TY - JOUR A1 - Schulz, K. A1 - Seppelt, Ralf A1 - Zehe, Erwin A1 - Vogel, Hans-Jörg A1 - Attinger, Sabine T1 - Importance of spatial structures in advancing hydrological sciences N2 - [1] Spatial patterns of land surface and subsurface characteristics often exert significant control over hydrological processes at many scales. Recognition of the dominant controls at the watershed scale, which is a prerequisite to successful prediction of system responses, will require significant progress in many different research areas. The development and improvement of techniques for mapping structures and spatiotemporal patterns using geophysical and remote sensing techniques would greatly benefit watershed science but still requires a significant synthesis effort. Effective descriptions of hydrological systems will also significantly benefit from new scaling and averaging techniques, from new mathematical description for spatial pattern/structures and their dynamics, and also from an understanding and quantification of structure and pattern-building processes in different compartments ( soils, rocks, and land surface) and at different scales. The advances that are needed to tackle these complex challenges could be greatly facilitated through the development of an interdisciplinary research framework that explores instrumentation, theory, and simulation components and that is implemented in a coordinated manner Y1 - 2006 UR - http://www.mendeley.com/research/importance-of-spatial-structures-in-advancing-hydrological-sciences/ #page-1 U6 - https://doi.org/10.1029/2005wr004301 ER - TY - JOUR A1 - Zech, Alraune A1 - Attinger, Sabine A1 - Bellin, Alberto A1 - Cvetkovic, Vladimir A1 - Dietrich, Peter A1 - Fiori, Aldo A1 - Teutsch, Georg A1 - Dagan, Gedeon T1 - A Critical Analysis of Transverse Dispersivity Field Data JF - Groundwater : journal of the Association of Ground-Water Scientists and Engineers, a division of the National Ground Water Association N2 - Transverse dispersion, or tracer spreading orthogonal to the mean flow direction, which is relevant e.g, for quantifying bio-degradation of contaminant plumes or mixing of reactive solutes, has been studied in the literature less than the longitudinal one. Inferring transverse dispersion coefficients from field experiments is a difficult and error-prone task, requiring a spatial resolution of solute plumes which is not easily achievable in applications. In absence of field data, it is a questionable common practice to set transverse dispersivities as a fraction of the longitudinal one, with the ratio 1/10 being the most prevalent. We collected estimates of field-scale transverse dispersivities from existing publications and explored possible scale relationships as guidance criteria for applications. Our investigation showed that a large number of estimates available in the literature are of low reliability and should be discarded from further analysis. The remaining reliable estimates are formation-specific, span three orders of magnitude and do not show any clear scale-dependence on the plume traveled distance. The ratios with the longitudinal dispersivity are also site specific and vary widely. The reliability of transverse dispersivities depends significantly on the type of field experiment and method of data analysis. In applications where transverse dispersion plays a significant role, inference of transverse dispersivities should be part of site characterization with the transverse dispersivity estimated as an independent parameter rather than related heuristically to longitudinal dispersivity. Y1 - 2018 U6 - https://doi.org/10.1111/gwat.12838 SN - 0017-467X SN - 1745-6584 VL - 57 IS - 4 SP - 632 EP - 639 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Baroni, Gabriele A1 - Schalge, Bernd A1 - Rakovec, Oldrich A1 - Kumar, Rohini A1 - Schüler, Lennart A1 - Samaniego, Luis A1 - Simmer, Clemens A1 - Attinger, Sabine T1 - A Comprehensive Distributed Hydrological Modeling Intercomparison to Support Process Representation and Data Collection Strategies JF - Water resources research N2 - The improvement of process representations in hydrological models is often only driven by the modelers' knowledge and data availability. We present a comprehensive comparison between two hydrological models of different complexity that is developed to support (1) the understanding of the differences between model structures and (2) the identification of the observations needed for model assessment and improvement. The comparison is conducted on both space and time and by aggregating the outputs at different spatiotemporal scales. In the present study, mHM, a process‐based hydrological model, and ParFlow‐CLM, an integrated subsurface‐surface hydrological model, are used. The models are applied in a mesoscale catchment in Germany. Both models agree in the simulated river discharge at the outlet and the surface soil moisture dynamics, lending their supports for some model applications (drought monitoring). Different model sensitivities are, however, found when comparing evapotranspiration and soil moisture at different soil depths. The analysis supports the need of observations within the catchment for model assessment, but it indicates that different strategies should be considered for the different variables. Evapotranspiration measurements are needed at daily resolution across several locations, while highly resolved spatially distributed observations with lower temporal frequency are required for soil moisture. Finally, the results show the impact of the