Modelling the daily probability of lightning-caused ignition in the Iberian Peninsula

Abstract

Background: Lightning is the most common origin of natural fires, being strongly linked to specific synoptic conditions associated with atmospheric instability, such as dry thunderstorms; dry fuels are required for ignition to take place and for subsequent propagation. Aims: The aim was to predict the daily probability of ignition by exploiting a large dataset of lightning and fire data to anticipate ignition over the entire Iberian Peninsula. Methods: We trained and tested a machine learning model using lightning strikes (>17 million) in the period 2009-2015. For each lightning strike, we extracted information relating to fuel condition, structural features of vegetation, topography, and the specific characteristics of the strikes (polarity, intensity and flash density). Key results: Naturally triggered ignitions are typically initiated at higher elevations (above 1000 m above sea level) under conditions of low dead fuel moisture (<10-13%) and moderate live moisture content (Drought Code > 300). Negative-polarity lightning strikes (-10 kA) appear to trigger fires more frequently. Conclusions and implications: Our approach was able to provide ignition forecasts at multiple temporal and spatial scales, thus enhancing forest fire risk assessment systems.