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Article

Simulating Impacts of Climate Change on Young-Aged Forest Succession and Carbon Dynamics

by
Wonhee Cho
1 and
Dongwook W. Ko
2,3,*
1
Department of Forest Sciences, Kongju National University, Yesan 32439, Chungcheongnam-do, Republic of Korea
2
Department of Forestry, Environment, and Systems, Kookmin University, Seongbuk-gu, Seoul 02707, Republic of Korea
3
Forest Carbon Graduate School, Kookmin University, Seongbuk-gu, Seoul 02707, Republic of Korea
*
Author to whom correspondence should be addressed.
Forests 2026, 17(7), 794; https://doi.org/10.3390/f17070794
Submission received: 27 May 2026 / Revised: 26 June 2026 / Accepted: 2 July 2026 / Published: 4 July 2026
(This article belongs to the Special Issue Impacts of Climate Change and Disturbances on Forest Ecosystems)
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Abstract

Young forests are recognized as important contributors to climate change mitigation due to their high productivity. However, their structural simplicity and transitional nature render them ecologically vulnerable to long-term climatic stress. We explored the long-term responses of young forests to climate change by applying the LANDIS-II forest landscape model coupled with a PnET-based physiological model to simulate 200 years of forest succession and carbon dynamics. Simulations were conducted under three climate scenarios (BAU, RCP45, and RCP85) to evaluate changes in aboveground biomass (AGB), carbon storage, and carbon absorption across elevation gradients. The results revealed that climate change significantly altered successional pathways and carbon capacity, with effects varying with elevation and initial species composition. Predominant species such as Quercus mongolica maintained dominance under the RCP45 and RCP85 scenarios across all elevations, whereas shade-tolerant mid and understory species showed suppressed growth. Sub-alpine species showed prominent declines in AGB, particularly in the RCP85 scenario. These divergent responses increased the spatial heterogeneity of forest productivity and reduced the predictability of forest carbon dynamics over time. Our findings emphasize the uncertainty of predicting forest development and carbon sequestration in young forests under future climatic conditions. They highlight the urgent need to plan forest management strategies incorporating site-specific ecological characteristics, promote successional advancement, and maintain functional stability for effective climate adaptation and mitigation.

1. Introduction

Climate change has emerged as a dominant driver of ecological dynamics globally, with forest ecosystems being particularly vulnerable to its complex and multifaceted impacts [1,2]. Rising temperatures, shifting precipitation patterns, and unexpected extreme wildfire, largely induced by anthropogenic greenhouse gas emissions, disrupt the physiological and structural processes in forest ecosystems, affecting tree species growth, regeneration, mortality, and community structure [3,4,5,6]. These effects occur across various spatial and temporal scales, reshaping not only the ecological dynamics at the individual level, but also altering broader ecosystem functions, such as biodiversity, carbon sequestration, and habitat sustainability [7,8,9,10].
Forest succession, the gradual replacement of species, is a key process by which climate change influences ecosystem structure and function [11,12,13]. Successional dynamics are often driven by changes in species competition due to changes in species growth and mortality, disturbance regimes, and microscale climates, all of which are sensitive to climate change. However, significant uncertainty exists regarding the direction and pace of forest succession under climate change [14]. Although numerous studies examined the general impacts of climate on forest ecosystems, a major research gap persists in understanding the unfolding of these processes in early- and mid-successional forests, where forest succession is still actively shaping ecosystem development.
Forest age is a significant determinant of ecosystem responses to stress caused by climate change [11,15,16,17,18]. In the early successional stages, forests are typically dominated by fast-growing pioneer species with high photosynthetic capacity and rapid biomass accumulation. These attributes facilitate efficient short-term carbon sinks. However, the simplicity of young forests and their reliance on disturbance-adapted species render them vulnerable to unstable climate conditions, including drought, extreme temperatures, and increased wildfire frequency and intensity, which further disrupt regeneration and competition [19]. The vulnerability of young forests is, however, strongly context-dependent—contingent on disturbance regime, species traits, soils, and management—rather than a universal property of stand age [20]. Under some conditions, structurally simple young stands with continuous, disturbance-adapted fuels can be more prone to high-severity fire and to rapid post-fire shifts in composition and function, whereas in other settings, older stands are equally or more vulnerable [20]. We therefore treat young-forest vulnerability as conditional, and note that our simulations address climate-driven succession and carbon dynamics.
South Korea offers a compelling case study of the interplay between forest succession and climate change. The country experienced enormous deforestation periods until the 1950s, and forest transition began in 1955 to significantly increase both forest cover and growing stock [21]. Following severe deforestation until the mid-20th century, South Korea implemented extensive reforestation policies [22,23]. By implementing reforestation, approximately 64% of the country is now forested, with a large portion classified as young stands. In the Korean inventory scheme, more than 75% of forests fall into age classes V–VI (approximately 41–60 years) [23]. We use the term “young” in a successional rather than chronological sense: although these stands are no longer juvenile, they are early- to mid-successional—even-aged, structurally simple, pioneer-dominated stands established during the mid-20th-century reforestation that have not yet undergone the compositional transition toward shade-tolerant, late-successional dominance [21,24]. These inventories are dominated by Pinus densiflora and Quercus mongolica. It is this successional immaturity, not literal stand age, that underlies their transitional dynamics and is the focus of this study.
Numerous studies documented that late-successional species are gradually becoming established in Korean forests, although they are limited in scope and often constrained by short monitoring periods [25,26]. Monitoring long-term forest dynamics under various environmental conditions in natural systems is challenging because of limited resources. To overcome this issue, various studies applied simulation-based approaches to predict long-term forest dynamics in response to climate change [27,28,29,30]. Process-based modeling, which incorporates species-specific physiology, climatic sensitivity, and spatial heterogeneity, is particularly well suited for long-term predictions [31,32,33]. It allows researchers to estimate not only biomass changes and species turnover, but also ecosystem-level carbon storage and net primary productivity across diverse landscapes and future scenarios.
Here, we integrated a forest landscape model with a physiological model to simulate 200 years of forest succession and carbon dynamics in the Hangang River Basin, South Korea. We aimed to enhance our understanding of the response of early- and mid-successional forests to the long-term effects of climate change by evaluating the changes in aboveground biomass (AGB), carbon storage, and carbon absorption in species at the landscape level following multiple climate change scenarios. Our insights are expected to inform adaptive forest management strategies and climate mitigation planning for temperate forest ecosystems undergoing active ecological transitions.

