Forest Carbon Storage Research Articles

Earthworm influence on soil aggregate distribution and protected carbon at managed forest sites in Vermont, USA

The long-term effect of non-native earthworm species on forest soil carbon storage is not clear. While initial invasion into earthworm-free soils stimulates carbon losses, there is evidence that carbon stabilization in soil aggregates is enhanced. Fourteen managed forest sites throughout Vermont were sampled to identify and enumerate earthworms, and determine soil aggregate distribution and physically protected carbon. Most of these sites were northern hardwoods reforested in the mid-20th century after agricultural land use. Soil at six 50 × 50 cm subplots at each site was sampled to a depth of 20 cm below the Oe horizon and the mineral soil fractionated into free microaggregates (<250 μm), small macroaggregates (250–2000 μm), and large macroaggregates (>2000 μm). Microaggregates were then isolated from the macroaggregates and organic carbon determined. Mean earthworm numbers below the Oe horizon at each site ranged from 0 to 313 individuals m−2 and ten different species were identified, representing all earthworm ecotypes. Four sites had no earthworms in the soil layer sampled, three sites had earthworms in every subplot with up to six different species, and seven sites had subplots both with and without earthworms. When subplots were grouped by number of species present, there was a trend towards a greater fraction of the mineral soil in macroaggregates containing greater protected carbon, and a lower fraction of free microaggregates. A paired t-test of subplots from the seven sites with variable earthworm presence showed significant differences in the same trends and there was significantly greater mineral soil total carbon and protected carbon in the subplots with earthworms. These results are consistent with recent research into earthworm effects on soil carbon stabilization. While initial invasion likely resulted in a negative carbon balance, our findings suggest that earthworms can enhance transfer of carbon into physically protected pools in these forest soils.

Beijing Forest Carbon Storage Potential Capacity

Forests serve as a crucial carbon reservoir. Therefore, optimizing forest carbon storage is a pathway towards achieving carbon neutrality. In this study, the Forest Simulation Optimization System (FSOS) was used to simulate the carbon storage in Beijing forests over 250 years (2018-2268). It was found that under the no management scenario, carbon storage fluctuates with the natural growth and death of trees, with peaks of more than 90 million tons. It proves that forests have a strong capacity of carbon storage. In the management scenario, harvest trees and make them into furniture, total carbon storage is high and maintains a stable level of 108 million tons. This is almost 1.6 times higher than in the no management scenario on average. In addition, the growth rate of carbon storage is fastest in the middle-aged forest and the near-mature forest. Therefore, in order to optimize the carbon sequestration benefits of forests, the forestry sector must pay attention to the age structure of forests in the future. Based on the results of this study, recommendations were made to optimize carbon storage in Beijing forests and to integrate forest managements of Beijing forests into regional economic and environmental planning.

Using Knowledge Graphs to Analyze the Characteristics and Trends of Forest Carbon Storage Research at the Global Scale

Research on forest carbon storage (FCS) is crucial for the sustainable development of human society given the context of global climate change. Previous FCS studies formed the science base of the FCS field but lacked a macrolevel knowledge summary. This study combined the scientometric mapping tool VOSviewer and multiple statistical models to conduct a comprehensive knowledge graph mining and analysis of global FCS papers (covering 101 countries, 1712 institutions, 5435 authors, and 276 journals) in the Web of Science database as of 2022, focusing on revealing the macro spatiotemporal pattern, multidimensional research status, and topic evolution process of FCS research at the global scale, so as to grasp the status of global FCS research more clearly and comprehensively, thereby facilitating the future decision-making and practice of researchers. The results showed the following: (1) In the past three decades, the number of FCS papers indicated an increasing trend, with a growth rate of 4.66/yr, particularly significant after 2010. These papers were mainly from Europe, the Americas, and Asia, while there was a huge gap between Africa, Oceania, and the above regions. (2) For the research status at the national, institutional, scholar, and journal levels, the USA, with 331 FCS papers and 18,653 total citations, was the most active and influential country in global FCS research; the United States Forest Service topped the influential ranking with 4115 citations; Grant M. Domke and Jerome Chave were the most active and influential FCS researchers globally, respectively. China’s activity (237 papers) and influence (5403 citations) ranked second, and the Chinese Academy of Sciences was the most active research institution in the world. Currently, FCS research is published in a growing number of journals, among which Forest Ecology and Management ranked first in the number of papers (154 papers) and citations (6374 citations). (3) In recent years, the keyword frequency of monitoring methods, driving factors, and reasonable management for FCS has increased rapidly, and many new related keywords have emerged, which means that researchers are not only focusing on the estimation and monitoring of FCS but also increasingly concerned about its driving mechanism and sustainable development.

