Decoding carbon allocation in boreal forests: Integrating multi-proxy observations and process-based modelling

TRIPLEX-Mortality model for simulating drought-induced tree mortality in boreal forests: Model development and evaluation

<strong class="journal-contentHeaderColor">Abstract.</strong> Climate change and increased fire are eroding the resilience of boreal forests. This is problematic because boreal vegetation and the cold soils underneath store approximately 30 % of all terrestrial carbon. Society urgently needs projections of where, when, and why boreal forests are likely to change. Permafrost (i.e., subsurface material that remains frozen for at least two consecutive years) and the thick soil-surface organic layers (SOLs) that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and SOL module that operates at fine spatial (1 ha) and temporal (daily) resolutions. The module mechanistically simulates daily changes in depth to permafrost, annual SOL accumulation, and their complex effects on boreal forest structure and functions. We coupled the module to an established forest landscape model, iLand, and benchmarked the model in interior Alaska at spatial scales of stands (1 ha) to landscapes (61,000 ha) and over temporal scales of days to centuries. The coupled model could generate intra- and inter-annual patterns of snow accumulation and active layer depth (portion of soil column that thaws throughout the year) consistent with independent observations in 17 instrumented forest stands. The model was also skilled at representing the distribution of near-surface permafrost presence in a topographically complex landscape. We simulated 34.6 % of forested area in the landscape as underlain by permafrost; a close match to the estimated 33.4 % from the benchmarking product. We further determined that the model could accurately simulate moss biomass, SOL accumulation, fire activity, tree-species composition, and stand structure at the landscape scale. Modular and flexible representations of key biophysical processes that underpin 21^st-century ecological change are an essential next step in vegetation simulation to reduce uncertainty in future projections and to support innovative environmental decision making. We show that coupling a new permafrost and SOL module to an existing forest landscape model increases the model&rsquo;s utility for projecting forest futures at high latitudes. Process-based models that represent relevant dynamics will catalyze opportunities to address previously intractable questions about boreal forest resilience, biogeochemical cycling, and feedbacks to regional and global climate.&emsp;

<strong class="journal-contentHeaderColor">Abstract.</strong> Climate change and increased fire are eroding the resilience of boreal forests. This is problematic because boreal vegetation and the cold soils underneath store approximately 30 % of all terrestrial carbon. Society urgently needs projections of where, when, and why boreal forests are likely to change. Permafrost (i.e., subsurface material that remains frozen for at least two consecutive years) and the thick soil-surface organic layers (SOLs) that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and SOL module that operates at fine spatial (1 ha) and temporal (daily) resolutions. The module mechanistically simulates daily changes in depth to permafrost, annual SOL accumulation, and their complex effects on boreal forest structure and functions. We coupled the module to an established forest landscape model, iLand, and benchmarked the model in interior Alaska at spatial scales of stands (1 ha) to landscapes (61,000 ha) and over temporal scales of days to centuries. The coupled model could generate intra- and inter-annual patterns of snow accumulation and active layer depth (portion of soil column that thaws throughout the year) consistent with independent observations in 17 instrumented forest stands. The model was also skilled at representing the distribution of near-surface permafrost presence in a topographically complex landscape. We simulated 34.6 % of forested area in the landscape as underlain by permafrost; a close match to the estimated 33.4 % from the benchmarking product. We further determined that the model could accurately simulate moss biomass, SOL accumulation, fire activity, tree-species composition, and stand structure at the landscape scale. Modular and flexible representations of key biophysical processes that underpin 21^st-century ecological change are an essential next step in vegetation simulation to reduce uncertainty in future projections and to support innovative environmental decision making. We show that coupling a new permafrost and SOL module to an existing forest landscape model increases the model&rsquo;s utility for projecting forest futures at high latitudes. Process-based models that represent relevant dynamics will catalyze opportunities to address previously intractable questions about boreal forest resilience, biogeochemical cycling, and feedbacks to regional and global climate.&emsp;

