@article{SchuelerCalabreseAttinger2021, author = {Sch{\"u}ler, Lennart and Calabrese, Justin M. and Attinger, Sabine}, title = {Data driven high resolution modeling and spatial analyses of the COVID-19 pandemic in Germany}, series = {PLoS one}, volume = {16}, journal = {PLoS one}, number = {8}, publisher = {PLoS}, address = {San Fransisco}, issn = {1932-6203}, doi = {10.1371/journal.pone.0254660}, pages = {14}, year = {2021}, abstract = {The SARS-CoV-2 virus has spread around the world with over 100 million infections to date, and currently many countries are fighting the second wave of infections. With neither sufficient vaccination capacity nor effective medication, non-pharmaceutical interventions (NPIs) remain the measure of choice. However, NPIs place a great burden on society, the mental health of individuals, and economics. Therefore the cost/benefit ratio must be carefully balanced and a target-oriented small-scale implementation of these NPIs could help achieve this balance. To this end, we introduce a modified SEIRD-class compartment model and parametrize it locally for all 412 districts of Germany. The NPIs are modeled at district level by time varying contact rates. This high spatial resolution makes it possible to apply geostatistical methods to analyse the spatial patterns of the pandemic in Germany and to compare the results of different spatial resolutions. We find that the modified SEIRD model can successfully be fitted to the COVID-19 cases in German districts, states, and also nationwide. We propose the correlation length as a further measure, besides the weekly incidence rates, to describe the current situation of the epidemic.}, language = {en} } @article{SarrazinKumarBasuetal.2022, author = {Sarrazin, Fanny J. and Kumar, Rohini and Basu, Nandita B. and Musolff, Andreas and Weber, Michael and Van Meter, Kimberly J. and Attinger, Sabine}, title = {Characterizing catchment-scale nitrogen legacies and constraining their uncertainties}, series = {Water resources research}, volume = {58}, journal = {Water resources research}, number = {4}, publisher = {American Geophysical Union}, address = {Washington}, issn = {0043-1397}, doi = {10.1029/2021WR031587}, pages = {32}, year = {2022}, abstract = {Improving nitrogen (N) status in European water bodies is a pressing issue. N levels depend not only on current but also past N inputs to the landscape, that have accumulated through time in legacy stores (e.g., soil, groundwater). Catchment-scale N models, that are commonly used to investigate in-stream N levels, rarely examine the magnitude and dynamics of legacy components. This study aims to gain a better understanding of the long-term fate of the N inputs and its uncertainties, using a legacy-driven N model (ELEMeNT) in Germany's largest national river basin (Weser; 38,450 km(2)) over the period 1960-2015. We estimate the nine model parameters based on a progressive constraining strategy, to assess the value of different observational data sets. We demonstrate that beyond in-stream N loading, soil N content and in-stream N concentration allow to reduce the equifinality in model parameterizations. We find that more than 50\% of the N surplus denitrifies (1480-2210 kg ha(-1)) and the stream export amounts to around 18\% (410-640 kg ha(-1)), leaving behind as much as around 230-780 kg ha(-1) of N in the (soil) source zone and 10-105 kg ha(-1) in the subsurface. A sensitivity analysis reveals the importance of different factors affecting the residual uncertainties in simulated N legacies, namely hydrologic travel time, denitrification rates, a coefficient characterizing the protection of organic N in source zone and N surplus input. Our study calls for proper consideration of uncertainties in N legacy characterization, and discusses possible avenues to further reduce the equifinality in water quality modeling.}, language = {en} } @article{HoubenPujadesKalbacheretal.2022, author = {Houben, Timo and Pujades, Estanislao and Kalbacher, Thomas and Dietrich, Peter and Attinger, Sabine}, title = {From dynamic groundwater level measurements to regional aquifer parameters - assessing the power of spectral analysis}, series = {Water resources research}, volume = {58}, journal = {Water resources research}, number = {5}, publisher = {Wiley}, address = {New York}, issn = {0043-1397}, doi = {10.1029/2021WR031289}, pages = {22}, year = {2022}, abstract = {Large-scale groundwater models are required to estimate groundwater availability and to inform water management strategies on the national scale. However, parameterization of large-scale groundwater models covering areas of major river basins and more is challenging due to the lack of observational data and the mismatch between the scales of modeling and measurements. In this work, we propose to bridge the scale gap and derive regional hydraulic parameters by spectral analysis of groundwater level fluctuations. We hypothesize that specific locations in aquifers can reveal regional parameters of the hydraulic system. We first generate ensembles of synthetic but realistic