@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} } @misc{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 = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe}, number = {851}, issn = {1866-8372}, doi = {10.25932/publishup-42703}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-427030}, 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} } @misc{HesseComunianAttinger2019, author = {Heße, Falk and Comunian, Alessandro and Attinger, Sabine}, title = {What We Talk About When We Talk About Uncertainty}, series = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe}, number = {754}, issn = {1866-8372}, doi = {10.25932/publishup-43658}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-436582}, pages = {20}, year = {2019}, language = {en} } @article{HesseComunianAttinger2019, author = {Heße, Falk and Comunian, Alessandro and Attinger, Sabine}, title = {What We Talk About When We Talk About Uncertainty}, series = {Frontiers in Earth Science}, volume = {7}, journal = {Frontiers in Earth Science}, publisher = {Frontiers Media}, address = {Lausanne}, issn = {2296-6463}, doi = {10.3389/feart.2019.00118}, pages = {20}, year = {2019}, language = {en} } @misc{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 = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe}, number = {545}, issn = {1866-8372}, doi = {10.25932/publishup-41917}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-419174}, pages = {20}, 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} } @article{AlMashaikhiOswaldAttingeretal.2012, author = {Al-Mashaikhi, K. and Oswald, Sascha and Attinger, Sabine and B{\"u}chel, G. and Kn{\"o}ller, K. and Strauch, G.}, title = {Evaluation of groundwater dynamics and quality in the Najd aquifers located in the Sultanate of Oman}, series = {Environmental earth sciences}, volume = {66}, journal = {Environmental earth sciences}, number = {4}, publisher = {Springer}, address = {New York}, issn = {1866-6280}, doi = {10.1007/s12665-011-1331-2}, pages = {1195 -- 1211}, year = {2012}, abstract = {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.}, language = {en} } @article{SchulzSeppeltZeheetal.2006, author = {Schulz, K. and Seppelt, Ralf and Zehe, Erwin and Vogel, Hans-J{\"o}rg and Attinger, Sabine}, title = {Importance of spatial structures in advancing hydrological sciences}, doi = {10.1029/2005wr004301}, year = {2006}, abstract = {[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}, language = {en} }