<strong class="journal-contentHeaderColor">Abstract.</strong> Climate change and increased fire are eroding the resilience of boreal forests. This is problematic because boreal vegetation and the cold soils underneath store approximately 30 % of all terrestrial carbon. Society urgently needs projections of where, when, and why boreal forests are likely to change. Permafrost (i.e., subsurface material that remains frozen for at least two consecutive years) and the thick soil-surface organic layers (SOLs) that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and SOL module that operates at fine spatial (1 ha) and temporal (daily) resolutions. The module mechanistically simulates daily changes in depth to permafrost, annual SOL accumulation, and their complex effects on boreal forest structure and functions. We coupled the module to an established forest landscape model, iLand, and benchmarked the model in interior Alaska at spatial scales of stands (1 ha) to landscapes (61,000 ha) and over temporal scales of days to centuries. The coupled model could generate intra- and inter-annual patterns of snow accumulation and active layer depth (portion of soil column that thaws throughout the year) consistent with independent observations in 17 instrumented forest stands. The model was also skilled at representing the distribution of near-surface permafrost presence in a topographically complex landscape. We simulated 34.6 % of forested area in the landscape as underlain by permafrost; a close match to the estimated 33.4 % from the benchmarking product. We further determined that the model could accurately simulate moss biomass, SOL accumulation, fire activity, tree-species composition, and stand structure at the landscape scale. Modular and flexible representations of key biophysical processes that underpin 21^st-century ecological change are an essential next step in vegetation simulation to reduce uncertainty in future projections and to support innovative environmental decision making. We show that coupling a new permafrost and SOL module to an existing forest landscape model increases the model&rsquo;s utility for projecting forest futures at high latitudes. Process-based models that represent relevant dynamics will catalyze opportunities to address previously intractable questions about boreal forest resilience, biogeochemical cycling, and feedbacks to regional and global climate.&emsp;

<strong class="journal-contentHeaderColor">Abstract.</strong> Climate change and increased fire are eroding the resilience of boreal forests. This is problematic because boreal vegetation and the cold soils underneath store approximately 30 % of all terrestrial carbon. Society urgently needs projections of where, when, and why boreal forests are likely to change. Permafrost (i.e., subsurface material that remains frozen for at least two consecutive years) and the thick soil-surface organic layers (SOLs) that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and SOL module that operates at fine spatial (1 ha) and temporal (daily) resolutions. The module mechanistically simulates daily changes in depth to permafrost, annual SOL accumulation, and their complex effects on boreal forest structure and functions. We coupled the module to an established forest landscape model, iLand, and benchmarked the model in interior Alaska at spatial scales of stands (1 ha) to landscapes (61,000 ha) and over temporal scales of days to centuries. The coupled model could generate intra- and inter-annual patterns of snow accumulation and active layer depth (portion of soil column that thaws throughout the year) consistent with independent observations in 17 instrumented forest stands. The model was also skilled at representing the distribution of near-surface permafrost presence in a topographically complex landscape. We simulated 34.6 % of forested area in the landscape as underlain by permafrost; a close match to the estimated 33.4 % from the benchmarking product. We further determined that the model could accurately simulate moss biomass, SOL accumulation, fire activity, tree-species composition, and stand structure at the landscape scale. Modular and flexible representations of key biophysical processes that underpin 21^st-century ecological change are an essential next step in vegetation simulation to reduce uncertainty in future projections and to support innovative environmental decision making. We show that coupling a new permafrost and SOL module to an existing forest landscape model increases the model&rsquo;s utility for projecting forest futures at high latitudes. Process-based models that represent relevant dynamics will catalyze opportunities to address previously intractable questions about boreal forest resilience, biogeochemical cycling, and feedbacks to regional and global climate.&emsp;

