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
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  -