Understanding each other's models: a standard representation of global water models to support improvement, intercomparison, and communication
- 1Institute of Physical Geography, Johann Wolfgang Goethe University Frankfurt, Frankfurt am Main, 60438, Germany
- 2Senckenberg Leibniz Biodiversity and Climate Research Centre (SBiK-F), Frankfurt am Main, 60325, Germany
- 3Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel, Brussels, 1050, Belgium
- 4Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
- 5International Institute for Applied Systems Analysis, Laxenburg, 2361, Austria
- 6National Institute for Environmental Studies, Tsukuba, 305–8506, Japan
- 7Potsdam Institute for Climate Impact Research, Potsdam, 14473, Germany
- 8Institute for Mediterranean Studies, Foundation for Research and Technology-Hellas, Rethymno, 74100, Greece
- 9School of Geography, University of Nottingham, Nottingham, NG7 2RD, United Kingdom of Great Britain and Northern Ireland
- 10Department Computational Hydrosystems, UFZ-Helmholtz Centre for Environmental Research, Leipzig, 04318, Germany
- 11Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, 16500, Czech Republic
- 12Institute of Coastal Research, Helmholtz-Zentrum Geesthacht, Geesthacht, 21502, Germany
- 13College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
- 14Laboratoire des Sciences du Climat et de l’Environnement, CEA-CNRS-UVSQ/IPSL, Université Paris Saclay, Gif sur Yvette, 91191, France
- 15Department of Physical Geography, Utrecht University, Utrecht, 3508, The Netherlands
- 16ndian Institute of Technology Gandhinagar, Palaj, Gandhinagar, 382355, India
- 17School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
- 18School of Environmental Engineering, Technical University of Crete, Chania, 73100, Greece
- 19Department of Civil and Environmental Engineering, Michigan State University, East Lansing, Michigan, 48824, United States of America
- 20School of Geographic Sciences, East China Normal University, Shanghai, 200241, China
- 21Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research / Atmospheric Environmental Research, Garmisch-Partenkirchen, 82467, Germany
Abstract. Global water models (GWMs) simulate the terrestrial water cycle, on the global scale, and are used to assess the impacts of climate change on freshwater systems. GWMs are developed within different modeling frameworks and consider different underlying hydrological processes, leading to varied model structures. Furthermore, the equations used to describe various processes take different forms and are generally accessible only from within the individual model codes. These factors have hindered a holistic and detailed understanding of how different models operate, yet such an understanding is crucial for explaining the results of model evaluation studies, understanding inter-model differences in their simulations, and identifying areas for future model development. This study provides a comprehensive overview of how state-of-the-art GWMs are designed. We analyze water storage compartments, water flows, and human water use sectors included in 16 GWMs that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). We develop a standard writing style for the model equations to further enhance model improvement, intercomparison, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Seven models used six compartments, while three models (JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments. WaterGAP2 simulates five human water use sectors, while four models (CLM4.5, CLM5.0, LPJmL, and MPI-HM) simulate only water used by humans for the irrigation sector. We conclude that even though hydrologic processes are often based on similar equations, in the end, these equations have been adjusted or have used different values for specific parameters or specific variables. Our results highlight that the predictive uncertainty of GWMs can be reduced through improvements of the existing hydrologic processes, implementation of new processes in the models, and high-quality input data.
Camelia-Eliza Telteu et al.
Camelia-Eliza Telteu et al.
Model code and software
CESM1.2.2_CLM4.5_freeze_ISIMIP2b (Version CESM1.2.2 - CLM4.5) https://doi.org/10.5281/zenodo.4277137
ESCOMP/CTSM: Update documentation for release-clm5.0 branch, and fix issues with no-anthro surface dataset creation (Version release-clm5.0.34) https://doi.org/10.5281/zenodo.3779821
Community Water Model (CwatM) (Version v1.04) https://doi.org/10.5281/zenodo.3361478
H08 Version 20190101 https://doi.org/10.5281/zenodo.4263375
mesoscale Hydrologic Model (Version v5.8) https://doi.org/10.5281/zenodo.1069203
Camelia-Eliza Telteu et al.
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2 citations as recorded by crossref.
- The global water resources and use model WaterGAP v2.2d: model description and evaluation H. Müller Schmied et al. 10.5194/gmd-14-1037-2021
- Uncertainty of simulated groundwater recharge at different global warming levels: a global-scale multi-model ensemble study R. Reinecke et al. 10.5194/hess-25-787-2021