A Bayesian method for comprehensive water quality evaluation of the Danjiangkou Reservoir water source area, for the middle route of the South-to-North Water Diversion Project in China

Abstract

The Danjiangkou Reservoir is the water source for the middle route of the South-to-North Water Diversion Project in China. Thus, its water quality status is of great concern. Five water quality indicators (dissolved oxygen, permanganate index, ammonia nitrogen, total nitrogen, and total phosphorus), were measured at three monitoring sites (the Danjiangkou Reservoir dam, the Hejiawan and the Jiangbei bridge), to investigate changing trends, and spatiotemporal characteristics of water quality in the Danjiangkou Reservoir area from January 2006 to May 2012. We then applied a Bayesian statistical method to evaluate the water quality comprehensively. The normal distribution sampling method was used to calculate likelihood, and the entropy weight method was used to determine indicator weights for variables of interest in to the study. The results indicated that concentrations of all five indicators increased during the last six years. In addition, the water quality in the reservoir was worse during the wet season (from May to October), than during the dry season (from November to April of the next year). Overall, the probability of the water’s belonging to quality category of type Ⅱ, according to environmental quality standards for surface water in China, was 27.7%–33.7%, larger than that of its belonging to the other four water quality types. The increasing concentrations of nutrients could result in eutrophication of the Danjiangkou Reservoir. This method reduced the subjectivity that is commonly associated with determining indicator weights and artificial classifications, achieving more reliable results. These results indicate that it is important for the interbasin water diversion project to implement integrated water quality management in the Danjiangkou Reservoir area.

Keywords

water quality evaluation / Danjiangkou Reservoir / Bayesian method / normal distribution sampling method / entropy weight method

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Acknowledgements

This study was supported by the National Science and Technology Support Program (No. 2011BAC12B02), the National Science Foundation for Innovative Research Group (Grant No. 51121003), and the Outstanding Doctoral Thesis Fund of Beijing Normal University (No. 105512GK). We are grateful to the editors and anonymous reviewers’ careful review of this paper, whom have contributed substantially to improving the paper.

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg