| 基于InVEST模型和PLUS模型的环杭州湾生态系统碳储量 |
| 摘要点击 1573 全文点击 440 投稿时间：2022-04-07 修订日期：2022-08-22 |
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| 中文关键词 碳储量 InVEST模型 PLUS模型 环杭州湾大湾区 未来情景模拟 |
| 英文关键词 carbon storage InVEST model PLUS model Greater Bay area around Hangzhou Bay future scenario simulation |
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| 中文摘要 |
| 研究土地利用方式与生态系统服务碳储量的关系,对于区域碳排放管理具有重要意义.利用InVEST模型碳储量模块和PLUS模型,探究并预测研究区2000~2018年和2018~2030年生态系统碳储量时空变化特征及其与土地利用方式之间的关系.结果表明,研究区2000、2010和2018年碳储量分别为7.250×108、7.227×108和7.241×108 t,呈先减后增趋势.土地利用类型变化是导致生态系统碳储量变化的主要因素,建设用地的快速扩张导致碳储量降低.与土地利用类型相对应,研究区碳储量空间分异显著,并以碳储量分界线为界,呈现"东北低西南高"特征.预测结果显示,至2030年碳储量为7.344×108 t,较2018年增加1.42%,林地面积的增长是主要原因. |
| 英文摘要 |
| The study of the relationship between the land use and carbon storage of ecosystem services is of great significance to regional carbon emission management. It can provide an important scientific basis for the management of regional ecosystem carbon pools and the formulation of policies for emission reduction and foreign exchange increases. The carbon storage component of the InVEST model and the PLUS model were used to study and predict the temporal and spatial variation characteristics of carbon storage in the ecological system and their relationship with land use type for the periods of 2000-2018 and 2018-2030 in the research area. The results were as follows:the carbon storage in 2000, 2010, and 2018 in the research area was 7.250×108, 7.227×108, and 7.241×108 t, respectively, which suggested that it first decreased and then increased. The change in land use pattern was the main cause of changed carbon storage in the ecological system, and the fast expansion of construction land resulted in the decrease of carbon storage. With its correspondence to land use patterns, the carbon storage in the research area demonstrated significant spatial differentiation and was characterized by low storage in the northeast and high storage in the southwest according to the demarcation line of carbon storage. The resulting prediction was that the carbon storage in 2030 will be 7.344×108 t, with an increase of 1.42% compared with that in 2018, owing mainly to increased forest land. Soil type and population were the two driving factors with the highest contribution to construction land, and soil type and DEM had the highest contribution to forest land. |
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