Developing Lebanon's fire danger forecast

Abstract

Lebanon lacks a system for forecasting wildfire danger with the combined use of dynamic weather forecasts and comprehensive fire risk information (i.e., hazard and vulnerability). Accordingly, the aim of this study was to develop a new national fire danger forecast system for use as an operational fire management tool in Lebanon. The specific objectives were to 1) produce an advanced fire risk map of Lebanon, 2) customize an index for use in fire danger forecasting, and 3) develop an automated system for daily fire danger forecasts. Producing an advanced fire risk map involved the combined use of collected fire risk data and predicted wildfire occurrence. Forward stepwise binary logistic regression analysis of 24 socio-economic and biophysical variables was initially adopted to predict wildfire occurrence. Geographic object-based image analysis (GEOBIA) approach was employed to map the different classes of fire risk. Consequently, the customization of an index for fire danger forecasting involved the use of both fire risk and Fire Weather Index (FWI) data using also the GEOBIA approach. Overall, modeling socio-economic and biophysical factors correctly predicted an average of 85.7% of fire occurrences between 1998 and 2012 across the country. In addition, a comparison of FWI forecasts and fire occurrence (i.e., observed fire events) during the fire season of 2015 resulted in sensitivity (76.2%) and specificity (18.6%) estimates. Finally, automated daily fire danger forecasts are expected to serve as essential tools for both scientific and operational purposes.