Carbon Density Research Articles

Modified soil physicochemical properties promoted sequestration of organic and inorganic carbon synergistically during revegetation in desertified land

Large-scale revegetation in desertified land might promote the sequestration of global soil carbon, but the influencing factors on inorganic carbon accumulation processes that absorb CO2 through abiotic “inorganic respiration” processes remain unclear. Several study sites were selected in the Mu Us Sandy Land: quicksand land (0 years), semi-fixed sand land (6 years), and three nearby shrub and arbor forest lands (20, 30, and 51 years). During 20–51 years of revegetation, soil organic carbon density (SOCD) and inorganic carbon density (SICD) significantly increased by 2.5–7.5 times and 1.7–7.0 times for shrub forest and 3.1–7.5 times and 2.6–7.8 times for arbor forest compared with quicksand land, but the difference between the two studied forest lands were not statistically significant. After revegetation in desertified land, the content of total nitrogen (TN), available potassium (AK), cation exchange capacity (CEC), clay, Ca2+, and Mg2+ also greatly enhanced, and they were positively correlated with SOC and SIC, while bulk density and pH slightly decreased, which were negatively correlated with SOC and SIC. T-value biplot importance ranking further indicated that TN was the most sensitive factor affecting the accumulation of SOC and SIC, and its contribution rate to SOC and SIC reached 89.8% and 88.1%, respectively. Moreover, SOC was also related to AK, CEC, and clay, while SIC was more affected by clay, Ca2+, and Mg2+ during revegetation. In conclusion, afforestation with shrubs and arbors on the Mu Us Sandy Land promoted the sequestration of SOC and SIC synergistically by modified soil physicochemical properties.

Conservation tillage and organic nutrients management improve soil properties, productivity, and economics of a maize‐vegetable pea system in the Eastern Himalayas

AbstractSoil quality restoration and sustainable crop production in the rainfed ecosystem of the Indian Himalayas can be achieved through effective conservation tillage and organic management. Hence, a six‐year (2013 to 2019) study was conducted to quantify the effect of tillage and organic nutrient management on soil properties, productivity, and profitability of the maize‐vegetable pea (Pisum sativum) system. Three tillage practices [conventional (CT), reduced (RT), and no‐till (NT)] and four organic nutrients management practices [(ONM)‐farmyard manure @ 8 Mg ha−1farmers practice (ONM1), 100% recommended dose of nitrogen (RDN) through manures (ONM2), 75% RDN through manures + maize/vegetable pea stover in either of the crops (ONM3), and 50% RDN through manures + maize/vegetable pea stover in either of the crops (ONM4)] were tested. The results indicated that the NT had higher soil organic carbon (SOC, 16.49 g kg−1), available N (354.5 kg ha−1), and lesser bulk density (1.31 Mg m−3) and penetration resistance (1.85 MPa) in comparison with that of CT at 0–10 cm depth. The system productivity under NT was 9.6% higher than that obtained under CT. The ONM3had higher SOC content, plant‐available N, soil microbial biomass carbon (SMBC), and dehydrogenase activity (DHA) than ONM1. The integration of RT‐ONM2enhanced SMBC, DHA, maize, and vegetable pea yield by 27.2%, 35.7%, 38.0%, and 60.3%, respectively, over CT‐ONM1. Thus, the study suggested that the adoption of effective conservation tillage with adequate organic nutrient management has the potential to advance the soil properties and productivity of maize‐vegetable pea system in the Himalayan Region.

Scale- and location-specific multivariate controls of topsoil organic carbon density depend on landform heterogeneity