aquifers which systematically differ in complexity. Applying Liang and Zhang's (2013), , semi-analytical solution for the spectrum of hydraulic head time series, we identify for each ensemble member and at different locations representative aquifer parameters. Next, we extend our study to investigate the use of spectral analysis in more complex numerical models and in real settings. Our analyses indicate that the variance of inferred effective transmissivity and storativity values for stochastic aquifer ensembles is small for observation points which are far away from the Dirichlet boundary. Moreover, the head time series has to cover a period which is roughly 10 times as long as the characteristic time of the aquifer. In deterministic aquifer models we infer equivalent, regionally valid parameters. A sensitivity analysis further reveals that as long as the aquifer length and the position of the groundwater measurement location is roughly known, the parameters can be robustly estimated.}, language = {en} } @article{SchroenOswaldZachariasetal.2021, author = {Schr{\"o}n, Martin and Oswald, Sascha and Zacharias, Steffen and Kasner, Mandy and Dietrich, Peter and Attinger, Sabine}, title = {Neutrons on rails}, series = {Geophysical research letters : GRL / American Geophysical Union}, volume = {48}, journal = {Geophysical research letters : GRL / American Geophysical Union}, number = {24}, publisher = {Wiley}, address = {Hoboken, NJ}, issn = {0094-8276}, doi = {10.1029/2021GL093924}, pages = {10}, year = {2021}, abstract = {Large-scale measurements of the spatial distribution of water content in soils and snow are challenging for state-of-the-art hydrogeophysical methods. Cosmic-ray neutron sensing (CRNS) is a noninvasive technology that has the potential to bridge the scale gap between conventional in situ sensors and remote sensing products in both, horizontal and vertical domains. In this study, we explore the feasibility and potential of estimating water content in soils and snow with neutron detectors in moving trains. Theoretical considerations quantify the stochastic measurement uncertainty as a function of water content, altitude, resolution, and detector efficiency. Numerical experiments demonstrate that the sensitivity of measured water content is almost unperturbed by train materials. Finally, three distinct real-world experiments provide a proof of concept on short and long-range tracks. With our results a transregional observational soil moisture product becomes a realistic vision within the next years.}, language = {en} } @article{NguyenKumarMusolffetal.2022, author = {Nguyen, Tam and Kumar, Rohini and Musolff, Andreas and Lutz, Stefanie R. and Sarrazin, Fanny and Attinger, Sabine and Fleckenstein, Jan H.}, title = {Disparate Seasonal Nitrate Export From Nested Heterogeneous Subcatchments Revealed With StorAge Selection Functions}, series = {Water resources research}, volume = {58}, journal = {Water resources research}, number = {3}, publisher = {American Geophysical Union}, address = {Washington}, issn = {0043-1397}, doi = {10.1029/2021WR030797}, pages = {20}, year = {2022}, abstract = {Understanding catchment controls on catchment solute export is a prerequisite for water quality management. StorAge Selection (SAS) functions encapsulate essential information about catchment functioning in terms of discharge selection preference and solute export dynamics. However, they lack information on the spatial origin of solutes when applied at the catchment scale, thereby limiting our understanding of the internal (subcatchment) functioning. Here, we parameterized SAS functions in a spatially explicit way to understand the internal catchment responses and transport dynamics of reactive dissolved nitrate (N-NO3). The model was applied in a nested mesoscale catchment (457 km(2)), consisting of a mountainous partly forested, partly agricultural subcatchment, a middle-reach forested subcatchment, and a lowland agricultural subcatchment. The model captured flow and nitrate concentration dynamics not only at the catchment outlet but also at internal gauging stations. Results reveal disparate subsurface mixing dynamics and nitrate export among headwater and lowland subcatchments. The headwater subcatchment has high seasonal variation in subsurface mixing schemes and younger water in discharge, while the lowland subcatchment has less pronounced seasonality in subsurface mixing and much older water in discharge. Consequently, nitrate concentration in discharge from the headwater subcatchment shows strong seasonality, whereas that from the lowland subcatchment is stable in time. The temporally varying responses of headwater and lowland subcatchments alternate the dominant contribution to nitrate export in high and low-flow periods between subcatchments. Overall, our results demonstrate that the spatially explicit SAS modeling provides useful information about internal catchment functioning, helping to develop or evaluate spatial management practices.