2. Materials and Methods

2.1. Background of the Study: Study Area, Species, and Environment

The Hangang River Basin, one of the major watersheds of South Korea, extends from the eastern mountainous regions to the western Yellow Sea (Figure 1). The basin includes four primary sub-basins (Hangang River downstream, Nam-hangang River upstream and downstream, and Buk-hangang River), which encompass the Seoul metropolitan area, parts of Gyeonggi-do, and Gangwon-do provinces [34]. The basin represents 68% of the forested land of South Korea, covering 21,812 km2 and exhibits diverse topographic features, including central plains and mountainous areas exceeding 1000 m in elevation.
The basin contains 3304 plots (Figure 1B) from the National Forest Inventory (NFI). According to the NFI, in the basin, the forest communities consist of 12 dominant tree species and four subalpine species (Table S1), including Quercus species, Pinus densiflora located in the overstories, Fraxinus rhynchophylla, Acer pictum var. mono, and Tilia amurensis located in mid- and understory. For landscape stratification, we divided the study area into 14 ecoregions based on ecological zone, elevation, and aspect (Figure 1C). Here, we used 12 dominant tree species to design initial communities as tree cohorts, identified as species age groups representing the initial conditions in the LANDIS-II model [31]. To evaluate forest dynamics, we applied the initial communities for each ecoregion.
The NFI is the systematic permanent-plot program of the Korea Forest Service. Plots are arranged in clusters on a 4 km × 4 km grid, with each cluster comprising four subplots in which species, diameter at breast height, and tree height are recorded. We used the 5th NFI (2006–2010) to construct the initial communities and both the 5th and 6th NFI (2011–2015) for AGB verification. The simulation is spatially explicit and raster-based at 90 m resolution. Each cell is assigned an initial community and one of the 14 ecoregions (Figure 1C), which supply its climate; succession, growth, mortality, and competition for light are simulated per cell at an annual time step and aggregated to ecoregion and landscape scales.

