Investigating Compound Flood Using Remote Sensing And Hydrodynamic Modeling

<p>Flooding is a frequent and destructive natural disaster that poses serious risks to infrastructure and socio-environmental systems. It involves multiple processes (e.g., coastal, pluvial, and fluvial) that can combine to cause more devastation than any process alone, called compound flooding (CF). While hydrodynamic models remain central to flood modeling, recent studies have also implemented remote sensing for flood mapping. This study first develops a framework to investigate CF in a data-scarce, cloudy region and creates probabilistic flood hazard maps using satellite imagery. The results conclude that the flood delineation algorithm developed in this study is simple (not cloud restricted), yet effective and comparable to complex algorithms (cloud restricted) requiring multiple satellites’ images. The probabilistic flood hazard maps derived for the region indicate that a larger portion has a high probability (0.8 - 1) of flooding, with a flood depth exceeding 1m. This study hindcasts the CF hazards during two hurricane events (Harvey and Ike) in Galveston, Texas, by calibrating SFINCS hydrodynamic model to explore how various flooding processes affect the flood hazards. Results indicate that flooding is highly dynamic, with diverse drivers and processes contributing considerably across the duration and from storm to storm. Results show that flooding from Hurricane Harvey was a pluvial-dominated event, whereas Ike’s was coastal-dominated. However, compound flooding was observed during both hurricanes. The study introduces a remote sensing-based rapid flood detection algorithm under a framework for probabilistic flood hazard and uncertainty mapping, and the new insights into TC-driven CFs should be useful for further research.</p>

Name thesis:degree_name

Master of Science (MS)

Level thesis:degree_level

Masters Thesis

Year dc:date.available

2025

Author dc:creator

• Uddin Ahmed, Raihan

Contributors dc:contributor

• Dr. Md Mamunur Rashid
• Dr. Robert Leaf
• Dr. Wei Wu

• Flood
• Flood Hazard
• Remote Sensing
• Hydrodynamic Model
• SFINCS
• Tropical Cyclone
• Climate
• Hydraulic Engineering
• Multivariate Analysis
• Probability
• Risk Analysis

Repository record dc:identifier

https://aquila.usm.edu/masters_theses/1127

OAI identifier oai:identifier

oai:aquila.usm.edu:masters_theses-2219