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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 02 May 2019

Submitted as: research article | 02 May 2019

Review status
A revised version of this preprint is currently under review for the journal NHESS.

Satellite Hydrology Observations as Operational Indicators of Forecasted Fire Danger across the Contiguous United States

Alireza Farahmand1, E. Natasha Stavros1, John T. Reager1, Ali Behrangi2, James Randerson3, and Brad Quayle4 Alireza Farahmand et al.
  • 1Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA
  • 2University of Arizona, Tucson, AZ
  • 3University of California, Irvine
  • 4USDA Forest Service Geospatial Technology and Applications Center, 2222 West 2300 SouthSalt Lake City, Utah 84119

Abstract. Traditional methods for assessing fire danger often depend on meteorological forecasts, which have reduced reliability after ~ 10 days. Recent studies have demonstrated long lead-time correlations between pre-fire-season hydrological variables such as soil moisture and later fire occurrence or area burned, yet no potential value of these relationships for operational forecasting have not been studied. Here, we use soil moisture data refined by remote sensing observations of terrestrial water storage from NASA’s GRACE mission and vapor pressure deficit from NASA’s AIRS mission to generate monthly predictions of fire danger at scales commensurate with regional management. We test the viability of predictors within nine US Geographic Area Coordination Centers (GACCs) using regression models specific to each GACC. Results show that the model framework improves interannual wildfire burned area prediction relative to climatology for all GACCs. This demonstrates the importance of hydrological information to extend operational forecast ability into the months preceding wildfire activity.

Alireza Farahmand et al.

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Alireza Farahmand et al.

Alireza Farahmand et al.


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Latest update: 18 Feb 2020
Publications Copernicus
Short summary
Wildfires result in billions of dollars of losses each year. Most wildfire predictions have a 10-day lead time prediction of wildfires. This study introduces a framework for 1-month lead time prediction of wildfires based on Vapor Pressure Deficit and Surface Soil Moisture in the US. The results show that the model can successfully predict burned area with relatively small margins of error. This is especially important for the operational wildfire management such as national resource allocation.
Wildfires result in billions of dollars of losses each year. Most wildfire predictions have a...