To improve reservoir flood control and scheduling schemes under changing environmental conditions, we established an adaptive reservoir regulation method integrating hydrological non-stationarity diagnosis, hydrological frequency analysis, design flood calculations, and reservoir flood control optimization scheduling and applied it to the Chengbi River Reservoir. The results showed that the peak annual flood sequence and the variation point of the annual maximum 3-day flood sequence of the Chengbi River Reservoir was in 1979, and the variation point of the annual maximum 1-day flood sequence was in 1980. A mixed distribution model was developed via a simulated annealing algorithm, hydrological frequency analysis was carried out, and a non-stationary design flood considering the variation point was obtained according to the analysis results; the increases in the flood peak compared to the original design were 4.00% and 8.66%, respectively. A maximum peak reduction model for optimal reservoir scheduling using the minimum sum of squares of the downgradient flow as the objective function was established and solved via a particle swarm optimization algorithm. The proposed adaptive scheduling scheme reduced discharge flow to 2661 m³/s under 1000-year flood conditions, and the peak reduction rate reached 60.6%. Furthermore, the discharge flow was reduced to 2661 m³/s under 10,000-year flood conditions, and the peak reduction rate reached 65.9%.

As the runoff series exhibit nonlinear and nonstationary characteristics, capturing the embedded periodicity and regularity in the runoff series using a single model is challenging. To account for these runoff characteristics and enhance the forecasting precision, this research proposed a new empirical wavelet transform–particle swarm optimization–support vector machine (EWT–PSO–SVM) hybrid model based on “decomposition-forecasting-reconstruction” for runoff forecasting and investigated its effectiveness in the karst area. First, empirical wavelet transform (EWT) was employed to decompose the original runoff series into multiple subseries. Second, the support vector machine (SVM) optimized by particle swarm optimization (PSO) was applied to forecast every signal subseries. Finally, this study summarized the predictions of the subseries to reconstruct the ultimate runoff forecasting. The developed forecasting model was assessed by applying the monthly runoff series of the Chengbi River Karst Basin, and the composite rating index combined with five metrics was adopted as the performance evaluation tool. From the results of this research, it is clear that the EWT–PSO–SVM model outperforms both the PSO–SVM model and the SVM model in terms of the composite rating index, reaching 0.68. Furthermore, verifying the performance stability, the developed model was also compared with PSO–SVM and SVM models under different input data structures. The comparison demonstrated that the hybrid EWT–PSO–SVM model had a robust performance superiority and was an effective model that can be applied to karst area runoff forecasting.