shallow groundwater system simulated by ParFlow‐CLM and the need to account for the related soil moisture redistribution. Our comparison strategy can be applied to other models types and environmental conditions to strengthen the dialog between modelers and experimentalists for improving process representations in Earth system models. KW - hydrological models KW - assessments KW - monitoring strategies KW - improvements Y1 - 2019 U6 - https://doi.org/10.1029/2018WR023941 SN - 0043-1397 SN - 1944-7973 VL - 55 IS - 2 SP - 990 EP - 1010 PB - American Geophysical Union CY - Washington ER - TY - GEN A1 - Heße, Falk A1 - Comunian, Alessandro A1 - Attinger, Sabine T1 - What We Talk About When We Talk About Uncertainty BT - Toward a Unified, Data-Driven Framework for Uncertainty Characterization in Hydrogeology T2 - Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 754 KW - Bayesianism KW - uncertainty analysis KW - hydrogeology KW - data science KW - opinion KW - prior derivation Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-436582 SN - 1866-8372 IS - 754 ER - TY - JOUR A1 - Al-Mashaikhi, K. A1 - Oswald, Sascha Eric A1 - Attinger, Sabine A1 - Büchel, G. A1 - Knöller, K. A1 - Strauch, G. T1 - Evaluation of groundwater dynamics and quality in the Najd aquifers located in the Sultanate of Oman JF - Environmental earth sciences N2 - The Najd, Oman, is located in one of the most arid environments in the world. The groundwater in this region is occurring in four different aquifers A to D of the Hadhramaut Group consisting mainly of different types of limestone and dolomite. The quality of the groundwater is dominated by the major ions sodium, calcium, magnesium, sulphate, and chloride, but the hydrochemical character is varying among the four aquifers. Mineralization within the separate aquifers increases along the groundwater flow direction from south to north-northeast up to high saline sodium-chloride water in aquifer D in the northeast area of the Najd. Environmental isotope analyses of hydrogen and oxygen were conducted to monitor the groundwater dynamics and to evaluate the recharge conditions of groundwater into the Najd aquifers. Results suggest an earlier recharge into these aquifers as well as ongoing recharge takes place in the region down to present day. Mixing of modern and submodern waters was detected by water isotopes in aquifer D in the mountain chain (Jabal) area and along the northern side of the mountain range. In addition, delta H-2 and delta O-18 variations suggest that aquifers A, B, and C are assumed to be connected by faults and fractures, and interaction between the aquifers may occur. Low tritium concentrations support the mixing assumption in the recharge area. The knowledge about the groundwater development is an important factor for the sustainable use of water resources in the Dhofar region. KW - Environmental isotopes KW - Groundwater KW - Najd aquifer KW - Oman KW - Recharge KW - Water quality Y1 - 2012 U6 - https://doi.org/10.1007/s12665-011-1331-2 SN - 1866-6280 VL - 66 IS - 4 SP - 1195 EP - 1211 PB - Springer CY - New York ER - TY - JOUR A1 - Heße, Falk A1 - Comunian, Alessandro A1 - Attinger, Sabine T1 - What We Talk About When We Talk About Uncertainty BT - Toward a Unified, Data-Driven Framework for Uncertainty Characterization in Hydrogeology JF - Frontiers in Earth Science KW - Bayesianism KW - uncertainty analysis KW - hydrogeology KW - data science KW - opinion KW - prior derivation Y1 - 2019 U6 - https://doi.org/10.3389/feart.2019.00118 SN - 2296-6463 VL - 7 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Schmidt, Lennart A1 - Hesse, Falk A1 - Attinger, Sabine A1 - Kumar, Rohini T1 - Challenges in applying machine learning models for hydrological inference BT - a case study for flooding events across Germany JF - Water resources research N2 - Machine learning (ML) algorithms are being increasingly used in Earth and Environmental modeling studies owing to the ever-increasing availability of diverse data sets and computational resources as well as advancement in ML algorithms. Despite advances in their predictive accuracy, the usefulness of ML algorithms for inference remains elusive. In this study, we employ two popular ML algorithms, artificial neural networks and random forest, to analyze a large data set of flood events across Germany with the goals to analyze their predictive accuracy and their usability to provide insights to hydrologic system functioning. The results of the ML algorithms are contrasted against a parametric approach based on multiple linear regression. For analysis, we employ a model-agnostic framework named Permuted Feature Importance to derive the influence of models' predictors. This allows us to compare the results of different algorithms for the first time in the context of hydrology. Our main findings are that (1) the ML models achieve higher prediction accuracy than linear regression, (2) the results reflect basic hydrological principles, but (3) further inference is hindered by the heterogeneity of results across algorithms. Thus, we conclude that the problem of equifinality as known from classical hydrological modeling also exists for ML and severely hampers its potential for inference. To account for the observed problems, we propose that when employing ML for inference, this should be made by using multiple algorithms and multiple methods, of which the latter should be embedded in a cross-validation routine. KW - machine learning KW - inference KW - floods Y1 - 2020 U6 - https://doi.org/10.1029/2019WR025924 