干旱区研究

干旱区研究 >

2024 , Vol. 41 >Issue 6: 1059 - 1068

DOI: https://doi.org/10.13866/j.azr.2024.06.14

生态与环境

基于PLUS土地利用模拟的阿克苏河流域NEP时空格局研究

  • 李沛尧 ,
  • 王新军 ,
  • 许世贤 ,
  • 高胜寒 ,
  • 薛智暄 ,
  • 衡瑞
展开
  • 1.新疆农业大学新疆土壤与植物生态过程重点实验室，新疆 乌鲁木齐 830052
    2.兴林（北京）林业工程设计研究院有限公司，北京 100000
    3.中国科学院新疆生态与地理研究所荒漠与绿洲国家重点实验室，新疆 乌鲁木齐 830011
李沛尧（1996-），男，硕士研究生，主要研究方向为土地利用与碳排放. E-mail: lpy88542585@163.com

收稿日期: 2023-10-28

  修回日期: 2024-05-21

  网络出版日期: 2024-07-03

基金资助

新疆维吾尔自治区财政专项“天山北坡典型区水土流失与经济发展关系研究”(213031002)

收起

Spatiotemporal pattern of NEP in Aksu River Basin based on PLUS land use simulation

  • LI Peiyao ,
  • WANG Xinjun ,
  • XU Shixian ,
  • GAO Shenghan ,
  • XUE Zhixuan ,
  • HENG Rui
Expand
  • 1. Xinjiang Key Laboratory of Soil and Plant Ecological Processes General Station of Soil, Xinjiang Agricultural University, Urumqi 830052, Xinjiang, China
    2. Xinglin (Beijing) Forestry Engineering Design and Research Institute Co., Ltd., Beijing 100000, China
    3. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China

Received date: 2023-10-28

  Revised date: 2024-05-21

  Online published: 2024-07-03

Fold

摘要

净生态系统生产力（NEP）是评估陆地生态系统碳吸收量的重要指标，而土地利用/覆盖变化（LUCC）是影响区域碳吸收量变化的主要因素之一，分析LUCC与NEP的变化趋势，对区域实现“双碳”目标具有重要意义。基于阿克苏河流域2000—2020年LUCC与MODIS遥感数据估算区域内各土地利用/覆盖类型的年均固碳速率，借助PLUS模型模拟未来40 a的LUCC，预测未来40 a流域NEP时空变化趋势。结果表明：（1） 近20 a流域内总NEP呈上升趋势，上升速率为0.136 Mt C·（10a）-1，林地平均固碳速率最高；（2） 未来40 a阿克苏河流域总碳吸收量在不断上升。林地面积的增加是阿克苏河流域碳吸收量上升的主要途径，生态保护工程的积极性对流域内碳吸收量起到关键作用。

关键词： NEP; LUCC模拟; PLUS模型; 阿克苏河流域

本文引用格式

李沛尧 , 王新军 , 许世贤 , 高胜寒 , 薛智暄 , 衡瑞 . 基于PLUS土地利用模拟的阿克苏河流域NEP时空格局研究[J]. 干旱区研究, 2024 , 41(6) : 1059 -1068 . DOI: 10.13866/j.azr.2024.06.14

Abstract

Net Ecosystem Productivity (NEP) is a crucial indicator for assessing the carbon sequestration capacity of terrestrial ecosystems, and Land Use/Cover Change (LUCC) is a key factor influencing regional differences in carbon uptake. Analyzing the trends of LUCC and NEP is essential for achieving regional carbon peaking and carbon neutrality goals. Based on the LUCC and MODIS remote sensing data from 2000 to 2020 in the Aksu River Basin, the annual average carbon sequestration rate of each land use/cover type in the region was estimated. The LUCC for the next 40 years was simulated by the PLUS model, and the spatial and temporal trend of NEP for the next 40 years in the river basin was predicted. The results show that: (1) the total NEP in the basin has shown an increasing trend in the past 20 years, with an uptake rate of 0.136 Mt C·(10a)-1, and the average carbon sequestration rate of forest area is the highest; (2) the total carbon uptake in the Aksu River Basin will continue to increase in the future 40 years. The increase in forest area is the main way of increasing carbon uptake in the Aksu River Basin, and the positive role of ecological protection projects plays a key role in this process.

