Evaluation of Methods for Causal Discovery in Hydrometeorological Systems

Understanding causal relations is of utmost importance in hydrology and climate research for systems identification, prediction, and understanding systems behavior in a changing climate. Traditionally, researchers in hydrometeorology attempted to study causal questions by conducting controlled experiments using numerical models. This approach, however, in most cases of interest provides uncertain results because the models are approximate representation of the natural system. An alternative approach that has recently drawn significant attention in several fields is to infer causal relations from purely observational data. It possesses several traits to its utility particularly in hydrometeorology due to the rapid accumulation of in situ and remotely sensed data records. The first objective of this study is to present a brief description of four causal discovery methods (Granger causality, Transfer Entropy, graph‐based algorithms, and Convergent Cross Mapping) with special emphasis on the assumptions on which they are built. Second, using synthetic data generated from a hydrological model, we assess their performance in retrieving causal information taking into account sensitivity to sample size and presence of noise. Last, we use causal analysis to examine and formulate hypotheses on causal drivers of evapotranspiration in a shrubland region during summer and winter seasons. An interpretation of the hypotheses based on canopy seasonal dynamics and evapotranspiration processes is presented. It is hoped that the results presented here can be useful in guiding researchers studying hydrometeorological systems as to which causal method is most appropriate to the characteristics of the system under study. Plain Language Summary The old cliché “correlation does not imply causation” is grounded on the pitfalls of conventional correlation analysis. Despite its well‐known shortcomings, the use of correlation permeates the hydrometeorological literature. Recently, however, methods of causal inference have attracted a lot of attention, and they are increasingly being used to address a wide range of problems. Due to the rapid increase in hydrometeorological data acquisition whether in situ or remotely sensed, the use of causal discovery methods is nothing but opportune. This article aims to contribute to the burgeoning community effort in advancing the use of causal discovery methods in hydrometeorological applications. It firstly reviews the assumptions underlying fou