A Copula-based bayesian network for modeling compound flood hazard from riverine and coastal interactions at the catchment scale: An application to the houston ship channel, Texas

Anaïs Couasnon*, Antonia Sebastian, Oswaldo Morales-Nápoles

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Traditional flood hazard analyses often rely on univariate probability distributions; however, in many coastal catchments, flooding is the result of complex hydrodynamic interactions between multiple drivers. For example, synoptic meteorological conditions can produce considerable rainfall-runoff, while also generating wind-driven elevated sea-levels. When these drivers interact in space and time, they can exacerbate flood impacts, a phenomenon known as compound flooding. In this paper, we build a Bayesian Network based on Gaussian copulas to generate the equivalent of 500 years of daily stochastic boundary conditions for a coastal watershed in Southeast Texas. In doing so, we overcome many of the limitations of conventional univariate approaches and are able to probabilistically represent compound floods caused by riverine and coastal interactions. We model the resulting water levels using a one-dimensional (1D) steady-state hydraulic model and find that flood stages in the catchment are strongly affected by backwater effects from tributary inflows and downstream water levels. By comparing our results against a bathtub modeling approach, we show that simplifying the multivariate dependence between flood drivers can lead to an underestimation of flood impacts, highlighting that accounting for multivariate dependence is critical for the accurate representation of flood risk in coastal catchments prone to compound events.

Original languageEnglish
Article number1190
Pages (from-to)1-19
Number of pages19
JournalWater
Volume10
Issue number9
DOIs
Publication statusPublished - 4 Sept 2018

Funding

A.C. was supported by the Netherlands Organisation for Scientific Research (NWO) in the form of a VIDI grant (grant No. 016.161.324). A.S. was supported by the Netherlands America Foundation/Fulbright Fellowship forWater Management and the NSF PIRE Grant No. OISE-1545837. The authors would like to thank H. Winsemius for his comments and suggestions during the writing of this manuscript. We would also like to thank three anonymous reviewers for their valuable comments which helped to improve this manuscript.

FundersFunder number
National Science Foundation1545837
Nederlandse Organisatie voor Wetenschappelijk Onderzoek016.161.324

    Keywords

    • Bayesian Network
    • Compound events
    • Copula
    • Flood risk
    • Multivariate
    • Spatial dependence
    • Storm surge

    Fingerprint

    Dive into the research topics of 'A Copula-based bayesian network for modeling compound flood hazard from riverine and coastal interactions at the catchment scale: An application to the houston ship channel, Texas'. Together they form a unique fingerprint.

    Cite this