@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{SchroenRosolemKoehlietal.2018,
  author    = {Schr{\"o}n, Martin and Rosolem, Rafael and K{\"o}hli, Markus and Piussi, L. and Schr{\"o}ter, I. and Iwema, J. and K{\"o}gler, S. and Oswald, Sascha and Wollschl{\"a}ger, U. and Samaniego, Luis and Dietrich, Peter and Zacharias, Steffen},
  title     = {Cosmic-ray Neutron Rover Surveys of Field Soil Moisture and the Influence of Roads},
  series = {Water resources research},
  volume    = {54},
  journal   = {Water resources research},
  number    = {9},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0043-1397},
  doi       = {10.1029/2017WR021719},
  pages     = {6441 -- 6459},
  year      = {2018},
  abstract  = {Measurements of root-zone soil moisture across spatial scales of tens to thousands of meters have been a challenge for many decades. The mobile application of Cosmic Ray Neutron Sensing (CRNS) is a promising approach to measure field soil moisture noninvasively by surveying large regions with a ground-based vehicle. Recently, concerns have been raised about a potentially biasing influence of local structures and roads. We employed neutron transport simulations and dedicated experiments to quantify the influence of different road types on the CRNS measurement. We found that roads introduce a substantial bias in the CRNS estimation of field soil moisture compared to off-road scenarios. However, this effect becomes insignificant at distances beyond a few meters from the road. Neutron measurements on the road could overestimate the field value by up to 40 \% depending on road material, width, and the surrounding field water content. The bias could be largely removed with an analytical correction function that accounts for these parameters. Additionally, an empirical approach is proposed that can be used without prior knowledge of field soil moisture. Tests at different study sites demonstrated good agreement between road-effect corrected measurements and field soil moisture observations. However, if knowledge about the road characteristics is missing, measurements on the road could substantially reduce the accuracy of this method. Our results constitute a practical advancement of the mobile CRNS methodology, which is important for providing unbiased estimates of field-scale soil moisture to support applications in hydrology, remote sensing, and agriculture. Plain Language Summary Measurements of root-zone soil moisture across spatial scales of tens to thousands of meters have been a challenge for many decades. The mobile application of Cosmic Ray Neutron Sensing (CRNS) is a promising approach to measure field soil moisture noninvasively by surveying large regions with a ground-based vehicle. Recently, concerns have been raised about a potentially biasing influence of roads. We employed physics simulations and dedicated experiments to quantify the influence of different road types on the CRNS measurement. We found that the presence of roads biased the CRNS estimation of field soil moisture compared to nonroad scenarios. Neutron measurements could overestimate the field value by up to 40 \% depending on road material, width, surrounding field water content, and distance from the road. We proposed a correction function that successfully removed this bias and works even without prior knowledge of field soil moisture. Tests at different study sites demonstrated good agreement between corrected measurements and other field soil moisture observations. Our results constitute a practical advancement of the mobile CRNS methodology, which is important for providing unbiased estimates of field-scale soil moisture to support applications in hydrology, remote sensing, and agriculture.},
  language  = {en}
}