Spatial Analysis of Flood Susceptibility Throughout Currituck County, North Carolina

Giovannettone, Jason; Sangameswaran, Siva; Maderia, Chris; Batten, Brian K.

doi:10.1061/(asce)he.1943-5584.0001948

Cited by 4 publications

(1 citation statement)

References 36 publications

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“…In particular, in many areas where an enhanced flash flood risk and vulnerability assessment could be of significant value, the necessary in situ data is more likely to be sparse [173,174]. Using information on non-flash floods (such as information on riverine floods) to drive flash flood specific applications, such as flash flood risk modelling and flash flood disaster management, could lead to significant biases that will further propagate through the subsequent elements of the application, such as the development of policy and services related to flash flood risk reduction and resilience including developing flood-type specific financial instruments and insurance mechanisms [175,176]. Here, we demonstrate that flood events can be assigned the subtype of flash flood with different levels of confidence.…”

Section: Discussionmentioning

confidence: 99%

Development of a Flash Flood Confidence Index from Disaster Reports and Geophysical Susceptibility

Kruczkiewicz

Bucherie

Ayala³

et al. 2021

Remote Sensing

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The analysis of historical disaster events is a critical step towards understanding current risk levels and changes in disaster risk over time. Disaster databases are potentially useful tools for exploring trends, however, criteria for inclusion of events and for associated descriptive characteristics is not standardized. For example, some databases include only primary disaster types, such as ‘flood’, while others include subtypes, such as ‘coastal flood’ and ‘flash flood’. Here we outline a method to identify candidate events for assignment of a specific disaster subtype—namely, ‘flash floods’—from the corresponding primary disaster type—namely, ‘flood’. Geophysical data, including variables derived from remote sensing, are integrated to develop an enhanced flash flood confidence index, consisting of both a flash flood confidence index based on text mining of disaster reports and a flash flood susceptibility index from remote sensing derived geophysical data. This method was applied to a historical flood event dataset covering Ecuador. Results indicate the potential value of disaggregating events labeled as a primary disaster type into events of a particular subtype. The outputs are potentially useful for disaster risk reduction and vulnerability assessment if appropriately evaluated for fitness of use.

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