000125250 001__ 125250
000125250 005__ 20241125101135.0
000125250 0247_ $$2doi$$a10.1071/WF22123
000125250 0248_ $$2sideral$$a133043
000125250 037__ $$aART-2023-133043
000125250 041__ $$aeng
000125250 100__ $$0(orcid)0000-0002-0477-0796$$aRodrigues, Marcos$$uUniversidad de Zaragoza
000125250 245__ $$aModelling the daily probability of lightning-caused ignition in the Iberian Peninsula
000125250 260__ $$c2023
000125250 5060_ $$aAccess copy available to the general public$$fUnrestricted
000125250 5203_ $$aBackground. 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.
000125250 536__ $$9info:eu-repo/grantAgreement/EC/H2020/101003890/EU/FIREURISK - DEVELOPING A HOLISTIC, RISK-WISE STRATEGY FOR EUROPEAN WILDFIRE MANAGEMENT/FirEUrisk$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101003890-FirEUrisk$$9info:eu-repo/grantAgreement/ES/MICINN/PID2020-116556RA-I00
000125250 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000125250 590__ $$a2.9$$b2023
000125250 592__ $$a0.819$$b2023
000125250 591__ $$aFORESTRY$$b14 / 89 = 0.157$$c2023$$dQ1$$eT1
000125250 593__ $$aForestry$$c2023$$dQ1
000125250 593__ $$aEcology$$c2023$$dQ1
000125250 594__ $$a5.5$$b2023
000125250 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000125250 700__ $$0(orcid)0000-0001-7397-1818$$aJiménez-Ruano, Adrián
000125250 700__ $$aGelabert, Pere Joan
000125250 700__ $$ade Dios, Víctor Resco
000125250 700__ $$aTorres, Luis
000125250 700__ $$aRibalaygua, Jaime
000125250 700__ $$aVega-García, Cristina
000125250 7102_ $$13006$$2010$$aUniversidad de Zaragoza$$bDpto. Geograf. Ordenac.Territ.$$cÁrea Análisis Geográfico Regi.
000125250 773__ $$g(2023), 22123 [12 pp.]$$pInt. j. wildland fire$$tInternational Journal of Wildland Fire$$x1049-8001
000125250 8564_ $$s3629436$$uhttps://zaguan.unizar.es/record/125250/files/texto_completo.pdf$$yVersión publicada
000125250 8564_ $$s2722750$$uhttps://zaguan.unizar.es/record/125250/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000125250 909CO $$ooai:zaguan.unizar.es:125250$$particulos$$pdriver
000125250 951__ $$a2024-11-22-12:00:42
000125250 980__ $$aARTICLE