SN - 0043-1397 SN - 1944-7973 VL - 56 IS - 5 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Kawa, Nura A1 - Cucchi, Karina A1 - Rubin, Yoram A1 - Attinger, Sabine A1 - Hesse, Falk T1 - Defining Hydrogeological Site Similarity with Hierarchical Agglomerative Clustering JF - Groundwater : journal of the Association of Ground-Water Scientists and Engineers, a division of the National Ground Water Association N2 - Hydrogeological information about an aquifer is difficult and costly to obtain, yet essential for the efficient management of groundwater resources. Transferring information from sampled sites to a specific site of interest can provide information when site-specific data is lacking. Central to this approach is the notion of site similarity, which is necessary for determining relevant sites to include in the data transfer process. In this paper, we present a data-driven method for defining site similarity. We apply this method to selecting groups of similar sites from which to derive prior distributions for the Bayesian estimation of hydraulic conductivity measurements at sites of interest. We conclude that there is now a unique opportunity to combine hydrogeological expertise with data-driven methods to improve the predictive ability of stochastic hydrogeological models. Y1 - 2022 U6 - https://doi.org/10.1111/gwat.13261 SN - 0017-467X SN - 1745-6584 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Jing, Miao A1 - Hesse, Falk A1 - Kumar, Rohini A1 - Kolditz, Olaf A1 - Kalbacher, Thomas A1 - Attinger, Sabine T1 - Influence of input and parameter uncertainty on the prediction of catchment-scale groundwater travel time distributions JF - Hydrology and earth system sciences : HESS N2 - Groundwater travel time distributions (TTDs) provide a robust description of the subsurface mixing behavior and hydrological response of a subsurface system. Lagrangian particle tracking is often used to derive the groundwater TTDs. The reliability of this approach is subjected to the uncertainty of external forcings, internal hydraulic properties, and the interplay between them. Here, we evaluate the uncertainty of catchment groundwater TTDs in an agricultural catchment using a 3-D groundwater model with an overall focus on revealing the relationship between external forcing, internal hydraulic properties, and TTD predictions. Eight recharge realizations are sampled from a high-resolution dataset of land surface fluxes and states. Calibration-constrained hydraulic conductivity fields (Ks fields) are stochastically generated using the null-space Monte Carlo (NSMC) method for each recharge realization. The random walk particle tracking (RWPT) method is used to track the pathways of particles and compute travel times. Moreover, an analytical model under the random sampling (RS) assumption is fit against the numerical solutions, serving as a reference for the mixing behavior of the model domain. The StorAge Selection (SAS) function is used to interpret the results in terms of quantifying the systematic preference for discharging young/old water. The simulation results reveal the primary effect of recharge on the predicted mean travel time (MTT). The different realizations of calibration-constrained Ks fields moderately magnify or attenuate the predicted MTTs. The analytical model does not properly replicate the numerical solution, and it underestimates the mean travel time. Simulated SAS functions indicate an overall preference for young water for all realizations. The spatial pattern of recharge controls the shape and breadth of simulated TTDs and SAS functions by changing the spatial distribution of particles' pathways. In conclusion, overlooking the spatial nonuniformity and uncertainty of input (forcing) will result in biased travel time predictions. We also highlight the worth of reliable observations in reducing predictive uncertainty and the good interpretability of SAS functions in terms of understanding catchment transport processes. Y1 - 2019 U6 - https://doi.org/10.5194/hess-23-171-2019 SN - 1027-5606 SN - 1607-7938 VL - 23 IS - 1 SP - 171 EP - 190 PB - Copernicus CY - Göttingen ER - TY - GEN A1 - Jing, Miao A1 - Heße, Falk A1 - Kumar, Rohini A1 - Wang, Wenqing A1 - Fischer, Thomas A1 - Walther, Marc A1 - Zink, Matthias A1 - Zech, Alraune A1 - Samaniego, Luis A1 - Kolditz, Olaf A1 - Attinger, Sabine T1 - Improved regional-scale groundwater representation by the coupling of the mesoscale Hydrologic Model (mHM v5.7) to the groundwater model OpenGeoSys (OGS) T2 - Postprints der Universität Potsdam : Mathematisch Naturwissenschaftliche Reihe N2 - Most large-scale hydrologic models fall short in reproducing groundwater head dynamics and simulating transport process due to their oversimplified representation of groundwater flow. In this study, we aim to extend the applicability of the mesoscale Hydrologic Model (mHM v5.7) to subsurface hydrology by coupling it with the porous media simulator OpenGeoSys (OGS). The two models are one-way coupled through model interfaces GIS2FEM and RIV2FEM, by which the grid-based fluxes of groundwater recharge and the river-groundwater exchange generated by mHM are converted to fixed-flux boundary conditions of the groundwater model OGS. Specifically, the grid-based vertical reservoirs in mHM are completely preserved for the estimation of land-surface fluxes, while OGS acts as a plug-in to the original mHM modeling framework for groundwater flow and transport modeling. The applicability of the coupled model (mHM-OGS v1.0) is evaluated by a case