地理学报 ›› 2019, Vol. 74 ›› Issue (3): 446-459.doi: 10.11821/dlxb201903004

• 土地利用与生态系统服务 • 上一篇    下一篇

基于土地利用变化情景的生态系统碳储量评估——以太行山淇河流域为例

朱文博(), 张静静, 崔耀平, 郑辉, 朱连奇()   

  1. 河南大学环境与规划学院,开封 475004
  • 收稿日期:2018-03-15 修回日期:2019-02-14 出版日期:2019-03-25 发布日期:2019-03-19
  • 作者简介:

    朱文博(1989-), 女, 河南开封人, 博士, 主要从事全球变化与生态系统服务研究。E-mail: zhuwb517@163.com

  • 基金资助:
    国家重点基础研究发展计划(2015CB452702);国家自然科学基金项目(41671090)

Assessment of territorial ecosystem carbon storage based on land use change scenario: A case study in Qihe River Basin

Wenbo ZHU(), Jingjing ZHANG, Yaoping CUI, Hui ZHENG, Lianqi ZHU()   

  1. College of Environment and Planning, Henan University, Kaifeng 475004, Henan, China
  • Received:2018-03-15 Revised:2019-02-14 Online:2019-03-25 Published:2019-03-19
  • Supported by:
    National Basic Research Program of China, No.2015CB452702;National Natural Science Foundation of China. No.41671090

摘要:

区域土地利用变化是导致生态系统碳储量变化的主要原因,影响其碳源、碳汇效应,但以往结合时空尺度探讨流域未来土地利用变化对生态系统碳储量影响的研究尚不多见。以太行山淇河流域为例,分析2005-2015年土地利用变化,采用Markov-CLUE-S复合模型预测2025年自然增长、耕地保护及生态保护情景下的土地利用格局,并基于土地利用数据,运用InVEST模型的碳储量模块评估2005-2015年及未来不同情景下的生态系统碳储量。结果表明:① 2015年淇河流域生态系统碳储量和平均碳密度分别为3.16×107 t和141.9 t/hm2,自2005年以来分别下降0.07×107 t和2.89 t/hm2。② 2005-2015年碳密度在低海拔区域以减少为主,在高海拔区域增加区与减少区比例相当,淇河中下游地区建设用地的大肆扩张以及上游林地的退化是导致碳密度下降的主要原因。③ 2015-2025年自然增长情景下碳储量和碳密度下降仍较明显,主要是低海拔区域固碳能力的减弱;耕地保护情景减缓了碳储量和碳密度的下降幅度,主要是由于低海拔区固碳能力的增强;生态保护情景下,碳储量和碳密度显著增加,分别达到3.19×107 t和143.26 t/hm2,主要发生在海拔高于1100 m的区域。生态保护情景能够增强固碳能力,但不能有效控制耕地面积的减小。因此,研究区土地利用规划可统筹考虑生态保护和耕地保护情景,既能增加碳汇,又能保障耕地质量和粮食安全。

关键词: 土地利用, Markov-CLUE-S复合模型, InVEST模型, 碳储量, 情景模拟, 淇河流域

Abstract:

The change of regional land use is the main cause for the change of carbon storage in territorial ecosystem, which affects the process of carbon emission and sink. However, previous studies on the impact of future land use change on ecosystem carbon storage considering temporal and spatial scales in the basin are still absent. This study analyzed land use change from 2005 to 2015 in the Qihe River Basin, Taihang mountainous areas, and used Markov-CLUE-S composite models to predict land use pattern of this region in 2025, under three scenarios of natural growth, farmland protection and ecological conservation. Based on the data of land use, we used carbon storage module in InVEST model to evaluate carbon storage of territorial ecosystem during the past 10 years and the future. The results showed that: (1) The carbon storage and carbon density of the ecosystem in the Qihe River Basin in 2015 were 3.16×107 t and 141.9 t/hm2, respectively, and they both had decreased by 0.07×107 t and 2.89 t/hm2 during the 10 years. (2) From 2005 to 2015, the carbon density was mainly reduced in low altitude areas, and the ratio of the increased areas was similar to that of the reduced areas in the high altitude areas. The decrease of carbon density was mainly caused by expansion of construction land in the middle and lower reaches, and degradation of forestland in the upper reach of Qihe River Basin. (3) From 2015 to 2025, the carbon storage and carbon density of ecosystem will decrease by 0.03×107 t and 1.38 t/hm2 respectively in the natural growth scenario, mainly due to the reduction of carbon sequestration capacity in low altitude areas. The farmland conservation scenario will slow down the decrease of carbon storage and carbon density (0.01×107 t and 0.44 t/hm2), mainly due to the enhancement of carbon sequestration capacity in low altitude areas. The ecological protection scenario will increase carbon storage and carbon density significantly to 3.19×107 t and 143.26 t/hm2 respectively, mainly appearing in the area above 1100 m. The ecological protection scenario can enhance carbon sequestration capacity, but it cannot effectively control the loss of farmland area. Therefore, the land use planning of the study area can comprehensively consider the ecological protection scenario and farmland conservation scenario, which not only increases carbon sink, but also ensures the farmland quality and food security.

Key words: land use, Markov-CLUE-S composite model, InVEST model, carbon storage, scenario simulation, Qihe River Basin