2.2. Model Description (LANDIS-II Biomass Succession Extension)

Forest succession and carbon dynamics were simulated by coupling two simulation models: the forest landscape model, LANDIS-II version 7.0 [31], and the ecosystem process model, PnETforlandis version 5.1 [35]. LANDIS-II is a raster-based stochastic model simulating spatiotemporally explicit forest succession and disturbances [31], an open-source platform incorporating sub-models (“extensions”) that represent different growth and disturbance processes. Unlike empirical growth-and-yield models, which extrapolate statistical relationships fitted to observed stand conditions and therefore lose reliability when projected beyond their calibration window or into novel climates [30,36], LANDIS-II and its PnET-based physiological coupling represent the underlying ecophysiological mechanisms—photosynthesis, water balance, and competition for light—and were explicitly designed for decadal-to-multi-century simulation [31,35]. Multi-century LANDIS applications spanning 200–300 years are well established in the literature [28,29,37], which supports the 200-year horizon adopted here.
LANDIS-II incorporates modular extensions to simulate processes, such as forest growth, mortality, competition, and disturbances [31]. Here, we employed a biomass succession extension [38] to simulate the AGB and actual annual net primary production (ANPP) for each species age group at the spatial resolution of individual raster cells [38]. The Biomass Succession extension simplifies ecological dynamics by estimating annual changes in biomass based on the core parameters of maximum annual net primary production (ANPPmax) and maximum AGB (Maxbiomass).
The ANPPmax was estimated using PnETforlandis version 5.1 [35], a derivative of the PnET-II model [39] that simulates species-specific physiological responses, such as photosynthetic rates. In the current study, the PnET-II model was used to estimate the ANPPmax of the target species under various climate scenarios.

2.3. Parameterization and Verification

To simulate forest succession and carbon dynamics using the LANDIS-II Biomass-succession extension and PnETforlandis, the species life history and physiological parameters were utilized following Cho et al. [40] (Table S2). The life history parameters were directly applied to the LANDIS-II Biomass Succession extension, and species physiological parameters were applied to the PnETforlandis model to estimate the ANPPmax to represent species’ photosynthetic efficiency in climate scenarios. Cho et al. [40] conducted a parameterization study to simulate the LANDIS-II Biomass Succession extension for the dominant species in South Korea. The parameters were estimated from those of the earlier studies conducted to survey the physiological characteristics of species in the area including East Asian countries, such as the eastern and northern parts of China, Korea, and Japan. The parameters were calibrated by comparing the estimated AGB for 20 years from the LANDIS-II model with long-term ecological research sites in South Korea.
In terms of model verification, the LANDIS-II model has uncertainty, sensitivity, and spatiotemporal scale effects because of the lack of long-term and landscape observation data [31,37,38]. Here, the model was verified using empirical AGB data from the 5th and 6th NFI in South Korea. We compared the simulated AGB and empirical values across ecoregions, excluding areas above 1500 m due to limited data. The simulated AGB was assessed using R2 and the root mean square error (RMSE) to provide quantitative measures of model accuracy and reliability.

2.4. Model Initialization (Initial Community and Ecoregion)

Model initialization plays a significant role in establishing baseline conditions for simulating the long-term dynamics of forest ecosystems. Here, initialization was conducted to develop spatially explicit inputs defining the distribution of initial communities (species-age groups) across the landscape and identify environmental characteristics by applying ecoregions.
The initial communities were constructed using the NFI data and an ecoregion map. The species pool comprised 16 species. Dominant species were selected using an importance value (IV), based on relative density, cover, and frequency computed from all NFI plots in the study area, with threshold of 1.0 (Table S1); this yielded 12 dominant species. The four subalpine conifers (Abies holophylla, A. nephrolepis, Picea jezoensis, and Taxus cuspidata) are included represented high-elevation ecoregions. Representative age classes for each species were assigned from national diameter-increment data [41]. Initial communities were then defined by overlaying the forest-type map with the ecoregions: each forest-type × ecoregion combination (227 in total) was populated with the species recorded in the NFI plots falling within that combination, producing 227 initial communities.

2.5. Climate Scenario

To evaluate the responses of forest ecosystems to climate change, we examined the impact of climate change on forest succession and carbon dynamics. We applied three climate scenarios—business-as-usual (BAU), Representative Concentration Pathway 4.5 (RCP45), and Representative Concentration Pathway 8.5 (RCP85)—based on digital climate maps for Korean forests, which were specifically designed to improve the applicability of RCP-based climate projections to the mountainous regions of Korea [42]. Each scenario spans the 200-year period from 2010 to 2210. This horizon was chosen deliberately: the successional transitions of interest—replacement of pioneer P. densiflora and Quercus by shade-tolerant T. amurensis and A. pictum, and the response of long-lived subalpine conifers (longevity 300–500 years; Table S2)—unfold over one to several tree generations and are not detectable within a 100-year window. The BAU scenario is a constant-climate (no additional climate change) reference rather than a socioeconomic emissions scenario: the 1981–2010 30-year mean climate of the study area was held constant over the entire 200-year period, providing a stationary-climate control against which the RCP45 and RCP85 transient projections were compared and the climate change signal was separated from stand development dynamics.
Scenarios influencing species-specific ANPPmax were generated from 2010 to 2210. The dataset used for the scenario development included monthly baseline climate data and high-resolution (30 m) climate projections tailored for Korea. Here, we estimated the mean values for each ecoregion to develop climate scenarios. For the BAU scenario, the historical 30-year average climate was consistently applied over the entire 200-year simulation period. For RCP45 and RCP85, transient projected climate was applied from 2010 to 2100; thereafter, because the digital climate maps for Korea extend only to 2100, climate inputs for 2100–2210 were held constant at the 2091–2100 ten-year mean values for each ecoregion.

