Remote Sensing to Enhance Modeling of Post-Burnout Runoff Risk

EarthzineDEVELOP 2015 Summer VPS, DEVELOP Virtual Poster Session, Responding to Hydrologic Disasters

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Yearly Average MODIS NDVI compared to Yearly Average NOAA PERSIANN-CDR Precipitation. Image Credit: Southwest United States Disasters Team

Category:åÊResponding to Hydrologic Disasters

Project Team: Southwest United States Disasters

Team Location: NOAA National Centers for Environmental Information (NCEI) – Asheville, North Carolina


Jason Zylberman

Jennifer Holder

Lance Watkins


Dr. DeWayne Cecil (Global Science & Technology, Inc. National Centers for Environmental Information [NCEI])

Gregg Garfin (Climate Assessment for the Southwest [CLIMAS])

Tim Brown (Western Regional Climate Center [WRCC])

Michael Schaffner (National Weather Service Salt Lake City)


This study investigated the relationship between the vegetation regrowth process and flooding following wildfire events in Arizona within the Lower Colorado River Basin. Extensive studies have been conducted on post-burnout rainfall-runoff relationships or post-burnout vegetation regeneration, but few establish a relationship between both processes. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) Earth observations were first used to create a surface indicating vegetation regrowth rate on a per-pixel basis following historical wildfire events. Next, historical flood events were identified in the National Oceanic and Atmospheric Administration (NOAA) Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) precipitation Climate Data Record (CDR) to establish precipitation trends associated with increased post-wildfire flooding risk. The relationships between precipitation anomalies, time since the fire, and vegetation regrowth were then used to predict flooding. By utilizing remotely-sensed vegetation and precipitation data in a study area with limited in-situ data, this analysis developed an additional long-term predictive tool for managing future post-fire hazards.

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