The Improved Water Resource Stress Index (WRSI) Model in Humid Regions

Author:

Yang Yuxin1,Yan Feng1,Wu Hongliang2

Affiliation:

1. School of Infrastructure Engineering, Nanchang University, Nanchang 330031, China

2. Powerchina Guiyang Engineering Corporation Limited, Guiyang 550081, China

Abstract

Water scarcity is commonly evaluated using the water resource stress index (WRSI). However, the conventional WRSI model cannot effectively reflect the water shortage in humid areas, which may lead to distorted evaluation results. To solve this problem, an improved WRSI has been designed. In the improved WRSI, the assessment year is first divided into multiple periods. Then, the temporal WRSI (T-WRSI) is built to evaluate water scarcity in each period. Finally, the comprehensive WRSI (C-WRSI) is constructed based on the entropy weight model (EWM) to synthetically assess the water shortage condition. The water scarcity of Yongzhou City is taken as a sample for evaluation. The results are as follows. (i) The Lingling-Lengshuitan District and Dong’an County suffer from the most serious water shortage. Their C-WRSI values are 0.464 and 0.458, respectively, both of which are “high” grades. (ii) The values of T-WRSI are more than 0.4 from August to October in most areas, indicating that the major key water shortage period is from August to October. (iii) The reasons for water shortage are that the annual runoff distribution in Yongzhou City is uneven with a uniformity coefficient (Cv) of 0.83, and the main irrigation periods are coincidentally accompanied by the retreat of rainfall. (iv) The main measures to solve water scarcity are to strengthen water resource management, to optimize plant structure, and to construct more reservoirs. (v) The C-WRSI values calculated by the improved model in each region are 95–168% larger than those of conventional WRSI. The improved WRSI has better capacities in evaluating the water shortage induced by uneven runoff within the year and identifying the key water scarcity period of humid regions.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

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