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A Data-driven, Probabilistic, Multiple Return Period Method of Flood Depth Estimation
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  • Rubayet Bin Mostafiz,
  • Carol Friedland,
  • Md Adilur Rahim,
  • Robert Rohli,
  • Nazla Bushra
Rubayet Bin Mostafiz
Louisiana State University

Corresponding Author:[email protected]

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Carol Friedland
Louisiana State University
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Md Adilur Rahim
Louisiana State University
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Robert Rohli
Louisiana State University
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Nazla Bushra
Louisiana State University
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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.