In the context of global climate change, understanding cryosphere degradation and its impact on water resources in alpine regions is crucial for sustainable development. This study investigates the relationship between permafrost degradation and runoff variations in the Source Region of the Yangtze River (SRYR), a critical water tower for sustainable water supply in Asia. We propose a novel method for assessing permafrost sensitivity, which establishes the correlation between cryosphere changes and hydrological responses, contributing to sustainable water resource management. Our research quantifies key uncertainties in runoff change attribution, providing essential data for sustainable decision making. Results show that changes in watershed characteristics account for up to 20% of runoff variation, with permafrost degradation (−0.02 sensitivity) demonstrating a greater influence than NDVI variations. The findings offer critical insights for the development of sustainable adaptation strategies, particularly in maintaining ecosystem services and ensuring long-term water security under changing climate conditions. This study offers a scientific basis for climate-resilient water management policies in high-altitude regions.

Reservoir construction has profoundly altered natural runoff evolution in river basins. Dynamic conflicts among multi-objective operational strategies—such as flood control, water supply, and ecological compensation—across varying temporal scales exacerbate uncertainties in runoff prediction, primarily due to the complex interplay between hydrological rhythm variations and anthropogenic regulation. To address these challenges, this study proposes a hierarchical multi-scale coupling framework. Long short-term memory (LSTM) networks are employed to extract implicit operational patterns from long-term reservoir records at monthly and weekly scales, while short-term decision dynamics are captured through deviations from these established long-term rules. The proposed framework is validated in the Dongjiang River Basin, a key water source for the Guangdong–Hong Kong–Macao Greater Bay Area. Compared to single-scale models, the hierarchical approach improves prediction accuracy with an average Nash–Sutcliffe Efficiency (NSE) increase of 9.4% and reductions in the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) of 13.2% and 9.6%, respectively. When coupled with a hydrological model, the framework enhances simulation accuracy in reservoir-regulated basins by up to 37.8%. By integrating multi-source decision variables, the framework captures the feedback mechanisms between natural flow variability and human interventions across temporal scales, providing a transferable strategy to reconcile operational conflicts with ecological flow requirements. Its flexibility supports optimized water allocation in regulated river basins, contributing to enhanced water security for downstream urban agglomerations.