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A Nonstationary Analysis Based Interval Two-Stage Water Classified-Allocation Method for Regional Water Management Under Uncertainty

Author(s): Shu Chen

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Keywords: Classification thought; Nonstationary analysis; Two-stage stochastic programming; Uncertainty; Water management;

Abstract: Due to climate change and human activities, the assumption of the stationarity of hydrologic features will no longer hold. Moreover, uncertainties, such as the apparent randomness of hydrologic elements, and complexities, such as various water users with different characteristics, also introduce huge challenges for water managers. To address non-stationarity, uncertainty and complexity, a new approach is proposed for the optimal allocation of regional water resources. This objective is achieved via two steps: first, the generalized additive model is chosen to analyze the nonstationary probability distribution of the hydrologic dataset based on change point analysis; then, an interval two-stage water classified-allocation model is formulated by incorporating two-stage stochastic programming, interval parameter programming and classification thought. The model can not only address the uncertainties, which are expressed as interval parameters and probability distributions, but can also handle the complexities by classifying the water users into agricultural and non-agricultural users. The approach is applied to the Zhanghe Irrigation District to optimize available water allocation for municipality, industry, hydropower and agriculture in two planning years. The annual inflow of the Zhanghe Reservoir is found to be nonstationary. Moreover, the difference in output between the 2010 and 2020 with different inflow probability distributions indicates the need for nonstationary analysis. In addition, the optimal targets and optimal water allocation for different water users can help managers to accurately develop allocation plans under uncertainty.


Year: 2019

Source: Proceedings of the 38th IAHR World Congress (Panama)

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