Changes In Carbon Stocks Research Articles

Prolonged impacts of extreme precipitation events weakened annual ecosystem CO2 sink strength in a coastal wetland

It remains unclear whether coastal wetlands could maintain the expected carbon sink role since extreme climate events are predicted to occur more frequently under future climate change. Among these extreme events, extreme precipitation may cause significant changes in ecosystem carbon stocks in coastal wetlands. However, the impacts of extreme precipitation events on carbon cycle processes and the mechanisms responsible for associated changes in the ecosystem carbon balance remain uncertain. To address this issue, we investigated the effects of extreme precipitation events on the ecosystem carbon dioxide (CO2) budget based on a 10-year eddy covariance dataset (2010-2019) at a coastal wetland in the Yellow River Delta. Results showed that this coastal wetland was a stable sink for CO2, with the annual net ecosystem CO2 exchange (NEE) ranged between -94.49 and -240.70 g C m−2 yr−1. Meanwhile, the interannual variability of the ecosystem CO2 sink strength in the dry stage was linked to the vegetation condition, and it was strongly affected by extreme precipitation events in the wet stage. During the wet stage, there was a clear trend of less negative daily NEE (i.e., lower CO2 uptake rate) in the years with extreme precipitation events occurring (extreme years) than in normal years, leading to the wet stage cumulative CO2 uptake being 56% lower in extreme years. Eventually, the coastal wetland became a weaker annual CO2 sink due to the prolonged impact of the extreme precipitation event. This research provides a timely study of the effect of extreme precipitation on ecosystem CO2 budget in coastal wetlands and should be of value to researchers attempting to assess the future impact of climate change on coastal ecosystems.

Global sensitivities of forest carbon changes to environmental conditions.

The responses of forest carbon dynamics to fluctuations in environmental conditions at a global scale remain elusive. Despite the understanding that favourable environmental conditions promote forest growth, these responses have been challenging to observe across different ecosystems and climate gradients. Based on a global annual time series of aboveground biomass (AGB) estimated from radar satellites between 1992 and 2018, we present forest carbon changes and provide insights on their sensitivities to environmental conditions across scales. Our findings indicate differences in forest carbon changes across AGB classes, with regions with carbon stocks of 50-125MgC ha-1 depict the highest forest carbon gains and losses, while regions with 125-150MgC ha-1 have the lowest forest carbon gains and losses in absolute terms. Net forest carbon change estimates show that the arc-of-deforestation and the Congo Basin were the main hotspots of forest carbon loss, while a substantial part of European forest gained carbon during the last three decades. Furthermore, we observe that changes in forest carbon stocks were systematically positively correlated with changes in forest cover fraction. At the same time, it was not necessarily the case with other environmental variables, such as air temperature and water availability at the bivariate level. We also used a model attribution method to demonstrate that atmospheric conditions were the dominant control of forest carbon changes (56% of the total study area) followed by water-related (29% of the total study area) and vegetation (15% of the total study area) conditions. Regionally, we find evidence that carbon gains from long-term forest growth covary with long-term carbon sinks inferred from atmospheric inversions. Our results describe the contributions from the atmosphere, water-related and vegetation conditions to forest carbon changes and provide new insights into the underlying mechanisms of the coupling between forest growth and the global carbon cycle.