2.6. Experimental Design and Analysis

We simulated forest dynamics by estimating spatiotemporal changes in species-specific AGB (ton/ha), carbon storage (tonC/ha), and carbon absorption (tonC/ha/year). The simulation was conducted with 10 replications under three climate scenarios, and major outputs were produced every 50 years. To assess forest succession and species composition changes, we analyzed the projected AGB of the simulated species at representative time points. Based on these estimates, we calculated the species-specific AGB across ecoregions during the simulation periods, allowing us to evaluate the changes in carbon storage dynamics in the Hangang River Basin in response to climate change. Furthermore, we estimated carbon absorption dynamics by simulating the actual ANPP across ecoregions. To determine the long-term effects of climate change, we mapped the spatial distribution of carbon storage and above-average absorption under climate scenarios at two key time points: 2110 and 2210. Finally, we assessed the dynamics of carbon-related forest functionality in the Hangang River Basin under climate change conditions.

3. Results

3.1. Model Verification

Our simulation results showed reasonable agreement, following the comparison between the simulated and observed AGB. Through the comparison across the landscape, simulated AGB correlated with observed AGB estimated from NFI data, presenting an R2 of 0.45 (Figure 2). Our verification revealed varying AGB results based on elevation, aspects, and landscape characteristics. These results show that our model can simulate the spatiotemporal forest dynamics in the Hangang River Basin. From Figure 2, the simulated AGB was higher than that of the NFI data. However, the simulation of AGB growth during the verification period slightly underestimated the estimated 10% increase between 2010 and 2015, compared with the 16% increase in the NFI data. The RMSE improved from 25.6 to 19.5 ton/ha between 2010 and 2015, respectively, indicating reasonable model performance for evaluating landscape-scale forest dynamics. We emphasize, however, that it evaluates landscape-scale AGB over a short (2010–2015) interval against two NFI cycles and does not independently validate species composition, ANPP, successional rates, or carbon fluxes, for which spatially and temporally extensive observations do not yet exist in the basin. An R2 of 0.45 indicates moderate explanatory skill at the cell level, and the systematic positive bias (simulated AGB exceeding NFI AGB) reflects the absence of growth-limiting disturbances and stresses during initialization, so that cohort biomass is estimated near site potential—a known tendency of landscape models, and of LANDIS-II in particular, to overestimate biomass relative to inventory observations. We therefore interpret the model as validated for landscape-scale AGB magnitude and broad spatial pattern.

