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Evaluating the Impact of LiDAR DEM Uncertainties on Inundation Modeling in Coastal Sub-Watersheds: An Exploration Via Deterministic and Probabilistic Approaches

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Date Issued:
2024
Abstract/Description:
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.
Title: Evaluating the Impact of LiDAR DEM Uncertainties on Inundation Modeling in Coastal Sub-Watersheds: An Exploration Via Deterministic and Probabilistic Approaches.
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Name(s): Thapa, Madan Chhetri, author
Zhang, Caiyun, Thesis advisor
Su, Hongbo, Thesis advisor
Florida Atlantic University, Degree grantor
Department of Geosciences
Charles E. Schmidt College of Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2024
Date Issued: 2024
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 53 p.
Language(s): English
Abstract/Description: 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.
Identifier: FA00014476 (IID)
Degree granted: Thesis (MS)--Florida Atlantic University, 2024.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Biscayne Bay (Fla.)
Lidar
Digital elevation models
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00014476
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Host Institution: FAU