An Interpretable and Lightweight Dynamic Framework for Streamflow–Meteorology Networks to Enhance Daily Streamflow Forecasting

Abstract

Achieving high prediction accuracy while maintaining low‐complexity and interpretability has been a critical challenge in streamflow forecasting. To address this issue, this study proposes an improved reservoir computing (RC) framework that simultaneously supports structural inference and dynamic forecasting of streamflow‐meteorology networks. The framework operates in two stages: first, a cross‐validation‐based interpretable dynamic weight inference algorithm is designed to uncover the relational structures within the streamflow–meteorology network; subsequently, the inferred structural information is integrated into an architecture embedded with catchment similarity to predict future streamflow states. This process guides the internal dynamics of the framework toward representations that align with the data‐driven dependency relationships between streamflow and meteorological variables, thereby enhancing the interpretability of the predictions. We partition a subset of catchments from CAMELS data set into eight groups and conduct a series of experiments to evaluate the proposed framework. Results demonstrate that the proposed method achieves an average improvement of 38.87% in Nash–Sutcliffe Efficiency (NSE) and 38.21% in peak flow prediction accuracy compared to benchmark models and effectively mitigates the temporal lag and instability commonly observed in deep learning approaches, exhibiting superior adaptability and robustness in handling spatial heterogeneity under diverse hydrological conditions. With fewer than 10% of the trainable parameters of benchmark models and a runtime 2 to 3 times faster, the framework enables rapid inference, adaptation, and modeling in cross‐catchment scenarios within complex hydrological dynamical systems, thus holding considerable promise for practical applications.