Estimasi Cadangan Karbon Akibat Perubahan Tutupan Lahan di Kabupaten Kendal

Greenhouse gas emissions are the main cause of global warming through the Green House Effect event. This occurs due to an increase in greenhouse gas emissions from various emission sources, one of which is land cover change. Land cover on the earth's surface has a fixed carbon stock value that contributes to storing carbon stocks through its absorption capacity. So, if there is a change in land cover from one land cover to another, it will cause the carbon stock of a land cover to change. Research on changes in carbon stocks due to land cover changes were carried out in Kendal Regency. This study uses Landsat image data from 2008, 2013, and 2018. The method used is image interpretation with guided classifications and Stock Difference for calculating changes in carbon stocks. The results obtained from this study are the changes in land cover in Kendal Regency in 2008-2018. The occurrence of land cover conversion will affect the carbon stock stored in each type of land cover in the Kendal Regency. Kendal Regency's carbon stock for 10 years has decreased. In the period 2008-2013, the total decline in carbon stocks was -4,305,193.29 tons C. This situation was caused because land cover in 2008 had more total carbon stock than the total carbon stock in 2013. Meanwhile, in the 2013- period 2018, there was a change in the carbon stock of -1,450,080.51 ton C, where the land cover in 2013 had more total carbon stock compared to the total carbon stock stored in 2018. The difference in total carbon stock in a certain year is influenced by the area of land cover as well as the determination of the carbon stock that each land cover has. The results of calculations regarding the estimated carbon stock in the Kendal Regency can then be considered for stakeholders and policymakers to develop plans and scenarios (land use directions) that support the mitigation of land-based greenhouse gas emissions to address environmental problems.

Changes in Soil Organic Carbon Concentration and Stock after Forest Regeneration of Agricultural Fields in Taiwan

Afforestation or abandonment of agricultural fields to forest regeneration is a method of sequestering carbon to offset the increasing atmospheric concentration of CO2. We selected 11 sites with altitudes ranging from 14 to 2056 m and with paired forest regenerated and adjacent agricultural fields. Our objectives were to (1) examine the changes in soil organic carbon (SOC) concentration and stock after forest regeneration of agricultural fields and (2) identify the factors related to elevation and adjacent agricultural practices that affect the SOC accumulation rate. Our results demonstrated overall increases in both SOC concentrations and stocks after forest regeneration of the abandoned agricultural fields. The average increase rates of SOC concentrations in the forest regenerated soil samples were 1.65 and 0.95 g C kg−1 at 0–10 and 10–20 cm depths, respectively, representing 101% and 65% increases relative to those in the soil samples from agricultural fields. The average accumulation rates of SOC stocks in the regenerated forests were 13.0 and 6.7 ton C ha−1 at the 0–10 and 10–20 cm depths, respectively, representing 96% and 62% increases relative to those in the agricultural soil samples. The average annual sequestration rate was 1.03 Mg C ha−1 year−1 for the top 0–20 cm soils, which is greater than that observed by previous reviews and meta-analyses. The tropical/subtropical climate, sampling soil depth, forest regeneration period, and tree species in this study are likely to have contributed to the high average SOC accumulation levels. In addition, the SOC stock accumulation rates were higher at low-elevation sites than at middle-elevation sites, which could also be attributed to the favorable climatic conditions at the low-elevation sites. Along with the build-up of carbon sequestration in the forest floor and tree biomass, the afforestation/abandonment of agricultural fields to forest regeneration appears to be a promising carbon offset mechanism.

Accelerated Sea‐Level Rise Limits Vegetation Capacity to Sequester Soil Carbon in Coastal Wetlands: A Study Case in Southeastern Australia

AbstractEstimates of global carbon stocks in coastal wetlands reveal that these are some of the most efficient carbon‐sequestering environments in the world, which has prompted a renewed interest in conservation and restoration programs as an opportunity for greenhouse gas abatement. Accumulation of carbon in coastal wetlands is linked to diverse factors such as the type of vegetation, geomorphic setting, and sediment supply. Feedbacks between these factors and the tidal flow conditions drive the dynamics of carbon accumulation rates. Climate change‐induced sea‐level rise has been shown to increase the vulnerability to submergence of saltmarsh and mangroves in coastal wetlands, even if accommodation and landward colonization are possible. These potential losses of wetland vegetation combined with the reduced productivity of newly colonized areas will directly affect the capacity of the wetlands to sequester carbon from sediments and root growth. Here, we implement an eco‐geomorphic model to simulate vegetation dynamics, soil carbon accumulation, and changes in soil carbon stock for a restored mangrove‐saltmarsh wetland experiencing accelerated sea‐level rise. We evaluate model outcomes for existing conditions and two different management scenarios aimed at mitigating sea‐level rise effects and conserve wetland vegetation. Even though some management measures can result in partial conservation of wetland vegetation, they do not necessarily result in the best option for soil carbon capture. Our results suggest that accelerated sea‐level can trigger accelerated wetland colonization resulting in wetland areas with limited opportunities for soil carbon capture from sediment and root mineralization, an issue that has not been considered in previous studies.

