Abstract
In water resource system risk research, the risk identification problem should be addressed first, due to its significant impact on risk evaluation and management. Conventional risk identification methods are static and one-sided and are likely to induce problems such as ignored risk sources and ambiguous relationships among sub-systems. Hierarchical holographic modelling (HHM) and Risk filtering, ranking, and management (RFRM) were employed to identify the risk of water resources system. Firstly, water resource systems are divided into 11 major hierarchies and 39 graded holographic sub-subsystems by using the HHM framework. Iteration was applied on 4 graded holographic sub-subsystems, which were decomposed from water resource system in the time-space domain, to accurately identify 30 initial scenarios. Then, on the basis of RFRM theory, the risk probabilities of the initial scenarios are calculated and ranked, and 13 high risk scenarios are identified. Finally, the quantifiable 33 risk indicators that characterize the risk scenario are presented. Research results show that the risks affecting the water resources system include the composition, quantity, quality, and management of water resources, which involve many factors such as hydrology, human resources, resource allocation, and safety. Additionally, the study gives quantitative indicators for responding to high-risk scenarios to ensure that high-risk scenarios are addressed first, which is significant for the subsequent evaluation and management of water resource system risk.
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Acknowledgements
The authors wish to thank the reviewers and the editor for their valuable suggestions. In addition, the authors wish to thank the National Natural Science Foundation of China (grant no. 51679040); the Natural Science Foundation of Heilongjiang Province of China (General Project, grant no. E2016004) for their financial support; University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province of China (No.UNPYSCT-2017022) and Postdoctoral Scientific Research Developmental Fund of Heilongjiang Province of China (No.LBH-Q17022).
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Jiang, Q., Wang, T., Wang, Z. et al. HHM- and RFRM-Based Water Resource System Risk Identification. Water Resour Manage 32, 4045–4061 (2018). https://doi.org/10.1007/s11269-018-2037-y
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DOI: https://doi.org/10.1007/s11269-018-2037-y