Publications 2006

@article{aulenbach_composite_2006,
	title = {The composite method: an improved method for stream‐water solute load estimation},
	volume = {20},
	copyright = {http://onlinelibrary.wiley.com/termsAndConditions\#vor},
	issn = {0885-6087, 1099-1085},
	shorttitle = {The composite method},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/hyp.6147},
	doi = {10.1002/hyp.6147},
	abstract = {Abstract
            The composite method is an alternative method for estimating stream‐water solute loads, combining aspects of two commonly used methods: the regression‐model method (which is used by the composite method to predict variations in concentrations between collected samples) and a period‐weighted approach (which is used by the composite method to apply the residual concentrations from the regression model over time). The extensive dataset collected at the outlet of the Panola Mountain Research Watershed (PMRW) near Atlanta, Georgia, USA, was used in data analyses for illustrative purposes. A bootstrap (subsampling) experiment (using the composite method and the PMRW dataset along with various fixed‐interval and large storm sampling schemes) obtained load estimates for the 8‐year study period with a magnitude of the bias of less than 1\%, even for estimates that included the fewest number of samples. Precisions were always {\textless}2\% on a study period and annual basis, and {\textless}2\% precisions were obtained for quarterly and monthly time intervals for estimates that had better sampling. The bias and precision of composite‐method load estimates varies depending on the variability in the regression‐model residuals, how residuals systematically deviated from the regression model over time, sampling design, and the time interval of the load estimate. The regression‐model method did not estimate loads precisely during shorter time intervals, from annually to monthly, because the model could not explain short‐term patterns in the observed concentrations. Load estimates using the period‐weighted approach typically are biased as a result of sampling distribution and are accurate only with extensive sampling. The formulation of the composite method facilitates exploration of patterns (trends) contained in the unmodelled portion of the load. Copyright © 2006 John Wiley \& Sons, Ltd.},
	language = {en},
	number = {14},
	urldate = {2024-11-20},
	journal = {Hydrological Processes},
	author = {Aulenbach, Brent T. and Hooper, Richard P.},
	month = sep,
	year = {2006},
	pages = {3029--3047},
}

Abstract The composite method is an alternative method for estimating stream‐water solute loads, combining aspects of two commonly used methods: the regression‐model method (which is used by the composite method to predict variations in concentrations between collected samples) and a period‐weighted approach (which is used by the composite method to apply the residual concentrations from the regression model over time). The extensive dataset collected at the outlet of the Panola Mountain Research Watershed (PMRW) near Atlanta, Georgia, USA, was used in data analyses for illustrative purposes. A bootstrap (subsampling) experiment (using the composite method and the PMRW dataset along with various fixed‐interval and large storm sampling schemes) obtained load estimates for the 8‐year study period with a magnitude of the bias of less than 1%, even for estimates that included the fewest number of samples. Precisions were always \textless2% on a study period and annual basis, and \textless2% precisions were obtained for quarterly and monthly time intervals for estimates that had better sampling. The bias and precision of composite‐method load estimates varies depending on the variability in the regression‐model residuals, how residuals systematically deviated from the regression model over time, sampling design, and the time interval of the load estimate. The regression‐model method did not estimate loads precisely during shorter time intervals, from annually to monthly, because the model could not explain short‐term patterns in the observed concentrations. Load estimates using the period‐weighted approach typically are biased as a result of sampling distribution and are accurate only with extensive sampling. The formulation of the composite method facilitates exploration of patterns (trends) contained in the unmodelled portion of the load. Copyright © 2006 John Wiley & Sons, Ltd.

@article{bogena_towards_2006,
	title = {Towards a network of observatories in terrestrial environmental research},
	volume = {9},
	issn = {1680-7359},
	url = {https://adgeo.copernicus.org/articles/9/109/2006/},
	doi = {10.5194/adgeo-9-109-2006},
	abstract = {Abstract. In order to address the challenges of global change, interdisciplinary research in terrestrial environmental science is of great importance. Several environmental research networks have already been established in order to monitor, analyse and predict the impact of global change on different compartments and/or matter cycles of the environment. Typically these environmental research networks have focused on specific research questions, and compartments, such as CarboEurope, FLUXNET and ILTER. In this paper, we propose the establishment of a network of terrestrial observatories, defined as a system consisting of the subsurface environment, the land surface including the biosphere, the lower atmosphere and the anthroposphere. Hydrological units will be used as the basic scaling units in a hierarchy of evolving scales and structures ranging from the local scale to the regional scale for multi-disciplinary process studies. Although terrestrial systems are extremely complex, the terrestrial component in most process-based climate and biosphere models is typically represented in a very conceptual and often rudimentary way. Remedying this deficiency is therefore one of the most important challenges in environmental and terrestrial research, and we suggest that terrestrial observatories could be an important step towards a new quality in environmental and terrestrial research.},
	language = {en},
	urldate = {2023-06-19},
	journal = {Advances in Geosciences},
	author = {Bogena, H. and Schulz, K. and Vereecken, H.},
	month = sep,
	year = {2006},
	pages = {109--114},
}

Abstract. In order to address the challenges of global change, interdisciplinary research in terrestrial environmental science is of great importance. Several environmental research networks have already been established in order to monitor, analyse and predict the impact of global change on different compartments and/or matter cycles of the environment. Typically these environmental research networks have focused on specific research questions, and compartments, such as CarboEurope, FLUXNET and ILTER. In this paper, we propose the establishment of a network of terrestrial observatories, defined as a system consisting of the subsurface environment, the land surface including the biosphere, the lower atmosphere and the anthroposphere. Hydrological units will be used as the basic scaling units in a hierarchy of evolving scales and structures ranging from the local scale to the regional scale for multi-disciplinary process studies. Although terrestrial systems are extremely complex, the terrestrial component in most process-based climate and biosphere models is typically represented in a very conceptual and often rudimentary way. Remedying this deficiency is therefore one of the most important challenges in environmental and terrestrial research, and we suggest that terrestrial observatories could be an important step towards a new quality in environmental and terrestrial research.