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| 1 | Moisture regime influence on soil carbon stock and carbon sequestration rates in semi-arid forests of the National Capital Region, India显示文摘Understanding the dynamics of soil carbon is crucial for assessing the soil carbon storage and predicting the potential of mitigating carbon dioxide from the atmosphere to the biomass and soil.The present study evaluated variations of soil carbon stock in semi-arid forests in India under diff erent moisture regimes.Soil organic carbon(SOC)and soil inorganic carbon(SIC)stocks were determined in diff erent moisture regimes i.e.monsoon,post-monsoon,winter and pre-monsoon seasons at 0–10 and>10–20 cm depths.SOC stock showed signifi cant variations under different moisture regimes.The highest SOC stock was during winter(22.81 Mg C ha−1)and lowest during the monsoon season(2.34 Mg C ha−1)among all the ridge forests under study.SOC and SIC stock under diff erent moisture regimes showed signifi cant negative correlation with soil moisture(p<0.05),as a sudden increase in soil moisture after rainfall results in an increase in carbon loss due to microbial decomposition of accumulated carbon during the dry period.There was an increase in annual SOC stock and a decrease(or no change in some cases),in SIC stock at both the depths during the study period.The SOC and SIC sequestration rates were estimated as any increase/decrease in the respective stock during each successive year.SOC sequestered ranged between 0.046 and 0.741 Mg C ha−1 y−1.Similarly,SIC sequestration ranged between 0.013 and 0.023 Mg C ha−1 y−1 over all ridge forests up to 20 cm depth.The Delhi ridge forests,which accounts to 0.007%of the semi-arid regions of India,contribute 0.25–0.32%of the national potential(semi-arid region)for SOC sequestration up to 20 cm depth.The estimates of the rate of C sequestration in this study provide a realistic image of carbon dynamics under present climatic conditions of semi-arid forests,and could be used in developing a database and formulating new strategies for carbon dioxide mitigation by enhancing soil C sequestration rates. | Urvashi Tomar Ratul Baishya | 2020 | Journal of Forestry Research2020，31，6： | 2 |
| 2 | 森林经营项目碳汇核算与期权价值评估——以吉林红石项目为例显示文摘林业温室气体自愿减排项目既可以作为碳交易标的参与碳交易,又能作为生态产品助力“双碳”战略的实施,其减排量核算与价值评估对于项目决策及规划上市交易后反哺当地开发具有重要意义。基于蓄积—生物量方程法与缺省值法对吉林红石项目进行了碳汇核算与实物期权价值评估,结果表明:(1)该项目总计减排3.50×10^(7)t,年均减排量5.84×10^(5)t/a;项目占地2.95×10^(5)hm^(2),可以创造2334.50元/hm2的欧式看涨期权价值。(2)我国林业CCER的交易预期并不明朗,林业部门应及时调整采伐政策,交易部门需适时调整抵消比例以调动交易热度,开发者也要充分规划项目进程,避免产生较大沉没成本或难以达到预期收益。 | 高迎军 张颖 朱钰华 | 2022 | 资源开发与市场2022，38，9： | 1 |
| 3 | Assessment of above- and belowground carbon pools in a semi-arid forest ecosystem of Delhi, India显示文摘Background:Assessment of carbon pools in semi-arid forests of India is crucial in order to develop a better action plan for management of such ecosystems under global climate change and rapid urbanization.This study,therefore,aims to assess the above-and belowground carbon storage potential of a semi-arid forest ecosystem of Delhi.Methods:For the study,two forest sites were selected,i.e.,north ridge(NRF)and central ridge(CRF).Aboveground tree biomass was estimated by using growing stock volume equations developed by Forest Survey of India and specific wood density.Understory biomass was determined by harvest sampling method.Belowground(root)biomass was determined by using a developed equation.For soil organic carbon(SOC),soil samples were collected at 0–10-cm and 10–20-cm depth and carbon content was estimated.Results:The present study estimated 90.51 Mg ha−1 biomass and 63.49 Mg C ha−1 carbon in the semi-arid forest of Delhi,India.The lower diameter classes showed highest tree density,i.e.,240 and 328 individuals ha−1(11–20 cm),basal area,i.e.,8.7(31–40 cm)and 6.08m2 ha−1(11–20 cm),and biomass,i.e.,24.25 and 23.57 Mg ha−1(11–20 cm)in NRF and CRF,respectively.Furthermore,a significant contribution of biomass(7.8 Mg ha−1)in DBH class 81–90 cm in NRF suggested the importance of mature trees in biomass and carbon storage.The forests were predominantly occupied by Prosopis juliflora(Sw.)DC which also showed the highest contribution to the(approximately 40%)tree biomass.Carbon allocation was maximum in aboveground(40–49%),followed by soil(29.93–37.7%),belowground or root(20–22%),and litter(0.27–0.59%).Conclusion:Our study suggested plant biomass and soils are the potential pools of carbon storage in these forests.Furthermore,carbon storage in tree biomass was found to be mainly influenced by tree density,basal area,and species diversity.Trees belonging to lower DBH classes are the major carbon sinks in these forests.In the study,native trees contributed to the significant amount of carbon stored in their biomass and soils.The estimated data is important in framing forest management plans and strategies aimed at enhancing carbon sequestration potential of semi-arid forest ecosystems of India. | Archana Meena Ankita Bidalia M.Hanief JDinakaran K.S.Rao | 2019 | Ecological Processes2019，8，1： | 0 |
| 4 | A traceability analysis system for model evaluation on land carbon dynamics: design and applications显示文摘Background:An increasing number of ecological processes have been incorporated into Earth system models.However,model evaluations usually lag behind the fast development of models,leading to a pervasive simulation uncertainty in key ecological processes,especially the terrestrial carbon(C)cycle.Traceability analysis provides a theoretical basis for tracking and quantifying the structural uncertainty of simulated C storage in models.Thus,a new tool of model evaluation based on the traceability analysis is urgently needed to efficiently diagnose the sources of inter-model variations on the terrestrial C cycle in Earth system models.Methods:A new cloud-based model evaluation platform,i.e.,the online traceability analysis system for model evaluation(TraceME v1.0),was established.The TraceME was applied to analyze the uncertainties of seven models from the Coupled Model Intercomparison Project(CMIP6).Results:The TraceME can effectively diagnose the key sources of different land C dynamics among CMIIP6 models.For example,the analyses based on TraceME showed that the estimation of global land C storage varied about 2.4 folds across the seven CMIP6 models.Among all models,IPSL-CM6A-LR simulated the lowest land C storage,which mainly resulted from its shortest baseline C residence time.Over the historical period of 1850–2014,gross primary productivity and baseline C residence time were the major uncertainty contributors to the inter-model variation in ecosystem C storage in most land grid cells.Conclusion:TraceME can facilitate model evaluation by identifying sources of model uncertainty and provides a new tool for the next generation of model evaluation. | Jian Zhou Jianyang Xia Ning Wei Yufu Liu Chenyu Bian Yuqi Bai Yiqi Luo | 2021 | Ecological Processes2021，10，1： | 0 |
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