A Data-driven, Probabilistic, Multiple Return Period Method of Flood
Depth Estimation
Abstract
Flood depth grids from U.S. Federal Emergency Management Agency (FEMA)
provide model-output estimates of the depth of water that can, on
average, be expected to occur at various return periods for localized
areas. However, use of these depth grids can be limited by spurious data
and an insufficient number of return periods for certain planning
applications. This research proposes a new method for estimating flood
depth grids to address these shortcomings. The Gumbel distribution is
used to characterize the flood depth-return period relationship for grid
cells for which the data are plausible. Then the Gumbel parameters of
slope (α) and intercept (u) are used to project flood elevations for
extreme return periods for which an entire area can be assumed to be
submerged. Spatial interpolation methods are then used to impute the
flood elevations for spurious or missing grid cells. Then, the flood
depth is recomputed from the flood elevations, once they are
re-calculated at the shorter return periods. Validation of this
technique for a Metairie, Louisiana, U.S.A. study area suggests that the
cokriging spatial interpolation technique provides the most suitable
estimates of flood depth, provided that the FEMA-generated model output
is assumed to provide the “correct” results. These methods may assist
engineers, developers, planners, and others in mitigating the world’s
most widespread and expensive natural hazard.