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- Author or Editor: J. Vicencio Veloso x
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Abstract
The 2016/17 wildfire season in Chile was the worst on record, burning more than 600,000 ha. While wildfires are an important natural process in some areas of Chile, supporting its diverse ecosystems, wildfires are also one of the biggest threats to Chile’s unique biodiversity and its timber and wine industries. They also pose a danger to human life and property because of the sharp wildland–urban interface that exists in many Chilean towns and cities. Wildfires are, however, difficult to predict because of the combination of physical (meteorology, vegetation, and fuel condition) and human (population density and awareness level) factors. Most Chilean wildfires are started because of accidental ignition by humans. This accidental ignition could be minimized if an effective wildfire warning system alerted the population to the heightened danger of wildfires in certain locations and meteorological conditions. Here, we demonstrate the design of a novel probabilistic wildfire prediction system. The system uses ensemble forecast meteorological data together with a long time series of fire products derived from Earth observation to predict not only fire occurrence but also how intense wildfires could be. The system provides wildfire risk estimation and associated uncertainty for up to six days in advance and communicates it to a variety of end users. The advantage of this probabilistic wildfire warning system over deterministic systems is that it allows users to assess the confidence of a forecast and thus make more informed decisions regarding resource allocation and forest management. The approach used in this study could easily be adapted to communicate other probabilistic forecasts of natural hazards.
Abstract
The 2016/17 wildfire season in Chile was the worst on record, burning more than 600,000 ha. While wildfires are an important natural process in some areas of Chile, supporting its diverse ecosystems, wildfires are also one of the biggest threats to Chile’s unique biodiversity and its timber and wine industries. They also pose a danger to human life and property because of the sharp wildland–urban interface that exists in many Chilean towns and cities. Wildfires are, however, difficult to predict because of the combination of physical (meteorology, vegetation, and fuel condition) and human (population density and awareness level) factors. Most Chilean wildfires are started because of accidental ignition by humans. This accidental ignition could be minimized if an effective wildfire warning system alerted the population to the heightened danger of wildfires in certain locations and meteorological conditions. Here, we demonstrate the design of a novel probabilistic wildfire prediction system. The system uses ensemble forecast meteorological data together with a long time series of fire products derived from Earth observation to predict not only fire occurrence but also how intense wildfires could be. The system provides wildfire risk estimation and associated uncertainty for up to six days in advance and communicates it to a variety of end users. The advantage of this probabilistic wildfire warning system over deterministic systems is that it allows users to assess the confidence of a forecast and thus make more informed decisions regarding resource allocation and forest management. The approach used in this study could easily be adapted to communicate other probabilistic forecasts of natural hazards.
