Carbon Balance and Management最新文献

{"title":"China’s terrestrial ecosystem carbon balance during the 20th century: an analysis with a process-based biogeochemistry model","authors":"Yanyu Lu,&nbsp;Yao Huang,&nbsp;Qianlai Zhuang,&nbsp;Wei Sun,&nbsp;Shutao Chen,&nbsp;Jun Lu","doi":"10.1186/s13021-022-00215-9","DOIUrl":"10.1186/s13021-022-00215-9","url":null,"abstract":"<div><h3>Background</h3><p>China’s terrestrial ecosystems play a pronounced role in the global carbon cycle. Here we combine spatially-explicit information on vegetation, soil, topography, climate and land use change with a process-based biogeochemistry model to quantify the responses of terrestrial carbon cycle in China during the 20th century.</p><h3>Results</h3><p>At a century scale, China’s terrestrial ecosystems have acted as a carbon sink averaging at 96 Tg C yr^− 1, with large inter-annual and decadal variabilities. The regional sink has been enhanced due to the rising temperature and CO₂ concentration, with a slight increase trend in carbon sink strength along with the enhanced net primary production in the century. The areas characterized by C source are simulated to extend in the west and north of the Hu Huanyong line, while the eastern and southern regions increase their area and intensity of C sink, particularly in the late 20th century. Forest ecosystems dominate the C sink in China and are responsible for about 64% of the total sink. On the century scale, the increase in carbon sinks in China’s terrestrial ecosystems is mainly contributed by rising CO₂. Afforestation and reforestation promote an increase in terrestrial carbon uptake in China from 1950s. Although climate change has generally contributed to the increase of carbon sinks in terrestrial ecosystems in China, the positive effect of climate change has been diminishing in the last decades of the 20th century.</p><h3>Conclusion</h3><p>This study focuses on the impacts of climate, CO₂ and land use change on the carbon cycle, and presents the potential trends of terrestrial ecosystem carbon balance in China at a century scale. While a slight increase in carbon sink strength benefits from the enhanced vegetation carbon uptake in China’s terrestrial ecosystems during the 20th century, the increase trend may diminish or even change to a decrease trend under future climate change.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"17 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2022-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9548143/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33493623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the use of Earth Observation to support estimates of national greenhouse gas emissions and sinks for the Global stocktake process: lessons learned from ESA-CCI RECCAP2","authors":"Ana Bastos,&nbsp;Philippe Ciais,&nbsp;Stephen Sitch,&nbsp;Luiz E. O. C. Aragão,&nbsp;Frédéric Chevallier,&nbsp;Dominic Fawcett,&nbsp;Thais M. Rosan,&nbsp;Marielle Saunois,&nbsp;Dirk Günther,&nbsp;Lucia Perugini,&nbsp;Colas Robert,&nbsp;Zhu Deng,&nbsp;Julia Pongratz,&nbsp;Raphael Ganzenmüller,&nbsp;Richard Fuchs,&nbsp;Karina Winkler,&nbsp;Sönke Zaehle,&nbsp;Clément Albergel","doi":"10.1186/s13021-022-00214-w","DOIUrl":"10.1186/s13021-022-00214-w","url":null,"abstract":"<div><p>The Global Stocktake (GST), implemented by the Paris Agreement, requires rapid developments in the capabilities to quantify annual greenhouse gas (GHG) emissions and removals consistently from the global to the national scale and improvements to national GHG inventories. In particular, new capabilities are needed for accurate attribution of sources and sinks and their trends to natural and anthropogenic processes. On the one hand, this is still a major challenge as national GHG inventories follow globally harmonized methodologies based on the guidelines established by the Intergovernmental Panel on Climate Change, but these can be implemented differently for individual countries. Moreover, in many countries the capability to systematically produce detailed and annually updated GHG inventories is still lacking. On the other hand, spatially-explicit datasets quantifying sources and sinks of carbon dioxide, methane and nitrous oxide emissions from Earth Observations (EO) are still limited by many sources of uncertainty. While national GHG inventories follow diverse methodologies depending on the availability of activity data in the different countries, the proposed comparison with EO-based estimates can help improve our understanding of the comparability of the estimates published by the different countries. Indeed, EO networks and satellite platforms have seen a massive expansion in the past decade, now covering a wide range of essential climate variables and offering high potential to improve the quantification of global and regional GHG budgets and advance process understanding. Yet, there is no EO data that quantifies greenhouse gas fluxes directly, rather there are observations of variables or proxies that can be transformed into fluxes using models. Here, we report results and lessons from the ESA-CCI RECCAP2 project, whose goal was to engage with National Inventory Agencies to improve understanding about the methods used by each community to estimate sources and sinks of GHGs and to evaluate the potential for satellite and in-situ EO to improve national GHG estimates. Based on this dialogue and recent studies, we discuss the potential of EO approaches to provide estimates of GHG budgets that can be compared with those of national GHG inventories. We outline a roadmap for implementation of an EO carbon-monitoring program that can contribute to the Paris Agreement.