Abstract
Wildfire danger assessment is essential for operational allocation of fire management resources; with longer lead prediction, the more efficiently can resources be allocated regionally. Traditional studies focus on meteorological forecasts and fire danger index models (e.g., National Fire Danger Rating System—NFDRS) for predicting fire danger. Meteorological forecasts, however, lose accuracy beyond ~10 days; as such, there is no quantifiable method for predicting fire danger beyond 10 days. While some recent studies have statistically related hydrologic parameters and past wildfire area burned or occurrence to fire, no study has used these parameters to develop a monthly spatially distributed predictive model in the contiguous United States. Thus, the objective of this study is to introduce Fire Danger from Earth Observations (FDEO), which uses satellite data over the contiguous United States (CONUS) to enable two-month lead time prediction of wildfire danger, a sufficient lead time for planning purposes and relocating resources. In this study, we use satellite observations of land cover type, vapor pressure deficit, surface soil moisture, and the enhanced vegetation index, together with the United States Forest Service (USFS) verified and validated fire database (FPA) to develop spatially gridded probabilistic predictions of fire danger, defined as expected area burned as a deviation from “normal”. The results show that the model predicts spatial patterns of fire danger with 52% overall accuracy over the 2004–2013 record, and up to 75% overall accuracy during the fire season. Overall accuracy is defined as number of pixels with correctly predicted fire probability classes divided by the total number of the studied pixels. This overall accuracy is the first quantified result of two-month lead prediction of fire danger and demonstrates the potential utility of using diverse observational data sets for use in operational fire management resource allocation in the CONUS.
Highlights
Wildfires can result in tremendous economic loss across the United States
More burned area occurs in the West later in the fire season, there is consistent wildfire in the eastern and southern US year-round
This is due to the fact that we use satellite observations from two months prior to fire occurrence to build our fire danger models by land cover class (Figure 3)
Summary
Wildfires can result in tremendous economic loss across the United States. For example, the year 2018 was the largest fire year on record, resulting in approximately $3 billion in suppression costs (National Interagency Fire Center (NIFC) [1]). Fire suppression cost depends on multiple factors, including vegetation type and weather conditions and a successful fire suppression will reduce the area burned [2]. Contributing to these high suppression costs are logistical costs of relocating resources The current fire danger predictions that inform resource allocation are primarily based on meteorological forecasts and expert judgement ([3]). FPOs report fire potential in qualitative categories, such as below-normal, normal and above-normal These qualitative metrics are derived using expert knowledge informed by meteorological forecasts and fuel maps. Real-time remotely-sensed data, on the other hand, do not have these limitations and can potentially be used to improve the accuracy of wildfire danger predications and help fire managers make effective resource allocation decisions prior to wildfire occurrence
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