study in the central European mesoscale river basin - Nagelstedt. Different time steps, i.e., daily in mHM and monthly in OGS, are used to account for fast surface flow and slow groundwater flow. Model calibration is conducted following a two-step procedure using discharge for mHM and long-term mean of groundwater head measurements for OGS. Based on the model summary statistics, namely the Nash-Sutcliffe model efficiency (NSE), the mean absolute error (MAE), and the interquartile range error (QRE), the coupled model is able to satisfactorily represent the dynamics of discharge and groundwater heads at several locations across the study basin. Our exemplary calculations show that the one-way coupled model can take advantage of the spatially explicit modeling capabilities of surface and groundwater hydrologic models and provide an adequate representation of the spatiotemporal behaviors of groundwater storage and heads, thus making it a valuable tool for addressing water resources and management problems. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 851 KW - travel-time distributions KW - surface-water KW - land-surface KW - surface/subsurface flow KW - parameter-estimation KW - subsurface flow KW - transport model KW - climate-change KW - river-basins KW - catchment Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-427030 SN - 1866-8372 IS - 851 SP - 1989 EP - 2007 ER - TY - JOUR A1 - Schweppe, Robert A1 - Thober, Stephan A1 - Müller, Sebastian A1 - Kelbling, Matthias A1 - Kumar, Rohini A1 - Attinger, Sabine A1 - Samaniego, Luis T1 - MPR 1.0: a stand-alone multiscale parameter regionalization tool for improved parameter estimation of land surface models JF - Geoscientific model development : an interactive open access journal of the European Geosciences Union N2 - Distributed environmental models such as land surface models (LSMs) require model parameters in each spatial modeling unit (e.g., grid cell), thereby leading to a high-dimensional parameter space. One approach to decrease the dimensionality of the parameter space in these models is to use regularization techniques. One such highly efficient technique is the multiscale parameter regionalization (MPR) framework that translates high-resolution predictor variables (e.g., soil textural properties) into model parameters (e.g., porosity) via transfer functions (TFs) and upscaling operators that are suitable for every modeled process. This framework yields seamless model parameters at multiple scales and locations in an effective manner. However, integration of MPR into existing modeling workflows has been hindered thus far by hard-coded configurations and non-modular software designs. For these reasons, we redesigned MPR as a model-agnostic, stand-alone tool. It is a useful software for creating graphs of NetCDF variables, wherein each node is a variable and the links consist of TFs and/or upscaling operators. In this study, we present and verify our tool against a previous version, which was implemented in the mesoscale hydrologic model (mHM; https://www.ufz.de/mhm, last access: 16 January 2022). By using this tool for the generation of continental-scale soil hydraulic parameters applicable to different models (Noah-MP and HTESSEL), we showcase its general functionality and flexibility. Further, using model parameters estimated by the MPR tool leads to significant changes in long-term estimates of evapotranspiration, as compared to their default parameterizations. For example, a change of up to 25 % in long-term evapotranspiration flux is observed in Noah-MP and HTESSEL in the Mississippi River basin. We postulate that use of the stand-alone MPR tool will considerably increase the transparency and reproducibility of the parameter estimation process in distributed (environmental) models. It will also allow a rigorous uncertainty estimation related to the errors of the predictors (e.g., soil texture fields), transfer function and its parameters, and remapping (or upscaling) algorithms. Y1 - 2022 U6 - https://doi.org/10.5194/gmd-15-859-2022 SN - 1991-959X SN - 1991-9603 VL - 15 IS - 2 SP - 859 EP - 882 PB - Copernicus CY - Göttingen ER - TY - GEN A1 - Baroni, Gabriele A1 - Zink, Matthias A1 - Kumar, Rohini A1 - Samaniego, Luis A1 - Attinger, Sabine T1 - Effects of uncertainty in soil properties on simulated hydrological states and fluxes at different spatio-temporal scales T2 - Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe N2 - Soil properties show high heterogeneity at different spatial scales and their correct characterization remains a crucial challenge over large areas. The aim of the study is to quantify the impact of different types of uncertainties that arise from the unresolved soil spatial variability on simulated hydrological states and fluxes. Three perturbation methods are presented for the characterization of uncertainties in soil properties. The methods are applied on the soil map of the upper Neckar catchment (Germany), as an example. The uncertainties are propagated through the distributed mesoscale hydrological model (mHM) to assess the impact on the simulated states and fluxes. The model outputs are analysed by aggregating the results at different spatial and temporal scales. These results show that the impact of the different uncertainties introduced in the original soil map is equivalent when the simulated model outputs are analysed at the model grid resolution (i.e. 500 m). However, several differences are identified by aggregating states and fluxes at different spatial scales (by subcatchments