Spatial variability of surface soil organic carbon density (SOCD) and its controls may be scale-specific and localized depending on the landforms which drive distribution of environmental factors such as soil moisture and soil texture. We hypothesize that SOCD variation dominates at larger scales and is controlled by less number of environmental variables at more homogeneous landforms. The objective of this study was to test this hypothesis by revealing the scale-location specific multivariate relationships between surface SOCD and environmental factors at three transects (i.e., upstream, midstream, and downstream) with different landform heterogeneities in a typical basin using bivariate wavelet coherency and multiple wavelet coherence. Results indicated that variations in SOCD was dominated at large scales (>10.6 km) across the entire transect (i.e., global) at the more homogeneous midstream transect, while those were more localized and mainly occurred at small (<2.6 km, upstream) or middle scales (2.6–10.6 km, downstream) at the other two transects. Soil water content was the best and could significantly explain the SOCD variations in 58% of the scale-location domains with significant (68% significance level) variations of SOCD at the midstream transect. However, more (two and seven at upstream and downstream, respectively) variables were needed but did not obviously increase (64% at downstream) or even decreased (23% at upstream) the percentage of scale-location domains where SOCD variations were significantly explained. This study verified that variations in SOCD and its scale-location specific controls were associated with landform heterogeneity. This study also indicated the outperformance of wavelet methods over traditional correlation analyses in terms of untangling the controls of multivariate factors on SOCD variation at different locations and scales. Meanwhile, a new index (percentage area of both significant coherence and significant SOCD variations relative to domains with significant SOCD variation, PASCSV) was developed, and was proved to be better than a previously used index (percentage area of significant coherence relative to all scale-location domains, PASC) in identifying the best factors in controlling SOCD variations in multiple location-scale domains.

Predicting downed woody material carbon stocks in forests of the conterminous United States

Downed woody material (DWM) is a unique part of the forest carbon cycle serving as a pool between living biomass and subsequent atmospheric emission or transference to other forest pools. Thus, DWM is an individually defined pool in national greenhouse gas inventories. The diversity of DWM carbon drivers (e.g., decay, tree mortality, or wildfire) and associated high spatial variability make this a difficult-to-predict component of forest ecosystems. Using the now fully established nationwide inventory of DWM across the United States (US), we developed models, which substantially improved predictions of stand-level DWM carbon density relative to the current national-reporting model (‘previous’ model, here). The previous model was developed from published DWM carbon densities prior to the NFI DWM inventory. Those predictions were tested using NFI DWM carbon densities resulting in a poor fit to the data (coefficient of determination, or R2 = 0.03).We present new random forest (RF) and stochastic gradient boosted (SGB) regression models to prediction DWM carbon density on all NFI plots and spatially on all forest land pixels. We evaluated various biotic and abiotic regression predictors, and the most important were standing dead trees, long-term annual precipitation, and long-term maximum summer temperature. A RF model scored best for expanding predictions to NFI plots (R2 = 0.31), while an SGB model was identified for DWM carbon predictions based on purely spatial data (i.e., NFI-plot-independent, with R2 = 0.23). The new RF model predicts conterminous US DWM carbon stocks to be 15% lower than the previous model and 2% higher than NFI data expanded according to inventory design-based inference. The new NFI data-driven models not only improve the predictions of DWM carbon density on all plots, they also provide flexibility in extending these predictions beyond the NFI to make spatially explicit and spatially continuous estimates of DWM carbon on all forest land in the US.

Strong effect of scandium source purity on chemical and electronic properties of epitaxial ScxAl1−xN/GaN heterostructures

Epitaxial multilayer heterostructures of ScxAl1−xN/GaN with Sc contents x = 0.11–0.45 are found to exhibit significant differences in structural quality, chemical impurity levels, and electronic properties depending on the starting Sc source impurity levels. A higher purity source leads to a 2–3 orders of magnitude reduction in the carbon, oxygen, and fluorine unintentional doping densities in MBE-grown ScxAl1−xN/GaN multilayers. Electrical measurements of ScxAl1−xN/n+GaN single heterostructure barriers show a 5–7 orders of magnitude reduction in the electrical leakage for films grown with a higher purity Sc source at most Sc contents. The measured chemical and electrical properties of epitaxial ScxAl1−xN highlight the importance of the starting Sc source material purity for epitaxial device applications that need these highly piezoelectric and/or ferroelectric transition-metal nitride alloys.

