SMU Data Science Review
Abstract
Abstract. In this paper, we present an innovative framework for evaluating the increased risk of flash flooding in areas that have been subjected to wildfires. Wildfires cause large-scale damage to an area’s soil and vegetation thus increasing both the likelihood and severity of flash flooding. Utilizing remote sensing to analyze aerial imagery of areas that have been affected by wildfires, we can investigate how much a landscape has changed and how that may adversely affect downstream areas in the event of a flash flooding event. There are currently no established frameworks from which downstream local officials can quickly assess the impact of a wildfire on the likelihood for flash floods in their area. The creation of flood maps from hydrologic and hydraulic models can take months due to the political nature of the discussion between local officials, insurance agents, and land developers. Our primary objective is to ensure the public’s safety. Therefore, we developed a framework for quickly updating flood maps and preparing communities in response to a sudden environmental change.
Recommended Citation
Cunningham, Brian; Benepe, David; Cikatz, Bryan; and Giakoumakis, Evangelos
(2018)
"Framework for Evaluation of Flash Flood Models in Wildfire-Prone Areas,"
SMU Data Science Review: Vol. 1:
No.
4, Article 9.
Available at:
https://scholar.smu.edu/datasciencereview/vol1/iss4/9
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Emergency and Disaster Management Commons, Geological Engineering Commons, Spatial Science Commons