Two large-scale forest scenario modelling approaches for reporting CO2 removal: a comparison for the Romanian forests

BackgroundForest carbon models are recognized as suitable tools for the reporting and verification of forest carbon stock and stock change, as well as for evaluating the forest management options to enhance the carbon sink provided by sustainable forestry. However, given their increased complexity and data availability, different models may simulate different estimates. Here, we compare carbon estimates for Romanian forests as simulated by two models (CBM and EFISCEN) that are often used for evaluating the mitigation options given the forest-management choices.ResultsThe models, calibrated and parameterized with identical or harmonized data, derived from two successive national forest inventories, produced similar estimates of carbon accumulation in tree biomass. According to CBM simulations of carbon stocks in Romanian forests, by 2060, the merchantable standing stock volume will reach an average of 377 m3 ha−1, while the carbon stock in tree biomass will reach 76.5 tC ha−1. The EFISCEN simulations produced estimates that are about 5% and 10%, respectively, lower. In addition, 10% stronger biomass sink was simulated by CBM, whereby the difference reduced over time, amounting to only 3% toward 2060.ConclusionsThis model comparison provided valuable insights on both the conceptual and modelling algorithms, as well as how the quality of the input data may affect calibration and projections of the stock and stock change in the living biomass pool. In our judgement, both models performed well, providing internally consistent results. Therefore, we underline the importance of the input data quality and the need for further data sampling and model improvements, while the preference for one model or the other should be based on the availability and suitability of the required data, on preferred output variables and ease of use.

The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations

Abstract. The terrestrial forest carbon pool is poorly quantified, in particular in regions with low forest inventory capacity. By combining multiple satellite observations of synthetic aperture radar (SAR) backscatter around the year 2010, we generated a global, spatially explicit dataset of above-ground live biomass (AGB; dry mass) stored in forests with a spatial resolution of 1 ha. Using an extensive database of 110 897 AGB measurements from field inventory plots, we show that the spatial patterns and magnitude of AGB are well captured in our map with the exception of regional uncertainties in high-carbon-stock forests with AGB >250 Mg ha−1, where the retrieval was effectively based on a single radar observation. With a total global AGB of 522 Pg, our estimate of the terrestrial biomass pool in forests is lower than most estimates published in the literature (426–571 Pg). Nonetheless, our dataset increases knowledge on the spatial distribution of AGB compared to the Global Forest Resources Assessment (FRA) by the Food and Agriculture Organization (FAO) and highlights the impact of a country's national inventory capacity on the accuracy of the biomass statistics reported to the FRA. We also reassessed previous remote sensing AGB maps and identified major biases compared to inventory data, up to 120 % of the inventory value in dry tropical forests, in the subtropics and temperate zone. Because of the high level of detail and the overall reliability of the AGB spatial patterns, our global dataset of AGB is likely to have significant impacts on climate, carbon, and socio-economic modelling schemes and provides a crucial baseline in future carbon stock change estimates. The dataset is available at https://doi.org/10.1594/PANGAEA.894711 (Santoro, 2018).

Estimating soil organic carbon stock change at multiple scales using machine learning and multivariate geostatistics

