Long-term flood risk assessment of watersheds under climate change based on the game cross-efficiency DEA

Qingmu Su ()
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Qingmu Su: National Cheng Kung University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2020, vol. 104, issue 3, No 13, 2213-2237

Abstract: Abstract Climate change has significantly increased extreme precipitation and altered regional hydrological cycle, aggravating flood in the watershed. The effective measurement of the risk brought by climate change is an effective way to cope with flood hazard in the future. At the same time, the quality of the simulation of climate change scenarios will also affect the accuracy of flood risk assessment. Therefore, a comprehensive method is needed to measure the long-term disaster risk. However, the current method of subjectively assigning indicator weights is still subjective and difficult to be promoted and applied. So a new model for assessing watershed risk is constructed in this study. Based on the game cross-efficiency data envelopment analysis method and the combination of simulations of climate scenarios, the model can determine the input factors of the assessment and the influencing level of the input factors by using the Principal Component Analysis and Tobit model. The model comprehensively evaluates the flood risk level in the watershed with the results of the simulation of hazard in different climate scenarios, hazard exposure and social vulnerability as input factors, and the degree of disaster loss as the output factor. Results: (1) the hazard, exposure, and social vulnerability are spatially mismatched; (2) the overall risk in the watershed presents such a pattern: upstream (0.751) > downstream (0.418) > midstream (0.362); (3) the long-term flood hazard may be reduced under the influence of climate change. The research is helpful to formulate long-term flood mitigation strategies in the future.

Keywords: Climate scenario simulation; Flood disaster; Risk assessment; Data envelopment analysis (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s11069-020-04269-1

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