Causal Discovery Methods for Functional Performance of Evapotranspiration Models

Abstract

Evapotranspiration (ET) plays a key role in agricultural water resources management. However, it is challenging to predict as it is driven by water and energy availability as well as soil, vegetation, and meteorological factors, and models vary widely in complexity and assumptions. Causal discovery methods can identify drivers and interactions based on time‐series data from both observations and models, and can be used as metrics of model “functional performance” that evaluate how models capture source‐target relationships. With many approaches to causal discovery, it is important to compare how functional performance metrics align with predictive accuracy and behave across temporal scales. We compare four methods (Granger causality, Transfer Entropy, PCMCI, and Convergent Cross Mapping) to analyze the functional performance of ET models in a corn‐soybean agricultural landscape based on 7 years of eddy covariance measurements, which we use as an empirical reference benchmark. We identify causal sources, among observed weather and soil variables, for Priestly‐Taylor (PT), Surface Flux Equilibrium (SFE), Soil Water Balance (SWB), and satellite‐based ET products from OpenET, and evaluate how closely model‐derived and observation‐based causal structures align. Methods consistently identify model forcings as sources, but otherwise vary widely in terms of sources and strengths across sub‐hourly to weekly timescales. OpenET products have high functional performance, indicating that they capture key processes although they are not forced by tower observations. Finally, some functional metrics align better with predictive performance than others, which highlights the importance of selecting robust metrics that both capture interactions and align with predictive accuracy.