Hydrological forecasting plays a crucial role in mitigating flood risks and managing water resources. Data-driven hydrological models demonstrate exceptional fitting capabilities and adaptability. Recognizing the limitations of single-model forecasting, this study introduces an innovative approach known as the Improved K-Nearest Neighbor Multi-Model Ensemble (IKNN-MME) method to enhance the runoff prediction. IKNN-MME dynamically adjusts model weights based on the similarity of historical data, acknowledging the influence of different training data features on localized predictions. By combining an enhanced K-Nearest Neighbor (KNN) algorithm with adaptive weighting, it offers a more powerful and flexible ensemble. This study evaluates the performance of the IKNN-MME method across four basins in the United States and compares it to other multi-model ensemble methods and benchmark models. The results underscore its outstanding performance and adaptability, offering a promising avenue for improving runoff forecasting.

Reliable and precise joint probabilistic forecasting of wind and solar power is crucial for optimizing renewable energy utilization and maintaining the safety and stability of modern power systems. This paper presents an innovative joint probabilistic forecasting model designed to address probabilistic spatiotemporal power output forecasting challenges. Leveraging a multi-network deep learning framework, the model integrated the temporal convolutional network for temporal feature extraction, the convolutional neural network for spatial feature analysis, and the attention mechanism for spatiotemporal focus enhancement, thereby capturing the spatiotemporal complementarity of wind and solar power. It also incorporated a quantile regression-based uncertainty quantification technique, contributing to reliable probabilistic predictions. A wind farm and two solar farms in China were used as a case study. Comparison results between the proposed model and ten established models demonstrated its superior performance in both reliable deterministic and probabilistic predictions, offering valuable insights for sustainable and resilient energy system operation.

Monthly extreme precipitation (EP) forecasts are of vital importance in water resources management and storage behind dams. Machine learning (ML) is extensively used for forecasting monthly EP, and improvements in model performance have been a popular issue. The innovation of this study is summarized as follows. First, a distance correlation-Pearson correlation (DC-PC) method was proposed to identify the complex nonlinear relationship between global sea surface temperature (SST) and EP and select key input factors from SST. Second, a random forest (RF) model was used for forecasting monthly EP, and the physical mechanism of EP was obtained based on the feature importance (FI) of RF and DC–PC relationship. The middle and lower reaches of the Yangtze River (MLYR) were selected as a case study, and monthly EP in summer (June, July and August) was forecasted. Furthermore, the physical mechanism between key predictors with a large proportion of FI and EP was investigated. Results showed that the proposed model had high accuracy and robustness, in which R² in the test period was above 0.81, and RMSE as well as MAE were below 10 mm. Meanwhile, the key predictors in the high SST years could cause eastward extension of the South Asian High, westward extension of the Western Pacific Subtropical High, water vapor rising motion and an increase in the duration of atmospheric rivers exceeding 66 h, which lead to increasing EP in the MLYR. The results indicated that the DC–PC method could replace Pearson correlation for investigating the nonlinear relationship between SST and EP, as well as for selecting the factors. Further, the key predictors that account for a large proportion of FI can be used for explaining the physical mechanism of EP and directing forecasts.

The partition of surface runoff and infiltration is crucial in hydrologic modeling. To improve the flood prediction, we designed four strategies to explore the influences of the runoff partition method on the flexible hybrid runoff generation model. The runoff partition strategies consist of a hydrological model without the runoff partition module, a two-source runoff partition method, an improved two-source runoff partition method considering the heterogeneity of the subsurface topography and land cover, and a three-source runoff partition method. The Xin’anjiang hydrological model was used as the modeling framework to simulate a six-hourly stream flow for the Xun River watershed in Shaanxi Province, China. And the saturation-excess runoff generation and infiltration-excess runoff generation mechanisms were combined to construct the flexible hybrid runoff generation model. The performances of the four strategies were compared and analyzed based on the continuous flow discharge as well as the flood events. The runoff components analysis method was used to test the model’s conformity with the reality of the watershed. The results showed that the three-source runoff partition method was not applicable to the flexible hybrid runoff generation model because it overestimated the surface runoff and almost ignored the subsurface stormflow runoff. The improved two-source runoff partition method outperformed the others as it considered the heterogeneity of the watershed.