Abstract/Summary

Global Hydrological and Land Surface Models (GHM/LSMs) embody numerous interacting predictors and equations, complicating the understanding of primary hydrological relationships. We propose a model diagnostic approach based on Random Forest (RF) feature importance to detect the input variables that most influence simulated hydrological fluxes. We analyzed the JULES, ORCHIDEE, HTESSEL, SURFEX, and PCR-GLOBWB models for the relative importance of precipitation, climate, soil, land cover and topographic slope as predictors of simulated average evaporation, runoff, and surface and subsurface runoff. RF models functioned as a metamodel and could reproduce GHM/LSMs outputs with a coefficient of determination (R2) over 0.85 in all cases and often considerably better. The GHM/LSMs agreed that precipitation, climate and land cover share equal importance for evaporation prediction, and mean precipitation is the most important predictor of runoff, while topographic slope and soil texture have no influence on the total variance of the water balance. However, the GHM/LSMs disagreed on which features determine surface and subsurface runoff processes, especially with regard to the relative importance of soil texture and topographic slope. Finally, the selection of soil maps was only important for target variables of which soil is a relevant predictor. We conclude that estimating feature importance is a useful diagnostic approach for model intercomparison projects.