and Duarte Rocha, Alby and Förster, Michael and Heistermann, Maik and Köhli, Markus and Rasche, Daniel and Reich, Marvin and Schattan, Paul and Scheiffele, Lena and Schrön, Martin},
	month = aug,
	year = {2024},
}

Abstract. The major challenge of multiscale measurement methods beyond the point scale is their complex interpretation in the light of landscape heterogeneity. For example, methods like cosmic-ray neutron sensing, remote sensing, or hydrogravimetry are all able to provide an integral value on the water storage, representative for their individual measurement volume. A rigorous assessment of their performance is often hindered by the lack of knowledge about the truth at their corresponding scale, given the high complexity and detail of natural landscapes. In this study we suggest a synthetic virtual landscape that allows for an exact definition of all variables of interest and, consequently, constitutes the so-called "virtual truth" free of knowledge gaps. Such a landscape can be explored in various "virtual field campaigns" using "virtual sensors" that mimic the response and characteristics of actual devices. We use dedicated physically-based models to simulate the signal a sensor would receive. These model outputs termed "virtual observations" can be explored and also allow the reconstruction of water storage, which can then readily be compared to the "virtual truth". Insights from this comparison could help to better understand real measurements and their uncertainties, and to challenge accepted knowledge about signal processing and data interpretation. The "Virtual Joint Field Campaign" is an open collaborative framework for constructing such landscapes. It comprises data and methods to create and combine different compartments of the landscape (e.g. atmosphere, soil, vegetation). The present study demonstrates virtual observations with Cosmic Ray Neutron Sensing, Hydrogravimetry, and Remote Sensing in three exemplary landscapes. It enables unprecedented opportunities for the systematic assessment of the sensor’s strengths and weaknesses and even their combined use.

@article{fu_global_2024,
	title = {Global critical soil moisture thresholds of plant water stress},
	volume = {15},
	issn = {2041-1723},
	url = {https://www.nature.com/articles/s41467-024-49244-7},
	doi = {10.1038/s41467-024-49244-7},
	abstract = {Abstract
            
              During extensive periods without rain, known as dry-downs, decreasing soil moisture (SM) induces plant water stress at the point when it limits evapotranspiration, defining a critical SM threshold (θ
              crit
              ). Better quantification of θ
              crit
              is needed for improving future projections of climate and water resources, food production, and ecosystem vulnerability. Here, we combine systematic satellite observations of the diurnal amplitude of land surface temperature (dLST) and SM during dry-downs, corroborated by in-situ data from flux towers, to generate the observation-based global map of θ
              crit
              . We find an average global θ
              crit
              of 0.19 m
              3
              /m
              3
              , varying from 0.12 m
              3
              /m
              3
              in arid ecosystems to 0.26 m
              3
              /m
              3
              in humid ecosystems. θ
              crit
              simulated by Earth System Models is overestimated in dry areas and underestimated in wet areas. The global observed pattern of θ
              crit
              reflects plant adaptation to soil available water and atmospheric demand. Using explainable machine learning, we show that aridity index, leaf area and soil texture are the most influential drivers. Moreover, we show that the annual fraction of days with water stress, when SM stays below θ
              crit
              , has increased in the past four decades. Our results have important implications for understanding the inception of water stress in models and identifying SM tipping points.},
	language = {en},
	number = {1},
	urldate = {2024-11-26},
	journal = {Nature Communications},
	author = {Fu, Zheng and Ciais, Philippe and Wigneron, Jean-Pierre and Gentine, Pierre and Feldman, Andrew F. and Makowski, David and Viovy, Nicolas and Kemanian, Armen R. and Goll, Daniel S. and Stoy, Paul C. and Prentice, Iain Colin and Yakir, Dan and Liu, Liyang and Ma, Hongliang and Li, Xiaojun and Huang, Yuanyuan and Yu, Kailiang and Zhu, Peng and Li, Xing and Zhu, Zaichun and Lian, Jinghui and Smith, William K.},
	month = jun,
	year = {2024},
	pages = {4826},
}

Abstract During extensive periods without rain, known as dry-downs, decreasing soil moisture (SM) induces plant water stress at the point when it limits evapotranspiration, defining a critical SM threshold (θ crit ). Better quantification of θ crit is needed for improving future projections of climate and water resources, food production, and ecosystem vulnerability. Here, we combine systematic satellite observations of the diurnal amplitude of land surface temperature (dLST) and SM during dry-downs, corroborated by in-situ data from flux towers, to generate the observation-based global map of θ crit . We find an average global θ crit of 0.19 m 3 /m 3 , varying from 0.12 m 3 /m 3 in arid ecosystems to 0.26 m 3 /m 3 in humid ecosystems. θ crit simulated by Earth System Models is overestimated in dry areas and underestimated in wet areas. The global observed pattern of θ crit reflects plant adaptation to soil available water and atmospheric demand. Using explainable machine learning, we show that aridity index, leaf area and soil texture are the most influential drivers. Moreover, we show that the annual fraction of days with water stress, when SM stays below θ crit , has increased in the past four decades. Our results have important implications for understanding the inception of water stress in models and identifying SM tipping points.

@article{gaber_using_2024,
	title = {Using automated machine learning for the upscaling of gross primary productivity},
	volume = {21},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1726-4189},
	url = {https://bg.copernicus.org/articles/21/2447/2024/},
	doi = {10.5194/bg-21-2447-2024},
	abstract = {Abstract. Estimating gross primary productivity (GPP) over space and time is fundamental for understanding the response of the terrestrial biosphere to climate change. Eddy covariance flux towers provide in situ estimates of GPP at the ecosystem scale, but their sparse geographical distribution limits larger-scale inference. Machine learning (ML) techniques have been used to address this problem by extrapolating local GPP measurements over space using satellite remote sensing data. However, the accuracy of the regression model can be affected by uncertainties introduced by model selection, parameterization, and choice of explanatory features, among others. Recent advances in automated ML (AutoML) provide a novel automated way to select and synthesize different ML models. In this work, we explore the potential of AutoML by training three major AutoML frameworks on eddy covariance measurements of GPP at 243 globally distributed sites. We compared their ability to predict GPP and its spatial and temporal variability based on different sets of remote sensing explanatory variables. Explanatory variables from only Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and photosynthetically active radiation explained over 70 \% of the monthly variability in GPP, while satellite-derived proxies for canopy structure, photosynthetic activity, environmental stressors, and meteorological variables from reanalysis (ERA5-Land) further improved the frameworks' predictive ability. We found that the AutoML framework Auto-sklearn consistently outperformed other AutoML frameworks as well as a classical random forest regressor in predicting GPP but with small performance differences, reaching an r2 of up to 0.75. We deployed the best-performing framework to generate global wall-to-wall maps highlighting GPP patterns in good agreement with satellite-derived reference data. This research benchmarks the application of AutoML in GPP estimation and assesses its potential and limitations in quantifying global photosynthetic activity.},
	language = {en},
	number = {10},
	urldate = {2024-11-26},
	journal = {Biogeosciences},
	author = {Gaber, Max and Kang, Yanghui and Schurgers, Guy and Keenan, Trevor},
	month = may,
	year = {2024},
	pages = {2447--2472},
}

Abstract. Estimating gross primary productivity (GPP) over space and time is fundamental for understanding the response of the terrestrial biosphere to climate change. Eddy covariance flux towers provide in situ estimates of GPP at the ecosystem scale, but their sparse geographical distribution limits larger-scale inference. Machine learning (ML) techniques have been used to address this problem by extrapolating local GPP measurements over space using satellite remote sensing data. However, the accuracy of the regression model can be affected by uncertainties introduced by model selection, parameterization, and choice of explanatory features, among others. Recent advances in automated ML (AutoML) provide a novel automated way to select and synthesize different ML models. In this work, we explore the potential of AutoML by training three major AutoML frameworks on eddy covariance measurements of GPP at 243 globally distributed sites. We compared their ability to predict GPP and its spatial and temporal variability based on different sets of remote sensing explanatory variables. Explanatory variables from only Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and photosynthetically active radiation explained over 70 % of the monthly variability in GPP, while satellite-derived proxies for canopy structure, photosynthetic activity, environmental stressors, and meteorological variables from reanalysis (ERA5-Land) further improved the frameworks' predictive ability. We found that the AutoML framework Auto-sklearn consistently outperformed other AutoML frameworks as well as a classical random forest regressor in predicting GPP but with small performance differences, reaching an r2 of up to 0.75. We deployed the best-performing framework to generate global wall-to-wall maps highlighting GPP patterns in good agreement with satellite-derived reference data. This research benchmarks the application of AutoML in GPP estimation and assesses its potential and limitations in quantifying global photosynthetic activity.

@article{garcia-franco_rapid_2024,
	title = {Rapid loss of organic carbon and soil structure in mountainous grassland topsoils induced by simulated climate change},
	volume = {442},
	issn = {00167061},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0016706124000363},
	doi = {10.1016/j.geoderma.2024.116807},
	language = {en},
	urldate = {2024-11-26},
	journal = {Geoderma},
	author = {Garcia-Franco, Noelia and Wiesmeier, Martin and Buness, Vincent and Berauer, Bernd J. and Schuchardt, Max A. and Jentsch, Anke and Schlingmann, Marcus and Andrade-Linares, Diana and Wolf, Benjamin and Kiese, Ralf and Dannenmann, Michael and Kögel-Knabner, Ingrid},
	month = feb,
	year = {2024},
	pages = {116807},
}

@article{ghaffar_toward_2024,
	title = {Toward a {Data}‐{Effective} {Calibration} of a {Fully} {Distributed} {Catchment} {Water} {Quality} {Model}},
	volume = {60},
	issn = {0043-1397, 1944-7973},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023WR036527},
	doi = {10.1029/2023WR036527},
	abstract = {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 effective 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 the 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  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; however, it 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.
            
          , 
            Plain Language Summary
            Water quality management in catchment areas is crucial for maintaining the health of aquatic ecosystems and ensuring safe drinking water. Catchment hydrological water quality models have become valuable tools for predicting and managing water quality in complex catchment systems. Typically, these models are calibrated using data from a single site (at the outlet), assuming that this location represents the entire catchment. However, recent studies have shown contrasting findings regarding the performance of hydrological water quality models calibrated at a single site versus multiple sites. To address this issue, the study focuses on achieving effective calibration of a fully distributed catchment water quality model known as mHM‐Nitrate. This study investigates the impacts of multi‐site calibration compared to single‐site calibration on the model’s accuracy in estimating nitrate concentration at non‐calibrated locations within a heterogenous catchment. By comparing different calibration schemes and analyzing the model’s performance in estimating nitrate concentration at non‐calibrated stations, the study highlights the importance of incorporating multi‐site calibration in a data‐effective manner to enhance the reliability and accuracy of catchment water quality models. The findings have important implications for the design of monitoring networks and the selection of calibration data, ultimately contributing to more effective water quality management strategies.
          , 
            Key Points
            
              
                
                  
                    Single‐ and multi‐site calibration approaches generally led to similar model performance for discharge (
                    Q
                    ) at the catchment outlet
                  
                
                
                  Influence of calibration stations on the spatiotemporal performance of a fully distributed process‐based hydrological water quality model
                
                
                  Quality of the nitrate simulation depends on representativeness of their catchment characteristics than the number of calibration stations},
	language = {en},
	number = {9},
	urldate = {2024-11-26},
	journal = {Water Resources Research},
	author = {Ghaffar, Salman and Zhou, Xiangqian and Jomaa, Seifeddine and Yang, Xiaoqiang and Meon, Günter and Rode, Michael},
	month = sep,
	year = {2024},
	pages = {e2023WR036527},
}

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 effective 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 the 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 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; however, it 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. , Plain Language Summary Water quality management in catchment areas is crucial for maintaining the health of aquatic ecosystems and ensuring safe drinking water. Catchment hydrological water quality models have become valuable tools for predicting and managing water quality in complex catchment systems. Typically, these models are calibrated using data from a single site (at the outlet), assuming that this location represents the entire catchment. However, recent studies have shown contrasting findings regarding the performance of hydrological water quality models calibrated at a single site versus multiple sites. To address this issue, the study focuses on achieving effective calibration of a fully distributed catchment water quality model known as mHM‐Nitrate. This study investigates the impacts of multi‐site calibration compared to single‐site calibration on the model’s accuracy in estimating nitrate concentration at non‐calibrated locations within a heterogenous catchment. By comparing different calibration schemes and analyzing the model’s performance in estimating nitrate concentration at non‐calibrated stations, the study highlights the importance of incorporating multi‐site calibration in a data‐effective manner to enhance the reliability and accuracy of catchment water quality models. The findings have important implications for the design of monitoring networks and the selection of calibration data, ultimately contributing to more effective water quality management strategies. , Key Points Single‐ and multi‐site calibration approaches generally led to similar model performance for discharge ( Q ) at the catchment outlet Influence of calibration stations on the spatiotemporal performance of a fully distributed process‐based hydrological water quality model Quality of the nitrate simulation depends on representativeness of their catchment characteristics than the number of calibration stations

@article{gibon_estimating_2024,
	title = {Estimating the uncertainties of satellite derived soil moisture at global scale},
	volume = {10},
	issn = {26660172},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S2666017224000312},
	doi = {10.1016/j.srs.2024.100147},
	language = {en},
	urldate = {2024-11-26},
	journal = {Science of Remote Sensing},
	author = {Gibon, François and Mialon, Arnaud and Richaume, Philippe and Rodríguez-Fernández, Nemesio and Aberer, Daniel and Boresch, Alexander and Crapolicchio, Raffaele and Dorigo, Wouter and Gruber, Alexander and Himmelbauer, Irene and Preimesberger, Wolfgang and Sabia, Roberto and Stradiotti, Pietro and Tercjak, Monika and Kerr, Yann H.},
	month = dec,
	year = {2024},
	pages = {100147},
}

@article{goswami_water_2024,
	title = {Water budget-based evapotranspiration product captures natural and human-caused variability},
	volume = {19},
	issn = {1748-9326},
	url = {https://iopscience.iop.org/article/10.1088/1748-9326/ad63bd},
	doi = {10.1088/1748-9326/ad63bd},
	abstract = {Abstract
            
              Evapotranspiration (
              ET
              ) is one of the most important yet highly uncertain components of the water cycle. Available modeled
              ET
              products do not necessarily agree with each other at various spatiotemporal scales, either due to limitations on input data and/or due to model assumptions and simplifications. Therefore, using the water budget equation to estimate
              ET
              has gained attention. However, numerous water budget combinations with large uncertainties are available, which increases ambiguity in choosing the best
              ET
              estimate. Here, the Kalman filter is employed to ingest 96 water budget-based
              ET
              estimates, and produce a global
              ET
              product with uncertainty
              
                
                  
                
                
                  
                    
                      {\textless}
                    
                    2
                  
                
              
               mm month
              −1
              , and capture the general spatiotemporal pattern of
              ET
              and the inter-annual variability over all continents. Since the water budget includes storage changes due to human interventions, our
              ET
              estimates are superior over regions with strong irrigation signals, such as the Ganges basin. We verify our claim by using a modified variable infiltration capacity model that also simulates irrigation activities. Our
              ET
              estimates have a global mean positive trend of
              
                
                  
                
                
                  
                    0.18
                    
                    ±
                    
                    0.02
                  
                
              
              mm yr
              −1
              with larger regional variations, which we discuss.},
	number = {9},
	urldate = {2025-02-14},
	journal = {Environmental Research Letters},
	author = {Goswami, Shubham and Rajendra Ternikar, Chirag and Kandala, Rajsekhar and Pillai, Netra S and Kumar Yadav, Vivek and {Abhishek} and Joseph, Jisha and Ghosh, Subimal and Dutt Vishwakarma, Bramha},
	month = sep,
	year = {2024},
	pages = {094034},
}

Abstract Evapotranspiration ( ET ) is one of the most important yet highly uncertain components of the water cycle. Available modeled ET products do not necessarily agree with each other at various spatiotemporal scales, either due to limitations on input data and/or due to model assumptions and simplifications. Therefore, using the water budget equation to estimate ET has gained attention. However, numerous water budget combinations with large uncertainties are available, which increases ambiguity in choosing the best ET estimate. Here, the Kalman filter is employed to ingest 96 water budget-based ET estimates, and produce a global ET product with uncertainty \textless 2  mm month −1 , and capture the general spatiotemporal pattern of ET and the inter-annual variability over all continents. Since the water budget includes storage changes due to human interventions, our ET estimates are superior over regions with strong irrigation signals, such as the Ganges basin. We verify our claim by using a modified variable infiltration capacity model that also simulates irrigation activities. Our ET estimates have a global mean positive trend of 0.18 ± 0.02 mm yr −1 with larger regional variations, which we discuss.

@article{gottschalk_monitoring_2024,
	title = {Monitoring cropland daily carbon dioxide exchange at field scales with {Sentinel}-2 satellite imagery},
	volume = {21},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1726-4189},
	url = {https://bg.copernicus.org/articles/21/3593/2024/},
	doi = {10.5194/bg-21-3593-2024},
	abstract = {Abstract. Improving the accuracy of monitoring cropland CO2 exchange at heterogeneous spatial scales is of great importance for reducing spatial and temporal uncertainty in estimating terrestrial carbon (C) dynamics. In this study, an approach to estimate daily cropland C fluxes is developed and tested by combining time series of field-scale eddy covariance (EC) CO2 flux data and Sentinel-2 satellite-based vegetation indices (VIs) after appropriately accounting for the spatial alignment between the two time series datasets. The study was carried out for an agricultural field (118 ha) in the lowlands of northeastern Germany. The ability of different VIs to estimate daily net ecosystem exchange (NEE) and gross primary productivity (GPP) based on linear regression models was assessed. Most VIs showed high ({\textgreater}0.9) and statistically significant (p{\textless}0.001) correlations with GPP and NEE, although some VIs deviated from the seasonal pattern of CO2 exchange. By contrast, correlations between ecosystem respiration (Reco) and VIs were weak and not statistically significant, and no attempt was made to estimate Reco from VIs. Linear regression models explained generally more than 80 \% and 70 \% of the variability in NEE and GPP, respectively, with high variability among the individual VIs. The performance in estimating daily C fluxes varied among VIs depending on the C flux component (NEE or GPP) and observation period. Root mean square error (RMSE) values ranged from 1.35 g C m−2 d−1 using the green normalized difference vegetation index (GNDVI) for NEE to 5 g C m−2 d−1 using the simple ratio (SR) for GPP. This equated to an underestimated net C uptake of only 41 g C m−2 (18 \%) and an overestimation of gross C uptake of 854 g C m−2 (73 \%). Differences between the measured and estimated C fluxes were mainly explained by the diversion of the C flux and VI signal during winter when C uptake remained low, while VI values indicated an increased C uptake due to relatively high crop leaf area. Overall, the results exhibited similar error margins to mechanistic crop models. Thus, they indicated the suitability and expandability of the proposed approach for monitoring cropland C exchange with satellite-derived VIs.},
	language = {en},
	number = {16},
	urldate = {2024-11-26},
	journal = {Biogeosciences},
	author = {Gottschalk, Pia and Kalhori, Aram and Li, Zhan and Wille, Christian and Sachs, Torsten},
	month = aug,
	year = {2024},
	pages = {3593--3616},
}

Abstract. Improving the accuracy of monitoring cropland CO2 exchange at heterogeneous spatial scales is of great importance for reducing spatial and temporal uncertainty in estimating terrestrial carbon (C) dynamics. In this study, an approach to estimate daily cropland C fluxes is developed and tested by combining time series of field-scale eddy covariance (EC) CO2 flux data and Sentinel-2 satellite-based vegetation indices (VIs) after appropriately accounting for the spatial alignment between the two time series datasets. The study was carried out for an agricultural field (118 ha) in the lowlands of northeastern Germany. The ability of different VIs to estimate daily net ecosystem exchange (NEE) and gross primary productivity (GPP) based on linear regression models was assessed. Most VIs showed high (\textgreater0.9) and statistically significant (p\textless0.001) correlations with GPP and NEE, although some VIs deviated from the seasonal pattern of CO2 exchange. By contrast, correlations between ecosystem respiration (Reco) and VIs were weak and not statistically significant, and no attempt was made to estimate Reco from VIs. Linear regression models explained generally more than 80 % and 70 % of the variability in NEE and GPP, respectively, with high variability among the individual VIs. The performance in estimating daily C fluxes varied among VIs depending on the C flux component (NEE or GPP) and observation period. Root mean square error (RMSE) values ranged from 1.35 g C m−2 d−1 using the green normalized difference vegetation index (GNDVI) for NEE to 5 g C m−2 d−1 using the simple ratio (SR) for GPP. This equated to an underestimated net C uptake of only 41 g C m−2 (18 %) and an overestimation of gross C uptake of 854 g C m−2 (73 %). Differences between the measured and estimated C fluxes were mainly explained by the diversion of the C flux and VI signal during winter when C uptake remained low, while VI values indicated an increased C uptake due to relatively high crop leaf area. Overall, the results exhibited similar error margins to mechanistic crop models. Thus, they indicated the suitability and expandability of the proposed approach for monitoring cropland C exchange with satellite-derived VIs.

@article{gui_better_2024,
	title = {Better representation of vegetation phenology improves estimations of annual gross primary productivity},
	volume = {82},
	issn = {15749541},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1574954124003091},
	doi = {10.1016/j.ecoinf.2024.102767},
	language = {en},
	urldate = {2024-11-26},
	journal = {Ecological Informatics},
	author = {Gui, Hanliang and Xin, Qinchuan and Zhou, Xuewen and Wu, Wei and Xiong, Zhenhua},
	month = sep,
	year = {2024},
	pages = {102767},
}

@article{guo_multimodel_2024,
	title = {Multimodel ensemble estimation of {Landsat}-like global terrestrial latent heat flux using a generalized deep {CNN}-{LSTM} integration algorithm},
	volume = {349},
	issn = {01681923},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0168192324000777},
	doi = {10.1016/j.agrformet.2024.109962},
	language = {en},
	urldate = {2024-11-26},
	journal = {Agricultural and Forest Meteorology},
	author = {Guo, Xiaozheng and Yao, Yunjun and Tang, Qingxin and Liang, Shunlin and Shao, Changliang and Fisher, Joshua B. and Chen, Jiquan and Jia, Kun and Zhang, Xiaotong and Shang, Ke and Yang, Junming and Yu, Ruiyang and Xie, Zijing and Liu, Lu and Ning, Jing and Zhang, Lilin},
	month = apr,
	year = {2024},
	pages = {109962},
}

@article{han_disentangling_2024,
	title = {Disentangling the {Impacts} of {Environmental} {Factors} on {Evaporative} {Fraction} {Across} {Climate} {Regimes}},
	volume = {129},
	issn = {2169-897X, 2169-8996},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024JD041515},
	doi = {10.1029/2024JD041515},
	abstract = {Abstract
            
              Evaporative fraction (EF) is a useful measure for quantifying land surface energy partitioning processes and determining evaporative regimes; however, its influencing factors remain highly uncertain. Here, global data sets were compiled to disentangle the effects of environmental variables on EF variations along climate and land surface gradients. We found that (a) at annual timescales, ecosystem‐level EF could be expressed as a power law function of aridity index. The relationships of mean annual soil water content (SWC) and leaf area index (LAI) with mean annual EF resembled the traditional evaporative regime theory; (b) at daily timescales, the boosted regression tree method quantitatively revealed that the impacts of environmental variables (including meteorological variables) on EF showed equal importance, especially at humid sites, primarily due to the different response direction and magnitude of latent heat (LE) and sensible heat (H) fluxes to environmental changes. Particularly, the contrasting responses of LE (positive) and H (negative) to SWC, LAI, and relative humidity enhanced the positive effects of those influencing variables on EF; whereas, the correlations between EF and energy‐related factors (i.e., net radiation‐Rn and air temperature‐Ta) deteriorated as both LE and H showed positive response patterns to those variables; (c) meteorological factors were also found to have nonlinear effects on daily EF, further modified by climatic conditions. Rn near 150 W/m
              2
              and Ta near 15°C appeared to be important energy‐partitioning thresholds at drier and humid sites, respectively. Moreover, changing interactions among environmental variables with climates were demonstrated to be important for better explaining EF variations.
            
          , 
            Plain Language Summary
            Evaporative fraction (EF; defined as latent heat flux‐LE divided by the sum of LE and sensible heat flux‐H) can vary under different climatic and land surface conditions, but its influencing factors remain poorly understood. To this end, we explored the effects of environmental variables on annual and daily EF variations at different sites around the globe. We found that mean annual EF decreased with increasing aridity index and increased with mean annual soil water content and leaf area index. By comparison, the boosted regression tree method quantitatively showed that environmental variables exerted equally important roles in regulating daily EF, especially at humid sites. The complex interplays of daily EF with environmental variables were mainly due to the different responses of LE and H to surrounding environments and the strong nonlinear and interactive effects of environmental variables. These results are important for understanding the driving mechanisms of EF and land surface energy partitioning processes along climate and land surface gradients.
          , 
            Key Points
            
              
                
                  Environmental variables exerted equally important roles in regulating daily evaporative fraction, especially at humid sites
                
                
                  Synchronous responses of latent and sensible heat to surroundings determined how environmental variables affected evaporative fraction
                
                
                  Environmental factors had strong nonlinear and interactive effects on daily evaporative fraction, which was further modified by climates},
	language = {en},
	number = {19},
	urldate = {2024-11-26},
	journal = {Journal of Geophysical Research: Atmospheres},
	author = {Han, Qiong and Wang, Tiejun and Kong, Zhe and Dai, Yibin and Wang, Lichun},
	month = oct,
	year = {2024},
	pages = {e2024JD041515},
}

Abstract Evaporative fraction (EF) is a useful measure for quantifying land surface energy partitioning processes and determining evaporative regimes; however, its influencing factors remain highly uncertain. Here, global data sets were compiled to disentangle the effects of environmental variables on EF variations along climate and land surface gradients. We found that (a) at annual timescales, ecosystem‐level EF could be expressed as a power law function of aridity index. The relationships of mean annual soil water content (SWC) and leaf area index (LAI) with mean annual EF resembled the traditional evaporative regime theory; (b) at daily timescales, the boosted regression tree method quantitatively revealed that the impacts of environmental variables (including meteorological variables) on EF showed equal importance, especially at humid sites, primarily due to the different response direction and magnitude of latent heat (LE) and sensible heat (H) fluxes to environmental changes. Particularly, the contrasting responses of LE (positive) and H (negative) to SWC, LAI, and relative humidity enhanced the positive effects of those influencing variables on EF; whereas, the correlations between EF and energy‐related factors (i.e., net radiation‐Rn and air temperature‐Ta) deteriorated as both LE and H showed positive response patterns to those variables; (c) meteorological factors were also found to have nonlinear effects on daily EF, further modified by climatic conditions. Rn near 150 W/m 2 and Ta near 15°C appeared to be important energy‐partitioning thresholds at drier and humid sites, respectively. Moreover, changing interactions among environmental variables with climates were demonstrated to be important for better explaining EF variations. , Plain Language Summary Evaporative fraction (EF; defined as latent heat flux‐LE divided by the sum of LE and sensible heat flux‐H) can vary under different climatic and land surface conditions, but its influencing factors remain poorly understood. To this end, we explored the effects of environmental variables on annual and daily EF variations at different sites around the globe. We found that mean annual EF decreased with increasing aridity index and increased with mean annual soil water content and leaf area index. By comparison, the boosted regression tree method quantitatively showed that environmental variables exerted equally important roles in regulating daily EF, especially at humid sites. The complex interplays of daily EF with environmental variables were mainly due to the different responses of LE and H to surrounding environments and the strong nonlinear and interactive effects of environmental variables. These results are important for understanding the driving mechanisms of EF and land surface energy partitioning processes along climate and land surface gradients. , Key Points Environmental variables exerted equally important roles in regulating daily evaporative fraction, especially at humid sites Synchronous responses of latent and sensible heat to surroundings determined how environmental variables affected evaporative fraction Environmental factors had strong nonlinear and interactive effects on daily evaporative fraction, which was further modified by climates

@article{han_asymmetry_2024,
	title = {Asymmetry in the {Diurnal} {Variation} of {Land} {Surface} {Albedo} and {Its} {Impacts} on {Daily} {Mean} {Albedo} {Estimation}},
	volume = {129},
	issn = {2169-897X, 2169-8996},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JD039728},
	doi = {10.1029/2023JD039728},
	abstract = {Abstract
            Daily mean albedo, a crucial variable of the earth radiation budget, is significantly affected by the diurnal variation of land surface albedo (DVLSA). The DVLSA typically exhibits asymmetry, thereby affecting the estimation of the daily mean albedo. However, the asymmetry in the DVLSA is generally ignored in daily mean albedo estimation. In this study, we investigated the influencing factors of the asymmetry in the DVLSA and evaluated its impacts on estimating the daily mean albedo based on field observations and simulated data. Our findings reveal that the asymmetry in the DVLSA varies among land cover types, with forests exhibiting more pronounced asymmetry compared to croplands, grasslands, and bare soil. The diurnal variation of the atmospheric conditions is the primary factor controlling the asymmetry in the DVLSA, with that of land surface conditions being a secondary factor. Neglecting the asymmetry in the DVLSA leads to estimation error in daily mean albedo, particularly pronounced during winter. The relative error of daily mean albedo can exceed 10\% when the mean asymmetry index of diffuse irradiance fraction reaches 40\%. However, the DVLSA retrieved from the satellite Bidirectional Reflectance Distribution Function product inadequately captures asymmetry, resulting in a relative error of approximately 13.7\% in estimating daily mean albedo.
          , 
            Plain Language Summary
            The amount of sunlight reflected by the ground is different in the morning and afternoon, which is important for climate research. This study looked at factors that cause this difference in reflection and found that it depends on things like land cover types and atmospheric visibility. Ignoring this difference in reflection can make it difficult to accurately estimate how much heat the earth absorbs each day, especially in the winter. Existing methods for estimating this are not perfect and can introduce errors of up to 13.7\%. A better understanding of these variations can improve our ability to study and address climate change.
          , 
            Key Points
            
              
                
                  The asymmetry in the diurnal variation of albedo varies with land cover types
                
                
                  The atmospheric conditions control the asymmetry in the diurnal variation of albedo
                
                
                  The asymmetry in the diurnal variation of albedo impacts the daily mean albedo estimation},
	language = {en},
	number = {14},
	urldate = {2025-02-14},
	journal = {Journal of Geophysical Research: Atmospheres},
	author = {Han, Yuan and Wen, Jianguang and Xiao, Qing and You, Dongqin and Meng, Lei and Wu, Shengbiao and Hao, Dalei and Tang, Yong and Chen, Xi and Liu, Qinhuo and Zhao, Congcong},
	month = jul,
	year = {2024},
	pages = {e2023JD039728},
}

Abstract Daily mean albedo, a crucial variable of the earth radiation budget, is significantly affected by the diurnal variation of land surface albedo (DVLSA). The DVLSA typically exhibits asymmetry, thereby affecting the estimation of the daily mean albedo. However, the asymmetry in the DVLSA is generally ignored in daily mean albedo estimation. In this study, we investigated the influencing factors of the asymmetry in the DVLSA and evaluated its impacts on estimating the daily mean albedo based on field observations and simulated data. Our findings reveal that the asymmetry in the DVLSA varies among land cover types, with forests exhibiting more pronounced asymmetry compared to croplands, grasslands, and bare soil. The diurnal variation of the atmospheric conditions is the primary factor controlling the asymmetry in the DVLSA, with that of land surface conditions being a secondary factor. Neglecting the asymmetry in the DVLSA leads to estimation error in daily mean albedo, particularly pronounced during winter. The relative error of daily mean albedo can exceed 10% when the mean asymmetry index of diffuse irradiance fraction reaches 40%. However, the DVLSA retrieved from the satellite Bidirectional Reflectance Distribution Function product inadequately captures asymmetry, resulting in a relative error of approximately 13.7% in estimating daily mean albedo. , Plain Language Summary The amount of sunlight reflected by the ground is different in the morning and afternoon, which is important for climate research. This study looked at factors that cause this difference in reflection and found that it depends on things like land cover types and atmospheric visibility. Ignoring this difference in reflection can make it difficult to accurately estimate how much heat the earth absorbs each day, especially in the winter. Existing methods for estimating this are not perfect and can introduce errors of up to 13.7%. A better understanding of these variations can improve our ability to study and address climate change. , Key Points The asymmetry in the diurnal variation of albedo varies with land cover types The atmospheric conditions control the asymmetry in the diurnal variation of albedo The asymmetry in the diurnal variation of albedo impacts the daily mean albedo estimation

@article{han_modeling_2024,
	title = {Modeling {Diurnal} {Variation} of {Land} {Surface} {Albedo} {Over} {Rugged} {Terrain}},
	volume = {62},
	copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
	issn = {0196-2892, 1558-0644},
	url = {https://ieeexplore.ieee.org/document/10689624/},
	doi = {10.1109/TGRS.2024.3466951},
	urldate = {2025-02-14},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	author = {Han, Yuan and Wen, Jianguang and You, Dongqin and Xiao, Qing and Hao, Dalei and Tang, Yong and Piao, Sen and Liu, Guokai and Liu, Qinhuo},
	year = {2024},
	pages = {1--13},
}

@article{hartmann_evolution_2024,
	title = {The {Evolution} of {Hillslope} {Hydrology}: {Links} {Between} {Form}, {Function} and the {Underlying} {Control} of {Geology}},
	volume = {60},
	issn = {0043-1397, 1944-7973},
	shorttitle = {The {Evolution} of {Hillslope} {Hydrology}},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023WR035937},
	doi = {10.1029/2023WR035937},
	abstract = {Abstract
            Form and function are two major characteristics of hydrological systems. While form summarizes the structure of the system, function represents the hydrological response. Little is known about how these characteristics evolve and how form relates to function in young hydrological systems. We investigated how form and function evolve during the first millennia of landscape evolution. We analyzed two hillslope chronosequences in glacial forelands, one developed from siliceous and the other from calcareous parent material. Variables describing hillslope form included soil physical properties, surface, and vegetation characteristics. Variables describing hydrological function included soil water response times, soil water storage, drainage, and dominant subsurface flow types. We identified links between form and hydrological function via cluster analysis. Clusters identified based on form were compared in terms of their hydrological functioning. The comparison of the two different parent materials shows how strongly landscape evolution is controlled by the underlying geology. Soil pH appears to be a key variable influencing vegetation, soil formation and subsequently hydrology. At the calcareous site, the high buffering capacity of the soil leads to less soil formation and fast, vertical subsurface water transport dominates the water redistribution even after more than 10,000 years of landscape evolution. At the siliceous site, soil acidification results in accumulation of organic material, a high water storage capacity, and in podsolization. Under these conditions water redistribution changes from vertical subsurface water transport at the young age classes to water storage in the organic surface layer and lateral subsurface water transport within 10,000 years.
          , 
            Key Points
            
              
                
                  The underlying geology controls landscape evolution in glacial forefields
                
                
                  After 10,000 years of evolution, hillslope form and hydrological functioning differ between the calcareous and siliceous sites
                
                
                  Soil pH is a key variable indicative of differences in soil evolution and hydrological response between the two forefields},
	language = {en},
	number = {3},
	urldate = {2024-11-26},
	journal = {Water Resources Research},
	author = {Hartmann, Anne and Blume, Theresa},
	month = mar,
	year = {2024},
	pages = {e2023WR035937},
}

Abstract Form and function are two major characteristics of hydrological systems. While form summarizes the structure of the system, function represents the hydrological response. Little is known about how these characteristics evolve and how form relates to function in young hydrological systems. We investigated how form and function evolve during the first millennia of landscape evolution. We analyzed two hillslope chronosequences in glacial forelands, one developed from siliceous and the other from calcareous parent material. Variables describing hillslope form included soil physical properties, surface, and vegetation characteristics. Variables describing hydrological function included soil water response times, soil water storage, drainage, and dominant subsurface flow types. We identified links between form and hydrological function via cluster analysis. Clusters identified based on form were compared in terms of their hydrological functioning. The comparison of the two different parent materials shows how strongly landscape evolution is controlled by the underlying geology. Soil pH appears to be a key variable influencing vegetation, soil formation and subsequently hydrology. At the calcareous site, the high buffering capacity of the soil leads to less soil formation and fast, vertical subsurface water transport dominates the water redistribution even after more than 10,000 years of landscape evolution. At the siliceous site, soil acidification results in accumulation of organic material, a high water storage capacity, and in podsolization. Under these conditions water redistribution changes from vertical subsurface water transport at the young age classes to water storage in the organic surface layer and lateral subsurface water transport within 10,000 years. , Key Points The underlying geology controls landscape evolution in glacial forefields After 10,000 years of evolution, hillslope form and hydrological functioning differ between the calcareous and siliceous sites Soil pH is a key variable indicative of differences in soil evolution and hydrological response between the two forefields

@article{heistermann_technical_2024,
	title = {Technical {Note}: {Revisiting} the general calibration of cosmic-ray neutron sensors to estimate soil water content},
	volume = {28},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1607-7938},
	shorttitle = {Technical {Note}},
	url = {https://hess.copernicus.org/articles/28/989/2024/},
	doi = {10.5194/hess-28-989-2024},
	abstract = {Abstract. Cosmic-ray neutron sensing (CRNS) is becoming increasingly popular for monitoring soil water content (SWC). To retrieve SWC from observed neutron intensities, local measurements of SWC are typically required to calibrate a location-specific parameter, N0, in the corresponding transfer function. In this study, we develop a generalized conversion function that explicitly takes into account the different factors that govern local neutron intensity. Thus, the parameter N0 becomes location independent, i.e. generally applicable. We demonstrate the feasibility of such a “general calibration function” by analysing 75 CRNS sites from four recently published datasets. Given the choice between the two calibration strategies – local or general – users will wonder which one is preferable. To answer this question, we estimated the resulting uncertainty in the SWC by means of error propagation. While the uncertainty in the local calibration depends on both the local reference SWC itself and its error, the uncertainty in the general calibration is mainly governed by the errors in vegetation biomass and soil bulk density. Our results suggest that a local calibration – generally considered best practice – might often not be the best option. In order to support the decision which calibration strategy – local or general – is actually preferable in the user-specific application context, we provide an interactive online tool that assesses the uncertainty in both options (https://cosmic-sense.github.io/local-or-global, last access: 23 February 2024).},
	language = {en},
	number = {4},
	urldate = {2024-11-26},
	journal = {Hydrology and Earth System Sciences},
	author = {Heistermann, Maik and Francke, Till and Schrön, Martin and Oswald, Sascha E.},
	month = feb,
	year = {2024},
	pages = {989--1000},
}

Abstract. Cosmic-ray neutron sensing (CRNS) is becoming increasingly popular for monitoring soil water content (SWC). To retrieve SWC from observed neutron intensities, local measurements of SWC are typically required to calibrate a location-specific parameter, N0, in the corresponding transfer function. In this study, we develop a generalized conversion function that explicitly takes into account the different factors that govern local neutron intensity. Thus, the parameter N0 becomes location independent, i.e. generally applicable. We demonstrate the feasibility of such a “general calibration function” by analysing 75 CRNS sites from four recently published datasets. Given the choice between the two calibration strategies – local or general – users will wonder which one is preferable. To answer this question, we estimated the resulting uncertainty in the SWC by means of error propagation. While the uncertainty in the local calibration depends on both the local reference SWC itself and its error, the uncertainty in the general calibration is mainly governed by the errors in vegetation biomass and soil bulk density. Our results suggest that a local calibration – generally considered best practice – might often not be the best option. In order to support the decision which calibration strategy – local or general – is actually preferable in the user-specific application context, we provide an interactive online tool that assesses the uncertainty in both options (https://cosmic-sense.github.io/local-or-global, last access: 23 February 2024).

