Joint Risk of Rainfall and Storm Surges during Typhoons in a Coastal City of Haidian Island, China

Xu, Hongshi; Xu, Kui; Bin, Lingling; Lian, Jijian; Ma, Chao

doi:10.3390/ijerph15071377

Cited by 36 publications

(25 citation statements)

References 51 publications

(68 reference statements)

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“…Coastal flood risk attributed to storm surge and rainfall (through pluvial or fluvial processes) has been reported in many regions around the world, including Asia (Lian et al 2013, Ikeuchi et al 2017, Xu et al 2018, Ghosh et al 2019, Europe (Svensson and Jones 2004, Klerk et al 2015, Van den Hurk et al 2015, Bevacqua et al 2017, Hu et al 2019, North America (Wahl et al 2015, Bass and Bedient 2018, Shao et al 2019, North Africa (Zellou and Rahali 2019) and Australia (Zheng et al 2013, Kumbier et al 2018, Wu et al 2018. There is strong evidence that the joint occurrence of extreme storm surge and heavy rainfall is related to large scale weather patterns, such as tropical cyclones, ex-tropical cyclones, frontal systems and East Coast Lows in Australia (McInnes et al 2002, Van den Hurk et al 2015, Wahl et al 2015, Kumbier et al 2018, Wu et al 2018.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

Impact of ENSO on dependence between extreme rainfall and storm surge

Leonard

2019

Environ. Res. Lett.

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“…According to Sklar's Theorem (Sklar, 1959), there exists a unique bivariate copula C S,R that generates the joint cumulative distribution function (CDF) F S,R given the continuous marginal CDFs F S and F R . Copulas have been widely implemented to assess compound flood hazard between storm tides and rainfall (Sebastian et al, 2017; Wahl et al, 2015; Xu et al, 2018; Zellou & Rahali, 2019). Rather than fitting a parametric copula to the synthetic data, we simply represent the copula structure using a non‐parametric kernel density estimate.…”

Section: Methodsmentioning

confidence: 99%

Tropical Cyclone Compound Flood Hazard Assessment: From Investigating Drivers to Quantifying Extreme Water Levels

Gori

Lin

2020

Earth's Future

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Compound flooding, characterized by the co-occurrence of multiple flood mechanisms, is a major threat to coastlines across the globe. Tropical cyclones (TCs) are responsible for many compound floods due to their storm surge and intense rainfall. Previous efforts to quantify compound flood hazard have typically adopted statistical approaches that may be unable to fully capture spatio-temporal dynamics between rainfall-runoff and storm surge, which ultimately impact total water levels. In contrast, we pose a physics-driven approach that utilizes a large set of realistic TC events and a simplified physics-based rainfall model and simulates each event within a hydrodynamic model framework. We apply our approach to investigate TC flooding in the Cape Fear River, NC. We find TC approach angle, forward speed, and intensity are relevant for compound flood potential, but rainfall rate and time lag between the centroid of rainfall and peak storm tide are the strongest predictors of compounding magnitude. Neglecting rainfall underestimates 100-year flood depths across 28% of the floodplain, and taking the maximum of each hazard modeled separately still underestimates 16% of the floodplain. We find the main stem of the river is surge-dominated, upstream portions of small streams and pluvial areas are rainfall dominated, but midstream portions of streams are compounding zones, and areas close to the coastline are surge dominated for lower return periods but compounding zones for high return periods (100 years). Our method links joint rainfall-surge occurrence to actual flood impacts and demonstrates how compound flooding is distributed across coastal catchments. Plain Language Summary Compound flooding can result when multiple sources of flooding (such as storm surges at the coast and inland rainfall) overlap in space or time and interact in such a way that overall flooding is exacerbated. Tropical cyclones (TCs) often produce both storm surges and intense rainfall, and have caused many observed compound floods. We analyze compound flooding from TCs by using a large set of realistic TC events and a suite of physics-based models that estimate TC rainfall, storm tides, and simulate their interaction in a coastal study area (Cape Fear River, NC). We find that compound flooding is more severe during TCs that produce high-intensity rainfall and when significant rainfall occurs near the time of peak ocean water level. It is crucial to incorporate rainfall in coastal flood hazard assessment, and we find that neglecting rainfall results in an underestimation of extreme water levels in the study area. It is also necessary to model rainfall and storm surges together within physics-based models to accurately represent their interaction and compounding during TC events.

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“…As one of only two Category 5 super typhoons recorded in the South China Sea, Rammasun made landfall at its peak intensity over the island province of Hainan in China on 18 th July 2014. It brought both heavy rainfall and strong surge with return periods of more than 100 years to the City of Haiko, the capital of Hainan province located on the estuary of Nandu River (Xu et al, 2018). Heavy rain caused widespread flooding in Haiko City and nearby urban areas.…”

Section: Introductionmentioning

confidence: 99%

“…Storm surge over three meters was observed on the northern coast 55 of the island, which prevented water from the Nandu River from draining into the sea, further exacerbating the impacts of floods in and nearby Haiko City (Wang et al, 2017). Yet flood estimation in this region proved problematic (Wang et al, 2017;Xu et al, 2018): historical flood records are short, the region has experienced rapid and extensive urbanisation including significant hydraulic changes in Nandu River leading to nonstationarity, and climate change is already modifying key flood-generating processes such as mean sea level and 60 heavy rainfall (IPCC, 2012). This is not an isolated example; with large human populations situated at low elevations in close proximity to where rivers meet the ocean, there are many cases where interacting processes lead to complex flood dynamics and substantial impacts (e.g.…”

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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Joint Risk of Rainfall and Storm Surges during Typhoons in a Coastal City of Haidian Island, China

Cited by 36 publications

References 51 publications

Impact of ENSO on dependence between extreme rainfall and storm surge

Impact of ENSO on dependence between extreme rainfall and storm surge

Tropical Cyclone Compound Flood Hazard Assessment: From Investigating Drivers to Quantifying Extreme Water Levels

Estimating the Probability of Compound Floods in Estuarine Regions

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