Machine learning based soil maps for a wide range of soil properties for the forested area of Switzerland

Spatial soil information in forests is crucial to assess ecosystem services such as carbon storage, water purification or biodiversity. However, spatially continuous information on soil properties at adequate resolution is rare in forested areas, especially in mountain regions. Therefore, we aimed to build high-resolution soil property maps for pH, soil organic carbon, clay, sand, gravel and soil density for six depth intervals as well as for soil thickness for the entire forested area of Switzerland. We used legacy data from 2071 soil profiles and evaluated six different modelling approaches of digital soil mapping, namely lasso, robust external-drift kriging, geoadditive modelling, quantile regression forest (QRF), cubist and support vector machines. Moreover, we combined the predictions of the individual models by applying a weighted model averaging approach. All models were built from a large set of potential covariates which included e.g. multi-scale terrain attributes and remote sensing data characterizing vegetation cover.Model performances, evaluated against an independent dataset were similar for all methods. However, QRF achieved the best prediction performance in most cases (18 out of 37 models), while model averaging outperformed the individual models in five cases. For the final soil property maps we therefore used the QRF predictions. Prediction performance showed large differences for the individual soil properties. While for fine earth density the R2 of QRF varied between 0.51 and 0.64 across all depth intervals, soil organic carbon content was more difficult to predict (R2 = 0.19–0.32). Since QRF was used for map prediction, we assessed the 90% prediction intervals from which we derived uncertainty maps. The latter are valuable to better interpret the predictions and provide guidance for future mapping campaigns to improve the soil maps.

Relating macrofungal diversity and forest characteristics in boreal forests in China: Conservation effects, inter-forest-type variations, and association decoupling.

QuestionHow conservation and forest type affect macrofungal compositional diversity is not well understood. Even less is known about macrofungal associations with plants, soils, and geoclimatic conditions.LocationSouthern edge of boreal forest distribution in China, named as Huzhong Nature Reserve.MethodsWe surveyed a total of 72 plots for recording macrofungi, plants, and topography in 2015 and measured soil organic carbon, nitrogen, and bulk density. Effects of conservation and forest types on macrofungi and plants were compared, and their associations were decoupled by structural equation modeling (SEM) and redundancy ordination (RDA).ResultsConservation and forest type largely shaped macrofungal diversity. Most of the macrofungal traits declined with the conservation intensities or peaked at the middle conservation region. Similarly, 91% of macrofungal traits declined or peaked in the middle succession stage of birch‐larch forests. Forest conservation resulted in the observation of sparse, larch‐dominant, larger tree forests. Moreover, the soil outside the Reserve had more water, higher fertility, and lower bulk density, showing miscellaneous wood forest preference. There is a complex association between conservation site characteristics, soils, plants, and macrofungi. Variation partitioning showed that soil N was the top‐one factor explaining the macrofungal variations (10%). As shown in SEM coefficients, conservation effect to macrofungi (1.1–1.2, p < .05) was like those from soils (1.2–1.6, p < .05), but much larger than the effect from plants (0.01–0.14, p > .10). For all tested macrofungal traits, 89%–97% of their variations were from soils, and 5%–21% were from conservation measures, while plants compensated 1%–10% of these effects. Our survey found a total of 207 macrofungal species, and 65 of them are new updates in this Reserve, indicating data shortage for the macrofungi list here.ConclusionOur findings provide new data for the joint conservation of macrofungi and plant communities, highlighting the crucial importance of soil matrix for macrofungal conservation in boreal forests.

Superior flexibility of planar graphene allotropes with pentagons and heptagons

Superior flexibility of the planar graphene allotropes containing pentagons and heptagons was demonstrated by evaluating the bending modulus through first-principles calculations. The bending moduli of planar graphene allotropes depend closely on the geometry and bending direction. Compared with pristine graphene, incorporation of pentagons and heptagons will change the bond length, planar carbon density and geometric contribution of carbon atoms, which further degrade the bending moduli of graphene allotropes. Generally, the longer the average bond length is, the lower bending modulus will be. While large planar carbon density and geometric contribution of the hexagons result in high bending modulus. Meanwhile, bending direction depended bending modulus suggests the mechanical anisotropy of the graphene allotropes. Our calculations not only provide a general approach to understand the flexibility of graphene allotropes, but also give an important clue to design high-performance flexible devices.

