Optimizing the joint drawdown operation of mega reservoirs presents a significant opportunity to enhance the comprehensive benefits among hydropower output, water release, and carbon emission reduction. However, achieving the complementary drawdown operation of mega reservoirs while considering reservoir carbon emissions poses a notable challenge. In this context, this study introduces an innovative multi-objective optimization framework tailored for the joint drawdown operation of mega reservoirs. Firstly, a multi-objective optimization model, leveraging an intelligent evolutionary algorithm, is developed to minimize reservoir carbon emissions (Objective 1), maximize hydropower output (Objective 2), and maximize water release (Objective 3). Subsequently, a multi-criteria decision-making approach to search for the optimal scheme is employed. The proposed framework is applied to seven mega reservoirs within the Hanjiang River basin, China. The results show that the framework is effective in promoting comprehensive benefits, improving hydropower production by 8.3%, reservoir carbon emission reduction by 5.6%, and water release by 6.2% from the optimal solution under wet scenarios, compared to standard operation policies. This study not only provides a fresh perspective on the multi-objective drawdown operation of mega reservoirs but also offers valuable support to stakeholders and decision-makers in formulating viable strategic recommendations that take potential carbon emissions and advantages into account.

Effective flood forecasting is essential for implementing proactive flood management and risk reduction strategies. However, conventional artificial neural networks often fail to capture the complex spatiotemporal dependencies among hydrometeorological variables, resulting in system biases and time‐lag errors, especially during extreme flood events. This study introduces a spatiotemporal Pathformer‐based deep learning framework for multi‐step‐ahead flood forecasting that dynamically adapts to flood magnitude and duration. The model integrates a dual self‐attention mechanism and adaptive path selection, enhancing its ability to model nonlinear rainfall‐runoff relationships and long horizon dependencies. Using a case study in the Jianxi basin with 25,341 hydrometeorological records at a 3‐hr resolution, the spatiotemporal Pathformer's performance was evaluated across 1‐ to 7‐step forecast horizons. A comparative analysis with Long Short‐Term Memory (LSTM) and Transformer models demonstrates the spatiotemporal Pathformer's superior predictive accuracy and stability. It improves nash‐sutcliffe efficiency by 3.0% and 7.4%, increases Volume Efficiency by 3.4% and 9.6%, reduces root mean square error by 18.4% and 34.9%, and lowers mean absolute error by 17.5% and 36.1% compared to LSTM and Transformer, respectively. By effectively mitigating time‐lag errors and prediction bottlenecks, the spatiotemporal Pathformer ensures robust and reliable forecasting, even during extreme flood events. The application of SHapley Additive exPlanations analysis increases the model's interpretability, transparency, and trustworthiness by revealing the key hydrometeorological drivers behind its predictions. These results establish the spatiotemporal Pathformer as an advanced solution for next‐generation flood forecasting, with strong potential to improve real‐world applications in flood prevention and water resource management.

Aimed at the information loss problem of using discrete indicators in wind power output characteristics analysis, a self-organizing map neural network-based clustering method is proposed in this study. By identifying the appropriate representativeness and topological structure of the competition layer, cluster analysis of the wind power output process in four seasons is realized. The output characteristics are evaluated through multiple evaluation indicators. Taking the wind power output of the Hunan power grid as a case study, the results underscore that the 1 × 3-dimensional competition layer structure had the highest representativeness (72.9%), and the wind power output processes of each season were divided into three categories, with a robust and stable topology structure. Summer and winter were the most representative seasons. Summer had strong volatility and small wind power outputs, which required the utilization of other power sources to balance power supply and load demand. Winter featured low volatility and large wind power outputs, necessitating cooperation with peak-shaving power sources to enhance the power grid’s absorbability to wind power. The seasonal clustering analysis of wind power outputs will be helpful to analyze the seasonality of wind power outputs and can provide scientific and technical support for guiding the power grid’s operation and management.