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

被引:11
|
作者
Rodrigues, Marcos [1 ,2 ]
Jimenez-Ruano, Adrian [1 ,2 ]
Gelabert, Pere Joan [3 ]
de Dios, Victor Resco [4 ,5 ,6 ]
Torres, Luis [7 ]
Ribalaygua, Jaime [7 ]
Vega-Garcia, Cristina [3 ]
机构
[1] Univ Zaragoza, Dept Geog & Land Management, Pedro Cerbuna 12, Zaragoza 5009, Spain
[2] Univ Zaragoza, Univ Inst Res Environm Sci Aragon IUCA, GEOFOREST Grp, Pedro Cerbuna 12, Zaragoza 5009, Spain
[3] Univ Lleida, Dept Agr & Forest Engn, Alcalde Rovira Roure 191, Lleida 25198, Spain
[4] Univ Lleida, Dept Crop & Forest Sci, Alcalde Rovira Roure 191, Lleida 25198, Spain
[5] CERCA Ctr, Joint Res Unit CTFC, AGROTECNIO, Alcalde Rovira Roure 191, Lleida 25198, Spain
[6] Southwest Univ Sci & Technol, Sch Life Sci & Engn, 59 Qinlong Rd, Mianyang 621010, Peoples R China
[7] MeteoGRID SL, Calle Almansa 88, Madrid 28040, Spain
关键词
fire danger; forecast; fuel moisture; Iberian Peninsula; ignition probability; lightning strike; machine learning; wildfires; FOREST-FIRE OCCURRENCE; CLIMATE-CHANGE; WILDFIRES; DISTRIBUTIONS; MOISTURE; DRIVERS; TRENDS;
D O I
10.1071/WF22123
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
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.
引用
收藏
页码:351 / 362
页数:12
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