Measurement of the density of carbon dioxide/toluene homogeneous mixtures and correlation with equations of state

Equations of state (EoS) can be used to estimate a wide variety of physical properties. However, there has been limited verification of the applicability of parameter sets derived from specific physical properties to correlate and estimate various other physical properties. The densities of homogeneous phase fluid mixtures of the carbon dioxide (CO2)/toluene (Tol) binary system were measured and correlated to three equations of state. The density measurements were performed using a high-pressure vibration-type density meter equipped with a circulation pump and variable-volume viewing cell, which guaranteed the homogeneity of the mixtures. The densities were measured at temperatures ranging from 313 to 353 K, pressures up to 20 MPa, and CO2 concentrations from 0 to 80 mol%. The experimental data obtained were correlated to the Tait density equation and three EoS, namely Peng–Robinson (PR), Sanchez–Lacombe (SL), and Perturbed Chain statistical associating fluid theory (PC-SAFT) EoS. Among them, SL and PC-SAFT EoS agree better with the experimental data compared to the PR EoS, presumably due to differences in the pure component parameters used in each EoS. Using parameter sets determined from the density measurements, the vapor liquid equilibrium (VLE) of the CO2/Tol mixtures was estimated. While we were unsuccessful in estimating VLE using the PR EoS, SL and PC-SAFT EoS were successfully used for the estimations. We also attempted to determine the densities from VLE correlations. It was found that SL and PC-SAFT EoS could be used to estimate the density well, whereas the PR was not predictive. From the results of the study, it is clear that for estimating density using EoS, the basic data used for determining the pure component parameters are important.

Effects of altitude, aspect, and soil depth on carbon stocks and properties of soils in a tea plantation in the humid Black Sea region

AbstractPhysiographic factors effectively alter soil properties and carbon storage. However, previous research on the effect of topographic factors affecting soil properties on carbon stocks in tea plantations has been limited in the humid Black Sea region. The aim of this study was to determine the effects of elevation, aspect, and soil depth on carbon stocks and soil properties of tea plantations in the Black Sea region. The tea plantations in the research area were divided into two elevation steps and two different aspect groups. Twelve soil sampling plots were then determined according to the stratified random sampling method in each elevation and aspect group; soil samples were taken at 0–10 cm and 10–30 cm soil depths. The amount of carbon stored at a soil depth of 30 cm was found to be 49.38 and 32.36 Mg C ha−1 at the upper and lower altitudes, respectively. Highest carbon density in the tea plantations in the research area was found to be 16.34 kg m−3 at a depth of 0–10 cm. The changes in the soil organic carbon (SOC) values according to elevation and soil depth were statistically significant, while the difference among the carbon storage values was not statistically significant. According to the results of our investigation, elevation is a more effective factor than aspect on SOC in tea plantation areas of highly humid climates.

Carbon Stocks, Species Diversity and Their Spatial Relationships in the Yucatán Peninsula, Mexico

Integrating information about the spatial distribution of carbon stocks and species diversity in tropical forests over large areas is fundamental for climate change mitigation and biodiversity conservation. In this study, spatial models showing the distribution of carbon stocks and the number of species were produced in order to identify areas that maximize carbon storage and biodiversity in the tropical forests of the Yucatan Peninsula, Mexico. We mapped carbon density and species richness of trees using L-band radar backscatter data as well as radar texture metrics, climatic and field data with the random forest regression algorithm. We reduced sources of errors in plot data of the national forest inventory by using correction factors to account for carbon stocks of small trees (&lt;7.5 cm DBH) and for the temporal difference between field data collection and imagery acquisition. We created bivariate maps to assess the spatial relationship between carbon stocks and diversity. Model validation of the regional maps obtained herein using an independent data set of plots resulted in a coefficient of determination (R2) of 0.28 and 0.31 and a relative mean square error of 38.5% and 33.0% for aboveground biomass and species richness, respectively, at pixel level. Estimates of carbon density were influenced mostly by radar backscatter and climatic data, while those of species richness were influenced mostly by radar texture and climatic variables. Correlation between carbon density and species richness was positive in 79.3% of the peninsula, while bivariate maps showed that 39.6% of the area in the peninsula had high carbon stocks and species richness. Our results highlight the importance of combining carbon and diversity maps to identify areas that are critical—both for maintaining carbon stocks and for conserving biodiversity.

