The objective of this project is to develop and test two qualitative flood risk models for use in first responder and planning roles. The first, the Obstruction Detection Model (ODM), uses Light Detection and Ranging (LiDAR) derived Digital Elevation Models (DEMs) and a slope analysis to detect changes in the free surface of the water that might indicate the presence of a sub-surface obstruction. The product of the ODM can be used as a guide for field inspection, as well as an input scenario for the Risk Assessment Model (RAM). The RAM is the second model developed and serves to create an output product that displays the risk factor of each given parcel in order to help prioritize first responder efforts, as well as planning and mitigation efforts when used as a scenario generation tool. The RAM incorporates various vector data comprised of parcels, Monroe County Critical Infrastructure (CIKR), population, and assessed value in order to generate the Risk Factor. A third model, the Flood Extent Generator (FEG), uses an input scenario from the ODM to generate vector flood extents rapidly. These extents are used with the RAM to create a map that displays the Risk Factor in the flooded parcels.
The ODM appears to pick up riverine obstructions in the various river reaches tested within New York State. The FEG flood extents have 15% spatial agreement when constrained to Monroe County and 32% when constrained upriver of the Ford Street Bridge obstruction. The over-estimated flood extents lead to the RAM over-predicting populations and infrastructure at risk.
Model results, when compared to the more complex Hazus model, suggest that the simplified approach presented needs additional predictor variables or data pre-processing to improve accuracy of each model component.
Environmental Science (MS)
Department, Program, or Center
Thomas H. Gosnell School of Life Sciences (COS)
Karl F. Korfmacher
Jan Van Aardt
Carlock, Brett Edmond, "Analytical Flood Risk Models for First Responder Use: Obstruction Detection and Risk Assessment" (2017). Thesis. Rochester Institute of Technology. Accessed from
RIT – Main Campus