Modelling the daily probability of lightning-caused ignition in the Iberian Peninsula
Marcos Rodrigues
A B * , Adrián Jiménez-Ruano A B , Pere Joan Gelabert C , Víctor Resco de Dios D E F , Luis Torres G , Jaime Ribalaygua G and Cristina Vega-García C
+ Author Affiliations
- Author Affiliations
A Department of Geography and Land Management, University of Zaragoza, Pedro Cerbuna 12, 5009, Zaragoza, Spain.
B GEOFOREST Group, University Institute for Research in Environmental Sciences of Aragon (IUCA), University of Zaragoza, Pedro Cerbuna 12, 5009, Zaragoza, Spain.
C Department of Agriculture and Forest Engineering, University of Lleida, Alcalde Rovira Roure 191, 25198, Lleida, Spain.
D Department of Crop and Forest Sciences, University of Lleida, Alcalde Rovira Roure 191, 25198, Lleida, Spain.
E Joint Research Unit CTFC-AGROTECNIO-CERCA Center, Alcalde Rovira Roure 191, 25198, Lleida, Spain.
F School of Life Science and Engineering, Southwest University of Science and Technology, 59 Qinlong Road, 621010, Mianyang, China.
G MeteoGRID SL, Calle de Almansa 88, 28040, Madrid, Spain.
* Correspondence to: rmarcos@unizar.es
International Journal of Wildland Fire 32(3) 351-362 https://doi.org/10.1071/WF22123
Submitted: 30 June 2022 Accepted: 27 December 2022 Published: 24 January 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
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.
Keywords: fire danger, forecast, fuel moisture, Iberian Peninsula, ignition probability, lightning strike, machine learning, wildfires.
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