Forest Carbon Storage in the Western United States: Distribution, Drivers, and Trends

Forest Carbon Storage in the Western United States: Distribution, Drivers, and Trends

Controversies and Misconceptions related to Forest Carbon Storage and the Policy of Forest Bioenergy and Sustainability

The present commentary elaborates on the question whether the sequestration of carbon in forests can be interpreted as a renewable form of energy. Serious concerns have been raised regarding this classification of forest wood among the alternatives for fossil carbon reserves. The hypothesis of (Zhao et al. 2022), namely that taller forests (as a volumetric measure) procure a larger C-sequestration (storage) to the planet’s biosphere, not only is extrapolated to all latitudes. Moreover, a number of 10 misconceptions is envisaged and critically answered, that are well-known from discourses of deforestation lobbyists. Finally, a case study is presented of the impact of deforestation and landscape reforms on bird biodiversity in the Netherlands, measured using a newly developed quick-scan for bird diversity in European temperate regions. It is observed that with broadening and increasing the impact of the political decision making process, the risk of generating bigger ecological mistakes may increase too.

Effects of Soil Microorganisms on Carbon Sequestration under Different Mixed Modification Models in Pinus massoniana L. Plantation

In forests, microbial populations in the soil can directly influence the decomposition of carbon from surface plants, promoting carbon storage and stability. However, in sustainable forest management, it is still unclear how soil microorganisms under different plantation types affect organic carbon sequestration and whether the mechanisms of influence are the same. In this research, we focused on four mixed forests and pure Pinus massoniana-planted forest in the state-owned forest farm of Dushan County. Three replicated plots were set up for each model, and soil samples were collected from different layers (0–20 cm, 20–40 cm, and 40–60 cm), totaling 45 samples. We elucidated the effects of soil microorganisms on carbon sequestration under five mixed modification models of P. massoniana and further explored the mechanisms by which microbial functional communities regulate soil carbon sequestration under different mixed models through molecular sequencing and collinear network analysis. Variance analysis indicated that the soil organic carbon (SOC) of the same soil layer varied significantly, and there were also significant differences in the composition of soil bacterial and fungal microbial communities. Moreover, the bacterial community was more sensitive to changes in the vegetation environment, while the fungal community structure was more resistant to changes in the soil environment. Correlation analysis indicated that the diversity and composition of the bacterial community had more positive effects on soil organic carbon than those of the fungal community. Linear fitting and redundancy analysis (RDA) showed that particulate organic carbon (POC) in soil had the strongest correlation with SOC content. Soil microorganisms affected the storage and stability of soil carbon mainly by regulating the conversion of litter (carbon sources) into POC. The soil environment of different mixed models had different effects on soil carbon accumulation. Both correlation and collinearity network analyses indicated that soil microbial functional groups could enhance carbon storage by regulating readily oxidizable carbon (EOC) and POC content in mixed forest plantations. The results of our study provide a sound basis for replanting a reasonable forest model structure to improve forest carbon storage.

The spatial distribution of tree-tree interaction effects on soil microbial biomass and respiration.

