125250 20241125101135.0 doi 10.1071/WF22123 sideral 133043 ART-2023-133043 eng Rodrigues, Marcos Universidad de Zaragoza (orcid)0000-0002-0477-0796 Modelling the daily probability of lightning-caused ignition in the Iberian Peninsula 2023 Access copy available to the general public Unrestricted 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. info:eu-repo/grantAgreement/EC/H2020/101003890/EU/FIREURISK - DEVELOPING A HOLISTIC, RISK-WISE STRATEGY FOR EUROPEAN WILDFIRE MANAGEMENT/FirEUrisk This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101003890-FirEUrisk info:eu-repo/grantAgreement/ES/MICINN/PID2020-116556RA-I00 info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 2.9 2023 FORESTRY 14 / 89 = 0.157 2023 Q1 T1 0.819 2023 Forestry 2023 Q1 Ecology 2023 Q1 5.5 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Jiménez-Ruano, Adrián (orcid)0000-0001-7397-1818 Gelabert, Pere Joan de Dios, Víctor Resco Torres, Luis Ribalaygua, Jaime Vega-García, Cristina 3006 010 Universidad de Zaragoza Dpto. Geograf. Ordenac.Territ. Área Análisis Geográfico Regi. (2023), 22123 [12 pp.] Int. j. wildland fire International Journal of Wildland Fire 1049-8001 3629436 http://zaguan.unizar.es/record/125250/files/texto_completo.pdf Versión publicada 2722750 http://zaguan.unizar.es/record/125250/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:125250 articulos driver 2024-11-22-12:00:42 ARTICLE