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Due to climate change and human activities, the assumption of the stationarity of hydrological variables will no longer hold. Moreover, the stochastic uncertainty of hydrological variables introduces huge challenges for water managers. To address nonstationarity and randomness, a new methodology called the nonstationary stochastic simulation-based water allocation approach is proposed to optimize regional water management. This objective is achieved via four steps. First, the generalized additive model is chosen to analyze the nonstationary probability distribution of the hydrological series. Second, the Monte Carlo method is applied to generate an optimized stochastic sample with many possible inflows according to the probability distribution obtained in the previous step. Third, a preallocated water optimization model is set up to optimize water allocation under each possible inflow. Last, the final water allocation plan can be made through statistical analysis of all optimized water allocation schemes obtained in the previous step. The approach can describe the probability distribution of regional water inflow in the planning year more accurately and reflect more stochastic information, which makes the final water allocation plan more reliable. To demonstrate its applicability, the approach was applied to the Zhanghe Irrigation District to optimize available water allocation for municipality, industry, hydropower, and agriculture in a planning year. The annual inflow of the Zhanghe Reservoir was found to be nonstationary. Moreover, an appropriate final water allocation plan in the Zhanghe Irrigation District in the planning year can be obtained through the nonstationary stochastic simulation-based water allocation method, which provides a foundation to water managers for managing water resources under nonstationary and stochastic conditions.