@article{henrique_lima_alencar_flash_2024,
	title = {Flash droughts and their impacts—using newspaper articles to assess the perceived consequences of rapidly emerging droughts},
	volume = {19},
	issn = {1748-9326},
	url = {https://iopscience.iop.org/article/10.1088/1748-9326/ad58fa},
	doi = {10.1088/1748-9326/ad58fa},
	abstract = {Abstract
            Flash droughts (FDs) have attracted increasing attention in the past decade. They are characterised by a rapid depletion of soil moisture resulting from interactions between the soil and atmospheric conditions. To date, there is a lack of consistent FD definitions and an understanding of their socio-economic impacts. Here, we explore the relationship between biophysical FD conditions and the perceived impacts of FDs in Germany between 2000 and 2022. We measured perceived impacts by analysing consequences reported in newspaper articles (2000–2022) and online search behaviour using Google trends data (2004–2022). To characterise the physical process, we considered root zone soil moisture data. Our results show that FDs are becoming increasingly frequent in Germany, occurring once every two years on average. Despite the lack of knowledge from the general public regarding the phenomenon of FDs, the peaks of interest in drought impacts correspond to the physical occurrence of FDs across the country. We identified an average time gap of four weeks between FD onset and the reporting of perceived impacts. This gap is longer than the average duration of FDs’ onset. Consequently, our findings highlight that consistent monitoring of FD conditions and drivers is necessary to guarantee effective preparedness. As impact perception is too slow to allow the adoption of mitigation measures, FDs require new schemes for response measures compared with slowly emerging (conventional) drought events. The novel method also allows the consistent and impact-based validation of FD identification methods.},
	number = {7},
	urldate = {2024-11-21},
	journal = {Environmental Research Letters},
	author = {Henrique Lima Alencar, Pedro and Sodoge, Jan and Nora Paton, Eva and Madruga De Brito, Mariana},
	month = jul,
	year = {2024},
	pages = {074048},
}

Abstract Flash droughts (FDs) have attracted increasing attention in the past decade. They are characterised by a rapid depletion of soil moisture resulting from interactions between the soil and atmospheric conditions. To date, there is a lack of consistent FD definitions and an understanding of their socio-economic impacts. Here, we explore the relationship between biophysical FD conditions and the perceived impacts of FDs in Germany between 2000 and 2022. We measured perceived impacts by analysing consequences reported in newspaper articles (2000–2022) and online search behaviour using Google trends data (2004–2022). To characterise the physical process, we considered root zone soil moisture data. Our results show that FDs are becoming increasingly frequent in Germany, occurring once every two years on average. Despite the lack of knowledge from the general public regarding the phenomenon of FDs, the peaks of interest in drought impacts correspond to the physical occurrence of FDs across the country. We identified an average time gap of four weeks between FD onset and the reporting of perceived impacts. This gap is longer than the average duration of FDs’ onset. Consequently, our findings highlight that consistent monitoring of FD conditions and drivers is necessary to guarantee effective preparedness. As impact perception is too slow to allow the adoption of mitigation measures, FDs require new schemes for response measures compared with slowly emerging (conventional) drought events. The novel method also allows the consistent and impact-based validation of FD identification methods.

@article{heyvaert_joint_2024,
	title = {Joint assimilation of satellite-based surface soil moisture and vegetation conditions into the {Noah}-{MP} land surface model},
	volume = {9},
	issn = {26660172},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S2666017224000130},
	doi = {10.1016/j.srs.2024.100129},
	language = {en},
	urldate = {2024-11-26},
	journal = {Science of Remote Sensing},
	author = {Heyvaert, Zdenko and Scherrer, Samuel and Dorigo, Wouter and Bechtold, Michel and De Lannoy, Gabriëlle},
	month = jun,
	year = {2024},
	pages = {100129},
}

@article{huang_global_2024,
	title = {Global synchronous increase in light-saturated and peak vegetation productivity},
	issn = {26673258},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S2667325824003522},
	doi = {10.1016/j.fmre.2024.09.001},
	language = {en},
	urldate = {2024-11-26},
	journal = {Fundamental Research},
	author = {Huang, Kun and Xia, Jianyang},
	month = sep,
	year = {2024},
	pages = {S2667325824003522},
}

@article{hovel_repeating_2024,
	title = {Repeating patterns in runoff time series: {A} basis for exploring hydrologic similarity of precipitation and catchment wetness conditions},
	volume = {629},
	issn = {00221694},
	shorttitle = {Repeating patterns in runoff time series},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0022169423015275},
	doi = {10.1016/j.jhydrol.2023.130585},
	language = {en},
	urldate = {2024-11-26},
	journal = {Journal of Hydrology},
	author = {Hövel, Adriane and Stumpp, Christine and Bogena, Heye and Lücke, Andreas and Strauss, Peter and Blöschl, Günter and Stockinger, Michael},
	month = feb,
	year = {2024},
	pages = {130585},
}

@article{kalhori_temporally_2024,
	title = {Temporally dynamic carbon dioxide and methane emission factors for rewetted peatlands},
	volume = {5},
	issn = {2662-4435},
	url = {https://www.nature.com/articles/s43247-024-01226-9},
	doi = {10.1038/s43247-024-01226-9},
	abstract = {Abstract
            
              Rewetting drained peatlands is recognized as a leading and effective natural solution to curb greenhouse gas emissions. However, rewetting creates novel ecosystems whose emission behaviors are not adequately captured by currently used emission factors. These emission factors are applied immediately after rewetting, thus do not reflect the temporal dynamics of greenhouse gas emissions during the period wherein there is a transition to a rewetted steady-state. Here, we provide long-term data showing a mismatch between actual emissions and default emission factors and revealing the temporal patterns of annual carbon dioxide and methane fluxes in a rewetted peatland site in northeastern Germany. We show that site-level annual emissions of carbon dioxide and methane approach the IPCC default emission factors 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 carbon dioxide fluxes with a decreasing trend of −0.36 t CO
              2
              -C ha
              −1
              yr
              −1
              and a decrease in annual methane emissions of −23.6 kg CH
              4
              ha
              −1
              yr
              −1
              . Our results indicate that emission factors should represent the temporally dynamic nature of peatlands post-rewetting and consider the effect of site characteristics to better estimate associated annual emissions.},
	language = {en},
	number = {1},
	urldate = {2024-11-26},
	journal = {Communications Earth \& Environment},
	author = {Kalhori, Aram and Wille, Christian and Gottschalk, Pia and Li, Zhan and Hashemi, Josh and Kemper, Karl and Sachs, Torsten},
	month = feb,
	year = {2024},
	pages = {62},
}

Abstract Rewetting drained peatlands is recognized as a leading and effective natural solution to curb greenhouse gas emissions. However, rewetting creates novel ecosystems whose emission behaviors are not adequately captured by currently used emission factors. These emission factors are applied immediately after rewetting, thus do not reflect the temporal dynamics of greenhouse gas emissions during the period wherein there is a transition to a rewetted steady-state. Here, we provide long-term data showing a mismatch between actual emissions and default emission factors and revealing the temporal patterns of annual carbon dioxide and methane fluxes in a rewetted peatland site in northeastern Germany. We show that site-level annual emissions of carbon dioxide and methane approach the IPCC default emission factors 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 carbon dioxide fluxes with a decreasing trend of −0.36 t CO 2 -C ha −1 yr −1 and a decrease in annual methane emissions of −23.6 kg CH 4 ha −1 yr −1 . Our results indicate that emission factors should represent the temporally dynamic nature of peatlands post-rewetting and consider the effect of site characteristics to better estimate associated annual emissions.

@misc{kaufmann_assessing_2024,
	title = {Assessing soil fertilization effects using time-lapse electromagnetic induction},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	url = {https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2889/},
	doi = {10.5194/egusphere-2024-2889},
	abstract = {Abstract. Adding mineral fertilizers and mineral nutrient is a common practice in conventional farming and fundamental to maintain optimal yield and crop quality, whereby nitrogen is the most applied fertilizer often used excessively, leading to adverse environmental impacts. To assist farmers in optimal fertilization and crop management, non-invasive geophysical methods can provide knowledge about the spatial and temporal distributions of nutrients in the soil. In recent years, electromagnetic induction (EMI) is widely used for field characterization, to delineate soil units and management zones or to estimate soil properties and states. Additionally, ground penetrating radar (GPR) and electrical resistivity tomography (ERT) have been used in local studies to measure changes of soil properties. Unfortunately, the measured geophysical signals are confounded by horizontal and vertical changes of soil states and parameters and the single contributions of those states and parameters are not easy to disentangle. Within fields, and also between fields, fertilization management might vary in space and time, and therefore, the differences in pore fluid conductivity caused directly by fertilization, or indirectly by different crop performance, makes the interpretation of large-scale geophysical survey over field borders complicated. To study the direct effect of mineral fertilization and its effects on the soil electrical conductivity, a field experiment was performed on 21 bare soil plots with seven different fertilization treatments. As fertilizers, calcium ammonium nitrate (CAN) and potassium chloride (KCl) were chosen and applied in three dosages. Soil water content, soil temperature, and bulk electrical conductivity were recorded permanently over 450 days. Additionally, 20 EMI, 7 GPR, and 9 ERT surveys were performed and at days of ERT measurements soil samples for nitrate and reference soil electrical conductivity measurements were taken. The results showed that the commonly used CAN application dosage did not impact the geophysical signals significantly. On the other hand, EMI and ERT were able to trace back the temporal changes in nitrate concentrations in the soil profile over more than one year. On the other hand, the results also showed, that both techniques were not able to trace the nitrate concentrations in the very shallow soil layer of 0–10 cm. Irrespectively of the low impact of fertilization on the geophysical signal, the results indicated that past fertilization practices cannot be neglected in EMI studies, especially if surveys are performed over large areas with different fertilization practices or crop grown with different fertilizer demands or uptake.},
	urldate = {2024-11-26},
	publisher = {Soil sensing},
	author = {Kaufmann, Manuela S. and Klotzsche, Anja and Van Der Kruk, Jan and Langen, Anke and Vereecken, Harry and Weihermüller, Lutz},
	month = oct,
	year = {2024},
}

Abstract. Adding mineral fertilizers and mineral nutrient is a common practice in conventional farming and fundamental to maintain optimal yield and crop quality, whereby nitrogen is the most applied fertilizer often used excessively, leading to adverse environmental impacts. To assist farmers in optimal fertilization and crop management, non-invasive geophysical methods can provide knowledge about the spatial and temporal distributions of nutrients in the soil. In recent years, electromagnetic induction (EMI) is widely used for field characterization, to delineate soil units and management zones or to estimate soil properties and states. Additionally, ground penetrating radar (GPR) and electrical resistivity tomography (ERT) have been used in local studies to measure changes of soil properties. Unfortunately, the measured geophysical signals are confounded by horizontal and vertical changes of soil states and parameters and the single contributions of those states and parameters are not easy to disentangle. Within fields, and also between fields, fertilization management might vary in space and time, and therefore, the differences in pore fluid conductivity caused directly by fertilization, or indirectly by different crop performance, makes the interpretation of large-scale geophysical survey over field borders complicated. To study the direct effect of mineral fertilization and its effects on the soil electrical conductivity, a field experiment was performed on 21 bare soil plots with seven different fertilization treatments. As fertilizers, calcium ammonium nitrate (CAN) and potassium chloride (KCl) were chosen and applied in three dosages. Soil water content, soil temperature, and bulk electrical conductivity were recorded permanently over 450 days. Additionally, 20 EMI, 7 GPR, and 9 ERT surveys were performed and at days of ERT measurements soil samples for nitrate and reference soil electrical conductivity measurements were taken. The results showed that the commonly used CAN application dosage did not impact the geophysical signals significantly. On the other hand, EMI and ERT were able to trace back the temporal changes in nitrate concentrations in the soil profile over more than one year. On the other hand, the results also showed, that both techniques were not able to trace the nitrate concentrations in the very shallow soil layer of 0–10 cm. Irrespectively of the low impact of fertilization on the geophysical signal, the results indicated that past fertilization practices cannot be neglected in EMI studies, especially if surveys are performed over large areas with different fertilization practices or crop grown with different fertilizer demands or uptake.

@article{kawaguchi_co2_2024,
	title = {{CO}$_{\textrm{2}}$ {Dependence} in {Global} {Estimation} of {All}‐{Sky} {Downwelling} {Longwave}: {Parameterization} and {Model} {Comparison}},
	volume = {51},
	issn = {0094-8276, 1944-8007},
	shorttitle = {{CO}$_{\textrm{2}}$ {Dependence} in {Global} {Estimation} of {All}‐{Sky} {Downwelling} {Longwave}},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024GL110384},
	doi = {10.1029/2024GL110384},
	abstract = {Abstract
            
              The downwelling longwave radiation at the surface (DLR) is a key component of the Earth's surface energy budget. We present a novel set of equations that explicitly account for both clouds and the  effect to calculate the all‐sky DLR. This paper first extends the clear‐sky DLR model of Shakespeare and Roderick (2021,
              https://doi.org/10.1002/qj.4176
              ) to include temperature inversions and clouds. We parameterize relevant cloud properties through theoretical and empirical considerations to formulate an all‐sky model. Our model is more accurate than existing methods (reduces Root Mean Squared Error by 2.1–8.7  and 1.2–10.1  compared to ERA5 reanalysis and in‐situ data respectively), and provides a strong physical basis for the estimation of the downwelling longwave from near‐surface information. We highlight the important role of  dependence by showing our model largely captures the change in atmospheric emissivity purely due to  (i.e., the instantaneous radiative forcing) in CMIP6 models.
            
          , 
            Plain Language Summary
            The downwelling longwave radiation (DLR) at the surface is a key component of the energy balance at the Earth's surface. Understanding how the DLR will change under future climate conditions is vital. For the first time, we explicitly write a set of equations to calculate the DLR that sufficiently account for the impact of  and clouds simultaneously. Our model is more accurate than existing methods, and provides a much stronger physical basis for the estimation of the downwelling longwave from near‐surface information. In this paper, we extend an existing method for estimating the DLR under clear‐sky conditions (i.e., no clouds) to operate under all sky conditions. This method can be used to inform models where the DLR is needed, but only basic observations are available.
          , 
            Key Points
            
              
                
                  Downwelling longwave radiation (DLR) is a poorly estimated element of the surface energy budget by existing analytical models
                
                
                  Explicitly accounting for temperature inversions and cloud emissivities improves the accuracy of DLR estimation
                
                
                  Considering the radiative forcing from increasing  is necessary to produce unbiased future estimates of DLR},
	language = {en},
	number = {18},
	urldate = {2024-11-26},
	journal = {Geophysical Research Letters},
	author = {Kawaguchi, Koh and Shakespeare, Callum J. and Roderick, Michael L.},
	month = sep,
	year = {2024},
	pages = {e2024GL110384},
}

Abstract The downwelling longwave radiation at the surface (DLR) is a key component of the Earth's surface energy budget. We present a novel set of equations that explicitly account for both clouds and the effect to calculate the all‐sky DLR. This paper first extends the clear‐sky DLR model of Shakespeare and Roderick (2021, https://doi.org/10.1002/qj.4176 ) to include temperature inversions and clouds. We parameterize relevant cloud properties through theoretical and empirical considerations to formulate an all‐sky model. Our model is more accurate than existing methods (reduces Root Mean Squared Error by 2.1–8.7 and 1.2–10.1 compared to ERA5 reanalysis and in‐situ data respectively), and provides a strong physical basis for the estimation of the downwelling longwave from near‐surface information. We highlight the important role of dependence by showing our model largely captures the change in atmospheric emissivity purely due to (i.e., the instantaneous radiative forcing) in CMIP6 models. , Plain Language Summary The downwelling longwave radiation (DLR) at the surface is a key component of the energy balance at the Earth's surface. Understanding how the DLR will change under future climate conditions is vital. For the first time, we explicitly write a set of equations to calculate the DLR that sufficiently account for the impact of and clouds simultaneously. Our model is more accurate than existing methods, and provides a much stronger physical basis for the estimation of the downwelling longwave from near‐surface information. In this paper, we extend an existing method for estimating the DLR under clear‐sky conditions (i.e., no clouds) to operate under all sky conditions. This method can be used to inform models where the DLR is needed, but only basic observations are available. , Key Points Downwelling longwave radiation (DLR) is a poorly estimated element of the surface energy budget by existing analytical models Explicitly accounting for temperature inversions and cloud emissivities improves the accuracy of DLR estimation Considering the radiative forcing from increasing is necessary to produce unbiased future estimates of DLR

@article{knopf_field_2024,
	title = {Field phenotyping of ten wheat cultivars under elevated {CO2} shows seasonal differences in chlorophyll fluorescence, plant height and vegetation indices},
	volume = {14},
	issn = {1664-462X},
	url = {https://www.frontiersin.org/articles/10.3389/fpls.2023.1304751/full},
	doi = {10.3389/fpls.2023.1304751},
	abstract = {In the context of climate change and global sustainable development goals, future wheat cultivation has to master various challenges at a time, including the rising atmospheric carbon dioxide concentration ([CO
              2
              ]). To investigate growth and photosynthesis dynamics under the effects of ambient ({\textasciitilde}434 ppm) and elevated [CO
              2
              ] ({\textasciitilde}622 ppm), a Free-Air CO
              2
              Enrichment (FACE) facility was combined with an automated phenotyping platform and an array of sensors. Ten modern winter wheat cultivars (
              Triticum aestivum
              L.) were monitored over a vegetation period using a Light-induced Fluorescence Transient (LIFT) sensor, ground-based RGB cameras and a UAV equipped with an RGB and multispectral camera. The LIFT sensor enabled a fast quantification of the photosynthetic performance by measuring the operating efficiency of Photosystem II (F
              q
              ’/F
              m
              ’) and the kinetics of electron transport, i.e. the reoxidation rates F
              r1
              ’ and F
              r2
              ’. Our results suggest that elevated [CO
              2
              ] significantly increased F
              q
              ’/F
              m
              ’ and plant height during the vegetative growth phase. As the plants transitioned to the senescence phase, a pronounced decline in F
              q
              ’/F
              m
              ’ was observed under elevated [CO
              2
              ]. This was also reflected in the reoxidation rates F
              r1
              ’ and F
              r2
              ’. A large majority of the cultivars showed a decrease in the harvest index, suggesting a different resource allocation and indicating a potential plateau in yield progression under e[CO
              2
              ]. Our results indicate that the rise in atmospheric [CO
              2
              ] has significant effects on the cultivation of winter wheat with strong manifestation during early and late growth.},
	urldate = {2024-11-26},
	journal = {Frontiers in Plant Science},
	author = {Knopf, Oliver and Castro, Antony and Bendig, Juliane and Pude, Ralf and Kleist, Einhard and Poorter, Hendrik and Rascher, Uwe and Muller, Onno},
	month = jan,
	year = {2024},
	pages = {1304751},
}

In the context of climate change and global sustainable development goals, future wheat cultivation has to master various challenges at a time, including the rising atmospheric carbon dioxide concentration ([CO 2 ]). To investigate growth and photosynthesis dynamics under the effects of ambient (~434 ppm) and elevated [CO 2 ] (~622 ppm), a Free-Air CO 2 Enrichment (FACE) facility was combined with an automated phenotyping platform and an array of sensors. Ten modern winter wheat cultivars ( Triticum aestivum L.) were monitored over a vegetation period using a Light-induced Fluorescence Transient (LIFT) sensor, ground-based RGB cameras and a UAV equipped with an RGB and multispectral camera. The LIFT sensor enabled a fast quantification of the photosynthetic performance by measuring the operating efficiency of Photosystem II (F q ’/F m ’) and the kinetics of electron transport, i.e. the reoxidation rates F r1 ’ and F r2 ’. Our results suggest that elevated [CO 2 ] significantly increased F q ’/F m ’ and plant height during the vegetative growth phase. As the plants transitioned to the senescence phase, a pronounced decline in F q ’/F m ’ was observed under elevated [CO 2 ]. This was also reflected in the reoxidation rates F r1 ’ and F r2 ’. A large majority of the cultivars showed a decrease in the harvest index, suggesting a different resource allocation and indicating a potential plateau in yield progression under e[CO 2 ]. Our results indicate that the rise in atmospheric [CO 2 ] has significant effects on the cultivation of winter wheat with strong manifestation during early and late growth.

@misc{konkathi_novel_2024,
	title = {A {Novel} {Calibration} of {Global} {Soil} {Roughness} {Effects} for {Smos}-{Ic} {Soil} {Moisture} and {L}-{Vod} {Products}},
	url = {https://www.ssrn.com/abstract=4830200},
	doi = {10.2139/ssrn.4830200},
	urldate = {2024-11-26},
	publisher = {SSRN},
	author = {Konkathi, Preethi and Li, Xiaojun and Fernandez-Moran, Roberto and Liu, Xiangzhuo and Xing, Zanpin and Frappart, Frederic and Piles, María and Karthikeyan, Lanka and Wigneron, Jean-Pierre},
	year = {2024},
}

@article{lai_characterizing_2024,
	title = {Characterizing crop productivity under heat stress using {MODIS} data},
	volume = {355},
	issn = {01681923},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0168192324002314},
	doi = {10.1016/j.agrformet.2024.110116},
	language = {en},
	urldate = {2024-11-26},
	journal = {Agricultural and Forest Meteorology},
	author = {Lai, Peiyu and Marshall, Michael and Darvishzadeh, Roshanak and Tu, Kevin and Nelson, Andrew},
	month = aug,
	year = {2024},
	pages = {110116},
}

@misc{lasota_interpretable_2024,
	title = {Interpretable {Quality} {Control} of {Sparsely} {Distributed} {Environmental} {Sensor} {Networks} {Using} {Graph} {Neural} {Networks}},
	url = {https://eartharxiv.org/repository/view/7073/},
	doi = {10.31223/X5WT3W},
	abstract = {Environmental sensor networks play a crucial role in monitoring key parameters essential for understanding Earth’s systems. To ensure the reliability and accuracy of collected data, effective quality control (QC) measures are essential. Conventional QC methods struggle to handle the complexity of environmental data. Conversely, advanced techniques such as neural networks, are typically not designed to process data from sensor networks with irregular spatial distribution. In this study, we focus on anomaly detection in environmental sensor networks using graph neural networks, which can represent sensor network structures as graphs. We investigate its performance on two datasets with distinct dynamics and resolution: commercial microwave link (CML) signal levels used for rainfall estimation and SoilNet soil moisture measurements. To evaluate the benefits of incorporating neighboring sensor information for anomaly detection, we compare two models: Graph Convolution Network (GCN) and a graph-less baseline-long short- term memory (LSTM). Our robust evaluation through 5-fold cross-validation demonstrates the superiority of the GCN models. For CML, the mean area under curve values for the GCN was 0.941 compared to 0.885 for the baseline-LSTM, and for SoilNet, it was 0.858 for GCN and 0.816 for the baseline-LSTM. Visual inspection of CML time series revealed that the GCN proficiently classified anomalies and remained resilient against rain-induced events often misidentified by the baseline- LSTM. However, for SoilNet, the advantage of GCN was less pronounced likely due to a fragile labeling strategy. Through interpretable model analysis, we demonstrate how feature attributions vividly illustrate the significance of neighboring sensor data, particularly in distinguishing between anomalies and expected changes in signal level in the time series.},
	urldate = {2024-11-26},
	publisher = {Artificial Intelligence and Robotics},
	author = {Lasota, Elżbieta and Houben, Timo and Polz, Julius and Schmidt, Lennart and Glawion, Luca and Schäfer, David and Bumberger, Jan and Chwala, Christian},
	month = may,
	year = {2024},
}

Environmental sensor networks play a crucial role in monitoring key parameters essential for understanding Earth’s systems. To ensure the reliability and accuracy of collected data, effective quality control (QC) measures are essential. Conventional QC methods struggle to handle the complexity of environmental data. Conversely, advanced techniques such as neural networks, are typically not designed to process data from sensor networks with irregular spatial distribution. In this study, we focus on anomaly detection in environmental sensor networks using graph neural networks, which can represent sensor network structures as graphs. We investigate its performance on two datasets with distinct dynamics and resolution: commercial microwave link (CML) signal levels used for rainfall estimation and SoilNet soil moisture measurements. To evaluate the benefits of incorporating neighboring sensor information for anomaly detection, we compare two models: Graph Convolution Network (GCN) and a graph-less baseline-long short- term memory (LSTM). Our robust evaluation through 5-fold cross-validation demonstrates the superiority of the GCN models. For CML, the mean area under curve values for the GCN was 0.941 compared to 0.885 for the baseline-LSTM, and for SoilNet, it was 0.858 for GCN and 0.816 for the baseline-LSTM. Visual inspection of CML time series revealed that the GCN proficiently classified anomalies and remained resilient against rain-induced events often misidentified by the baseline- LSTM. However, for SoilNet, the advantage of GCN was less pronounced likely due to a fragile labeling strategy. Through interpretable model analysis, we demonstrate how feature attributions vividly illustrate the significance of neighboring sensor data, particularly in distinguishing between anomalies and expected changes in signal level in the time series.

@article{lausch_monitoring_2024,
	title = {Monitoring {Water} {Diversity} and {Water} {Quality} with {Remote} {Sensing} and {Traits}},
	volume = {16},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2072-4292},
	url = {https://www.mdpi.com/2072-4292/16/13/2425},
	doi = {10.3390/rs16132425},
	abstract = {Changes and disturbances to water diversity and quality are complex and multi-scale in space and time. Although in situ methods provide detailed point information on the condition of water bodies, they are of limited use for making area-based monitoring over time, as aquatic ecosystems are extremely dynamic. Remote sensing (RS) provides methods and data for the cost-effective, comprehensive, continuous and standardised monitoring of characteristics and changes in characteristics of water diversity and water quality from local and regional scales to the scale of entire continents. In order to apply and better understand RS techniques and their derived spectral indicators in monitoring water diversity and quality, this study defines five characteristics of water diversity and quality that can be monitored using RS. These are the diversity of water traits, the diversity of water genesis, the structural diversity of water, the taxonomic diversity of water and the functional diversity of water. It is essential to record the diversity of water traits to derive the other four characteristics of water diversity from RS. Furthermore, traits are the only and most important interface between in situ and RS monitoring approaches. The monitoring of these five characteristics of water diversity and water quality using RS technologies is presented in detail and discussed using numerous examples. Finally, current and future developments are presented to advance monitoring using RS and the trait approach in modelling, prediction and assessment as a basis for successful monitoring and management strategies.},
	language = {en},
	number = {13},
	urldate = {2024-11-26},
	journal = {Remote Sensing},
	author = {Lausch, Angela and Bannehr, Lutz and Berger, Stella A. and Borg, Erik and Bumberger, Jan and Hacker, Jorg M. and Heege, Thomas and Hupfer, Michael and Jung, András and Kuhwald, Katja and Oppelt, Natascha and Pause, Marion and Schrodt, Franziska and Selsam, Peter and Von Trentini, Fabian and Vohland, Michael and Glässer, Cornelia},
	month = jul,
	year = {2024},
	pages = {2425},
}

Changes and disturbances to water diversity and quality are complex and multi-scale in space and time. Although in situ methods provide detailed point information on the condition of water bodies, they are of limited use for making area-based monitoring over time, as aquatic ecosystems are extremely dynamic. Remote sensing (RS) provides methods and data for the cost-effective, comprehensive, continuous and standardised monitoring of characteristics and changes in characteristics of water diversity and water quality from local and regional scales to the scale of entire continents. In order to apply and better understand RS techniques and their derived spectral indicators in monitoring water diversity and quality, this study defines five characteristics of water diversity and quality that can be monitored using RS. These are the diversity of water traits, the diversity of water genesis, the structural diversity of water, the taxonomic diversity of water and the functional diversity of water. It is essential to record the diversity of water traits to derive the other four characteristics of water diversity from RS. Furthermore, traits are the only and most important interface between in situ and RS monitoring approaches. The monitoring of these five characteristics of water diversity and water quality using RS technologies is presented in detail and discussed using numerous examples. Finally, current and future developments are presented to advance monitoring using RS and the trait approach in modelling, prediction and assessment as a basis for successful monitoring and management strategies.

@article{lausch_monitoring_2024,
	title = {Monitoring vegetation- and geodiversity with remote sensing and traits},
	volume = {382},
	issn = {1364-503X, 1471-2962},
	url = {https://royalsocietypublishing.org/doi/10.1098/rsta.2023.0058},
	doi = {10.1098/rsta.2023.0058},
	abstract = {Geodiversity has shaped and structured the Earth's surface at all spatio-temporal scales, not only through long-term processes but also through medium- and short-term processes. Geodiversity is, therefore, a key control and regulating variable in the overall development of landscapes and biodiversity. However, climate change and land use intensity are leading to major changes and disturbances in bio- and geodiversity. For sustainable ecosystem management, temporal, economically viable and standardized monitoring is needed to monitor and model the effects and changes in vegetation- and geodiversity. RS approaches have been used for this purpose for decades. However, to understand in detail how RS approaches capture vegetation- and geodiversity, the aim of this paper is to describe how five features of vegetation- and geodiversity are captured using RS technologies, namely: (i) trait diversity, (ii) phylogenetic/genese diversity, (iii) structural diversity, (iv) taxonomic diversity and (v) functional diversity. Trait diversity is essential for establishing the other four. Traits provide a crucial interface between
              in situ
              , close-range, aerial and space-based RS monitoring approaches. The trait approach allows complex data of different types and formats to be linked using the latest semantic data integration techniques, which will enable ecosystem integrity monitoring and modelling in the future.
            
            This article is part of the Theo Murphy meeting issue ‘Geodiversity for science and society’.},
	language = {en},
	number = {2269},
	urldate = {2024-11-26},
	journal = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences},
	author = {Lausch, Angela and Selsam, Peter and Pause, Marion and Bumberger, Jan},
	month = apr,
	year = {2024},
	pages = {20230058},
}

Geodiversity has shaped and structured the Earth's surface at all spatio-temporal scales, not only through long-term processes but also through medium- and short-term processes. Geodiversity is, therefore, a key control and regulating variable in the overall development of landscapes and biodiversity. However, climate change and land use intensity are leading to major changes and disturbances in bio- and geodiversity. For sustainable ecosystem management, temporal, economically viable and standardized monitoring is needed to monitor and model the effects and changes in vegetation- and geodiversity. RS approaches have been used for this purpose for decades. However, to understand in detail how RS approaches capture vegetation- and geodiversity, the aim of this paper is to describe how five features of vegetation- and geodiversity are captured using RS technologies, namely: (i) trait diversity, (ii) phylogenetic/genese diversity, (iii) structural diversity, (iv) taxonomic diversity and (v) functional diversity. Trait diversity is essential for establishing the other four. Traits provide a crucial interface between in situ , close-range, aerial and space-based RS monitoring approaches. The trait approach allows complex data of different types and formats to be linked using the latest semantic data integration techniques, which will enable ecosystem integrity monitoring and modelling in the future. This article is part of the Theo Murphy meeting issue ‘Geodiversity for science and society’.