Many national and international initiatives rely on spatially explicit information on soil organic carbon (SOC) stock change at multiple scales to support policies aiming at land degradation neutrality and climate change mitigation. In this study, we used regression cokriging with random forest and spatial stochastic cosimulation to predict the SOC stock change between two years (i.e. 1992 and 2010) in Hungary at multiple aggregation levels (i.e. point support, 1 × 1 km, 10 × 10 km square blocks, Hungarian counties and entire Hungary). We also quantified the uncertainty associated with these predictions in order to identify and delimit areas with statistically significant SOC stock change. Our study highlighted that prediction of spatial totals and averages with quantified uncertainty requires a geostatistical approach and cannot be solved by machine learning alone, because it does not account for spatial correlation in prediction errors. The total topsoil SOC stock for Hungary was predicted to increase between 1992 and 2010 with 14.9Tg, with lower and upper limits of a 90% prediction interval equal to 11.2Tg and 18.2Tg, respectively. Results also showed that both the predictions and uncertainties of the average SOC stock change were smaller for larger spatial supports, while spatial aggregation also made it easier to obtain statistically significant SOC stock changes. The latter is important for carbon accounting studies that need to prove in Measurement, Reporting and Verification protocols that observed SOC stock changes are not only practically but also statistically significant.

Dynamics of carbon storage and status of standing vegetation in temperate coniferous forest ecosystem of north western Himalaya India.

Natural ecosystems, which operate as a sink, play an important role in determining the concentration of CO2 in the atmosphere and have a large storage capacity, assisting in mitigation of problem that has a negative impact on the human population. Forests are one of the most important carbon sinks in the terrestrial ecosystem, with the best example being the Western Himalaya, where healthy and sustainable vegetation is prized. Standard methodology was adopted for assessing the different parameters of carbon related information to enumerate the status of carbon storage and its trend in sustaining the ecosystem of the area. The current research displays the annual increment and carbon dynamics in various vegetation components and levels. Trees, shrubs, and herbs help to fix atmospheric carbon in a variety of forms, including AGC, BGC, and TC. The concentration of carbon-fixing potential was measured on an annual and seasonal basis, with herbs having the highest mean annual increment, followed by shrubs and trees. Pinus wallichiana had the largest annual carbon stock change among trees, followed by Cedrus deodara, Picea smithiana, and Abies pindrow. P. wallichiana topped the increase percentage with 60.58%, followed by C. deodara 33.35%, P. smithiana 5.61%, and A. pindrow 0.45%. Litter was also investigated as a potential source of mitigation, with the best results observed during the autumn months. Natural coniferous forests provide a regulating ecological service in the region by maintaining carbon dioxide levels in the form of biomass, according to the study.

Input database related uncertainty of Biome-BGCMuSo agro-environmental model outputs

ABSTRACT Gridded model assessments require at least one climatic and one soil database for carrying out the simulations. There are several parallel soil and climate database development projects that provide sufficient, albeit considerably different, observation based input data for crop model based impact studies. The input database related uncertainty of the Biome-BGCMuSo agro-environmental model outputs was investigated using three and four different gridded climatic and soil databases, respectively covering an area of nearly 100.000 km2 with 1104 grid cells. Spatial, temporal, climate and soil database selection related variances were calculated and compared for four model outputs obtained from 30-year-long simulations. The choice of the input database introduced model output variability that was comparable to the variability the year-to-year change of the weather or the spatial heterogeneity of the soil causes. Input database selection could be a decisive factor in carbon sequestration related studies as the soil carbon stock change estimates may either suggest that the simulated ecosystem is a carbon sink or to the contrary a carbon source on the long run. Careful evaluation of the input database quality seems to be an inevitable and highly relevant step towards more realistic plant production and carbon balance simulations.

Supply potential of lignocellulosic energy crops grown on marginal land and greenhouse gas footprint of advanced biofuels—A spatially explicit assessment under the sustainability criteria of the Renewable Energy Directive Recast