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"17 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9526973/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40387968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Required displacement factors for evaluating and comparing climate impacts of intensive and extensive forestry in Germany","authors":"Buschbeck Christian,&nbsp;Pauliuk Stefan","doi":"10.1186/s13021-022-00216-8","DOIUrl":"10.1186/s13021-022-00216-8","url":null,"abstract":"<div><h3>Background</h3><p>Forestry plays a major role in climate change mitigation. However, which intensity of logging is best suited for that task remains controversial. We contribute to the debate by quantitatively analyzing three different forest management scenarios in Germany—a baseline scenario which represents a continuation of current forest management practice as well as an intensive and an extensive logging scenario. We assess whether increased carbon storage in wood products and substitution of other emission-intensive materials can offset reduced carbon stocks in the forest due to increased harvesting. For that, we calculate annual required displacement factors (RDF)—a dimensionless quantity that indicates the minimal displacement factor (DF) so that intensive forestry outperforms extensive forestry from a climate perspective.</p><h3>Results</h3><p>If the intensive forest management scenario is included in the comparison, the RDF starts off with relatively high values (1 to 1.5) but declines over time and eventually even reaches negative values. Comparing the extensive scenario to a baseline yields RDF values between 0.1 and 0.9 with a slightly increasing trend. Compared to RDFs, expected future DFs are too low to favour the intensive forestry scenario and too high to favour the extensive forestry scenario, during the first 25 years of the modeling period. However, towards the end of the modeling period, the relationship between DFs and RDF is turned around in both comparisons. In the comparison between intensive and extensive forest management RDF values are very similar to future DF trajectories.</p><h3>Conclusion</h3><p>RDFs are a useful tool for comparing annual climate impacts of forest growth scenarios and can be used to benchmark material and energy substitution effects of wood. Our results indicate that the baseline scenario reflects an effective compromise between carbon stocks in the forest and carbon displacement by wood use. For a longer modeling period, however, this might not be the case. Which of the alternative scenarios would be best suited for climate change mitigation is heavily dependent on future DF trajectory. Hence, our findings highlight the necessity of robust projections of forest dynamics and industry decarbonization pathways.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"17 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9526925/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40387362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards a low carbon ASEAN: an environmentally extended MRIO optimization model","authors":"Adrianus Amheka,&nbsp;Hoa Thi Nguyen,&nbsp;Krista Danielle Yu,&nbsp;Robert Mesakh Noach,&nbsp;Viknesh Andiappan,&nbsp;Vincent Joseph Dacanay,&nbsp;Kathleen Aviso","doi":"10.1186/s13021-022-00213-x","DOIUrl":"10.1186/s13021-022-00213-x","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;p&gt;Economic growth is dependent on economic activity, which often translates to higher levels of carbon emissions. With the emergence of technologies that promote sustainable production, governments are working towards achieving their target economic growth while minimizing environmental emissions to meet their commitments to the international community. The IPCC reports that economic activities associated with electricity and heat production contributed most to GHG emissions and it led to the steady increase in global average temperatures. Currently, more than 90% of the total GHG emissions of the ASEAN region is attributable to Indonesia, Malaysia, the Philippines, Thailand, and Vietnam. These regions are expected to be greatly affected with climate change. This work analyzes how ASEAN nations can achieve carbon reduction targets while aspiring for economic growth rates in consideration of interdependencies between nations. We thus develop a multi-regional input–output model which can either minimize collective or individual carbon emissions. A high-level eight-sector economy is used for analyzing different economic strategies.&lt;/p&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;p&gt;This model shows that minimizing collective carbon emissions can still yield economic growth. Countries can focus on developing sectors that have potentials for growth and lower carbon intensity as new technologies become available. In the case study examined, results indicate that the services sector, agriculture, and food manufacturing sector have higher potential for economic growth under carbon reduction emission constraints. In addition, the simultaneous implementation of multiple carbon emission reduction strategies provides the largest reduction in regional carbon emissions.