Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 3 August 2018 / Approved: 3 August 2018 / Online: 3 August 2018 (15:46:14 CEST)
Version 2 : Received: 11 September 2018 / Approved: 11 September 2018 / Online: 11 September 2018 (14:19:43 CEST)

A peer-reviewed article of this Preprint also exists.

Couasnon, A.; Sebastian, A.; Morales-Nápoles, O. 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. Water 2018, 10, 1190. Couasnon, A.; Sebastian, A.; Morales-Nápoles, O. 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. Water 2018, 10, 1190.

Journal reference: Water 2018, 10, 1190
DOI: 10.3390/w10091190

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; this phenomenon is 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 calculate the resulting water levels using a 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 with 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.

Subject Areas

flood risk; copula; compound events; multivariate; storm surge; spatial dependence; coastal catchment; Bayesian Network.

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