Key words： NEP; LUCC simulation; PLUS model; Aksu River Basin

参考文献

[1] Wei X D, Yang J, Luo P P, et al. Assessment of the variation and influencing factors of vegetation NPP and carbon sink capacity under different natural conditions[J]. Ecological Indicators, 2022, 138(2): 108834.
[2] 杨元合, 石岳, 孙文娟, 等. 中国及全球陆地生态系统碳源汇特征及其对碳中和的贡献[J]. 中国科学: 生命科学, 2022, 52(4): 534-574.
  [Yang Yuanhe, Shi Yue, Sun Wenjuan, et al. Terrestrial carbon sinks in China and around the world and their contribution to carbon neutrality[J]. Science China-Life Sciences, 2022, 52(4): 534-574. ]
[3] Grant R F, Baldocchi D D, Ma S. Ecological controls on net ecosystem productivity of a seasonally dry annual grassland under current and future climates: Modelling with ecosys[J]. Agricultural and Forest Meteorology, 2011, 152(15): 189-200.
[4] 张海凤, 崔桂善. 土地利用/覆盖类型的变化对陆地生态系统碳收支的影响[J]. 中国资源综合利用, 2019, 37(1): 176-179.
  [Zhang Haifeng, Cui Guishan. Effects of different land use/cover types on terrestrial ecosystem carbon budgets[J]. China Resources Comprehensive Utilization, 2019, 37(1): 176-179. ]
[5] Liang S Q, Peng S Z, Chen Y M. Carbon cycles of forest ecosystems in a typical climate transition zone under future climate change: A case study of Shaanxi Province, China[J]. Forests, 2019, 10(12):1150.
[6] Huang Z, Du H, Li X, et al. Spatiotemporal LUCC simulation under different RCP scenarios based on the BPNN_CA_Markov model: A case study of bamboo forest in Anji County[J]. ISPRS International Journal of Geo-Information, 2020, 9(12): 718.
[7] Liang X, Guan Q F, Clarke C K, et al. Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: A case study in Wuhan, China[J]. Computers, Environment and Urban Systems, 2021, 85: 101569.
[8] 伍丹, 朱康文, 张晟, 等. 基于PLUS模型和InVEST模型的成渝经济区碳储量演化分析[J]. 三峡生态环境监测, 2022, 7(2): 85-96.
  [Wu Dan, Zhu Kangwen, Zhang Sheng, et al. Evolution analysis of carbon stock in Chengdu-Chongqing economic zone based on PLUS model and InVEST model[J]. Ecology and Environmental Monitoring of Three Gorges, 2022, 7(2): 85-96. ]
[9] 金一诺, 黄银洲, 尤凤, 等. 基于Markov-PLUS模型的石羊河流域土地利用及生态系统服务价值模拟[J]. 干旱区资源与环境, 2024, 38(5): 130-139.
  [Jin Yinuo, Huang Yinzhou, You Feng, et al. Simulation of land use and ecosystem service value in Shiyang River Basin based on Markov-PLUS model[J]. Journal of Arid Land Resources and Environment, 2024, 38(5): 130-139. ]
[10] 张波, 潘佩佩, 王新云, 等. 基于GMOP-PLUS耦合模型的京津冀土地利用变化多情景模拟及功能关系分析[J]. 地理与地理信息科学, 2023, 39(5): 8-16.
  [Zhang Bo, Pan Peipei, Wang Xinyun, et al. Multi-scenario simulation and functional relationship analysis of land use change in the Beijing-Tianjin-Hebei region based on GMOP-PLUS coupling model[J]. Geography and Geo-Information Science, 2023, 39(5): 8-16. ]
[11] 李克让, 王绍强, 曹明奎. 中国植被和土壤碳贮量[J]. 中国科学(D辑:地球科学), 2003, 33(1): 72-80.
  [Li Kerang, Wang Shaoqiang, Cao Mingkui. Carbon storage of vegetation and soil in China[J]. Science in China, 2003, 33(1): 72-80. ]
[12] 邵全琴, 刘树超, 宁佳, 等. 2000—2019年中国重大生态工程生态效益遥感评估[J]. 地理学报, 2022, 77(9): 2133-2153.
  [Shao Quanqin, Liu Shuchao, Ning Jia, et al. Assessment of ecological benefits of key national ecological projects in China in 2000-2019 using remote sensing[J]. Acta Geographica Sinica, 2022, 77(9): 2133-2153. ]
[13] 张祯祺, 蔡惠文, 张平平, 等. 基于GEE遥感云平台的三江源植被碳源/汇时空变化研究[J]. 自然资源遥感, 2023, 35(1): 231-242.
  [Zhang Zhenqi, Cai Huiwen, Zhang Pingping, et al. A GEE- based study on the temporal and spatial variations in the carbon source/sink function of vegetation in the Three-River Headwaters region[J]. Remote Sensing for Natural Resources, 2023, 35(1): 231-242. ]
