Date of Award
8-2025
Degree Type
Masters Thesis
Degree Name
Master of Science (MS)
School
Ocean Science and Engineering
Committee Chair
Dr. Md Mamunur Rashid
Committee Chair School
Ocean Science and Engineering
Committee Member 2
Dr. Robert Leaf
Committee Member 2 School
Ocean Science and Engineering
Committee Member 3
Dr. Wei Wu
Committee Member 3 School
Ocean Science and Engineering
Abstract
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.
ORCID ID
https://orcid.org/0000-0001-9094-0685
Copyright
Raihan Uddin Ahmed, 2025
Recommended Citation
Uddin Ahmed, Raihan, "Investigating Compound Flood Using Remote Sensing And Hydrodynamic Modeling" (2025). Master's Theses. 1127.
https://aquila.usm.edu/masters_theses/1127
Included in
Climate Commons, Hydraulic Engineering Commons, Multivariate Analysis Commons, Probability Commons, Risk Analysis Commons