@article{lee_improved_2024,
	title = {Improved {SMAP} {Soil} {Moisture} {Retrieval} {Using} a {Deep} {Neural} {Network}-{Based} {Replacement} of {Radiative} {Transfer} and {Roughness} {Model}},
	volume = {62},
	copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
	issn = {0196-2892, 1558-0644},
	url = {https://ieeexplore.ieee.org/document/10741325/},
	doi = {10.1109/TGRS.2024.3489974},
	urldate = {2025-02-13},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	author = {Lee, Jaese and Im, Jungho and Son, Bokyung and Cosio, Eric G. and Salinas, Norma},
	year = {2024},
	pages = {1--19},
}

@article{li_can_2024,
	title = {Can a {Sparse} {Network} of {Cosmic} {Ray} {Neutron} {Sensors} {Improve} {Soil} {Moisture} and {Evapotranspiration} {Estimation} at the {Larger} {Catchment} {Scale}?},
	volume = {60},
	issn = {0043-1397, 1944-7973},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023WR035056},
	doi = {10.1029/2023WR035056},
	abstract = {Abstract
            Cosmic‐ray neutron sensors (CRNS) fill the gap between locally measured in‐situ soil moisture (SM) and remotely sensed SM by providing accurate SM estimation at the field scale. This is promising for improving hydrologic model predictions, as CRNS can provide valuable information on SM in the root zone at the typical scale of a model grid cell. In this study, SM measurements from a network of 12 CRNS in the Rur catchment (Germany) were assimilated into the Terrestrial System Modeling Platform (TSMP) to investigate its potential for improving SM, evapotranspiration (ET) and river discharge characterization and estimating soil hydraulic parameters at the larger catchment scale. The data assimilation (DA) experiments (with and without parameter estimation) were conducted in both a wet year (2016) and a dry year (2018) with the ensemble Kalman filter (EnKF), and later verified with an independent year (2017) without DA. The results show that SM characterization was significantly improved at measurement locations (with up to 60\% root mean square error (RMSE) reduction), and that joint state‐parameter estimation improved SM simulation more than state estimation alone (more than 15\% additional RMSE reduction). Jackknife experiments showed that SM at verification locations had lower and different improvements in the wet and dry years (an RMSE reduction of 40\% in 2016 and 16\% in 2018). In addition, SM assimilation was found to improve ET characterization to a much lesser extent, with a 15\% RMSE reduction of monthly ET in the wet year and 9\% in the dry year.
          , 
            Key Points
            
              
                
                  Assimilation of soil moisture from a network of cosmic‐ray neutron sensors improves soil moisture characterization at the catchment scale
                
                
                  Soil moisture characterization improved more in a wet year than in a very dry year
                
                
                  Evapotranspiration and river discharge simulation are only slightly improved, despite better estimations of soil moisture},
	language = {en},
	number = {1},
	urldate = {2024-11-26},
	journal = {Water Resources Research},
	author = {Li, Fang and Bogena, Heye Reemt and Bayat, Bagher and Kurtz, Wolfgang and Hendricks Franssen, Harrie‐Jan},
	month = jan,
	year = {2024},
	pages = {e2023WR035056},
}

Abstract Cosmic‐ray neutron sensors (CRNS) fill the gap between locally measured in‐situ soil moisture (SM) and remotely sensed SM by providing accurate SM estimation at the field scale. This is promising for improving hydrologic model predictions, as CRNS can provide valuable information on SM in the root zone at the typical scale of a model grid cell. In this study, SM measurements from a network of 12 CRNS in the Rur catchment (Germany) were assimilated into the Terrestrial System Modeling Platform (TSMP) to investigate its potential for improving SM, evapotranspiration (ET) and river discharge characterization and estimating soil hydraulic parameters at the larger catchment scale. The data assimilation (DA) experiments (with and without parameter estimation) were conducted in both a wet year (2016) and a dry year (2018) with the ensemble Kalman filter (EnKF), and later verified with an independent year (2017) without DA. The results show that SM characterization was significantly improved at measurement locations (with up to 60% root mean square error (RMSE) reduction), and that joint state‐parameter estimation improved SM simulation more than state estimation alone (more than 15% additional RMSE reduction). Jackknife experiments showed that SM at verification locations had lower and different improvements in the wet and dry years (an RMSE reduction of 40% in 2016 and 16% in 2018). In addition, SM assimilation was found to improve ET characterization to a much lesser extent, with a 15% RMSE reduction of monthly ET in the wet year and 9% in the dry year. , Key Points Assimilation of soil moisture from a network of cosmic‐ray neutron sensors improves soil moisture characterization at the catchment scale Soil moisture characterization improved more in a wet year than in a very dry year Evapotranspiration and river discharge simulation are only slightly improved, despite better estimations of soil moisture

@article{li_trend_2024,
	title = {Trend {Analysis} of {High}-{Resolution} {Soil} {Moisture} {Data} {Based} on {GAN} in the {Three}-{River}-{Source} {Region} {During} the 21st {Century}},
	volume = {16},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2072-4292},
	url = {https://www.mdpi.com/2072-4292/16/23/4367},
	doi = {10.3390/rs16234367},
	abstract = {Soil moisture (SM) is a crucial factor in land-atmosphere interactions and climate systems, affecting surface energy, water budgets, and weather extremes. In the Three-River-Source Region (TRSR) of China, rapid climate change necessitates precise SM monitoring. This study employs a novel UNet-Gan model to integrate and downscale SM data from 17 CMIP6 models, producing a high-resolution (0.1°) dataset called CMIP6UNet-Gan. This dataset includes SM data for five depth layers (0–10 cm, 10–30 cm, 30–50 cm, 50–80 cm, 80–110 cm), four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). The UNet-Gan model demonstrates strong performance in data fusion and downscaling, especially in shallow soil layers. Analysis of the CMIP6UNet-Gan dataset reveals an overall increasing trend in SM across all layers, with higher rates under more intense emission scenarios. Spatially, moisture increases vary, with significant trends in the western Yangtze and northeastern Yellow River regions. Deeper soils show a slower response to climate change, and seasonal variations indicate that moisture increases are most pronounced in spring and winter, followed by autumn, with the least increase observed in summer. Future projections suggest higher moisture increase rates in the early and late 21st century compared to the mid-century. By the end of this century (2071–2100), compared to the Historical period (1995–2014), the increase in SM across the five depth layers ranges from: 5.5\% to 11.5\%, 4.6\% to 9.2\%, 4.3\% to 7.5\%, 4.5\% to 7.5\%, and 3.3\% to 6.5\%, respectively.},
	language = {en},
	number = {23},
	urldate = {2025-02-13},
	journal = {Remote Sensing},
	author = {Li, Zhuoqun and Luo, Siqiong and Tan, Xiaoqing and Wang, Jingyuan},
	month = nov,
	year = {2024},
	pages = {4367},
}

Soil moisture (SM) is a crucial factor in land-atmosphere interactions and climate systems, affecting surface energy, water budgets, and weather extremes. In the Three-River-Source Region (TRSR) of China, rapid climate change necessitates precise SM monitoring. This study employs a novel UNet-Gan model to integrate and downscale SM data from 17 CMIP6 models, producing a high-resolution (0.1°) dataset called CMIP6UNet-Gan. This dataset includes SM data for five depth layers (0–10 cm, 10–30 cm, 30–50 cm, 50–80 cm, 80–110 cm), four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). The UNet-Gan model demonstrates strong performance in data fusion and downscaling, especially in shallow soil layers. Analysis of the CMIP6UNet-Gan dataset reveals an overall increasing trend in SM across all layers, with higher rates under more intense emission scenarios. Spatially, moisture increases vary, with significant trends in the western Yangtze and northeastern Yellow River regions. Deeper soils show a slower response to climate change, and seasonal variations indicate that moisture increases are most pronounced in spring and winter, followed by autumn, with the least increase observed in summer. Future projections suggest higher moisture increase rates in the early and late 21st century compared to the mid-century. By the end of this century (2071–2100), compared to the Historical period (1995–2014), the increase in SM across the five depth layers ranges from: 5.5% to 11.5%, 4.6% to 9.2%, 4.3% to 7.5%, 4.5% to 7.5%, and 3.3% to 6.5%, respectively.

@article{lippmann_simultaneous_2024,
	title = {Simultaneous {Hot} and {Dry} {Extreme}‐{Events} {Increase} {Wetland} {Methane} {Emissions}: {An} {Assessment} of {Compound} {Extreme}‐{Event} {Impacts} {Using} {Ameriflux} and {FLUXNET}‐{CH}$_{\textrm{4}}$ {Site} {Data} {Sets}},
	volume = {38},
	issn = {0886-6236, 1944-9224},
	shorttitle = {Simultaneous {Hot} and {Dry} {Extreme}‐{Events} {Increase} {Wetland} {Methane} {Emissions}},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024GB008201},
	doi = {10.1029/2024GB008201},
	abstract = {Abstract
            
              Wetlands are the largest natural source of global atmospheric methane (CH
              4
              ). Despite advances to our understanding of changes in temperature and precipitation extremes, their impacts on carbon‐rich ecosystems such as wetlands, remain significantly understudied. Here, we quantify the impacts of extreme temperature, precipitation, and dry events on wetland CH
              4
              dynamics by investigating the effects of both compound and discrete extreme‐events. We use long‐term climate data to identify extreme‐events and 45 eddy covariance sites data sets sourced from the FLUXNET‐CH
              4
              database and Ameriflux project to assess impacts on wetland CH
              4
              emissions. These findings reveal that compound hot + dry extreme‐events lead to large increases in daily CH
              4
              emissions. However, per event, discrete dry‐only extreme‐events cause the largest total decrease in CH
              4
              emissions, due to their long duration. Despite dry‐only extreme‐events leading to an overall reduction in CH
              4
              emissions, enhanced fluxes are often observed for the first days of dry‐only extreme‐events. These effects differ depending on wetland type, where marsh sites tend to be sensitive to most types of extreme‐events. Lagged impacts are significant for at least the 12 months following several types of extreme‐events. These findings have implications for understanding how extreme‐event impacts may evolve in the context of climate change, where changes in the frequency and intensity of temperature and precipitation extreme‐events are already observed. With increasing occurrences of enhanced CH
              4
              fluxes in response to hot‐only extreme‐events and hot + wet extreme‐events and fewer occurrences of reduced CH
              4
              fluxes during cold‐only extreme‐events, the impact of wetland CH
              4
              emissions on climate warming may be increasing.
            
          , 
            Key Points
            
              
                
                  
                    Compound extreme‐events (e.g., hot + dry extreme‐events) cause large impacts on daily CH
                    4
                    emissions relative to discrete extreme‐events
                  
                
                
                  
                    Dry‐only extreme‐events show large total decreases in CH
                    4
                    emissions due to the long duration of events, despite initial flux increases
                  
                
                
                  Lagged impacts are significant for at least the 12 months following most types of extreme‐events},
	language = {en},
	number = {9},
	urldate = {2024-11-26},
	journal = {Global Biogeochemical Cycles},
	author = {Lippmann, T. J. R. and Van Der Velde, Y. and Naudts, K. and Hensgens, G. and Vonk, J. E. and Dolman, H.},
	month = sep,
	year = {2024},
	pages = {e2024GB008201},
}

Abstract Wetlands are the largest natural source of global atmospheric methane (CH 4 ). Despite advances to our understanding of changes in temperature and precipitation extremes, their impacts on carbon‐rich ecosystems such as wetlands, remain significantly understudied. Here, we quantify the impacts of extreme temperature, precipitation, and dry events on wetland CH 4 dynamics by investigating the effects of both compound and discrete extreme‐events. We use long‐term climate data to identify extreme‐events and 45 eddy covariance sites data sets sourced from the FLUXNET‐CH 4 database and Ameriflux project to assess impacts on wetland CH 4 emissions. These findings reveal that compound hot + dry extreme‐events lead to large increases in daily CH 4 emissions. However, per event, discrete dry‐only extreme‐events cause the largest total decrease in CH 4 emissions, due to their long duration. Despite dry‐only extreme‐events leading to an overall reduction in CH 4 emissions, enhanced fluxes are often observed for the first days of dry‐only extreme‐events. These effects differ depending on wetland type, where marsh sites tend to be sensitive to most types of extreme‐events. Lagged impacts are significant for at least the 12 months following several types of extreme‐events. These findings have implications for understanding how extreme‐event impacts may evolve in the context of climate change, where changes in the frequency and intensity of temperature and precipitation extreme‐events are already observed. With increasing occurrences of enhanced CH 4 fluxes in response to hot‐only extreme‐events and hot + wet extreme‐events and fewer occurrences of reduced CH 4 fluxes during cold‐only extreme‐events, the impact of wetland CH 4 emissions on climate warming may be increasing. , Key Points Compound extreme‐events (e.g., hot + dry extreme‐events) cause large impacts on daily CH 4 emissions relative to discrete extreme‐events Dry‐only extreme‐events show large total decreases in CH 4 emissions due to the long duration of events, despite initial flux increases Lagged impacts are significant for at least the 12 months following most types of extreme‐events

@article{liu_can_2024,
	title = {Can the atmospheric boundary layer-based potential evaporation model increase the accuracy of generalized complementary functions?},
	volume = {55},
	issn = {0029-1277, 2224-7955},
	url = {https://iwaponline.com/hr/article/55/12/1249/105998/Can-the-atmospheric-boundary-layer-based-potential},
	doi = {10.2166/nh.2024.092},
	abstract = {ABSTRACT
            
            Complementary functions could reliably and effectively estimate actual evaporation and have received wide attention in recent years. However, estimating potential evaporation (Epo) greatly influences the accuracy of complementary functions. In this study, we compare the atmospheric boundary layer model (ABL2021) with the Priestly–Taylor model (P-T) and the maximum evaporation model (YR2019) for estimating Epo. Eighty-six flux sites are utilized to fit parameters for three generalized complementary functions, including the sigmoid function (H2018), the polynomial function (B2015), and the exponential function (G2021) with various potential evaporation models. The results suggest that ABL2021 shows the best agreement with the observations at large lake sites. The uncertainties for estimation of actual evaporation induced by different potential evaporation models are larger than different complementary functions. ABL2021 significantly reduces the differences in performance between different complementary functions. It suggests that ABL2021 improves the accuracy of the generalized complementary functions in most cases and can provide a calibration-free method for Epo estimation. G2021 performs better and shows more flexibility than the other generalized complementary functions. Therefore, G2021 combined with ABL2021 shows potential to develop a robust method for estimating actual evaporation based on the complementary principle.},
	language = {en},
	number = {12},
	urldate = {2025-02-14},
	journal = {Hydrology Research},
	author = {Liu, Weina and Gao, Bing},
	month = dec,
	year = {2024},
	pages = {1249--1270},
}

ABSTRACT Complementary functions could reliably and effectively estimate actual evaporation and have received wide attention in recent years. However, estimating potential evaporation (Epo) greatly influences the accuracy of complementary functions. In this study, we compare the atmospheric boundary layer model (ABL2021) with the Priestly–Taylor model (P-T) and the maximum evaporation model (YR2019) for estimating Epo. Eighty-six flux sites are utilized to fit parameters for three generalized complementary functions, including the sigmoid function (H2018), the polynomial function (B2015), and the exponential function (G2021) with various potential evaporation models. The results suggest that ABL2021 shows the best agreement with the observations at large lake sites. The uncertainties for estimation of actual evaporation induced by different potential evaporation models are larger than different complementary functions. ABL2021 significantly reduces the differences in performance between different complementary functions. It suggests that ABL2021 improves the accuracy of the generalized complementary functions in most cases and can provide a calibration-free method for Epo estimation. G2021 performs better and shows more flexibility than the other generalized complementary functions. Therefore, G2021 combined with ABL2021 shows potential to develop a robust method for estimating actual evaporation based on the complementary principle.

@article{liu_evaluation_2024,
	title = {Evaluation of 22 {CMIP6} model-derived global soil moisture products of different shared socioeconomic pathways},
	volume = {636},
	issn = {00221694},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S002216942400636X},
	doi = {10.1016/j.jhydrol.2024.131241},
	language = {en},
	urldate = {2024-11-26},
	journal = {Journal of Hydrology},
	author = {Liu, Yangxiaoyue and Chen, Xiaona and Bai, Yongqing and Zeng, Jiangyuan},
	month = jun,
	year = {2024},
	pages = {131241},
}

@article{larm_linking_2024,
	title = {Linking horizontal crosshole {GPR} variability with root image information for maize crops},
	volume = {23},
	issn = {1539-1663, 1539-1663},
	url = {https://acsess.onlinelibrary.wiley.com/doi/10.1002/vzj2.20293},
	doi = {10.1002/vzj2.20293},
	abstract = {Abstract
            
              Non‐invasive imaging of processes within the soil–plant continuum, particularly root and soil water distributions, can help optimize agricultural practices such as irrigation and fertilization. In this study, in‐situ time‐lapse horizontal crosshole ground penetrating radar (GPR) measurements and root images were collected over three maize crop growing seasons at two minirhizotron facilities (Selhausen, Germany). Root development and GPR permittivity were monitored at six depths (0.1–1.2 m) for different treatments within two soil types. We processed these data in a new way that gave us the information of the “trend‐corrected spatial permittivity deviation of vegetated field,” allowing us to investigate whether the presence of roots increases the variability of GPR permittivity in the soil. This removed the main non‐root‐related influencing factors: static influences, such as soil heterogeneities and rhizotube deviations, and dynamic effects, such as seasonal moisture changes. This trend‐corrected spatial permittivity deviation showed a clear increase during the growing season, which could be linked with a similar increase in root volume fraction. Additionally, the corresponding probability density functions of the permittivity variability were derived and cross‐correlated with the root volume fraction, resulting in a coefficient of determination (
              R
              2
              ) above 0.5 for 23 out of 46 correlation pairs. Although both facilities had different soil types and compaction levels, they had similar numbers of good correlations. A possible explanation for the observed correlation is that the presence of roots causes a redistribution of soil water, and therefore an increase in soil water variability.
            
          , 
            Core Ideas
            
              
                
                  Analying spatial and temporal belowground GPR signal variability and root images for maize crop roots.
                
                
                  Root images and GPR data show differences for different treatments.
                
                
                  Linking of root volume fraction and GPR permittivity variability.},
	language = {en},
	number = {1},
	urldate = {2024-11-26},
	journal = {Vadose Zone Journal},
	author = {Lärm, Lena and Bauer, Felix Maximilian and Van Der Kruk, Jan and Vanderborght, Jan and Morandage, Shehan and Vereecken, Harry and Schnepf, Andrea and Klotzsche, Anja},
	month = jan,
	year = {2024},
	pages = {e20293},
}

Abstract Non‐invasive imaging of processes within the soil–plant continuum, particularly root and soil water distributions, can help optimize agricultural practices such as irrigation and fertilization. In this study, in‐situ time‐lapse horizontal crosshole ground penetrating radar (GPR) measurements and root images were collected over three maize crop growing seasons at two minirhizotron facilities (Selhausen, Germany). Root development and GPR permittivity were monitored at six depths (0.1–1.2 m) for different treatments within two soil types. We processed these data in a new way that gave us the information of the “trend‐corrected spatial permittivity deviation of vegetated field,” allowing us to investigate whether the presence of roots increases the variability of GPR permittivity in the soil. This removed the main non‐root‐related influencing factors: static influences, such as soil heterogeneities and rhizotube deviations, and dynamic effects, such as seasonal moisture changes. This trend‐corrected spatial permittivity deviation showed a clear increase during the growing season, which could be linked with a similar increase in root volume fraction. Additionally, the corresponding probability density functions of the permittivity variability were derived and cross‐correlated with the root volume fraction, resulting in a coefficient of determination ( R 2 ) above 0.5 for 23 out of 46 correlation pairs. Although both facilities had different soil types and compaction levels, they had similar numbers of good correlations. A possible explanation for the observed correlation is that the presence of roots causes a redistribution of soil water, and therefore an increase in soil water variability. , Core Ideas Analying spatial and temporal belowground GPR signal variability and root images for maize crop roots. Root images and GPR data show differences for different treatments. Linking of root volume fraction and GPR permittivity variability.

@misc{ma_preliminary_2024,
	title = {A {Preliminary} {Evaluation} of {Newly} {Released} {Gnss}-{R} {Soil} {Moisture} {Product} {Against} {Distributed} {Ground} {Network} {Measurements} and {Existing} {Satellite} {Products}},
	url = {https://www.ssrn.com/abstract=4979325},
	doi = {10.2139/ssrn.4979325},
	urldate = {2024-11-26},
	publisher = {SSRN},
	author = {Ma, Chunfeng and Wang, Shuguo and Wu, Yueru and Li, Xingze and Li, Xin and Wang, Weizhen and Liang, Lifeng and El Hajj, Marcel  M. and Johansen, Kasper and McCabe, Matthew},
	year = {2024},
}

@article{ma_surface_2024,
	title = {Surface soil moisture from combined active and passive microwave observations: {Integrating} {ASCAT} and {SMAP} observations based on machine learning approaches},
	volume = {308},
	issn = {00344257},
	shorttitle = {Surface soil moisture from combined active and passive microwave observations},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0034425724002153},
	doi = {10.1016/j.rse.2024.114197},
	language = {en},
	urldate = {2024-11-26},
	journal = {Remote Sensing of Environment},
	author = {Ma, Hongliang and Zeng, Jiangyuan and Zhang, Xiang and Peng, Jian and Li, Xiaojun and Fu, Peng and Cosh, Michael H. and Letu, Husi and Wang, Shaohua and Chen, Nengcheng and Wigneron, Jean-Pierre},
	month = jul,
	year = {2024},
	pages = {114197},
}

@article{mahmood_estimation_2024,
	title = {Estimation of 100 m root zone soil moisture by downscaling 1 km soil water index with machine learning and multiple geodata},
	volume = {196},
	issn = {0167-6369, 1573-2959},
	url = {https://link.springer.com/10.1007/s10661-024-12969-5},
	doi = {10.1007/s10661-024-12969-5},
	abstract = {Abstract
            
              Root zone soil moisture (RZSM) is crucial for agricultural water management and land surface processes. The 1 km soil water index (SWI) dataset from Copernicus Global Land services, with eight fixed characteristic time lengths (
              T
              ), requires root zone depth optimization (
              T
              opt
              ) and is limited in use due to its low spatial resolution. To estimate RZSM at 100-m resolution, we integrate the depth specificity of SWI and employed random forest (RF) downscaling. Topographic synthetic aperture radar (SAR) and optical datasets were utilized to develop three RF models (RF1: SAR, RF2: optical, RF3: SAR + optical). At the DEMMIN experimental site in northeastern Germany,
              T
              opt
              (in days) varies from 20 to 60 for depths of 10 to 30 cm, increasing to 100 for 40–60 cm. RF3 outperformed other models with 1 km test data. Following residual correction, all high-resolution predictions exhibited strong spatial accuracy (
              R
               ≥ 0.94). Both products (1 km and 100 m) agreed well with observed RZSM during summer but overestimated in winter. Mean
              R
              between observed RZSM and 1 km (100 m; RF1, RF2, and RF3) SWI ranges from 0.74 (0.67, 0.76, and 0.68) to 0.90 (0.88, 0.81, and 0.82), with the lowest and highest
              R
              achieved at 10 cm and 30 cm depths, respectively. The average RMSE using 1 km (100 m; RF1, RF2, and RF3) SWI increased from 2.20 Vol.\% (2.28, 2.28, and 2.35) at 30 cm to 3.40 Vol.\% (3.50, 3.70, and 3.60) at 60 cm. These negligible accuracy differences underpin the potential of the proposed method to estimate RZSM for precise local applications, e.g., irrigation management.},
	language = {en},
	number = {9},
	urldate = {2024-11-26},
	journal = {Environmental Monitoring and Assessment},
	author = {Mahmood, Talha and Löw, Johannes and Pöhlitz, Julia and Wenzel, Jan Lukas and Conrad, Christopher},
	month = sep,
	year = {2024},
	pages = {823},
}

Abstract Root zone soil moisture (RZSM) is crucial for agricultural water management and land surface processes. The 1 km soil water index (SWI) dataset from Copernicus Global Land services, with eight fixed characteristic time lengths ( T ), requires root zone depth optimization ( T opt ) and is limited in use due to its low spatial resolution. To estimate RZSM at 100-m resolution, we integrate the depth specificity of SWI and employed random forest (RF) downscaling. Topographic synthetic aperture radar (SAR) and optical datasets were utilized to develop three RF models (RF1: SAR, RF2: optical, RF3: SAR + optical). At the DEMMIN experimental site in northeastern Germany, T opt (in days) varies from 20 to 60 for depths of 10 to 30 cm, increasing to 100 for 40–60 cm. RF3 outperformed other models with 1 km test data. Following residual correction, all high-resolution predictions exhibited strong spatial accuracy ( R  ≥ 0.94). Both products (1 km and 100 m) agreed well with observed RZSM during summer but overestimated in winter. Mean R between observed RZSM and 1 km (100 m; RF1, RF2, and RF3) SWI ranges from 0.74 (0.67, 0.76, and 0.68) to 0.90 (0.88, 0.81, and 0.82), with the lowest and highest R achieved at 10 cm and 30 cm depths, respectively. The average RMSE using 1 km (100 m; RF1, RF2, and RF3) SWI increased from 2.20 Vol.% (2.28, 2.28, and 2.35) at 30 cm to 3.40 Vol.% (3.50, 3.70, and 3.60) at 60 cm. These negligible accuracy differences underpin the potential of the proposed method to estimate RZSM for precise local applications, e.g., irrigation management.

@article{manfreda_advancing_2024,
	title = {Advancing river monitoring using image-based techniques: challenges and opportunities},
	volume = {69},
	issn = {0262-6667, 2150-3435},
	shorttitle = {Advancing river monitoring using image-based techniques},
	url = {https://www.tandfonline.com/doi/full/10.1080/02626667.2024.2333846},
	doi = {10.1080/02626667.2024.2333846},
	language = {en},
	number = {6},
	urldate = {2024-11-26},
	journal = {Hydrological Sciences Journal},
	author = {Manfreda, Salvatore and Miglino, Domenico and Saddi, Khim Cathleen and Jomaa, Seifeddine and Eltner, Anette and Perks, Matthew and Peña-Haro, Salvador and Bogaard, Thom and Van Emmerik, Tim H.M. and Mariani, Stefano and Maddock, Ian and Tauro, Flavia and Grimaldi, Salvatore and Zeng, Yijian and Gonçalves, Gil and Strelnikova, Dariia and Bussettini, Martina and Marchetti, Giulia and Lastoria, Barbara and Su, Zhongbo and Rode, Michael},
	month = apr,
	year = {2024},
	pages = {657--677},
}

@article{massart_mitigating_2024,
	title = {Mitigating the impact of dense vegetation on the {Sentinel}-1 surface soil moisture retrievals over {Europe}},
	volume = {57},
	issn = {2279-7254},
	url = {https://www.tandfonline.com/doi/full/10.1080/22797254.2023.2300985},
	doi = {10.1080/22797254.2023.2300985},
	language = {en},
	number = {1},
	urldate = {2024-11-26},
	journal = {European Journal of Remote Sensing},
	author = {Massart, Samuel and Vreugdenhil, Mariette and Bauer-Marschallinger, Bernhard and Navacchi, Claudio and Raml, Bernhard and Wagner, Wolfgang},
	month = dec,
	year = {2024},
	pages = {2300985},
}

@article{meng_validation_2024,
	title = {Validation and expansion of the soil moisture index for assessing soil moisture dynamics from {AMSR2} brightness temperature},
	volume = {303},
	issn = {00344257},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0034425724000294},
	doi = {10.1016/j.rse.2024.114018},
	language = {en},
	urldate = {2024-11-26},
	journal = {Remote Sensing of Environment},
	author = {Meng, Xiangjin and Peng, Jian and Hu, Jia and Li, Ji and Leng, Guoyong and Ferhatoglu, Caner and Li, Xueying and García-García, Almudena and Yang, Yingbao},
	month = mar,
	year = {2024},
	pages = {114018},
}

@article{mi_optimizing_2024,
	title = {Optimizing selective withdrawal strategies to mitigate hypoxia under water-level reduction in {Germany}'s largest drinking water reservoir},
	volume = {146},
	issn = {10010742},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1001074223002760},
	doi = {10.1016/j.jes.2023.06.025},
	language = {en},
	urldate = {2024-11-15},
	journal = {Journal of Environmental Sciences},
	author = {Mi, Chenxi and Rinke, Karsten and Shatwell, Tom},
	month = dec,
	year = {2024},
	pages = {127--139},
}

@misc{miglino_technical_2024,
	title = {Technical note: {Image} processing for continuous river turbidity monitoring – full scale tests and potential applications},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	shorttitle = {Technical note},
	url = {https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2172/},
	doi = {10.5194/egusphere-2024-2172},
	abstract = {Abstract. The development of continuous river turbidity monitoring systems is essential, since it is a critical water quality metric linked to the presence of organic and inorganic suspended matter. Current monitoring practices are mainly limited by low spatial and temporal resolution, and costs. This results in the huge challenge to provide extensive and timely water quality monitoring at global scale. In this work, we propose an image analysis procedure for river turbidity assessment using different camera systems (i.e., fixed trap camera, camera on board of an Unmanned Aerial Vehicle, and a multispectral camera). We explored multiple types of camera installation setup during a river turbidity event artificially re-created on site. The outcomes prove that processed digital camera data can properly represent the turbidity trends. Specifically, the experimental activities revealed that single band values were the most reliable proxy for turbidity monitoring in short terms, better than band ratios and indexes. The best camera positioning, orientation and lens sensitivity, as well as daily and seasonal changes in lightning and river flow conditions, may affect the accuracy of the results. The reliability of this application will be tested under different hydrological and environmental conditions during our next field experiments. The final goal of the work is the implementation of this camera system to support existing monitoring techniques with early warning strategies and help in finding innovative solutions to water resources management.},
	urldate = {2024-11-26},
	publisher = {Rivers and Lakes/Instruments and observation techniques},
	author = {Miglino, Domenico and Saddi, Khim Cathleen and Isgrò, Francesco and Jomaa, Seifeddine and Rode, Michael and Manfreda, Salvatore},
	month = sep,
	year = {2024},
}

Abstract. The development of continuous river turbidity monitoring systems is essential, since it is a critical water quality metric linked to the presence of organic and inorganic suspended matter. Current monitoring practices are mainly limited by low spatial and temporal resolution, and costs. This results in the huge challenge to provide extensive and timely water quality monitoring at global scale. In this work, we propose an image analysis procedure for river turbidity assessment using different camera systems (i.e., fixed trap camera, camera on board of an Unmanned Aerial Vehicle, and a multispectral camera). We explored multiple types of camera installation setup during a river turbidity event artificially re-created on site. The outcomes prove that processed digital camera data can properly represent the turbidity trends. Specifically, the experimental activities revealed that single band values were the most reliable proxy for turbidity monitoring in short terms, better than band ratios and indexes. The best camera positioning, orientation and lens sensitivity, as well as daily and seasonal changes in lightning and river flow conditions, may affect the accuracy of the results. The reliability of this application will be tested under different hydrological and environmental conditions during our next field experiments. The final goal of the work is the implementation of this camera system to support existing monitoring techniques with early warning strategies and help in finding innovative solutions to water resources management.

@article{munoz-martin_scattering_2024,
	title = {Scattering {Matrix} {Retrieval} {Using} {Full}-{Polarimetric} {GNSS}-{R}},
	volume = {62},
	copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
	issn = {0196-2892, 1558-0644},
	url = {https://ieeexplore.ieee.org/document/10556619/},
	doi = {10.1109/TGRS.2024.3414261},
	urldate = {2024-11-26},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	author = {Munoz-Martin, Joan Francesc and Rodriguez-Alvarez, Nereida and Bosch-Lluis, Xavier and Oudrhiri, Kamal},
	year = {2024},
	pages = {1--15},
}

@article{musolff_forest_2024,
	title = {Forest {Dieback} {Alters} {Nutrient} {Pathways} in a {Temperate} {Headwater} {Catchment}},
	volume = {38},
	issn = {0885-6087, 1099-1085},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.15308},
	doi = {10.1002/hyp.15308},
	abstract = {ABSTRACT
            
              Forested headwater catchments ensure good water quality for downstream ecosystems and human consumption. Climate change and the exacerbating likelihood of extreme events elevate the risk of severe forest dieback. However, the effects of forest dieback on the quantity and quality of stream water are not fully understood. Here, we analyse high‐frequency observations of discharge, electrical conductivity (EC), dissolved organic carbon (DOC) and nitrate (NO
              3
              N) obtained before, during and after a drought‐induced forest dieback in a headwater stream in the German Harz Mountains. We focus on the characteristics of concentration‐discharge (C‐Q) relationships at the scale of runoff events to assess the effects of forest dieback on the sources, mobilisation and pathways of EC, DOC and NO
              3
              N. When comparing pre‐ and post‐dieback conditions, we found a significant increase in runoff efficiency and a doubling of DOC loads exported from the catchment, while DOC concentrations increased only moderately and their C‐Q patterns did not change. EC exhibit no changes in concentrations but a steepening of C‐Q dilution patterns. We explain these findings with a dieback‐induced decrease in evapotranspiration, which leads to more intensive drainage of the upper organic soil layers in the riparian zone. In contrast, we observed a strong increase in NO
              3
              N concentrations and fluxes by a factor of {\textasciitilde}5, while C‐Q patterns at the event scale changed from enrichment to dilution. We argue that the dieback led to an excess of NO
              3
              N on the hillslopes that connect to the stream via surficial flowpaths. In this way, NO
              3
              N bypasses the riparian zone, reducing the catchment's efficiency in attenuating this nutrient. Our study emphasises the pivotal role of riparian zones in mediating water quality in headwater streams. Different configurations of the riparian zone and its connection to the hillslopes and the stream network may be a missing piece in explaining differences in water quality responses of catchments to forest dieback.},
	language = {en},
	number = {10},
	urldate = {2024-11-20},
	journal = {Hydrological Processes},
	author = {Musolff, Andreas and Tarasova, Larisa and Rinke, Karsten and Ledesma, José L. J.},
	month = oct,
	year = {2024},
	pages = {e15308},
}

ABSTRACT Forested headwater catchments ensure good water quality for downstream ecosystems and human consumption. Climate change and the exacerbating likelihood of extreme events elevate the risk of severe forest dieback. However, the effects of forest dieback on the quantity and quality of stream water are not fully understood. Here, we analyse high‐frequency observations of discharge, electrical conductivity (EC), dissolved organic carbon (DOC) and nitrate (NO 3 N) obtained before, during and after a drought‐induced forest dieback in a headwater stream in the German Harz Mountains. We focus on the characteristics of concentration‐discharge (C‐Q) relationships at the scale of runoff events to assess the effects of forest dieback on the sources, mobilisation and pathways of EC, DOC and NO 3 N. When comparing pre‐ and post‐dieback conditions, we found a significant increase in runoff efficiency and a doubling of DOC loads exported from the catchment, while DOC concentrations increased only moderately and their C‐Q patterns did not change. EC exhibit no changes in concentrations but a steepening of C‐Q dilution patterns. We explain these findings with a dieback‐induced decrease in evapotranspiration, which leads to more intensive drainage of the upper organic soil layers in the riparian zone. In contrast, we observed a strong increase in NO 3 N concentrations and fluxes by a factor of ~5, while C‐Q patterns at the event scale changed from enrichment to dilution. We argue that the dieback led to an excess of NO 3 N on the hillslopes that connect to the stream via surficial flowpaths. In this way, NO 3 N bypasses the riparian zone, reducing the catchment's efficiency in attenuating this nutrient. Our study emphasises the pivotal role of riparian zones in mediating water quality in headwater streams. Different configurations of the riparian zone and its connection to the hillslopes and the stream network may be a missing piece in explaining differences in water quality responses of catchments to forest dieback.

