TY - GEN A1 - Guse, Björn A1 - Pfannerstill, Matthias A1 - Gafurov, Abror A1 - Kiesel, Jens A1 - Lehr, Christian A1 - Fohrer, Nicola T1 - Identifying the connective strength between model parameters and performance criteria T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - In hydrological models, parameters are used to represent the time-invariant characteristics of catchments and to capture different aspects of hydrological response. Hence, model parameters need to be identified based on their role in controlling the hydrological behaviour. For the identification of meaningful parameter values, multiple and complementary performance criteria are used that compare modelled and measured discharge time series. The reliability of the identification of hydrologically meaningful model parameter values depends on how distinctly a model parameter can be assigned to one of the performance criteria.& para;& para;To investigate this, we introduce the new concept of connective strength between model parameters and performance criteria. The connective strength assesses the intensity in the interrelationship between model parameters and performance criteria in a bijective way. In our analysis of connective strength, model simulations are carried out based on a latin hypercube sampling. Ten performance criteria including Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE) and its three components (alpha, beta and r) as well as RSR (the ratio of the root mean square error to the standard deviation) for different segments of the flow duration curve (FDC) are calculated.& para;& para;With a joint analysis of two regression tree (RT) approaches, we derive how a model parameter is connected to different performance criteria. At first, RTs are constructed using each performance criterion as the target variable to detect the most relevant model parameters for each performance criterion. Secondly, RTs are constructed using each parameter as the target variable to detect which performance criteria are impacted by changes in the values of one distinct model parameter. Based on this, appropriate performance criteria are identified for each model parameter.& para;& para;In this study, a high bijective connective strength between model parameters and performance criteria is found for low- and mid-flow conditions. Moreover, the RT analyses emphasise the benefit of an individual analysis of the three components of KGE and of the FDC segments. Furthermore, the RT analyses highlight under which conditions these performance criteria provide insights into precise parameter identification. Our results show that separate performance criteria are required to identify dominant parameters on low- and mid-flow conditions, whilst the number of required performance criteria for high flows increases with increasing process complexity in the catchment. Overall, the analysis of the connective strength between model parameters and performance criteria using RTs contribute to a more realistic handling of parameters and performance criteria in hydrological modelling. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 657 KW - flow-duration curves KW - hydrological models KW - multiobjective calibration KW - sensitivity-analysis KW - landscape controls KW - lowland catchment KW - regional patterns KW - physical controls KW - water-balance KW - part 1 Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-419142 SN - 1866-8372 IS - 657 ER - TY - JOUR A1 - Guse, Björn A1 - Pfannerstill, Matthias A1 - Gafurov, Abror A1 - Kiesel, Jens A1 - Lehr, Christian A1 - Fohrer, Nicola T1 - Identifying the connective strength between model parameters and performance criteria JF - Hydrology and earth system sciences : HESS N2 - In hydrological models, parameters are used to represent the time-invariant characteristics of catchments and to capture different aspects of hydrological response. Hence, model parameters need to be identified based on their role in controlling the hydrological behaviour. For the identification of meaningful parameter values, multiple and complementary performance criteria are used that compare modelled and measured discharge time series. The reliability of the identification of hydrologically meaningful model parameter values depends on how distinctly a model parameter can be assigned to one of the performance criteria.& para;& para;To investigate this, we introduce the new concept of connective strength between model parameters and performance criteria. The connective strength assesses the intensity in the interrelationship between model parameters and performance criteria in a bijective way. In our analysis of connective strength, model simulations are carried out based on a latin hypercube sampling. Ten performance criteria including Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE) and its three components (alpha, beta and r) as well as RSR (the ratio of the root mean square error to the standard deviation) for different segments of the flow duration curve (FDC) are calculated.& para;& para;With a joint analysis of two regression tree (RT) approaches, we derive how a model parameter is connected to different performance criteria. At first, RTs are constructed using each performance criterion as the target variable to detect the most relevant model parameters for each performance criterion. Secondly, RTs are constructed using each parameter as the target variable to detect which performance criteria are impacted by changes in the values of one distinct model parameter. Based on this, appropriate performance criteria are identified for each model parameter.