Predicting downstream impacts of post-fire sediment inputs to transportation assets over management time scales
This project will develop user-friendly models and geospatial tools to predict secondary, routed impacts to critical infrastructure (i.e., depth and rate of sediment erosion/deposition) caused by the natural down-stream transport of wildfire-derived sediment inputs over management relevant time-scales. The primary objectives are to: (1) develop Machine Learning models to predict post-fire streamflow changes and post-fire burn severity, and then (2) predict potential downstream risks to critical transportation infrastructure and aquatic habitat over time. The resulting geospatial toolkit and risk assessments for selected burned and unburned watersheds will help Colorado Department of Transportation (CDOT) mitigate damages associated with recent wildfires and prioritize long-term infrastructure planning and design in high-risk watersheds.
Language
- English
Project
- Status: Programmed
-
Sponsor Organizations:
Colorado Department of Transportation
Applied Research and Innovation Branch
Denver, CO United States 80204 -
Managing Organizations:
Colorado Department of Transportation
Applied Research and Innovation Branch
Denver, CO United States 80204 -
Project Managers:
Tran, Thien
- Performing Organizations: Logan, Utah United States 84322
- Start Date: 20250501
- Expected Completion Date: 0
- Actual Completion Date: 0
Subject/Index Terms
- TRT Terms: Machine learning; Risk assessment; Sediments; Spatial analysis; Streamflow; Wildfires
- Subject Areas: Environment; Geotechnology; Hydraulics and Hydrology; Planning and Forecasting; Transportation (General);
Filing Info
- Accession Number: 01930283
- Record Type: Research project
- Source Agency: Colorado Department of Transportation
- Files: RIP, STATEDOT
- Created Date: Sep 16 2024 8:44AM