Spatial analysis and simulation of extreme coastal flooding scenarios for national-scale emergency planning

Wyncoll, David; Haigh, Ivan D.; Gouldby, Ben; Hames, Dominic; Laeger, Stefan; Wall, Andy; Hawkes, P.J.; Hammond, Anthony

doi:10.1051/e3sconf/20160701001

Cited by 5 publications

(6 citation statements)

References 8 publications

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“…It is standard practice to transform the data to achieve stationarity through detrending (e.g. Wyncoll et al, 2016). The long-term mean sea level signal is superimposed onto inter-annual to multi-decadal sea level variability caused by tidal modulations associated with the nodal (18.61 year) and perigean (8.5 year) cycles, as well as other oceanic-atmospheric processes (e.g.…”

Section: Case Study Sites and Datamentioning

confidence: 99%

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Multivariate statistical modelling of the drivers of compound flood events in south Florida

Jane

Cadavid

Obeysekera

et al. 2020

Nat. Hazards Earth Syst. Sci.

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Abstract. Miami-Dade County (south-east Florida) is among the most vulnerable regions to sea level rise in the United States, due to a variety of natural and human factors. The co-occurrence of multiple, often statistically dependent flooding drivers – termed compound events – typically exacerbates impacts compared with their isolated occurrence. Ignoring dependencies between the drivers will potentially lead to underestimation of flood risk and under-design of flood defence structures. In Miami-Dade County water control structures were designed assuming full dependence between rainfall and Ocean-side Water Level (O-sWL), a conservative assumption inducing large safety factors. Here, an analysis of the dependence between the principal flooding drivers over a range of lags at three locations across the county is carried out. A two-dimensional analysis of rainfall and O-sWL showed that the magnitude of the conservative assumption in the original design is highly sensitive to the regional sea level rise projection considered. Finally, the vine copula and Heffernan and Tawn (2004) models are shown to outperform five standard higher-dimensional copulas in capturing the dependence between the principal drivers of compound flooding: rainfall, O-sWL, and groundwater level. The work represents a first step towards the development of a new framework capable of capturing dependencies between different flood drivers that could potentially be incorporated into future Flood Protection Level of Service (FPLOS) assessments for coastal water control structures.

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Section: Case Study Sites and Datamentioning

confidence: 99%

“…The model has also been applied to capture the dependence in the variables contributing to extreme sea states at a single location (e.g. Gouldby et al, 2014) and at multiple sites (Wyncoll et al, 2016).…”

Section: Heffernan and Tawn (Ht04) Approachmentioning

confidence: 99%

Multivariate statistical modelling of the drivers of compound flood events in south Florida

Jane

Cadavid

Obeysekera

et al. 2020

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…The models thus require stationarity, i.e., the statistical parameters such as mean and variance should remain constant over time and be free of "trends, shifts, or periodicity" (Salas, 1993). It is standard practice to transform the data to achieve stationarity through detrending (e.g., Wyncoll et al, 2016). The long-term mean sea level signal is superimposed onto inter-annual to multi-decadal sea level variability caused by tidal modulations associated with the nodal (18.61 year) and perigean (8.5 year) cycles, and other oceanic-atmospheric processes (e.g.…”

Section: Case Study Sites and Datamentioning

confidence: 99%

“…The model has also been applied to capture the dependence in the variables contributing to extreme sea states at a single location (e.g., Gouldby et al, 2014) and at multiple sites (Wyncoll et al, 2016).…”

Section: Reject Sample Unless Is a Maximummentioning

confidence: 99%

Multivariate statistical modelling of the drivers of compound flood events in South Florida

Jane¹,

Cadavid²,

Obeysekera³

et al. 2020

Preprint

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Abstract. Miami-Dade County (south-east Florida) is among the most vulnerable regions to sea-level rise in the United States, due to a variety of natural and human factors. The co-occurrence of multiple, often statistically dependent flooding drivers – termed compound events – typically exacerbates impacts compared with their isolated occurrence. Ignoring dependencies between the drivers will potentially lead to underestimation of flood risk and under-design of flood defence structures. At present, design assessments of flood defence structures in Miami-Dade County assume rainfall and Ocean-side Water Level (O-sWL) are fully dependent, a conservative assumption inducing large safety factors. Here, an analysis of the dependence between the principal flooding drivers over a range of lags at three locations across the county is carried out. The conservative nature of the existing structural design assessment is subsequently explored, by combining a two-dimensional analysis of rainfall and O-sWL with regional sea-level rise projections. Finally, the vine copula and Heffernan and Tawn (2004) models are shown to outperform five standard higher dimensional copulas in capturing the dependence between the principal drivers of compound flooding: rainfall, O-sWL, and groundwater level. This leads to recommendations for revised future design frameworks able to capture and represent dependencies between different flood drivers.

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“…Thus, for example, 86% of extreme sea level events were found to be only moderate surges but imposed on high tides, indicating a dominance of the tidal component (Haigh et al, ). Analysis has also revealed examples of simultaneous flooding on unconnected stretches of coastline (Santos, Haigh, & Wahl, ; Wyncoll et al, ). This has important implications for risk assessment given the potential for stretching emergency services beyond capacity (Wahl et al, ; Wyncoll et al, ).…”

Section: Bdas For Coastal Flood Risk Assessmentmentioning

confidence: 99%

Big Data Approaches for coastal flood risk assessment and emergency response

Pollard

Spencer

Jude

2018

WIREs Climate Change

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Big Data Approaches (BDAs) refers to the combined use of historic datasets, incoming data streams, and the array of related technologies designed to shed new light on societal and environmental complexities through novel organizational, storage, and analytical capabilities. Despite widespread recognition of the commercial benefits of BDAs, application in the environmental domain is less well articulated. This represents a missed opportunity given that the dimensions used to characterize BDAs (volume, variety, velocity, and veracity) appear apt in describing the intractable challenges posed by global climate change. This paper employs coastal flood risk management as an illustrative case study to explore the potential applications in the environmental domain. Trends in global change including accelerating sea level rise, concentration of people and assets in low‐lying areas and deterioration of protective coastal ecosystems are expected to manifest locally as increased future flood risk. Two branches of coastal flood risk management are considered. First, coastal flood risk assessment, focusing on better characterization of hazard sources, facilitative pathways, and vulnerable receptors. Second, flood emergency response procedures, focusing on forecasting of flooding events, dissemination of warnings, and response monitoring. Critical commentary regarding technical, contextual, institutional, and behavioral barriers to the implementation of BDAs is offered throughout including a discussion of two fundamental difficulties associated with applying BDAs to coastal flood risk management: the role of BDAs in the broader flood system and the skill requirements for a generation of data scientists capable of implementing Big Data Approaches. This article is categorized under: Social Status of Climate Change Knowledge > Knowledge and Practice Climate, History, Society, Culture > Technological Aspects and Ideas

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Spatial analysis and simulation of extreme coastal flooding scenarios for national-scale emergency planning

Cited by 5 publications

References 8 publications

Multivariate statistical modelling of the drivers of compound flood events in south Florida

Multivariate statistical modelling of the drivers of compound flood events in south Florida

Multivariate statistical modelling of the drivers of compound flood events in South Florida

Big Data Approaches for coastal flood risk assessment and emergency response

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