&lt;/p&gt;&lt;h3&gt;Conclusions&lt;/h3&gt;&lt;p&gt;This model provides a more holistic view of how the generation of carbon emissions are influenced by the interdependence of nations. The emissions reduction achieved by each country varied depending on the state of technology and the level of economic development in the different regions. Though the presented case focused on the ASEAN region, the model framework can be used for the analysis of other multi-regional systems at various levels of resolution if data is available. Insights obtained from the model results can be used to help nations identify more appropriate and achievable carbon reduction targets and to develop coordinated and more customized policies to target priority sectors in a country. This model is currently limited by the assumption of fixed technical coefficients in the exchange and interdependence of different regions. Future work can investigate modelling flexible multi-regional trade where regions have the option of substituting goods and products in its import or export structure. Other strategies for reducing carbon emission intensity can also be explored, such as modelling transport mode choices, or establishing sectors for waste management. Hybri","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"17 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2022-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454141/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40354537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating spaceborne LiDAR and Sentinel-2 images to estimate forest aboveground biomass in Northern China","authors":"Fugen Jiang,&nbsp;Muli Deng,&nbsp;Jie Tang,&nbsp;Liyong Fu,&nbsp;Hua Sun","doi":"10.1186/s13021-022-00212-y","DOIUrl":"10.1186/s13021-022-00212-y","url":null,"abstract":"<div><h3>Background</h3><p>Fast and accurate forest aboveground biomass (AGB) estimation and mapping is the basic work of forest management and ecosystem dynamic investigation, which is of great significance to evaluate forest quality, resource assessment, and carbon cycle and management. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), as one of the latest launched spaceborne light detection and ranging (LiDAR) sensors, can penetrate the forest canopy and has the potential to obtain accurate forest vertical structure parameters on a large scale. However, the along-track segments of canopy height provided by ICESat-2 cannot be used to obtain comprehensive AGB spatial distribution. To make up for the deficiency of spaceborne LiDAR, the Sentinel-2 images provided by google earth engine (GEE) were used as the medium to integrate with ICESat-2 for continuous AGB mapping in our study. Ensemble learning can summarize the advantages of estimation models and achieve better estimation results. A stacking algorithm consisting of four non-parametric base models which are the backpropagation (BP) neural network, k-nearest neighbor (kNN), support vector machine (SVM), and random forest (RF) was proposed for AGB modeling and estimating in Saihanba forest farm, northern China.</p><h3>Results</h3><p>The results show that stacking achieved the best AGB estimation accuracy among the models, with an R² of 0.71 and a root mean square error (RMSE) of 45.67 Mg/ha. The stacking resulted in the lowest estimation error with the decreases of RMSE by 22.6%, 27.7%, 23.4%, and 19.0% compared with those from the BP, kNN, SVM, and RF, respectively.</p><h3>Conclusion</h3><p>Compared with using Sentinel-2 alone, the estimation errors of all models have been significantly reduced after adding the LiDAR variables of ICESat-2 in AGB estimation. The research demonstrated that ICESat-2 has the potential to improve the accuracy of AGB estimation and provides a reference for dynamic forest resources management and monitoring.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"17 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9438156/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40336982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GHG Monitoring Project for the Global Stocktake 2023: implications of the COP26 Japan Pavilion seminar","authors":"Tomohiro Oda","doi":"10.1186/s13021-022-00211-z","DOIUrl":"10.1186/s13021-022-00211-z","url":null,"abstract":"<div><p>During the 2021 Glasgow Climate Change Conference (COP26), a hybrid seminar event “Greenhouse gas (GHG) Monitoring Project for the Global Stocktake 2023” was held at the COP26 Japan Pavilion on 2nd of November 2011. The participants presented and discussed science-based GHG monitoring approaches in support of the Global Stocktake. This report summarizes the five research talks given at the event.