}, language = {en} } @misc{KumarHesseRaoetal.2020, author = {Kumar, Rohini and Hesse, Fabienne and Rao, P. Srinivasa and Musolff, Andreas and Jawitz, James and Sarrazin, Francois and Samaniego, Luis and Fleckenstein, Jan H. and Rakovec, Oldrich and Thober, S. and Attinger, Sabine}, title = {Strong hydroclimatic controls on vulnerability to subsurface nitrate contamination across Europe}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {1}, issn = {1866-8372}, doi = {10.25932/publishup-54987}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-549875}, pages = {12}, year = {2020}, abstract = {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.}, language = {en} } @article{KumarHesseRaoetal.2020, author = {Kumar, Rohini and Hesse, Fabienne and Rao, P. Srinivasa and Musolff, Andreas and Jawitz, James and Sarrazin, Francois and Samaniego, Luis and Fleckenstein, Jan H. and Rakovec, Oldrich and Thober, S. and Attinger, Sabine}, title = {Strong hydroclimatic controls on vulnerability to subsurface nitrate contamination across Europe}, series = {Nature Communications}, volume = {11}, journal = {Nature Communications}, number = {1}, publisher = {Nature Publishing Group UK}, address = {London}, issn = {2041-1723}, doi = {10.1038/s41467-020-19955-8}, pages = {1 -- 10}, year = {2020}, abstract = {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.}, language = {en} } @misc{JingKumarHesseetal.2020, author = {Jing, Miao and Kumar, Rohini and Heße, Falk and Thober, Stephan and Rakovec, Oldrich and Samaniego, Luis and Attinger, Sabine}, title = {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}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {3}, issn = {1866-8372}, doi = {10.25932/publishup-50934}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-509343}, pages = {18}, year = {2020}, abstract = {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 (N{\"a}gelstedt). 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.}, language = {en} } @article{JingKumarHesseetal.2020, author = {Jing, Miao and Kumar, Rohini and Heße, Falk and Thober, Stephan and Rakovec, Oldrich and Samaniego, Luis and Attinger, Sabine}, title = {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}, series = {Hydrology and Earth System Sciences}, volume = {24}, journal = {Hydrology and Earth System Sciences}, number = {3}, publisher = {Copernicus Publ.}, address = {G{\"o}ttingen}, issn = {1607-7938}, doi = {10.5194/hess-24-1511-2020}, pages = {1511 -- 1526}, year = {2020}, abstract = {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 (N{\"a}gelstedt). 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.}, language = {en} } @article{SchweppeThoberMuelleretal.2022, author = {Schweppe, Robert and Thober, Stephan and M{\"u}ller, Sebastian and Kelbling, Matthias and Kumar, Rohini and Attinger, Sabine and Samaniego, Luis}, title = {MPR 1.0: a stand-alone multiscale parameter regionalization tool for improved parameter estimation of land surface models}, series = {Geoscientific model development : an interactive open access journal of the European Geosciences Union}, volume = {15}, journal = {Geoscientific model development : an interactive open access journal of the European Geosciences Union}, number = {2}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1991-959X}, doi = {10.5194/gmd-15-859-2022}, pages = {859 -- 882}, year = {2022}, abstract = {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.}, language = {en} } @article{KawaCucchiRubinetal.2022, author = {Kawa, Nura and Cucchi, Karina and Rubin, Yoram and Attinger, Sabine and Hesse, Falk}, title = {Defining Hydrogeological Site Similarity with Hierarchical Agglomerative Clustering}, series = {Groundwater : journal of the Association of Ground-Water Scientists and Engineers, a division of the National Ground Water Association}, journal = {Groundwater : journal of the Association of Ground-Water Scientists and Engineers, a division of the National Ground Water Association}, publisher = {Wiley}, address = {Hoboken}, issn = {0017-467X}, doi = {10.1111/gwat.13261}, pages = {11}, year = {2022}, abstract = {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.}, language = {en} } @article{SchmidtHesseAttingeretal.2020, author = {Schmidt, Lennart and Hesse, Falk and Attinger, Sabine and Kumar, Rohini}, title = {Challenges in applying machine learning models for hydrological inference}, series = {Water resources research}, volume = {56}, journal = {Water resources research}, number = {5}, publisher = {American Geophysical Union}, address = {Washington}, issn = {0043-1397}, doi = {10.1029/2019WR025924}, pages = {10}, year = {2020}, abstract = {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.}, language = {en} } @misc{AyllonGrimmAttingeretal.2018, author = {Ayllon, Daniel and Grimm, Volker and Attinger, Sabine and Hauhs, Michael and Simmer, Clemens and Vereecken, Harry and Lischeid, Gunnar}, title = {Cross-disciplinary links in environmental systems science}, series = {The science of the total environment : an international journal for scientific research into the environment and its relationship with man}, volume = {622}, journal = {The science of the total environment : an international journal for scientific research into the environment and its relationship with man}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0048-9697}, doi = {10.1016/j.scitotenv.2017.12.007}, pages = {954 -- 973}, year = {2018}, abstract = {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.