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Center Independent Research & Development: JPL IRAD

Fire Danger Assessment System (FDAS)

Active Technology Project

Project Introduction

A fire danger dataset for the continental US based on satellite hydrology information, this can be updated monthly and used operationally to distribute fire response resources.  The project involved working together with fire-community end-users and developing a data product that can be utilized for future NASA research solicitations in hydrology, disasters, and applied sciences programs, as well as other agency (e.g. NOAA) program solicitations.

Quantify fire-moisture relationships.  At seasonal to annual time-scales, we will use the published relationships between sea surface temperatures and regional fire likelihood as a baseline predictor for a good or bad fire year [Chen et al., 2016], and extend this methodology to include terrestrial water storage information from GRACE.  That long-term prediction will then be combined with a seasonal-to-monthly predictive approach based on surface moisture conditions [Rousseau et al., 2015].  We also will use GRACE-assimilated land surface model outputs of surface soil moisture (top 2.5 cm) at 12.5-km resolution in the continental US.  At sub-seasonal time-scales (i.e. daily to monthly) we will use assimilated soil moisture and AIRS VPD to estimate future fire risk [e.g., Stavros et al., 2014].  The GFED4 fire occurrence and burned area database [Giglio et al., 2013], which has several significant improvements compared with earlierGFED versions, will be used with the predictor variables in a multi-regression approach that employs efficient parameterization. This results in a multi-variable algorithm for global seasonal/monthly/weekly fire danger.  Two additional exploratory analyses will be undertaken if time permits: (i) Investigation of whether canopy and ground fuel moisture can be predicted using the SMAP vegetation water content product; (ii) Investigation of ECOSTRESS data usefulness and predictive viability.  Both are subject to data quality and availability limitations. A remote sensing-based methodology for wall-to-wall fuel moisture levels would be extremely valuable to fire managers for optimizing suppression and firefighting resources.

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