Improved Understanding of How Catchment Properties Control Hydrological Partitioning Through Machine Learning

Abstract Long‐term hydrological partitioning of catchments can be well described by the Budyko framework with a parameter (e.g., Fu's equations with parameter ω ). The Budyko framework considers aridity index as the dominant control on hydrological partitioning, while the parameter represents integrated influences of catchment properties. Our understanding regarding the controls of catchment properties on the parameter is still limited. In this study, two machine learning methods, that is, boosted regression tree (BRT) and CUBIST, were used to model ω . Interpretable machine learning methods were adopted for better physical understanding including feature importance, accumulated local effects (ALE), and local interpretable model‐agnostic explanations. Among the 15 properties of 443 Australian catchments, analysis of feature importance showed that root zone storage capacity (SR), vapor pressure, vegetation coverage (M), precipitation depth, climate seasonality and asynchrony index (SAI), and water use efficiency (WUE) were the six primary control factors on ω . ALE showed that ω varied nonlinearly with all factors, and varied non‐monotonically with M, SAI, and WUE. LIME showed that the importance of the six dominant factors on ω varied between regions. CUBIST was further used to build regionally varying relationships between ω and the primary factors. Continental scale ω and evapotranspiration were further mapped across Australia based on the most robust BRT‐trained parameterization scheme with a resolution of 0.05°. Instead of using the machine learning method as a black box, we employed interpretability approaches to identify the controls. Our findings not only improved the capability of the Budyko method for hydrological partitioning across Australia, but also demonstrated that the controls of catchment properties on hydrological partitioning vary in different regions.