The capacity of forests to sequester carbon in both above- and belowground compartments is a crucial tool to mitigate rising atmospheric carbon concentrations. Belowground carbon storage in forests is strongly linked to soil microbial communities that are the key drivers of soil heterotrophic respiration, organic matter decomposition and thus nutrient cycling. However, the relationships between tree diversity and soil microbial properties such as biomass and respiration remain unclear with inconsistent findings among studies. It is unknown so far how the spatial configuration and soil depth affect the relationship between tree richness and microbial properties. Here, we studied the spatial distribution of soil microbial properties in the context of a tree diversity experiment by measuring soil microbial biomass and respiration in subtropical forests (BEF-China experiment). We sampled soil cores at two depths at five locations along a spatial transect between the trees in mono- and hetero-specific tree pairs of the native deciduous species Liquidambar formosana and Sapindus saponaria. Our analyses showed decreasing soil microbial biomass and respiration with increasing soil depth and distance from the tree in mono-specific tree pairs. We calculated belowground overyielding of soil microbial biomass and respiration - which is higher microbial biomass or respiration than expected from the monocultures - and analysed the distribution patterns along the transect. We found no general overyielding across all sampling positions and depths. Yet, we encountered a spatial pattern of microbial overyielding with a significant microbial overyielding close to L. formosana trees and microbial underyielding close to S. saponaria trees. We found similar spatial patterns across microbial properties and depths that only differed in the strength of their effects. Our results highlight the importance of small-scale variations of tree-tree interaction effects on soil microbial communities and functions and are calling for better integration of within-plot variability to understand biodiversity-ecosystem functioning relationships.

The effects of environmental variability and forest management on natural forest carbon stock in northwestern Ethiopia

AbstractNatural forests are crucial for climate change mitigation and adaptation, but deforestation and degradation challenges highly reduce their value. This study evaluates the potential of natural forest carbon stock and the influence of management interventions on enhancing forest carbon storage capacity. Based on forest area cover, a study was conducted in nine purposely selected forest patches across various forest ecosystems. Data on diameter, height, and environmental variables from various forest management approaches were collected and analyzed with R Ver. 4.1. The findings revealed a substantial difference (p .029) in carbon stock between environmental variables and management interventions. The findings revealed a strong connection between environmental variables and the overall pool of carbon stock within forest patches (p .029). Carbon stocks were highest in the Moist‐montane forest ecosystem (778.25 ton/ha), moderate slope (1019.5 ton/ha), lower elevation (614.50 ton/ha), southwest‐facing (800.1 ton/ha) and area exclosures (993.2 ton/ha). Accordingly, natural forests, particularly unmanaged parts, are sensitive to anthropogenic stresses, decreasing their ability to efficiently store carbon. As a result, the study highlighted the importance of sustainable forest management, particularly area exclosures and participatory forest management, in increasing forest carbon storage potential.

Carbon storage through China’s planted forest expansion

China’s extensive planted forests play a crucial role in carbon storage, vital for climate change mitigation. However, the complex spatiotemporal dynamics of China’s planted forest area and its carbon storage remain uncaptured. Here we reveal such changes in China’s planted forests from 1990 to 2020 using satellite and field data. Results show a doubling of planted forest area, a trend that intensified post-2000. These changes lead to China’s planted forest carbon storage increasing from 675.6 ± 12.5 Tg C in 1990 to 1,873.1 ± 16.2 Tg C in 2020, with an average rate of ~ 40 Tg C yr−1. The area expansion of planted forests contributed ~ 53% (637.2 ± 5.4 Tg C) of the total above increased carbon storage in planted forests compared with planted forest growth. This proactive policy-driven expansion of planted forests has catalyzed a swift increase in carbon storage, aligning with China’s Carbon Neutrality Target for 2060.

Assessment of Phytomass and Carbon Stock in the Ecosystems of the Central Forest Steppe of the East European Plain: Integrated Approach of Terrestrial Environmental Monitoring and Remote Sensing with Unmanned Aerial Vehicles.

The rapid and accurate estimation of aboveground forest phytomass remains a challenging research task. In general, methods for estimating phytomass fall mainly into the category of field measurements performed by ground-based methods, but approaches based on remote sensing and ecological modelling have been increasingly applied. The aim is to develop the scientific and methodological framework for the remote sensing estimation of qualitative and quantitative characteristics of forest stands, using the combination of surveys and machine learning models to determine phytomass of forest stands and calculate the carbon balance. Even-aged stands of different tree species growing in the forest steppe zone of the East European Plain were chosen as test objects. We have applied the modernized methodological approaches to compare and integrate forest and tree stand characteristics obtained by ground-based and UAV-based comprehensive surveys; additionally, we developed computer vision models and methods for determining the same characteristics by remote sensing methods. The key advantage of the proposed methodology for remote monitoring and carbon balance control over existing analogues is the minimization of the amount of groundwork and, consequently, the reduction inlabor costs without loss of information quality. Reliable data on phytomass volumes will allow for operational control of the forest carbon storage, which is essential for decision-making processes. This is important for the environmental monitoring of forests and green spaces of various economic categories. The proposed methodology is necessary for the monitoring and control of ecological-climatic and anthropogenic-technogenic transformations in various landscapes. The development is useful for organizing the management of ecosystems, environmental protection, and managing the recreational and economic resources of landscapes with natural forests and forest plantations.

