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.

Forests play an important role in regulating the carbon (C) cycle. The main objective of this study was to quantify the effects of South Korean national reforestation programs on carbon budgets. We estimated the changes in C stocks and annual C sequestration in the years 1961–2014 using Korea-specific models, a forest cover map (FCM), national forest inventory (NFI) data, and climate data. Furthermore, we examined the differences in C budgets between Cool forests (forests at elevations above 700 m) and forests in lower-altitude areas. Simulations including the effects of climate conditions on forest dynamics showed that the C stocks of the total forest area increased from 6.65 Tg C in 1961 to 476.21 Tg C in 2014. The model developed here showed a high degree of spatiotemporal reliability. The mean C stocks of the Cool forests and other forests increased from 4.03 and 0.43 Mg C ha⁻¹, respectively, to 102.43 and 73.76 Mg C ha⁻¹ at a rate of 1.82 and 1.36 Mg C ha⁻¹ yr⁻¹ during the same period. These results imply that, although the total Cool forest area of South Korea occupied only about 12.3% (772,788 ha) of the total forest area, the Cool forests play important roles in C balances and forest ecosystems in South Korea. Annual C sequestration totals are projected to decrease at a low rate in the near future because the overall growth rate of a mature forest decreases as the stand ages. Our results quantified forest C dynamics in South Korean forests before and after national reforestation programs. Furthermore, our results can help in development of regional and national forest management strategies to allow for sustainable development of society and to cope with climate change in South Korea.

The IPCC emphasizes the role of forests in the sequestration of greenhouse gases, a significant cause of climate change. Accordingly, it shows the importance of predicting changes in forests due to climate change, evaluating them to reduce vulnerability under adaptive capacity, and finding ways to find climate resilient development pathways. In this study, the KO-G-Dynamic model, a Korean growth model, was linked with the frameworks of AR5 and 6 to assess risk dynamics in the forest growth sector. At this time, the sensitivity is a variability due to the reduction in forest growth, the exposure is the forest as an object, the hazard is climate change, the adaptive capacity is forest management, and the vulnerability is a mechanism that sensitivity could not be adjusted according to adaptive capacity. The risk was assessed by ranking overall risks derived from the process of vulnerability generated by the interaction of the above factors. As a result, the current forests in Korea are age class imbalanced, and the effects of distribution are centered on fast-growing tree species. If climate change and overprotection continue, the vulnerable area expands as sensitivity increases, since the total growth reduces due to increasing over-matured forests. From the regional-based analysis, Gangwon-do and Gyeongsangnam-do mostly consist of the higher V age class, the ratio of ‘very high’ risk grade was high and the area of ‘high’ risk grade changed rapidly. However, after applying forest management scenarios of adaptive capacity such as harvesting, reforestation, and thinning based on Republic of Korea’s forest management policy, the ratio of ‘Low’ risk grades increased according to the reduction of vulnerability areas. Therefore, forest management can act as an important factor to reduce the risk of forest growth in response to climate change.

The seriousness of climate change is being discussed in various fields, including the importance of efficient afforestation and forest management as a major carbon absorption sink to help achieve the 2050 carbon neutral aim. The scheme of climate change data has been changed from Representative Concentration Pathways (RCP) to Shared Socioeconomic Pathways (SSP) to reflect socioeconomic land use. The SSP can be used to predict the distribution of future tree species when used with the existing Hydrological and Thermal Analogy Groups (HyTAG) model. Therefore, this study predicted potential distribution changes by tree species using RCP and SSP scenario data and compared the trends of the two scenarios to actual climate change data. For the analysis, climatic indices were constructed by period and used in the HyTAG model to predict potential distribution of tree species. Growth of needleleaf and broadleaf trees was confirmed only in the vicinity of the alpine region of Gangwon-do, with decreasing proportions of 81% (RCP 8.5) and 97% (SSP 5-8.5) in the RCP and SSP scenarios, respectively. In addition, the area of such trees decreased by 50% in the long-term (2051-2080) compared to the mid-term (2021-2050) and was nearly depleted in severe cases. Comparison of hydrological and thermal indices by climate change scenario confirmed that actual climate change was progressing faster than predicted in any climate change scenarios. These results show that forests, which are major carbon absorption sinks, are facing serious risks from rapid climate change and should be considered in decision-making on climate change adaptation measures and future afforestation strategies.

Forests play a crucial role in South Korea’s carbon neutrality goal and require sustainable management strategies to overcome age-class imbalances. The Generic Carbon Budget Model (GCBM) offers a spatially explicit approach to simulate carbon dynamics at a regional scale. In this study, we utilized the GCBM to analyze the carbon budget of forests in South Korea and produce spatiotemporal maps for distribution of the forest biomass. The growth parameters of five representative tree species (Pinus densiflora Siebold & Zucc., Larix kaempferi Carr., Pinus koraiensis Siebold & Zucc., Quercus mongolica Fisch. ex Ledeb., Quercus variabilis Blume), which are the main species in South Korea, were used to operate the model. In addition, spatial data for harvest and thinning management activities were used to analyze the effects of anthropogenic activities. In 2020, the aboveground and belowground biomass were 112.98 and 22.84 tC ha⁻¹, and the net primary productivity was 8.30 tC ha⁻¹ year⁻¹. These results were verified using comparison with statistics, a literature review, and MODIS NPP. In particular, broadleaf is higher than conifer forest in net primary production. The Canadian GCBM with Korean forest inventory data and yield curves successfully estimated the aboveground and belowground biomass of forests in South Korea. Our study demonstrates that these estimates can be mapped in detail, thereby supporting decision-makers and stakeholders in analyzing the carbon budget of the forests in South Korea and developing novel schemes that can serve regional and national aims related to forest management, wood utilization, and ecological preservation. Further studies are needed to improve the initialization of dead organic matter pools, given the large-scale afforestation efforts in recent decades that have established South Korea’s forests on predominantly non-forest sites.

Climate change has a significant impact on the occurrence of landslides, and as it intensifies, abnormal weather such as cold waves, heavy snowfall, torrential rains, and typhoons will become more frequent, leading to an increase in disasters. This study constructed a maximum entropy (MaxEnt) model using geospatial information and meteorological factors (using representative concentration pathways (RCP) 8.5 and RCP 4.5 scenarios) from landslide occurrence locations from 2010 to 2020 and simulated the national probability of landslide occurrence per year from 2021 to 2085. A vulnerability assessment was performed considering the government’s policy and budget, and the possible damage caused by landslides was quantitatively evaluated. The results of the impact assessment showed that the probability of landslide occurrence based on the RCP 8.5 scenario was 1.8 times higher in the 2030s, 1.7 times higher in the 2050s, and 7.2 times higher in the 2080s than that based on the RCP 4.5 scenario. In addition, passive and active mitigation, assuming a constant increasing rate of policy budget, showed a difference in reduction of landslide risk. Applying passive mitigation in the impact and vulnerability assessments reduced the risk of landslides by a minimum of 47.7% to a maximum of 52.6%. Active mitigation reduced the risk of landslide occurrence from a minimum of 63.6% to a maximum of 68.4%. The results of this study provide basic data for local governments to determine the priorities of landslide prevention projects. However, future research should consider landslide damage intensity and the physical limitations in spatialization of the adaptive capacity considered in the vulnerability evaluation.