TY - JOUR A1 - Schüler, Lennart A1 - Calabrese, Justin M. A1 - Attinger, Sabine T1 - Data driven high resolution modeling and spatial analyses of the COVID-19 pandemic in Germany JF - PLoS one N2 - 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. Y1 - 2021 U6 - https://doi.org/10.1371/journal.pone.0254660 SN - 1932-6203 VL - 16 IS - 8 PB - PLoS CY - San Fransisco ER - TY - JOUR A1 - Sarrazin, Fanny J. A1 - Kumar, Rohini A1 - Basu, Nandita B. A1 - Musolff, Andreas A1 - Weber, Michael A1 - Van Meter, Kimberly J. A1 - Attinger, Sabine T1 - Characterizing catchment-scale nitrogen legacies and constraining their uncertainties JF - Water resources research N2 - 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. KW - nitrogen legacies KW - water quality modeling KW - equifinality KW - parameter KW - estimation KW - sensitivity analysis Y1 - 2022 U6 - https://doi.org/10.1029/2021WR031587 SN - 0043-1397 SN - 1944-7973 VL - 58 IS - 4 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Houben, Timo A1 - Pujades, Estanislao A1 - Kalbacher, Thomas A1 - Dietrich, Peter A1 - Attinger, Sabine T1 - From dynamic groundwater level measurements to regional aquifer parameters - assessing the power of spectral analysis JF - Water resources research N2 - 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. KW - spectral analysis of groundwater level fluctuations KW - proof of concept in numerical environments KW - homogeneous KW - stochastic and deterministic numerical model design KW - regional aquifer parameters KW - sensitivity analysis with field data KW - plausibility test with field data Y1 - 2022 U6 - https://doi.org/10.1029/2021WR031289 SN - 0043-1397 SN - 1944-7973 VL - 58 IS - 5 PB - Wiley CY - New York ER - TY - JOUR A1 - Schrön, Martin A1 - Oswald, Sascha A1 - Zacharias, Steffen A1 - Kasner, Mandy A1 - Dietrich, Peter A1 - Attinger, Sabine T1 - Neutrons on rails BT - Transregional monitoring of soil moisture and snow water equivalent JF - Geophysical research letters : GRL / American Geophysical Union N2 - 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. KW - soil moisture KW - transregional KW - multiscale KW - snow water equivalent KW - cosmic-ray neutron sensing KW - railway Y1 - 2021 U6 - https://doi.org/10.1029/2021GL093924 SN - 0094-8276 SN - 1944-8007 VL - 48 IS - 24 PB - Wiley CY - Hoboken, NJ ER - TY - JOUR A1 - Nguyen, Tam A1 - Kumar, Rohini A1 - Musolff, Andreas A1 - Lutz, Stefanie R. A1 - Sarrazin, Fanny A1 - Attinger, Sabine A1 - Fleckenstein, Jan H. T1 - Disparate Seasonal Nitrate Export From Nested Heterogeneous Subcatchments Revealed With StorAge Selection Functions JF - Water resources research N2 - 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. KW - catchment nitrate export KW - StorAge Selection function KW - travel time distribution KW - mesoscale heterogeneous catchment KW - subcatchment response Y1 - 2022 U6 - https://doi.org/10.1029/2021WR030797 SN - 0043-1397 SN - 1944-7973 VL - 58 IS - 3 PB - American Geophysical Union CY - Washington 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - Al-Mashaikhi, K. A1 - Oswald, Sascha 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 - 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 -