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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. [ABSTRACT FROM AUTHOR]

Climate change-induced heat waves increase the global risk of forest fires, intensifying biomass burning and accelerating climate change in a vicious cycle. This presents a challenge to the response system in heavily forested South Korea, increasing the risk of more frequent and large-scale fire outbreaks. This study aims to optimize IIASA's wildFire cLimate impacts and Adaptation Model (FLAM)—a processed-based model integrating biophysical and human impacts—to South Korea for projecting the pattern and scale of future forest fires. The developments performed in this study include: (1) the optimization of probability algorithms in FLAM based on the national GIS data downscaled to 1 km2 with additional factors introduced for national specific modeling; (2) the improvement of soil moisture computation by adjusting the Fine Fuel Moisture Code (FFMC) to represent vegetation feedbacks by fitting soil moisture to daily remote sensing data; and (3) projection of future forest fire frequency and burned area. Our results show that optimization has considerably improved the modeling of seasonal patterns of forest fire frequency. Pearson's correlation coefficient between monthly predictions and observations from national statistics over 2016–2022 was improved from 0.171 in the non-optimized to 0.893 in the optimized FLAM. These findings imply that FLAM's main algorithms for interpreting biophysical and human impacts on forest fire at a global scale are only applicable to South Korea after the optimization of all modules, and climate change is the main driver of the recent increases in forest fires. Projections for forest fire were produced for four periods until 2100 based on the forest management plan, which included three management scenarios (current, ideal, and overprotection). Ideal management led to a reduction of 60–70% of both fire frequency and burned area compared to the overprotection scenario. This study should be followed by research for developing adaptation strategies corresponding to the projected risks of future forest fires. [ABSTRACT FROM AUTHOR]