AbstractAdvanced biofuels produced from lignocellulosic crops grown on marginal lands can become an important part of the European Union (EU) climate change mitigation strategy to reduce CO2 emissions and meet biofuel demand. This study quantifies spatially explicit the availability of marginal land in the EU, its production biomass potentials for eight different crops, and the greenhouse gas (GHG) performance of advanced biofuel supply chains. Available land is mapped based on land marginality and Renewable Energy Directive Recast (REDII) land‐related sustainability criteria. Biomass potentials are assessed with a water‐use‐to‐biomass‐production approach while considering the available land, location‐specific biophysical conditions and crop‐specific phenological characteristics. The GHG balance of advanced biofuels from energy crops produced on marginal lands is assessed considering both land‐related carbon stock changes and supply chain emissions with the carbon footprint approach from the REDII. Available marginal land that meets REDII criteria is projected at 20.5–21 Mha 2030 and 2050, respectively. Due to biophysical limitations, not all available land is suitable for energy crop production. The maximum biomass potential of lignocellulosic energy crops (optimal crop choice with maximum yield for each available location) varies between 1951 PJ year−1 in 2030 and 2265 PJ year−1 in 2050. The GHG emission performance (net emissions) of different advanced biofuel supply chains varies on average between −32 g CO2‐eq MJfuel−1 for poplar/willow diesel to 38 g CO2‐eq for reed canary grass renewable jet fuel. The large variability in GHG performance is strongly determined by the spatial heterogeneity, which dictates the type of feedstock produced under specific local biophysical conditions, the crop characteristics, and the best conversion pathway. Negative GHG emissions are related to increased carbon stocks for the biomass and soil organic carbon pools compared to the land prior to conversion. When for each location, the advanced biofuel supply chain with the highest GHG performance (lowest net GHG emissions) is selected, 618 PJ year−1 of advanced biofuels can be produced by 2030. Under REDII GHG emission criteria, slightly less (552 PJ year−1) is viable. Smart choices on location, crop type and supply chain design are paramount to achieve maximum benefits of bioenergy systems.

Changes in plant debris and carbon stocks across a subalpine forest successional series

BackgroundAs a structurally and functionally important component in forest ecosystems, plant debris plays a crucial role in the global carbon cycle. Although it is well known that plant debris stocks vary greatly with tree species composition, forest type, forest origin, and stand age, simultaneous investigation on the changes in woody and non-woody debris biomass and their carbon stock with forest succession has not been reported. Therefore, woody and non-woody debris and carbon stocks were investigated across a subalpine forest successional gradient in Wanglang National Nature Reserve on the eastern Qinghai-Tibet Plateau.ResultsPlant debris ranged from 25.19 to 82.89 Mg∙ha− 1 and showed a global increasing tendency across the subalpine forest successional series except for decreasing at the S4 successional stage. Accordingly, the ratios of woody to non-woody debris stocks ranged from 26.58 to 208.89, and the highest and lowest ratios of woody to non-woody debris stocks were respectively observed in mid-successional coniferous forest and shrub forest, implying that woody debris dominates the plant debris. In particular, the ratios of coarse to fine woody debris stocks varied greatly with the successional stage, and the highest and lowest ratios were found in later and earlier successional subalpine forests, respectively. Furthermore, the woody debris stock varied greatly with diameter size, and larger diameter woody debris dominated the plant debris. Correspondingly, the carbon stock of plant debris ranged from 10.30 to 38.87 Mg∙ha− 1 across the successional series, and the highest and lowest values were observed in the mid-coniferous stage and shrub forest stage, respectively. Most importantly, the carbon stored in coarse woody debris in later successional forests was four times higher than in earlier successional forests.ConclusionsThe stock and role of woody debris, particularly coarse woody debris, varied greatly with the forest successional stage and dominated the carbon cycle in the subalpine forest ecosystem. Thus, preserving coarse woody debris is a critical strategy for sustainable forest management.

Modeling land use change and forest carbon stock changes in temperate forests in the United States

