A three-parameter flood damage function, part Ⅰ. Theory and development

被引:0
|
作者
Li C. [1 ]
Cheng X. [2 ]
Wang Y. [2 ]
Fu D. [3 ]
机构
[1] School of Civil and Hydraulic Engineering, Ningxia University, Yinchuan
[2] China Institute of Water Resources and Hydropower Research, Beijing
[3] Ningxia Capital Sponge City Construction & Development Co., LTD, Guyuan
来源
Shuili Xuebao/Journal of Hydraulic Engineering | 2020年 / 51卷 / 03期
关键词
Driving mechanism; Evolution trend; Flood damage-return period function; Flood disaster risk; S-shaped curve;
D O I
10.13243/j.cnki.slxb.20190824
中图分类号
学科分类号
摘要
In order to describe the evolution trend of flood risk under the background of social economic growth, rapid urbanization, and improvement of flood control standards, the driving mechanism of flood disaster risk evolution is discussed on the basis of the flood risk theory, chain-reaction and mutability of urban flood damage. The flood damage-return period function is analyzed as S-shaped curve with three parameters. critical damage Dc, critical return period Rc and regional integrated vulnerability index k, respectively. The function can be used as a simple and fast method for flood risk assessment, prediction, and flood risk reduction benefits assessment. The turning points of the curve provide scientific basis for disaster prevention and reduction decision-making. © 2020, China Water Power Press. All right reserved.
引用
收藏
页码:349 / 357
页数:8
相关论文
共 15 条
  • [1] Muis S., Guneralp B., Jongman B., Et al., Flood risk and adaptation strategies under climate change and ur-ban expansion: A probabilistic analysis using global data, Science of the Total Environment, 538, pp. 445-457, (2015)
  • [2] Konrad C.P., Effects of Urban Development on Floods, (2003)
  • [3] Penning-Roswell E.C., Chatterton J.B., The Benefits of Flood Alleviation: A Manual of Assessment Techniques, (1977)
  • [4] Yu J.J., Qin X.S., Larsen O., Joint Monte Carlo and possibilistic simulation for flood damage assessment, Stochastic Environmental Research and Risk Assessment, 27, pp. 725-735, (2013)
  • [5] Crichton D., The risk triangle, Natural Disaster Management, (1999)
  • [6] Smith A., Martin D., Cockings S., Spatiotemporal population modeling for enhanced assessment of urban ex-posure to flood risk, Applied Spatial Analysis and Policy, 9, pp. 145-163, (2014)
  • [7] Tapia C., Abajo B., Feliu E., Et al., Profiling urban vulnerabilities to climate change: An indicator-based vul-nerability assessment for European cities, Ecological Indicators, 78, pp. 142-155, (2017)
  • [8] Cho S.Y., Chang H., Recent research approaches to urban flood vulnerability, 2006-2016, Natural Haz-ards, 88, pp. 633-649, (2017)
  • [9] Climate Change: Impacts, Adaptation, and Vulnerability. Part A: Global and Sect Oral Aspects. Contribu-tion of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, (2014)
  • [10] Resndiz-Munoz J., Corona-Rivera M.A., Fernandez-Munoz J.L., Et al., Mathematical model of Boltzmann's sigmoidal equation applicable to the set-up of the RF-magnetron co-sputtering in thin films deposi-tion of Ba<sub>x</sub>Sr<sub>1-x</sub>TiO<sub>3</sub> , Bulletin of Materials Science, 40, 5, pp. 1043-1047, (2017)