Alternative biome states of African terrestrial vegetation and the potential drivers: A continental-scale study

The alternative stable state (ASS) theory provides a plausible framework to explain the spatial distribution of biomes and their dynamics. Existing studies to test the alternative biome states (ABSs) mainly focused on tree-dominated biomes. It is still uncertain whether ABSs are present in a wide range of terrestrial biomes. This study was to examine the ABSs in the terrestrial vegetated areas of Africa and the maintaining factors. The potential landscapes were reconstructed separately for forest, grassland and shrubland using the MODIS Vegetation Continuous Field data along the gradients of temperature, precipitation and aridity index (AI, the ratio of precipitation to potential evapotranspiration). The differences of soil organic carbon density (SOCD), fire count and grazing intensity were compared to test the feedback hypothesis to maintain the ABSs. The results showed that AI performed well in detecting the ABSs at the continental scale of Africa. Forest (at the wetter end) and shrubland (at the drier end) were well separated along the AI axis. Forest had three stable states (i.e. closed forest, woody savanna, and savanna) and shrubland had two stable states (i.e. closed shrubland and open shrubland). Grassland had two stable states (i.e. dense grassland and sparse grassland) distributing in a large AI range. The stable states that shared a specific AI range were regarded as the ABSs. Climate aridity greatly determined the distribution of the ABSs but the positive feedbacks between vegetation and SOCD, fire count, and livestock density played potential roles in driving the shifts between the ABSs. Our study indicated that the ABSs commonly existed in varied biomes (both tree-dominated and non-tree-dominated) in the African continent, which provided an enlarged picture of the ABSs of the terrestrial biomes. The findings contribute to a deeper understanding of large scale vegetation patterns and their dynamics and facilitate to macro management of the terrestrial ecosystems in facing the possible regime shifts of the biomes.

There Is No Planet B: Polycentric Governance In The Amazon Rainforest

How do firms design incentives compatible with environmental protection? The new institutional economics identifies the challenges of governing common-pool resources and the difficulties of internalizing environmental externalities into regular market transactions. New stakeholder management theory suggests that firms may avoid the tragedy of the commons through the formation of a polycentric governance structure among stakeholders. This paper evaluates these theoretical claims by analyzing the activities of Natura, a Brazilian cosmetics company, regarding Amazon rainforest preservation. We argue that, in line with theoretical predictions, Natura internalizes positive externalities arising from environmental protection by sharing value with stakeholders in rural Amazon communities. To test this proposition, the paper presents a differences-in-differences analysis comparing forest preservation in the municipalities that Natura entered versus those in which it did not. The study employs an instrumental variable based on missing satellite images, which Natura relies upon to make decisions about entry into different municipalities. Images are missing due to technical problems in satellite operations, which are uncorrelated with stakeholder needs on the ground. Quantitative results show that Natura’s entry into a municipality helps to preserve forested areas. Analysis of three mechanisms using information on crop yields and carbon density ties Natura’s involvement with stakeholder decisions to cultivate forest-generated crops rather than to engage in clear-cutting. The polycentric governance system that Natura stimulated also led to reforestation of previously cleared areas. This study contributes to the management literature by suggesting how firms can foster environmental protection through incentive alignment with critical stakeholders.

Internet plus and China industrial system's low-carbon development

The Internet Plus concept has deeply affected the development mode of the industrial system. To achieve a low-carbon transition, it is essential to examine how Internet Plus effects the industrial system's carbon intensity. In the existing literature, few studies focus on the influence of Internet usage on the low-carbon development of industries. Based on the Kaya identity, this study explores the relationship and interaction mechanism between Internet Plus, output structure, energy intensity, carbon density, and carbon intensity involved in industrial systems. Based on threshold and dynamic panel models, the Internet effect on carbon intensity, energy intensity, and carbon density of China's industrial system from 2000 to 2015 is studied empirically. Results indicate that the Internet encourages decreased carbon intensity, energy intensity, and carbon density of China's industrial system to various degrees. Specifically, regarding its contribution, the Internet's reducing effect on energy intensity is obvious, whereas that on carbon density is less notable. Further, different threshold effects of the Internet are exerted on carbon intensity, energy intensity, and carbon density.

Peat Soil Burning in the Mezzano Lowland (Po Plain, Italy): Triggering Mechanisms and Environmental Consequences.