Spatio-Temporal Analysis of Carbon Storage in Urban Areas After Wildfires: The Case of Marmaris Fire

Cities and urban areas are the primary source of CO2 worldwide by using around 70% of global energy and emitting more than 71% of CO2. Urban vegetation, referring to all trees and shrubs, are important components of urban environments. They provide many ecosystem services to human beings both directly and indirectly. Especially, they play a key role in reducing carbon emissions in urban areas by storing and capturing the carbon. However, recently, an increase in the number and intensity of wildfires that occur within urban areas has been observed. It resulted in losing stored carbon, releasing GHG to the atmosphere. Hence, quantifying above-ground carbon stored by urban trees and its distribution is essential to better understanding urban vegetation's role in urban environments and to better urban vegetation management. This study aimed to examine how forest fire affects the amount and distribution of stored carbon in the urban environment for the case of the Marmaris fire in the Summer of 2021 in Türkiye. For the study, urban forest carbon storage maps were generated before and after the Marmaris forest fire using remote sensing-based methodology with freely available remote sensing (RS) data. The results indicated that using the existing methodology could be rapid and cost-effective in monitoring the carbon storage change after an anthropogenic and natural disaster. However, for precise and reliable estimation of total carbon storage and the change in total urban carbon storage, the methodology needs to be developed at a local scale using field sampling along with RS data.

Extracting the DBH of Moso Bamboo Forests Using LiDAR: Parameter Optimization and Accuracy Evaluation

The accurate determination of the Diameter at Breast Height (DBH) of Moso bamboo is crucial for estimating biomass and carbon storage in Moso bamboo forests. In this research, we utilized handheld LiDAR point cloud data to extract the DBH of Moso bamboo and enhanced the accuracy of diameter fitting by optimizing denoising parameters. Specifically, we fine-tuned two denoising parameters, neighborhood point number and standard deviation multiplier, across five gradient levels for denoising. Subsequently, DBH fitting was conducted on data processed with varying denoising parameters, followed by a precision evaluation to investigate the key factors influencing the accuracy of Moso bamboo DBH fitting. The research results indicate that a handheld laser was used to scan six plots, from which 132 single Moso bamboo trees were selected. Out of these, 122 single trees were successfully segmented and identified, achieving an accuracy rate of 92.4% in identifying single Moso bamboo trees, with an average accuracy of 95.64% in extracting DBH for individual plants; the mean error was ±1.8 cm. Notably, setting the minimum neighborhood point to 10 resulted in the highest fitting accuracy for DBH. Moreover, the optimal standard deviation multiplier threshold was found to be 1 in high-density forest plots and 2 in low-density forest plots. Forest condition and slope were identified as the primary factors impacting the accuracy of Moso bamboo DBH fitting.

Organic soil greenhouse gas flux rates in hemiboreal old-growth Scots pine forests at different groundwater levels

