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Correlation models in flood risk analysis

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  • Diermanse, F.L.M.
  • Geerse, C.P.M.

Abstract

Statistical dependence among random hydraulic variables generally stems from a common meteorological cause. Usually, this dependence increases the probability of occurrence of floods. Therefore, in many (potential) applications in flood risk analysis there is a need for techniques that properly describe the statistical dependence among random variables. This study makes an inventory of existing bivariate correlation models as available from international literature and/or ‘Dutch practice’ in the design and testing of flood defence structures. Differences in correlation structures are described and quantified. Subsequently, their practical applicability is tested in two case studies.

Suggested Citation

  • Diermanse, F.L.M. & Geerse, C.P.M., 2012. "Correlation models in flood risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 105(C), pages 64-72.
    • Handle: RePEc:eee:reensy:v:105:y:2012:i:c:p:64-72
    DOI: 10.1016/j.ress.2011.12.004
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    References listed on IDEAS

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    Cited by:

    1. Yaolong Liu & Guorui Feng & Ye Xue & Huaming Zhang & Ruoguang Wang, 2015. "Small-scale natural disaster risk scenario analysis: a case study from the town of Shuitou, Pingyang County, Wenzhou, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(3), pages 2167-2183, February.
    2. Wang, Fan & Li, Heng, 2018. "System reliability under prescribed marginals and correlations: Are we correct about the effect of correlations?," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 94-104.
    3. Gong, Yu & Liu, Pan & Zhang, Jun & Liu, Dedi & Zhang, Xiaoqi & Zhang, Xiaojing, 2020. "Considering different streamflow forecast horizons in the quantitative flood risk analysis for a multi-reservoir system," Reliability Engineering and System Safety, Elsevier, vol. 204(C).

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