土地利用变化对区域碳源汇的影响研究进展

土地利用变化对陆地生态系统碳循环有着重要的影响,既可能成为碳源,也可能是碳汇。在国内外相关研究的基础上,综述了土地利用变化对全球及区域尺度上森林、草地和农业生态系统碳循环的影响。全球范围内,森林砍伐后向草地和农田的转化发挥碳源的作用,在毁林碳排放中占主导地位,其中热带地区森林转变为农田和草场的碳排放均高于温带和北方森林。另一方面,土地利用变化可促进森林的碳贮存,如退耕还林、改善森林管理等。各区域森林生态系统通过土地利用变化贮存碳的潜力存在显著差别,热带湿润和半湿润地区具有较大的碳汇潜力,而干旱地区减少碳排放的空间相对较少。开垦活动是影响草地生态系统碳储存最主要的人类活动,草地转变为农田伴随着土壤碳的流失。森林或草场转变为农田的过程伴随着植被和土壤碳储量的减少,生态系统碳储量降低,因此它是一个碳排放的过程。伴随着城市的扩张,农田向建设用地的转化也是一个碳排放的过程。当前评估土地利用变化影响的研究方法主要有遥感观测和遥感模型、统计估算、生态系统模型以及土地利用与生态系统模型的耦合。研究方法得到不断地完善和改进的同时,还存在着一些不确定性,因此需要建立统一的观测统计方法,降低数据中的不确定性;完善土地利用与生态系统模型的耦合研究;建立多尺度土地利用变化及生态系统综合技术方法体系;开展碳减排目标下土地利用最优化布局研究。

Progress in the study on the impact of land-use change on regional carbon sources and sinks

Land-use change may be carbon sources or sinks that play vital roles in the terrestrial carbon cycle. This paper reviewed the effects of land-use change on the carbon cycle of forests, grasslands, and agricultural ecosystems based on researches carried out in China and worldwide. Globally, the effects of the conversion of forest to cropland and grassland on the global carbon cycle dominate the carbon emissions from deforestation. Regional differences in carbon emissions due to transformation of forests into croplands and grasslands reflect on the regional climate. The carbon emissions per/hm² from tropical forest converted into cropland and grassland were 151 and 120 tC/hm² higher than the emissions from temperate or boreal forests, respectively. Furthermore, land-use change could promote forest carbon storage through reforestation and improved forest management. Significant differences exist in the potential carbon sinks created through change in land use. Tropical humid and semi-humid regions have greater potential for carbon sequestration than temperate regions, while the potential for carbon sequestration in arid regions is relatively small. Reclamation of grassland to cropland is one of the main human activities that affects the carbon stock of grassland ecosystems, causing the soil carbon stock to fall by 59%. When a forest or grassland is converted to cropland, carbon stocks in both vegetation and soil are reduced as well as the ecosystem''s carbon storage capacity. With the expansion of cities, the conversion of cropland to land for construction further increases carbon emissions. Currently, researchers employ several methods when analyzing the effects of land-use change including remote sensing and models based on remote sensing, statistical estimation, ecosystem models, and the coupling models of land-use change and ecosystem. Temporal land-use change data can be obtained from remote sensing data, as well as the data related to temperature, soil moisture, and vegetation structure parameters. These data can be used to drive remote sensing models such as the Carnegie-Ames-Stanford Approach (CASA) and Global Production Efficiency Modeling (GLO-PEM). The bookkeeping model is a typical statistical model and is easy to use, while the simulation results cannot provide sufficient accuracy. Ecosystem models include models of static and dynamic types. CENTURY, CASA, and BIOME are believed to be the most widely used static models, while currently the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM) and Terrestrial Ecosystem Model (TEM) are the most widely used dynamic models. Although ecosystem models can simulate the carbon balance of an ecosystem quite well, the current models do not consider the impact of land-use change on the carbon cycle and could be applied only at certain geographical scales. Therefore, a land-use model should be coupled with ecosystem models, such as the Integrated Model to Assess the Global Environment (IMAGE) and the Lund-Potsdam-Jena managed Land Dynamic Global Vegetation, Agriculture, and Water Balance Model (LPJmL). Although the research methods have been continually refined and improved, uncertainties remain in both the data and the models. Therefore, unified statistical observation methods should be established to reduce the uncertainties within various datasets, which will improve the accuracy of simulation results. Additionally, we need to improve the use of land-use models coupled with ecosystem models, which is expected to be the main research direction of this field. Integrated technology systems using multi-scale land-use change and ecosystem data should be established while considering social, economic, and environmental factors that drive carbon storage and emission. Furthermore, we need to carry out research designed to aid in the optimization of land-use layout with the target of reducing carbon emissions to provide more valuable information and sound policy recommendations.