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乌江流域固碳服务时空格局及驱动机制

杨俊毅 李俊生 关潇

杨俊毅, 李俊生, 关潇. 乌江流域固碳服务时空格局及驱动机制[J]. 环境科学研究, 2023, 36(4): 757-767. doi: 10.13198/j.issn.1001-6929.2023.01.08
引用本文: 杨俊毅, 李俊生, 关潇. 乌江流域固碳服务时空格局及驱动机制[J]. 环境科学研究, 2023, 36(4): 757-767. doi: 10.13198/j.issn.1001-6929.2023.01.08
YANG Junyi, LI Junsheng, GUAN Xiao. Spatio-Temporal Pattern and Driving Mechanism of Ecosystem Carbon Sequestration Services in the Wujiang River Basin[J]. Research of Environmental Sciences, 2023, 36(4): 757-767. doi: 10.13198/j.issn.1001-6929.2023.01.08
Citation: YANG Junyi, LI Junsheng, GUAN Xiao. Spatio-Temporal Pattern and Driving Mechanism of Ecosystem Carbon Sequestration Services in the Wujiang River Basin[J]. Research of Environmental Sciences, 2023, 36(4): 757-767. doi: 10.13198/j.issn.1001-6929.2023.01.08

乌江流域固碳服务时空格局及驱动机制

doi: 10.13198/j.issn.1001-6929.2023.01.08
基金项目: 国家重点研发计划项目(No.2020YFC1807704)
详细信息
    作者简介:

    杨俊毅(1998-),男(瑶族),广西桂林人,yangjunyi20@mails.ucas.ac.cn

    通讯作者:

    关潇(1978-),女,山西怀仁人,副研究员,博士,主要从事草地资源与生态保护修复研究,cynthia815@126.com

  • 中图分类号: X21

Spatio-Temporal Pattern and Driving Mechanism of Ecosystem Carbon Sequestration Services in the Wujiang River Basin

Funds: National Key Research and Development Program of China (No.2020YFC1807704)
  • 摘要: 生态系统提供的固碳服务是实现碳中和的重要手段之一,明确环境因子的驱动机制是有效提高固碳服务的重要基础. 该研究以乌江流域为研究区域,利用InVEST模型核算乌江流域2000—2020年碳储量,并综合考虑社会-经济-自然系统中的影响因素,使用随机森林模型和部分依赖图模型(PDP模型)分析各因子对固碳服务的贡献度及驱动机制. 结果表明:①2000—2020年,乌江流域固碳服务空间上呈东北高西南低的分布特征,表现为下游>中游>上游,时间上呈显著增加趋势,且上游的增速大于中游及下游. ②时空尺度上,自然地表类因子均是对固碳服务贡献最大的环境要素,其中植被覆盖度在空间尺度上的贡献度最高,为55.08%;坡度在时间尺度上的贡献度最高,为34.46%. ③各影响因子在时空上呈现出不同的驱动机制,其中人口密度的驱动机制变化最为强烈,在空间上,固碳服务随人口的增加而降低,在时间上,固碳服务整体随人口的增加而增加. ④2000—2020年,人类活动的改变是乌江流域固碳服务增加的重要原因,气候变化也是较重要的影响因素. 研究显示,固碳服务的高低虽受限于自然地表类环境因子,但人类活动的正面干扰能显著提高固碳服务.

     

  • 图  1  乌江流域区位

    Figure  1.  Location of the Wujiang River Basin

    图  2  2000—2020年乌江流域固碳服务变化

    Figure  2.  Changes in carbon sequestration services in the Wujiang River Basin from 2000 to 2020

    图  3  2000—2020年乌江流域固碳服务的空间格局

    Figure  3.  Spatial pattern of carbon sequestration services in the Wujiang River Basin from 2000 to 2020

    图  4  2000—2020年乌江流域碳库储量

    Figure  4.  Carbon storage in the Wujiang River Basin from 2000 to 2020

    图  5  2000—2020年乌江流域固碳服务时序变化

    Figure  5.  Temporal variation of carbon sequestration services in the Wujiang River Basin from 2000 to 2020

    图  6  2000—2020年乌江流域环境因子的时序变化

    Figure  6.  Temporal variation of environmental factors in the Wujiang River Basin from 2000 to 2020

    图  7  2000—2020年乌江流域环境因子的时空贡献度

    注:Dem—海拔;Slo—坡度;FVC—植被覆盖度;Pre—年均降水量;Tem—年均气温;Pet—年均潜在蒸散量;Pop—人口密度;lig—夜间灯光指数;Bu—建筑地密度;pH—土壤pH;Som—土壤有机质含量;AWC—土壤可利用水量.

