A hurricane surge risk assessment framework using the joint probability method and surge response functions

Hsu, Chih‐Hung; Olivera, Francisco; Irish, Jennifer L.

doi:10.1007/s11069-017-3108-8

Cited by 10 publications

(8 citation statements)

References 43 publications

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“…However, a lack of information on the dependence structure of different environmental variables often limits their joint analysis (Defra, ). Joint probabilities have been mostly applied to tide and storm surges time‐series for the analysis of extreme water levels RPs, regardless of wave characteristics (Hsu, Olivera, & Irish, ; Wahl & Plant, ). With the increase in the availability of measured or modelled wave data, the latter are more than ever being integrated to multivariate assessments (Leijala et al, ; Lerma et al, ; Sayol & Marcos, ) but sometimes even with joint time series as short as 6 years (Masina, Lamberti, & Archetti, ).…”

Section: Introductionmentioning

confidence: 99%

Multihazard simulation for coastal flood mapping: Bathtub versus numerical modelling in an open estuary, Eastern Canada

David

Baudry

Bernatchez

et al. 2018

J Flood Risk Management

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Funding informationFonds Québécois de la recherche sur la nature et les technologies; Québec Ministry of Public Security Coastlines along the St. Lawrence Estuary and Gulf, Eastern Canada, are under increasing risk of flooding due to sea level rise and sea ice shrinking. Efficient and validated regional-scale coastal flood mapping approaches that include storm surges and waves are hence required to better prepare for the increased hazard. This paper compares and validates two different flood mapping methods: numerical flood simulations using XBeach and bathtub mapping based on total water levels, forced with multihazard scenarios of compound wave and water level events. XBeach is validated with hydrodynamic measurements. Simulations of a historical storm event are performed and validated against observed flood data over ã 25 km long coastline using multiple fit metrics. XBeach and the bathtub method correctly predict flooded areas (66 and 78%, respectively), but the latter overpredicts the flood extent by 36%. XBeach is a slightly more robust flood mapping approach with a fit of 51% against 48% for the bathtub maps. Deeper floodwater by~0.5 m is expected with the bathtub approach, and more buildings are vulnerable to a 100-year flood level. For coastal management at regional-scale, despite similar flood extents with both flood mapping approaches, results suggest that numerical simulati on with XBeach outperforms bathtub flood mapping.

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Section: Introductionmentioning

confidence: 99%

Multihazard simulation for coastal flood mapping: Bathtub versus numerical modelling in an open estuary, Eastern Canada

David

Baudry

Bernatchez

et al. 2018

J Flood Risk Management

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“…Masina et al (2015) produced the joint probability distribution of extreme water levels and wave heights at Ravenna coast in Italy and used the direct integration method to assess the flooding probability. Hsu et al (2018) developed an approach based on the joint probability method with optimal sampling using surge response functions to predict extreme surge elevations as a function of hurricane parameters and to assess impacts of the estimated storm surge hazard at three study sites in the northern Gulf of Mexico. Mazas and Hamm (2017) presented an approach for determining extreme joint probabilities of wave heights and sea levels focusing on the sampling of the two variables, proposed to be based on the event generating the variables or resulting from their combined effect.…”

Section: Literature Reviewmentioning

confidence: 99%

Nonstationary joint probability analysis of extreme marine variables to assess design water levels at the shoreline in a changing climate

