From Mechanisms to Modeling: Causal Inference for Hydro‐Sedimentary Responses

Abstract Hydro‐sedimentary responses critically influence water and ecological security, yet their nonlinear complexity challenges mechanistic understanding and reliable modeling, a gap that conventional association‐based methods hardly address. Emerging data‐driven causal inference offers promise in elucidating directional relationships and filtering spurious correlations, but its applicability in hydro‐sediment systems remains underexplored due to unclear system assumptions and intricate driver–sediment linkages. This study fills this gap by developing a generalizable causal framework integrating representative causal methods, Granger causality (GC), transfer entropy (TE), and convergent cross mapping (CCM), rigorously validated with 41‐year real‐world data from seven subtropical watersheds in China. Specifically, the framework evaluates method applicability in detecting precipitation–streamflow–sediment causality (direction/strength/time‐delay), analyzes environmental controls, and identifies key predictors to refine causal insights in machine learning (ML)‐based sediment predictions. Key findings indicate: (a) CCM excels in causality detection despite high variability, GC is flexible but insensitive in feature selection, and TE only suits confirmatory studies—collectively highlighting the dual deterministic‐stochastic nature of watershed hydro‐sediment systems. (b) Precipitation exerts similar causal effects on sediment and streamflow dynamics. Over 41 years, precipitation–sediment causality strength weakens, with reduced time‐delays and seasonal variations. (c) Precipitation–streamflow causality is shaped by precipitation and short‐term drought, while precipitation–sediment causality by land‐use and long‐term drought, with all causalities impacted by soil moisture and economic factors. (d) ML‐based predictions can partially reflect causality with precipitation/land‐use predictors but deteriorate with excessive antecedent hydroclimate predictors. This causality‐driven framework complements mechanistic understanding of hydro‐sedimentary responses, urging a shift from correlative to causal modeling in earth surface processes.