of different sizes or coarsening the grid resolution). Streamflow is only sensitive to the perturbation of long spatial structures while distributed states and fluxes (e.g. soil moisture and groundwater recharge) are only sensitive to the local noise introduced to the original soil properties. A clear identification of the temporal and spatial scale for which finer-resolution soil information is (or is not) relevant is unlikely to be universal. However, the comparison of the impacts on the different hydrological components can be used to prioritize the model improvements in specific applications, either by collecting new measurements or by calibration and data assimilation approaches. In conclusion, the study underlines the importance of a correct characterization of uncertainty in soil properties. With that, soil maps with additional information regarding the unresolved soil spatial variability would provide strong support to hydrological modelling applications. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 545 KW - global sensitivity analysis KW - hydraulic conductivity KW - pedotransfer functions KW - parameter uncertainty KW - physical properties KW - solute transport KW - model KW - rainfall KW - Evapotranspiration KW - impact Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-419174 SN - 1866-8372 IS - 545 ER - TY - JOUR A1 - Ayllon, Daniel A1 - Grimm, Volker A1 - Attinger, Sabine A1 - Hauhs, Michael A1 - Simmer, Clemens A1 - Vereecken, Harry A1 - Lischeid, Gunnar T1 - Cross-disciplinary links in environmental systems science BT - Current state and claimed needs identified in a meta-review of process models JF - The science of the total environment : an international journal for scientific research into the environment and its relationship with man N2 - Terrestrial environmental systems are characterised by numerous feedback links between their different compartments. However, scientific research is organized into disciplines that focus on processes within the respective compartments rather than on interdisciplinary links. Major feedback mechanisms between compartments might therefore have been systematically overlooked so far. Without identifying these gaps, initiatives on future comprehensive environmental monitoring schemes and experimental platforms might fail. We performed a comprehensive overview of feedbacks between compartments currently represented in environmental sciences and explores to what degree missing links have already been acknowledged in the literature. We focused on process models as they can be regarded as repositories of scientific knowledge that compile findings of numerous single studies. In total, 118 simulation models from 23 model types were analysed. Missing processes linking different environmental compartments were identified based on a meta-review of 346 published reviews, model inter-comparison studies, and model descriptions. Eight disciplines of environmental sciences were considered and 396 linking processes were identified and ascribed to the physical, chemical or biological domain. There were significant differences between model types and scientific disciplines regarding implemented interdisciplinary links. The most wide-spread interdisciplinary links were between physical processes in meteorology, hydrology and soil science that drive or set the boundary conditions for other processes (e.g., ecological processes). In contrast, most chemical and biological processes were restricted to links within the same compartment. Integration of multiple environmental compartments and interdisciplinary knowledge was scarce in most model types. There was a strong bias of suggested future research foci and model extensions towards reinforcing existing interdisciplinary knowledge rather than to open up new interdisciplinary pathways. No clear pattern across disciplines exists with respect to suggested future research efforts. There is no evidence that environmental research would clearly converge towards more integrated approaches or towards an overarching environmental systems theory. (c) 2017 Elsevier B.V. All rights reserved. KW - Review KW - Interdisciplinary links KW - Integrated environmental modelling KW - Research needs Y1 - 2018 U6 - https://doi.org/10.1016/j.scitotenv.2017.12.007 SN - 0048-9697 SN - 1879-1026 VL - 622 SP - 954 EP - 973 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Baroni, Gabriele A1 - Zink, Matthias A1 - Kumar, Rohini A1 - Samaniego, Luis A1 - Attinger, Sabine T1 - Effects of uncertainty in soil properties on simulated hydrological states and fluxes at different spatio-temporal scales JF - Hydrology and earth system sciences : HESS N2 - Soil properties show high heterogeneity at different spatial scales and their correct characterization remains a crucial challenge over large areas. The aim of the study is to quantify the impact of different types of uncertainties that arise from the unresolved soil spatial variability on simulated hydrological states and fluxes. Three perturbation methods are presented for the characterization of uncertainties in soil properties. The methods are applied on the soil map of the upper Neckar catchment (Germany), as an example. The uncertainties are propagated through the distributed mesoscale hydrological model (mHM) to assess the impact on the simulated states and fluxes. The model outputs are analysed by aggregating the results at different spatial and temporal scales. These results show that the impact of the different uncertainties introduced in the original soil map