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

Couasnon, A.A.O.; Sebastian, Antonia; Morales Napoles, O.

doi:10.3390/w10091190

A Copula-Based Bayesian Network for Modeling Compound Flood Hazard from Riverine and Coastal Interactions at the Catchment Scale

Title

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

Author

Couasnon, A.A.O. (TU Delft Hydraulic Structures and Flood Risk; Vrije Universiteit Amsterdam)
Sebastian, Antonia (TU Delft Hydraulic Structures and Flood Risk)
Morales Napoles, O. (TU Delft Hydraulic Structures and Flood Risk)

Date

2018-09-04

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

Subject

flood risk
copula
compound events
multivariate
storm surge
spatial dependence
Bayesian Network
OA-Fund TU Delft

To reference this document use:

http://resolver.tudelft.nl/uuid:297695bc-74c6-417b-8481-40477c9840a6

DOI

https://doi.org/10.3390/w10091190

ISSN

2073-4441

Source

Water, 10 (9)

Part of collection

Institutional Repository