et al. 2019

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In the present study, a recently developed novel approach (Bender et al. in J Hydrol 514:123-130, 2014) has been further extended to investigate the changes in the joint probabilities of extreme offshore and nearshore marine variables with time and to assess design the total water level (TWL) at the shoreline under the effects of climate change. The nonstationary generalised extreme value (GEV) distribution has been utilised to model the marginal distribution functions of marine variables (wave characteristics and sea levels), within a 40-year moving window. All parameters of the GEV were tested for statistically significant linear and polynomial trends over time, and best-fitted trends have been detected. Different copula functions were fitted at the 40-year moving windows, to model the dependence structure of extreme offshore significant wave heights and peak spectral periods, and of wave-induced sea levels on the shoreline and nearshore sea levels due to storm surges. The most appropriate bivariate models were then selected. Statistically significant polynomial trends were detected in the dependence parameters of the selected copulas, and time-dependent most likely bivariate events were extracted to be used in the estimation of the TWL at the shoreline. The methods of the present work were implemented in three selected Greek coastal areas in the Aegean Sea. The analysis revealed different variations in the most likely estimates of the offshore wave characteristics and nearshore storm surges in the three study areas, as well as in the time-dependent estimates of TWL at the shoreline. The approach combines nonstationarity and bivariate analysis, blends coastal and offshore marine features and finally provides non-trivial alterations in the response of coastal sea level dynamics to climate change signals, compared to former work on the subject. The methodology produces reasonable estimates of design quantities for coastal structures and boundary conditions for the assessment of flood hazard and risk in coastal areas.

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“…Observed global sea level rise (SLR) (Nerem et al., 2018 ) and the consensus expectation of accelerated SLR in the future (e.g., Sweet et al., 2017 ) further motivate research efforts to quantify spatial and temporal exposure to future coastal flood and erosion hazards. Numerous efforts have focused on the development and application of high‐fidelity, but computationally expensive, numerical modeling suites to quantify the impacts of specific storm events (e.g., Barnard et al., 2019 ; Bilskie et al., 2014 ; Dietrich et al., 2011 ; Hsu et al., 2018 ; Warner et al., 2010 ; Wolf, 2009 ) or use output from a limited number of dynamically downscaled multi‐decadal general circulation models (GCMs) (e.g., Muis et al., 2020 ). Other work has investigated statistical approaches to generate 1,000s of multivariate combinations of waves, sea levels, precipitation, and river flows that could compound to create future extreme events (e.g., Callaghan et al., 2008 ; Moftakhari et al., 2017 ; Serafin & Ruggiero, 2014 ; Wahl & Chambers, 2015 ), enabling an estimate of the intrinsic variability within climate‐dependent processes.…”

Section: Introductionmentioning

confidence: 99%

Projecting Climate Dependent Coastal Flood Risk With a Hybrid Statistical Dynamical Model

et al. 2021

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Numerical models for tides, storm surge, and wave runup have demonstrated ability to accurately define spatially varying flood surfaces. However these models are typically too computationally expensive to dynamically simulate the full parameter space of future oceanographic, atmospheric, and hydrologic conditions that will constructively compound in the nearshore to cause both extreme event and nuisance flooding during the 21st century. A surrogate modeling framework of waves, winds, and tides is developed in this study to efficiently predict spatially varying nearshore and estuarine water levels contingent on any combination of offshore forcing conditions. The surrogate models are coupled with a time‐dependent stochastic climate emulator that provides efficient downscaling for hypothetical iterations of offshore conditions. Together, the hybrid statistical‐dynamical framework can assess present day and future coastal flood risk, including the chronological characteristics of individual flood and wave‐induced dune overtopping events and their changes into the future. The framework is demonstrated at Naval Base Coronado in San Diego, CA, utilizing the regional Coastal Storm Modeling System (CoSMoS; composed of Delft3D and XBeach) as the dynamic simulator and Gaussian process regression as the surrogate modeling tool. Validation of the framework uses both in‐situ tide gauge observations within San Diego Bay, and a nearshore cross‐shore array deployment of pressure sensors in the open beach surf zone. The framework reveals the relative influence of large‐scale climate variability on future coastal flood resilience metrics relevant to the management of an open coast artificial berm, as well as the stochastic nature of future total water levels.

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A hurricane surge risk assessment framework using the joint probability method and surge response functions

Cited by 10 publications

References 43 publications

Multihazard simulation for coastal flood mapping: Bathtub versus numerical modelling in an open estuary, Eastern Canada

Multihazard simulation for coastal flood mapping: Bathtub versus numerical modelling in an open estuary, Eastern Canada

Nonstationary joint probability analysis of extreme marine variables to assess design water levels at the shoreline in a changing climate

Projecting Climate Dependent Coastal Flood Risk With a Hybrid Statistical Dynamical Model

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