Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2015, Article ID 120873, 16 pages
Research Article

A Bayesian Method for Water Resources Vulnerability Assessment: A Case Study of the Zhangjiakou Region, North China

1State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China
2Key Laboratory of Water and Sediment Sciences of Ministry of Education, School of Environment, Beijing Normal University, Beijing 100875, China
3Shidu Town People’s Government, Beijing 102411, China

Received 18 June 2014; Accepted 23 August 2014

Academic Editor: Jian Li

Copyright © 2015 Xuan Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Water resources vulnerability (WRV) assessment is an important basis for maintaining water resources security in a basin. In this paper, considering the complexity of the water resources system and the uncertainty of the assessment information, a method based on the Bayesian theory was developed for performing WRV assessments while using the constructed indicator system. This system includes four subsystems, the hydrological subsystem, the socioeconomic subsystem, the ecoenvironmental subsystem and the hydraulic engineering subsystem. The WRV degree for each subsystem and the integrated water resources system were assessed. Finally, the assessment results and the characteristics of the Bayesian method were compared with those of the grey relational analysis method and the parametric-system method. The results showed the following. (1) The WRV of the integrated water resources system of the entire Zhangjiakou region was very high; Zhangjiakou City and Xuanhua County have tendencies to belong to Extreme WRV, with probabilities of 26.8% and 25%, respectively, while the other seven administrative counties have tendencies to belong to High WRV, with probabilities ranging from 24.6% to 27%. (2) Compared with the parametric-system method and the grey relational analysis method, the Bayesian method is simple and can effectively address the uncertainty issues with the reliable WRV assessment results.