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.

Instream ecological flow is an essential determinant of river health. Intra- and interannual distribution characteristics of runoff have been altered to different degrees by dam construction. Historical runoff series with alterations, as basic data for ecological flow calculation, provide minimal instream hydrological process information, which affects the credibility of calculation results. Considering the influence of the alterations in runoff series on ecological flow calculation, the Gini coefficient (GI) is introduced to study the evenness degrees of the intra-annual runoff distribution of four hydrological stations located in the Naolihe basin of the Sanjiang Plain. The hydrological alteration diagnosis system is used to examine the alteration points in the GI series of each hydrological station for selecting reasonable subsequences. Based on the selected subsequences, the ecological flow of each station is calculated using three hydrological methods, and the comprehensive ecological flow is calculated using weighted calculation results from the three hydrological methods. The study results show that ecological flow and natural flow have similar processes with two peaks occurring in the process in May and August, respectively. Also, dams decrease the ecological water requirement damage frequency in dry seasons, but overuse of water resources increases the ecological water requirement damage frequency in flood seasons.

Medium- and long-term runoff forecasting has always been a problem, especially in the wet season. Forecasting performance can be improved using complementary ensemble empirical mode decomposition (CEEMD) to produce clearer signals as model inputs. In the forecasting models based on CEEMD, the entire time series is decomposed into several sub-series, each sub-series is divided into training and validation datasets and forecasted by some common models, such as least squares support vector machine (LSSVM), and finally an ensemble forecasting result is obtained by summing the forecasted results of each sub-series. This model was applied to forecast the inflow runoff of the Shitouxia Reservoir (STX Reservoir). The forecasting results show that the Nash efficiency coefficient of the LSSVM model is 0.815, and the Nash efficiency coefficient of the CEEMD-LSSVM model is 0.954, an increase of 13.9%. The root mean square error value is reduced from 20.654 to 10.235, a decrease of 50.4%. The runoff forecasting performance can be effectively improved by applying the CEEMD-LSSVM model. When analyzing the annual runoff forecasting results month by month, it was found that the forecasting results for November to April were unsatisfactory compared results from the nearest neighbor bootstrapping regressive (NNBR) model, which was more suitable for the dry season, but the forecasting results for May to October improved significantly. This also proves that the CEEMD-LSSVM model has a great advantage in the forecasting of inflow runoff during the wet season. In the optimized operation of reservoirs, the forecasting result of inflow runoff in the wet season is more important than in the dry season. Therefore, when forecasting annual runoff month by month, the CEEMD-LSSVM model is recommended for the wet season combined with the NNBR model for the dry season.

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.