Causal Analysis for Model Evaluation in Large Sample Hydrology

Hydrological and land surface models rely on strong prior assumptions about system functioning, including which processes are represented, their parametrization and how they are simplified across space and time. Model evaluation, however, is often based on measures of predictive performance that provide limited insights into whether models capture underlying processes correctly. Causal discovery methods offer a complementary perspective by learning causal interaction networks directly from time series data to reveal how system components influence each other. Here, we apply the PCMCI+ algorithm for causal discovery in combination with a causal effect estimation to hydrometeorological observations and model simulations from 671 U.S. catchments to infer monthly causal interaction networks and associated effect strengths. We show that inferred interaction strengths vary systematically across gradients of water and energy availability and reflect structural differences in how three hydrological models represent key processes of snow and evapotranspiration dynamics. Our results illustrate how causal inference can complement traditional model evaluation approaches in complex environmental systems by providing process-level insights that help bridge theory, observations, and models across disciplines.