Abstract. Climate change and increased fire are eroding the resilience of boreal forests. This is problematic because boreal vegetation and the cold soils underneath store approximately 30 % of all terrestrial carbon. Society urgently needs projections of where, when, and why boreal forests are likely to change. Permafrost (i.e., subsurface material that remains frozen for at least 2 consecutive years) and the thick soil-surface organic layers (SOLs) that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and SOL module named the Permafrost and Organic LayEr module for Forest Models (POLE-FM) that operates at fine spatial (1 ha) and temporal (daily) resolutions. The module mechanistically simulates daily changes in depth to permafrost, annual SOL accumulation, and their complex effects on boreal forest structure and functions. We coupled the module to an established forest landscape model, iLand, and benchmarked the model in interior Alaska at spatial scales of stands (1 ha) to landscapes (61 000 ha) and over temporal scales of days to centuries. The coupled model generated intra- and inter-annual patterns of snow accumulation and active layer depth (portion of soil column that thaws throughout the year) generally consistent with independent observations in 17 instrumented forest stands. The model also represented the distribution of near-surface permafrost presence in a topographically complex landscape. We simulated 39.3 % of forested area in the landscape as underlain by permafrost, compared to the estimated 33.4 % from the benchmarking product. We further determined that the model could accurately simulate moss biomass, SOL accumulation, fire activity, tree species composition, and stand structure at the landscape scale. Modular and flexible representations of key biophysical processes that underpin 21st-century ecological change are an essential next step in vegetation simulation to reduce uncertainty in future projections and to support innovative environmental decision-making. We show that coupling a new permafrost and SOL module to an existing forest landscape model increases the model's utility for projecting forest futures at high latitudes. Process-based models that represent relevant dynamics will catalyze opportunities to address previously intractable questions about boreal forest resilience, biogeochemical cycling, and feedbacks to regional and global climate.

Climate change, including increasing atmospheric CO2 concentrations ([CO2]), nitrogen deposition, and recovery from past management have led to changes in forest productivity in many parts of the world. Process-based forest models have been widely used to project productivity changes under changing environmental conditions into the future. Based on a review of published simulation results from a large number of process-based models, a synthesis of impacts of environmental change on forest productivity and carbon pools is presented. This synthesis shows that most stand-scale process-based model studies have been carried out in temperate and boreal forests, focusing mostly on monospecific forests with tree species that are relevant for forestry and on analyses of the impacts of climate change and of increasing [CO2] rather than that of other environmental drivers. Forest productivity and biomass carbon pools in these forests mainly respond positively to environmental change especially if the effects of increasing [CO2] are included. If climate change is considered in isolation 61 % of the simulations show positive responses, but 35 % of the simulations show decreasing forest productivity and declining biomass carbon pools. Boreal forests mostly become more productive and sequester more carbon under climate change and increasing [CO2], while temperate and especially Mediterranean forests show more mixed responses depending on the importance of individual environmental driving variables. It is recommended that future modeling studies should increasingly strive to incorporate mixed stands and tropical forests, and include other environmental drivers besides climate and [CO2] to better capture the totality of future changes in forest productivity and carbon pools.

Ammonium fertilization causes a decoupling of ammonium cycling in a boreal forest

Abstract. Boreal forest and tundra are the major ecosystems in the northern high latitudes in which a large amount of carbon is stored. These ecosystems are nitrogen-limited due to slow mineralization rate of the soil organic nitrogen. Recently, abundant field studies have found that organic nitrogen is another important nitrogen supply for boreal forest and tundra ecosystems. In this study, we incorporated a mechanism that allowed boreal plants to uptake small molecular amino acids into a process-based biogeochemical model, the Terrestrial Ecosystem Model (TEM), to evaluate the impact of organic nitrogen uptake on ecosystem carbon cycling. The new version of the model was evaluated for both boreal forest and tundra sites. We found that the modeled organic nitrogen uptake accounted for 36–87% of total nitrogen uptake by plants in tundra ecosystems and 26–50% for boreal forests, suggesting that tundra ecosystem might have more relied on the organic form of nitrogen than boreal forests. The simulated monthly gross ecosystem production (GPP) and net ecosystem production (NEP) tended to be larger with the new version of the model since the plant uptake of organic nitrogen alleviated the soil nitrogen limitation especially during the growing season. The sensitivity study indicated that the most important factors controlling the plant uptake of organic nitrogen was the soil amino acid diffusion coefficient (De) in our model, suggesting that the organic nitrogen uptake by plants is likely to be regulated by the edaphic characteristics of diffusion. The model uncertainty due to uncertain parameters associated with organic nitrogen uptake of the tundra ecosystem was larger than the boreal forest ecosystems. This study suggests that considering the organic nitrogen uptake by plants is important to carbon modeling of boreal forest and tundra ecosystems.