& para;& para;In this study, a high bijective connective strength between model parameters and performance criteria is found for low- and mid-flow conditions. Moreover, the RT analyses emphasise the benefit of an individual analysis of the three components of KGE and of the FDC segments. Furthermore, the RT analyses highlight under which conditions these performance criteria provide insights into precise parameter identification. Our results show that separate performance criteria are required to identify dominant parameters on low- and mid-flow conditions, whilst the number of required performance criteria for high flows increases with increasing process complexity in the catchment. Overall, the analysis of the connective strength between model parameters and performance criteria using RTs contribute to a more realistic handling of parameters and performance criteria in hydrological modelling. Y1 - 2017 U6 - https://doi.org/10.5194/hess-21-5663-2017 SN - 1027-5606 SN - 1607-7938 VL - 21 SP - 5663 EP - 5679 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Lehr, Christian A1 - Dannowski, Ralf A1 - Kalettka, Thomas A1 - Merz, Christoph A1 - Schröder, Boris A1 - Steidl, Jörg A1 - Lischeid, Gunnar T1 - Detecting dominant changes in irregularly sampled multivariate water quality data sets JF - Hydrology and earth system sciences : HESS N2 - Time series of groundwater and stream water quality often exhibit substantial temporal and spatial variability, whereas typical existing monitoring data sets, e.g. from environmental agencies, are usually characterized by relatively low sampling frequency and irregular sampling in space and/or time. This complicates the differentiation between anthropogenic influence and natural variability as well as the detection of changes in water quality which indicate changes in single drivers. We suggest the new term "dominant changes" for changes in multivariate water quality data which concern (1) multiple variables, (2) multiple sites and (3) long-term patterns and present an exploratory framework for the detection of such dominant changes in data sets with irregular sampling in space and time. Firstly, a non-linear dimension-reduction technique was used to summarize the dominant spatiotemporal dynamics in the multivariate water quality data set in a few components. Those were used to derive hypotheses on the dominant drivers influencing water quality. Secondly, different sampling sites were compared with respect to median component values. Thirdly, time series of the components at single sites were analysed for long-term patterns. We tested the approach with a joint stream water and groundwater data set quality consisting of 1572 samples, each comprising sixteen variables, sampled with a spatially and temporally irregular sampling scheme at 29 sites in northeast Germany from 1998 to 2009. The first four components were interpreted as (1) an agriculturally induced enhancement of the natural background level of solute concentration, (2) a redox sequence from reducing conditions in deep groundwater to post-oxic conditions in shallow groundwater and oxic conditions in stream water, (3) a mixing ratio of deep and shallow groundwater to the streamflow and (4) sporadic events of slurry application in the agricultural practice. Dominant changes were observed for the first two components. The changing intensity of the first component was interpreted as response to the temporal variability of the thickness of the unsaturated zone. A steady increase in the second component at most stream water sites pointed towards progressing depletion of the denitrification capacity of the deep aquifer. Y1 - 2018 U6 - https://doi.org/10.5194/hess-22-4401-2018 SN - 1027-5606 SN - 1607-7938 VL - 22 IS - 8 SP - 4401 EP - 4424 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Lischeid, Gunnar A1 - Kalettka, Thomas A1 - Holländer, Matthias A1 - Steidl, Jörg A1 - Merz, Christoph A1 - Dannowski, Ralf A1 - Hohenbrink, Tobias Ludwig A1 - Lehr, Christian A1 - Onandia, Gabriela A1 - Reverey, Florian A1 - Pätzig, Marlene T1 - Natural ponds in an agricultural landscape BT - external drivers, internal processes, and the role of the terrestrial-aquatic interface JF - Limnologica : ecology and management of inland waters N2 - The pleistocenic landscape in North Europe, North Asia and North America is spotted with thousands of natural ponds called kettle holes. They are biological and biogeochemical hotspots. Due to small size, small perimeter and shallow depth biological and biogeochemical processes in kettle holes are closely linked to the dynamics and the emissions of the terrestrial environment. On the other hand, their intriguing high spatial and temporal variability makes a sound understanding of the terrestrial-aquatic link very difficult. It is presumed that intensive agricultural land use during the last decades has resulted in a ubiquitous high nutrient load. However, the water quality encountered at single sites highly depends on internal biogeochemical processes and thus can differ substantially even between adjacent sites. This study aimed at elucidating the interplay between external drivers and internal processes based on a thorough analysis of a comprehensive kettle hole water quality data set. To study the role of external drivers, effects of land use in the adjacent terrestrial environment, effects of vegetation at the interface between terrestrial and aquatic systems, and that of kettle hole morphology on water quality was investigated. None of these drivers was prone to strong with-in year variability. Thus temporal variability of spatial patterns could point to the role of internal biogeochemical processes. To that end, the temporal stability of the respective spatial patterns was studied as well for various solutes. All of these analyses were performed for a set of different variables. Different results for different solutes were then used as a source of information about the respective driving processes. In the Quillow catchment in the Uckermark region, about 100 km north of Berlin, Germany, 62 kettle holes have been regularly sampled since 2013. Kettle hole catchments were determined based on a groundwater level map of the uppermost aquifer. The catchments were not clearly related to topography. Spatial patterns of kettle hole water concentration of (earth) alkaline metals and chloride were fairly stable, presumably reflecting solute concentration of the uppermost aquifer. In contrast, spatial patterns of nutrients and redox-sensitive solutes within the kettle holes were hardly correlated