[14] Xiao J F, Sun G, Chen J Q, et al. Carbon fluxes, evapotranspiration, and water use efficiency of terrestrial ecosystems in China[J]. Agricultural and Forest Meteorology, 2013, 182: 76-90.
[15] 潘竟虎, 文岩. 中国西北干旱区植被碳汇估算及其时空格局[J]. 生态学报, 2015, 35(23): 7718-7728.
  [Pan Jinghu, Wen Yan. Estimation and spatial-temporal characteristics of carbon sink in the arid region of northwest China[J]. Acta Ecologica Sinica, 2015, 35(23): 7718-7728. ]
[16] 李远航, 郝兴明, 张静静, 等. 近17 a阿克苏绿洲农田净碳汇功能的时空变化[J]. 干旱区研究, 2022, 39(4): 1303-1311.
  [Li Yuanhang, Hao Xingming, Zhang Jingjing, et al. Spatial and temporal variation of net carbon sink function of farmland in Aksu oasis in the past 17 years[J]. Arid Zone Research, 2022, 39(4): 1303-1311. ]
[17] 方精云. 碳中和的生态学透视[J]. 植物生态学报, 2021, 45(11): 1173-1176.
  [Fang Jingyun. Ecological perspectives of carbon neutrality[J]. Chinese Journal of Plant Ecology, 2021, 45(11): 1173-1176. ]
[18] Yu G R, Zheng Z M, Wang Q F, et al. Spatiotemporal pattern of soil respiration of terrestrial ecosystems in China: The development of a geostatistical model and its simulatio[J]. Environmental Science & Technology, 2010, 44(16): 6074-6080.
[19] Zhang J, Liu M Y, Zhang M M, et al. Changes of vegetation carbon sequestration in the tableland of Loess Plateau and its influencing factors[J]. Environmental Science and Pollution Research, 2019, 26(22): 22160-22172.
[20] Liang L, Geng D, Yan J, et al. Remote sensing estimation and spatiotemporal pattern analysis of terrestrial net ecosystem productivity in China[J]. Remote Sensing, 2022, 14(8): 1902.
[21] 谢薇, 陈书涛, 胡正华. 中国陆地生态系统土壤异养呼吸变异的影响因素[J]. 环境科学, 2014, 35(1): 334-340.
  [Xie Wei, Chen Shutao, Hu Zhenghua. Factors influencing the variability in soil heterotrophic respiration from terrestrial ecosystem in China[J]. Environmental Science, 2014, 35(1): 334-340. ]
[22] Xu X J, Liu J, Jiao F S, et al. Ecological engineering induced carbon sinks shifting from decreasing to increasing during 1981-2019 in China[J]. The Science of the Total Environment, 2023, 864: 161037.
[23] Ding Z M, Yao S B. Assessing the ecological effectiveness of Sloping Land Conversion Programme to identify vegetation restoration types: A case study of Northern Shaanxi Loess Plateau, China[J]. Ecological Indicators, 2022, 145: 109671.
[24] 张斌, 李璐, 夏秋月, 等. “三线”约束下土地利用变化及其对碳储量的影响——以武汉城市圈为例[J]. 生态学报, 2022, 42(6): 2265-2280.
  [Zhang Bin, Li Lu, Xia Qiuyue, et al. Land use change and its impact on carbon storage under the constraints of“three lines”: A case study of Wuhan City circle[J]. Acta Ecologica Sinica, 2022, 42(6): 2265-2280. ]
[25] 李玮麒, 兰泽英, 陈德权, 等. 广州市土地利用多情景模拟及其生态风险时空响应[J]. 水土保持通报, 2020, 40(4): 204-210.
  [Li Weiqi, Lan Zeying, Chen Dequan, et al. Multi-scenario simulation of land use and its spatial-temporal response to ecological risk in Guangzhou City[J]. Bulletin of Soil and Water Conservation, 2020, 40(4): 204-210. ]
[26] 李琛, 高彬嫔, 吴映梅, 等. 基于PLUS模型的山区城镇景观生态风险动态模拟[J]. 浙江农林大学学报, 2022, 39(1): 84-94.
  [Li Chen, Gao Binpin, Wu Yingmei, et al. Dynamic simulation of landscape ecological risk in mountain towns based on PLUS model[J]. Journal of Zhejiang A & F University, 2022, 39(1): 84-94. ]
[27] Wang C Y, Li T Z, Guo X H, et al. Plus-InVEST study of the Chengdu-Chongqing urban agglomeration’s land-use change and carbon storage[J]. Land, 2022, 11(10):1617.