@article{mwanake_divergent_2024,
	title = {Divergent drivers of the spatial variation in greenhouse gas concentrations and fluxes along the {Rhine} {River} and the {Mittelland} {Canal} in {Germany}},
	volume = {31},
	issn = {1614-7499},
	url = {https://link.springer.com/10.1007/s11356-024-33394-8},
	doi = {10.1007/s11356-024-33394-8},
	abstract = {Abstract
            
              Lotic ecosystems are sources of greenhouse gases (GHGs) to the atmosphere, but their emissions are uncertain due to longitudinal GHG heterogeneities associated with point source pollution from anthropogenic activities. In this study, we quantified summer concentrations and fluxes of carbon dioxide (CO
              2
              ), methane (CH
              4
              ), nitrous oxide (N
              2
              O), and dinitrogen (N
              2
              ), as well as several water quality parameters along the Rhine River and the Mittelland Canal, two critical inland waterways in Germany. Our main objectives were to compare GHG concentrations and fluxes along the two ecosystems and to determine the main driving factors responsible for their longitudinal GHG heterogeneities. The results indicated that the two ecosystems were sources of GHG fluxes to the atmosphere, with the Mittelland Canal being a hotspot for CH
              4
              and N
              2
              O fluxes. We also found significant longitudinal GHG flux discontinuities along the mainstems of both ecosystems, which were mainly driven by divergent drivers. Along the Mittelland Canal, peak CO
              2
              and CH
              4
              fluxes coincided with point pollution sources such as a joining river tributary or the presence of harbors, while harbors and in-situ biogeochemical processes such as methanogenesis and respiration mainly explained CH
              4
              and CO
              2
              hotspots along the Rhine River. In contrast to CO
              2
              and CH
              4
              fluxes, N
              2
              O longitudinal trends along the two lotic ecosystems were better predicted by in-situ parameters such as chlorophyll-
              a
              concentrations and N
              2
              fluxes. Based on a positive relationship with N
              2
              fluxes, we hypothesized that in-situ denitrification was driving N
              2
              O hotspots in the Canal, while a negative relationship with N
              2
              in the Rhine River suggested that coupled biological N
              2
              fixation and nitrification accounted for N
              2
              O hotspots. These findings stress the need to include N
              2
              flux estimates in GHG studies, as it can potentially improve our understanding of whether nitrogen is fixed through N
              2
              fixation or lost through denitrification.},
	language = {en},
	number = {22},
	urldate = {2024-11-26},
	journal = {Environmental Science and Pollution Research},
	author = {Mwanake, Ricky Mwangada and Imhof, Hannes Klaus and Kiese, Ralf},
	month = apr,
	year = {2024},
	pages = {32183--32199},
}

Abstract Lotic ecosystems are sources of greenhouse gases (GHGs) to the atmosphere, but their emissions are uncertain due to longitudinal GHG heterogeneities associated with point source pollution from anthropogenic activities. In this study, we quantified summer concentrations and fluxes of carbon dioxide (CO 2 ), methane (CH 4 ), nitrous oxide (N 2 O), and dinitrogen (N 2 ), as well as several water quality parameters along the Rhine River and the Mittelland Canal, two critical inland waterways in Germany. Our main objectives were to compare GHG concentrations and fluxes along the two ecosystems and to determine the main driving factors responsible for their longitudinal GHG heterogeneities. The results indicated that the two ecosystems were sources of GHG fluxes to the atmosphere, with the Mittelland Canal being a hotspot for CH 4 and N 2 O fluxes. We also found significant longitudinal GHG flux discontinuities along the mainstems of both ecosystems, which were mainly driven by divergent drivers. Along the Mittelland Canal, peak CO 2 and CH 4 fluxes coincided with point pollution sources such as a joining river tributary or the presence of harbors, while harbors and in-situ biogeochemical processes such as methanogenesis and respiration mainly explained CH 4 and CO 2 hotspots along the Rhine River. In contrast to CO 2 and CH 4 fluxes, N 2 O longitudinal trends along the two lotic ecosystems were better predicted by in-situ parameters such as chlorophyll- a concentrations and N 2 fluxes. Based on a positive relationship with N 2 fluxes, we hypothesized that in-situ denitrification was driving N 2 O hotspots in the Canal, while a negative relationship with N 2 in the Rhine River suggested that coupled biological N 2 fixation and nitrification accounted for N 2 O hotspots. These findings stress the need to include N 2 flux estimates in GHG studies, as it can potentially improve our understanding of whether nitrogen is fixed through N 2 fixation or lost through denitrification.

@article{nabi_best_2024,
	title = {Best {Linear} {Unbiased} {Estimators} for {Fusion} of {Multiple} {CYGNSS} {Soil} {Moisture} {Products}},
	volume = {17},
	copyright = {https://creativecommons.org/licenses/by-nc-nd/4.0/},
	issn = {1939-1404, 2151-1535},
	url = {https://ieeexplore.ieee.org/document/10637276/},
	doi = {10.1109/JSTARS.2024.3443100},
	urldate = {2025-02-13},
	journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	author = {Nabi, M M and Senyurek, Volkan and Kurum, Mehmet and Gurbuz, Ali Cafer},
	year = {2024},
	pages = {16108--16118},
}

@article{nagavciuc_past_2024,
	title = {A past and present perspective on the {European} summer vapor pressure deficit},
	volume = {20},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1814-9332},
	url = {https://cp.copernicus.org/articles/20/573/2024/},
	doi = {10.5194/cp-20-573-2024},
	abstract = {Abstract. The response of evapotranspiration to anthropogenic warming is of critical importance for the water and carbon cycle. Contradictory conclusions about evapotranspiration changes are caused primarily by their brevity in time and sparsity in space, as well as the strong influence of internal variability. Here, we present the first gridded reconstruction of the summer (June, July, and August) vapor pressure deficit (VPD) for the past 4 centuries at the European level. This gridded reconstruction is based on 26 European tree ring oxygen isotope records and is obtained using a random forest approach. According to validation scores obtained with the Nash–Sutcliffe model efficiency, our reconstruction is robust over large parts of Europe since 1600, in particular for the westernmost and northernmost regions, where most tree ring records are located. Based on our reconstruction, we show that from the mid-1700s a trend towards higher summer VPD occurred in central Europe and the Mediterranean region that is related to a simultaneous increase in temperature and decrease in precipitation. This increasing summer VPD trend continues throughout the observational period and in recent times. Moreover, our summer VPD reconstruction helps to visualize the local and regional impacts of the current climate change, as well as to minimize statistical uncertainties of historical VPD variability. This paper provides also new insights into the relationship between summer VPD and large-scale atmospheric circulation, and we show that summer VPD has two preferred modes of variability, namely a NW–SE dipole-like mode and a N–S dipole-like mode. Furthermore, the interdisciplinary use of the data should be emphasized, as summer VPD is a crucial parameter for many climatological feedback processes in the Earth's surface system. The reconstructed summer VPD gridded data over the last 400 years are available at the following link: https://doi.org/10.5281/zenodo.5958836 (Balting et al., 2022).},
	language = {en},
	number = {3},
	urldate = {2024-11-26},
	journal = {Climate of the Past},
	author = {Nagavciuc, Viorica and Michel, Simon L. L. and Balting, Daniel F. and Helle, Gerhard and Freund, Mandy and Schleser, Gerhard H. and Steger, David N. and Lohmann, Gerrit and Ionita, Monica},
	month = mar,
	year = {2024},
	pages = {573--595},
}

Abstract. The response of evapotranspiration to anthropogenic warming is of critical importance for the water and carbon cycle. Contradictory conclusions about evapotranspiration changes are caused primarily by their brevity in time and sparsity in space, as well as the strong influence of internal variability. Here, we present the first gridded reconstruction of the summer (June, July, and August) vapor pressure deficit (VPD) for the past 4 centuries at the European level. This gridded reconstruction is based on 26 European tree ring oxygen isotope records and is obtained using a random forest approach. According to validation scores obtained with the Nash–Sutcliffe model efficiency, our reconstruction is robust over large parts of Europe since 1600, in particular for the westernmost and northernmost regions, where most tree ring records are located. Based on our reconstruction, we show that from the mid-1700s a trend towards higher summer VPD occurred in central Europe and the Mediterranean region that is related to a simultaneous increase in temperature and decrease in precipitation. This increasing summer VPD trend continues throughout the observational period and in recent times. Moreover, our summer VPD reconstruction helps to visualize the local and regional impacts of the current climate change, as well as to minimize statistical uncertainties of historical VPD variability. This paper provides also new insights into the relationship between summer VPD and large-scale atmospheric circulation, and we show that summer VPD has two preferred modes of variability, namely a NW–SE dipole-like mode and a N–S dipole-like mode. Furthermore, the interdisciplinary use of the data should be emphasized, as summer VPD is a crucial parameter for many climatological feedback processes in the Earth's surface system. The reconstructed summer VPD gridded data over the last 400 years are available at the following link: https://doi.org/10.5281/zenodo.5958836 (Balting et al., 2022).

@article{nandintsetseg_future_2024,
	title = {Future drought risk and adaptation of pastoralism in {Eurasian} rangelands},
	volume = {7},
	issn = {2397-3722},
	url = {https://www.nature.com/articles/s41612-024-00624-2},
	doi = {10.1038/s41612-024-00624-2},
	abstract = {Abstract
            Drought risk threatens pastoralism in rangelands, which are already under strain from climatic and socioeconomic changes. We examine the future drought risk (2031–2060 and 2071–2100) to rangeland productivity across Eurasia (West, Central, and East Asia) using a well-tested process-based ecosystem model and projections of five climate models under three shared socioeconomic pathway (SSP) scenarios of low (SSP1−2.6), medium (SSP3−7.0), and high (SSP5−8.5) warming relative to 1985–2014. We employ a probabilistic approach, with risk defined as the expected productivity loss induced by the probability of hazardous droughts (determined by a precipitation-based index) and vulnerability (the response of rangeland productivity to hazardous droughts). Drought risk and vulnerability are projected to increase in magnitude and area across Eurasian rangelands, with greater increases in 2071–2100 under the medium and high warming scenarios than in 2031–2060. Increasing risk in West Asia is caused by longer and more intense droughts and vulnerability, whereas higher risk in Central and East Asia is mainly associated with increased vulnerability, indicating overall risk is higher where vulnerability increases. These findings suggest that future droughts may exacerbate livestock feed shortages and negatively impact pastoralism. The results have practical implications for rangeland management that should be adapted to the ecological and socioeconomic contexts of the different countries in the region. Existing traditional ecological knowledge can be promoted to adapt to drought risk and embedded in a wider set of adaptation measures involving management improvements, social transformations, capacity building, and policy reforms addressing multiple stakeholders.},
	language = {en},
	number = {1},
	urldate = {2025-02-13},
	journal = {npj Climate and Atmospheric Science},
	author = {Nandintsetseg, Banzragch and Chang, Jinfeng and Sen, Omer L. and Reyer, Christopher P. O. and Kong, Kaman and Yetemen, Omer and Ciais, Philippe and Davaadalai, Jamts},
	month = mar,
	year = {2024},
	pages = {82},
}

Abstract Drought risk threatens pastoralism in rangelands, which are already under strain from climatic and socioeconomic changes. We examine the future drought risk (2031–2060 and 2071–2100) to rangeland productivity across Eurasia (West, Central, and East Asia) using a well-tested process-based ecosystem model and projections of five climate models under three shared socioeconomic pathway (SSP) scenarios of low (SSP1−2.6), medium (SSP3−7.0), and high (SSP5−8.5) warming relative to 1985–2014. We employ a probabilistic approach, with risk defined as the expected productivity loss induced by the probability of hazardous droughts (determined by a precipitation-based index) and vulnerability (the response of rangeland productivity to hazardous droughts). Drought risk and vulnerability are projected to increase in magnitude and area across Eurasian rangelands, with greater increases in 2071–2100 under the medium and high warming scenarios than in 2031–2060. Increasing risk in West Asia is caused by longer and more intense droughts and vulnerability, whereas higher risk in Central and East Asia is mainly associated with increased vulnerability, indicating overall risk is higher where vulnerability increases. These findings suggest that future droughts may exacerbate livestock feed shortages and negatively impact pastoralism. The results have practical implications for rangeland management that should be adapted to the ecological and socioeconomic contexts of the different countries in the region. Existing traditional ecological knowledge can be promoted to adapt to drought risk and embedded in a wider set of adaptation measures involving management improvements, social transformations, capacity building, and policy reforms addressing multiple stakeholders.

@article{nasta_temperature-corrected_2024,
	title = {Temperature-{Corrected} {Calibration} of {GS3} and {TEROS}-12 {Soil} {Water} {Content} {Sensors}},
	volume = {24},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1424-8220},
	url = {https://www.mdpi.com/1424-8220/24/3/952},
	doi = {10.3390/s24030952},
	abstract = {The continuous monitoring of soil water content is commonly carried out using low-frequency capacitance sensors that require a site-specific calibration to relate sensor readings to apparent dielectric bulk permittivity (Kb) and soil water content (θ). In fine-textured soils, the conversion of Kb to θ is still challenging due to temperature effects on the bound water fraction associated with clay mineral surfaces, which is disregarded in factory calibrations. Here, a multi-point calibration approach accounts for temperature effects on two soils with medium to high clay content. A calibration strategy was developed using repacked soil samples in which the Kb-θ relationship was determined for temperature (T) steps from 10 to 40 °C. This approach was tested using the GS3 and TEROS-12 sensors (METER Group, Inc. Pullman, WA, USA; formerly Decagon Devices). Kb is influenced by T in both soils with contrasting T-Kb relationships. The measured data were fitted using a linear function θ = aKb + b with temperature-dependent coefficients a and b. The slope, a(T), and intercept, b(T), of the loam soil were different from the ones of the clay soil. The consideration of a temperature correction resulted in low RMSE values, ranging from 0.007 to 0.033 cm3 cm−3, which were lower than the RMSE values obtained from factory calibration (0.046 to 0.11 cm3 cm−3). However, each experiment was replicated only twice using two different sensors. Sensor-to-sensor variability effects were thus ignored in this study and will be systematically investigated in a future study. Finally, the applicability of the proposed calibration method was tested at two experimental sites. The spatial-average θ from a network of GS3 sensors based on the new calibration fairly agreed with the independent area-wide θ from the Cosmic Ray Neutron Sensor (CRNS). This study provided a temperature-corrected calibration to increase the accuracy of commercial sensors, especially under dry conditions, at two experimental sites.},
	language = {en},
	number = {3},
	urldate = {2024-11-26},
	journal = {Sensors},
	author = {Nasta, Paolo and Coccia, Francesca and Lazzaro, Ugo and Bogena, Heye R. and Huisman, Johan A. and Sica, Benedetto and Mazzitelli, Caterina and Vereecken, Harry and Romano, Nunzio},
	month = feb,
	year = {2024},
	pages = {952},
}

The continuous monitoring of soil water content is commonly carried out using low-frequency capacitance sensors that require a site-specific calibration to relate sensor readings to apparent dielectric bulk permittivity (Kb) and soil water content (θ). In fine-textured soils, the conversion of Kb to θ is still challenging due to temperature effects on the bound water fraction associated with clay mineral surfaces, which is disregarded in factory calibrations. Here, a multi-point calibration approach accounts for temperature effects on two soils with medium to high clay content. A calibration strategy was developed using repacked soil samples in which the Kb-θ relationship was determined for temperature (T) steps from 10 to 40 °C. This approach was tested using the GS3 and TEROS-12 sensors (METER Group, Inc. Pullman, WA, USA; formerly Decagon Devices). Kb is influenced by T in both soils with contrasting T-Kb relationships. The measured data were fitted using a linear function θ = aKb + b with temperature-dependent coefficients a and b. The slope, a(T), and intercept, b(T), of the loam soil were different from the ones of the clay soil. The consideration of a temperature correction resulted in low RMSE values, ranging from 0.007 to 0.033 cm3 cm−3, which were lower than the RMSE values obtained from factory calibration (0.046 to 0.11 cm3 cm−3). However, each experiment was replicated only twice using two different sensors. Sensor-to-sensor variability effects were thus ignored in this study and will be systematically investigated in a future study. Finally, the applicability of the proposed calibration method was tested at two experimental sites. The spatial-average θ from a network of GS3 sensors based on the new calibration fairly agreed with the independent area-wide θ from the Cosmic Ray Neutron Sensor (CRNS). This study provided a temperature-corrected calibration to increase the accuracy of commercial sensors, especially under dry conditions, at two experimental sites.

@misc{neumann_agrasim_2024,
	title = {The {AgraSim} ({Agricultural} {Simulator}) facility for the comprehensive experimental simulation and analysis of environmental impacts on processes in the soil-plant-atmosphere system},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	url = {https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1598/},
	doi = {10.5194/egusphere-2024-1598},
	abstract = {Abstract. The AgraSim (Agricultural Simulator) large-scale research infrastructure is an experimental simulator consisting of six mesocosms (each mesocosm consisting of an integrated climate chamber, plant chamber and lysimeter system) for studying the effects of future climate conditions on plant physiological, biogeochemical, hydrological and atmospheric processes in agroecosystems, which was designed and built by the Forschungszentrum Jülich. AgraSim makes it possible to simulate the environmental conditions in the mesocosms in a fully controlled manner under different weather and climate conditions ranging from tropical to boreal climate. Moreover, it provides a unique way of imposing future climate conditions which presently cannot be implemented under real-world conditions. It allows monitoring and controlling states and fluxes of a broad range of processes in the soil-plant-atmosphere system. This information can then be used to give input to process-models, to improve process descriptions and to serve as a platform for the development of a digital twin of the soil-plant-atmosphere system.},
	urldate = {2024-11-26},
	publisher = {System design},
	author = {Neumann, Joschka and Brüggemann, Nicolas and Chaumet, Patrick and Hermes, Normen and Huwer, Jan and Kirchner, Peter and Lesmeister, Werner and Mertens, Wilhelm August and Pütz, Thomas and Wolters, Jörg and Vereecken, Harry and Natour, Ghaleb},
	month = jul,
	year = {2024},
}

Abstract. The AgraSim (Agricultural Simulator) large-scale research infrastructure is an experimental simulator consisting of six mesocosms (each mesocosm consisting of an integrated climate chamber, plant chamber and lysimeter system) for studying the effects of future climate conditions on plant physiological, biogeochemical, hydrological and atmospheric processes in agroecosystems, which was designed and built by the Forschungszentrum Jülich. AgraSim makes it possible to simulate the environmental conditions in the mesocosms in a fully controlled manner under different weather and climate conditions ranging from tropical to boreal climate. Moreover, it provides a unique way of imposing future climate conditions which presently cannot be implemented under real-world conditions. It allows monitoring and controlling states and fluxes of a broad range of processes in the soil-plant-atmosphere system. This information can then be used to give input to process-models, to improve process descriptions and to serve as a platform for the development of a digital twin of the soil-plant-atmosphere system.

@misc{nguyen_responses_2024,
	title = {Responses of field-grown maize to different soil types, water regimes, and contrasting vapor pressure deficit},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	url = {https://egusphere.copernicus.org/preprints/2024/egusphere-2023-2967/},
	doi = {10.5194/egusphere-2023-2967},
	abstract = {Abstract. Drought is a serious constraint to crop growth and production of important staple crops such as maize. Improved understanding of the responses of crops to drought can be incorporated into cropping system models to support crop breeding, varietal selection and management decisions for minimizing negative impacts. We investigate the impacts of different soil types (stony and silty) and water regimes (irrigated and rainfed) on hydraulic linkages between soil and plant, as well as root: shoot growth characteristics. Our analysis is based on a comprehensive dataset measured along the soil-plant-atmosphere pathway at field scale in two growing seasons (2017, 2018) with contrasting climatic conditions (low and high VPD). Roots were observed mostly in the topsoil (10–20 cm) of the stony soil while more roots were found in the subsoil (60–80 cm) of the silty soil. The difference in root length was pronounced at silking and harvest between the soil types. Total root length was 2.5–6 times higher in the silty soil compared to the stony soil with the same water treatment. At silking time, the ratios of root length to shoot biomass in the rainfed plot of the silty soil (F2P2) were 3 times higher than those in the irrigated silty soil (F2P3) while the ratio was similar for two water treatments in the stony soil. With the same water treatment, the ratios of root length to shoot biomass of silty soil was higher than stony soil. The observed minimum leaf water potential (ψleaf) varied from around -1.5 MPa in the rainfed plot in 2017 to around -2.5 MPa in the same plot of the stony soil in 2018. In the rainfed plot, the mimimum ψleaf in the stony soil was lower than in silty soil from -2 to -1.5 MPa in 2017, respectively while these were from -2.5 to -2 MPa in 2018, respectively. Leaf water potential, water potential gradients from soil to plant roots, plant hydraulic conductance (Ksoil\_plant), stomatal conductance, transpiration, and photosynthesis were considerably modulated by the soil water content and the conductivity of the rhizosphere. When the stony soil and silt soil are compared, the higher 'stress' due to the lower water availability in the stony soil resulted in less roots with a higher root tissue conductance in the soil with more stress. When comparing the rainfed with the irrigated plot in the silty soil, the higher stress in the rainfed soil resulted in more roots with a lower root tissue conductance in the treatment with more stress. This illustrates that the 'response' to stress can be completely opposite depending on conditions or treatments that lead to the differences in stress that are compared. To respond to water deficit, maize had higher water uptake rate per unit root length and higher root segment conductance in the stony soil than in the silty soil, while the crop reduced transpired water via reduced aboveground plant size. Future improvements of soil-crop models in simulating gas exchange and crop growth should further emphasize the role of soil textures on stomatal function, dynamic root growth, and plant hydraulic system together with aboveground leaf area adjustments.},
	urldate = {2024-11-26},
	publisher = {Earth System Science/Response to Global Change: Climate Change},
	author = {Nguyen, Thuy Huu and Gaiser, Thomas and Vanderborght, Jan and Schnepf, Andrea and Bauer, Felix and Klotzsche, Anja and Lärm, Lena and Hüging, Hubert and Ewert, Frank},
	month = jan,
	year = {2024},
}

Abstract. Drought is a serious constraint to crop growth and production of important staple crops such as maize. Improved understanding of the responses of crops to drought can be incorporated into cropping system models to support crop breeding, varietal selection and management decisions for minimizing negative impacts. We investigate the impacts of different soil types (stony and silty) and water regimes (irrigated and rainfed) on hydraulic linkages between soil and plant, as well as root: shoot growth characteristics. Our analysis is based on a comprehensive dataset measured along the soil-plant-atmosphere pathway at field scale in two growing seasons (2017, 2018) with contrasting climatic conditions (low and high VPD). Roots were observed mostly in the topsoil (10–20 cm) of the stony soil while more roots were found in the subsoil (60–80 cm) of the silty soil. The difference in root length was pronounced at silking and harvest between the soil types. Total root length was 2.5–6 times higher in the silty soil compared to the stony soil with the same water treatment. At silking time, the ratios of root length to shoot biomass in the rainfed plot of the silty soil (F2P2) were 3 times higher than those in the irrigated silty soil (F2P3) while the ratio was similar for two water treatments in the stony soil. With the same water treatment, the ratios of root length to shoot biomass of silty soil was higher than stony soil. The observed minimum leaf water potential (ψleaf) varied from around -1.5 MPa in the rainfed plot in 2017 to around -2.5 MPa in the same plot of the stony soil in 2018. In the rainfed plot, the mimimum ψleaf in the stony soil was lower than in silty soil from -2 to -1.5 MPa in 2017, respectively while these were from -2.5 to -2 MPa in 2018, respectively. Leaf water potential, water potential gradients from soil to plant roots, plant hydraulic conductance (Ksoil_plant), stomatal conductance, transpiration, and photosynthesis were considerably modulated by the soil water content and the conductivity of the rhizosphere. When the stony soil and silt soil are compared, the higher 'stress' due to the lower water availability in the stony soil resulted in less roots with a higher root tissue conductance in the soil with more stress. When comparing the rainfed with the irrigated plot in the silty soil, the higher stress in the rainfed soil resulted in more roots with a lower root tissue conductance in the treatment with more stress. This illustrates that the 'response' to stress can be completely opposite depending on conditions or treatments that lead to the differences in stress that are compared. To respond to water deficit, maize had higher water uptake rate per unit root length and higher root segment conductance in the stony soil than in the silty soil, while the crop reduced transpired water via reduced aboveground plant size. Future improvements of soil-crop models in simulating gas exchange and crop growth should further emphasize the role of soil textures on stomatal function, dynamic root growth, and plant hydraulic system together with aboveground leaf area adjustments.

@article{nguyen_multi-year_2024,
	title = {Multi-year aboveground data of minirhizotron facilities in {Selhausen}},
	volume = {11},
	issn = {2052-4463},
	url = {https://www.nature.com/articles/s41597-024-03535-2},
	doi = {10.1038/s41597-024-03535-2},
	abstract = {Abstract
            
              Improved understanding of crops’ response to soil water stress is important to advance soil-plant system models and to support crop breeding, crop and varietal selection, and management decisions to minimize negative impacts. Studies on eco-physiological crop characteristics from leaf to canopy for different soil water conditions and crops are often carried out at controlled conditions. In-field measurements under realistic field conditions and data of plant water potential, its links with CO
              2
              and H
              2
              O gas fluxes, and crop growth processes are rare. Here, we presented a comprehensive data set collected from leaf to canopy using sophisticated and comprehensive sensing techniques (leaf chlorophyll, stomatal conductance and photosynthesis, canopy CO
              2
              exchange, sap flow, and canopy temperature) including detailed crop growth characteristics based on destructive methods (crop height, leaf area index, aboveground biomass, and yield). Data were acquired under field conditions with contrasting soil types, water treatments, and different cultivars of wheat and maize. The data from 2016 up to now will be made available for studying soil/water-plant relations and improving soil-plant-atmospheric continuum models.},
	language = {en},
	number = {1},
	urldate = {2024-11-26},
	journal = {Scientific Data},
	author = {Nguyen, Thuy Huu and Lopez, Gina and Seidel, Sabine J. and Lärm, Lena and Bauer, Felix Maximilian and Klotzsche, Anja and Schnepf, Andrea and Gaiser, Thomas and Hüging, Hubert and Ewert, Frank},
	month = jun,
	year = {2024},
	pages = {674},
}

Abstract Improved understanding of crops’ response to soil water stress is important to advance soil-plant system models and to support crop breeding, crop and varietal selection, and management decisions to minimize negative impacts. Studies on eco-physiological crop characteristics from leaf to canopy for different soil water conditions and crops are often carried out at controlled conditions. In-field measurements under realistic field conditions and data of plant water potential, its links with CO 2 and H 2 O gas fluxes, and crop growth processes are rare. Here, we presented a comprehensive data set collected from leaf to canopy using sophisticated and comprehensive sensing techniques (leaf chlorophyll, stomatal conductance and photosynthesis, canopy CO 2 exchange, sap flow, and canopy temperature) including detailed crop growth characteristics based on destructive methods (crop height, leaf area index, aboveground biomass, and yield). Data were acquired under field conditions with contrasting soil types, water treatments, and different cultivars of wheat and maize. The data from 2016 up to now will be made available for studying soil/water-plant relations and improving soil-plant-atmospheric continuum models.

@article{nicholson_pesticide_2024,
	title = {Pesticide use negatively affects bumble bees across {European} landscapes},
	volume = {628},
	issn = {0028-0836, 1476-4687},
	url = {https://www.nature.com/articles/s41586-023-06773-3},
	doi = {10.1038/s41586-023-06773-3},
	abstract = {Abstract
            
              Sustainable agriculture requires balancing crop yields with the effects of pesticides on non-target organisms, such as bees and other crop pollinators. Field studies demonstrated that agricultural use of neonicotinoid insecticides can negatively affect wild bee species
              1,2
              , leading to restrictions on these compounds
              3
              . However, besides neonicotinoids, field-based evidence of the effects of landscape pesticide exposure on wild bees is lacking. Bees encounter many pesticides in agricultural landscapes
              4–9
              and the effects of this landscape exposure on colony growth and development of any bee species remains unknown. Here we show that the many pesticides found in bumble bee-collected pollen are associated with reduced colony performance during crop bloom, especially in simplified landscapes with intensive agricultural practices. Our results from 316
              Bombus terrestris
              colonies at 106 agricultural sites across eight European countries confirm that the regulatory system fails to sufficiently prevent pesticide-related impacts on non-target organisms, even for a eusocial pollinator species in which colony size may buffer against such impacts
              10,11
              . These findings support the need for postapproval monitoring of both pesticide exposure and effects to confirm that the regulatory process is sufficiently protective in limiting the collateral environmental damage of agricultural pesticide use.},
	language = {en},
	number = {8007},
	urldate = {2024-11-15},
	journal = {Nature},
	author = {Nicholson, Charlie C. and Knapp, Jessica and Kiljanek, Tomasz and Albrecht, Matthias and Chauzat, Marie-Pierre and Costa, Cecilia and De La Rúa, Pilar and Klein, Alexandra-Maria and Mänd, Marika and Potts, Simon G. and Schweiger, Oliver and Bottero, Irene and Cini, Elena and De Miranda, Joachim R. and Di Prisco, Gennaro and Dominik, Christophe and Hodge, Simon and Kaunath, Vera and Knauer, Anina and Laurent, Marion and Martínez-López, Vicente and Medrzycki, Piotr and Pereira-Peixoto, Maria Helena and Raimets, Risto and Schwarz, Janine M. and Senapathi, Deepa and Tamburini, Giovanni and Brown, Mark J. F. and Stout, Jane C. and Rundlöf, Maj},
	month = apr,
	year = {2024},
	pages = {355--358},
}

Abstract Sustainable agriculture requires balancing crop yields with the effects of pesticides on non-target organisms, such as bees and other crop pollinators. Field studies demonstrated that agricultural use of neonicotinoid insecticides can negatively affect wild bee species 1,2 , leading to restrictions on these compounds 3 . However, besides neonicotinoids, field-based evidence of the effects of landscape pesticide exposure on wild bees is lacking. Bees encounter many pesticides in agricultural landscapes 4–9 and the effects of this landscape exposure on colony growth and development of any bee species remains unknown. Here we show that the many pesticides found in bumble bee-collected pollen are associated with reduced colony performance during crop bloom, especially in simplified landscapes with intensive agricultural practices. Our results from 316 Bombus terrestris colonies at 106 agricultural sites across eight European countries confirm that the regulatory system fails to sufficiently prevent pesticide-related impacts on non-target organisms, even for a eusocial pollinator species in which colony size may buffer against such impacts 10,11 . These findings support the need for postapproval monitoring of both pesticide exposure and effects to confirm that the regulatory process is sufficiently protective in limiting the collateral environmental damage of agricultural pesticide use.

@article{nogueira_combined_2024,
	title = {Combined effects of geological heterogeneity and discharge events on groundwater and surface water mixing},
	volume = {638},
	issn = {00221694},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0022169424008631},
	doi = {10.1016/j.jhydrol.2024.131467},
	language = {en},
	urldate = {2024-11-26},
	journal = {Journal of Hydrology},
	author = {Nogueira, Guilherme E.H. and Partington, Daniel and Heidbüchel, Ingo and Fleckenstein, Jan H.},
	month = jul,
	year = {2024},
	pages = {131467},
}

@article{ohnemus_elter_2024,
	title = {The {eLTER} research infrastructure: {Current} design and coverage of environmental and socio-ecological gradients},
	volume = {23},
	issn = {26659727},
	shorttitle = {The {eLTER} research infrastructure},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S2665972724001247},
	doi = {10.1016/j.indic.2024.100456},
	language = {en},
	urldate = {2024-11-26},
	journal = {Environmental and Sustainability Indicators},
	author = {Ohnemus, Thomas and Zacharias, Steffen and Dirnböck, Thomas and Bäck, Jaana and Brack, Werner and Forsius, Martin and Mallast, Ulf and Nikolaidis, Nikolaos P. and Peterseil, Johannes and Piscart, Christophe and Pando, Francisco and Poppe Terán, Christian and Mirtl, Michael},
	month = sep,
	year = {2024},
	pages = {100456},
}

@article{oswald_hydrology_2024,
	title = {Hydrology on {Solid} {Grounds}? {Integration} {Is} {Key} to {Closing} {Knowledge} {Gaps} {Concerning} {Landscape} {Subsurface} {Water} {Storage} {Dynamics}},
	volume = {38},
	issn = {0885-6087, 1099-1085},
	shorttitle = {Hydrology on {Solid} {Grounds}?},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.15320},
	doi = {10.1002/hyp.15320},
	abstract = {ABSTRACT
            Individual approaches to observe water dynamics across our landscape, from the land surface to groundwater, are many though they individually only provide glimpses into the real world due to their specific space–time scales. Comprehensive integration across all available observations is still largely lacking, limiting both our ability to reduce scientific knowledge gaps, and to guide land and water management using the best available scientific evidence. We argue that a stronger focus on integration of observational products, while utilising machine learning and accounting for current perceptual understanding is urgently needed to overcome this limitation. Since Europe is warming faster than any other continent, central Europe is undergoing a dramatic hydroclimatic transition about which such integrated observations would provide timely and valuable insights. Here, we present potential and gaps of current and planned observational methods. We argue that hyperresolution (sub km) integrated estimates of landscape water dynamics are feasible, which could significantly improve our ability to simulate vadose zone and groundwater dynamics, ultimately closing gaps in our current perception of hydrological processes in a temperate region under strong influence from climate change. We close by arguing that an interdisciplinary effort of various scientific communities is needed to enable this advancement.},
	language = {en},
	number = {11},
	urldate = {2024-11-26},
	journal = {Hydrological Processes},
	author = {Oswald, Sascha E. and Angermann, Lisa and Bogena, Heye R. and Förster, Michael and García‐García, Almudena and Lischeid, Gunnar and Paton, Eva N. and Altdorff, Daniel and Attinger, Sabine and Güntner, Andreas and Hartmann, Andreas and Hendricks Franssen, Harrie‐Jan and Hildebrandt, Anke and Kleinschmit, Birgit and Orth, Rene and Peng, Jian and Ryo, Masahiro and Schrön, Martin and Wagner, Wolfgang and Wagener, Thorsten},
	month = nov,
	year = {2024},
	pages = {e15320},
}

ABSTRACT Individual approaches to observe water dynamics across our landscape, from the land surface to groundwater, are many though they individually only provide glimpses into the real world due to their specific space–time scales. Comprehensive integration across all available observations is still largely lacking, limiting both our ability to reduce scientific knowledge gaps, and to guide land and water management using the best available scientific evidence. We argue that a stronger focus on integration of observational products, while utilising machine learning and accounting for current perceptual understanding is urgently needed to overcome this limitation. Since Europe is warming faster than any other continent, central Europe is undergoing a dramatic hydroclimatic transition about which such integrated observations would provide timely and valuable insights. Here, we present potential and gaps of current and planned observational methods. We argue that hyperresolution (sub km) integrated estimates of landscape water dynamics are feasible, which could significantly improve our ability to simulate vadose zone and groundwater dynamics, ultimately closing gaps in our current perception of hydrological processes in a temperate region under strong influence from climate change. We close by arguing that an interdisciplinary effort of various scientific communities is needed to enable this advancement.

@article{oleary_linking_2024,
	title = {Linking electromagnetic induction data to soil properties at field scale aided by neural network clustering},
	volume = {4},
	issn = {2673-8619},
	url = {https://www.frontiersin.org/articles/10.3389/fsoil.2024.1346028/full},
	doi = {10.3389/fsoil.2024.1346028},
	abstract = {Introduction
              The mapping of soil properties, such as soil texture, at the field scale is important Q6 in the context of national agricultural planning/policy and precision agriculture. Electromagnetic Induction (EMI) surveys are commonly used to measure soil apparent electrical conductivity and can provide valuable insights into such subsurface properties.
            
            
              Methods
              Multi-receiver or multi-frequency instruments provide a vertical distribution of apparent conductivity beneath the instrument, while the mobility of such instruments allows for spatial coverage. Clustering is the grouping together of similar multi-dimensional data, such as the processed EMI data over a field. A neural network clustering process, where the number of clusters can be objectively determined, results in a set of one-dimensional apparent electrical conductivity cluster centers, which are representative of the entire three-dimensional dataset. These cluster centers are used to guide inversions of apparent conductivity data to give an estimate of the true electrical conductivity distribution at a site.
            
            
              Results and discussion
              The method is applied to two sites and the results demonstrate a correlation between (true) electrical conductivity with soil texture (sampled prior to the EMI surveys) which is superior to correlations where no clustering is included. The method has the potential to be developed further, with the aim of improving the prediction of soil properties at cluster scale, such as texture, from EMI data. A particularly important conclusion from this initial study is that EMI data should be acquired prior to a focused soil sampling campaign to calibrate the electrical conductivity – soil property correlations.},
	urldate = {2024-11-26},
	journal = {Frontiers in Soil Science},
	author = {O’Leary, Dave and Brogi, Cosimo and Brown, Colin and Tuohy, Pat and Daly, Eve},
	month = feb,
	year = {2024},
	pages = {1346028},
}

Introduction The mapping of soil properties, such as soil texture, at the field scale is important Q6 in the context of national agricultural planning/policy and precision agriculture. Electromagnetic Induction (EMI) surveys are commonly used to measure soil apparent electrical conductivity and can provide valuable insights into such subsurface properties. Methods Multi-receiver or multi-frequency instruments provide a vertical distribution of apparent conductivity beneath the instrument, while the mobility of such instruments allows for spatial coverage. Clustering is the grouping together of similar multi-dimensional data, such as the processed EMI data over a field. A neural network clustering process, where the number of clusters can be objectively determined, results in a set of one-dimensional apparent electrical conductivity cluster centers, which are representative of the entire three-dimensional dataset. These cluster centers are used to guide inversions of apparent conductivity data to give an estimate of the true electrical conductivity distribution at a site. Results and discussion The method is applied to two sites and the results demonstrate a correlation between (true) electrical conductivity with soil texture (sampled prior to the EMI surveys) which is superior to correlations where no clustering is included. The method has the potential to be developed further, with the aim of improving the prediction of soil properties at cluster scale, such as texture, from EMI data. A particularly important conclusion from this initial study is that EMI data should be acquired prior to a focused soil sampling campaign to calibrate the electrical conductivity – soil property correlations.

