Achieving causality-aware interpretable modeling of riverine suspended sediment via collaborative causal paths and...

Accurate sediment modeling is essential for effective water and environmental management, yet practitioners face a choice between two modeling paradigms. Process-based models are physically interpretable yet typically limited in generalizability and flexibility, while data-driven models are the opposite, leaving either paradigm alone insufficient to support robust management. To bridge these gaps, this study introduced a causality-aware framework embedding multitemporal response pathways within data-driven architecture. Guided by domain knowledge, we developed a daily suspended sediment concentration (SSC) prediction model with high accuracy and mechanistic transparency by encoding causal relationships as structural constraints and matching multi-temporal dynamics. Encoding the same causal structure into Shapley value-based attribution enhanced the reliability and plausibility of explainable attribution analysis for SSC variations and prediction uncertainty/errors. Validation across six subtropical watersheds in China using three algorithms (LightGBM/XGBoost/Random Forest) showed that the proposed framework outperformed conventional data-driven modeling, improving the accuracy across algorithms by 4.2% ± 3.0%, 6.2% ± 5.2%, and 1.5% ± 1.3% in NSE, KGE, and RMSE, respectively. Causality-guided Shapley values successfully disentangled root drivers (e.g., precipitation and land use) from mediators (e.g., soil water content and discharge). Consequently, feature importance shifted into physically meaningful patterns in terms of both overall causal contributions and directional relationships. Causality-guided error analysis broadly agreed with the SSC attribution, while identifying long-lagged precipitation and impervious surface factors as noteworthy contributors given their stronger influence on model uncertainty. This work highlights the practical value of integrating causal frameworks into hydro-geosciences for interpretable watershed management.