Using information theory to understand process controls in Global Water Models

Global Water Models (GWMs) are essential for understanding and projecting global hydrological fluxes under changing climate conditions, yet their outputs often diverge from each other, limiting their utility for robust decision-making. We can evaluate GWM using functional relationships that capture the spatial co-variability of over 100 forcing variables, model parameters and key output variables (such as groundwater recharge). Uncovering and identifying relationships and interactions embedded in the high-dimensional and complex input-output datasets created by these simulation models requires measures of dependence that can capture a wide range of functional behaviors. In this study, we test the Maximal Information Coefficient (MIC), an information theory based, non-parametric measure of dependence, to systematically explore and characterize input-output relationships in the Community Water Model (CWatM) forced with ISIMIP3a observed climate data. Our results demonstrate that MIC not only recovers expected hydrological controls but also reveals previously unnoticed functional relationships that Pearson and Spearman correlation coefficients would have overlooked. Additional analysis steps enable us to isolate key explanatory factors from the model’s internal structure and domain-specific factors. This information theory based approach provides a systematic methodology to improve model diagnostic capabilities, guide targeted research directions, and ultimately strengthen the credibility and interpretability of large-scale hydrological simulations.