is equivalent when the simulated model outputs are analysed at the model grid resolution (i.e. 500 m). However, several differences are identified by aggregating states and fluxes at different spatial scales (by subcatchments of different sizes or coarsening the grid resolution). Streamflow is only sensitive to the perturbation of long spatial structures while distributed states and fluxes (e.g. soil moisture and groundwater recharge) are only sensitive to the local noise introduced to the original soil properties. A clear identification of the temporal and spatial scale for which finer-resolution soil information is (or is not) relevant is unlikely to be universal. However, the comparison of the impacts on the different hydrological components can be used to prioritize the model improvements in specific applications, either by collecting new measurements or by calibration and data assimilation approaches. In conclusion, the study underlines the importance of a correct characterization of uncertainty in soil properties. With that, soil maps with additional information regarding the unresolved soil spatial variability would provide strong support to hydrological modelling applications. Y1 - 2017 U6 - https://doi.org/10.5194/hess-21-2301-2017 SN - 1027-5606 SN - 1607-7938 VL - 21 SP - 2301 EP - 2320 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Jing, Miao A1 - Hesse, Falk A1 - Kumar, Rohini A1 - Wang, Wenqing A1 - Fischer, Thomas A1 - Walther, Marc A1 - Zink, Matthias A1 - Zech, Alraune A1 - Samaniego, Luis A1 - Kolditz, Olaf A1 - Attinger, Sabine T1 - Improved regional-scale groundwater representation by the coupling of the mesoscale Hydrologic Model (mHM v5.7) to the groundwater model OpenGeoSys (OGS) JF - Geoscientific model development : an interactive open access journal of the European Geosciences Union N2 - Most large-scale hydrologic models fall short in reproducing groundwater head dynamics and simulating transport process due to their oversimplified representation of groundwater flow. In this study, we aim to extend the applicability of the mesoscale Hydrologic Model (mHM v5.7) to subsurface hydrology by coupling it with the porous media simulator OpenGeoSys (OGS). The two models are one-way coupled through model interfaces GIS2FEM and RIV2FEM, by which the grid-based fluxes of groundwater recharge and the river-groundwater exchange generated by mHM are converted to fixed-flux boundary conditions of the groundwater model OGS. Specifically, the grid-based vertical reservoirs in mHM are completely preserved for the estimation of land-surface fluxes, while OGS acts as a plug-in to the original mHM modeling framework for groundwater flow and transport modeling. The applicability of the coupled model (mHM-OGS v1.0) is evaluated by a case study in the central European mesoscale river basin - Nagelstedt. Different time steps, i.e., daily in mHM and monthly in OGS, are used to account for fast surface flow and slow groundwater flow. Model calibration is conducted following a two-step procedure using discharge for mHM and long-term mean of groundwater head measurements for OGS. Based on the model summary statistics, namely the Nash-Sutcliffe model efficiency (NSE), the mean absolute error (MAE), and the interquartile range error (QRE), the coupled model is able to satisfactorily represent the dynamics of discharge and groundwater heads at several locations across the study basin. Our exemplary calculations show that the one-way coupled model can take advantage of the spatially explicit modeling capabilities of surface and groundwater hydrologic models and provide an adequate representation of the spatiotemporal behaviors of groundwater storage and heads, thus making it a valuable tool for addressing water resources and management problems. Y1 - 2018 U6 - https://doi.org/10.5194/gmd-11-1989-2018 SN - 1991-959X SN - 1991-9603 VL - 11 IS - 5 SP - 1989 EP - 2007 PB - Copernicus CY - Göttingen ER - TY - GEN A1 - Jing, Miao A1 - Kumar, Rohini A1 - Heße, Falk A1 - Thober, Stephan A1 - Rakovec, Oldrich A1 - Samaniego, Luis A1 - Attinger, Sabine T1 - Assessing the response of groundwater quantity and travel time distribution to 1.5, 2, and 3 °C global warming in a mesoscale central German basin T2 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Groundwater is the biggest single source of high-quality freshwater worldwide, which is also continuously threatened by the changing climate. In this paper, we investigate the response of the regional groundwater system to climate change under three global warming levels (1.5, 2, and 3 ∘C) in a central German basin (Nägelstedt). This investigation is conducted by deploying an integrated modeling workflow that consists of a mesoscale hydrologic model (mHM) and a fully distributed groundwater model, OpenGeoSys (OGS). mHM is forced with climate simulations of five general circulation models under three representative concentration pathways. The diffuse recharges estimated by mHM are used as boundary forcings to the OGS groundwater model to compute changes in groundwater levels and travel time distributions. Simulation results indicate that groundwater recharges and levels are expected to increase slightly under future climate scenarios. Meanwhile, the mean travel time is expected to decrease compared to the historical average. However, the ensemble simulations do not all agree on the sign of relative change. Changes in mean travel time exhibit a larger variability than those in groundwater levels. The ensemble simulations do not show a systematic relationship between the projected change (in both groundwater levels and travel times) and the warming level, but they indicate an increased variability in projected changes with adjusting the enhanced warming level from 1.5 to 3 ∘C. Correspondingly, it is highly recommended to restrain the trend of global warming. