In the background of water ecological protection, how to utilize the superfluous coal mine water efficiently has become an urgent problem, especially in northwest China, where the fragile ecological environment needs to be protected but lacks water. To solve this problem, this study proposes a new procedure for the allocation and utilization of mine water aimed at the ecological protection of lakes in an arid and semi-arid area. Based on the water balance method, the ecological water supplement of regional lakes is first estimated according to their different protection goals. Next, a trend analysis of water demand and supply is carried out, and the mine water inflow and available quantity are calculated. Meanwhile, the water resource allocation plan is evaluated systematically. In this study, the procedure is applied to the mine water and lakes in Wushen Banner, Inner Mongolia Autonomous Region of China. The results show that: (1) the lakes can be divided into three classifications according to their ecological protection goals, (2) the available amount of mine water will reach 57.17–81.97 million m³ in 2030, and there are about 46.7 million m³ of water that can be adjusted to reach the optimal utilization in 2020, and (3) the mine water after advanced treatment could meet the requirement of lakes. Finally, it outputs the water supplement path and the water quantity, as well as the water transmission pipelines to each lake, which makes up a new water resources allocation plan and a utilization mode of regional mine water. This utilization mode can provide solutions and ideas for improving the ecological environment of regional lakes and promote the construction of regional ecological safety barriers. Moreover, it can be very helpful for optimizing the allocation of regional water resources, and for improving the reasonable utilization of coal mine water.

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.

Actual evapotranspiration (ETa) is a key component of the hydrological cycle, and its partitioning into soil evaporation (Es) and vegetation transpiration (Ec) is essential for understanding hydrological processes. Focusing on the Yiluo River Basin during 1960–2020, this study developed a hybrid framework combining the physically based Penman–Monteith–Leuning (PML) model with machine learning to dynamically parameterize the soil evaporation coefficient f. ERA5-Land reanalysis data were used to drive the model, while the Pettitt change-point test and ridge regression were applied to identify potential change points and quantify driving factors. The results show that the framework improved the agreement of Es simulations with the GLEAM-derived reference product (R² and NSE > 0.8) and reduced the difference in ETa estimates by approximately 10% within the product-constrained modeling framework. ETa exhibited a significant upward trend (0.28 mm·yr⁻¹) with a potential change point around 2004, while its components responded earlier, with Ec and Es changing in 1994 and 2002. Ec dominated ETa, accounting for about 70% of the total. Net radiation, temperature and leaf area index were primary controls, while increasing vapor pressure deficit, together with changes in relative humidity and precipitation, jointly regulated the identified shifts. These findings provide a process-based understanding of ETa dynamics and improve the representation of ETa components in hydrological modeling.