RT info:eu-repo/semantics/article
T1 Anticipating future risks of climate-driven wildfires in boreal forests
A1 Corning, Shelby
A1 Krasovskiy, Andrey
A1 Kiparisov, Pavel
A1 San Pedro, Johanna
A1 Maciel Viana, Camila
A1 Kraxner, Florian
K1 Forest fires
K1 Bosques - Incendios
K1 Wildfire risk
K1 Wildfires - Prevention and control
K1 Bosques - Incendios - Prevención y control
K1 Climate change
K1 Clima - Cambios
K1 Climatology
K1 Forests and forestry
K1 Bosques y Silvicultura - Gestión
K1 Taiga ecology
K1 Ecología forestal
K1 Environmental policy
K1 3106 Ciencia Forestal
K1 3106.08 Silvicultura
K1 2502 Climatología
K1 5902.08 Política del Medio Ambiente
AB Extreme forest fires have historically been a significant concern in Canada, the Russian Federation, the USA, and now pose an increasing threat in boreal Europe. This paper deals with application of the wildFire cLimate impacts and Adaptation Model (FLAM) in boreal forests. FLAM operates on a daily time step and utilizes mechanistic algorithms to quantify the impact of climate, human activities, and fuel availability on wildfire probabilities, frequencies, and burned areas. In our paper, we calibrate the model using historical remote sensing data and explore future projections of burned areas under different climate change scenarios. The study consists of the following steps: (i) analysis of the historical burned areas over 2001–2020; (ii) analysis of temperature and precipitation changes in the future projections as compared to the historical period; (iii) analysis of the future burned areas projected by FLAM and driven by climate change scenarios until the year 2100; (iv) simulation of adaptation options under the worst-case scenario. The modeling results show an increase in burned areas under all Representative Concentration Pathway (RCP) scenarios. Maintaining current temperatures (RCP 2.6) will still result in an increase in burned area (total and forest), but in the worst-case scenario (RCP 8.5), projected burned forest area will more than triple by 2100. Based on FLAM calibration, we identify hotspots for wildland fires in the boreal forest and suggest adaptation options such as increasing suppression efficiency at the hotspots. We model two scenarios of improved reaction times—stopping a fire within 4 days and within 24 h—which could reduce average burned forest areas by 48.6% and 79.2%, respectively, compared to projected burned areas without adaptation from 2021–2099.
PB MDPI
SN 2571-6255
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/68122
UL https://uvadoc.uva.es/handle/10324/68122
LA eng
NO Fire, 2024, Vol. 7, Nº. 4, 144
NO Producción Científica
DS UVaDOC
RD 22-dic-2024