Efficient Tropical Cyclone Scenario Selection Based on Cumulative Likelihood of Potential Impacts

Sohrabi, Meraj; Moftakhari, Hamed; Moradkhani, Hamid

doi:10.1029/2023ef003731

Cited by 3 publications

(1 citation statement)

References 85 publications

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“…Non-linearity refers to the complex interactions among different flood drivers, which can lead to impacts that are not simply the sum of their individual effects (Arns et al, 2020;Muñoz et al, 2020Muñoz et al, , 2022. Multidimensionality recognizes that flood risk is influenced by a multitude of factors, including not only meteorological and hydrological variables but also the built environment and human behavior (Jongman et al, 2012;Alipour et al, 2022;Sohrabi et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Challenges for compound coastal flood risk management in a warming climate: a case study of the Gulf Coast of the United States

Lewis,

Moftakhari,

Passalacqua

2024

Front. Water

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Compound flooding (CF) events, driven by coincident/concurrent and mutually reinforcing factors such as heavy rainfall, storm surges, and river discharge, pose severe threats to coastal communities around the Globe. Moreover, the exacerbating influence of climate change and sea-level rise further amplifies these risks. This study delves into the complex and multifaceted issue of compound coastal flooding in two freshwater-influenced systems on the Gulf Coast of the United States – Southeast Texas and South Alabama. We first conduct a robust statistical analysis to evaluate the significance of non-stationarity, multi-dimensionality, and non-linearity of interactions among various drivers of CF. Second, to assess the extent to which current flood resilience policies and guidelines account for these characteristics of CF events, we perform a critical review of existing policy documents. The results of the statistical analysis reveal significant compounding and shifts in the statistics of flood drivers that emphasize the pressing need for a multi-mechanism, non-stationary approach to flood hazard assessment. We also found an evident lack of appropriate language/recommendation in policy documents of solid tools that systematically take non-stationarity, multi-dimensionality, and non-linearity of CF into account. By identifying the gaps between current policy measures and the detected complexities of CF, we seek to provide insights that can inform more effective flood resilience policies and design guidelines. Through this robust analysis, we aspire to bridge the divide between research and policy.

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Section: Introductionmentioning

confidence: 99%

Challenges for compound coastal flood risk management in a warming climate: a case study of the Gulf Coast of the United States

Lewis,

Moftakhari,

Passalacqua

2024

Front. Water

Self Cite

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Downscaling, bias correction, and spatial adjustment of extreme tropical cyclone rainfall in ERA5 using deep learning

Ascenso,

Ficchì,

Giuliani

et al. 2024

Weather and Climate Extremes

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Multivariate compound events drive historical floods and associated losses along the U.S. East and Gulf coasts

Ali,

Wahl,

Morim

et al. 2024

Preprint

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Compound flooding events are a threat to many coastal regions and can have widespread socio-economic implications. However, their frequency of occurrence, underlying flood drivers, and direct link to past socio-economic losses are largely unknown despite being key to supporting risk and adaptation assessments. Here, we present an impact-based analysis of compound flooding for 203 coastal counties along the U.S. Gulf and East coasts by combining data from multiple flood drivers and socio-economic loss information from 1980 to 2018. We find that ~ 80% of all flood events recorded in our study area were compound rather than univariate. In addition, we show that historical compound flooding events in most counties were driven by more than two flood drivers (hydrological, meteorological, and/or oceanographic) and distinct spatial clusters exist that exhibit variability in the underlying driver of compound flood events. Furthermore, we find that in more than 80% of the counties over 80% of recorded property and crop losses were linked to compound flooding. The median cost of compound events is more than 26 times that of univariate events in terms of property loss and 76 times in terms of crop loss. Our analysis overcomes some of the limitations of previous compound-event studies based on pre-defined flood drivers and offers new insights into the complex relationship between hazards and associated socio-economic impacts.

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Efficient Tropical Cyclone Scenario Selection Based on Cumulative Likelihood of Potential Impacts

Cited by 3 publications

References 85 publications

Challenges for compound coastal flood risk management in a warming climate: a case study of the Gulf Coast of the United States

Challenges for compound coastal flood risk management in a warming climate: a case study of the Gulf Coast of the United States

Downscaling, bias correction, and spatial adjustment of extreme tropical cyclone rainfall in ERA5 using deep learning

Multivariate compound events drive historical floods and associated losses along the U.S. East and Gulf coasts

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