3.2. Changes in Forest Composition and Succession Under Climate Change

The AGB of the species exhibited distinct variations depending on elevation (Figure 3). In particular, the AGB in the initially dominant species, such as P. densiflora and Q. mongolica, and the post-dominant species, T. amurensis and A. pictum var. mono, and subalpine species varied significantly during the simulation periods depending on the climate scenario. These changes are caused by species competition, which can be simulated in LANDIS-II based on the physiological responses of species to climatic conditions applied to PnET-based species growth parameters [31,39].
Under the BAU scenario, in mountainous areas below 1000 m, Q. mongolica remained the dominant species for 150 years, but its AGB gradually declined, whereas A. pictum var. mono and T. amurensis continued to increase. Contrastingly, under RCP45 and RCP85 scenarios, Q. mongolica maintained high AGB levels, whereas T. amurensis and A. pictum var. mono consisted of AGB levels up to 20 tons/ha lower in 2210 than those in the BAU scenario.
In the 1000–1500 m elevation range, distinct changes in the AGB patterns were observed in Q. mongolica, A. pictum var. mono, T. amurensis, and the subalpine species (Figure 3B). Under the BAU scenario, Q. mongolica initially exhibited a continuous increase in AGB over the first 100 years, maintaining its dominance. However, the AGB declined sharply during the later simulation period. During the same time scale, T. amurensis and A. pictum var. mono exhibited sustained AGB growth, ultimately replacing Q. mongolica and emerging as the dominant species. Subalpine species, including Abies holophylla, Abies nephrolepis, Picea jezoensis, and Taxus cuspidata, demonstrated relatively higher AGB retention and growth than those at lower elevations (below 1000 m).
Contrastingly, under the RCP45 and RCP85 scenarios, the AGB of Q. mongolica continued to increase throughout the simulation period, maintaining its dominance. A. pictum var. mono and T. amurensis also showed an increasing trend, and their AGB levels remained lower than those in the BAU scenario. Furthermore, subalpine species exhibited either reduced AGB accumulation or an overall declining trend compared with that in the BAU scenario, indicating a potential long-term contraction of their distribution due to climate change.
Above 1500 m, changes in AGB for Q. mongolica and the subalpine species were particularly pronounced (Figure 3C). Under the BAU scenario, Q. mongolica maintained an AGB below 30 tons/ha, whereas that of the subalpine species continued to increase. However, under RCP45 and RCP85 scenarios, the subalpine species exhibited either lower AGB levels or a slight decline compared with that in the BAU scenario, while that of Q. mongolica continued to increase.
In the central plain and mountainous regions, species-specific AGB changes with elevation followed a similar pattern as those in the mountainous region. However, under RCP45 and RCP85 scenarios, T. amurensis and A. pictum var. mono exhibited lower AGB than that in the mountainous region below 1000 m, while Q. mongolica showed a decline (Figure 4A). In areas between 1000 and 1500 m, no subalpine species were observed, and similar to those in the mountainous region, Q. mongolica exhibited increased AGB, whereas T. amurensis and A. pictum var. mono increased but remained at lower levels compared with that in the BAU scenario (Figure 4B).
Overall, the total AGB over time was estimated to be lower in the central plain and mountainous regions than that in the mountainous regions. Species-specific responses to climate change showed that, while the general trends were similar, some differences existed in AGB variations between the two regions.

3.3. Carbon Dynamics

3.3.1. Carbon Storage

The aboveground carbon storage in the study area initially increased across all climate scenarios over the first 100 years, and then declined continuously until 2210 (Figure 5A). In 2010, aboveground carbon storage was estimated at 75 tonC/ha. By 2110, it had increased by 91% to 144 tonC/ha under BAU, and by 82% (to 137 tonC/ha) and 75% (to 132 tonC/ha) under RCP45 and RCP85, respectively.
After 2110, aboveground carbon storage declined continuously across all scenarios. Under BAU, it decreased by approximately 11% from its 2110 peak, reaching 128 tonC/ha by 2210. A more substantial decline occurred under RCP45 (−19%, to 117 tonC/ha), and the most pronounced reduction under RCP85 (−42%, to 84 tonC/ha)—only 11% above the initial 2010 level, in stark contrast to the net increases under BAU (+70%) and RCP45 (+55%).
By examining the spatial characteristics of carbon storage, the total carbon stock in the study area was estimated to be 133 million tons in 2010 (Figure 5B). By 2110, carbon storage is projected to increase to 255, 242, and 231 million tons under the BAU, RCP 45, and RCP 85 scenarios, respectively. By 2210, carbon storage will decline to 227 (BAU), 207 (RCP 45), and 149 (RCP 85) million tons.

3.3.2. Carbon Absorption

In the study area, the average aboveground carbon absorption decreased continuously over 50 years under all climate change scenarios (Figure 6A). In the early 50 years (2010–2060), the average aboveground carbon absorption were 2.07, 1.67, and 1.51 tonC/ha/yr for the BAU, RCP 45, and RCP 85 scenarios, respectively. From 2060 to 2110, the average aboveground carbon absorption rates were 1.55, 1.16, and 0.99 tonC/ha/yr for BAU, RCP 45, and RCP 85 scenarios, showing a decrease of 25%, 31%, and 34%, respectively, compared with those of the first 50-year period.
Between 2110 and 2160, the average aboveground carbon absorption were 1.49, 1.14, and 0.93 tonC/ha/yr under the BAU, RCP 45, and RCP 85 scenarios, with a decrease of 28%, 32%, and 38%, respectively, compared with those of the first 50-year period (Figure 6A). From 2160 to 2210, it declined further to 1.40, 1.12, and 0.87 tonC/ha/yr for the BAU, RCP 45, and RCP 85 scenarios, representing a decrease of 32%, 33%, and 42%, respectively.
The spatial distribution of aboveground carbon absorption showed that the annual absorption in the study area started at 3.31 million tons (Figure 6B). This value decreased continuously during the simulation, to 2.75, 2.05, and 1.76 million tonsC yr−1 under BAU, RCP45, and RCP85 in 2110, respectively, and to 2.49, 1.99, and 1.54 million tonsC yr−1 in 2210. Thus, by 2210, the annual net aboveground carbon absorption (uptake) under RCP85 was only 62% of that under BAU.