@article{paulus_interpretability_2024,
	title = {Interpretability of negative latent heat fluxes from eddy covariance measurements in dry conditions},
	volume = {21},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1726-4189},
	url = {https://bg.copernicus.org/articles/21/2051/2024/},
	doi = {10.5194/bg-21-2051-2024},
	abstract = {Abstract. It is known from arid and semi-arid ecosystems that atmospheric water vapor can directly be adsorbed by the soil matrix. Soil water vapor adsorption was typically neglected and only recently received attention because of improvements in measurement techniques. One technique rarely explored for the measurement of soil water vapor adsorption is eddy covariance (EC). Soil water vapor adsorption may be detectable as downwardly directed (i.e., negative) EC latent heat (λE) flux measurements under dry conditions, but a systematic assessment of the use of negative λE fluxes from EC flux stations to characterize adsorption is missing. We propose a classification method to characterize soil water vapor adsorption, excluding conditions of dew and fog when λE derived from EC is not trustworthy due to stable atmospheric conditions. We compare downwardly directed λE fluxes from EC with measurements from weighing lysimeters for 4 years in a Mediterranean savanna ecosystem and 3 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 was measured predominantly under an extreme drought event in 2018. During 30 \% of nights in the 4-year measurement period at the Mediterranean site, the EC technique detected downwardly directed λE fluxes of which 88.8 \% were confirmed to be soil water vapor adsorption by at least one lysimeter. At the temperate site, downwardly 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. This relationship slightly improved to 61 \% 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 amounts of soil water vapor adsorption between the two methods revealed a substantial underestimation of the EC compared to the lysimeters. This underestimation was, however, comparable with the underestimation in evaporation by the eddy covariance and improved in conditions of higher turbulence. Based on a random-forest-based feature selection, we found the mismatch between the methods being dominantly related to the site's inherent variability 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 for EC or lysimeter, respectively, 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 measured ratio of adsorption to evaporation over 24 h 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 thanks to the greater abundance of EC measurements compared to lysimeter observations.},
	language = {en},
	number = {8},
	urldate = {2024-11-26},
	journal = {Biogeosciences},
	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 Sebastian and Carrara, Arnaud and Moreno, Gerardo and Mauder, Matthias and Groh, Jannis and Graf, Alexander and Reichstein, Markus and Migliavacca, Mirco},
	month = apr,
	year = {2024},
	pages = {2051--2085},
}

Abstract. It is known from arid and semi-arid ecosystems that atmospheric water vapor can directly be adsorbed by the soil matrix. Soil water vapor adsorption was typically neglected and only recently received attention because of improvements in measurement techniques. One technique rarely explored for the measurement of soil water vapor adsorption is eddy covariance (EC). Soil water vapor adsorption may be detectable as downwardly directed (i.e., negative) EC latent heat (λE) flux measurements under dry conditions, but a systematic assessment of the use of negative λE fluxes from EC flux stations to characterize adsorption is missing. We propose a classification method to characterize soil water vapor adsorption, excluding conditions of dew and fog when λE derived from EC is not trustworthy due to stable atmospheric conditions. We compare downwardly directed λE fluxes from EC with measurements from weighing lysimeters for 4 years in a Mediterranean savanna ecosystem and 3 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 was measured predominantly under an extreme drought event in 2018. During 30 % of nights in the 4-year measurement period at the Mediterranean site, the EC technique detected downwardly directed λE fluxes of which 88.8 % were confirmed to be soil water vapor adsorption by at least one lysimeter. At the temperate site, downwardly 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. This relationship slightly improved to 61 % 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 amounts of soil water vapor adsorption between the two methods revealed a substantial underestimation of the EC compared to the lysimeters. This underestimation was, however, comparable with the underestimation in evaporation by the eddy covariance and improved in conditions of higher turbulence. Based on a random-forest-based feature selection, we found the mismatch between the methods being dominantly related to the site's inherent variability 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 for EC or lysimeter, respectively, 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 measured ratio of adsorption to evaporation over 24 h 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 thanks to the greater abundance of EC measurements compared to lysimeter observations.

@article{peng_first_2024,
	title = {First mapping of polarization-dependent vegetation optical depth and soil moisture from {SMAP} {L}-band radiometry},
	volume = {302},
	issn = {00344257},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0034425723005229},
	doi = {10.1016/j.rse.2023.113970},
	language = {en},
	urldate = {2024-11-26},
	journal = {Remote Sensing of Environment},
	author = {Peng, Zhiqing and Zhao, Tianjie and Shi, Jiancheng and Hu, Lu and Rodríguez-Fernández, Nemesio J. and Wigneron, Jean-Pierre and Jackson, Thomas J. and Walker, Jeffrey P. and Cosh, Michael H. and Yang, Kun and Lu, Hui and Bai, Yu and Yao, Panpan and Zheng, Jingyao and Wei, Zushuai},
	month = mar,
	year = {2024},
	pages = {113970},
}

@article{petrovic_heat_2024,
	title = {Heat wave characteristics: evaluation of regional climate model performances for {Germany}},
	volume = {24},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1684-9981},
	shorttitle = {Heat wave characteristics},
	url = {https://nhess.copernicus.org/articles/24/265/2024/},
	doi = {10.5194/nhess-24-265-2024},
	abstract = {Abstract. Heat waves are among the most severe climate extreme events. In this study, we address the impact of increased model resolution and tailored model settings on the reproduction of these events by evaluating different regional climate model outputs for Germany and its near surroundings between 1980–2009. Outputs of an ensemble of six EURO-CORDEX models with 12.5 km grid resolution and outputs from a high-resolution (5 km) WRF (Weather Research and Forecasting) model run are employed. The latter was especially tailored for the study region regarding the physics configuration. We analyze the reproduction of the maximum temperature, number of heat wave days, heat wave characteristics (frequency, duration and intensity), the 2003 major event, and trends in the annual number of heat waves. E-OBS is used as the reference, and we utilize the Taylor diagram, the Mann–Kendall trend test and the spatial efficiency metric, while the cumulative heat index is used as a measure of intensity. Averaged over the domain, heat waves occurred about 31 times in the study period, with an average duration of 4 d and an average heat excess of 10 ∘C. The maximum temperature was only reproduced satisfactorily by some models. Despite using the same forcing, the models exhibited a large spread in heat wave reproduction. The domain mean conditions for heat wave frequency and duration were captured reasonably well, but the intensity was reproduced weakly. The spread was particularly pronounced for the 2003 event, indicating how difficult it was for the models to reproduce single major events. All models underestimated the spatial extent of the observed increasing trends. WRF generally did not perform significantly better than the other models. We conclude that increasing the model resolution does not add significant value to heat wave simulation if the base resolution is already relatively high. Tailored model settings seem to play a minor role. The sometimes pronounced differences in performance, however, highlight that the choice of model can be crucial.},
	language = {en},
	number = {1},
	urldate = {2024-11-15},
	journal = {Natural Hazards and Earth System Sciences},
	author = {Petrovic, Dragan and Fersch, Benjamin and Kunstmann, Harald},
	month = jan,
	year = {2024},
	pages = {265--289},
}

Abstract. Heat waves are among the most severe climate extreme events. In this study, we address the impact of increased model resolution and tailored model settings on the reproduction of these events by evaluating different regional climate model outputs for Germany and its near surroundings between 1980–2009. Outputs of an ensemble of six EURO-CORDEX models with 12.5 km grid resolution and outputs from a high-resolution (5 km) WRF (Weather Research and Forecasting) model run are employed. The latter was especially tailored for the study region regarding the physics configuration. We analyze the reproduction of the maximum temperature, number of heat wave days, heat wave characteristics (frequency, duration and intensity), the 2003 major event, and trends in the annual number of heat waves. E-OBS is used as the reference, and we utilize the Taylor diagram, the Mann–Kendall trend test and the spatial efficiency metric, while the cumulative heat index is used as a measure of intensity. Averaged over the domain, heat waves occurred about 31 times in the study period, with an average duration of 4 d and an average heat excess of 10 ∘C. The maximum temperature was only reproduced satisfactorily by some models. Despite using the same forcing, the models exhibited a large spread in heat wave reproduction. The domain mean conditions for heat wave frequency and duration were captured reasonably well, but the intensity was reproduced weakly. The spread was particularly pronounced for the 2003 event, indicating how difficult it was for the models to reproduce single major events. All models underestimated the spatial extent of the observed increasing trends. WRF generally did not perform significantly better than the other models. We conclude that increasing the model resolution does not add significant value to heat wave simulation if the base resolution is already relatively high. Tailored model settings seem to play a minor role. The sometimes pronounced differences in performance, however, highlight that the choice of model can be crucial.

@article{piatka_precipitation_2024,
	title = {Precipitation fuels dissolved greenhouse gas ({CO2}, {CH4}, {N2O}) dynamics in a peatland-dominated headwater stream: results from a continuous monitoring setup},
	volume = {5},
	issn = {2624-9375},
	shorttitle = {Precipitation fuels dissolved greenhouse gas ({CO2}, {CH4}, {N2O}) dynamics in a peatland-dominated headwater stream},
	url = {https://www.frontiersin.org/articles/10.3389/frwa.2023.1321137/full},
	doi = {10.3389/frwa.2023.1321137},
	abstract = {Stream ecosystems are actively involved in the biogeochemical cycling of carbon (C) and nitrogen (N) from terrestrial and aquatic sources. Streams hydrologically connected to peatland soils are suggested to receive significant quantities of particulate, dissolved, and gaseous C and N species, which directly enhance losses of greenhouse gases (GHGs), i.e., carbon dioxide (CO
              2
              ), methane (CH
              4
              ), and nitrous oxide (N
              2
              O), and fuel in-stream GHG production. However, riverine GHG concentrations and emissions are highly dynamic due to temporally and spatially variable hydrological, meteorological, and biogeochemical conditions. In this study, we present a complete GHG monitoring system in a peatland stream, which can continuously measure dissolved GHG concentrations and allows to infer gaseous fluxes between the stream and the atmosphere and discuss the results from March 31 to August 25 at variable hydrological conditions during a cool spring and warm summer period. Stream water was continuously pumped into a water-air equilibration chamber, with the equilibrated and actively dried gas phase being measured with two GHG analyzers for CO
              2
              and N
              2
              O and CH
              4
              based on Off-Axis Integrated Cavity Output Spectroscopy (OA-ICOS) and Non-Dispersive Infra-Red (NDIR) spectroscopy, respectively. GHG measurements were performed continuously with only shorter measurement interruptions, mostly following a regular maintenance program. The results showed strong dynamics of GHGs with hourly mean concentrations up to 9959.1, 1478.6, and 9.9 parts per million (ppm) and emissions up to 313.89, 1.17, and 0.40 mg C or N m
              −2
              h
              −1
              for CO
              2
              , CH
              4
              , and N
              2
              O, respectively. Significantly higher GHG concentrations and emissions were observed shortly after intense precipitation events at increasing stream water levels, contributing 59\% to the total GHG budget of 762.2 g m
              −2
              CO
              2
              -equivalents (CO
              2
              -eq). The GHG data indicated a constantly strong terrestrial signal from peatland pore waters, with high concentrations of dissolved GHGs being flushed into the stream water after precipitation. During drier periods, CO
              2
              and CH
              4
              dynamics were strongly influenced by in-stream metabolism. Continuous and high-frequency GHG data are needed to assess short- and long-term dynamics in stream ecosystems and for improved source partitioning between
              in-situ
              and
              ex-situ
              production.},
	urldate = {2024-11-26},
	journal = {Frontiers in Water},
	author = {Piatka, David R. and Nánási, Raphaela L. and Mwanake, Ricky M. and Engelsberger, Florian and Willibald, Georg and Neidl, Frank and Kiese, Ralf},
	month = jan,
	year = {2024},
	pages = {1321137},
}

Stream ecosystems are actively involved in the biogeochemical cycling of carbon (C) and nitrogen (N) from terrestrial and aquatic sources. Streams hydrologically connected to peatland soils are suggested to receive significant quantities of particulate, dissolved, and gaseous C and N species, which directly enhance losses of greenhouse gases (GHGs), i.e., carbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 O), and fuel in-stream GHG production. However, riverine GHG concentrations and emissions are highly dynamic due to temporally and spatially variable hydrological, meteorological, and biogeochemical conditions. In this study, we present a complete GHG monitoring system in a peatland stream, which can continuously measure dissolved GHG concentrations and allows to infer gaseous fluxes between the stream and the atmosphere and discuss the results from March 31 to August 25 at variable hydrological conditions during a cool spring and warm summer period. Stream water was continuously pumped into a water-air equilibration chamber, with the equilibrated and actively dried gas phase being measured with two GHG analyzers for CO 2 and N 2 O and CH 4 based on Off-Axis Integrated Cavity Output Spectroscopy (OA-ICOS) and Non-Dispersive Infra-Red (NDIR) spectroscopy, respectively. GHG measurements were performed continuously with only shorter measurement interruptions, mostly following a regular maintenance program. The results showed strong dynamics of GHGs with hourly mean concentrations up to 9959.1, 1478.6, and 9.9 parts per million (ppm) and emissions up to 313.89, 1.17, and 0.40 mg C or N m −2 h −1 for CO 2 , CH 4 , and N 2 O, respectively. Significantly higher GHG concentrations and emissions were observed shortly after intense precipitation events at increasing stream water levels, contributing 59% to the total GHG budget of 762.2 g m −2 CO 2 -equivalents (CO 2 -eq). The GHG data indicated a constantly strong terrestrial signal from peatland pore waters, with high concentrations of dissolved GHGs being flushed into the stream water after precipitation. During drier periods, CO 2 and CH 4 dynamics were strongly influenced by in-stream metabolism. Continuous and high-frequency GHG data are needed to assess short- and long-term dynamics in stream ecosystems and for improved source partitioning between in-situ and ex-situ production.

@inproceedings{preethi_new_2024,
	address = {Athens, Greece},
	title = {A {New} {Calibration} of {Soil} {Roughness} {Effects} in the {SMOS}-{IC} {Algorithm} for {Soil} {Moisture} and {VOD} {Retrievals}},
	copyright = {https://doi.org/10.15223/policy-029},
	isbn = {979-8-3503-6032-5},
	url = {https://ieeexplore.ieee.org/document/10642708/},
	doi = {10.1109/IGARSS53475.2024.10642708},
	urldate = {2025-02-13},
	booktitle = {{IGARSS} 2024 - 2024 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium}},
	publisher = {IEEE},
	author = {Preethi, Konkathi and Li, Xiaojun and Fernandez-Moran, Roberto and Liu, Xiangzhuo and Xing, Zanpin and Frappart, F. and Piles, Maria and Lanka, Karthikeyan and Wigneron, Jean-Pierre},
	month = jul,
	year = {2024},
	pages = {6701--6704},
}

@article{placzkowska_solute_2024,
	title = {Solute fluxes in headwater catchments with contrasting anthropogenic impact},
	volume = {454},
	issn = {0169555X},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0169555X24001168},
	doi = {10.1016/j.geomorph.2024.109166},
	language = {en},
	urldate = {2024-11-26},
	journal = {Geomorphology},
	author = {Płaczkowska, Eliza and Kijowska-Strugała, Małgorzata and Ketzler, Gunnar and Bogena, Heye Reemt and Leuchner, Michael},
	month = jun,
	year = {2024},
	pages = {109166},
}

@article{placzkowska_solute_2024,
	title = {Solute fluxes in headwater catchments with contrasting anthropogenic impact},
	volume = {454},
	issn = {0169555X},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0169555X24001168},
	doi = {10.1016/j.geomorph.2024.109166},
	language = {en},
	urldate = {2024-11-17},
	journal = {Geomorphology},
	author = {Płaczkowska, Eliza and Kijowska-Strugała, Małgorzata and Ketzler, Gunnar and Bogena, Heye Reemt and Leuchner, Michael},
	month = jun,
	year = {2024},
	pages = {109166},
}

@misc{radtke_nitrate_2024,
	title = {Nitrate and {Water} {Isotopes} as {Tools} to {Resolve} {Nitrate} {Transit} {Times} in a {Mixed} {Land} {Use} {Catchment}},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	url = {https://hess.copernicus.org/preprints/hess-2024-109/},
	doi = {10.5194/hess-2024-109},
	abstract = {Abstract. To understand the transport and fate of nitrate in catchments and its potential hazardous impact on ecosystems, knowledge about transit times (TT) and age of nitrate is needed. To add to that knowledge, we analyzed a 5-year low-frequency dataset followed by a 3-year high-frequency data set of water and nitrate isotopic signatures from a 11.5 km2 headwater catchment with mixed land use within the Northern lowlands of the Harz mountains in Germany. For the first time, a combination of water and nitrate isotope data was used to investigate nitrate age and transport and their relation to water transit times. To do so, the numerical model tran-SAS based on Storage Age Selection (SAS) functions was extended using biogeochemical equations describing nitrate turnover processes to model nitrification and denitrification dynamics along with the age composition of discharge fluxes. The analysis revealed a temporally varying offset between nitrate and water median transit times, with a larger offset at the beginning of wet periods due to higher proportions of young nitrate that is released more quickly with increasing discharge compared to water with larger transit times. Our findings of the varying offset between water and nitrate transit times underline the importance of analyses of solute transport and transformation in the light of projected more frequent hydrological extremes (droughts and floods) under future climate conditions.},
	urldate = {2024-11-26},
	publisher = {Catchment hydrology/Modelling approaches},
	author = {Radtke, Christina Franziska and Yang, Xiaoqiang and Müller, Christin and Rouhiainen, Jarno and Merz, Ralf and Lutz, Stefanie R. and Benettin, Paolo and Wei, Hong and Knöller, Kay},
	month = may,
	year = {2024},
}

Abstract. To understand the transport and fate of nitrate in catchments and its potential hazardous impact on ecosystems, knowledge about transit times (TT) and age of nitrate is needed. To add to that knowledge, we analyzed a 5-year low-frequency dataset followed by a 3-year high-frequency data set of water and nitrate isotopic signatures from a 11.5 km2 headwater catchment with mixed land use within the Northern lowlands of the Harz mountains in Germany. For the first time, a combination of water and nitrate isotope data was used to investigate nitrate age and transport and their relation to water transit times. To do so, the numerical model tran-SAS based on Storage Age Selection (SAS) functions was extended using biogeochemical equations describing nitrate turnover processes to model nitrification and denitrification dynamics along with the age composition of discharge fluxes. The analysis revealed a temporally varying offset between nitrate and water median transit times, with a larger offset at the beginning of wet periods due to higher proportions of young nitrate that is released more quickly with increasing discharge compared to water with larger transit times. Our findings of the varying offset between water and nitrate transit times underline the importance of analyses of solute transport and transformation in the light of projected more frequent hydrological extremes (droughts and floods) under future climate conditions.

@article{rahi_combining_2024,
	title = {Combining signal decomposition and deep learning model to predict noisy runoff coefficient},
	volume = {641},
	issn = {00221694},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0022169424012113},
	doi = {10.1016/j.jhydrol.2024.131815},
	language = {en},
	urldate = {2024-11-26},
	journal = {Journal of Hydrology},
	author = {Rahi, Arash and Rahmati, Mehdi and Dari, Jacopo and Bogena, Heye and Vereecken, Harry and Morbidelli, Renato},
	month = sep,
	year = {2024},
	pages = {131815},
}

@article{rajwa-kuligiewicz_streamflow_2024,
	title = {Streamflow response to catastrophic windthrow and forest recovery in subalpine spruce forest},
	volume = {634},
	issn = {00221694},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0022169424004736},
	doi = {10.1016/j.jhydrol.2024.131078},
	language = {en},
	urldate = {2024-11-17},
	journal = {Journal of Hydrology},
	author = {Rajwa-Kuligiewicz, Agnieszka and Bojarczuk, Anna},
	month = may,
	year = {2024},
	pages = {131078},
}

@article{rasche_depth_2024,
	title = {Depth extrapolation of field-scale soil moisture time series derived with cosmic-ray neutron sensing ({CRNS}) using the soil moisture analytical relationship ({SMAR}) model},
	volume = {10},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2199-398X},
	url = {https://soil.copernicus.org/articles/10/655/2024/},
	doi = {10.5194/soil-10-655-2024},
	abstract = {Abstract. Ground-based soil moisture measurements at the field scale are highly beneficial for different hydrological applications, including the validation of space-borne soil moisture products, landscape water budgeting, or multi-criteria calibration of rainfall–runoff models from field to catchment scale. Cosmic-ray neutron sensing (CRNS) allows for the non-invasive monitoring of field-scale soil moisture across several hectares around the instrument but only for the first few tens of centimeters of the soil. Many of these applications require information on soil water dynamics in deeper soil layers. Simple depth-extrapolation approaches often used in remote sensing may be used to estimate soil moisture in deeper layers based on the near-surface soil moisture information. However, most approaches require a site-specific calibration using depth profiles of in situ soil moisture data, which are often not available. The soil moisture analytical relationship (SMAR) is usually also calibrated to sensor data, but due to the physical meaning of each model parameter, it could be applied without calibration if all its parameters were known. However, its water loss parameter in particular is difficult to estimate. In this paper, we introduce and test a simple modification of the SMAR model to estimate the water loss in the second layer based on soil physical parameters and the surface soil moisture time series. We apply the model with and without calibration at a forest site with sandy soils. Comparing the model results with in situ reference measurements down to depths of 450 cm shows that the SMAR models both with and without modification as well as the calibrated exponential filter approach do not capture the observed soil moisture dynamics well. While, on average, the latter performs best over different tested scenarios, the performance of the SMAR models nevertheless meets a previously used benchmark RMSE of ≤ 0.06 cm3 cm−3 in both the calibrated original and uncalibrated modified version. Different transfer functions to derive surface soil moisture from CRNS do not translate into markedly different results of the depth-extrapolated soil moisture time series simulated by SMAR. Despite the fact that the soil moisture dynamics are not well represented at our study site using the depth-extrapolation approaches, our modified SMAR model may provide valuable first estimates of soil moisture in a deeper soil layer derived from surface measurements based on stationary and roving CRNS as well as remote sensing products where in situ data for calibration are not available.},
	language = {en},
	number = {2},
	urldate = {2024-11-26},
	journal = {SOIL},
	author = {Rasche, Daniel and Blume, Theresa and Güntner, Andreas},
	month = sep,
	year = {2024},
	pages = {655--677},
}

Abstract. Ground-based soil moisture measurements at the field scale are highly beneficial for different hydrological applications, including the validation of space-borne soil moisture products, landscape water budgeting, or multi-criteria calibration of rainfall–runoff models from field to catchment scale. Cosmic-ray neutron sensing (CRNS) allows for the non-invasive monitoring of field-scale soil moisture across several hectares around the instrument but only for the first few tens of centimeters of the soil. Many of these applications require information on soil water dynamics in deeper soil layers. Simple depth-extrapolation approaches often used in remote sensing may be used to estimate soil moisture in deeper layers based on the near-surface soil moisture information. However, most approaches require a site-specific calibration using depth profiles of in situ soil moisture data, which are often not available. The soil moisture analytical relationship (SMAR) is usually also calibrated to sensor data, but due to the physical meaning of each model parameter, it could be applied without calibration if all its parameters were known. However, its water loss parameter in particular is difficult to estimate. In this paper, we introduce and test a simple modification of the SMAR model to estimate the water loss in the second layer based on soil physical parameters and the surface soil moisture time series. We apply the model with and without calibration at a forest site with sandy soils. Comparing the model results with in situ reference measurements down to depths of 450 cm shows that the SMAR models both with and without modification as well as the calibrated exponential filter approach do not capture the observed soil moisture dynamics well. While, on average, the latter performs best over different tested scenarios, the performance of the SMAR models nevertheless meets a previously used benchmark RMSE of ≤ 0.06 cm3 cm−3 in both the calibrated original and uncalibrated modified version. Different transfer functions to derive surface soil moisture from CRNS do not translate into markedly different results of the depth-extrapolated soil moisture time series simulated by SMAR. Despite the fact that the soil moisture dynamics are not well represented at our study site using the depth-extrapolation approaches, our modified SMAR model may provide valuable first estimates of soil moisture in a deeper soil layer derived from surface measurements based on stationary and roving CRNS as well as remote sensing products where in situ data for calibration are not available.

@article{roosch_exploring_2024,
	title = {Exploring the mechanisms of diverging mechanical and water stability in macro‐ and microaggregates},
	volume = {187},
	issn = {1436-8730, 1522-2624},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/jpln.202300245},
	doi = {10.1002/jpln.202300245},
	abstract = {Abstract
            
              Background
              Soil stability is often evaluated using either mechanical or hydraulic stress. The few studies that use both approaches suggest that these two types of stability behave differently.
            
            
              Aims
              Our aim was to explore the mechanisms of aggregate stability regarding mechanical and water stability at the macro‐ and microscale, among other things, the effect of differing pore structure and soil organic matter content.
            
            
              Methods
              Samples were taken from two adjacent plots that were expected to differ in stability due to land use, that is, cropped versus bare fallow (BF). The stability of dry‐separated macroaggregates (8–16 mm) and microaggregates (53–250 µm) was determined via wet sieving and unconfined uniaxial compression tests. To explore the mechanisms of stability, 3D pore characteristics were analyzed with microtomography scans. Furthermore, the contents of carbon and exchangeable polyvalent cations as well as contact angles were determined.
            
            
              Results
              Water stability of macroaggregates was much higher in the cropped plot (geometric mean diameter 0.65–2.37 mm [cropped] vs. 0.31–0.56 mm [BF]), while mechanical stability was very similar (median work 17.3 [cropped] and 17.5 N mm [BF]). The two size fractions behaved similarly regarding both types of stability, with more pronounced differences in macroaggregates. Several soil characteristics, like carbon, exchangeable calcium, and higher connectivity of pores to the aggregate exterior, contributed to water stability. Regarding mechanical stability, the destabilizing effect of lower carbon content and exchangeable calcium in the BF plot was counterbalanced by a lower porosity.
            
            
              Conclusions
              Mechanical and water stability behaved differently in the two plots due to the different deformation mechanisms.},
	language = {en},
	number = {1},
	urldate = {2024-11-15},
	journal = {Journal of Plant Nutrition and Soil Science},
	author = {Roosch, Svenja and Felde, Vincent J. M. N. L. and Uteau, Daniel and Peth, Stephan},
	month = feb,
	year = {2024},
	pages = {104--117},
}

Abstract Background Soil stability is often evaluated using either mechanical or hydraulic stress. The few studies that use both approaches suggest that these two types of stability behave differently. Aims Our aim was to explore the mechanisms of aggregate stability regarding mechanical and water stability at the macro‐ and microscale, among other things, the effect of differing pore structure and soil organic matter content. Methods Samples were taken from two adjacent plots that were expected to differ in stability due to land use, that is, cropped versus bare fallow (BF). The stability of dry‐separated macroaggregates (8–16 mm) and microaggregates (53–250 µm) was determined via wet sieving and unconfined uniaxial compression tests. To explore the mechanisms of stability, 3D pore characteristics were analyzed with microtomography scans. Furthermore, the contents of carbon and exchangeable polyvalent cations as well as contact angles were determined. Results Water stability of macroaggregates was much higher in the cropped plot (geometric mean diameter 0.65–2.37 mm [cropped] vs. 0.31–0.56 mm [BF]), while mechanical stability was very similar (median work 17.3 [cropped] and 17.5 N mm [BF]). The two size fractions behaved similarly regarding both types of stability, with more pronounced differences in macroaggregates. Several soil characteristics, like carbon, exchangeable calcium, and higher connectivity of pores to the aggregate exterior, contributed to water stability. Regarding mechanical stability, the destabilizing effect of lower carbon content and exchangeable calcium in the BF plot was counterbalanced by a lower porosity. Conclusions Mechanical and water stability behaved differently in the two plots due to the different deformation mechanisms.

@article{rupp_influence_2024,
	title = {The influence of increasing mineral fertilizer application on nitrogen leaching of arable land and grassland—results of a long-term lysimeter study},
	volume = {4},
	issn = {2673-8619},
	url = {https://www.frontiersin.org/articles/10.3389/fsoil.2024.1345073/full},
	doi = {10.3389/fsoil.2024.1345073},
	abstract = {Introduction
              Despite various efforts to reduce nitrogen leaching from agricultural land, the permissible nitrate concentrations in groundwater have often been exceeded in the past. Intensive farming is often seen as the cause of the deterioration in water quality. Therefore, the present lysimeter study aimed to quantify nitrogen (N) leaching at different N fertilization levels for the agricultural land use systems of arable land and grassland to derive suitable management measures for improving groundwater quality.
            
            
              Methods
              The effects of three different of mineral fertilization treatments (50\%, 100\%, and 150\%) in arable land and grassland use on four distinct soil types (loamy sand, sand, loam, loess) concerning seepage formation, nitrogen concentrations, nitrogen loads, dry matter yields and nitrogen balances were tested. The study was conducted at the lysimeter facility of the Helmholtz Centre of Environmental Research – UFZ at Falkenberg (northeast Germany). Twenty-four non-weighable lysimeters with a surface area of 1 m² and a depth of 1.25 m were managed as grassland and arable land with three different fertilization treatments since 1985.
            
            
              Results and Discussion
              
                For arable land use, N leaching differed between the studied soil types, with the highest N loads from the sand (36.6 kg ha
                –1
                yr
                –1
                ) and loamy sand (30.7 kg ha
                –1
                yr
                –1
                ) and the lowest N loads from loess (12.1 kg ha
                –1
                yr
                –1
                ) and loam soil (13.1 kg ha
                –1
                yr
                –1
                ). In contrast to grassland use, a reduction of N fertilization level by 50 \% did not result in reduced N leaching for arable land, whereas a maximal 29\% reduced dry matter yields was observed. An increase of N fertilization by 50 \% did not cause significant enhanced N leaching at arable land use. Soil-and management-related factors (soil type, texture, soil tillage, crop rotation, and others) mask the effect of increased N fertilization rates in arable land using lysimeters. For arable land use, a reduction of N fertilizer levels as the only measure was insufficient to reduce NO
                3
                –
                leaching, and other strategies besides N fertilization levels are required to improve groundwater quality. Measures should be targeted to reduce N losses by mineralization processes.},
	urldate = {2024-11-26},
	journal = {Frontiers in Soil Science},
	author = {Rupp, Holger and Tauchnitz, Nadine and Meissner, Ralph},
	month = mar,
	year = {2024},
	pages = {1345073},
}

Introduction Despite various efforts to reduce nitrogen leaching from agricultural land, the permissible nitrate concentrations in groundwater have often been exceeded in the past. Intensive farming is often seen as the cause of the deterioration in water quality. Therefore, the present lysimeter study aimed to quantify nitrogen (N) leaching at different N fertilization levels for the agricultural land use systems of arable land and grassland to derive suitable management measures for improving groundwater quality. Methods The effects of three different of mineral fertilization treatments (50%, 100%, and 150%) in arable land and grassland use on four distinct soil types (loamy sand, sand, loam, loess) concerning seepage formation, nitrogen concentrations, nitrogen loads, dry matter yields and nitrogen balances were tested. The study was conducted at the lysimeter facility of the Helmholtz Centre of Environmental Research – UFZ at Falkenberg (northeast Germany). Twenty-four non-weighable lysimeters with a surface area of 1 m² and a depth of 1.25 m were managed as grassland and arable land with three different fertilization treatments since 1985. Results and Discussion For arable land use, N leaching differed between the studied soil types, with the highest N loads from the sand (36.6 kg ha –1 yr –1 ) and loamy sand (30.7 kg ha –1 yr –1 ) and the lowest N loads from loess (12.1 kg ha –1 yr –1 ) and loam soil (13.1 kg ha –1 yr –1 ). In contrast to grassland use, a reduction of N fertilization level by 50 % did not result in reduced N leaching for arable land, whereas a maximal 29% reduced dry matter yields was observed. An increase of N fertilization by 50 % did not cause significant enhanced N leaching at arable land use. Soil-and management-related factors (soil type, texture, soil tillage, crop rotation, and others) mask the effect of increased N fertilization rates in arable land using lysimeters. For arable land use, a reduction of N fertilizer levels as the only measure was insufficient to reduce NO 3 – leaching, and other strategies besides N fertilization levels are required to improve groundwater quality. Measures should be targeted to reduce N losses by mineralization processes.

@article{schmidt_comprehensive_2024,
	title = {Comprehensive quality assessment of satellite- and model-based soil moisture products against the {COSMOS} network in {Germany}},
	volume = {301},
	issn = {00344257},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0034425723004820},
	doi = {10.1016/j.rse.2023.113930},
	language = {en},
	urldate = {2024-11-26},
	journal = {Remote Sensing of Environment},
	author = {Schmidt, Toni and Schrön, Martin and Li, Zhan and Francke, Till and Zacharias, Steffen and Hildebrandt, Anke and Peng, Jian},
	month = feb,
	year = {2024},
	pages = {113930},
}

@article{schron_buoybased_2024,
	title = {Buoy‐{Based} {Detection} of {Low}‐{Energy} {Cosmic}‐{Ray} {Neutrons} to {Monitor} the {Influence} of {Atmospheric}, {Geomagnetic}, and {Heliospheric} {Effects}},
	volume = {11},
	issn = {2333-5084, 2333-5084},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023EA003483},
	doi = {10.1029/2023EA003483},
	abstract = {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 a bare and a moderated neutron detector in a buoy on a lake in Germany from July 15 to 02 December 2014. We found that the count rate over water has been better predicted by a theory from Köhli et al. (2021,
              https://doi.org/10.3389/frwa.2020.544847
              ) compared to the traditional approach from Desilets et al. (2010,
              https://doi.org/10.1029/2009wr008726
              ). We further found strong linear correlation parameters to air pressure (
              β
               = 0.0077 mb
              −1
              ) and air humidity (
              α
               = 0.0054 m
              3
              /g) for epithermal neutrons, while thermal neutrons responded with
              α
               = 0.0023 m
              3
              /g. Both approaches, from Rosolem et al. (2013,
              https://doi.org/10.1175/jhm‐d‐12‐0120.1
              ) and from Köhli et al. (2021,
              https://doi.org/10.3389/frwa.2020.544847
              ), were similarly able to remove correlations of epithermal neutrons to air humidity. Correction for incoming radiation proved to be necessary for both thermal and epithermal neutrons, for which we tested different neutron monitor stations and correction methods. Here, the approach from Zreda et al. (2012,
              https://doi.org/10.5194/hess‐16‐4079‐2012
              ) worked best with the Jungfraujoch monitor in Switzerland, while the approach from McJannet and Desilets (2023,
              https://doi.org/10.1029/2022wr033889
              ) 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 on 13 September, to which thermal and epithermal neutrons showed a comparatively strong response. The buoy detector experiment provided a unique data set for empirical testing of traditional and new theories on Cosmic‐Ray Neutron Sensing. It could serve as a local alternative to reference data from remote neutron monitors.
            