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1402 KW - climate change impacts KW - hydrological models KW - coupled surface KW - water fluxes KW - catchment KW - recharge KW - dynamics KW - aquifer KW - flow KW - parameterization Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-509343 SN - 1866-8372 IS - 3 ER - TY - GEN A1 - Schmidt, Lennart A1 - Heße, Falk A1 - Attinger, Sabine A1 - Kumar, Rohini T1 - Challenges in applying machine learning models for hydrological inference: a case study for flooding events across Germany T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Machine learning (ML) algorithms are being increasingly used in Earth and Environmental modeling studies owing to the ever-increasing availability of diverse data sets and computational resources as well as advancement in ML algorithms. Despite advances in their predictive accuracy, the usefulness of ML algorithms for inference remains elusive. In this study, we employ two popular ML algorithms, artificial neural networks and random forest, to analyze a large data set of flood events across Germany with the goals to analyze their predictive accuracy and their usability to provide insights to hydrologic system functioning. The results of the ML algorithms are contrasted against a parametric approach based on multiple linear regression. For analysis, we employ a model-agnostic framework named Permuted Feature Importance to derive the influence of models' predictors. This allows us to compare the results of different algorithms for the first time in the context of hydrology. Our main findings are that (1) the ML models achieve higher prediction accuracy than linear regression, (2) the results reflect basic hydrological principles, but (3) further inference is hindered by the heterogeneity of results across algorithms. Thus, we conclude that the problem of equifinality as known from classical hydrological modeling also exists for ML and severely hampers its potential for inference. To account for the observed problems, we propose that when employing ML for inference, this should be made by using multiple algorithms and multiple methods, of which the latter should be embedded in a cross-validation routine. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1193 KW - machine learning KW - inference KW - floods Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-523843 SN - 1866-8372 IS - 5 ER - TY - JOUR A1 - Schmidt, Lennart A1 - Heße, Falk A1 - Attinger, Sabine A1 - Kumar, Rohini T1 - Challenges in applying machine learning models for hydrological inference: a case study for flooding events across Germany JF - Water Resources Research N2 - Machine learning (ML) algorithms are being increasingly used in Earth and Environmental modeling studies owing to the ever-increasing availability of diverse data sets and computational resources as well as advancement in ML algorithms. Despite advances in their predictive accuracy, the usefulness of ML algorithms for inference remains elusive. In this study, we employ two popular ML algorithms, artificial neural networks and random forest, to analyze a large data set of flood events across Germany with the goals to analyze their predictive accuracy and their usability to provide insights to hydrologic system functioning. The results of the ML algorithms are contrasted against a parametric approach based on multiple linear regression. For analysis, we employ a model-agnostic framework named Permuted Feature Importance to derive the influence of models' predictors. This allows us to compare the results of different algorithms for the first time in the context of hydrology. Our main findings are that (1) the ML models achieve higher prediction accuracy than linear regression, (2) the results reflect basic hydrological principles, but (3) further inference is hindered by the heterogeneity of results across algorithms. Thus, we conclude that the problem of equifinality as known from classical hydrological modeling also exists for ML and severely hampers its potential for inference. To account for the observed problems, we propose that when employing ML for inference, this should be made by using multiple algorithms and multiple methods, of which the latter should be embedded in a cross-validation routine. KW - machine learning KW - inference KW - floods Y1 - 2019 VL - 56 IS - 5 PB - John Wiley & Sons, Inc. CY - New Jersey ER - TY - JOUR A1 - Jing, Miao A1 - Kumar, Rohini A1 - Heße, Falk A1 - Thober, Stephan A1 - Rakovec, Oldrich A1 - Samaniego, Luis A1 - Attinger, Sabine T1 - Assessing the response of groundwater quantity and travel time distribution to 1.5, 2, and 3 °C global warming in a mesoscale central German basin JF - Hydrology and Earth System Sciences N2 - Groundwater is the biggest single source of high-quality freshwater worldwide, which is also continuously threatened by the changing climate. In this paper, we investigate the response of the regional groundwater system to climate change under three global warming levels (1.5, 2, and 3 ∘C) in a central German basin (Nägelstedt). This investigation is conducted by deploying an integrated modeling workflow that consists of a mesoscale hydrologic model (mHM) and a fully distributed groundwater