4. Discussion

We demonstrated that climate change significantly alters forest successional trajectories and carbon dynamics in early- and mid-successional forest ecosystems in South Korea. By simulating 200 years of forest ecosystem dynamics under various climate scenarios, we offer insights into the response of AGB and carbon dynamics to climate change.

4.1. Species AGB Change in Young-Aged Forests

South Korea implemented reforestation policies to overcome severe deforestation in the mid-20th century [22]. This has led to an increase in forest area in South Korea, up to approximately 64% of the entire country. During the restoration period, South Korea planted pioneer species such as P. densiflora, and some of the areas were managed as commercial forests dominated by L. kaempferi and P. koraiensis with Quercus spp. in the mid- and understories [22]. Through successive reforestation policies, growing stocks in South Korea reached 165 m3/ha with various valuable forest services. However, these young forests increased vulnerability due to the impacts of climate change [43,44], especially the habitat suitability and growth of pioneer species in forested land and increased mortality of sub-alpine species [45,46].
Here, we simulated 200 years of forest succession by estimating the AGB changes in the Hangang River Basin. Following AGB changes in the BAU scenario, the initially dominant species, P. densiflora and Q. mongolica, were succeeded by A. pictum var. mono and T. amurensis (Figure 3 and Figure 4). This succession trend is affected by species competition related to shade tolerance and gradually replaced the initial dominant species (Table S2; [47,48]). Forest succession exhibits varying trajectories with climate change. In the RCP45 and RCP85 scenarios, the AGB of Q. mongolica continuously increased and maintained its dominance, especially over 1000 m elevation areas. The increasing temperatures applied in the RCP45 and RCP85 scenarios provided suitable habitat conditions for Q. mongolica, which could provide advantages in competition with growing understories and P. densiflora [47]. The AGB of subalpine species distributed over 1500 m increased in the BAU scenario during the simulation period (Figure 3). However, the vulnerability of subalpine species will increase with climate change [45,46]. In the RCP45 and RCP85 scenarios, the amount of AGB from subalpine species sharply declined compared with that in the BAU scenario (Figure 3). As the temperature increased during the simulation periods, it challenged the subalpine species—which have low-level temperature conditions for growth—to survive under climate change conditions (Table S2).

4.2. Carbon Vulnerability in Hangang River Basin

The trend of aboveground carbon storage presented different patterns during the simulation periods, according to the criteria for 2110 (Figure 5). In our simulation, aboveground carbon storage was evaluated to increase continuously from 2010 to 2110 in all of the climate scenarios. After 2110, aboveground carbon storage declined continuously. The decline in aboveground carbon storage was more pronounced under RCP 45 and RCP 85 scenarios than that under BAU climate conditions. In particular, under the RCP 85 scenario, the aboveground carbon storage in 2210 was 36% lower than that in 2110, marking the most significant reduction among the scenarios.
The reduction in aboveground carbon storage under climate change scenarios was followed by a decline in species growth during the simulation periods, as shown in Figure 3 and Figure 4. In particular, P. densiflora was the dominant species under the initial conditions, and A. pictum var. mono and T. amurensis significantly increased in the simulation and subalpine species. These findings emphasize that P. densiflora is affected by climate change and will be significantly vulnerable in the future. Many studies emphasized its vulnerability to climate change, particularly in South Korea [45,46,47]. Additionally, A. pictum var. mono and T. amurensis are expected to be vulnerable to climate change. These species are expected to be post-dominant in the Hangang River Basin (Figure 3 and Figure 4). However, the AGB after 2110 years was significantly deficient in the RCP45 and RCP85 scenarios compared with that in the BAU, emphasizing the expected overall decline in aboveground carbon storage in the Hangang River Basin. Regional differences in carbon storage due to climate change were particularly evident in the central plains and mountainous areas (Figure 5B). Under the RCP 85 scenario, most areas showed a substantial decrease in aboveground carbon storage compared with that in the BAU scenario after 2110.
Net aboveground carbon absorption (uptake) in the Hangang River Basin decreased continuously under all scenarios (Figure 6). The decline under BAU reflects stand development and aging—uptake necessarily slows as even-aged cohorts approach maximum biomass and longevity—whereas the steeper declines under RCP45 and RCP85 quantify the additional, climate-attributable suppression of uptake; forest aging is independently projected to reduce carbon uptake in South Korea [49,50]. Considering spatial differences, both the central-plain/mountainous and the subalpine areas showed decreasing uptake over time, but the central-plain and mountainous areas declined more steeply, with RCP85 driving net uptake below 1 tonC ha−1 yr−1 across most of the central plain and mountainous areas.
Our findings emphasize that extreme climate change could critically impact carbon storage and absorption in the forested areas of the Han River Basin. In particular, elevation played a critical role in species’ responses to the impact of climate change [51,52,53]. Subalpine areas are suitable for several species; however, predominant species such as subalpine species in the areas experienced consistent declines in AGB and ANPP under the RCP 45 and RCP85 scenarios, which highlights the loss of climatic habitat in high-elevation ecoregions [54,55,56,57]. Contrastingly, Q. mongolica maintained or increased its dominance across a range of elevations, revealing its resilience in sustaining forest ecosystems by maintaining growth and carbon dynamics in high-elevation ecoregions [58,59]. Our simulations include neither fire nor other disturbances, so they support conclusions about climate-driven growth and successional responses only, not about disturbance resilience per se; we therefore frame the following as hypotheses rather than findings. The simulated maintenance of Q. mongolica dominance under warming, together with the documented decline in P. densiflora habitat suitability in Korea [45,46,47], leads us to hypothesize that climate change may favor Q. mongolica over P. densiflora in ways that could also alter their relative exposure to fire, given their contrasting traits and stand structure [20]. Whether such climate-driven compositional shifts translate into different fire behavior or post-fire recovery cannot be determined from climate-only simulations and would require coupling LANDIS-II with a fire-disturbance extension [20,60]. We flag this as a priority for future work and as a caution for management interpretation [61].