          , 
            Plain Language Summary
            Cosmic radiation near the Earth's surface is influenced by solar activity, atmospheric conditions, and changes of nearby soil moisture or snow. To better understand how cosmic‐ray neutron measurements should be corrected for meteorological effects, we operated a detector for low‐energy neutrons in a buoy on a lake in Germany for 5 months in 2014. Since the water content in the surroundings is constant, we were able to isolate the signal from almost any ground‐related disturbances. With this instrument, we challenged traditional and recent theories on the neutron response to water, air humidity, and to reference data from high‐energy neutron monitors around the world. We found that in some cases, recent theories showed superior performance over traditional approaches. We also found a stronger response of the neutrons detected by the buoy to a major solar event than was observed by traditional neutron monitors. The concept of a neutron detector on a lake could be useful as a reference station for similar land‐side detectors and help provide more reliable soil moisture products.
          , 
            Key Points
            
              
                
                  Neutron detectors on a buoy were deployed in the center of a lake for 5 months
                
                
                  Thermal and epithermal signals correlated with air pressure, air humidity, and secondary cosmic rays from neutron monitors
                
                
                  Data was used to challenge traditional correction approaches and to serve as an alternative neutron monitor},
	language = {en},
	number = {6},
	urldate = {2024-11-26},
	journal = {Earth and Space Science},
	author = {Schrön, Martin and Rasche, Daniel and Weimar, Jannis and Köhli, Markus and Herbst, Konstantin and Boehrer, Bertram and Hertle, Lasse and Kögler, Simon and Zacharias, Steffen},
	month = jun,
	year = {2024},
	pages = {e2023EA003483},
}

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 a bare and a moderated neutron detector in a buoy on a lake in Germany from July 15 to 02 December 2014. We found that the count rate over water has been better predicted by a theory from Köhli et al. (2021, https://doi.org/10.3389/frwa.2020.544847 ) compared to the traditional approach from Desilets et al. (2010, https://doi.org/10.1029/2009wr008726 ). We further found strong linear correlation parameters to air pressure ( β  = 0.0077 mb −1 ) and air humidity ( α  = 0.0054 m 3 /g) for epithermal neutrons, while thermal neutrons responded with α  = 0.0023 m 3 /g. Both approaches, from Rosolem et al. (2013, https://doi.org/10.1175/jhm‐d‐12‐0120.1 ) and from Köhli et al. (2021, https://doi.org/10.3389/frwa.2020.544847 ), were similarly able to remove correlations of epithermal neutrons to air humidity. Correction for incoming radiation proved to be necessary for both thermal and epithermal neutrons, for which we tested different neutron monitor stations and correction methods. Here, the approach from Zreda et al. (2012, https://doi.org/10.5194/hess‐16‐4079‐2012 ) worked best with the Jungfraujoch monitor in Switzerland, while the approach from McJannet and Desilets (2023, https://doi.org/10.1029/2022wr033889 ) 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 on 13 September, to which thermal and epithermal neutrons showed a comparatively strong response. The buoy detector experiment provided a unique data set for empirical testing of traditional and new theories on Cosmic‐Ray Neutron Sensing. It could serve as a local alternative to reference data from remote neutron monitors. , Plain Language Summary Cosmic radiation near the Earth's surface is influenced by solar activity, atmospheric conditions, and changes of nearby soil moisture or snow. To better understand how cosmic‐ray neutron measurements should be corrected for meteorological effects, we operated a detector for low‐energy neutrons in a buoy on a lake in Germany for 5 months in 2014. Since the water content in the surroundings is constant, we were able to isolate the signal from almost any ground‐related disturbances. With this instrument, we challenged traditional and recent theories on the neutron response to water, air humidity, and to reference data from high‐energy neutron monitors around the world. We found that in some cases, recent theories showed superior performance over traditional approaches. We also found a stronger response of the neutrons detected by the buoy to a major solar event than was observed by traditional neutron monitors. The concept of a neutron detector on a lake could be useful as a reference station for similar land‐side detectors and help provide more reliable soil moisture products. , Key Points Neutron detectors on a buoy were deployed in the center of a lake for 5 months Thermal and epithermal signals correlated with air pressure, air humidity, and secondary cosmic rays from neutron monitors Data was used to challenge traditional correction approaches and to serve as an alternative neutron monitor

@article{schunck_ultra-low_2024,
	title = {Ultra-low esfenvalerate exposure may disrupt interspecific competition},
	volume = {906},
	issn = {00489697},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0048969723060825},
	doi = {10.1016/j.scitotenv.2023.167455},
	language = {en},
	urldate = {2024-11-26},
	journal = {Science of The Total Environment},
	author = {Schunck, Florian and Liess, Matthias},
	month = jan,
	year = {2024},
	pages = {167455},
}

@article{selsam_ecosystem_2024,
	title = {Ecosystem {Integrity} {Remote} {Sensing}—{Modelling} and {Service} {Tool}—{ESIS}/{Imalys}},
	volume = {16},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2072-4292},
	url = {https://www.mdpi.com/2072-4292/16/7/1139},
	doi = {10.3390/rs16071139},
	abstract = {One of the greatest challenges of our time is monitoring the rapid environmental changes taking place worldwide at both local and global scales. This requires easy-to-use and ready-to-implement tools and services to monitor and quantify aspects of bio- and geodiversity change and the impact of land use intensification using freely available and global remotely sensed data, and to derive remotely sensed indicators. Currently, there are no services for quantifying both raster- and vector-based indicators in a “compact tool”. Therefore, the main innovation of ESIS/Imalys is having a remote sensing (RS) tool that allows for RS data processing, data management, and continuous and discrete quantification and derivation of RS indicators in one tool. With the ESIS/Imalys project (Ecosystem Integrity Remote Sensing—Modelling and Service Tool), we try to present environmental indicators on a clearly defined and reproducible basis. The Imalys software library generates the RS indicators and remote sensing products defined for ESIS. This paper provides an overview of the functionality of the Imalys software library. An overview of the technical background of the implementation of the Imalys library, data formats and the user interfaces is given. Examples of RS-based indicators derived using the Imalys tool at pixel level and at zone level (vector level) are presented. Furthermore, the advantages and disadvantages of the Imalys tool are discussed in detail in order to better assess the value of Imalys for users and developers. The applicability of the indicators will be demonstrated through three ecological applications, namely: (1) monitoring landscape diversity, (2) monitoring landscape structure and landscape fragmentation, and (3) monitoring land use intensity and its impact on ecosystem functions. Despite the integration of large amounts of data, Imalys can run on any PC, as the processing and derivation of indicators has been greatly optimised. The Imalys source code is freely available and is hosted and maintained under an open source license. Complete documentation of all methods, functions and derived indicators can be found in the freely available Imalys manual. The user-friendliness of Imalys, despite the integration of a large amount of RS data, makes it another important tool for ecological research, modelling and application for the monitoring and derivation of ecosystem indicators from local to global scale.},
	language = {en},
	number = {7},
	urldate = {2024-11-26},
	journal = {Remote Sensing},
	author = {Selsam, Peter and Bumberger, Jan and Wellmann, Thilo and Pause, Marion and Gey, Ronny and Borg, Erik and Lausch, Angela},
	month = mar,
	year = {2024},
	pages = {1139},
}

One of the greatest challenges of our time is monitoring the rapid environmental changes taking place worldwide at both local and global scales. This requires easy-to-use and ready-to-implement tools and services to monitor and quantify aspects of bio- and geodiversity change and the impact of land use intensification using freely available and global remotely sensed data, and to derive remotely sensed indicators. Currently, there are no services for quantifying both raster- and vector-based indicators in a “compact tool”. Therefore, the main innovation of ESIS/Imalys is having a remote sensing (RS) tool that allows for RS data processing, data management, and continuous and discrete quantification and derivation of RS indicators in one tool. With the ESIS/Imalys project (Ecosystem Integrity Remote Sensing—Modelling and Service Tool), we try to present environmental indicators on a clearly defined and reproducible basis. The Imalys software library generates the RS indicators and remote sensing products defined for ESIS. This paper provides an overview of the functionality of the Imalys software library. An overview of the technical background of the implementation of the Imalys library, data formats and the user interfaces is given. Examples of RS-based indicators derived using the Imalys tool at pixel level and at zone level (vector level) are presented. Furthermore, the advantages and disadvantages of the Imalys tool are discussed in detail in order to better assess the value of Imalys for users and developers. The applicability of the indicators will be demonstrated through three ecological applications, namely: (1) monitoring landscape diversity, (2) monitoring landscape structure and landscape fragmentation, and (3) monitoring land use intensity and its impact on ecosystem functions. Despite the integration of large amounts of data, Imalys can run on any PC, as the processing and derivation of indicators has been greatly optimised. The Imalys source code is freely available and is hosted and maintained under an open source license. Complete documentation of all methods, functions and derived indicators can be found in the freely available Imalys manual. The user-friendliness of Imalys, despite the integration of a large amount of RS data, makes it another important tool for ecological research, modelling and application for the monitoring and derivation of ecosystem indicators from local to global scale.

@article{shan_assimilating_2024,
	title = {Assimilating {ASCAT} normalized backscatter and slope into the land surface model {ISBA}-{A}-gs using a {Deep} {Neural} {Network} as the observation operator: {Case} studies at {ISMN} stations in western {Europe}},
	volume = {308},
	issn = {00344257},
	shorttitle = {Assimilating {ASCAT} normalized backscatter and slope into the land surface model {ISBA}-{A}-gs using a {Deep} {Neural} {Network} as the observation operator},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0034425724001780},
	doi = {10.1016/j.rse.2024.114167},
	language = {en},
	urldate = {2024-11-26},
	journal = {Remote Sensing of Environment},
	author = {Shan, Xu and Steele-Dunne, Susan and Hahn, Sebastian and Wagner, Wolfgang and Bonan, Bertrand and Albergel, Clement and Calvet, Jean-Christophe and Ku, Ou},
	month = jul,
	year = {2024},
	pages = {114167},
}

@article{short_gianotti_local_2024,
	title = {Local and {General} {Patterns} of {Terrestrial} {Water}‐{Carbon} {Coupling}},
	volume = {51},
	issn = {0094-8276, 1944-8007},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024GL109625},
	doi = {10.1029/2024GL109625},
	abstract = {Abstract
            
              Terrestrial carbon uptake and water availability have coupled feedbacks; specifically water uptake for plant growth and soil drying via transpiration. While we might expect this coupling over time at arid sites, climatic water availability also widely covaries geographically with biomass variables that control photosynthetic rates. Using eddy covariance data globally, we find convex, positively‐covarying relations between carbon uptake and a turbulent flux metric controlled by land surface moisture (
              r
               = 0.73 monthly across sites) at the site level. We estimate a general, empirical relationship based on site‐wise water‐carbon dynamics. Most sites, and the general relationship, show strong power‐law dependence, implicating the role of sub‐seasonal land‐cover dynamics. We also find that long‐term mean carbon/water states follow a similar convex relationship to the site‐specific temporal dynamics. We discuss opportunities and caveats for space‐for‐time frameworks of carbon/water feedback processes globally.
            
          , 
            Plain Language Summary
            The amount of carbon dioxide that plants take from the air depends on how plants respond to water and water stress. At the same time, plants also control the loss of water from the landscape through transpiration. While we think of individual stressed plants in dry conditions as being very sensitive to the time‐variability in water available to them, we find here that the average carbon uptake by plants across climate zones is similarly related to climatic water availability. Water availability and carbon uptake rates seem not to depend strongly on location, so that most locations behave quite similarly, across many climates. This can be explained by similar structural changes in the vegetation canopy in response to water stress, whether it be over time at a site or across regions with different mean climate conditions.
          , 
            Key Points
            
              
                
                  Landscape carbon uptake displays strong relationships with landscape evaporative fraction
                
                
                  These relations are non‐linear both at single sites and across bioclimates at eddy covariance tower sites
                
                
                  Changes in structural canopy‐scale variables (e.g., leaf‐area) may dominate the coupling of water and carbon cycles similarly in both space and time},
	language = {en},
	number = {12},
	urldate = {2024-11-26},
	journal = {Geophysical Research Letters},
	author = {Short Gianotti, Daniel J. and Entekhabi, Dara},
	month = jun,
	year = {2024},
	pages = {e2024GL109625},
}

Abstract Terrestrial carbon uptake and water availability have coupled feedbacks; specifically water uptake for plant growth and soil drying via transpiration. While we might expect this coupling over time at arid sites, climatic water availability also widely covaries geographically with biomass variables that control photosynthetic rates. Using eddy covariance data globally, we find convex, positively‐covarying relations between carbon uptake and a turbulent flux metric controlled by land surface moisture ( r  = 0.73 monthly across sites) at the site level. We estimate a general, empirical relationship based on site‐wise water‐carbon dynamics. Most sites, and the general relationship, show strong power‐law dependence, implicating the role of sub‐seasonal land‐cover dynamics. We also find that long‐term mean carbon/water states follow a similar convex relationship to the site‐specific temporal dynamics. We discuss opportunities and caveats for space‐for‐time frameworks of carbon/water feedback processes globally. , Plain Language Summary The amount of carbon dioxide that plants take from the air depends on how plants respond to water and water stress. At the same time, plants also control the loss of water from the landscape through transpiration. While we think of individual stressed plants in dry conditions as being very sensitive to the time‐variability in water available to them, we find here that the average carbon uptake by plants across climate zones is similarly related to climatic water availability. Water availability and carbon uptake rates seem not to depend strongly on location, so that most locations behave quite similarly, across many climates. This can be explained by similar structural changes in the vegetation canopy in response to water stress, whether it be over time at a site or across regions with different mean climate conditions. , Key Points Landscape carbon uptake displays strong relationships with landscape evaporative fraction These relations are non‐linear both at single sites and across bioclimates at eddy covariance tower sites Changes in structural canopy‐scale variables (e.g., leaf‐area) may dominate the coupling of water and carbon cycles similarly in both space and time

@article{siebers_synergistic_2024,
	title = {Synergistic relationships between the age of soil organic matter, {Fe} speciation, and aggregate stability in an arable {Luvisol}},
	volume = {187},
	issn = {1436-8730, 1522-2624},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/jpln.202300020},
	doi = {10.1002/jpln.202300020},
	abstract = {Abstract
            
              Background
              Knowledge of soil aggregate formation and stability is essential, as this is important for maintaining soil functions.
            
            
              Aims
              This study aimed to investigate the influence of organic matter (OM), the content of pedogenic iron (Fe) (oxyhydr)oxides, and aggregate size on the stability of aggregates in arable soil.
            
            
              Methods
              To this end, the Ap and Bt horizons of a Luvisol were sampled after 14 years of bare fallow, and the results were compared with a control field that had been permanently cropped.
            
            
              Results
              
                In the Ap horizon, bare fallow decreased the median diameter of the 53–250 µm size fraction by 26\%. Simultaneously, the mass of the 20–53 µm size fraction increased by 65\%, indicating reduced stability—particularly of larger soil microaggregates—due to the lack of input of fresh OM. The range of
                14
                carbon (
                14
                C) fraction of modern C (F
                14
                C) under bare fallow was between 0.50 and 0.90, and thus lower than the cropped site (F
                14
                C between 0.75 and 1.01), which is particularly pronounced in the smallest size fraction, indicating the presence of older C. This higher stability and the reduced C turnover in {\textless}20 µm aggregates is probably also due to having the highest content of poorly crystalline Fe (oxy)hydroxides, compared to the other size fractions, which act as a cementing agent. Colloid transport from the Ap to the Bt horizon was observed under bare fallow treatment.
              
            
            
              Conclusions
              The lack of input of OM decreased the stability of microaggregates and led to a release of mobile colloids, the transport of which can initiate elemental fluxes with as‐yet unknown environmental consequences.},
	language = {en},
	number = {1},
	urldate = {2024-11-15},
	journal = {Journal of Plant Nutrition and Soil Science},
	author = {Siebers, Nina and Voggenreiter, Eva and Joshi, Prachi and Rethemeyer, Janet and Wang, Liming},
	month = feb,
	year = {2024},
	pages = {77--88},
}

Abstract Background Knowledge of soil aggregate formation and stability is essential, as this is important for maintaining soil functions. Aims This study aimed to investigate the influence of organic matter (OM), the content of pedogenic iron (Fe) (oxyhydr)oxides, and aggregate size on the stability of aggregates in arable soil. Methods To this end, the Ap and Bt horizons of a Luvisol were sampled after 14 years of bare fallow, and the results were compared with a control field that had been permanently cropped. Results In the Ap horizon, bare fallow decreased the median diameter of the 53–250 µm size fraction by 26%. Simultaneously, the mass of the 20–53 µm size fraction increased by 65%, indicating reduced stability—particularly of larger soil microaggregates—due to the lack of input of fresh OM. The range of 14 carbon ( 14 C) fraction of modern C (F 14 C) under bare fallow was between 0.50 and 0.90, and thus lower than the cropped site (F 14 C between 0.75 and 1.01), which is particularly pronounced in the smallest size fraction, indicating the presence of older C. This higher stability and the reduced C turnover in \textless20 µm aggregates is probably also due to having the highest content of poorly crystalline Fe (oxy)hydroxides, compared to the other size fractions, which act as a cementing agent. Colloid transport from the Ap to the Bt horizon was observed under bare fallow treatment. Conclusions The lack of input of OM decreased the stability of microaggregates and led to a release of mobile colloids, the transport of which can initiate elemental fluxes with as‐yet unknown environmental consequences.

@article{sinha_characterizing_2024,
	title = {Characterizing {Satellite} {Soil} {Moisture} {Drydown}: {A} {Bivariate} {Filtering} {Approach}},
	volume = {60},
	issn = {0043-1397, 1944-7973},
	shorttitle = {Characterizing {Satellite} {Soil} {Moisture} {Drydown}},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR034019},
	doi = {10.1029/2022WR034019},
	abstract = {Abstract
            Drying of soil impacts land energy and water balance, influences the sustainability of vegetation growth, and modulates hydrological extremes including floods. While satellite soil moisture data are widely used for a range of environmental applications, systematic differences from regional in‐situ data prevent their optimal use as key physical signatures (such as soil moisture recession, also termed drydown) are represented differently. This study investigates differences in drydowns from the Soil Moisture Active Passive (SMAP) level 4 product with reference to in‐situ observations. A bivariate filtering alternative is proposed to minimize the disparity noted by modeling the relationship between the rate of drying and initial soil wetness and representing the same as in‐situ. Considerable improvements are observed in the resulting SMAP soil moisture filtered estimates. Although the algorithm assumes spatial stationarity, improvements exist across different soil properties and climatic conditions, providing a parsimonious alternative to better capture the dynamics of soil moisture loss.
          , 
            Plain Language Summary
            Soil drying affects the environment by changing how land uses energy and water. It also affects plant growth and can lead to extreme events like floods. Scientists use data from satellites to understand soil moisture, but this data sometimes differs from what's measured directly on the ground. Our study looks at these differences, focusing on how soil dries out, using data from a satellite program called the Soil Moisture Active Passive (SMAP). We suggest a new method to make satellite data closer to what's observed on the ground by adjusting it based on initial soil wetness and drying rates. This new approach showed better results and worked well in different types of soil and weather conditions. It helps us track how soil moisture decreases at ground level more accurately, which is important for understanding and managing our environment.
          , 
            Key Points
            
              
                
                  Coarse‐scale satellite‐derived soil moisture dries faster than in‐situ measurements
                
                
                  We propose a bivariate recursive filtering approach to characterize soil moisture drying rates and initial wetness conditions
                
                
                  The proposed approach is applied to SMAP L4, eliminating systematic bias in drying rates for varied sand fractions and aridity profiles},
	language = {en},
	number = {7},
	urldate = {2025-02-13},
	journal = {Water Resources Research},
	author = {Sinha, Jhilam and Sharma, Ashish and Marshall, Lucy and Kim, Seokhyeon},
	month = jul,
	year = {2024},
	pages = {e2022WR034019},
}

Abstract Drying of soil impacts land energy and water balance, influences the sustainability of vegetation growth, and modulates hydrological extremes including floods. While satellite soil moisture data are widely used for a range of environmental applications, systematic differences from regional in‐situ data prevent their optimal use as key physical signatures (such as soil moisture recession, also termed drydown) are represented differently. This study investigates differences in drydowns from the Soil Moisture Active Passive (SMAP) level 4 product with reference to in‐situ observations. A bivariate filtering alternative is proposed to minimize the disparity noted by modeling the relationship between the rate of drying and initial soil wetness and representing the same as in‐situ. Considerable improvements are observed in the resulting SMAP soil moisture filtered estimates. Although the algorithm assumes spatial stationarity, improvements exist across different soil properties and climatic conditions, providing a parsimonious alternative to better capture the dynamics of soil moisture loss. , Plain Language Summary Soil drying affects the environment by changing how land uses energy and water. It also affects plant growth and can lead to extreme events like floods. Scientists use data from satellites to understand soil moisture, but this data sometimes differs from what's measured directly on the ground. Our study looks at these differences, focusing on how soil dries out, using data from a satellite program called the Soil Moisture Active Passive (SMAP). We suggest a new method to make satellite data closer to what's observed on the ground by adjusting it based on initial soil wetness and drying rates. This new approach showed better results and worked well in different types of soil and weather conditions. It helps us track how soil moisture decreases at ground level more accurately, which is important for understanding and managing our environment. , Key Points Coarse‐scale satellite‐derived soil moisture dries faster than in‐situ measurements We propose a bivariate recursive filtering approach to characterize soil moisture drying rates and initial wetness conditions The proposed approach is applied to SMAP L4, eliminating systematic bias in drying rates for varied sand fractions and aridity profiles

@article{sirota_tracing_2024,
	title = {Tracing rate and extent of human-induced hypoxia during the last 200 years in the mesotrophic lake, {Tiefer} {See} ({NE} {Germany})},
	volume = {21},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1726-4189},
	url = {https://bg.copernicus.org/articles/21/4317/2024/},
	doi = {10.5194/bg-21-4317-2024},
	abstract = {Abstract. The global spread of lake hypoxia, [O2] {\textless} 2 mg L−1, during the last 2 centuries has had a severe impact on ecological systems and sedimentation processes. While the occurrence of hypoxia was observed in many lakes, a detailed quantification of hypoxia spread at centennial timescales remained largely unquantified. We track the evolution of hypoxia and its controls during the past 200 years in a lake, Tiefer See (TSK; NE Germany), using 17 gravity cores recovered from between 10 and 62 m water depth in combination with lake monitoring data. Lake hypoxia was associated with the onset of varve preservation in the TSK and has been dated by varve counting to 1918 ± 1 at 62 m water depth and reached a lake floor depth of 16 m in 1997 ± 1. This indicates that oxygen concentration fell below the threshold for varve preservation at the lake floor ({\textgreater} 16 m). Sediment cores at 10–12 m depth do not contain varves indicating good oxygenation of the upper-water column. Monitoring data show that the threshold for hypoxia, and the intensity and duration of hypoxia which are sufficient for varve preservation, is a period of 5 months of [O2] {\textless} 5 mg L−1 and 2 months of [O2] {\textless} 2 mg L−1. Detailed total organic carbon (TOC), δ13Corg, and X-ray fluorescence (XRF) core scanning analyses of the short cores indicate that the decline in dissolved oxygen (DO) started several decades prior to the varve preservation. This proves a change in the depositional conditions in the lake, following a transition phase of several decades during which varves were not preserved. Furthermore, varve preservation does occur at seasonal stratification and does not necessarily require permanent stratification.},
	language = {en},
	number = {19},
	urldate = {2024-11-26},
	journal = {Biogeosciences},
	author = {Sirota, Ido and Tjallingii, Rik and Pinkerneil, Sylvia and Schroeder, Birgit and Albert, Marlen and Kearney, Rebecca and Heiri, Oliver and Breu, Simona and Brauer, Achim},
	month = oct,
	year = {2024},
	pages = {4317--4339},
}

Abstract. The global spread of lake hypoxia, [O2] \textless 2 mg L−1, during the last 2 centuries has had a severe impact on ecological systems and sedimentation processes. While the occurrence of hypoxia was observed in many lakes, a detailed quantification of hypoxia spread at centennial timescales remained largely unquantified. We track the evolution of hypoxia and its controls during the past 200 years in a lake, Tiefer See (TSK; NE Germany), using 17 gravity cores recovered from between 10 and 62 m water depth in combination with lake monitoring data. Lake hypoxia was associated with the onset of varve preservation in the TSK and has been dated by varve counting to 1918 ± 1 at 62 m water depth and reached a lake floor depth of 16 m in 1997 ± 1. This indicates that oxygen concentration fell below the threshold for varve preservation at the lake floor (\textgreater 16 m). Sediment cores at 10–12 m depth do not contain varves indicating good oxygenation of the upper-water column. Monitoring data show that the threshold for hypoxia, and the intensity and duration of hypoxia which are sufficient for varve preservation, is a period of 5 months of [O2] \textless 5 mg L−1 and 2 months of [O2] \textless 2 mg L−1. Detailed total organic carbon (TOC), δ13Corg, and X-ray fluorescence (XRF) core scanning analyses of the short cores indicate that the decline in dissolved oxygen (DO) started several decades prior to the varve preservation. This proves a change in the depositional conditions in the lake, following a transition phase of several decades during which varves were not preserved. Furthermore, varve preservation does occur at seasonal stratification and does not necessarily require permanent stratification.

@article{slabbert_climate_2024,
	title = {Climate and land use primarily drive the diversity of multi-taxonomic communities in agroecosystems},
	volume = {79},
	issn = {14391791},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1439179124000422},
	doi = {10.1016/j.baae.2024.06.003},
	language = {en},
	urldate = {2024-11-26},
	journal = {Basic and Applied Ecology},
	author = {Slabbert, El and Knight, Tm. and Wubet, T. and Frenzel, M. and Singavarapu, B. and Schweiger, O.},
	month = sep,
	year = {2024},
	pages = {65--73},
}

@article{song_soil_2024,
	title = {Soil moisture estimation based on {FY}-{3E} backscattering data for enhanced daily coverage to {SMAP} observations in the dawn-dusk orbit},
	volume = {309},
	issn = {00344257},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S003442572400227X},
	doi = {10.1016/j.rse.2024.114209},
	language = {en},
	urldate = {2024-11-26},
	journal = {Remote Sensing of Environment},
	author = {Song, Peilin and Liu, Xiangzhuo and Sun, Ling and Zhai, Xiaochun and Wang, Jiao and He, Liang and Wang, Yuanyuan and Zhang, Yongqiang and Li, Guicai},
	month = aug,
	year = {2024},
	pages = {114209},
}

@inproceedings{spinosa_automated_2024,
	address = {Athens, Greece},
	title = {Automated {Gross} {Primary} {Production} {Application} for {Monitoring} {Ecosystem} {Health} {Within} {GEOSS}},
	copyright = {https://doi.org/10.15223/policy-029},
	isbn = {9798350360325},
	url = {https://ieeexplore.ieee.org/document/10642481/},
	doi = {10.1109/IGARSS53475.2024.10642481},
	urldate = {2024-11-26},
	booktitle = {{IGARSS} 2024 - 2024 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium}},
	publisher = {IEEE},
	author = {Spinosa, Anna and Eleveld, Marieke and Mallast, Ulf and Peterseil, Johannes and Mobilia, Valeria and Karisma, Karisma and Fuentes-Monjaraz, Mario Alberto and El Serafy, Ghada},
	month = jul,
	year = {2024},
	pages = {4544--4547},
}

@article{steger_site_2024,
	title = {Site matters - {Canopy} conductance regulation in mature temperate trees diverges at two sites with contrasting soil water availability},
	volume = {345},
	issn = {01681923},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0168192323005403},
	doi = {10.1016/j.agrformet.2023.109850},
	language = {en},
	urldate = {2024-11-26},
	journal = {Agricultural and Forest Meteorology},
	author = {Steger, David N. and Peters, Richard L. and Blume, Theresa and Hurley, Alexander G. and Balanzategui, Daniel and Balting, Daniel F. and Heinrich, Ingo},
	month = feb,
	year = {2024},
	pages = {109850},
}

@article{strebel_evapotranspiration_2024,
	title = {Evapotranspiration prediction for {European} forest sites does not improve with assimilation of in situ soil water content data},
	volume = {28},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1607-7938},
	url = {https://hess.copernicus.org/articles/28/1001/2024/},
	doi = {10.5194/hess-28-1001-2024},
	abstract = {Abstract. Land surface models (LSMs) are an important tool for advancing our knowledge of the Earth system. LSMs are constantly improved to represent the various terrestrial processes in more detail. High-quality data, freely available from various observation networks, are being used to improve the prediction of terrestrial states and fluxes of water and energy. To optimize LSMs with observations, data assimilation methods and tools have been developed in the past decades. We apply the coupled Community Land Model version 5 (CLM5) and Parallel Data Assimilation Framework (PDAF) system (CLM5-PDAF) for 13 forest field sites throughout Europe covering different climate zones. The goal of this study is to assimilate in situ soil moisture measurements into CLM5 to improve the modeled evapotranspiration fluxes. The modeled fluxes will be evaluated using the predicted evapotranspiration fluxes with eddy covariance (EC) systems. Most of the sites use point-scale measurements from sensors placed in the ground; however, for three of the forest sites we use soil water content data from cosmic-ray neutron sensors, which have a measurement scale closer to the typical land surface model grid scale and EC footprint. Our results show that while data assimilation reduced the root-mean-square error for soil water content on average by 56 \% to 64 \%, the root-mean-square error for the evapotranspiration estimation is increased by 4 \%. This finding indicates that only improving the soil water content (SWC) estimation of state-of-the-art LSMs such as CLM5 is not sufficient to improve evapotranspiration estimates for forest sites. To improve evapotranspiration estimates, it is also necessary to consider the representation of leaf area index (LAI) in magnitude and timing, as well as uncertainties in water uptake by roots and vegetation parameters.},
	language = {en},
	number = {4},
	urldate = {2024-11-26},
	journal = {Hydrology and Earth System Sciences},
	author = {Strebel, Lukas and Bogena, Heye and Vereecken, Harry and Andreasen, Mie and Aranda-Barranco, Sergio and Hendricks Franssen, Harrie-Jan},
	month = feb,
	year = {2024},
	pages = {1001--1026},
}

Abstract. Land surface models (LSMs) are an important tool for advancing our knowledge of the Earth system. LSMs are constantly improved to represent the various terrestrial processes in more detail. High-quality data, freely available from various observation networks, are being used to improve the prediction of terrestrial states and fluxes of water and energy. To optimize LSMs with observations, data assimilation methods and tools have been developed in the past decades. We apply the coupled Community Land Model version 5 (CLM5) and Parallel Data Assimilation Framework (PDAF) system (CLM5-PDAF) for 13 forest field sites throughout Europe covering different climate zones. The goal of this study is to assimilate in situ soil moisture measurements into CLM5 to improve the modeled evapotranspiration fluxes. The modeled fluxes will be evaluated using the predicted evapotranspiration fluxes with eddy covariance (EC) systems. Most of the sites use point-scale measurements from sensors placed in the ground; however, for three of the forest sites we use soil water content data from cosmic-ray neutron sensors, which have a measurement scale closer to the typical land surface model grid scale and EC footprint. Our results show that while data assimilation reduced the root-mean-square error for soil water content on average by 56 % to 64 %, the root-mean-square error for the evapotranspiration estimation is increased by 4 %. This finding indicates that only improving the soil water content (SWC) estimation of state-of-the-art LSMs such as CLM5 is not sufficient to improve evapotranspiration estimates for forest sites. To improve evapotranspiration estimates, it is also necessary to consider the representation of leaf area index (LAI) in magnitude and timing, as well as uncertainties in water uptake by roots and vegetation parameters.

@article{sun_global_2024,
	title = {Global evapotranspiration simulation research using a coupled deep learning algorithm with physical mechanisms},
	volume = {73},
	issn = {1531-0353, 1531-0361},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/ird.2942},
	doi = {10.1002/ird.2942},
	abstract = {Abstract
            
              Evapotranspiration (ET) and actual evapotranspiration (AET) serve as critical parameters in the water vapour exchange between terrestrial surfaces and the atmosphere. ET denotes the theoretical maximum evapotranspiration achievable under ideal conditions, whereas AET represents the actual evapotranspiration observed, factoring in the constraints imposed by available water resources. Precise estimation of AET is imperative for the optimization of water resource management and the advancement of sustainable development initiatives. In recent years, deep learning techniques have been extensively utilized in AET estimation. However, traditional deep learning models often lack the incorporation of essential physical constraints. We proceeded to enhance the loss function of the temporal convolutional network (TCN) by taking into account the physical relationships that exist among soil water content (SWC), potential evapotranspiration (PET) and AET, thereby introducing a novel physically coupled deep learning model (AET, SWC after kernel principal component analysis, PET, TCN and AKP‐TCN), and checked the rationality of the model with the FLUXNET 2015 dataset. These findings underscore that the AKP‐TCN model exhibits heightened sensitivity to peak fluctuations in AET under the imposition of physical constraints. This approach notably enhances the precision of AET simulations in areas marked by complex and variable climatic conditions, such as the Mediterranean climate zone and Oceania, achieving determination coefficient (
              R
              2
              ) values surpassing the threshold of 0.900. Compared to traditional models, which include long short‐term memory (LSTM), convolutional neural networks (CNN) and TCN, the AKP‐TCN delivers substantial
              R
              2
              improvements of 16\%, 16\% and 9\%, respectively. This advancement offers a novel perspective for coupling deep learning with physical mechanisms.
            
          , 
            Résumé
            
              L'évapotranspiration (ET) et l'évapotranspiration réelle (AET) sont des paramètres essentiels de l'échange de vapeur d'eau entre les surfaces terrestres et l'atmosphère. L'ET représente l'évapotranspiration maximale théorique réalisable dans des conditions idéales, tandis que l'AET représente l'évapotranspiration réelle observée, en tenant compte de la limitation des ressources en eau disponibles. Une estimation précise de l'AET est essentielle pour optimiser la gestion des ressources en eau et faire progresser les initiatives de développement durable. Ces dernières années, les techniques d'apprentissage profond ont été largement appliquées pour l'estimation de l'AET. Cependant, les modèles traditionnels d'apprentissage profond ne tiennent généralement pas compte des contraintes physiques essentielles. Nous avons augmenté la fonction de perte du réseau convolutif temporel (TCN) en tenant compte des relations physiques qui existent entre la teneur en eau du sol (SWC), l'évapotranspiration potentielle (PET) et l'AET, introduisant ainsi un nouveau modèle d'apprentissage profond couplé physiquement (AET, SWC après l'analyse en composantes principales du noyau; PET, TCN, et AKP‐TCN) et vérifié la rationalité du modèle avec l'ensemble de données FLUXNET 2015. Ces résultats soulignent que le modèle AKP‐TCN présente une plus grande sensibilité aux fluctuations maximales de l'AET lorsque des contraintes physiques sont imposées. La méthode améliore significativement la précision des simulations d'AET dans les régions aux conditions climatiques complexes et variables, telles que la zone climatique méditerranéenne et l'Océanie, avec des valeurs de coefficient de détermination R
              2
              dépassant le seuil de 0,900. Par rapport aux modèles traditionnels qui comprennent la mémoire à long terme (LSTM), les réseaux neuronaux convolutifs (CNN) et les TCN, le R
              2
              de l'AKP‐TCN s'est amélioré de 16\%, 16\% et 9\%, respectivement. Ces progrès offrent une nouvelle perspective sur le couplage de l'apprentissage profond avec les mécanismes physiques.},
	language = {en},
	number = {4},
	urldate = {2024-11-26},
	journal = {Irrigation and Drainage},
	author = {Sun, Yongxi and Dong, Yuru and Chen, Yanfei},
	month = oct,
	year = {2024},
	pages = {1373--1390},
}

Abstract Evapotranspiration (ET) and actual evapotranspiration (AET) serve as critical parameters in the water vapour exchange between terrestrial surfaces and the atmosphere. ET denotes the theoretical maximum evapotranspiration achievable under ideal conditions, whereas AET represents the actual evapotranspiration observed, factoring in the constraints imposed by available water resources. Precise estimation of AET is imperative for the optimization of water resource management and the advancement of sustainable development initiatives. In recent years, deep learning techniques have been extensively utilized in AET estimation. However, traditional deep learning models often lack the incorporation of essential physical constraints. We proceeded to enhance the loss function of the temporal convolutional network (TCN) by taking into account the physical relationships that exist among soil water content (SWC), potential evapotranspiration (PET) and AET, thereby introducing a novel physically coupled deep learning model (AET, SWC after kernel principal component analysis, PET, TCN and AKP‐TCN), and checked the rationality of the model with the FLUXNET 2015 dataset. These findings underscore that the AKP‐TCN model exhibits heightened sensitivity to peak fluctuations in AET under the imposition of physical constraints. This approach notably enhances the precision of AET simulations in areas marked by complex and variable climatic conditions, such as the Mediterranean climate zone and Oceania, achieving determination coefficient ( R 2 ) values surpassing the threshold of 0.900. Compared to traditional models, which include long short‐term memory (LSTM), convolutional neural networks (CNN) and TCN, the AKP‐TCN delivers substantial R 2 improvements of 16%, 16% and 9%, respectively. This advancement offers a novel perspective for coupling deep learning with physical mechanisms. , Résumé L'évapotranspiration (ET) et l'évapotranspiration réelle (AET) sont des paramètres essentiels de l'échange de vapeur d'eau entre les surfaces terrestres et l'atmosphère. L'ET représente l'évapotranspiration maximale théorique réalisable dans des conditions idéales, tandis que l'AET représente l'évapotranspiration réelle observée, en tenant compte de la limitation des ressources en eau disponibles. Une estimation précise de l'AET est essentielle pour optimiser la gestion des ressources en eau et faire progresser les initiatives de développement durable. Ces dernières années, les techniques d'apprentissage profond ont été largement appliquées pour l'estimation de l'AET. Cependant, les modèles traditionnels d'apprentissage profond ne tiennent généralement pas compte des contraintes physiques essentielles. Nous avons augmenté la fonction de perte du réseau convolutif temporel (TCN) en tenant compte des relations physiques qui existent entre la teneur en eau du sol (SWC), l'évapotranspiration potentielle (PET) et l'AET, introduisant ainsi un nouveau modèle d'apprentissage profond couplé physiquement (AET, SWC après l'analyse en composantes principales du noyau; PET, TCN, et AKP‐TCN) et vérifié la rationalité du modèle avec l'ensemble de données FLUXNET 2015. Ces résultats soulignent que le modèle AKP‐TCN présente une plus grande sensibilité aux fluctuations maximales de l'AET lorsque des contraintes physiques sont imposées. La méthode améliore significativement la précision des simulations d'AET dans les régions aux conditions climatiques complexes et variables, telles que la zone climatique méditerranéenne et l'Océanie, avec des valeurs de coefficient de détermination R 2 dépassant le seuil de 0,900. Par rapport aux modèles traditionnels qui comprennent la mémoire à long terme (LSTM), les réseaux neuronaux convolutifs (CNN) et les TCN, le R 2 de l'AKP‐TCN s'est amélioré de 16%, 16% et 9%, respectivement. Ces progrès offrent une nouvelle perspective sur le couplage de l'apprentissage profond avec les mécanismes physiques.

