A Data-driven Spatial Approach to Characterize the Flood Hazard

Carol Friedland, Kleinpeter, Shelly

Federal Emergency Management Agency (FEMA) modeled 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). Properties located outside the SFHA are generally categorized as shaded and unshaded X-zone based on a 500-year return period flood grid (Figure 1). As the 100-year return period flood is the benchmark set by FEMA for insurance purposes, the FEMA flood grids are not generally modeled for higher return-period (i.e., recurrence interval, or the reciprocal of the annual exceedance probability (AEP)) flood events. So, the flood hazard, and subsequently flood risk, of properties located outside the SFHA still cannot be quantified using these flood surfaces. Therefore, the goal of this research is the development of a novel method to characterize flood hazard based on existing hydrologic-modeled flood surfaces for properties located both inside and outside of SFHA.

FEMA provides flood surfaces for 10-year, 50-year, 100-year, and 500-year return periods. We used these existing flood surfaces to characterize flood hazard for properties using a Gumbel extreme value distribution. The Gumbel parameters, namely location (μ) and scale (σ), are fit using the available flood depth data for a property located inside SFHA. To estimate the Gumbel parameters for properties that are located outside SFHA where only one (500-year in case of shaded X zone) or no (unshaded X zone) flood depth data is available, we estimated extreme return period flood surfaces where most of the study area is assumed to be submerged (Figure 1). These extrapolated surfaces are generated using the already estimated Gumbel parameters for areas inside SFHA. Then, spatial interpolation is conducted on the extrapolated flood surface to impute the missing values. Gumbel parameters are then fit using these imputed values. Thus, the Gumbel parameters are estimated for the entire study area which can then be used to develop flood hazard estimates that are more reasonable to expect within the useful life of the building or settlement.

Zones of spatial interpolation.

Figure 1. Extrapolated flood surfaces and zones of spatial interpolation

Interestingly, the output of the method is relatively insensitive to the spatial interpolation technique chosen, at least for this study area. The relatively small errors from the method are interpreted as evidence that the procedure is successful. The Gumbel distribution is deemed to provide an acceptable result. Moreover, the relatively small RMSE values imply that D can be estimated relatively accurately and precisely. Such estimates can provide engineers and planners with useful information for enhancing infrastructure to accommodate low-frequency, large-magnitude flood events.

To see the results in detail and read more about our peer-reviewed publication on this research click on the link below.

Frontiers In: A data-driven spatial approach to characterize the flood hazard

Mostafiz RB, Rahim MA, Friedland CJ, Rohli RV, Bushra N and Orooji F (2022) A data-driven spatial approach to characterize the flood hazard. Frontiers Big Data 5:1022900. doi: 10.3389/fdata.2022.1022900
2/22/2023 5:46:27 PM
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