BackgroundForests provide the largest terrestrial sink of carbon (C). However, these C stocks are threatened by forest land conversion. Land use change has global impacts and is a critical component when studying C fluxes, but it is not always fully considered in C accounting despite being a major contributor to emissions. An urgent need exists among decision-makers to identify the likelihood of forest conversion to other land uses and factors affecting C loss. To help address this issue, we conducted our research in California, Colorado, Georgia, New York, Texas, and Wisconsin. The objectives were to (1) model the probability of forest conversion and C stocks dynamics using USDA Forest Service Forest Inventory and Analysis (FIA) data and (2) create wall-to-wall maps showing estimates of the risk of areas to convert from forest to non-forest. We used two modeling approaches: a machine learning algorithm (random forest) and generalized mixed-effects models. Explanatory variables for the models included ecological attributes, topography, census data, forest disturbances, and forest conditions. Model predictions and Landsat spectral information were used to produce wall-to-wall probability maps of forest change using Google Earth Engine.ResultsDuring the study period (2000–2017), 3.4% of the analyzed FIA plots transitioned from forest to mixed or non-forested conditions. Results indicate that the change in land use from forests is more likely with increasing human population and housing growth rates. Furthermore, non-public forests showed a higher probability of forest change compared to public forests. Areas closer to cities and coastal areas showed a higher risk of transition to non-forests. Out of the six states analyzed, Colorado had the highest risk of conversion and the largest amount of aboveground C lost. Natural forest disturbances were not a major predictor of land use change.ConclusionsLand use change is accelerating globally, causing a large increase in C emissions. Our results will help policy-makers prioritize forest management activities and land use planning by providing a quantitative framework that can enhance forest health and productivity. This work will also inform climate change mitigation strategies by understanding the role that land use change plays in C emissions.

Significant loss of soil inorganic carbon at the continental scale.

Widespread soil acidification due to atmospheric acid deposition and agricultural fertilization may greatly accelerate soil carbonate dissolution and CO2 release. However, to date, few studies have addressed these processes. Here, we use meta-analysis and nationwide-survey datasets to investigate changes in soil inorganic carbon (SIC) stocks in China. We observe an overall decrease in SIC stocks in topsoil (0–30 cm) (11.33 g C m–2 yr–1) from the 1980s to the 2010s. Total SIC stocks have decreased by ∼8.99 ± 2.24% (1.37 ± 0.37 Pg C). The average SIC losses across China (0.046 Pg C yr–1) and in cropland (0.016 Pg C yr–1) account for ∼17.6%–24.0% of the terrestrial C sink and 57.1% of the soil organic carbon sink in cropland, respectively. Nitrogen deposition and climate change have profound influences on SIC cycling. We estimate that ∼19.12%–19.47% of SIC stocks will be further lost by 2100. The consumption of SIC may offset a large portion of global efforts aimed at ecosystem carbon sequestration, which emphasizes the importance of achieving a better understanding of the indirect coupling mechanisms of nitrogen and carbon cycling and of effective countermeasures to minimize SIC loss.

Changes in global terrestrial live biomass over the 21st century.

Live woody vegetation is the largest reservoir of biomass carbon, with its restoration considered one of the most effective natural climate solutions. However, terrestrial carbon fluxes remain the largest uncertainty in the global carbon cycle. Here, we develop spatially explicit estimates of carbon stock changes of live woody biomass from 2000 to 2019 using measurements from ground, air, and space. We show that live biomass has removed 4.9 to 5.5 PgC year-1 from the atmosphere, offsetting 4.6 ± 0.1 PgC year-1 of gross emissions from disturbances and adding substantially (0.23 to 0.88 PgC year-1) to the global carbon stocks. Gross emissions and removals in the tropics were four times larger than temperate and boreal ecosystems combined. Although live biomass is responsible for more than 80% of gross terrestrial fluxes, soil, dead organic matter, and lateral transport may play important roles in terrestrial carbon sink.

Changes in Carbon and Nutrient Stocks of Secondary Forest Transformations Under Ultisol in Leyte Island, Philippines

Ultisols are old soils, inferior and acidic. However, tropical secondary forests (SF) under this soil are transformed into grasslands (GL) and forest plantations (FP) which could profoundly affect the carbon and nutrient stocks. To support efforts in conserving tropical forest, estimates of these are essential, especially in addressing climate change and carbon sequestering. This study aimed to quantify soil organic carbon (SOC), total aboveground biomass (TAGB) and nutrient stocks of SF transformations using three adjacent land-use types, namely SF, GL and FP with comparable climate, parent material, soil type and geology. Results showed that land-use mean of organic carbon, TAGB, potential cation exchange capacity, effective cation exchange capacity, exchangeable acidity, hydrogen ion, aluminum, magnesium, calcium, potassium, sodium and the base saturation decreased when SF was converted into GL and then into FP. Additionally, there was a slight increase in SOC stocks, total nitrogen, available phosphorus and pH in H2O when SF was converted into GL and then into FP. This study indicates that SF conversion into GL and FL did not affect SOC stocks and has no significant relationship with TAGB.