@article{sun_modeling_2024,
	title = {Modeling and prediction of high-precision global evapotranspiration: based on a different model of physical relationships},
	volume = {15},
	issn = {2040-2244, 2408-9354},
	shorttitle = {Modeling and prediction of high-precision global evapotranspiration},
	url = {https://iwaponline.com/jwcc/article/15/5/2532/101814/Modeling-and-prediction-of-high-precision-global},
	doi = {10.2166/wcc.2024.162},
	abstract = {ABSTRACT
            
            The interchange of water vapor between the land and the atmosphere is influenced by actual evapotranspiration (AET). A nonlinear model (AET-SWC-PET-GPP, ASPG) was developed in this study to combine potential evapotranspiration (PET), soil water content (SWC), and gross primary productivity (GPP) in order to quantitatively estimate AET. The Fluxnet Network 2015 global flux station dataset was used to compare the AET models (AET-SWC, AS; AET-SWC-PET, ASP and AET-SWC-PET2, ASP2) with various combinations of influencing factors. The results show that the simulation accuracy of the ASPG model is higher than that of AS, ASP, and ASP2, with improvements in a coefficient of determination (R2) of 45.3, 8.1, and 5.7\%, respectively.The ASPG performed well for various vegetation types, geographical regions, and time scales. It was also discovered that the fitting coefficients vary depending on the type of vegetation, each with its own range of values. The ASPG model put forth in this study can be used to more effectively estimate AET quantitatively on a global scale and can serve as a theoretical foundation for the accurate calculation of global evapotranspiration and the wise use of water resources.},
	language = {en},
	number = {5},
	urldate = {2024-11-26},
	journal = {Journal of Water and Climate Change},
	author = {Sun, Yongxi and He, Chao and Dong, Yuru and Chen, Yanfei},
	month = may,
	year = {2024},
	pages = {2532--2546},
}

ABSTRACT The interchange of water vapor between the land and the atmosphere is influenced by actual evapotranspiration (AET). A nonlinear model (AET-SWC-PET-GPP, ASPG) was developed in this study to combine potential evapotranspiration (PET), soil water content (SWC), and gross primary productivity (GPP) in order to quantitatively estimate AET. The Fluxnet Network 2015 global flux station dataset was used to compare the AET models (AET-SWC, AS; AET-SWC-PET, ASP and AET-SWC-PET2, ASP2) with various combinations of influencing factors. The results show that the simulation accuracy of the ASPG model is higher than that of AS, ASP, and ASP2, with improvements in a coefficient of determination (R2) of 45.3, 8.1, and 5.7%, respectively.The ASPG performed well for various vegetation types, geographical regions, and time scales. It was also discovered that the fitting coefficients vary depending on the type of vegetation, each with its own range of values. The ASPG model put forth in this study can be used to more effectively estimate AET quantitatively on a global scale and can serve as a theoretical foundation for the accurate calculation of global evapotranspiration and the wise use of water resources.

@article{teuling_technical_2024,
	title = {Technical note: {Investigating} the potential for smartphone-based monitoring of evapotranspiration and land surface energy-balance partitioning},
	volume = {28},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1607-7938},
	shorttitle = {Technical note},
	url = {https://hess.copernicus.org/articles/28/3799/2024/},
	doi = {10.5194/hess-28-3799-2024},
	abstract = {Abstract. Evapotranspiration plays a key role in the terrestrial water cycle, climate extremes, and vegetation functioning. However, the understanding of spatio-temporal variability of evapotranspiration is limited by a lack of measurement techniques that are low cost and that can be applied anywhere at any time. Here we investigate the estimation of evapotranspiration and land surface energy-balance partitioning by only using observations made by smartphone sensors. Individual variables known to effect evapotranspiration as measured by smartphone sensors generally showed a high correlation with routine observations during a multiday field test. In combination with a simple multivariate regression model fitted on observed evapotranspiration, the smartphone observations had a mean RMSE of 0.10 and 0.05 mm h−1 during validation against lysimeter and eddy covariance observations, respectively. This is comparable to an error of 0.08 mm h−1 that is associated with estimating the eddy covariance ET from the lysimeter or vice versa. The results suggests that smartphone-based ET monitoring could provide a realistic and low-cost alternative for real-time ET estimation in the field.},
	language = {en},
	number = {16},
	urldate = {2024-11-26},
	journal = {Hydrology and Earth System Sciences},
	author = {Teuling, Adriaan J. and Holthuis, Belle and Lammers, Jasper F. D.},
	month = aug,
	year = {2024},
	pages = {3799--3806},
}

Abstract. Evapotranspiration plays a key role in the terrestrial water cycle, climate extremes, and vegetation functioning. However, the understanding of spatio-temporal variability of evapotranspiration is limited by a lack of measurement techniques that are low cost and that can be applied anywhere at any time. Here we investigate the estimation of evapotranspiration and land surface energy-balance partitioning by only using observations made by smartphone sensors. Individual variables known to effect evapotranspiration as measured by smartphone sensors generally showed a high correlation with routine observations during a multiday field test. In combination with a simple multivariate regression model fitted on observed evapotranspiration, the smartphone observations had a mean RMSE of 0.10 and 0.05 mm h−1 during validation against lysimeter and eddy covariance observations, respectively. This is comparable to an error of 0.08 mm h−1 that is associated with estimating the eddy covariance ET from the lysimeter or vice versa. The results suggests that smartphone-based ET monitoring could provide a realistic and low-cost alternative for real-time ET estimation in the field.

@article{van_der_breggen_influence_2024,
	title = {Influence of atmospheric rivers on extreme rainfall and high streamflow events in northwestern {Europe}: {Rur} ({Roer}) {River} basin},
	volume = {51},
	issn = {22145818},
	shorttitle = {Influence of atmospheric rivers on extreme rainfall and high streamflow events in northwestern {Europe}},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S2214581823003312},
	doi = {10.1016/j.ejrh.2023.101644},
	language = {en},
	urldate = {2024-11-26},
	journal = {Journal of Hydrology: Regional Studies},
	author = {Van Der Breggen, Noah N. and Hudson, Paul F.},
	month = feb,
	year = {2024},
	pages = {101644},
}

@article{vitale_pre-whitening_2024,
	title = {A pre-whitening with block-bootstrap cross-correlation procedure for temporal alignment of data sampled by eddy covariance systems},
	volume = {31},
	issn = {1352-8505, 1573-3009},
	url = {https://link.springer.com/10.1007/s10651-024-00615-9},
	doi = {10.1007/s10651-024-00615-9},
	abstract = {Abstract
            
              The eddy covariance (EC) method is a standard micrometeorological technique for monitoring the exchange rate of the main greenhouse gases across the interface between the atmosphere and ecosystems. One of the first EC data processing steps is the temporal alignment of the raw, high frequency measurements collected by the sonic anemometer and gas analyser. While different methods have been proposed and are currently applied, the application of the EC method to trace gases measurements highlighted the difficulty of a correct time lag detection when the fluxes are small in magnitude. Failure to correctly synchronise the time series entails a systematic error on covariance estimates and can introduce large uncertainties and biases in the calculated fluxes. This work aims at overcoming these issues by introducing a new time lag detection procedure based on the assessment of the cross-correlation function (CCF) between variables subject to (i) a pre-whitening based on autoregressive filters and (ii) a resampling technique based on block-bootstrapping. Combining pre-whitening and block-bootstrapping facilitates the assessment of the CCF, enhancing the accuracy of time lag detection between variables with correlation of low order of magnitude (i.e. lower than
              
                
                  \$\$-1\$\$
                  
                    
                      -
                      1
                    
                  
                
              
              ) and allowing for a proper estimate of the associated uncertainty. We expect the proposed procedure to significantly improve the temporal alignment of the EC time-series measured by two physically separate sensors, and to be particularly beneficial in centralised data processing pipelines of research infrastructures (e.g. the Integrated Carbon Observation System, ICOS-RI) where the use of robust and fully data-driven methods, like the one we propose, constitutes an essential prerequisite.},
	language = {en},
	number = {2},
	urldate = {2024-11-26},
	journal = {Environmental and Ecological Statistics},
	author = {Vitale, Domenico and Fratini, Gerardo and Helfter, Carole and Hortnagl, Lukas and Kohonen, Kukka-Maaria and Mammarella, Ivan and Nemitz, Eiko and Nicolini, Giacomo and Rebmann, Corinna and Sabbatini, Simone and Papale, Dario},
	month = jun,
	year = {2024},
	pages = {219--244},
}

Abstract The eddy covariance (EC) method is a standard micrometeorological technique for monitoring the exchange rate of the main greenhouse gases across the interface between the atmosphere and ecosystems. One of the first EC data processing steps is the temporal alignment of the raw, high frequency measurements collected by the sonic anemometer and gas analyser. While different methods have been proposed and are currently applied, the application of the EC method to trace gases measurements highlighted the difficulty of a correct time lag detection when the fluxes are small in magnitude. Failure to correctly synchronise the time series entails a systematic error on covariance estimates and can introduce large uncertainties and biases in the calculated fluxes. This work aims at overcoming these issues by introducing a new time lag detection procedure based on the assessment of the cross-correlation function (CCF) between variables subject to (i) a pre-whitening based on autoregressive filters and (ii) a resampling technique based on block-bootstrapping. Combining pre-whitening and block-bootstrapping facilitates the assessment of the CCF, enhancing the accuracy of time lag detection between variables with correlation of low order of magnitude (i.e. lower than $$-1$$ - 1 ) and allowing for a proper estimate of the associated uncertainty. We expect the proposed procedure to significantly improve the temporal alignment of the EC time-series measured by two physically separate sensors, and to be particularly beneficial in centralised data processing pipelines of research infrastructures (e.g. the Integrated Carbon Observation System, ICOS-RI) where the use of robust and fully data-driven methods, like the one we propose, constitutes an essential prerequisite.

@article{wagner_global_2024,
	title = {Global {Scale} {Mapping} of {Subsurface} {Scattering} {Signals} {Impacting} {ASCAT} {Soil} {Moisture} {Retrievals}},
	volume = {62},
	copyright = {https://creativecommons.org/licenses/by/4.0/legalcode},
	issn = {0196-2892, 1558-0644},
	url = {https://ieeexplore.ieee.org/document/10601171/},
	doi = {10.1109/TGRS.2024.3429550},
	urldate = {2024-11-26},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	author = {Wagner, Wolfgang and Lindorfer, Roland and Hahn, Sebastian and Kim, Hyunglok and Vreugdenhil, Mariette and Gruber, Alexander and Fischer, Milan and Trnka, Miroslav},
	year = {2024},
	pages = {1--20},
}

@article{wang_all-season_2024,
	title = {All-{Season} {Liquid} {Soil} {Moisture} {Retrieval} {From} {SMAP}},
	volume = {17},
	copyright = {https://creativecommons.org/licenses/by-nc-nd/4.0/},
	issn = {1939-1404, 2151-1535},
	url = {https://ieeexplore.ieee.org/document/10480549/},
	doi = {10.1109/JSTARS.2024.3382315},
	urldate = {2025-02-13},
	journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	author = {Wang, Chi and Yang, Na and Zhao, Tianjie and Xue, Huazhu and Peng, Zhiqing and Zheng, Jingyao and Pan, Jinmei and Yao, Panpan and Gao, Xiaowen and Yan, Hongbo and Song, Peilin and Liou, Yuei-An and Shi, Jiancheng},
	year = {2024},
	pages = {8258--8270},
}

@article{wang_sampling_2024,
	title = {Sampling frequency, load estimation and the disproportionate effect of storms on solute mass flux in rivers},
	volume = {906},
	issn = {00489697},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0048969723060060},
	doi = {10.1016/j.scitotenv.2023.167379},
	language = {en},
	urldate = {2024-11-18},
	journal = {Science of The Total Environment},
	author = {Wang, Jinyu and Bouchez, Julien and Dolant, Antoine and Floury, Paul and Stumpf, Andrew J. and Bauer, Erin and Keefer, Laura and Gaillardet, Jérôme and Kumar, Praveen and Druhan, Jennifer L.},
	month = jan,
	year = {2024},
	pages = {167379},
}

@article{wang_estimates_2024,
	title = {Estimates of the {Priestley}-{Taylor} coefficient based on {FLUXNET} data at multiple spatiotemporal scales},
	volume = {629},
	issn = {00221694},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0022169424000301},
	doi = {10.1016/j.jhydrol.2024.130636},
	language = {en},
	urldate = {2024-11-26},
	journal = {Journal of Hydrology},
	author = {Wang, Junping and Xue, Baolin and Wang, Yuntao and A, Yinglan and Wang, Guoqiang and Long, Di and Huang, Jinhai},
	month = feb,
	year = {2024},
	pages = {130636},
}

@article{wang_soil_2024,
	title = {Soil water content and vapor pressure deficit affect ecosystem water use efficiency through different pathways},
	volume = {640},
	issn = {00221694},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0022169424011284},
	doi = {10.1016/j.jhydrol.2024.131732},
	language = {en},
	urldate = {2024-11-26},
	journal = {Journal of Hydrology},
	author = {Wang, Licheng and Li, Yi and Zhang, Xinchen and Chen, Ke and Siddique, Kadambot H.M.},
	month = aug,
	year = {2024},
	pages = {131732},
}

@article{wang_novel_2024,
	title = {A novel {AMSR2} retrieval algorithm for global {C}-band vegetation optical depth and soil moisture ({AMSR2} {IB}): {Parameters}' calibration, evaluation and inter-comparison},
	volume = {313},
	issn = {00344257},
	shorttitle = {A novel {AMSR2} retrieval algorithm for global {C}-band vegetation optical depth and soil moisture ({AMSR2} {IB})},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0034425724003961},
	doi = {10.1016/j.rse.2024.114370},
	language = {en},
	urldate = {2024-11-26},
	journal = {Remote Sensing of Environment},
	author = {Wang, Mengjia and Ciais, Philippe and Frappart, Frédéric and Tao, Shengli and Fan, Lei and Sun, Rui and Li, Xiaojun and Liu, Xiangzhuo and Wang, Huan and Wigneron, Jean-Pierre},
	month = nov,
	year = {2024},
	pages = {114370},
}

@article{wang_global-scale_2024,
	title = {Global-{Scale} {Assessment} of {Multiple} {Recently} {Developed}/{Reprocessed} {Remotely} {Sensed} {Soil} {Moisture} {Datasets}},
	volume = {62},
	copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
	issn = {0196-2892, 1558-0644},
	url = {https://ieeexplore.ieee.org/document/10419373/},
	doi = {10.1109/TGRS.2024.3361890},
	urldate = {2024-11-26},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	author = {Wang, Panshan and Zeng, Jiangyuan and Chen, Kun-Shan and Ma, Hongliang and Zhang, Xiang and Shi, Pengfei and Peng, Chenchen and Bi, Haiyun},
	year = {2024},
	pages = {1--18},
}

@article{wang_estimating_2024,
	title = {Estimating {Global} {Gross} {Primary} {Production} {Using} an {Improved} {MODIS} {Leaf} {Area} {Index} {Dataset}},
	volume = {16},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2072-4292},
	url = {https://www.mdpi.com/2072-4292/16/19/3731},
	doi = {10.3390/rs16193731},
	abstract = {Remote sensing and process-coupled ecological models are widely used for the simulation of GPP, which plays a key role in estimating and monitoring terrestrial ecosystem productivity. However, most such models do not differentiate the C3 and C4 photosynthetic pathways and neglect the effect of nitrogen content on Vmax and Jmax, leading to considerable bias in the estimation of gross primary productivity (GPP). Here, we developed a model driven by the leaf area index, climate, and atmospheric CO2 concentration to estimate global GPP with a spatial resolution of 0.1° and a temporal interval of 1 day from 2000 to 2022. We validated our model with ground-based GPP measurements at 128 flux tower sites, which yielded an accuracy of 72.3\%. We found that the global GPP ranged from 116.4 PgCyear−1 to 133.94 PgCyear−1 from 2000 to 2022, with an average of 125.93 PgCyear−1. We also found that the global GPP showed an increasing trend of 0.548 PgCyear−1 during the study period. Further analyses using the structure equation model showed that atmospheric CO2 concentration and air temperature were the main drivers of the global GPP changes, total associations of 0.853 and 0.75, respectively, while precipitation represented a minor but negative contribution to global GPP.},
	language = {en},
	number = {19},
	urldate = {2025-02-14},
	journal = {Remote Sensing},
	author = {Wang, Shujian and Zhang, Xunhe and Hou, Lili and Sun, Jiejie and Xu, Ming},
	month = oct,
	year = {2024},
	pages = {3731},
}

Remote sensing and process-coupled ecological models are widely used for the simulation of GPP, which plays a key role in estimating and monitoring terrestrial ecosystem productivity. However, most such models do not differentiate the C3 and C4 photosynthetic pathways and neglect the effect of nitrogen content on Vmax and Jmax, leading to considerable bias in the estimation of gross primary productivity (GPP). Here, we developed a model driven by the leaf area index, climate, and atmospheric CO2 concentration to estimate global GPP with a spatial resolution of 0.1° and a temporal interval of 1 day from 2000 to 2022. We validated our model with ground-based GPP measurements at 128 flux tower sites, which yielded an accuracy of 72.3%. We found that the global GPP ranged from 116.4 PgCyear−1 to 133.94 PgCyear−1 from 2000 to 2022, with an average of 125.93 PgCyear−1. We also found that the global GPP showed an increasing trend of 0.548 PgCyear−1 during the study period. Further analyses using the structure equation model showed that atmospheric CO2 concentration and air temperature were the main drivers of the global GPP changes, total associations of 0.853 and 0.75, respectively, while precipitation represented a minor but negative contribution to global GPP.

@article{wang_increasing_2024,
	title = {Increasing {Optimum} {Temperature} of {Vegetation} {Activity} {Over} the {Past} {Four} {Decades}},
	volume = {12},
	issn = {2328-4277, 2328-4277},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024EF004489},
	doi = {10.1029/2024EF004489},
	abstract = {Abstract
            
              Over the past four decades, global temperatures have increased more rapidly than before, potentially reducing vegetation activity if temperatures exceed the optimum temperature (T
              opt
              ). However, plants have the capacity to acclimate to rising temperatures by adjusting T
              opt
              , thereby maintaining or even enhancing photosynthesis and carbon uptake. Despite this, it remains unclear how T
              opt
              of vegetation activity changes over time and to what extent global vegetation can acclimate to current temperature changes. In this study, we evaluated the temporal trends of T
              opt
              of vegetation activity and the thermal acclimation magnitudes globally using three remote‐sensed vegetation indices and eddy‐covariance observations of gross primary productivity from 1982 to 2020. We found that the global T
              opt
              of vegetation activity has increased at an average rate of 0.63°C per decade over the past four decades. The increase in T
              opt
              closely tracked the rise in annual maximum daily mean temperature (T
              max
              ), indicating that thermal acclimation has occurred widely across the globe. Globally, we found an average thermal acclimation magnitude of 0.38°C per 1°C increase in T
              max
              . Notably, polar and continental regions exhibited the highest thermal acclimation magnitudes, while arid areas showed the lowest. Additionally, the thermal acclimation magnitude was positively affected by interannual temperature variability and negatively affected by soil moisture and vapor pressure deficits. Our findings indicate that terrestrial ecosystems have acclimated to current climate warming trends with varying degrees, suggesting a greater potential for land carbon uptake. Moreover, these results highlight the necessity for earth system models to integrate the thermal acclimation of T
              opt
              to better forecast the global carbon cycle.
            
          , 
            Plain Language Summary
            Global warming affects vegetation growth, and plants may acclimate to temperature changes to enhance their growth and activity. However, how effectively global vegetation can adjust to temperature changes is unclear. To examine this, we analyzed satellite and ground data from 1982 to 2020 to determine changes in the optimum temperature of vegetation activity. We found that global vegetation has acclimated to rising temperatures by increasing the optimum temperatures. Polar and continental vegetation have undergone the greatest acclimation. In addition, ecosystems with humid climates and higher temperature variability have a greater capacity to acclimate to rising temperatures. These findings have important implications for better predicting how warming will affect the global carbon cycle.
          , 
            Key Points
            
              
                
                  
                    The optimum temperature (T
                    opt
                    ) of global vegetation activity has increased rapidly, by 0.63 per decade over 1982–2020
                  
                
                
                  
                    The increase in T
                    opt
                    closely tracked the increase in air temperature, revealing widespread thermal acclimation across global vegetation
                  
                
                
                  Continental and polar ecosystems showed the greatest capacity for acclimation},
	language = {en},
	number = {10},
	urldate = {2024-11-26},
	journal = {Earth's Future},
	author = {Wang, Yiheng and Sarmah, Sangeeta and Singha, Mrinal and Chen, Weinan and Ge, Yong and Liang, Liyin L. and Goswami, Santonu and Niu, Shuli},
	month = oct,
	year = {2024},
	pages = {e2024EF004489},
}

Abstract Over the past four decades, global temperatures have increased more rapidly than before, potentially reducing vegetation activity if temperatures exceed the optimum temperature (T opt ). However, plants have the capacity to acclimate to rising temperatures by adjusting T opt , thereby maintaining or even enhancing photosynthesis and carbon uptake. Despite this, it remains unclear how T opt of vegetation activity changes over time and to what extent global vegetation can acclimate to current temperature changes. In this study, we evaluated the temporal trends of T opt of vegetation activity and the thermal acclimation magnitudes globally using three remote‐sensed vegetation indices and eddy‐covariance observations of gross primary productivity from 1982 to 2020. We found that the global T opt of vegetation activity has increased at an average rate of 0.63°C per decade over the past four decades. The increase in T opt closely tracked the rise in annual maximum daily mean temperature (T max ), indicating that thermal acclimation has occurred widely across the globe. Globally, we found an average thermal acclimation magnitude of 0.38°C per 1°C increase in T max . Notably, polar and continental regions exhibited the highest thermal acclimation magnitudes, while arid areas showed the lowest. Additionally, the thermal acclimation magnitude was positively affected by interannual temperature variability and negatively affected by soil moisture and vapor pressure deficits. Our findings indicate that terrestrial ecosystems have acclimated to current climate warming trends with varying degrees, suggesting a greater potential for land carbon uptake. Moreover, these results highlight the necessity for earth system models to integrate the thermal acclimation of T opt to better forecast the global carbon cycle. , Plain Language Summary Global warming affects vegetation growth, and plants may acclimate to temperature changes to enhance their growth and activity. However, how effectively global vegetation can adjust to temperature changes is unclear. To examine this, we analyzed satellite and ground data from 1982 to 2020 to determine changes in the optimum temperature of vegetation activity. We found that global vegetation has acclimated to rising temperatures by increasing the optimum temperatures. Polar and continental vegetation have undergone the greatest acclimation. In addition, ecosystems with humid climates and higher temperature variability have a greater capacity to acclimate to rising temperatures. These findings have important implications for better predicting how warming will affect the global carbon cycle. , Key Points The optimum temperature (T opt ) of global vegetation activity has increased rapidly, by 0.63 per decade over 1982–2020 The increase in T opt closely tracked the increase in air temperature, revealing widespread thermal acclimation across global vegetation Continental and polar ecosystems showed the greatest capacity for acclimation

@article{wang_increasing_2024,
	title = {Increasing drought sensitivity of plant photosynthetic phenology and physiology},
	volume = {166},
	issn = {1470160X},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1470160X24009269},
	doi = {10.1016/j.ecolind.2024.112469},
	language = {en},
	urldate = {2024-11-26},
	journal = {Ecological Indicators},
	author = {Wang, Yiheng and Tian, Dashuan and Xiao, Jingfeng and Li, Xing and Niu, Shuli},
	month = sep,
	year = {2024},
	pages = {112469},
}

@article{ward_estimating_2024,
	title = {Estimating {Soil} {Organic} {Carbon} using multitemporal {PRISMA} imaging spectroscopy data},
	volume = {450},
	issn = {00167061},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0016706124002544},
	doi = {10.1016/j.geoderma.2024.117025},
	language = {en},
	urldate = {2024-11-26},
	journal = {Geoderma},
	author = {Ward, Kathrin J. and Foerster, Saskia and Chabrillat, Sabine},
	month = oct,
	year = {2024},
	pages = {117025},
}

@article{wei_bridging_2024,
	title = {Bridging spatio-temporal discontinuities in global soil moisture mapping by coupling physics in deep learning},
	volume = {313},
	issn = {00344257},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0034425724003973},
	doi = {10.1016/j.rse.2024.114371},
	language = {en},
	urldate = {2024-11-26},
	journal = {Remote Sensing of Environment},
	author = {Wei, Zushuai and Miao, Linguang and Peng, Jian and Zhao, Tianjie and Meng, Lingkui and Lu, Hui and Peng, Zhiqing and Cosh, Michael H. and Fang, Bin and Lakshmi, Venkat and Shi, Jiancheng},
	month = nov,
	year = {2024},
	pages = {114371},
}

@inproceedings{wilson_prediction_2024,
	address = {Athens, Greece},
	title = {Prediction of {Soil} {Moisture} {From} {Near}-{Global} {Cygnss} {Gnss}-{Reflectometry} {Using} a {Random} {Forest} {Machine} {Learning} {Model}},
	copyright = {https://doi.org/10.15223/policy-029},
	isbn = {979-8-3503-6032-5},
	url = {https://ieeexplore.ieee.org/document/10642723/},
	doi = {10.1109/IGARSS53475.2024.10642723},
	urldate = {2025-02-13},
	booktitle = {{IGARSS} 2024 - 2024 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium}},
	publisher = {IEEE},
	author = {Wilson, M.D. and Datta, R. and Savarimuthu, S. and Moller, D. and Ruf, C.},
	month = jul,
	year = {2024},
	pages = {4465--4471},
}

@article{xing_seasonal-scale_2024,
	title = {Seasonal-scale intercomparison of {SMAP} and fused {SMOS}-{SMAP} soil moisture products},
	volume = {5},
	issn = {2673-6187},
	url = {https://www.frontiersin.org/articles/10.3389/frsen.2024.1440891/full},
	doi = {10.3389/frsen.2024.1440891},
	abstract = {Two L-band passive microwave satellite sensors, onboard the Soil Moisture and Ocean Salinity (SMOS) launched in 2009 and Soil Moisture Active Passive (SMAP) launched in 2015, are specifically designed for surface soil moisture (SM) monitoring. The first global continuous fused L-band satellite SM product based on SMOS and SMAP observations (SMOS-SMAP-INRAE-BORDEAUX, the so-called Fused-IB) was recently released to the public. Currently, the performance of Fused-IB has only been evaluated collectively over the entire data records in the study period, without specific evaluation for individual seasons. To fill this gap, this study intercompared the Fused-IB and the enhanced SMAP-L3 version 6 (SMAP-E) SM products against
              in situ
              SM data from the International Soil Moisture Network (ISMN) from 2016 to 2020 regarding the whole period and different seasons. We aim to investigate the performance of these two products at different time scales and to explore the potential eco-hydrological factors (i.e., precipitation and vegetation) driving their seasonal variations. Results show that both SM products are in good agreement with the
              in situ
              measurements, demonstrating high median correlation (
              R
              ) and low
              ub
              RMSD (median
              R
              = 0.70 and
              ub
              RMSD = 0.058 m
              3
              /m
              3
              for Fused-IB vs.
              R
              = 0.68 and
              ub
              RMSD = 0.059 m
              3
              /m
              3
              for SMAP-E) during 2016–2020. For most land use and land cover (LULC) types, Fused-IB outperformed SMAP-E with higher accuracy and lower errors, particularly in forests, partly due to the advantage of the robust SMAP-IB (SMAP-INRAE-BORDEAUX) algorithm used to generate Fused-IB in forests, which avoids the pronounced saturation effects of vegetation optical depth caused by relying on optical information. Besides, both products had superior performances across most LULC types in summer (JJA) and autumn (SON), yet increased uncertainties were observed in forests, grasslands, and croplands during spring (MAM) and winter (DJF). These uncertainties could be mainly attributed to the effects of vegetation growth in forests, grasslands and croplands, and the interception of water from rainfall events in grasslands. The results of this study can serve as a reference for algorithm developers to enhance the accuracy of SM and thus promote hydro-meteorological applications that benefit from L-band radiometer soil moisture products.},
	urldate = {2025-02-13},
	journal = {Frontiers in Remote Sensing},
	author = {Xing, Zanpin and Li, Xiaojun and Fan, Lei and Frappart, Frédéric and Kim, Hyunglok and Karthikeyan, Lanka and Konkathi, Preethi and Liu, Yuqing and Zhao, Lin and Wigneron, Jean-Pierre},
	month = jul,
	year = {2024},
	pages = {1440891},
}

Two L-band passive microwave satellite sensors, onboard the Soil Moisture and Ocean Salinity (SMOS) launched in 2009 and Soil Moisture Active Passive (SMAP) launched in 2015, are specifically designed for surface soil moisture (SM) monitoring. The first global continuous fused L-band satellite SM product based on SMOS and SMAP observations (SMOS-SMAP-INRAE-BORDEAUX, the so-called Fused-IB) was recently released to the public. Currently, the performance of Fused-IB has only been evaluated collectively over the entire data records in the study period, without specific evaluation for individual seasons. To fill this gap, this study intercompared the Fused-IB and the enhanced SMAP-L3 version 6 (SMAP-E) SM products against in situ SM data from the International Soil Moisture Network (ISMN) from 2016 to 2020 regarding the whole period and different seasons. We aim to investigate the performance of these two products at different time scales and to explore the potential eco-hydrological factors (i.e., precipitation and vegetation) driving their seasonal variations. Results show that both SM products are in good agreement with the in situ measurements, demonstrating high median correlation ( R ) and low ub RMSD (median R = 0.70 and ub RMSD = 0.058 m 3 /m 3 for Fused-IB vs. R = 0.68 and ub RMSD = 0.059 m 3 /m 3 for SMAP-E) during 2016–2020. For most land use and land cover (LULC) types, Fused-IB outperformed SMAP-E with higher accuracy and lower errors, particularly in forests, partly due to the advantage of the robust SMAP-IB (SMAP-INRAE-BORDEAUX) algorithm used to generate Fused-IB in forests, which avoids the pronounced saturation effects of vegetation optical depth caused by relying on optical information. Besides, both products had superior performances across most LULC types in summer (JJA) and autumn (SON), yet increased uncertainties were observed in forests, grasslands, and croplands during spring (MAM) and winter (DJF). These uncertainties could be mainly attributed to the effects of vegetation growth in forests, grasslands and croplands, and the interception of water from rainfall events in grasslands. The results of this study can serve as a reference for algorithm developers to enhance the accuracy of SM and thus promote hydro-meteorological applications that benefit from L-band radiometer soil moisture products.

@article{xiufang_new_2024,
	title = {A new global time-series {GPP} production: {DFRF}-{GPP}},
	volume = {158},
	issn = {1470160X},
	shorttitle = {A new global time-series {GPP} production},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1470160X24000086},
	doi = {10.1016/j.ecolind.2024.111551},
	language = {en},
	urldate = {2024-11-26},
	journal = {Ecological Indicators},
	author = {Xiufang, Zhu and Shizhe, Zhang and Kun, Xu and Rui, Guo and Tingting, Liu},
	month = jan,
	year = {2024},
	pages = {111551},
}

@article{xu_estimating_2024,
	title = {Estimating global annual gross primary production based on satellite-derived phenology and maximal carbon uptake capacity},
	volume = {252},
	issn = {00139351},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S001393512400968X},
	doi = {10.1016/j.envres.2024.119063},
	language = {en},
	urldate = {2024-11-26},
	journal = {Environmental Research},
	author = {Xu, Xiaojun and Chen, Danna},
	month = jul,
	year = {2024},
	pages = {119063},
}

@article{xue_causal_2024,
	title = {Causal inference of root zone soil moisture performance in drought},
	volume = {305},
	issn = {03783774},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0378377424004591},
	doi = {10.1016/j.agwat.2024.109123},
	language = {en},
	urldate = {2025-02-13},
	journal = {Agricultural Water Management},
	author = {Xue, Shouye and Wu, Guocan},
	month = dec,
	year = {2024},
	pages = {109123},
}

@article{yang_asynchrony_2024,
	title = {Asynchrony of the seasonal dynamics of gross primary production and ecosystem respiration},
	volume = {19},
	issn = {1748-9326},
	url = {https://iopscience.iop.org/article/10.1088/1748-9326/ad5d08},
	doi = {10.1088/1748-9326/ad5d08},
	abstract = {Abstract
            The phenological cycles of terrestrial ecosystems have shifted with the changing climate, and the altered timings of biogeochemical fluxes may also exert feedback on the climate system. As regulators of land carbon balance, relative shifts in photosynthetic and respiratory phenology under climate change are of great importance. However, the relative seasonal dynamics of these individual processes and their sensitivity to climate factors as well as the implications for carbon cycling are not well understood. In this study, we examined the relationship in the seasonality of gross primary production (GPP) and ecosystem respiration (RE) as well as their temperature sensitivities and the implications for carbon uptake with around 1500 site-years’ of data from FLUXNET 2015 and Boreal Ecosystem Productivity Simulator (BEPS) at 212 sites. The results showed that RE started earlier in the spring and ended later in the autumn than GPP over most biomes. Furthermore, the flux phenology metrics responded differently to temperature: GPP phenology was more sensitive to changes during the spring temperature than RE phenology, and less sensitive to autumn temperature than RE. We found large BEPS-observation discrepancies in seasonality metrics and their apparent temperature sensitivity. The site-based BEPS projections did not capture the observed seasonal metrics and temperature sensitivities in either GPP or RE seasonality metrics. Improved understanding of the asynchrony of GPP and RE as well as different sensitivity of environmental factors are of great significance for reliable future carbon balance projections.},
	number = {8},
	urldate = {2024-11-26},
	journal = {Environmental Research Letters},
	author = {Yang, Linqing and Noormets, Asko},
	month = aug,
	year = {2024},
	pages = {084049},
}

Abstract The phenological cycles of terrestrial ecosystems have shifted with the changing climate, and the altered timings of biogeochemical fluxes may also exert feedback on the climate system. As regulators of land carbon balance, relative shifts in photosynthetic and respiratory phenology under climate change are of great importance. However, the relative seasonal dynamics of these individual processes and their sensitivity to climate factors as well as the implications for carbon cycling are not well understood. In this study, we examined the relationship in the seasonality of gross primary production (GPP) and ecosystem respiration (RE) as well as their temperature sensitivities and the implications for carbon uptake with around 1500 site-years’ of data from FLUXNET 2015 and Boreal Ecosystem Productivity Simulator (BEPS) at 212 sites. The results showed that RE started earlier in the spring and ended later in the autumn than GPP over most biomes. Furthermore, the flux phenology metrics responded differently to temperature: GPP phenology was more sensitive to changes during the spring temperature than RE phenology, and less sensitive to autumn temperature than RE. We found large BEPS-observation discrepancies in seasonality metrics and their apparent temperature sensitivity. The site-based BEPS projections did not capture the observed seasonal metrics and temperature sensitivities in either GPP or RE seasonality metrics. Improved understanding of the asynchrony of GPP and RE as well as different sensitivity of environmental factors are of great significance for reliable future carbon balance projections.

