This study proposes a hedging rule model which is composed of a two-period reservior operation model considering the damage depth and hedging rule parameter optimization model. The former solves hedging rules based on a given poriod’s water supply weighting factor and carryover storage target, while the latter optimization model is used to optimize the weighting factor and carryover storage target based on the hedging rules. The coupling model gives the optimal poriod’s water supply weighting factor and carryover storage target to guide release. The conclusions achieved from this study as follows: (1) the water supply weighting factor and carryover storage target have a direct impact on the three elements of the hedging rule; (2) parameters can guide reservoirs to supply water reasonably after optimization of the simulation and optimization model; and (3) in order to verify the utility of the hedging rule, the Heiquan reservoir is used as a case study and particle swarm optimization algorithm with a simulation model is adopted for optimizing the parameter. The results show that the proposed hedging rule can improve the operation performances of the water supply reservoir.

The optimization of irrigation schedules is of great significance for saving water resources and ensuring food security. For the downstream of the Songhua River Basin, the key growth stages of crops were determined by the coupling degree between the effective precipitation and crop water requirement and the sensitivity indexes of the crop water production function. A Soil and Water Assessment Tool (SWAT) model was used to simulate 16 irrigation schedules in different scenarios. Taking four factors into consideration, Analytic Hierarchy Process (AHP)-Gray Interconnect Degree Analysis (GIDA) was used to establish the optimal irrigation schedule. The results showed that the key growth stages of corn and soybean were vegetative, reproductive and pod formation, seed enlargement. Deficit treatments were beneficial to improving crop yield and WUE. The optimal schedules were: the corn was irrigated with four times in key growth stages, and the irrigation quota was 21 mm; irrigation occurred six times in both normal and dry year, with quotas of 84 mm and 134 mm, respectively; the soybean was filled with six times in key growth stages, and the irrigation quotas were 10 mm, 28 mm and 89 mm in wet, normal and dry year, respectively. The evaluation method of irrigation schedule provided a theoretical basis for agricultural management and planting in the basin.

Data mining technology is applied to extract the water supply operation rules in this study. Five characteristic attributes—reservoir storage water, operation period number, water demand, runoff, and hydrological year—are chosen as the dataset, and these characteristic attributes are applied to build a mapping relation with the optimal operation mode calculated by dynamic programming (DP). A Levenberg-Marquardt (LM) neural network and a classification and regression tree (CART) are chosen as data mining algorithms to build the LM neural network classifier and CART decision tree classifier, respectively. In order to verify the classification effect of the LM and CART, the two classifiers are applied to the operation mode recognition for the Heiquan reservoir, which is located in the Qinghai Province of China. The accuracies of the two classifiers are 73.6% and 86.9% for the training sample, and their accuracies are 65.8% and 83.3%, respectively, for the test sample, which indicates that the classification result of the CART classifier is better than that of the LM neural network classifier. Thus, the CART classifier is chosen to guide the long-series water supply operation. Compared to the operation result with the other operation scheme, the result shows that the water deficit index of the CART is mostly closest to the DP scheme, which indicates that the CART classifier can guide reservoir water supply operation effectively.