Brief   communication:  Post-event  analysis   of   loss   of   life   due   to 
hurricane Harvey

Jonkman, Sebastiaan N.; Godfroy, Maartje; Sebastian, Antonia; Kolen, B.

doi:10.5194/nhess-2017-436

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…Yet, long flood events spanning multiple days have been observed in the catchment, such as Hurricane Harvey in August 2017. The tropical cyclone stalled over Texas generating extreme coastal water surface elevations and precipitation lasting for several days which impeded runoff from the system [82], causing critical road cutoffs, the release of toxic materials, and more than 70 fatalities [83,84]. Therefore, it is important to consider the temporal aspect of flood not only to correctly determine the flood hazard but also to properly model their impact in flood risk assessments.…”

Section: Discussionmentioning

confidence: 99%

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

Couasnon¹,

Sebastian²,

Morales-Nápoles³

2018

Preprint

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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.

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Section: Discussionmentioning

confidence: 99%

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

Couasnon¹,

Sebastian²,

Morales-Nápoles³

2018

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Yet, long flood events spanning multiple days have been observed in the catchment, such as Hurricane Harvey in August 2017. The tropical cyclone stalled over Texas generating extreme coastal water surface elevations and precipitation lasting for several days which impeded runoff from the system (van Oldenborgh et al, 2017), causing critical road cutoffs, the release of toxic materials, and more than 70 fatalities (Jonkman et al, 2018;Sebastian et al, 2017b). Therefore, it is important to consider the temporal aspect of flood not only to correctly determine the flood hazard but also to properly model their impact in flood risk assessments.…”

Section: Discussionmentioning

confidence: 99%

The role of (in)dependence in flood risk assessments

Couasnon¹

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Flooding remains the most frequent natural hazard, affecting more people than any other weather-related disasters. The increase in frequency, duration and severity of floods in many parts of the world is projected to rise due to changes in climate, population and land use. Compound flooding, which occurs when multiple flood drivers interact to cause or exacerbate flooding, is a significant challenge in estuarine environments. However, the influence of flood drivers on compound flooding in estuaries remains poorly understood. To address this issue, the goal of this thesis is to improve flood risk assessments in estuarine environments by advancing our understanding and modelling of compound flood hazard. The thesis first identifies potential hotspots of compound flooding at the global scale by analyzing discharge and storm surge time series from state-of-the-art global models. The study is performed at 3,434 river mouths, encompassing all catchments along the coast with an upstream area larger than 1,000 km2. About 6% of the locations are classified as having the highest compound flooding potential. The results show that the dependence between river discharge and storm surge is significant in many areas, including the coasts of Madagascar, northern Morocco, northern Australia, Vietnam, and Taiwan. Furthermore, the study highlights the complex temporal dynamics between discharge and storm surge that cannot be captured by one indicator alone. Next, the thesis develops different probabilistic assessment methods to account for the dependence of flood drivers in flood risk assessment through two case studies. The first case study assesses the impact of the spatial dependence between river discharges and storm surge on the flood hazard quantification for a small catchment in Galveston Bay, Texas. The second case study expands the probabilistic assessment method into a computationally efficient probabilistic framework for calculating flood risk using Ho Chi Minh City as a case study. The framework allows for multiple dependence structures and is applied at a monthly time scale to account for seasonality. Lastly, the thesis moves beyond traditional expected annual damage (EAD) as the indicator of flood risk and discusses the many implications of spatial and temporal dynamics on disaster risk management. The development of plausible storylines and their impact on risk management decisions are explored within a serious game called Breaking the Silos. Breaking the Silos is a collaborative role-playing game designed to explore the impacts of these complexities on risk management decisions. Overall, the thesis provides important insights into the challenges of compound flooding in estuarine environments and proposes new methods to account for these challenges in flood risk assessments. The study highlights the need to include compound flooding in flood hazard assessments in locations where fluvial, pluvial, and coastal interactions are important. The thesis also emphasizes the need for disaster management measures to consider the spatial and temporal dynamics between hazards and their impact on residual risk.

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Brief communication: Post-event analysis of loss of life due to hurricane Harvey

Cited by 2 publications

References 4 publications

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

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

The role of (in)dependence in flood risk assessments

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