Version 1
: Received: 21 November 2017 / Approved: 21 November 2017 / Online: 21 November 2017 (09:28:07 CET)
How to cite:
Wang, H.; Zhang, J.; Yun, R.; Zhao, R. Sensitivity Analysis of Mountain Flash Flood Risk: Case of Licheng County in China. Preprints2017, 2017110138. https://doi.org/10.20944/preprints201711.0138.v1
Wang, H.; Zhang, J.; Yun, R.; Zhao, R. Sensitivity Analysis of Mountain Flash Flood Risk: Case of Licheng County in China. Preprints 2017, 2017110138. https://doi.org/10.20944/preprints201711.0138.v1
Wang, H.; Zhang, J.; Yun, R.; Zhao, R. Sensitivity Analysis of Mountain Flash Flood Risk: Case of Licheng County in China. Preprints2017, 2017110138. https://doi.org/10.20944/preprints201711.0138.v1
APA Style
Wang, H., Zhang, J., Yun, R., & Zhao, R. (2017). Sensitivity Analysis of Mountain Flash Flood Risk: Case of Licheng County in China. Preprints. https://doi.org/10.20944/preprints201711.0138.v1
Chicago/Turabian Style
Wang, H., Ruan Yun and Ranhang Zhao. 2017 "Sensitivity Analysis of Mountain Flash Flood Risk: Case of Licheng County in China" Preprints. https://doi.org/10.20944/preprints201711.0138.v1
Abstract
Flash flood is one of the most significant natural disasters in China, particularly in mountainous area, causing heavy economic damage and casualties of life. Accurate risk assessment is critical to an efficient flash flood management. There are more than 530,000 small watersheds in 2058 counties in China where flash flood should be prevented. In practice, with limited fund and different risk levels, the priorities of each small watershed for flash flood prevention and control are also needed for an efficient flash flood management. This paper, take Licheng county in China as an example, aims to give out these priorities for management. First, sensitive indexes are identified among index system, which includes 9 indexes based on underlying surface characteristics of small watershed in hilly region. Second, the range of each index and the rank division of each index for evaluation are determined. Based on the rank divisions for evaluation, the flash flood risk grade eigenvalue (H) is calculated by Variable Fuzzy Method (VFM ) using 1000 samplings generated by Latin hypercube sampling method. Third, the key sensitivity factors that affect flash flood risk grade eigenvalue (H) are assessed by two different global sensitivity analysis methods -- stepwise regression analysis and mutual entropy. Both results indicate that watershed slope (S) is the most sensitive factor; the second is antecedent precipitation index (CN); while other factors are slightly different sensitive in sequence. This study shows that stepwise regression analysis and mutual information analysis are appropriate for the sensitivity analysis of mountain flash flood risk. Finally, based on watershed slope (S), the priorities of flash flood prevention and control of 119 small watersheds in Licheng county are given out.
Engineering, Safety, Risk, Reliability and Quality
Copyright:
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.