Carbon stock estimation by dual-polarized synthetic aperture radar (SAR) and forest inventory data in a Mediterranean forest landscape

Forest ecosystems play a crucial role in mitigating global climate change by forming massive carbon sinks. Their carbon stocks and stock changes need to be quantified for carbon budget balancing and international reporting schemes. However, direct sampling and biomass weighing may not always be possible for quantification studies conducted in large forests. In these cases, indirect methods that use forest inventory information combined with remote sensing data can be beneficial. Synthetic aperture radar (SAR) images offer numerous opportunities to researchers as freely distributed remote sensing data. This study aims to estimate the amount of total carbon stock (TCS) in forested lands of the Kizildag Forest Enterprise. To this end, the actual storage capacities of five carbon pools, i.e. above- and below-ground, deadwood, litter, and soil, were calculated using the indirect method based on ground measurements of 264 forest inventory plots. They were then associated with the backscattered values from Sentinel-1 and ALOS-2 PALSAR-2 data in a Geographical Information System (GIS). Finally, TCS was separately modelled and mapped. The best regression model was developed using the HH polarization of ALOS-2 PALSAR-2 with an adjusted R2 of 0.78 (p < 0.05). According to the model, the estimated TCS was about 2 Mt for the entire forest, with an average carbon storage of 133 t ha−1. The map showed that the distribution of TCS was heterogenic across the study area. Carbon hotspots were mostly composed of pure stands of Anatolian black pine and mixed, over-mature stands of Lebanese cedar and Taurus fir. It was concluded that the total carbon stocks of forest ecosystems could be estimated using appropriate SAR images at acceptable accuracy levels for forestry purposes. The use of additional ancillary data may provide more delicate and reliable estimations in the future. Given the implications of this study, the spatiotemporal dynamics of carbon can be effectively controlled by forest management when coupled with easily accessible space-borne radar data.

Estimating temporal and spatial changes in soil organic carbon stocks and its controlling factors in moraine landscapes in Denmark

Assessing temporal changes in soil organic carbon (SOC) stocks is important when evaluating the SOC sequestration potential of soil management strategies. The monitoring of SOC stocks is challenging, particularly in eroding landscapes where erosion-induced changes in SOC stocks are superimposed on changes related to management. In this study we focused on the spatio-temporal variations of SOC in eroding cropland soils of the northeast of thepeninsulaofJutland (Denmark) at field scale. We examined soil redistribution processes as control factors of SOC changes (ΔSOC) during a 16-year period by using soil data (both spatial and with depth) collected over two distinct periods in 1998 and 2014. Topographic changes between 1998 and 2014, derived from high resolution digital elevation models, were used to identify eroded, stable and depositional areas within the field. There was a predominance of soil loss, with 44% of the sampling points corresponding to eroded sites and 28% to depositional sites, while the remainder was considered as stable soils. Substantial erosion was detected and at the field scale, soil deposition equalled (29.7 ± 17.5 Mg ha−1 y−1) while soil erosion rates were lower with a mean of 25.8 ± 12.9 Mg ha−1 y−1. Comparing SOC stocks over the 16 year-period showed that on average, and when considering the whole soil profile, SOC contents and stocks were stable. However, this masked substantial dynamics that were related to erosion processes. Despite the occurrence of net soil erosion at the field scale, there was an overall increase in SOC storage of 1.88 Mg ha (SOC2014:SOC1998 ratio ≥ 1). Losses of SOC in topsoils (6%) and SOC gain (34%) in soil layers below the plough depth (25–45 cm) could be related to soil disturbances caused by tillage and the significant downward transport of topsoil SOC within the soil profile. Soil disturbances caused by tillage practices (i.e. soil management) and SOC transportation from topsoil to subsoil may be responsible for the downward movement of organic carbon and consequent SOC accumulation in subsoils. In addition the successive deposition and deep burial of SOC rich topsoils at depositional sites due to the effect of topography and slope position favours the enrichment of SOC in subsoils. It was estimated that as much as 11% of the sampling points changed from eroded to depositional sites and 15% of them varied from erosion to stable sites, and this might have favoured SOC accumulation. These results clearly show that understanding and quantifying soil redistribution processes is key to assess SOC temporal changes in agricultural soils. This is important to develop site-specific management strategies that improve SOC sequestration at local scale.

