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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

1
Institute for Environmental Studies, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1087, 1081 HV Amsterdam, The Netherlands
2
Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands
*
Author to whom correspondence should be addressed.
Water 2018, 10(9), 1190; https://doi.org/10.3390/w10091190
Received: 3 August 2018 / Revised: 28 August 2018 / Accepted: 30 August 2018 / Published: 4 September 2018
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. View Full-Text
Keywords: flood risk; copula; compound events; multivariate; storm surge; spatial dependence; Bayesian Network flood risk; copula; compound events; multivariate; storm surge; spatial dependence; Bayesian Network
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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.

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