Developing models to establish seasonal forest fire early warning system

Abstract

During a typically dry season in Sumatra or Kalimantan, the forest fire starts. In 2015, an El Nino year, forest fires in Sumatra and Kalimantan ranked among the worst episodes on record. Understanding the connection between accumulated monthly rainfall and the risk of hotspot occurrence is key to improving forest fire management decision-making. This study addresses model development to predict the number of 6-month fire hotspots, by combining the prediction of rainfall with hotspot patterns. Hotspot data were obtained from the Fire Information for Resources Management System (FIRMS) for the period of 2001–2018. For rainfall prediction, we used the output model of the European Centre for Medium-Range Weather Forecasts (ECMWF). The threshold of more than 10 hotspot events has been used to establish hotspot climatology. To get a threshold for rainfall that can cause forest fires, we used the Pulang Pisau rain station. We applied two rainfall thresholds to determine three categorical forecasts (low, moderate, high) as environment quality indicator. The two thresholds are 100 mm/month for the lower threshold and 130 mm/month for the upper threshold. The verification of the observational data showed an accuracy of > 0.83, which is relatively consistent and persistent with forest fire events. The weakness of this system is that it cannot determine the exact location of the forest fire because the spatial resolution used is 0.25 degrees. The predictions of the monthly climate index values were reasonably good suggesting the potential to be used as an operational tool to predict the number of fire hotspots expected. The seasonal forest fire early warning system is expected to be an effort to anticipate forest fires for the next six months. The modeling strategy presented in this study could be replicated for any fire index in any region, based on predictive rainfall information and patterns of the hotspot.