Source Relationships and Model Structures Determine Information Flow Paths in Ecohydrologic Models

Abstract In a complex ecohydrologic system, vegetation and soil variables combine to dictate heat fluxes, and these fluxes may vary depending on the extent to which drivers are linearly or nonlinearly interrelated. From a modeling and causality perspective, uncertainty, sensitivity, and performance measures all relate to how information from different sources “flows” through a model to produce a target, or output. We address how model structure, broadly defined as a mapping from inputs to an output, combines with source dependencies to produce a range of information flow pathways from sources to a target. We apply information decomposition, which partitions reductions in uncertainty into synergistic, redundant, and unique information types, to a range of model cases. Toy models show that model structure and source dependencies both restrict the types of interactions that can arise between sources and targets. Regressions based on weather data illustrate how different model structures vary in their sensitivity to source dependencies, thus affecting predictive and functional performance. Finally, we compare the Surface Flux Equilibrium theory, a land‐surface model, and neural networks in estimating the Bowen ratio and find that models trade off information types particularly when sources have the highest and lowest dependencies. Overall, this study extends an information theory‐based model evaluation framework to incorporate the influence of source dependency on information pathways. This could be applied to explore behavioral ranges for both machine learning and process‐based models, and guide model development by highlighting model deficiencies based on information flow pathways that would not be apparent based on existing measures.