between different sampling campaigns. Correspondingly, effects of season, hydrogeomorphic kettle hole type, shore vegetation or land use in the respective catchments were significant but explained only a minor portion of the total variance. It is concluded that internal processes mask effects of the terrestrial environment. There is some evidence that denitrification and phosphorus release from the sediment during frequent periods of hypoxia might play a major role. The latter seems to boost primary production occasionally. These processes do not follow a clear seasonal pattern and are still not well understood. KW - Ponds KW - Kettle holes KW - Water quality KW - Land use KW - Hydrogeomorphic type KW - Shore vegetationa Y1 - 2018 U6 - https://doi.org/10.1016/j.limno.2017.01.003 SN - 0075-9511 SN - 1873-5851 VL - 68 SP - 5 EP - 16 PB - Elsevier GMBH CY - München ER - TY - JOUR A1 - Lehr, Christian A1 - Lischeid, Gunnar T1 - Efficient screening of groundwater head monitoring data for anthropogenic effects and measurement errors JF - Hydrology and Earth System Sciences N2 - Groundwater levels are monitored by environmental agencies to support the sustainable use of groundwater resources. For this purpose continuous and spatially comprehensive monitoring in high spatial and temporal resolution is desired. This leads to large datasets that have to be checked for quality and analysed to distinguish local anthropogenic influences from natural variability of the groundwater level dynamics at each well. Both technical problems with the measurements as well as local anthropogenic influences can lead to local anomalies in the hydrographs. We suggest a fast and efficient screening method for the identification of well-specific peculiarities in hydrographs of groundwater head monitoring networks. The only information required is a set of time series of groundwater heads all measured at the same instants of time. For each well of the monitoring network a reference hydrograph is calculated, describing expected "normal" behaviour at the respective well as is typical for the monitored region. The reference hydrograph is calculated by multiple linear regression of the observed hydrograph with the "stable" principal components (PCs) of a principal component analysis of all groundwater head series of the network as predictor variables. The stable PCs are those PCs which were found in a random subsampling procedure to be rather insensitive to the specific selection of the analysed observation wells, i.e. complete series, and to the specific selection of measurement dates. Hence they can be considered to be representative for the monitored region in the respective period. The residuals of the reference hydrograph describe local deviations from the normal behaviour. Peculiarities in the residuals allow the data to be checked for measurement errors and the wells with a possible anthropogenic influence to be identified. The approach was tested with 141 groundwater head time series from the state authority groundwater monitoring network in northeastern Germany covering the period from 1993 to 2013 at an approximately weekly frequency of measurement. KW - streamflow variability KW - principal components KW - united states KW - time-seriesa KW - water KW - network KW - nonstationarity KW - fluctuations KW - rotation Y1 - 2020 U6 - https://doi.org/10.5194/hess-24-501-2020 SN - 1027-5606 SN - 1607-7938 VL - 24 IS - 2 SP - 501 EP - 513 PB - Copernicus CY - Göttingen ER - TY - GEN A1 - Lehr, Christian A1 - Lischeid, Gunnar T1 - Efficient screening of groundwater head monitoring data for anthropogenic effects and measurement errors T2 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Groundwater levels are monitored by environmental agencies to support the sustainable use of groundwater resources. For this purpose continuous and spatially comprehensive monitoring in high spatial and temporal resolution is desired. This leads to large datasets that have to be checked for quality and analysed to distinguish local anthropogenic influences from natural variability of the groundwater level dynamics at each well. Both technical problems with the measurements as well as local anthropogenic influences can lead to local anomalies in the hydrographs. We suggest a fast and efficient screening method for the identification of well-specific peculiarities in hydrographs of groundwater head monitoring networks. The only information required is a set of time series of groundwater heads all measured at the same instants of time. For each well of the monitoring network a reference hydrograph is calculated, describing expected “normal” behaviour at the respective well as is typical for the monitored region. The reference hydrograph is calculated by multiple linear regression of the observed hydrograph with the “stable” principal components (PCs) of a principal component analysis of all groundwater head series of the network as predictor variables. The stable PCs are those PCs which were found in a random subsampling procedure to be rather insensitive to the specific selection of the analysed observation wells, i.e. complete series, and to the specific selection of measurement dates. Hence they can be considered to be representative for the monitored region in the respective period. The residuals of the reference hydrograph describe local deviations from the normal behaviour. Peculiarities in the residuals allow the data to be checked for measurement errors and the wells with a possible anthropogenic influence to be identified. The approach was tested with 141 groundwater head time series from the state authority groundwater monitoring network in northeastern Germany covering the period from 1993 to 2013 at an approximately weekly frequency of measurement. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1424 KW - streamflow variability KW - principal components KW - united states KW - time-series KW - water KW - network KW - nonstationarity KW - fluctuations KW - rotation Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-511992 SN - 1866-8372 IS - 2 ER -