Baseflow plays a vital role in protecting the environment and ensuring a stable water supply for farming. There are still gaps in the current understanding of baseflow convergence rates in the humid region due to the abundance of rainfall and the high‐water table. Therefore, this study focused on the evolution and hysteresis characteristics of baseflow in humid basins of southeastern China. The baseflow ensemble simulation (BES) method was established to improve the reliability and applicability of baseflow simulation. We suggest a way of differentiating the wet and dry seasons based on the multi‐year average monthly baseflow index (BFI) to determine the intra‐annual distribution of water effectively and simply. The hydrological hysteresis effect of baseflow on precipitation is revealed by characterizing baseflow response to precipitation under precipitation events during wet and dry seasons. A methodology for assessing the performance of baseflow simulation was proposed from observations of streamflow and precipitation. We found that the BES method performed better in baseflow simulation than other single separation methods. Using the BES method, the lag time of baseflow to precipitation during the wet and dry seasons was found to be 3.09 and 4.04 days after utilizing the BFI to divide the hydrological situation into wet and dry seasons. Additionally, precipitation had nearly twice as much intensity influence on baseflow during the dry season compared to the wet season. These findings have significant ramifications for the use, management, and planning of water resources in humid areas of China.

Informing reservoirs with forecasts is highly important for real‐time flood control. This study proposed a forecast‐informed methodology framework for reservoir flood control operation under uncertainty. A new combination of two post‐processing methods, that is, the Cloud model and error‐based copula functions, were developed to merge individual AI‐based forecasts to ensemble flood forecasts, so called stochastic errors‐based Cloud (SE‐Cloud). A multi‐objective robust optimization model (MRO) integrating the risk, resilience, and vulnerability was then proposed to tackle flood control problems under ensemble forecasts; for comparison, a two‐objective stochastic optimization model (TSO) was developed to minimize the expected highest reservoir level and peak release. The proposed methodology was applied to the Lishimen reservoir in the Shifeng River subbasin, China, aiming to comprehensively verify the relationships among deterministic forecasts, ensemble forecasts, and flood control performance. Results showed that the Cloud model could effectively integrate different models and improve forecast accuracy. But a higher deterministic forecast quality did not consistently result in improved flood control performance. SE‐Cloud could capture the peak flow and effectively characterize forecast uncertainties and increased hypervolume values by 13.14%–39.65% compared to the Cloud model, indicating the superiority of ensemble forecasts in generating robust solutions over individual deterministic forecasts. MRO released more inflow than TSO, decreasing the expected highest water level by 0.05 m and incrementing the expected peak release by 4.29%. However, with downstream resilience value remaining at zero, it is demonstrated that MRO improving upstream vulnerability did not necessarily diminish resilience. The enhanced robustness highlights the potential of AI‐based ensemble forecasts in flood control.

Urban agglomeration polders type of flood control pattern (UAPFCP) is an extensively used pattern for urban flood control in plain water system areas. Urbanization and polders are two main factors that affect the runoff process in these regions. The Qinhuai River basin, one of the representative watersheds of this flood control pattern in East China, was selected to perform the study. Five urbanization scenarios (the historical, current, and three assumed future urbanization scenarios) of the basin were defined in this paper. The Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) was used to simulate basin runoff. The results indicate that the UAPFCP increased the flood volume (Q_v) and peak flow (Q_p) compared to the results under the condition without polders. With the constant improvement of the urbanization level of the basin, Q_v and Q_p under the with polder condition increased correspondingly, and the potential changes show a linear relationship. The urbanization and urban agglomeration polders have interactions with flood events. The effect of urbanization on the flood process is weakened because of the existence of urban agglomeration polders. With the constant improvement of the urbanization level, the effect on the flood process caused by urban agglomeration polders becomes gradually weaker.