    Figure  7.  Spatial and temporal contribution of environmental factors in the Wujiang River Basin from 2000 to 2020

    图  8  乌江流域各环境因子的空间驱动机制

    Figure  8.  Spatial driving mechanism of environmental factors in the Wujiang River Basin

    图  9  乌江流域各环境因子的时间驱动机制

    Figure  9.  Temporal driving mechanism of environmental factors in the Wujiang River Basin

    图  10  乌江流域人口密度的时空驱动机制

    Figure  10.  Spatio-temporal driving mechanism of population density in the Wujiang River Basin

    表  1  研究所需数据及其来源

    Table  1.   Data required for by this the study and their sources

    数据类型数据名称详细信息
    土地利用数据 土地利用 2000—2020年逐年的土地利用数据来源于Earth System Science Data(https://www.earth-system-science-data.net),空间分辨率为30 m
    生态系统净生
    态系统生产力
    生态系统净生
    态系统生产力
    2000—2020年逐年的陆地生态系统净生态系统生产力来源于国家地球系统科学数据中心(http://loess.geodata.cn),空间分辨率为500 m
    人类因子 人口密度 2000—2020年逐年的人口密度数据来源于美国能源部橡树岭国家实验室(https://landscan.ornl.gov),空间分辨率为1 km
    夜间灯光指数 2000—2020年校正后的逐年夜间灯光指数来源于全球变化科学研究数据出版系统(http://geodoi.ac.cn),空间分辨率为500 m
    建筑地密度 2000—2020年逐年的建筑地密度基于土地利用数据计算得来,空间分辨率为1 km
    气象因子 年均气温 2000—2020年逐年的年均气温数据来源于国家青藏高原科学数据中心(http://www.tpdc.ac.cn),空间分辨率为1 km
    年均降水量 2000—2020年逐年的年均降水数据来源于国家地球系统科学数据中心(http://loess.geodata.cn),空间分辨率为1 km
    年均潜在蒸散量 2000—2020年逐年的年均潜在蒸散发数据来源于国家青藏高原科学数据中心(http://www.tpdc.ac.cn),空间分辨率为1 km
    自然地表
    因子
    植被覆盖度 植被覆盖度是基于NDVI使用像元二分模型计算得到,2000—2020年逐年的NDVI数据来源于MODIS植被指数产品数据MOD13Q1(https://earthdata.nasa.gov),空间分辨率为250 m
    海拔 海拔数据来源于地理空间数据云(http://www.gscloud.cn),空间分辨率为30 m
    坡度 基于海拔数据在ArcGIS软件上计算,空间分辨率为30 m
    土壤因子 土壤可利用水量 由第二次全国土地调查中的土壤砂粒、粉粒、黏粒及土壤有机质含量计算得到,空间分辨率为1 km
    土壤pH 土壤pH来源于国家青藏高原科学数据中心(http://www.tpdc.ac.cn),空间分辨率为1 km
    土壤有机质含量 土壤有机质含量数据来源于国家青藏高原科学数据中心(http://www.tpdc.ac.cn),空间分辨率为1 km
    下载: 导出CSV

    表  2  固碳服务驱动因子

    Table  2.   The drivers of carbon sequestration services

    影响因子具体因子
    人为因子 人口密度、夜间灯光指数、建筑地密度
    气候因子 年均气温、年均降水量、年均潜在蒸散量
    自然地表因子 海拔、坡度、植被覆盖度
    土壤因子 土壤pH、土壤有机质含量、土壤可利用水量
    下载: 导出CSV
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