Abstract. Stand-replacing fires are the dominant fire type in North American boreal forests. They leave a historical legacy of a mosaic landscape of different aged forest cohorts. This forest age dynamics must be included in vegetation models to accurately quantify the role of fire in the historical and current regional forest carbon balance. The present study adapted the global process-based vegetation model ORCHIDEE to simulate the CO2 emissions from boreal forest fire and the subsequent recovery after a stand-replacing fire; the model represents postfire new cohort establishment, forest stand structure and the self-thinning process. Simulation results are evaluated against observations of three clusters of postfire forest chronosequences in Canada and Alaska. The variables evaluated include: fire carbon emissions, CO2 fluxes (gross primary production, total ecosystem respiration and net ecosystem exchange), leaf area index, and biometric measurements (aboveground biomass carbon, forest floor carbon, woody debris carbon, stand individual density, stand basal area, and mean diameter at breast height). When forced by local climate and the atmospheric CO2 history at each chronosequence site, the model simulations generally match the observed CO2 fluxes and carbon stock data well, with model-measurement mean square root of deviation comparable with the measurement accuracy (for CO2 flux ~100 g C m−2 yr−1, for biomass carbon ~1000 g C m−2 and for soil carbon ~2000 g C m−2). We find that the current postfire forest carbon sink at the evaluation sites, as observed by chronosequence methods, is mainly due to a combination of historical CO2 increase and forest succession. Climate change and variability during this period offsets some of these expected carbon gains. The negative impacts of climate were a likely consequence of increasing water stress caused by significant temperature increases that were not matched by concurrent increases in precipitation. Our simulation results demonstrate that a global vegetation model such as ORCHIDEE is able to capture the essential ecosystem processes in fire-disturbed boreal forests and produces satisfactory results in terms of both carbon fluxes and carbon-stock evolution after fire. This makes the model suitable for regional simulations in boreal regions where fire regimes play a key role in the ecosystem carbon balance.

Boreal forests are under strong influences from climate change, and alterations in forest dynamics will have significant impacts on global climate‐biosphere feedback as well as local to regional conservation and resource management. To understand the mechanisms of forest dynamics and to assess the fate of boreal forests, simulation studies should be based on plant ecophysiological responses onto environmental conditions. In central Canadian boreal forests, local geomorphology created by past glacial activities often generates a mosaic of very distinctive forest types. On sandy hilltop of a glacial till, due to limitations in moisture availability and short fire return intervals, drought‐tolerant and fire‐adapted jack pine usually becomes the dominant species. On mesic and nutrient‐rich slopes, fast‐growing and resource‐demanding trembling aspen forms mixed forests with coniferous species. In bottomland, black spruce, slowly growing but tolerant species, is often the only species that can survive to the adult stage. These three very distinctive forest types often occur within a scale of 10 m. Simulation models of boreal forests should be able to reproduce this heterogeneity in forest structure and composition as an emergent property of plant ecophysiological responses to varying environmental properties. In this study, a process‐based forest dynamics model, ecosystem demography model version 1.0, is used to mechanically reproduce the landscape heterogeneity due to edaphic variations. First, boreal tree species of northern Manitoba, Canada, are parameterized according to field observations, and, to explicitly capture interactions among tree saplings, allometric equations based on diameter at height of 0.15 m, instead of the conventional breast height of 1.37 m, is parameterized. Then, soil moisture regime and nutrient concentrations are statistically incorporated from a dataset. The resultant simulation successfully reproduces the distinctive forest dynamics influenced by the edaphic heterogeneity. The sequences of succession and the trajectories of forest development are generally consistent with the field observations. The differences in resource availability are the essential control on equilibrium values of total forest leaf area index. Next, to show the effect of anthropogenic atmospheric changes, changes in temperature and CO 2 concentrations are studied by a set of factorial experiments. The magnitude of CO 2 fertilization is largely affected by soil fertility. The temperature rise will increase the length of growing season, but can have a negative impact on forest growth by increasing aridity and autotrophic respiration. Overall, the boreal forest responses to climate change are complex due to the inherent edaphic variations and ecophysiological responses.