model, OpenGeoSys (OGS). mHM is forced with climate simulations of five general circulation models under three representative concentration pathways. The diffuse recharges estimated by mHM are used as boundary forcings to the OGS groundwater model to compute changes in groundwater levels and travel time distributions. Simulation results indicate that groundwater recharges and levels are expected to increase slightly under future climate scenarios. Meanwhile, the mean travel time is expected to decrease compared to the historical average. However, the ensemble simulations do not all agree on the sign of relative change. Changes in mean travel time exhibit a larger variability than those in groundwater levels. The ensemble simulations do not show a systematic relationship between the projected change (in both groundwater levels and travel times) and the warming level, but they indicate an increased variability in projected changes with adjusting the enhanced warming level from 1.5 to 3 ∘C. Correspondingly, it is highly recommended to restrain the trend of global warming. KW - climate change impacts KW - hydrological models KW - coupled surface KW - water fluxes KW - catchment KW - recharge KW - dynamics KW - aquifer KW - flow KW - parameterization Y1 - 2020 U6 - https://doi.org/10.5194/hess-24-1511-2020 SN - 1607-7938 SN - 1027-5606 VL - 24 IS - 3 SP - 1511 EP - 1526 PB - Copernicus Publ. CY - Göttingen ER - TY - GEN A1 - Kumar, Rohini A1 - Hesse, Fabienne A1 - Rao, P. Srinivasa A1 - Musolff, Andreas A1 - Jawitz, James A1 - Sarrazin, Francois A1 - Samaniego, Luis A1 - Fleckenstein, Jan H. A1 - Rakovec, Oldrich A1 - Thober, S. A1 - Attinger, Sabine T1 - Strong hydroclimatic controls on vulnerability to subsurface nitrate contamination across Europe T2 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Subsurface contamination due to excessive nutrient surpluses is a persistent and widespread problem in agricultural areas across Europe. The vulnerability of a particular location to pollution from reactive solutes, such as nitrate, is determined by the interplay between hydrologic transport and biogeochemical transformations. Current studies on the controls of subsurface vulnerability do not consider the transient behaviour of transport dynamics in the root zone. Here, using state-of-the-art hydrologic simulations driven by observed hydroclimatic forcing, we demonstrate the strong spatiotemporal heterogeneity of hydrologic transport dynamics and reveal that these dynamics are primarily controlled by the hydroclimatic gradient of the aridity index across Europe. Contrasting the space-time dynamics of transport times with reactive timescales of denitrification in soil indicate that similar to 75% of the cultivated areas across Europe are potentially vulnerable to nitrate leaching for at least onethird of the year. We find that neglecting the transient nature of transport and reaction timescale results in a great underestimation of the extent of vulnerable regions by almost 50%. Therefore, future vulnerability and risk assessment studies must account for the transient behaviour of transport and biogeochemical transformation processes. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1352 KW - travel time distributions KW - groundwater vulnerability KW - flux tracking KW - transit-time KW - water age KW - nitrogen KW - model KW - dynamics KW - pollution KW - patterns Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-549875 SN - 1866-8372 IS - 1 ER - TY - JOUR A1 - Kumar, Rohini A1 - Hesse, Fabienne A1 - Rao, P. Srinivasa A1 - Musolff, Andreas A1 - Jawitz, James A1 - Sarrazin, Francois A1 - Samaniego, Luis A1 - Fleckenstein, Jan H. A1 - Rakovec, Oldrich A1 - Thober, S. A1 - Attinger, Sabine T1 - Strong hydroclimatic controls on vulnerability to subsurface nitrate contamination across Europe JF - Nature Communications N2 - Subsurface contamination due to excessive nutrient surpluses is a persistent and widespread problem in agricultural areas across Europe. The vulnerability of a particular location to pollution from reactive solutes, such as nitrate, is determined by the interplay between hydrologic transport and biogeochemical transformations. Current studies on the controls of subsurface vulnerability do not consider the transient behaviour of transport dynamics in the root zone. Here, using state-of-the-art hydrologic simulations driven by observed hydroclimatic forcing, we demonstrate the strong spatiotemporal heterogeneity of hydrologic transport dynamics and reveal that these dynamics are primarily controlled by the hydroclimatic gradient of the aridity index across Europe. Contrasting the space-time dynamics of transport times with reactive timescales of denitrification in soil indicate that similar to 75% of the cultivated areas across Europe are potentially vulnerable to nitrate leaching for at least onethird of the year. We find that neglecting the transient nature of transport and reaction timescale results in a great underestimation of the extent of vulnerable regions by almost 50%. Therefore, future vulnerability and risk assessment studies must account for the transient behaviour of transport and biogeochemical transformation processes. KW - travel time distributions KW - groundwater vulnerability KW - flux tracking KW - transit-time KW - water age KW - nitrogen KW - model KW - dynamics KW - pollution KW - patterns Y1 - 2020 U6 - https://doi.org/10.1038/s41467-020-19955-8 SN - 2041-1723 VL - 11 IS - 1 SP - 1 EP - 10 PB - Nature Publishing Group UK CY - London ER -