}, language = {en} } @article{JingHesseKumaretal.2018, author = {Jing, Miao and Hesse, Falk and Kumar, Rohini and Wang, Wenqing and Fischer, Thomas and Walther, Marc and Zink, Matthias and Zech, Alraune and Samaniego, Luis and Kolditz, Olaf and Attinger, Sabine}, title = {Improved regional-scale groundwater representation by the coupling of the mesoscale Hydrologic Model (mHM v5.7) to the groundwater model OpenGeoSys (OGS)}, series = {Geoscientific model development : an interactive open access journal of the European Geosciences Union}, volume = {11}, journal = {Geoscientific model development : an interactive open access journal of the European Geosciences Union}, number = {5}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1991-959X}, doi = {10.5194/gmd-11-1989-2018}, pages = {1989 -- 2007}, year = {2018}, abstract = {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.}, language = {en} } @article{SchmidtHesseAttingeretal.2020, author = {Schmidt, Lennart and Heße, Falk and Attinger, Sabine and Kumar, Rohini}, title = {Challenges in applying machine learning models for hydrological inference: a case study for flooding events across Germany}, series = {Water Resources Research}, volume = {56}, journal = {Water Resources Research}, number = {5}, publisher = {John Wiley \& Sons, Inc.}, address = {New Jersey}, pages = {10}, year = {2020}, abstract = {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.}, language = {en} } @misc{SchmidtHesseAttingeretal.2020, author = {Schmidt, Lennart and Heße, Falk and Attinger, Sabine and Kumar, Rohini}, title = {Challenges in applying machine learning models for hydrological inference: a case study for flooding events across Germany}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {5}, issn = {1866-8372}, doi = {10.25932/publishup-52384}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-523843}, pages = {12}, year = {2020}, abstract = {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.}, language = {en} } @article{JingHesseKumaretal.2019, author = {Jing, Miao and Hesse, Falk and Kumar, Rohini and Kolditz, Olaf and Kalbacher, Thomas and Attinger, Sabine}, title = {Influence of input and parameter uncertainty on the prediction of catchment-scale groundwater travel time distributions}, series = {Hydrology and earth system sciences : HESS}, volume = {23}, journal = {Hydrology and earth system sciences : HESS}, number = {1}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1027-5606}, doi = {10.5194/hess-23-171-2019}, pages = {171 -- 190}, year = {2019}, abstract = {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.}, language = {en} } @article{BaroniSchalgeRakovecetal.2019, author = {Baroni, Gabriele and Schalge, Bernd and Rakovec, Oldrich and Kumar, Rohini and Sch{\"u}ler, Lennart and Samaniego, Luis and Simmer, Clemens and Attinger, Sabine}, title = {A Comprehensive Distributed Hydrological Modeling Intercomparison to Support Process Representation and Data Collection Strategies}, series = {Water resources research}, volume = {55}, journal = {Water resources research}, number = {2}, publisher = {American Geophysical Union}, address = {Washington}, issn = {0043-1397}, doi = {10.1029/2018WR023941}, pages = {990 -- 1010}, year = {2019}, abstract = {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.}, language = {en} } @article{ZechAttingerBellinetal.2019, author = {Zech, Alraune and Attinger, Sabine and Bellin, Alberto and Cvetkovic, Vladimir and Dietrich, Peter and Fiori, Aldo and Teutsch, Georg and Dagan, Gedeon}, title = {A Critical Analysis of Transverse Dispersivity Field Data}, series = {Groundwater : journal of the Association of Ground-Water Scientists and Engineers, a division of the National Ground Water Association}, volume = {57}, journal = {Groundwater : journal of the Association of Ground-Water Scientists and Engineers, a division of the National Ground Water Association}, number = {4}, publisher = {Wiley}, address = {Hoboken}, issn = {0017-467X}, doi = {10.1111/gwat.12838}, pages = {632 -- 639}, year = {2019}, abstract = {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.}, language = {en} } @article{BaroniZinkKumaretal.2017, author = {Baroni, Gabriele and Zink, Matthias and Kumar, Rohini and Samaniego, Luis and Attinger, Sabine}, title = {Effects of uncertainty in soil properties on simulated hydrological states and fluxes at different spatio-temporal scales}, series = {Hydrology and earth system sciences : HESS}, volume = {21}, journal = {Hydrology and earth system sciences : HESS}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1027-5606}, doi = {10.5194/hess-21-2301-2017}, pages = {2301 -- 2320}, year = {2017}, abstract = {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.}, language = {en} }