We calculated annual mean stem volume increment (AMSVI) and total litter fall to produce forest net primary production (NPP) maps at 1-km 2 and half-degree resolutions in Finland and Sweden. We used a multi-scale methodology to link field inventory data reported at plot and forestry district levels through a remotely sensed total plant biomass map derived from 1-km2 AVHRR image. Total litter fall was estimated as function of elevation and latitude. Leaf litter fall, a surrogate for fine root production, was estimated from total litter fall by forest type. The gridded NPP estimates agreed well with previously reported NPP values, based on point measurements. Regional NPP increases from northeast to southwest. It is positively related to annual mean temperature and annual mean total precipitation (strongly correlated with temperature) and is negatively related to elevation at broad scale. Total NPP (TNPP) values for representative cells se- lected based on three criteria were highly correlated with simulated values from a process-based model (CEVSA) at 0.5∞ ∞ 0.5∞ resolution. At 1-km 2 resolution, mean above-ground NPP in the re- gion was 408 g/m 2 /yr ranging from 172 to 1091 (standard deviation (SD) = 134). Mean TNPP was 563 (252 to 1426, SD = 176). Ranges and SD were reduced while the mean values of the estimated NPP stayed almost constant as cell size in- creased from 1-km 2 to 0.5∞ ∞ 0.5∞, as expected. Nordic boreal forests seem to have lower productivity among the world boreal forests.

BackgroundRising temperatures and altered precipitation patterns are expected to have profound impacts on the composition and condition of boreal forests. As a result there are growing needs for climate adaptation strategies in boreal forest management; one potential solution to achieve these goals is the utilization of nature-based climate-informed adaption solutions including afforestation using deciduous species which can help offset carbon emissions and sequester carbon at an increased rate. Deciduous afforestation has the potential to allow mangers to adapt fire-risk, while increasing carbon storage. Here, we investigated the impact of deciduous compared to coniferous afforestation on biomass accumulation in the Canadian boreal using a process-based model (3-PG). 3-PG utilises physiological principals to predict the growth of individual species across a variety of climate scenarios. This approach is valuable for projecting forest growth under changing climate, as it can account for plant responses to environmental factors which may not be captured by empirical models based on historical data. We simulated forest growth under three future climate scenarios to 2080, and compared the aboveground biomass (AGB, tons of Dry Matter per hectare; tDM ha−1) accumulated to baseline estimates using locally adapted coniferous species. In addition, we investigated the modelled effects of converting from conifer to deciduous species on stand level soil water and vapor pressure deficit responses to climate.ResultsWe found that deciduous simulations sequester more carbon under all climate scenarios, with the greatest difference occurring in the warmest scenario (171 tDM ha−1 for coniferous species compared to 347.1 tDM ha−1 for deciduous species). Coniferous species were generally more water stressed than deciduous species; conifers were generally 65.6% more stressed compared to deciduous species in August under the warmest climate scenario, while northern sites were less stressed than southern sites.ConclusionsSimulations such as these highlight the importance of modelling and consideration of different planting scenarios in decision-making to ensure successful resource allocation. They also demonstrate the potential of nature-based adaptation solutions projects, and the role deciduous afforestation can play in provision of habitat, modifying wildfire risk and northern boreal biomass and timber supply.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13021-025-00385-2.

See the forest not the trees! Ecosystem-based assessment of response, resilience, and scope for growth of global forests

Wildfires significantly change boreal forest ecosystem carbon balance through both direct combustion and post-fire carbon dynamics. Affected vegetation influences soil thermal regime and carbon cycling by impacting the surface energy balance of boreal forests. This study uses a process-based biogeochemistry model to quantify carbon budget of North American boreal forests during 1986–2020 based on satellite-derived burn severity data. During the study period, burn severity generally increases. Fires remove ecosystem carbon of 2.4 Pg C and reduce net ecosystem production (NEP) from 32.6 to 0.8 Tg C yr−1, making the forest ecosystems lose 3.5 Pg C, shifting a carbon sink to a source. The canopy’s cooling effect leads to lower soil temperature and lower net primary production due to lower nitrogen mineralization and uptake. Post-fire NEP decreases from 1.6 to 0.8 Tg C yr−1. This reduction accounts for 50% of the simulated NEP when the effects of fire-affected canopy are not considered. Our study highlights the importance of wildfires and their induced-canopy changes in soil thermal and ecosystem carbon dynamics of boreal forests.