Predictor Importance for Hydrological Fluxes of Global Hydrological and Land Surface Models

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. Plain Language Summary Simulations of hydrological fluxes such as evaporation and runoff at a global scale are uncertain. This happens because the models that produce global simulations are different in terms of structure, parametrization and meteorological data. So, several model intercomparison projects (MIP) have tried to identify where the hydrological fluxes estimates are most discrepant. In order to make MIPs even more useful, we are proposing an additional method focusing on understanding why the models disagree. This method consists of replacing the original global model with a random forest model and then identifying which input variables are more relevant using the feature importance functionality. More specifically, we detected how important meteorological variables, soil properties, land cover and topography are for each global model. We observed that the models agree that precipitation, climate and land cover are equally important for evaporation and that precipitation is the most important feature