A basis function approach for exploring the seasonal and spatial features of storm surge events

Wu, Wenyan; Westra, Seth; Leonard, Michael

doi:10.1002/2017gl074357

Cited by 12 publications

(13 citation statements)

References 33 publications

(42 reference statements)

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“…Port Hedland in Western Australia, Weipa in northern Queensland and Bundaberg in east Queensland in figure S1 in the supplementary material), which are most likely resulted from the impact of localised features (e.g. orography, bathymetry, tropical cyclones, etc) on individual flood drivers (Resio and Westerink 2008, Foresti and Seed 2015, Wu et al 2017 or their joint occurrence (Wu et al 2018).…”

Section: Impact Of Enso On Dependencementioning

confidence: 99%

Impact of ENSO on dependence between extreme rainfall and storm surge

Leonard

2019

Environ. Res. Lett.

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Dependence between extreme rainfall and storm surge can have significant implications for coastal floods, which are often caused by joint occurrence of these flood drivers (through pluvial or fluvial processes). The effect of multiple drivers leading to a compound flood event poses higher risk than those caused by a single flood-driving process. There is strong evidence that compound floods caused by joint occurrence of extreme storm surge and heavy rainfall are related to meteorological forcing (e.g. large scale pressure systems and wind) and climate phenomena (e.g. the El Niño Southern Oscillation or ENSO). Therefore, understanding how climate phenomena affect the co-occurrence of coastal flood drivers is an important step towards understanding future coastal flood risk under climate change. Here we examine the impact of one of the most important climate phenomena-ENSO-on dependence between storm surge and rainfall in Australia, using both observed surge and modelled surge from a linked ocean-climate modelthe Regional Ocean Modeling System. Our results show that ENSO has a significant impact on the dependence between extreme rainfall and storm surge, thus flood risk resulted from these drivers. The overall dependence is largely driven by La Niña in Australia, with increased dependence observed during La Niña along most of the Australian coastline. However, there can be increased dependence during El Niño in some locations. The results demonstrate dependence is contributed by unequally-weighted mechanisms due to the interaction between climate phenomena and local features, indicating the need for greater understanding of composition of compound flood risk. Where climate phenomena are anticipated to change into the future, it is possible to use integrated process-driven models to establish a better understanding of whether extremes are more likely to co-occur and exacerbate compound flood risk.

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Section: Impact Of Enso On Dependencementioning

confidence: 99%

Impact of ENSO on dependence between extreme rainfall and storm surge

Leonard

2019

Environ. Res. Lett.

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“…The magnitude, timing and duration of elevated estuarine water levels due to this mechanism depends on the dynamics (e.g. timing and duration) of the storm surge, its superposition on the astronomic tide (with the greatest effects during 'spring tides' (Cowell and Thom, 1995)), and various bathymetric effects that influence propagation of the flood wave up the estuary (Resio 80 and Westerink, 2008;Wu et al, 2017). Although these two physical processes are often treated separately, the flood level within an estuary is not a simple addition of a fluvial hydrograph and an elevated coastal water level.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Probability of Compound Floods in Estuarine Regions

Wu¹,

Westra²,

Leonard³

2020

Preprint

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Abstract. The quantification of flood risk in estuarine regions relies on accurate estimation of flood probability, which is often challenging due to the rareness of flood events and their multi-causal (or compound) nature. Failure to consider the compounding nature of estuarine floods can lead to significant underestimation of flood risk in these regions. This study provides a comparative review of alternative approaches for estuarine flood estimation; namely, traditional univariate flood frequency analysis applied to both observed historical data and simulated data, and multivariate frequency analysis applied to flood events. Three specific implementations of the above approaches are evaluated on a case study – the estuarine portion of Swan River in Western Australia, highlighting the advantages and disadvantages of each approach. The theoretical understanding of the three approaches, combined with findings from the case study, enable generation of guidance on method selection for estuarine flood probability estimation, recognising issues such as data availability, complexity of the application/analysis process, location of interest within the estuarine region, computational demands and whether or not future conditions need to be assessed.

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“…The concept of risk in natural disasters emphasizes the integration of hazard, exposure, and vulnerability, and promotes the decision process of hazard-projection-social impact evaluation-risk-estimation-risk-countermeasures. For coastal regions, the risk from tropical storm surge inundation is responsible for most of the damage and loss [10][11][12] and is projected to worsen as coastal cities grow and the natural environment changes [13,14]. To estimate storm surge inundation risk, it is necessary to consider climate change scenarios [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Simulation-Based Exceedance Probability Curves to Assess the Economic Impact of Storm Surge Inundations due to Climate Change: A Case Study in Ise Bay, Japan

et al. 2019

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Understanding storm surge inundation risk is essential for developing countermeasures and adaptation strategies for tackling climate change. Consistent assessment of storm surge inundation risk that links probability of hazard occurrence to distribution of economic consequence are scarce due to the lack of historical data and uncertainty of climate change, especially at local scales. This paper proposes a simulation-based method to construct exceedance probability (EP) curves for representing storm surge risk and identifying the economic impact of climate change in the coastal areas of Ise Bay, Japan. The region-specific exceedance probability curves show that risk could be different among different districts. The industry-specific exceedance probability curves show that manufacturing, transport and postal activities, electricity, gas, heat supply and water, and wholesale and retail trade are the most affected sectors in terms of property damage. Services also need to be of concern in terms of business interruption loss. Exceedance probability curves provide complete risk information and our simulation-based approach can contribute to a better understanding of storm surge risk, improve the quantitative assessment of the climate change-driven impacts on coastal areas, and identify vulnerable regions and industrial sectors in detail.

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A basis function approach for exploring the seasonal and spatial features of storm surge events

Cited by 12 publications

References 33 publications

Impact of ENSO on dependence between extreme rainfall and storm surge

Impact of ENSO on dependence between extreme rainfall and storm surge

Estimating the Probability of Compound Floods in Estuarine Regions

Simulation-Based Exceedance Probability Curves to Assess the Economic Impact of Storm Surge Inundations due to Climate Change: A Case Study in Ise Bay, Japan

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