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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Uddin Ahmed, Raihan"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Masters Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Dr. Md Mamunur Rashid","Dr. Robert Leaf","Dr. Wei Wu"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-08-01T07:00:00Z","date_published":"2025-08-01T07:00:00Z","updated_at":"2026-07-24T05:45:47Z","subjects":["Flood","Flood Hazard","Remote Sensing","Hydrodynamic Model","SFINCS","Tropical Cyclone","Climate","Hydraulic Engineering","Multivariate Analysis","Probability","Risk Analysis"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://aquila.usm.edu/masters_theses/1127","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Md Mamunur Rashid","Dr. Robert Leaf","Dr. Wei Wu"]},{"key":"dc:creator","label":"Author","values":["Uddin Ahmed, Raihan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2026-12-31T08:00:00Z"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Flood","Flood Hazard","Remote Sensing","Hydrodynamic Model","SFINCS","Tropical Cyclone","Climate","Hydraulic Engineering","Multivariate Analysis","Probability","Risk Analysis"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://aquila.usm.edu/masters_theses/1127"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Investigating Compound Flood Using Remote Sensing And Hydrodynamic Modeling"]}]}],"canonical_facts":{"dc:contributor":["Dr. Md Mamunur Rashid","Dr. Robert Leaf","Dr. Wei Wu"],"dc:creator":["Uddin Ahmed, Raihan"],"dc:date.available":["2026-12-31T08:00:00Z"],"dc:description.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.
"],"dc:identifier":["https://aquila.usm.edu/masters_theses/1127"],"dc:subject":["Flood","Flood Hazard","Remote Sensing","Hydrodynamic Model","SFINCS","Tropical Cyclone","Climate","Hydraulic Engineering","Multivariate Analysis","Probability","Risk Analysis"],"dc:title":["Investigating Compound Flood Using Remote Sensing And Hydrodynamic Modeling"],"thesis:degree_level":["Masters Thesis"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T05:45:47Z"}