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"17 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9294744/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40534982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal dynamics of forest ecosystem carbon budget in Guizhou: customisation and application of the CBM-CFS3 model for China","authors":"Yuzhi Tang,&nbsp;Quanqin Shao,&nbsp;Tiezhu Shi,&nbsp;Zhensheng Lu,&nbsp;Guofeng Wu","doi":"10.1186/s13021-022-00210-0","DOIUrl":"10.1186/s13021-022-00210-0","url":null,"abstract":"<div><h3>Background</h3><p>Countries seeking to mitigate climate change through forests require suitable modelling approaches to predict carbon (C) budget dynamics in forests and their responses to disturbance and management. The Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) is a feasible and comprehensive tool for simulating forest C stock dynamics across broad levels, but discrepancies remain to be addressed in China. Taking Guizhou as the case study, we customised the CBM-CFS3 model according to China’s context, including the modification of aboveground biomass C stock algorithm, addition of C budget accounting for bamboo forests, economic forests, and shrub forests, improvement of non-forest land belowground slow dead organic matter (DOM) pool initialisation, and other model settings.</p><h3>Results</h3><p>The adequate linear relationship between the estimated and measured C densities (<i>R</i>² = 0.967, <i>P</i> &lt; 0.0001, <i>slope</i> = 0.904) in the model validation demonstrated the high accuracy and reliability of our customised model. We further simulated the spatiotemporal dynamics of forest C stocks and disturbance impacts in Guizhou for the period 1990–2016 using our customised model. Results shows that the total ecosystem C stock and C density, and C stocks in biomass, litter, dead wood, and soil in Guizhou increased continuously and significantly, while the soil C density decreased over the whole period, which could be attributed to deforestation history and climate change. The total ecosystem C stock increased from 1220 Tg C in 1990 to 1684 Tg C in 2016 at a rate of 18 Tg C yr⁻¹, with significant enhancement in most areas, especially in the south and northwest. The total decrease in ecosystem C stock and C expenditure caused by disturbances reached 97.6 Tg C and 120.9 Tg C, respectively, but both represented significant decreasing trends owing to the decline of disturbed forest area during 1990–2016. Regeneration logging, deforestation for agriculture, and harvest logging caused the largest C stock decrease and C expenditure, while afforestation and natural expansion of forest contributed the largest increases in C stock.</p><h3>Conclusions</h3><p>The forests in Guizhou were a net carbon sink under large-scale afforestation throughout the study period; Our customised CBM-CFS3 model can serve as a more effective and accurate method for estimating forest C stock and disturbance impacts in China and further enlightens model customisation to other areas.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"17 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2022-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9250733/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40464911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An empirical model for estimating daily atmospheric column-averaged CO2 concentration above São Paulo state, Brazil","authors":"Luis Miguel da Costa,&nbsp;Gustavo André de Araújo Santos,&nbsp;Alan Rodrigo Panosso,&nbsp;Glauco de Souza Rolim,&nbsp;Newton La Scala","doi":"10.1186/s13021-022-00209-7","DOIUrl":"10.1186/s13021-022-00209-7","url":null,"abstract":"<div><h3>Background</h3><p>The recent studies of the variations in the atmospheric column-averaged CO₂ concentration (<span>({text{X}}_{{{text{CO}}_{{2}} }})</span>) above croplands and forests show a negative correlation between <span>({text{X}}_{{{text{CO}}_{{2}} }})</span>and Sun Induced Chlorophyll Fluorescence (SIF) and confirmed that photosynthesis is the main regulator of the terrestrial uptake for atmospheric CO₂. The remote sensing techniques in this context are very important to observe this relation, however, there is still a time gap in orbital data, since the observation is not daily. Here we analyzed the effects of several variables related to the photosynthetic capacity of vegetation on <span>({text{X}}_{{{text{CO}}_{{2}} }})</span> above São Paulo state during the period from 2015 to 2019 and propose a daily model to estimate the natural changes in atmospheric CO₂.</p><h3>Results</h3><p>The data retrieved from the Orbiting Carbon Observatory-2 (OCO-2), NASA-POWER and Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) show that Global Radiation (Qg), Sun Induced Chlorophyll Fluorescence (SIF) and, Relative Humidity (RH) are the most significant factors for predicting the annual <span>({text{X}}_{{{text{CO}}_{{2}} }})</span> cycle. The daily model of <span>({text{X}}_{{{text{CO}}_{{2}} }})</span> estimated from Qg and RH predicts daily <span>({text{X}}_{{{text{CO}}_{{2}} }})</span> with root mean squared error of 0.47 ppm (the coefficient of determination is equal to 0.44, p &lt; 0.01).</p><h3>Conclusion</h3><p>The obtained results imply that a significant part of daily <span>({text{X}}_{{{text{CO}}_{{2}} }})</span> variations could be explained by meteorological factors and that further research should be done to quantify the effects of the atmospheric transport and anthropogenic emissions.