Amid rapid urbanization and climate change, global urban water consumption, particularly household water use, has continuously increased in recent years. However, the impact of climate change on individual and household water use behavior remains insufficiently understood. In this study, we conducted tracking surveys in Beijing, China, to determine the correlation between climatic factors (e.g., temperature, precipitation, and wind) and household water use behaviors and consumption patterns. Furthermore, we proposed a genetic programming-based algorithm to identify and quantify key meteorological factors influencing household and personal water use. The results demonstrated that water use is mainly affected by temperature, particularly the daily maximum (TASMAX) and minimum (TASMIN) near-surface air temperature. In addition, showering and personal cleaning account for the largest proportion of water use and are most affected by meteorological factors. For every 10 °C increase in TASMAX, showering water use nonlinearly increases by 3.46 L/d/person and total water use nonmonotonically increases by 1.14 L/d/person. When TASMIN varies between −10 °C and 0 °C, a significant change in personal cleaning water use is observed. We further employed shared socioeconomic pathway scenarios of the Coupled Model Intercomparison Project 6 to forecast household water use. The results showed that residential water use in Beijing will increase by 21–33% by 2035 compared with 2020. This study offers a groundbreaking perspective and transferable methodology for understanding the effects of climate change on household water use behavior, providing empirical foundations for developing sustainable water resource management strategies.

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.