[28] Yang J, Huang X. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019[J]. Earth System Science Data, 2021, 13(8): 3907-3925.
[29] 付晶莹, 江东, 黄耀欢. 中国公里网格人口分布数据集[J]. 地理学报, 2014, 69(s1): 41-44.
  [Fu Jingying, Jiang Dong, Huang Yaohuan. 1 km grid population dataset of China (2005, 2010)[J]. Acta Geographica Sinica, 2014, 69(s1): 41-44. ]
[30] 黄耀欢, 江东, 付晶莹. 中国公里网格GDP分布数据集[J]. 地理学报, 2014, 69(s1): 45-48.
  [Huang Yaohuan, Jiang Dong, Fu Jingying. 1 km grid GDP data of China (2005, 2010)[J]. Acta Geographica Sinica, 2014, 69(s1): 45-48. ]
[31] 仲晓雅, 闫庆武, 李桂娥. 中国长时间序列夜间灯光数据集的研发(2000—2020)[J]. 全球变化数据学报(中英文), 2022, 6(3): 416-424.
  [Zhong Xiaoya, Yan Qingwu, Li Gui’e. Development of time series of nighttime light dataset of China (2000-2020)[J]. Journal of Global Change Data & Discovery, 2022, 6(3): 416-424. ]
[32] 乔木, 周生斌, 卢磊, 等. 新疆渭干河流域土壤盐渍化时空变化及成因分析[J]. 地理科学进展, 2012, 31(7): 904-910.
  [Qiao Mu, Zhou Shengbin, Lu Lei, et al. Causes and spatial-temporal changes of soil salinization in Weigan River Basin, Xinjiang[J]. Progress in Geography, 2012, 31(7): 904-910. ]
[33] 廖杰, 文星. 基于遥感的近30年来河西地区盐碱地动态变化研究[J]. 干旱区资源与环境, 2011, 25(9): 96-101.
  [Liao Jie, Wen Xing. Dynamic change of saline and alkali land in Hexi Region based on remote-sensing[J]. Journal of Arid Land Resources and Environment, 2011, 25(9): 96-101. ]
[34] Peng S Z, Ding Y X, Liu W Z, et al. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017[J]. Earth System Science Data, 2019, 11(4): 1931-1946.
[35] 王玉辉, 井长青, 白洁, 等. 亚洲中部干旱区3个典型生态系统生长季水碳通量特征[J]. 植物生态学报, 2014, 38(8): 795-808.
  [Wang Yuhui, Jing Changqing, Bai Jie, et al. Characteristics of water and carbon fluxes during growing season in three typical aridecosystems in Central Asia[J]. Chinese Journal of Plant Ecology, 2014, 38(8): 795-808. ]
[36] 许世贤, 井长青, 高胜寒, 等. 遥感GPP模型在中亚干旱区4个典型生态系统的适用性评价[J]. 生态学报, 2022, 42(23): 9689-9700.
  [Xu Shixian, Jing Changqing, Gao Shenghan, et al. Evaluation of the applicability of remote sensing GPP models in four typical ecosystems in the arid zone of Central Asia[J]. Acta Ecologica Sinica, 2022, 42(23): 9689-9700. ]
[37] 朴世龙, 何悦, 王旭辉, 等. 中国陆地生态系统碳汇估算: 方法、进展、展望[J]. 中国科学:地球科学, 2022, 52(6): 1010-1020.
  [Piao Shilong, He Yue, Wang Xuhui, et al. Estimation of China’s terrestrial ecosystem carbon sink: Methods, progress and prospects[J]. Scientia Sinica Terrae, 2022, 52(6): 1010-1020. ]
[38] 焦伟, 陈亚宁, 李稚, 等. 基于多种回归分析方法的西北干旱区植被NPP遥感反演研究[J]. 资源科学, 2017, 39(3): 545-556.
  [Jiao Wei, Chen Yaning, Li Zhi, et al. Inversion of net primary productivity in the arid region of Northwest China based on various regressions[J]. Resources Science, 2017, 39(3): 545-556. ]
[39] Schurgers G, Ahlstrom A, Arneth A, et al. Climate sensitivity controls uncertainty in future terrestrial carbon sink[J]. Geophysical Research Letters, 2018, 45(9): 4329-4336.
[40] Xu S X, Su Y, Yan W, et al. Influences of ecological restoration programs on ecosystem services in sandy areas, northern China[J]. Romotes Sensing, 2023, 15(14): 3519.
[41] Lu F, Hu H, Sun W, et al. Effects of national ecological restoration projects on carbon sequestration in China from 2001 to 2010[J]. Proceedings of the National Academy of Sciences of the United States of America, 2018, 115(16): 4039-4044.
[42] 师庆东, 吴友均, 赵福生, 等. 干旱区造林碳汇项目综合效益评估指标体系研究[J]. 干旱区研究, 2015, 32(2): 368-375.
  [Shi Qingdong, Wu Youjun, Zhao Fusheng, et al. Evaluation index system of comprehensive benefit for afforestation carbon sequestration projects in arid area[J]. Arid Zone Research, 2015, 32(2): 368-375. ]
Options
文章导航

/

〈 〉