4.3. Uncertainties and Future Research

Although the LANDIS-II model, which is calibrated with species-specific physiological parameters, captures forest ecosystem succession and carbon dynamics, uncertainties remain [61,62,63]. Particularly in sub-alpine regions, the sample size remains above 1500 m, which affects the model accuracy in high-elevation ecoregions. A formal sensitivity analysis and the parameterization of the South Korean species were carried out in the underlying parameterization study [40]. Propagating parameter, climate-ensemble, and stochastic uncertainty nonetheless remains an important direction for future work [32,35].
Future research should incorporate denser and more localized growth data, refine parameter estimates for Korean species, and expand monitoring at various time and spatial scales, particularly in high-elevation areas. Linking such empirical data with long-term simulations will enhance predictive capacity and support more robust forest policies in the context of accelerating climate change. South Korea developed various long-term ecological monitoring sites in the country [64,65,66]. These trials and efforts can enhance the reliability of future research.
Succession under changing climates also appeared to be less predictable in these systems. However, mid- and late-successional species such as T. amurensis and A. pictum var. mono began to increase under BAU, and its growth was significantly constrained under the RCP scenarios. This indicates that climate change not only alters the timing of successional transitions, but may also suppress them in some cases. Additionally, suppressing long-term forest succession can have a significant effect on carbon storage and absorption in early successional forests. Forests in South Korea developed through massive reforestation policies lacking biodiversity and structural complexity. This limitation influences the carbon dynamics in South Korea. To overcome this, successful carbon management studies considering forest composition, successional stage, and spatial heterogeneity should be conducted to enhance species diversity and succession toward greater stability.
Our results suggest three management priorities. First, where warming is projected to suppress natural succession toward shade-tolerant late-successional species, managers could facilitate the establishment of T. amurensis and A. pictum var. mono (for example, through enrichment planting and release thinning) to sustain successional advance and structural complexity. Second, high-elevation ecoregions, where subalpine conifers (Abies spp., P. jezoensis, and T. cuspidata) show consistent AGB declines under the RCP scenarios, should be prioritized for conservation and monitoring as potential climatic refugia. Third, because the RCP85 projection implies near-complete loss of the basin’s mid-century carbon gains by 2210 (aboveground carbon storage only ~11% above the 2010 level), carbon-oriented management should not assume the persistence of current sink strength and should instead diversify species and age structure to buffer long-term carbon functionality.

