Understanding each other's models: a standard representation of global water models to support improvement, intercomparison, and communication

Abstract

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.

Amplified Drought and Flood Risk Under Future Socioeconomic and Climatic Change

“…A comprehensive overview of the modeling frameworks of these GHMs is provided by Telteu et al. (2021). We consider the same historical and future periods and concentration pathways as the CMIP5 simulations for the ISIMIP2b simulations.…”

Comment on gmd-2020-367

“…Venant equation (Chow et al, 1998), linked with dynamic reservoir and lake operation. Further, CWatM computes runoff concentrated in creeks and rivulets, with a lag time before entering the river storage, by using a triangular weighting function (Burek et al, 2020).…”

Understanding process controls on groundwater recharge variability across Africa through recharge landscapes

“…We characterized the influence of landcover on groundwater recharge via transpiration and canopy storage processes, by attributing vegetation specific transpiration coefficients to a landcover dataset and by looking at the Leaf Area Index, respectively. This approach is also often taken when parameterizing these processes in continental scale hydrological modelling (Telteu et al, 2021). To avoid having multiple indices to describe soil textures we instead calculated the ratio of soils which promote infiltration (i.e., sand) to those which restrict infiltration (i.e., silt and clay) .…”

“…However, these models largely rely upon global datasets for their parameterisation with only very limited levels of evaluation against hydrologic fluxes -especially fluxes rarely estimated locally such as groundwater recharge (Bierkens, 2015;Telteu et al, 2021;Wagener et al, 2021). Global models thus far also include only a limited number of process representations and neglect regionally dominant controls, such as karst (Hartmann et al, 2015;Hartmann et al, 2014) or dryland-specific hydrological processes (Quichimbo et al 2021).…”