Understanding each other's models: an introduction and a standard representation of 16 global water models to support intercomparison, improvement, 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 modelling 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 16 state-of-the-art GWMs are designed. We analyse water storage compartments, water flows, and human water use sectors included in models 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 enhance model intercomparison, improvement, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Six models used six compartments, while four models (DBH, 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 for the irrigation sector. We conclude that, even though hydrological processes are often based on similar equations for various processes, in the end these equations have been adjusted or models have used different values for specific parameters or specific variables. The similarities and differences found among the models analysed in this study are expected to enable us to reduce the uncertainty in multi-model ensembles, improve existing hydrological processes, and integrate new processes.

Hydrostreamer v1.0 – improved streamflow predictions for local applications from an ensemble of downscaled global runoff products

“…If a target zone is at the boundary of the area of interest, the boundary condition is set as the starting value of the target zone at the beginning of PP. The boundary condition does not change with iterations, consistent with the suggestion of Tobler (1979). It should be noted that averaging over neighbours is done for density (i.e.…”

“…While other interpolation methods may also be volume or mass preserving, pycnophylactic interpolation (PP) refers to a class of methods developed by Tobler (1979) to estimate the internal variation in a variable within a certain source zone. Tobler's application first subdivides a source zone into a regular grid and creates a smooth representation of the interpolated variable which preserves the volume within the source zone.…”

“…The smoothing involves solving an integral in both the x and y direction, which is subject to the condition in Eq. ( 5) that preserves total volume RO s across the parts of target zones t within the original zone s (Kar and Hodgson, 2012;Tobler, 1979).…”

“…Hydrostreamer v1.0, a software library written in the R language (R Core Team, 2019), uniquely combines (1) advanced areal interpolation methods, (2) lateral routing methods, and (3) functions for data assimilation via a model averaging approach in order to improve and extend the usability of off-the-shelf runoff products. Hydrostreamer particularly implements advanced areal interpolation methods: (1) dasymetric mapping (DM), areal interpolation which is guided by ancillary variables (Comber and Zeng, 2019;Eicher and Brewer, 2001;Wright, 1936), and (2) pycnophylactic interpolation (PP), a technique to estimate internal variable distribution within a specified zone (Tobler, 1979). To our knowledge, there are currently no other software solutions for R which implement DM, but areaweighted interpolation is available in at least sf (Pebesma, 2018) and areal (Prener and Revord, 2019) R packages.…”

Amplified Drought and Flood Risk Under Future Socioeconomic and Climatic Change

“…4 of 24 Thiery et al, 2017), H08 (Hanasaki et al, 2008), JULES-W1 (Best et al, 2011), LPJmL (Bondeau et al, 2007), MATSIRO (Takata et al, 2003), ORCHIDEE (Guimberteau et al, 2014), andWaterGAP2 (Mueller Schmied et al, 2016). 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.…”