A reasonable flood season delineation can effectively implement staged reservoir scheduling and improve water resource efficiency. Therefore, this study is aimed at analyzing the flood period segmentation and optimizing the staged flood limit water levels (FLWLs) for a multi-purpose reservoir, the Longtan Reservoir, China. The rainfall seasonality index ($S{I}_P$) and the runoff seasonality index ($S{I}_R$) are used to evaluate the feasibility and rationality of the flood period staging. The fractal method is then used to segment the flood season. Finally, the design flood is carried out to optimize the staged FLWLs. The results show that the $SI$ is an effective indicator for judging the feasibility and verifying the rationality of flood segmentation. The flood period can be segmented into the pre-flood season (12 April–29 May), the main flood season (30 May–3 September), and the post-flood season (4 September–9 November). The FLWLs in the main flood and the post-flood season can be raised by 2.05 m and 3.45 m, and the effective reservoir capacity is increased by 5.810 billion m³ and 6.337 billion m³, according to the results of the flood season division.

Frequent flood disasters have caused serious damage to karst areas with insufficient measured rainfall data, and the analysis of the applicability of satellite rainfall data in runoff simulation is helpful to the local water management. Therefore, the purpose of this study is to analyze the accuracy of IMERG satellite rainfall data and apply it to long-term runoff simulations in a karst area—the Xiajia River basin, China. First, R (correlation coefficient) and POD (probability of detection) are applied to analyze the accuracy of the IMERG data, and the SWAT model is used for runoff simulation. The results show that the accuracy of the original IMERG data is poor (R range from 0.412 to 0.884 and POD range from 47.33 to 100), and the simulation results are “Unsatisfactory” (NSE (Nash-Sutcliffe efficiency coefficient) ranged from 0.17 to 0.32 and RSR (root mean square standard deviation ratio) ranged from 0.81 to 0.92). Therefore, the GDA correction method is used to correct the original IMERG data, and then the accuracy analysis and runoff simulation are carried out. The results show that the accuracy of the corrected IMERG data is better than that of the original data (R range from 0.886 to 0.987 and POD range from 94.08 to 100), and the simulation results of the corrected IMERG data are “Satisfactory” (NSE is over 0.55 and RSR is approximately 0.65). Therefore, the corrected data have a certain applicability in long-term continuous runoff simulations.

Flooding at small basins is characterized by weak predictability, sudden onset, and rapid disaster formation, especially in karst areas. Therefore, an accurate flood simulation will be helpful for flood control and disaster reduction. In this study, the reservoir unit is added into the original HEC-HMS model to improve the model and analyze the stagnation of the runoff process in karst basins. Then, the HEC-HMS model before and after improvement is used to simulate floods in the Xiajia basin, a typical karst area in southwest China. Before improvement, the calibration result shows that the accuracy of 31 flood simulations is poor, and the qualified rate is only 38.71%. After improvement, the qualified rate increases to 51.61% during calibration, and the simulation accuracy is increased by 12.90%. Moreover, the qualified rate reaches 61.11% during validation, and the simulation accuracy is increased by 22.40%. The improved HEC-HMS model can be applied to flood simulations in the study area and the study results can provide useful insights for flood warning and management in karst areas.

In order to solve the problem that the existing optimal operation model of reservoirs cannot coordinate the contradiction between long-term and short-term benefits, the paper nested the long-term optimal operation and mid-long-term optimal operations of reservoirs and established the multi-objective optimal operation nested model of reservoirs. At the same time, based on this model, the optimal control mode is determined when there are errors in the predicted runoff. In the optimal scheduling nested model, the dynamic programming algorithm is used to determine the long-term optimal scheduling solution, and the genetic algorithm is used to solve the mid-long-term optimal scheduling. The optimal control mode is determined by three indicators: power generation benefit, water level over limit risk rate and the not-exploited water volume. The results show that, on the premise of meeting the flood control objectives, the nested model optimal dispatching plan has higher benefits than the long-term optimal dispatching plan and the actual dispatching plan, which verifies the superiority of the nested model in the reservoir optimal dispatching problem. When there is error in predicting runoff, among the water level control mode, flow control mode and output control mode, the average power generation benefit of output control mode is 150.05 GW·h, the low-risk rate of water level overrun is 0.29, and the not-exploited water volume is 39,270 m³. Compared with the water level control mode and the flow control mode, the output control mode has the advantages of higher power generation efficiency, lower water level over limit risk rate and less not-exploited water volume. Therefore, from the perspective of economic benefit and risk balance, the output control mode in the optimization scheduling nested mode is the optimal control mode.

A reasonable analysis of flood season staging is significant to the management of floods and the alleviation of water shortage. For this paper, the case of the Chengbi River Reservoir in China was selected for study. Based on fractal theory, the flood season is divided into several sub-seasons by using four indexes (multi-year average daily rainfall, multi-year maximum rainfall, multi-year average daily runoff, and multi-year maximum daily runoff) in this study. Also the Benefit-Risk theory is applied to evaluate the effects of staged dispatching. The results show that the flood season of the Chengbi River basin should be divided into the pre-flood season (13 April–6 June), the main flood season (7 June–9 September) and the post-flood season (10 September–31 October). After adjusting the flood limit water level for sub-season and benefit assessment, the probability of exceedance after reservoir flood season operation increases by $0.13\times {10}^{-5}$, the average annual expected risk is 0.2264 million RMB, and the average annual benefit increases by 0.88–1.62 million RMB. The benefits obtained far outweigh the risks, indicating the importance of staging the flood season.