基于PLUS土地利用模拟的阿克苏河流域NEP时空格局研究

doi:10.13866/j.azr.2024.06.14

Abstract

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

LI Peiyao, WANG Xinjun, XU Shixian, GAO Shenghan, XUE Zhixuan, HENG Rui. Spatiotemporal pattern of NEP in Aksu River Basin based on PLUS land use simulation[J].Arid Zone Research, 2024, 41(6): 1059-1068.

Figures/Tables 9

Fig. 1

Tab. 1

Tab. 2

Land use/cover type transfer probability correction rules"

生态保护重点工程类型	修正规则
退耕还林还草工程	耕地转变至林地、草地、灌木地转移概率提高 $α i j (i = 耕地, j = 森林、草地、灌木地)$
“三北”防护林工程	裸地转变至林地、草地、灌木地转移概率提高 $α i j (i = 裸地, j = 森林、草地、灌木地)$

Tab. 2

Tab. 3

Fig. 2

Fig. 3

Fig. 4

Tab. 4

Fig. 5

References 42

[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. doi: 10.11821/dlxb202209001
	[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. ] doi: 10.11821/dlxb202209001
[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. doi: 10.17521/cjpe.2021.0394
	[Fang Jingyun. Ecological perspectives of carbon neutrality[J]. Chinese Journal of Plant Ecology, 2021, 45(11): 1173-1176. ] doi: 10.17521/cjpe.2021.0394
[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. doi: 10.1007/s11356-019-05561-9
[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. doi: 10.11821/dlxb2014S006
	[Fu Jingying, Jiang Dong, Huang Yaohuan. 1 km grid population dataset of China (2005, 2010)[J]. Acta Geographica Sinica, 2014, 69(s1): 41-44. ] doi: 10.11821/dlxb2014S006
[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. doi: 10.11821/dlxb2014S007
	[Huang Yaohuan, Jiang Dong, Fu Jingying. 1 km grid GDP data of China (2005, 2010)[J]. Acta Geographica Sinica, 2014, 69(s1): 45-48. ] doi: 10.11821/dlxb2014S007
[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. ] doi: 10.11820/dlkxjz.2012.07.009
[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. doi: 10.3724/SP.J.1258.2014.00075
	[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. ] doi: 10.3724/SP.J.1258.2014.00075
[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. doi: 10.18402/resci.2017.03.16
	[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. ] doi: 10.18402/resci.2017.03.16
[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. doi: 10.1073/pnas.1700294115 pmid: 29666317
[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. ]

政策类型	修正规则
生态保护红线	生态保护红线内人造地表、耕地扩张概率为0
永久基本农田	永久基本农田内耕地扩张概率为1
城市开发边界	城市开发边界内人造地表扩张概率为1
退耕还林还草工程	坡度≥25°、强盐渍化、盐土、沙漠化地区耕地扩张概率为0
“三北”防护林工程	强盐渍化、盐土、沙漠化地区林地、灌木地扩张概率为1

数据	年份	空间分辨率/m	来源
土地利用/覆盖数据	2000—2020年	30	[28]
高程	2010年	30	https://earthexplorer.usgs.gov/
土壤类型、土壤有机碳含量	2000年	1000	https://www.fao.org/soils-portal/data-hub/
人口密度	2010年	1000	[29]
GDP	2010年	1000	[30]
夜间灯光DN平均值	2010年	1000	[31]
各级道路（高速、省道、国道、县道）、政府中心位置、铁路、河流	2014年	30	https://www.openstreetmap.org/
生态保护红线、永久基本农田、城市开发边界			阿克苏地区自然资源局
土地沙漠化	2010年	1000	https://cstr.cn/CSTR:11738.11.ncdc.973Desert.2020.117
土地盐碱化数据	2010年	1000	[32-33]
NPP数据	2000—2020年	500	https://ladsweb.modaps.eosdis.nasa.gov/
月平均气温、月总降水量	2000—2020年	1000	[34]

情景	年份	土地覆盖/利用类型面积/km²
情景	年份	耕地	林地	草地	灌木地	水体	人造地表	裸地	冰川和永久积雪
情景A	2030年	8855.53	219.44	9852.00	322.73	373.60	835.35	22719.59	2141.56
	2040年	9763.15	215.89	9734.02	318.53	500.11	1099.64	21553.24	2135.20
	2050年	10627.83	212.49	9612.50	314.33	572.68	1348.70	20505.70	2125.55
	2060年	11447.67	209.16	9483.88	310.15	612.77	1583.85	19559.23	2113.07
情景B	2030年	8745.56	221.01	10149.08	324.37	373.60	835.35	22529.27	2141.56
	2040年	9554.27	219.80	10274.81	322.20	502.49	1094.95	21216.22	2135.05
	2050年	10329.34	219.08	10353.51	320.30	578.53	1336.05	20057.83	2125.15
	2060年	11067.54	218.54	10389.45	318.57	622.42	1560.98	19029.91	2112.38
情景C	2030年	8635.59	213.33	9252.30	320.43	373.60	835.35	23547.63	2141.56
	2040年	9349.25	202.97	8643.31	313.07	496.24	1091.97	23086.20	2136.80
	2050年	10041.12	192.63	8122.08	305.12	563.31	1328.01	22637.45	2130.07
	2060年	10705.78	182.64	7670.64	296.83	597.51	1546.51	22198.28	2121.58

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

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