@article{yankelzon_lysimeter-based_2024,
	title = {Lysimeter-based full fertilizer {15N} balances corroborate direct dinitrogen emission measurements using the {15N} gas flow method},
	issn = {0178-2762, 1432-0789},
	url = {https://link.springer.com/10.1007/s00374-024-01801-4},
	doi = {10.1007/s00374-024-01801-4},
	abstract = {Abstract
            
              The
              15
              N gas flux (
              15
              NGF) method allows for direct in situ quantification of dinitrogen (N
              2
              ) emissions from soils, but a successful cross-comparison with another method is missing. The objectives of this study were to quantify N
              2
              emissions of a wheat rotation using the
              15
              NGF method, to compare these N
              2
              emissions with those obtained from a lysimeter-based
              15
              N fertilizer mass balance approach, and to contextualize N
              2
              emissions with
              15
              N enrichment of N
              2
              in soil air. For four sampling periods, fertilizer-derived N
              2
              losses (
              15
              NGF method) were similar to unaccounted fertilizer N fates as obtained from the
              15
              N mass balance approach. Total N
              2
              emissions (
              15
              NGF method) amounted to 21 ± 3 kg N ha
              − 1
              , with 13 ± 2 kg N ha
              − 1
              (7.5\% of applied fertilizer N) originating from fertilizer. In comparison, the
              15
              N mass balance approach overall indicated fertilizer-derived N
              2
              emissions of 11\%, equivalent to 18 ± 13 kg N ha
              − 1
              . Nitrous oxide (N
              2
              O) emissions were small (0.15���± 0.01 kg N ha
              − 1
              or 0.1\% of fertilizer N), resulting in a large mean N
              2
              :(N
              2
              O + N
              2
              ) ratio of 0.94 ± 0.06. Due to the applied drip fertigation, ammonia emissions accounted for {\textless} 1\% of fertilizer-N, while N leaching was negligible. The temporal variability of N
              2
              emissions was well explained by the δ
              15
              N
              2
              in soil air down to 50 cm depth. We conclude the
              15
              NGF method provides realistic estimates of field N
              2
              emissions and should be more widely used to better understand soil N
              2
              losses. Moreover, combining soil air δ
              15
              N
              2
              measurements with diffusion modeling might be an alternative approach for constraining soil N
              2
              emissions.},
	language = {en},
	urldate = {2024-11-26},
	journal = {Biology and Fertility of Soils},
	author = {Yankelzon, Irina and Schilling, Lexie and Butterbach-Bahl, Klaus and Gasche, Rainer and Han, Jincheng and Hartl, Lorenz and Kepp, Julia and Matson, Amanda and Ostler, Ulrike and Scheer, Clemens and Schneider, Katrin and Tenspolde, Arne and Well, Reinhard and Wolf, Benjamin and Wrage-Moennig, Nicole and Dannenmann, Michael},
	month = feb,
	year = {2024},
}

Abstract The 15 N gas flux ( 15 NGF) method allows for direct in situ quantification of dinitrogen (N 2 ) emissions from soils, but a successful cross-comparison with another method is missing. The objectives of this study were to quantify N 2 emissions of a wheat rotation using the 15 NGF method, to compare these N 2 emissions with those obtained from a lysimeter-based 15 N fertilizer mass balance approach, and to contextualize N 2 emissions with 15 N enrichment of N 2 in soil air. For four sampling periods, fertilizer-derived N 2 losses ( 15 NGF method) were similar to unaccounted fertilizer N fates as obtained from the 15 N mass balance approach. Total N 2 emissions ( 15 NGF method) amounted to 21 ± 3 kg N ha − 1 , with 13 ± 2 kg N ha − 1 (7.5% of applied fertilizer N) originating from fertilizer. In comparison, the 15 N mass balance approach overall indicated fertilizer-derived N 2 emissions of 11%, equivalent to 18 ± 13 kg N ha − 1 . Nitrous oxide (N 2 O) emissions were small (0.15���± 0.01 kg N ha − 1 or 0.1% of fertilizer N), resulting in a large mean N 2 :(N 2 O + N 2 ) ratio of 0.94 ± 0.06. Due to the applied drip fertigation, ammonia emissions accounted for \textless 1% of fertilizer-N, while N leaching was negligible. The temporal variability of N 2 emissions was well explained by the δ 15 N 2 in soil air down to 50 cm depth. We conclude the 15 NGF method provides realistic estimates of field N 2 emissions and should be more widely used to better understand soil N 2 losses. Moreover, combining soil air δ 15 N 2 measurements with diffusion modeling might be an alternative approach for constraining soil N 2 emissions.

@article{yi_responses_2024,
	title = {Responses of {Marginal} and {Intrinsic} {Water}‐{Use} {Efficiency} to {Changing} {Aridity} {Using} {FLUXNET} {Observations}},
	volume = {129},
	issn = {2169-8953, 2169-8961},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JG007875},
	doi = {10.1029/2023JG007875},
	abstract = {Abstract
            
              According to classic stomatal optimization theory, plant stomata are regulated to maximize carbon assimilation for a given water loss. A key component of stomatal optimization models is marginal water‐use efficiency (mWUE), the ratio of the change of transpiration to the change in carbon assimilation. Although the mWUE is often assumed to be constant, variability of mWUE under changing hydrologic conditions has been reported. However, there has yet to be a consensus on the patterns of mWUE variabilities and their relations with atmospheric aridity. We investigate the dynamics of mWUE in response to vapor pressure deficit (VPD) and aridity index using carbon and water fluxes from 115 eddy covariance towers available from the global database FLUXNET. We demonstrate a non‐linear mWUE‐VPD relationship at a sub‐daily scale in general; mWUE varies substantially at both low and high VPD levels. However, mWUE remains relatively constant within the mid‐range of VPD. Despite the highly non‐linear relationship between mWUE and VPD, the relationship can be informed by the strong linear relationship between ecosystem‐level inherent water‐use efficiency (IWUE) and mWUE using the slope,
              m
              *. We further identify site‐specific
              m
              * and its variability with changing site‐level aridity across six vegetation types. We suggest accurately representing the relationship between IWUE and VPD using Michaelis–Menten or quadratic functions to ensure precise estimation of mWUE variability for individual sites.
            
          , 
            Plain Language Summary
            Plants use diverse strategies for water utilization during growth. Marginal water‐use efficiency (mWUE) quantifies how effectively plants gain carbon relative to the water they lose through their leaves. A scientific debate exists regarding how mWUE responds to dry conditions. To investigate this, we analyze data from various vegetation types worldwide, observing changes in mWUE under dry conditions. Contrary to common assumptions, mWUE is not a constant; it varies substantially based on moisture levels. Additionally, we show that a simpler measure called inherent water‐use efficiency (IWUE) can help explain this complicated relationship, which is useful for predicting plant growth under different moisture conditions.
          , 
            Key Points
            
              
                
                  The relationship between marginal water‐use efficiency (mWUE) and vapor pressure deficit (VPD) is highly non‐linear at a sub‐daily scale in general
                
                
                  Despite the highly non‐linear relationship between mWUE and VPD, the relationship can be informed by simpler inherent water‐use efficiency (IWUE)
                
                
                  We identify the site‐specific relationship between mWUE and IWUE and its variability with changing aridity across six vegetation types},
	language = {en},
	number = {6},
	urldate = {2024-11-26},
	journal = {Journal of Geophysical Research: Biogeosciences},
	author = {Yi, Koong and Novick, Kimberly A. and Zhang, Quan and Wang, Lixin and Hwang, Taehee and Yang, Xi and Mallick, Kanishka and Béland, Martin and Senay, Gabriel B. and Baldocchi, Dennis D.},
	month = jun,
	year = {2024},
	pages = {e2023JG007875},
}

Abstract According to classic stomatal optimization theory, plant stomata are regulated to maximize carbon assimilation for a given water loss. A key component of stomatal optimization models is marginal water‐use efficiency (mWUE), the ratio of the change of transpiration to the change in carbon assimilation. Although the mWUE is often assumed to be constant, variability of mWUE under changing hydrologic conditions has been reported. However, there has yet to be a consensus on the patterns of mWUE variabilities and their relations with atmospheric aridity. We investigate the dynamics of mWUE in response to vapor pressure deficit (VPD) and aridity index using carbon and water fluxes from 115 eddy covariance towers available from the global database FLUXNET. We demonstrate a non‐linear mWUE‐VPD relationship at a sub‐daily scale in general; mWUE varies substantially at both low and high VPD levels. However, mWUE remains relatively constant within the mid‐range of VPD. Despite the highly non‐linear relationship between mWUE and VPD, the relationship can be informed by the strong linear relationship between ecosystem‐level inherent water‐use efficiency (IWUE) and mWUE using the slope, m *. We further identify site‐specific m * and its variability with changing site‐level aridity across six vegetation types. We suggest accurately representing the relationship between IWUE and VPD using Michaelis–Menten or quadratic functions to ensure precise estimation of mWUE variability for individual sites. , Plain Language Summary Plants use diverse strategies for water utilization during growth. Marginal water‐use efficiency (mWUE) quantifies how effectively plants gain carbon relative to the water they lose through their leaves. A scientific debate exists regarding how mWUE responds to dry conditions. To investigate this, we analyze data from various vegetation types worldwide, observing changes in mWUE under dry conditions. Contrary to common assumptions, mWUE is not a constant; it varies substantially based on moisture levels. Additionally, we show that a simpler measure called inherent water‐use efficiency (IWUE) can help explain this complicated relationship, which is useful for predicting plant growth under different moisture conditions. , Key Points The relationship between marginal water‐use efficiency (mWUE) and vapor pressure deficit (VPD) is highly non‐linear at a sub‐daily scale in general Despite the highly non‐linear relationship between mWUE and VPD, the relationship can be informed by simpler inherent water‐use efficiency (IWUE) We identify the site‐specific relationship between mWUE and IWUE and its variability with changing aridity across six vegetation types

@article{yu_quantifying_2024,
	title = {Quantifying albedo impact and radiative forcing of management practices in {European} wheat cropping systems},
	volume = {19},
	issn = {1748-9326},
	url = {https://iopscience.iop.org/article/10.1088/1748-9326/ad5859},
	doi = {10.1088/1748-9326/ad5859},
	abstract = {Abstract
            
              Management practices that increase the surface albedo of cultivated land could mitigate climate change, with similar effectiveness to practices that reduce greenhouse gas emissions or favor natural CO
              2
              sequestration. Yet, the efficiency of such practices is barely quantified. In this study, we quantified the impacts of seven different management practices on the surface albedo of winter wheat fields (nitrogen fertilizer, herbicide, fungicide, sowing, harvest, tillage, and crop residues) by analyzing observed daily albedo dynamics from eight European flux-tower sites with interpretable machine learning. We found that management practices have significant influences on surface albedo dynamics compared with climate and soil conditions. The nitrogen fertilizer application has the largest effect among the seven practices as it increases surface albedo by 0.015 ± 0.004 during the first two months after application, corresponding to a radiative forcing of −4.39 ± 1.22 W m
              −2
              . Herbicide induces a modest albedo decrease of 0.005 ± 0.002 over 150 d after application by killing weeds in the fallow period only, resulting in a magnitude of radiative forcing of 1.33 ± 1.06 W m
              −2
              which is higher than radiative forcing of other practices in the same period. The substantial temporal evolution of the albedo impacts of management practices increases uncertainties in the estimated albedo-mediated climate impacts of management practices. Although these albedo effects are smaller than published estimates of the greenhouse gas-mediated biogeochemical practices, they are nevertheless significant and should thus be accounted for in climate impact assessments.},
	number = {7},
	urldate = {2025-02-14},
	journal = {Environmental Research Letters},
	author = {Yu, Ke and Su, Yang and Ciais, Philippe and Lauerwald, Ronny and Ceschia, Eric and Makowski, David and Xu, Yidi and Abbessi, Ezzeddine and Bazzi, Hassan and Tallec, Tiphaine and Brut, Aurore and Heinesch, Bernard and Brümmer, Christian and Schmidt, Marius and Acosta, Manuel and Buysse, Pauline and Gruenwald, Thomas and Goll, Daniel S},
	month = jul,
	year = {2024},
	pages = {074042},
}

Abstract Management practices that increase the surface albedo of cultivated land could mitigate climate change, with similar effectiveness to practices that reduce greenhouse gas emissions or favor natural CO 2 sequestration. Yet, the efficiency of such practices is barely quantified. In this study, we quantified the impacts of seven different management practices on the surface albedo of winter wheat fields (nitrogen fertilizer, herbicide, fungicide, sowing, harvest, tillage, and crop residues) by analyzing observed daily albedo dynamics from eight European flux-tower sites with interpretable machine learning. We found that management practices have significant influences on surface albedo dynamics compared with climate and soil conditions. The nitrogen fertilizer application has the largest effect among the seven practices as it increases surface albedo by 0.015 ± 0.004 during the first two months after application, corresponding to a radiative forcing of −4.39 ± 1.22 W m −2 . Herbicide induces a modest albedo decrease of 0.005 ± 0.002 over 150 d after application by killing weeds in the fallow period only, resulting in a magnitude of radiative forcing of 1.33 ± 1.06 W m −2 which is higher than radiative forcing of other practices in the same period. The substantial temporal evolution of the albedo impacts of management practices increases uncertainties in the estimated albedo-mediated climate impacts of management practices. Although these albedo effects are smaller than published estimates of the greenhouse gas-mediated biogeochemical practices, they are nevertheless significant and should thus be accounted for in climate impact assessments.

@article{zacharias_fifteen_2024,
	title = {Fifteen {Years} of {Integrated} {Terrestrial} {Environmental} {Observatories} ({TERENO}) in {Germany}: {Functions}, {Services}, and {Lessons} {Learned}},
	volume = {12},
	issn = {2328-4277, 2328-4277},
	shorttitle = {Fifteen {Years} of {Integrated} {Terrestrial} {Environmental} {Observatories} ({TERENO}) in {Germany}},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024EF004510},
	doi = {10.1029/2024EF004510},
	abstract = {Abstract
            The need to develop and provide integrated observation systems to better understand and manage global and regional environmental change is one of the major challenges facing Earth system science today. In 2008, the German Helmholtz Association took up this challenge and launched the German research infrastructure TERrestrial ENvironmental Observatories (TERENO). The aim of TERENO is the establishment and maintenance of a network of observatories as a basis for an interdisciplinary and long‐term research program to investigate the effects of global environmental change on terrestrial ecosystems and their socio‐economic consequences. State‐of‐the‐art methods from the field of environmental monitoring, geophysics, remote sensing, and modeling are used to record and analyze states and fluxes in different environmental disciplines from groundwater through the vadose zone, surface water, and biosphere, up to the lower atmosphere. Over the past 15 years we have collectively gained experience in operating a long‐term observing network, thereby overcoming unexpected operational and institutional challenges, exceeding expectations, and facilitating new research. Today, the TERENO network is a key pillar for environmental modeling and forecasting in Germany, an information hub for practitioners and policy stakeholders in agriculture, forestry, and water management at regional to national levels, a nucleus for international collaboration, academic training and scientific outreach, an important anchor for large‐scale experiments, and a trigger for methodological innovation and technological progress. This article describes TERENO's key services and functions, presents the main lessons learned from this 15‐year effort, and emphasizes the need to continue long‐term integrated environmental monitoring programmes in the future.
          , 
            Plain Language Summary
            This paper discusses the importance of creating comprehensive environmental observation systems to better understand and address global and regional environmental changes. In 2008, a German research infrastructure named Terrestrial Environmental Observatories (TERENO) was established to build and maintain a network of observatories. The goal is to conduct interdisciplinary, long‐term research on the impacts of global environmental changes on terrestrial ecosystems and their socio‐economic effects. The TERENO network employs advanced methods from environmental monitoring, geophysics, remote sensing, and modeling to study various environmental aspects. Over the past 15 years, four observatories have been part of this network, contributing to valuable experience in overcoming challenges and exceeding expectations. Today, TERENO is a crucial component for environmental modeling and forecasting in Germany, serving as an information hub for practitioners and policymakers. It also fosters international collaboration, supports large‐scale experiments, and drives methodological and technological advancements. The article highlights key lessons learned from this 15‐year effort and emphasizes the importance of continuing such integrated environmental monitoring programs in the future.
          , 
            Key Points
            
              
                
                  Integrated observatories ensure a holistic Earth Systems perspective, offering data for current and future ecological challenges
                
                
                  The scientific and societal value of observatories is invaluable, but their design, construction and operation require considerable effort
                
                
                  For assured long‐term data collection, research infrastructure must have flexible design for adapting to changing research needs},
	language = {en},
	number = {6},
	urldate = {2024-11-26},
	journal = {Earth's Future},
	author = {Zacharias, Steffen and Loescher, Henry W. and Bogena, Heye and Kiese, Ralf and Schrön, Martin and Attinger, Sabine and Blume, Theresa and Borchardt, Dietrich and Borg, Erik and Bumberger, Jan and Chwala, Christian and Dietrich, Peter and Fersch, Benjamin and Frenzel, Mark and Gaillardet, Jérôme and Groh, Jannis and Hajnsek, Irena and Itzerott, Sibylle and Kunkel, Ralf and Kunstmann, Harald and Kunz, Matthias and Liebner, Susanne and Mirtl, Michael and Montzka, Carsten and Musolff, Andreas and Pütz, Thomas and Rebmann, Corinna and Rinke, Karsten and Rode, Michael and Sachs, Torsten and Samaniego, Luis and Schmid, Hans Peter and Vogel, Hans‐Jörg and Weber, Ute and Wollschläger, Ute and Vereecken, Harry},
	month = jun,
	year = {2024},
	pages = {e2024EF004510},
}

Abstract The need to develop and provide integrated observation systems to better understand and manage global and regional environmental change is one of the major challenges facing Earth system science today. In 2008, the German Helmholtz Association took up this challenge and launched the German research infrastructure TERrestrial ENvironmental Observatories (TERENO). The aim of TERENO is the establishment and maintenance of a network of observatories as a basis for an interdisciplinary and long‐term research program to investigate the effects of global environmental change on terrestrial ecosystems and their socio‐economic consequences. State‐of‐the‐art methods from the field of environmental monitoring, geophysics, remote sensing, and modeling are used to record and analyze states and fluxes in different environmental disciplines from groundwater through the vadose zone, surface water, and biosphere, up to the lower atmosphere. Over the past 15 years we have collectively gained experience in operating a long‐term observing network, thereby overcoming unexpected operational and institutional challenges, exceeding expectations, and facilitating new research. Today, the TERENO network is a key pillar for environmental modeling and forecasting in Germany, an information hub for practitioners and policy stakeholders in agriculture, forestry, and water management at regional to national levels, a nucleus for international collaboration, academic training and scientific outreach, an important anchor for large‐scale experiments, and a trigger for methodological innovation and technological progress. This article describes TERENO's key services and functions, presents the main lessons learned from this 15‐year effort, and emphasizes the need to continue long‐term integrated environmental monitoring programmes in the future. , Plain Language Summary This paper discusses the importance of creating comprehensive environmental observation systems to better understand and address global and regional environmental changes. In 2008, a German research infrastructure named Terrestrial Environmental Observatories (TERENO) was established to build and maintain a network of observatories. The goal is to conduct interdisciplinary, long‐term research on the impacts of global environmental changes on terrestrial ecosystems and their socio‐economic effects. The TERENO network employs advanced methods from environmental monitoring, geophysics, remote sensing, and modeling to study various environmental aspects. Over the past 15 years, four observatories have been part of this network, contributing to valuable experience in overcoming challenges and exceeding expectations. Today, TERENO is a crucial component for environmental modeling and forecasting in Germany, serving as an information hub for practitioners and policymakers. It also fosters international collaboration, supports large‐scale experiments, and drives methodological and technological advancements. The article highlights key lessons learned from this 15‐year effort and emphasizes the importance of continuing such integrated environmental monitoring programs in the future. , Key Points Integrated observatories ensure a holistic Earth Systems perspective, offering data for current and future ecological challenges The scientific and societal value of observatories is invaluable, but their design, construction and operation require considerable effort For assured long‐term data collection, research infrastructure must have flexible design for adapting to changing research needs

@article{zahorec_observation_2024,
	title = {Observation and {Local} {Prediction} of the {Vertical} {Gravity} {Gradient}: {Review} {Paper}},
	volume = {27},
	copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
	issn = {1094-6969, 1941-0123},
	shorttitle = {Observation and {Local} {Prediction} of the {Vertical} {Gravity} {Gradient}},
	url = {https://ieeexplore.ieee.org/document/10654722/},
	doi = {10.1109/MIM.2024.10654722},
	number = {6},
	urldate = {2024-11-21},
	journal = {IEEE Instrumentation \& Measurement Magazine},
	author = {Zahorec, Pavol and Papčo, Juraj and Greco, Filippo and Vajda, Peter and Pašteka, Roman and Cantarero, Massimo and Carbone, Daniele},
	month = sep,
	year = {2024},
	pages = {11--16},
}

@inproceedings{zhang_gap_2024,
	address = {Athens, Greece},
	title = {{GAP} {Filling} of {SMAP} {Soil} {Moisture} {Products} {Using} {Different} {Approaches}},
	copyright = {https://doi.org/10.15223/policy-029},
	isbn = {979-8-3503-6032-5},
	url = {https://ieeexplore.ieee.org/document/10640723/},
	doi = {10.1109/IGARSS53475.2024.10640723},
	urldate = {2025-02-13},
	booktitle = {{IGARSS} 2024 - 2024 {IEEE} {International} {Geoscience} and {Remote} {Sensing} {Symposium}},
	publisher = {IEEE},
	author = {Zhang, Chunlin and Zeng, Jiangyuan and Chen, Kun-Shan and Ma, Hongliang and Shi, Pengfei and Wang, Panshan and Bi, Haiyun and Chen, Quan and Letu, Husi},
	month = jul,
	year = {2024},
	pages = {5052--5055},
}

@article{zhang_new_2024,
	title = {A {New} {Post}‐{Hoc} {Method} to {Reduce} the {Energy} {Imbalance} in {Eddy} {Covariance} {Measurements}},
	volume = {51},
	issn = {0094-8276, 1944-8007},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023GL107084},
	doi = {10.1029/2023GL107084},
	abstract = {Abstract
            
              Latent and sensible heat flux observations are essential for understanding land–atmosphere interactions. Measurements from the eddy covariance technique are widely used but suffer from systematic energy imbalance problems, partly due to missing large eddies from sub‐mesoscale transport. Because available energy drives the development of large eddies, we propose an available energy based correction method for turbulent flux measurements. We apply our method to 172 flux tower sites and show that we can reduce the energy imbalance from −14.99 to −0.65 W m
              −2
              on average, together with improved consistency between turbulent fluxes and available energy and associated increases in
              r
              
                2
              
              at individual sites and across networks. Our results suggest that our method is conceptually and empirically preferable over the method implemented in the ONEFlux processing. This can contribute to the efforts in understanding and addressing the energy imbalance issue, which is crucial for the evaluation and calibration of land surface models.
            
          , 
            Plain Language Summary
            Eddy covariance measurements are key to understanding the exchange of energy and water between the Earth's surface and the atmosphere, which helps us validate Earth system models that predict how the land interacts with the atmosphere. However, these measurements often show an energy imbalance problem, meaning that the measured turbulent energy does not fully account for all the energy entering the system. For two decades, scientists have been using advanced simulations and multi‐tower measurements to find out why this happens, and have found that the movements of airflow in a horizontal direction play a large role. Taking this knowledge into account, we propose a simple, data‐driven method to make these measurements more accurate. This new approach reduces the error not just at one eddy covariance site, but at multiple sites around the globe, and it's also effective at reflecting the energy changes that occur with daily weather events like rain.
          , 
            Key Points
            
              
                
                  Observed systematic imbalance of energy flux (∼17\%) across the network of eddy covariance sites
                
                
                  A theoretically motivated correction method based on available energy variations is proposed
                
                
                  The available energy correction method has conceptual and empirical advantages compared to the method implemented in the ONEFlux pipeline},
	language = {en},
	number = {2},
	urldate = {2024-11-26},
	journal = {Geophysical Research Letters},
	author = {Zhang, Weijie and Nelson, Jacob A. and Miralles, Diego G. and Mauder, Matthias and Migliavacca, Mirco and Poyatos, Rafael and Reichstein, Markus and Jung, Martin},
	month = jan,
	year = {2024},
	pages = {e2023GL107084},
}

Abstract Latent and sensible heat flux observations are essential for understanding land–atmosphere interactions. Measurements from the eddy covariance technique are widely used but suffer from systematic energy imbalance problems, partly due to missing large eddies from sub‐mesoscale transport. Because available energy drives the development of large eddies, we propose an available energy based correction method for turbulent flux measurements. We apply our method to 172 flux tower sites and show that we can reduce the energy imbalance from −14.99 to −0.65 W m −2 on average, together with improved consistency between turbulent fluxes and available energy and associated increases in r 2 at individual sites and across networks. Our results suggest that our method is conceptually and empirically preferable over the method implemented in the ONEFlux processing. This can contribute to the efforts in understanding and addressing the energy imbalance issue, which is crucial for the evaluation and calibration of land surface models. , Plain Language Summary Eddy covariance measurements are key to understanding the exchange of energy and water between the Earth's surface and the atmosphere, which helps us validate Earth system models that predict how the land interacts with the atmosphere. However, these measurements often show an energy imbalance problem, meaning that the measured turbulent energy does not fully account for all the energy entering the system. For two decades, scientists have been using advanced simulations and multi‐tower measurements to find out why this happens, and have found that the movements of airflow in a horizontal direction play a large role. Taking this knowledge into account, we propose a simple, data‐driven method to make these measurements more accurate. This new approach reduces the error not just at one eddy covariance site, but at multiple sites around the globe, and it's also effective at reflecting the energy changes that occur with daily weather events like rain. , Key Points Observed systematic imbalance of energy flux (∼17%) across the network of eddy covariance sites A theoretically motivated correction method based on available energy variations is proposed The available energy correction method has conceptual and empirical advantages compared to the method implemented in the ONEFlux pipeline

@article{zhao_comparative_2024,
	title = {A {Comparative} {Analysis} of {Remote} {Sensing} {Soil} {Moisture} {Datasets} {Fusion} {Methods}: {Novel} {LSTM} {Approach} {Versus} {Widely} {Used} {Triple} {Collocation} {Technique}},
	volume = {17},
	copyright = {https://creativecommons.org/licenses/by-nc-nd/4.0/},
	issn = {1939-1404, 2151-1535},
	shorttitle = {A {Comparative} {Analysis} of {Remote} {Sensing} {Soil} {Moisture} {Datasets} {Fusion} {Methods}},
	url = {https://ieeexplore.ieee.org/document/10669050/},
	doi = {10.1109/JSTARS.2024.3455549},
	urldate = {2024-11-26},
	journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	author = {Zhao, Haojin and Montzka, Carsten and Vereecken, Harry and Franssen, Harrie-Jan Hendricks},
	year = {2024},
	pages = {16659--16671},
}

@article{zheng_evaluation_2024,
	title = {Evaluation of reanalysis soil moisture products using cosmic ray neutron sensor observations across the globe},
	volume = {28},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1607-7938},
	url = {https://hess.copernicus.org/articles/28/1999/2024/},
	doi = {10.5194/hess-28-1999-2024},
	abstract = {Abstract. Reanalysis soil moisture products are valuable for diverse applications, but their quality assessment is limited due to scale discrepancies when compared to traditional in situ point-scale measurements. The emergence of cosmic ray neutron sensors (CRNSs) with field-scale soil moisture estimates (∼ 250 m radius, up to 0.7 m deep) is more suitable for the product evaluation owing to their larger footprint. In this study, we perform a comprehensive evaluation of eight widely used reanalysis soil moisture products (ERA5-Land, CFSv2, MERRA2, JRA55, GLDAS-Noah, CRA40, GLEAM and SMAP L4 datasets) against 135 CRNS sites from the COSMOS-UK, COSMOS-Europe, COSMOS USA and CosmOz Australia networks. We evaluate the products using six metrics capturing different aspects of soil moisture dynamics. Results show that all reanalysis products generally exhibit good temporal correlation with the measurements, with the median temporal correlation coefficient (R) values spanning 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 time series, with R values varying from 0.46 to 0.66. The performance of reanalysis products differs greatly across regions, climate, land covers and topographic conditions. In general, all products tend to overestimate data in arid climates and underestimate data 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 the west of the UK, which might be associated with relatively low bulk density and high soil organic carbon. Overall, ERA5-Land, CRA40, CFSv2, SMAP L4 and GLEAM exhibit superior performance compared to MERRA2, GLDAS-Noah and JRA55. We recommend that ERA5-Land and CFSv2 could be used in humid climates, whereas SMAP L4 and CRA40 perform better in arid regions. SMAP L4 has good performance for cropland, while GLEAM is more effective in shrubland regions. Our findings also provide insights into directions for improvement of soil moisture products for product developers.},
	language = {en},
	number = {9},
	urldate = {2024-11-26},
	journal = {Hydrology and Earth System Sciences},
	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 = may,
	year = {2024},
	pages = {1999--2022},
}

Abstract. Reanalysis soil moisture products are valuable for diverse applications, but their quality assessment is limited due to scale discrepancies when compared to traditional in situ point-scale measurements. The emergence of cosmic ray neutron sensors (CRNSs) with field-scale soil moisture estimates (∼ 250 m radius, up to 0.7 m deep) is more suitable for the product evaluation owing to their larger footprint. In this study, we perform a comprehensive evaluation of eight widely used reanalysis soil moisture products (ERA5-Land, CFSv2, MERRA2, JRA55, GLDAS-Noah, CRA40, GLEAM and SMAP L4 datasets) against 135 CRNS sites from the COSMOS-UK, COSMOS-Europe, COSMOS USA and CosmOz Australia networks. We evaluate the products using six metrics capturing different aspects of soil moisture dynamics. Results show that all reanalysis products generally exhibit good temporal correlation with the measurements, with the median temporal correlation coefficient (R) values spanning 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 time series, with R values varying from 0.46 to 0.66. The performance of reanalysis products differs greatly across regions, climate, land covers and topographic conditions. In general, all products tend to overestimate data in arid climates and underestimate data 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 the west of the UK, which might be associated with relatively low bulk density and high soil organic carbon. Overall, ERA5-Land, CRA40, CFSv2, SMAP L4 and GLEAM exhibit superior performance compared to MERRA2, GLDAS-Noah and JRA55. We recommend that ERA5-Land and CFSv2 could be used in humid climates, whereas SMAP L4 and CRA40 perform better in arid regions. SMAP L4 has good performance for cropland, while GLEAM is more effective in shrubland regions. Our findings also provide insights into directions for improvement of soil moisture products for product developers.

@article{zhou_divergent_2024,
	title = {Divergent trajectories of future global gross primary productivity and evapotranspiration of terrestrial vegetation in {Shared} {Socioeconomic} {Pathways}},
	volume = {919},
	issn = {00489697},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0048969724007174},
	doi = {10.1016/j.scitotenv.2024.170580},
	language = {en},
	urldate = {2024-11-26},
	journal = {Science of The Total Environment},
	author = {Zhou, Xuewen and Gui, Hanliang and Xin, Qinchuan and Dai, Yongjiu},
	month = apr,
	year = {2024},
	pages = {170580},
}

@article{zhu_cross-resolution_2024,
	title = {A cross-resolution transfer learning approach for soil moisture retrieval from {Sentinel}-1 using limited training samples},
	volume = {301},
	issn = {00344257},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0034425723004960},
	doi = {10.1016/j.rse.2023.113944},
	language = {en},
	urldate = {2024-11-26},
	journal = {Remote Sensing of Environment},
	author = {Zhu, Liujun and Dai, Junjie and Liu, Yi and Yuan, Shanshui and Qin, Tianling and Walker, Jeffrey P.},
	month = feb,
	year = {2024},
	pages = {113944},
}

@article{ottl_millennium_2024,
	title = {A millennium of arable land use – the long-term impact of tillage and water erosion on landscape-scale carbon dynamics},
	volume = {10},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2199-398X},
	url = {https://soil.copernicus.org/articles/10/281/2024/},
	doi = {10.5194/soil-10-281-2024},
	abstract = {Abstract. In the last decades, soils and their agricultural management have received great scientific and political attention due to their potential to act as a sink of atmospheric carbon dioxide (CO2). Agricultural management has strong potential to accelerate soil redistribution, and, therefore, it is questioned if soil redistribution processes affect this potential CO2 sink function. Most studies analysing the effect of soil redistribution upon soil organic carbon (SOC) dynamics focus on water erosion and analyse only relatively small catchments and relatively short time spans of several years to decades. The aim of this study is to widen this perspective by including tillage erosion as another important driver of soil redistribution and by performing a model-based analysis in a 200 km2 sized arable region of northeastern Germany for the period since the conversion from forest to arable land (approx. 1000 years ago). The spatially explicit soil redistribution and carbon (C) turnover model SPEROS-C was applied to simulate lateral soil and SOC redistribution and SOC turnover. The model parameterisation uncertainty was estimated by simulating different realisations of the development of agricultural management over the past millennium. The results indicate that, in young moraine areas, which are relatively dry but have been intensively used for agriculture for centuries, SOC patterns and dynamics are substantially affected by tillage-induced soil redistribution processes. To understand the landscape-scale effect of these redistribution processes on SOC dynamics, it is essential to account for long-term changes following land conversion as typical soil-erosion-induced processes, e.g. dynamic replacement, only take place after former forest soils reach a new equilibrium following conversion. Overall, it was estimated that, after 1000 years of arable land use, SOC redistribution by tillage and water results in a current-day landscape-scale C sink of up to 0.66 ‰ yr−1 of the current SOC stocks.},
	language = {en},
	number = {1},
	urldate = {2024-11-26},
	journal = {SOIL},
	author = {Öttl, Lena Katharina and Wilken, Florian and Juřicová, Anna and Batista, Pedro V. G. and Fiener, Peter},
	month = apr,
	year = {2024},
	pages = {281--305},
}

Abstract. In the last decades, soils and their agricultural management have received great scientific and political attention due to their potential to act as a sink of atmospheric carbon dioxide (CO2). Agricultural management has strong potential to accelerate soil redistribution, and, therefore, it is questioned if soil redistribution processes affect this potential CO2 sink function. Most studies analysing the effect of soil redistribution upon soil organic carbon (SOC) dynamics focus on water erosion and analyse only relatively small catchments and relatively short time spans of several years to decades. The aim of this study is to widen this perspective by including tillage erosion as another important driver of soil redistribution and by performing a model-based analysis in a 200 km2 sized arable region of northeastern Germany for the period since the conversion from forest to arable land (approx. 1000 years ago). The spatially explicit soil redistribution and carbon (C) turnover model SPEROS-C was applied to simulate lateral soil and SOC redistribution and SOC turnover. The model parameterisation uncertainty was estimated by simulating different realisations of the development of agricultural management over the past millennium. The results indicate that, in young moraine areas, which are relatively dry but have been intensively used for agriculture for centuries, SOC patterns and dynamics are substantially affected by tillage-induced soil redistribution processes. To understand the landscape-scale effect of these redistribution processes on SOC dynamics, it is essential to account for long-term changes following land conversion as typical soil-erosion-induced processes, e.g. dynamic replacement, only take place after former forest soils reach a new equilibrium following conversion. Overall, it was estimated that, after 1000 years of arable land use, SOC redistribution by tillage and water results in a current-day landscape-scale C sink of up to 0.66 ‰ yr−1 of the current SOC stocks.