Impact of land cover changes on carbon stock trends in Kenya for spatial implementation of REDD+ policy

Terrestrial carbon stock estimates information has significant importance in planning decisions for global warming and climate change mitigation. This study aimed to estimate and analyze carbon stock changes in Kenya as consequence of land cover change (LCC) using free open data to provide affordable and timely information. Using Random Forest (RF) decision trees, the land cover for 2028 was modelled based on 2004 and 2016 land cover under a Business as Usual (BAU) and an alternative Reducing of Emissions from Forest Degradation and Deforestation (REDD+) scenarios. The InVEST carbon model was used for estimation and valuation of carbon stock between 2004 and 2028. Results show a 16% decline in carbon stock with a loss of 21 billion US$ under the BAU scenario. On a regional scale, results show a gradual decline in carbon stock in the Coastal and Central regions while other regions exhibited mixed results. This trend can be reversed by the implementation of a REDD + scenario with a possible increase of 1.6% between 2016 and 2028, translating to a gain of 1 billion US$. This study contributes to the understanding of spatiotemporal carbon stock changes under different scenarios for effective spatial planning aiming to a balanced natural resource utilization.

Natural forests in New Zealand \u2013 a large terrestrial carbon pool in a national state of equilibrium

BackgroundNatural forests cover approximately 29% of New Zealand’s landmass and represent a large terrestrial carbon pool. In 2002 New Zealand implemented its first representative plot-based natural forest inventory to assess carbon stocks and stock changes in these mostly undisturbed old-growth forests. Although previous studies have provided estimates of biomass or carbon stocks, these were either not fully representative or lacked data from important pools such as dead wood (coarse woody debris). The current analysis provides the most complete estimates of carbon stocks and stock changes in natural forests in New Zealand.ResultsWe present estimates of per hectare carbon stocks and stock changes in live and dead organic matter pools excluding soil carbon based on the first two measurement cycles of the New Zealand Natural Forest Inventory carried out from 2002 to 2014. These show that New Zealand’s natural forests are in balance and are neither a carbon source nor a carbon sink. The average total carbon stock was 227.0 ± 14.4 tC·ha− 1 (95% C.I.) and did not change significantly in the 7.7 years between measurements with the net annual change estimated to be 0.03 ± 0.18 tC·ha− 1·yr− 1. There was a wide variation in carbon stocks between forest groups. Regenerating forest had an averaged carbon stock of only 53.6 ± 9.4 tC·ha− 1 but had a significant sequestration rate of 0.63 ± 0.25 tC·ha− 1·yr− 1, while tall forest had an average carbon stock of 252.4 ± 15.5 tC·ha− 1, but its sequestration rate did not differ significantly from zero (− 0.06 ± 0.20 tC·ha− 1·yr− 1). The forest alliance with the largest average carbon stock in above and below ground live and dead organic matter pools was silver beech-red beech-kamahi forest carrying 360.5 ± 34.6 tC·ha− 1. Dead wood and litter comprised 27% of the total carbon stock.ConclusionsNew Zealand’s Natural Forest Inventory provides estimates of carbon stocks including estimates for difficult to measure pools such as dead wood and roots. It also provides estimates of uncertainties including effects of model prediction error and sampling variation between plots. Importantly it shows that on a national level New Zealand’s natural forests are in balance. Nevertheless, this is a nationally important carbon pool that requires continuous monitoring to identify potential negative or positive changes.