Tree biomass and soils (especially organic soils) are significant carbon pools in forest ecosystems, therefore forest management practices, in order to ensure carbon storage in these pools and to mitigate climate change, are essential in reaching climate neutrality goals set by the European Union. Overall studies have focused on diverse aspects of forest carbon storage and greenhouse gas (GHG) fluxes from mineral soils, and recently also from organic soils. However, the information about old-growth forests and the long-term effects of drainage on GHG fluxes of organic soils is missing. Additionally, a large proportion of Scots pine (Pinus sylvestris L.) forests on organic soils in the hemiboreal region are drained to regulate groundwater level and to improve above-ground carbon storage. The study aims to assess the intra-annual dynamics of soil carbon dioxide (CO2) and methane (CH4) fluxes in hemiboreal old-growth Scots pine stands on organic soils with diverse groundwater levels. Six old-growth stands (130–180 years old) were evaluated. In old-growth forests, the main source of soil CO2 emissions is ground vegetation and tree roots (autotrophic respiration), while heterotrophic respiration contributes to almost half (41%) of the total forest floor ecosystem (soil) respiration. The total forest floor respiration and soil heterotrophic respiration are mainly affected by soil temperature, with minor but statistically significant contribution of groundwater level (model R2 = 0.78 and R2 = 0.56, respectively). The CO2 fluxes have a significant, yet weak positive relationship with groundwater level (RtCO2 R2 = 0.06 RhCO2 R2 = 0.08). In contrast, total soil CH4 uptake or release depends primarily on groundwater level fluctuations, with a minor but significant contribution of soil temperature (model R2 = 0.67). CH4 flux has high variability between stands.

Interacting Sentinel-2A, Sentinel 1A, and GF-2 Imagery to Improve the Accuracy of Forest Aboveground Biomass Estimation in a Dry-Hot Valley

Carbon absorption and storage in forests is one of the important ways to mitigate climate change. Therefore, it is essential to use a variety of remote-sensing resources to accurately estimate forest aboveground biomass (AGB) in dry-hot valley regions. In this study, satellite images from the Sentinel-1A, Sentinel-2A, and Gaofen-2 satellites were utilized to estimate the forest AGB in Yuanmou County, Yunnan Province, China. Different combinations of image data, based on selected variables of stepwise regression and their performance in constructing linear stepwise regression (LSR) and random forest (RF) models, were explored. The results showed that: (1) after adding the polarized values of the synthetic aperture radar backscatter coefficients, the combination fitting effect was significantly improved; (2) the fitting effect of the Sentinel-1A + Sentinel-2A + Gaofen-2 data combination was superior to the other combinations, indicating that the effective extraction of forest horizon and vertical information can improve the estimation effect of the forest AGB; and (3) the RF model exhibited superior fitting performance compared to the LSR model across all permutations of remotely sensed image datasets, with R2 values of 0.71 and 0.65, and RMSE values of 30.67 and 33.79 Mg/ha, respectively. These findings lay the groundwork for enhancing the precision of AGB estimation in dry-hot valley areas by integrating Sentinel-2A, Sentinel-1A, and GF-2 imagery, providing valuable insights for future research and applications.

The distribution of tree biomass carbon within the Pacific Coastal Temperate Rainforest, a disproportionally carbon dense forest

Spatially explicit global estimates of forest carbon storage are typically coarsely scaled. While useful, these estimates do not account for the variability and distribution of carbon at management scales. We asked how climate, topography, and disturbance regimes interact across and within geopolitical boundaries to influence tree biomass carbon, using the perhumid region of the Pacific Coastal Temperate Rainforest, an infrequently disturbed carbon dense landscape, as a test case. We leveraged permanent sample plots in southeast Alaska and coastal British Columbia and used multiple quantile regression forests and generalized linear models to estimate tree biomass carbon stocks and the effects of topography, climate, and disturbance regimes. We estimate tree biomass carbon stocks are either 211 (SD = 163) Mg C ha−1 or 218 (SD = 169) Mg C ha−1. Natural disturbance regimes had no correlation with tree biomass but logging decreased tree biomass carbon and the effect diminished with increasing time since logging. Despite accounting for 0.3% of global forest area, this forest stores between 0.63% and 1.07% of global aboveground forest carbon as aboveground live tree biomass. The disparate impact of logging and natural disturbance regimes on tree biomass carbon suggests a mismatch between current forest management and disturbance history.

Analyzing temporal and spatial forest carbon storage using Google Plus Code: a case study of Zijin Mountain National Forest Park, China.

