HESS Opinions: Improving the evaluation of groundwater representation in continental to global scale models
- 1Department of Civil Engineering, University of Victoria, Canada
- 2School of Earth and Ocean Sciences, University of Victoria
- 3Department of Civil Engineering, University of Bristol, UK & Cabot Institute, University of Bristol, UK
- 4Institut für Physische Geographie, Goethe-Universität Frankfurt am Main and Senckenberg Leibniz Biodiversity and Climate Research Centre Frankfurt (SBiK-F), Frankfurt am Main, Germany
- 5Kansas Geological Survey, University of Kansas
- 6International Institute for Applied Systems Analysis, Laxenburg, Austria
- 7Department of Geography, University College London, UK
- 8Bureau of Economic Geology, The University of Texas at Austin, USA
- 9School of Earth and Ocean Sciences & Water Research Institute, Cardiff University, UK
- 10Department of Geology and Geological Engineering, Colorado School of Mines, USA
- 11Department of Atmospheric Sciences, National Taiwan University, Taiwan
- 12Institute of Industrial Science, The University of Tokyo
- 13Department of Geology, University of Kansas, USA
- 14Chair of Hydrological Modeling and Water Resources, University of Freiburg, Germany
- 15Department of Land, Air and Water Resources – UC Davis
- 16School of Environment and Sustainability and Global Institute for Water Security, University of Saskatchewan, Saskatoon, Canada
- 17Sorbonne Université, CNRS, EPHE, IPSL, UMR 7619 METIS, Paris, France
- 18Chair or Environmental Hydrological Systems, University of Freiburg, Germany
- 19Department of Hydrology & Atmospheric Sciences, University of Arizona, Tucson, Arizona, USA
- 20Korea Institute of Science and Technology, Seoul, South Korea
- 21Physical Geography, Utrecht University, Utrecht, Netherlands
- 22Deltares, Utrecht, Netherlands
Abstract. Continental- to global-scale hydrologic and land surface models increasingly include representations of the groundwater system, driven by crucial Earth science and sustainability problems. These models are essential for examining, communicating, and understanding the dynamic interactions between the Earth System above and below the land surface as well as the opportunities and limits of groundwater resources. A key question for this nascent and rapidly developing field is how to evaluate the realism and performance of such large-scale groundwater models given limitations in data availability and commensurability. Our objective is to provide clear recommendations for improving the evaluation of groundwater representation in continental- to global-scale models. We identify three evaluation approaches, including comparing model outputs with available observations of groundwater levels or other state or flux variables (observation-based evaluation); comparing several models with each other with or without reference to actual observations (model-based evaluation); and comparing model behavior with expert expectations of hydrologic behaviors that we expect to see in particular regions or at particular times (expert-based evaluation). Based on current and evolving practices in model evaluation as well as innovations in observations, machine learning and expert elicitation, we argue that combining observation-, model-, and expert-based model evaluation approaches may significantly improve the realism of groundwater representation in large-scale models, and thus our quantification, understanding, and prediction of crucial Earth science and sustainability problems. We encourage greater community-level communication and cooperation on these challenges, including among global hydrology and land surface modelers, local to regional hydrogeologists, and hydrologists focused on model development and evaluation.
Tom Gleeson et al.
Tom Gleeson et al.
Tom Gleeson et al.
Viewed (geographical distribution)
2 citations as recorded by crossref.
- Groundwater and baseflow drought responses to synthetic recharge stress tests J. Hellwig et al. 10.5194/hess-25-1053-2021
- Similarity-based approaches in hydrogeology: proposal of a new concept for data-scarce groundwater resource characterization and prediction R. Barthel et al. 10.1007/s10040-021-02358-4