Author Type

Graduate Student

Date of Award

2024

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Zhang, Caiyun

Thesis/Dissertation Co-Chair

Su, Hongbo

Abstract

This study examines the impact of uncertainty associated with Light Detection and Ranging (LiDAR) derived Digital Elevation Models (DEMs) on flood risk mapping in the North Biscayne Bay sub-watershed. A comparison of flood extent and generation of the probability of flooding was carried out using the bathtub and probabilistic approaches respectively. The water level was computed separately for original and refined DEM using Cascade 2001 hydrological model. Using land cover based corrected DEMs reveals a 12% reduction in flooded areas in contrast to original DEM, considering uncertainties associated with land cover. Probabilistic flood modeling via Gaussian Geostatistical Simulation accounts for DEM uncertainty, yielding nuanced probability flood risk maps (0-100%). Findings emphasize DEM refinement before conducting flood mapping to address uncertainties. Future research should explore other mediums of correction incorporating effects of point density of LiDAR, methods of DEM generation, use of diverse scenarios, and kriging techniques for flood modeling and mapping while using LiDAR derived DEM.

Extent

53 p.

Comments

Includes bibliography.|Thesis (MS)--Florida Atlantic University, 2024.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.

Identifier

FA00014476

Format

pdf

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