A data-driven spatial approach to characterize the flood hazard
Model output of localized flood grids are useful in characterizing flood hazards for properties located in the Special Flood Hazard Area (SFHA-areas expected to experience a 1% or greater annual chance of flooding). However, due to the unavailability of higher return-period [i.e., recurrence interval, or the reciprocal of the annual exceedance probability (AEP)] flood grids, the flood risk of properties located outside the SFHA cannot be quantified. Here, we present a method to estimate flood hazards that are located both inside and outside the SFHA using existing AEP surfaces. Flood hazards are characterized by the Gumbel extreme value distribution to project extreme flood event elevations for which an entire area is assumed to be submerged. Spatial interpolation techniques impute flood elevation values and are used to estimate flood hazards for areas outside the SFHA. The proposed method has the potential to improve the assessment of flood risk for properties located both inside and outside the SFHA and therefore to improve the decision-making process regarding flood insurance purchases, mitigation strategies, and long-term planning for enhanced resilience to one of the world's most ubiquitous natural hazards.
Publication Source (Journal or Book title)
Frontiers in big data
Mostafiz, R. B., Rahim, M. A., Friedland, C. J., Rohli, R. V., Bushra, N., & Orooji, F. (2022). A data-driven spatial approach to characterize the flood hazard. Frontiers in big data, 5, 1022900. https://doi.org/10.3389/fdata.2022.1022900