It is always a challenging job to compare forest resources as there is not a standardized spatial unit with location information. Google Plus Code, the newest alphanumeric geocoding system, uses 20 specifically selected letters and numbers to assign a unique global ID to every cell at different levels of a hierarchical grid system which is established based on latitude and longitude. It can be used as a standardized, unique global geospatial unit to segment, locate, quantitate, evaluate, and compare natural resources with area, boundary, and location information embedded. For this proof-of-concept case study, forest inventory data from 1987, 2002, and 2019 for the Zijin Mountain National Forest Park in Jiangsu Province, China was analyzed based on Google Plus Code grid/cell. This enabled the quantification of carbon storage at each cell allowing for the comparison of estimated carbon storage at same or different locations over time. This methodology is used to quantify the impacts of changing forest conditions and forest management activities on carbon storage with high spatial accuracy through the 32-year study period. Furthermore, this technique could be used for providing technical support and validation of carbon credit quantification and management.

Remote Sensing Estimation of Forest Carbon Stock Based on Machine Learning Algorithms

Improving the precision of remote sensing estimation and implementing the fusion and analysis of multi-source data are crucial for accurately estimating the aboveground carbon storage in forests. Using the Google Earth Engine (GEE) platform in conjunction with national forest resource inventory data and Landsat 8 multispectral remote sensing imagery, this research applies four machine learning algorithms available on the GEE platform: Random Forest (RF), Classification and Regression Trees (CART), Gradient Boosting Trees (GBT), and Support Vector Machine (SVM). Using these algorithms, the entire Yunnan Province is classified into seven categories, including broadleaf forest, coniferous forest, mixed broadleaf-coniferous forest, water bodies, built-up areas, cultivated land, and other types. After a thorough comparison, the research reveals that the RF algorithm surpasses others in terms of accuracy and reliability, making it the most suitable choice for estimating aboveground carbon storage in forests using remote sensing data. Therefore, the study used the RF algorithm for both forest classification and the estimation of carbon storage. By extracting remote sensing factors; by using the Pearson correlation coefficient to select the most relevant factors; and by utilizing multiple linear regression, RF regression, and decision tree regression, a model for estimating aboveground carbon stocks in forests was developed. The results indicate that among the four classification algorithms, the RF classifier demonstrates superior performance, with an overall accuracy of 84.96% and a Kappa coefficient of 76.46%. In the RF regression models, the R2 values for the coniferous forest, broadleaf forest, and mixed needle-broadleaf forest models are 0.636, 0.663, and 0.638, respectively. In both RF and CART, the R2 values for the three forest-type models are greater than 0.6, indicating satisfactory model fitting performance. This study aims to explore the possibility of improving the estimation of forest carbon stocks in large-scale areas through fine land use classification. Additionally, the data sources used are completely free, and medium to low resolution can provide a better reference value for practical applications, thereby reducing the cost of utilization.

Demographic change and loss of big trees in resprouting eucalypt forests exposed to megadisturbance

AbstractAimIncreased tree mortality linked to droughts and fires is occurring across temperate regions globally. Vegetation recovery has been widely reported; however, less is known about how disturbance may alter forests structurally and functionally across environmental gradients. We examined whether dry forests growing on low‐fertility soils were more resilient to coupled extreme drought and severe fire owing to lower tree mortality rates, higher resprouting success and persistence of juveniles relative to wetter forests on more fertile soils.LocationFire‐tolerant eucalypt forests of temperate southeastern Australia.Time period2020–2023.Major taxa studiedEucalyptus, Corymbia, Angophora.MethodsDemographic surveys of tree mortality and regeneration in all combinations of dry/wet forest, fertile/less fertile substrates exposed to extreme drought and fire were conducted. We used Bayesian regression modelling to compare tree mortality, diameter, response traits, population structure and occurrence of fire scars between substrates/forest types.ResultsOverall mortality (20%–33%) and topkill (34%–41%) were within historically reported ranges for various forests and soil types. However, we observed an atypical trend of increased mortality and topkill in the largest trees, particularly when they had structural damage from past fires. Trees in wet forests on more fertile soils had the highest levels of mortality. Numbers of persistent resprouting juveniles were highest in dry forests on low‐fertility soils. Dry forests growing on low‐fertility soils appear more resilient to compound disturbances due to lower rates of mortality and higher rates of juvenile persistence. Wet forests on more fertile soils may experience greater demographic change due to higher mortality of small and large trees.Main conclusionsMesic forests on relatively fertile soils were found to be at relatively high risk of demographic change from compound disturbances. Combined, fire and drought are likely to reduce the number of large trees in affected areas, with consequences for forest carbon cycling and storage.