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"17 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2022-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://cbmjournal.biomedcentral.com/counter/pdf/10.1186/s13021-022-00209-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43616905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stand carbon storage and net primary production in China’s subtropical secondary forests are predicted to increase by 2060","authors":"Jia Jin,&nbsp;Wenhua Xiang,&nbsp;Yelin Zeng,&nbsp;Shuai Ouyang,&nbsp;Xiaolu Zhou,&nbsp;Yanting Hu,&nbsp;Zhonghui Zhao,&nbsp;Liang Chen,&nbsp;Pifeng Lei,&nbsp;Xiangwen Deng,&nbsp;Hui Wang,&nbsp;Shirong Liu,&nbsp;Changhui Peng","doi":"10.1186/s13021-022-00204-y","DOIUrl":"10.1186/s13021-022-00204-y","url":null,"abstract":"<div><h3>Background</h3><p>Forest ecosystems play an important role in carbon sequestration, climate change mitigation, and achieving China's target to become carbon (C) neutral by 2060. However, changes in C storage and net primary production (NPP) in natural secondary forests stemming from tree growth and future climate change have not yet been investigated in subtropical areas in China. Here, we used data from 290 inventory plots in four secondary forests [evergreen broad-leaved forest (EBF), deciduous and evergreen broad-leaved mixed forest (DEF), deciduous broad-leaved forest (DBF), and coniferous and broad-leaved mixed forest (CDF)] at different restoration stages and run a hybrid model (TRIPLEX 1.6) to predict changes in stand carbon storage and NPP under two future climate change scenarios (RCP4.5 and RCP8.5).</p><h3>Results</h3><p>The runs of the hybrid model calibrated and validated by using the data from the inventory plots suggest significant increase in the carbon storage by 2060 under the current climate conditions, and even higher increase under the RCP4.5 and RCP8.5 climate change scenarios. In contrast to the carbon storage, the simulated EBF and DEF NPP declines slightly over the period from 2014 to 2060.</p><h3>Conclusions</h3><p>The obtained results lead to conclusion that proper management of China’s subtropical secondary forests could be considered as one of the steps towards achieving China’s target to become carbon neutral by 2060.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"17 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://cbmjournal.biomedcentral.com/counter/pdf/10.1186/s13021-022-00204-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46986822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncertainties of soil organic carbon stock estimation caused by paleoclimate and human footprint on the Qinghai Plateau","authors":"Xia Liu,&nbsp;Tao Zhou,&nbsp;Peijun Shi,&nbsp;Yajie Zhang,&nbsp;Hui Luo,&nbsp;Peixin Yu,&nbsp;Yixin Xu,&nbsp;Peifang Zhou,&nbsp;Jingzhou Zhang","doi":"10.1186/s13021-022-00203-z","DOIUrl":"10.1186/s13021-022-00203-z","url":null,"abstract":"<div><h3>Background</h3><p>Quantifying the stock of soil organic carbon (SOC) and evaluating its potential impact factors is important to evaluating global climate change. Human disturbances and past climate are known to influence the rates of carbon fixation, soil physiochemical properties, soil microbial diversity and plant functional traits, which ultimately affect the current SOC storage. However, whether and how the paleoclimate and human disturbances affect the distribution of SOC storage on the high-altitude Tibetan Plateau remain largely unknown. Here, we took the Qinghai Plateau, the main component of the Tibetan Plateau, as our study region and applied three machine learning models (random forest, gradient boosting machine and support vector machine) to estimate the spatial and vertical distributions of the SOC stock and then evaluated the effects of the paleoclimate during the Last Glacial Maximum and the mid-Holocene periods as well as the human footprint on SOC stock at 0 to 200 cm depth by synthesizing 827 soil observations and 71 environmental factors.</p><h3>Results</h3><p>Our results indicate that the vegetation and modern climate are the determinant factors of SOC stocks, while paleoclimate (i.e., paleotemperature and paleoprecipitation) is more important than modern temperature, modern precipitation and the human footprint in shaping current SOC stock distributions. Specifically, the SOC stock was deeply underestimated in near natural ecosystems and overestimated in the strongly human disturbance ecosystems if the model did not consider the paleoclimate. Overall, the total SOC stock of the Qinghai Plateau was underestimated by 4.69%, 12.25% and 6.67% at depths of 0 to 100 cm, 100 to 200 cm and 0 to 200 cm, respectively. In addition, the human footprint had a weak influence on the distributions of the SOC stock. We finally estimated that the total and mean SOC stock at 200 cm depth by including the paleoclimate effects was 11.36 Pg C and 16.31 kg C m⁻², respectively, and nearly 40% SOC was distributed in the top 30 cm.</p><h3>Conclusion</h3><p>The paleoclimate is relatively important for the accurate modeling of current SOC stocks. Overall, our study provides a benchmark for predicting SOC stock patterns at depth and emphasizes that terrestrial carbon cycle models should incorporate information on how the paleoclimate has influenced SOC stocks.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"17 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://cbmjournal.biomedcentral.com/counter/pdf/10.1186/s13021-022-00203-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42352960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}