5. Conclusions

We provide a long-term prediction of the response of young forests (early- to mid-successional forests) to climate change focusing specifically on forest succession and carbon dynamics in the Hangang River Basin, South Korea. Using an integrated process-based model approach, we demonstrated that both successional trajectories and carbon dynamics are highly sensitive to climate conditions, particularly in the RCP 85 emission scenarios. Our findings showed that the response of young forests to climate change varies with spatial heterogeneity. Climate change threatened the growth of subalpine, predominant, and post-dominant species. Additionally, it has become increasingly difficult to maintain long-term carbon storage and absorption capacity, especially in low-elevation areas. These results emphasize the importance of the forest succession stage and species composition in determining forest resilience. Our study suggests that future forest management plans should incorporate forest and environmental characteristics to sustain forest succession and carbon dynamics in response to climate change.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f17070794/s1, Table S1: Species pool in the study area; Table S2: Life history and physiological parameters of each species.

Author Contributions

Conceptualization, W.C. and D.W.K.; methodology, W.C. and D.W.K.; investigation, and writing, W.C.; software, W.C.; project administration, writing—review and editing, W.C. and D.W.K.; and funding acquisition, W.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by ‘R&D program for forest science technology (RS-2024-00402624)’ provided by Korea Forest Service (Korea Forestry Promotion Institute), the research grant of Kongju National University in 2024, and a grant (Project no.: FE0100-2024-04) from National Institute of Forest Science, Republic of Korea.

Data Availability Statement

The datasets used and/or analyzed are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Location of Hangang River Basin. (A–C) represent the sub-basins, national forest inventory (NFI) points, and environmental characteristics of Hangagn River Basin, respectively.
Figure 1. Location of Hangang River Basin. (A–C) represent the sub-basins, national forest inventory (NFI) points, and environmental characteristics of Hangagn River Basin, respectively.
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Figure 2. AGB comparison between model estimation and NFI estimation (R2 = 0.45). Left blue box in plot represents estimated AGB in 2010 between model and 5th NFI. Right orange box in plot represents estimated AGB in 2015 between model and 6th NFI.
Figure 2. AGB comparison between model estimation and NFI estimation (R2 = 0.45). Left blue box in plot represents estimated AGB in 2010 between model and 5th NFI. Right orange box in plot represents estimated AGB in 2015 between model and 6th NFI.
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Figure 3. Species AGB dynamics in mountain areas for each scenario in the simulation period. (A–C) are simulated results for below 1000 m, 1000 m to 1500 m, and over 1500 m area, respectively.
Figure 3. Species AGB dynamics in mountain areas for each scenario in the simulation period. (A–C) are simulated results for below 1000 m, 1000 m to 1500 m, and over 1500 m area, respectively.
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Figure 4. Species AGB dynamics in central plain and mountain areas for each scenario in the simulation period. (A,B) are simulated results for below 1000 m and 1000 m to 1500 m area, respectively.
Figure 4. Species AGB dynamics in central plain and mountain areas for each scenario in the simulation period. (A,B) are simulated results for below 1000 m and 1000 m to 1500 m area, respectively.
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Figure 5. Time-series changes in aboveground carbon storage for the Hangang River Basin during 2010–2210. (A,B) represent the quantified totals and the spatial distribution of aboveground carbon storage, respectively.
Figure 5. Time-series changes in aboveground carbon storage for the Hangang River Basin during 2010–2210. (A,B) represent the quantified totals and the spatial distribution of aboveground carbon storage, respectively.
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Figure 6. Time-series changes in net aboveground carbon absorption (uptake) for the Hangang River Basin during 2010–2210. (A,B) represent the 50-year-average quantified totals and the spatial distribution of net aboveground carbon uptake, respectively.
Figure 6. Time-series changes in net aboveground carbon absorption (uptake) for the Hangang River Basin during 2010–2210. (A,B) represent the 50-year-average quantified totals and the spatial distribution of net aboveground carbon uptake, respectively.
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Cho, W.; Ko, D.W. Simulating Impacts of Climate Change on Young-Aged Forest Succession and Carbon Dynamics. Forests 2026, 17, 794. https://doi.org/10.3390/f17070794

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Cho W, Ko DW. Simulating Impacts of Climate Change on Young-Aged Forest Succession and Carbon Dynamics. Forests. 2026; 17(7):794. https://doi.org/10.3390/f17070794

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Cho, Wonhee, and Dongwook W. Ko. 2026. "Simulating Impacts of Climate Change on Young-Aged Forest Succession and Carbon Dynamics" Forests 17, no. 7: 794. https://doi.org/10.3390/f17070794

APA Style

Cho, W., & Ko, D. W. (2026). Simulating Impacts of Climate Change on Young-Aged Forest Succession and Carbon Dynamics. Forests, 17(7), 794. https://doi.org/10.3390/f17070794

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