The effects of peat burning on organic‐rich agricultural soils of the Mezzano Lowland (NE Italy) were evaluated on soil profiles variously affected by smoldering. Profiles were investigated for pH, electrical conductivity, bulk density, elemental and isotopic composition of distinct carbon (and nitrogen) fractions. The results suggest that the horizons affected by carbon loss lie at depths 10–70 cm, where the highest temperatures are developed. We suggest that the exothermal oxidation of methane (mediated by biological activity) plays a significant role in the triggering mechanism. In the interested soils we estimated a potential loss of Soil Organic Carbon of approximately 110 kg m −2 within the first meter, corresponding to 580 kg CO2 m −3. The released greenhouse gas is coupled with a loss of soil structure and nutrients. Moreover, the process plausibly triggers mobility of metals bound in organometallic complexes. All these consequences negatively affect the environment, the agricultural activities and possibly also health of the local people.

Calculation method and model of carbon sequestration by urban buildings: An example from Shenyang

Owing to the carbonation of concrete, urban buildings can continuously absorb carbon dioxide in the air and have carbon fixation functions. Buildings, greenbelts, soil, etc. constitute the artificial and natural carbon sequestration system in a city. However, owing to the complexity and heterogeneity of buildings, a quantitative method for determining and understanding the spatial distribution of carbon sequestration in urban buildings is still needed. This study explored whether the neglected carbon sequestration capacity of buildings can act as a natural carbon sink in the carbon balance process and alleviate the adverse impacts of urban carbon sources on the environment. Shenyang city in China was chosen for building a concrete carbon fixation model using a regression analysis and spatial distribution map. The results showed that the carbon sequestration of the urban buildings in the Third Ring Road of Shenyang was about 1,701,600 tons, which was higher than that of natural carbon sinks of the same scale, such as soil and aboveground vegetation, and equivalent to 4.39% of the annual city carbon emissions. The average carbon density of urban buildings is 119.45t/hm2, and the carbon density gradually decreases from the center to the edge. In addition, different types of buildings showed large differences in carbon storage capacity and carbon density, with residential buildings having the highest carbon storage capacity and commercial buildings having the highest carbon density. These results can be used to evaluate the actual role of urban buildings in reducing atmospheric CO 2, visually reveal the spatial pattern of carbon storage in urban buildings and provide a reference for spatial planning.

Isotope effects on transport in LHD

Isotope effects are one of the most important issues for predicting future reactor operations. Large helical device (LHD) is the presently working largest stellarator/helical device using super conducting helical coils. In LHD, deuterium experiments started in 2017. Extensive studies regarding isotope effects on transport have been carried out. In this paper, the results of isotope effect studies in LHD are reported. The systematic studies were performed adjusting operational parameters and nondimensional parameters. In L mode like normal confinement plasma, where internal and edge transport barriers are not formed, the scaling of global energy confinement time (τ E) with operational parameters shows positive mass dependence (M 0.27; where M is effective ion mass) in electron cyclotron heating plasma and no mass dependence (M 0.0) in neutral beam injection heating plasma. The non-negative ion mass dependence is anti-gyro-Bohm scaling. The role of the turbulence in isotope effects was also found by turbulence measurements and gyrokinetic simulation. Better accessibility to electron and ion internal transport barrier (ITB) plasma is found in deuterium (D) plasma than in hydrogen (H). Gyro kinetic non-linear simulation shows reduced ion heat flux due to the larger generation of zonal flow in deuterium plasma. Peaked carbon density profile plays a prominent role in reducing ion energy transport in ITB plasma. This is evident only in plasma with deuterium ions. New findings on the mixing and non-mixing states of D and H particle transports are reported. In the mixing state, ion particle diffusivities are higher than electron particle diffusivities and D and H ion density profiles are almost identical. In the non-mixing state, ion particle diffusivity is much lower than electron diffusivity. Deuterium and hydrogen ion profiles are clearly different. Different turbulence structures were found in the mixing and non-mixing states suggesting different turbulence modes play a role.