Forest carbon storage and sink estimates under different management scenarios in China from 2020 to 2100

Forests play a crucial role in mitigating climate change through carbon storage and sequestration, though environmental change drivers and management scenarios are likely to influence these contributions across multiple spatial and temporal scales. In this study, we employed three tree growth models—the Richard, Hossfeld, and Korf models—that account for the biological characteristics of trees, alongside national forest inventory (NFI) datasets from 1994 to 2018, to evaluate the carbon sink potential of existing forests and afforested regions in China from 2020 to 2100, assuming multiple afforestation and forest management scenarios. Our results indicate that the Richard, Hossfeld, and Korf models provided a good fit for 26 types of vegetation biomass in both natural and planted Chinese forests. These models estimate that in 2020, carbon stocks in existing Chinese forests are 7.62 ± 0.05 Pg C, equivalent to an average of 44.32 ± 0.32 Mg C/ ha. Our predictions then indicate this total forest carbon stock is expected to increase to 15.51 ± 0.99 Pg C (or 72.26 ± 4.6 Mg C/ha) in 2060, and further to 19.59 ± 1.36 Pg C (or 91.31 ± 6.33 Mg C/ha) in 2100. We also show that plantation management measures, namely tree species replacement, would increase carbon sinks to 0.09 Pg C/ year (contributing 38.9 %) in 2030 and 0.06 Pg C/ year (contributing 32.4 %) in 2060. Afforestation using tree species with strong carbon sink capacity in existing plantations would further significantly increase carbon sinks from 0.02 Pg C/year (contributing 10.3 %) in 2030 to 0.06 Pg C/year (contributing 28.2 %) in 2060. Our results quantify the role plantation management plays in providing a strong increase in forest carbon sequestration at national scales, pointing to afforestation with native tree species with high carbon sequestration as key in achieving China's 2060 carbon neutrality target.

Prescribed fire placement matters more than increasing frequency and extent in a simulated Pacific Northwest landscape

AbstractPrescribed fire has been increasingly promoted to reduce wildfire risk and restore fire‐adapted ecosystems. Yet, the complexities of forest ecosystem dynamics in response to disturbances, climate change, and drought stress, combined with myriad social and policy barriers, have inhibited widespread implementation. Using the forest succession model LANDIS‐II, we investigated the likely impacts of increasing prescribed fire frequency and extent on wildfire severity and forest carbon storage at local and landscape scales. Specifically, we ask how much prescribed fire is required to maintain carbon storage and reduce the severity and extent of wildfires under divergent climate change scenarios? We simulated four prescribed fire scenarios (no prescribed fire, business‐as‐usual, moderate increase, and large increase) in the Siskiyou Mountains of northwest California and southwest Oregon. At the local site scale, prescribed fires lowered the severity of projected wildfires and maintained approximately the same level of ecosystem carbon storage when reapplied at a ~15‐year return interval for 50‐year simulations. Increased frequency and extent of prescribed fire decreased the likelihood of aboveground carbon combustion during wildfire events. However, at the landscape scale, prescribed fire did not decrease the projected severity and extent of wildfire, even when large increases (up to 10× the current levels) of prescribed fire were simulated. Prescribed fire was most effective at reducing wildfire severity under a climate change scenario with increased temperature and precipitation and on sites with north‐facing aspects and slopes greater than 30°. Our findings suggest that placement matters more than frequency and extent to estimate the effects of prescribed fire, and that prescribed fire alone would not be sufficient to reduce the risk of wildfire and promote carbon sequestration at regional scales in the Siskiyou Mountains. To improve feasibility, we propose targeting areas of high concern or value to decrease the risk of high‐severity fire and contribute to meeting climate mitigation and adaptation goals. Our results support strategic and targeted landscape prioritization of fire treatments to reduce wildfire severity and increase the pace and scale of forest restoration in areas of social and ecological importance, highlighting the challenges of using prescribed fire to lower wildfire risk.