Assessing and predicting soil carbon density in China using CMIP5 earth system models

Soil carbon (SC) is a key component of the carbon cycle and plays an important role in climate change; however, quantitatively assessing SC dynamics at the regional scale remains challenging. Earth system model (ESM) that considers multiple environmental factors and spatial heterogeneity has become a powerful tool to explore carbon cycle-climate feedbacks, although the performance of the ESM is diverse and highly uncertain. Thus, identifying reliable ESMs is a prerequisite for better understanding the response of SC dynamics to human activity and climate change. The 16 ESMs that participated in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) were employed to evaluate the skill performance of SC density simulation by comparison with reference data from the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS). Although ESMs generally reflect spatial patterns with lower SC in northwest China and higher SC in southeast China, 11 of 16 ESMs underestimated the SC in China, and 5 of 16 ESMs overestimated the SC density as most ESMs had large discrepancies in capturing the SC density in the northern high latitudes of China and the Qinghai-Tibet Plateau. According to a series of model performance statistics, SC simulated by Institute Pierre Simon Laplace (IPSL) Coupled Model had a close spatial pattern with IGBP-DIS and showed higher skills for SC predictions in China relative to other CMIP5 ESMs. The multimodel ensemble average obtained by IPSL family ESMs showed that SC density exhibited increasing trends under both the RCP4.5 scenario and RCP8.5 scenario. The SC density increased slowly under RCP8.5 compared with that under RCP4.5 and even displayed a decreasing trend in the late 21st century. The findings of this study can provide a reference for identifying the shortcomings of SC predictions in China and guide SC parameterization improvement in ESMs.

Nonlinear turnover rates of soil carbon following cultivation of native grasslands and subsequent afforestation of croplands

Abstract. Land use conversions can strongly impact soil organic matter (SOM) storage, which creates paramount opportunities for sequestering atmospheric carbon into the soil. It is known that land uses such as annual cropping and afforestation can decrease and increase SOM, respectively; however, the rates of these changes over time remain elusive. This study focused on extracting the kinetics (k) of turnover rates that describe these long-term changes in soil C storage and also quantifying the sources of soil C. We used topsoil organic carbon density and δ13C isotopic composition data from multiple chronosequences and paired sites in Russia and United States. Reconstruction of soil C storage trajectory over 250 years following conversion from native grassland to continual annual cropland revealed a C depletion rate of 0.010 yr−1 (first-order k rate constant), which translates into a mean residence time (MRT) of 100 years (R2≥0.90). Conversely, soil C accretion was observed over 70 years following afforestation of annual croplands at a much faster k rate of 0.055 yr−1. The corresponding MRT was only 18 years (R2=0.997) after a lag phase of 5 years. Over these 23 years of afforestation, trees contributed 14 Mg C ha−1 to soil C accrual in the 0 to 15 cm depth increment. This tree-C contribution reached 22 Mg C ha−1 at 70 years after tree planting. Over these 70 years of afforestation, the proportion of tree C to whole-soil C increased to reach a sizable 79 %. Furthermore, assuming steady state of soil C in the adjacent croplands, we also estimated that 45 % of the prairie C existent at the time of tree planting was still present in the afforested soils 70 years later. As an intrinsic property of k modeling, the derived turnover rates that represent soil C changes over time are nonlinear. Soil C changes were much more dynamic during the first decades following a land use conversion than afterwards when the new land use system approached equilibrium. Collectively, results substantiated that C sequestration in afforested lands is a suitable means to proactively mitigate escalating climate change within a typical person's lifetime, as indicated by MRTs of a few decades.

A Novel Spatial Simulation Method for Mapping the Urban Forest Carbon Density in Southern China by the Google Earth Engine

Urban forest is an important component of terrestrial ecosystems and is highly related to global climate change. However, because of complex city landscapes, deriving the spatial distribution of urban forest carbon density and conducting accuracy assessments are difficult. This study proposes a novel spatial simulation method, optimized geographically weighted logarithm regression (OGWLR), using Landsat 8 data acquired by the Google Earth Engine (GEE) and field survey data to map the forest carbon density of Shenzhen city in southern China. To verify the effectiveness of the novel method, multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF) and geographically weighted regression (GWR) models were established for comparison. The results showed that OGWLR achieved the highest coefficient of determination (R2 = 0.54) and the lowest root mean square error (RMSE = 13.28 Mg/ha) among all estimation models. In addition, OGWLR achieved a more consistent spatial distribution of carbon density with the actual situation. The carbon density of the forests in the study area was large in the central and western regions and coastal areas and small in the building and road areas. Therefore, this method can provide a new reference for urban forest carbon density estimation and mapping.