青藏高原山地区碳储量时空演变与预测——以黄河源区为例

doi:10.3976/j.issn.1002-4026.2025031

摘要/Abstract

摘要：

土地利用变化作为陆地生态系统碳循环的核心驱动因素,科学评估其关系对碳平衡与可持续发展至关重要。基于青藏高原山地区中的黄河源区2000—2020年土地利用变化规律,耦合CA-Markov-InVEST-OPGD模型,揭示了土地覆被变化与碳储量的时空演变格局及碳储量的驱动机制,并预测和分析2030年黄河源区自然变化情景下碳储量的变化特征。结果表明:2000—2020年黄河源区的草地减少,未利用地和水域扩大,碳储量整体下降,高值区向西北集中。NDVI、高程和温度是黄河源区碳储量的主要驱动因子,其中,NDVI与温度、降水及人口密度的交互作用最显著。2030年黄河源区自然变化情景下的草地面积持续减少,水域面积显著增加;东部和南部碳储量明显下降,西部和北部部分区域显著上升;整体碳储量空间集聚趋势趋缓,局部呈现显著增减变化,应积极加强草地生态保护与修复,提升碳汇能力,促进区域碳平衡。

关键词: 青藏高原山地区, 土地利用变化, 碳储量, CA-Markov-InVEST-OPGD模型

Abstract:

Land-use change, as a key driving force of the terrestrial ecosystem carbon cycle, plays a critical role in assessing carbon balance and promoting sustainable development. Based on the land-use change patterns in the source region of the Yellow River in the mountainous areas of the Qinghai-Xizang Plateau from 2000 to 2020, this study developed an integrated CA-Markov-InVEST-OPGD model to reveal the spatiotemporal evolution of land cover and carbon storage, analyze the driving mechanism of carbon-storage changes, and predict the characteristics of carbon storage changes in a natural change scenario in the source region of the Yellow River in 2030. The results showed that from 2000 to 2020, grasslands in the source region of the Yellow River declined, while unused land and water areas expanded, leading to an overall reduction in carbon storage, with high-value areas shifting toward the northwest. NDVI, elevation, and temperature were the main factors affecting carbon storage in the region, with NDVI interacting most significantly with temperature, precipitation, and population density. In the natural change scenario in 2030, the grassland area will continue to shrink while the water area will increase significantly. Carbon storage in the east and south will decline significantly, while carbon storage in some areas in the west and north will increase significantly. The overall spatial concentration of carbon storage will tend to decrease, with notable local increases and decreases. Therefore, it is essential to undertake grassland ecological protection and restoration efforts to enhance carbon sink capacity and promote regional carbon balance.

Key words: mountainous areas of the Qinghai-Xizang Plateau, land use changes, carbon storage, CA-Markov-InVEST-OPGD model

中图分类号:

X144

李珊, 申恩庭, 孟应鹏, 陈旭. 青藏高原山地区碳储量时空演变与预测——以黄河源区为例[J]. 山东科学, 2026, 39(3): 54-67.

LI Shan, SHEN Enting, MENG Yingpeng, CHEN Xu. Spatiotemporal evolution and prediction of carbon storage in the mountainous areas of the Qinghai-Xizang Plateau: A case study of the source region of the Yellow River[J]. Shandong Science, 2026, 39(3): 54-67.

图/表 16

图1

表1

表2

图2

图3

图4

图5

表3

图6

图7

图8

图9

图10

表4

图11

图12

参考文献 42

[1]	LU F, HU H F, SUN W J, 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
[2]	ANINDITA S, SLEUTEL S, VANDENBERGHE D, et al. Land use impacts on weathering, soil properties, and carbon storage in wet Andosols, Indonesia[J]. Geoderma, 2022, 423: 115963. DOI: 10.1016/j.geoderma.2022.115963.
[3]	BABBAR D, AREENDRAN G, SAHANA M, et al. Assessment and prediction of carbon sequestration using Markov chain and InVEST model in Sariska Tiger Reserve, India[J]. Journal of Cleaner Production, 2021, 278: 123333. DOI: 10.1016/j.jclepro.2020.123333.
[4]	PIYATHILAKE I D U H, UDAYAKUMARA E P N, RANAWEERA L V, et al. Modeling predictive assessment of carbon storage using InVEST model in Uva Province, Sri Lanka[J]. Modeling Earth Systems and Environment, 2022, 8(2): 2213-2223. DOI: 10.1007/s40808-021-01207-3.
[5]	郑吉林, 蔡艳龙, 郭晓宇, 等. 基于InVEST模型的晋北土地利用变化与碳储量研究[J]. 地质通报, 2024, 43(1):173-180. DOI:10.12097/gbc.2022.05.038.
[6]	王洪彦, 郑文璐, 盖兆雪. 基于InVEST模型的黑土区碳储量时空分异特征[J]. 环境科学学报, 2024, 44(7): 473-481. DOI: 10.13671/j.hjkxxb.2024.0098.
[7]	卢雅焱, 徐晓亮, 李基才, 等. 基于InVEST模型的新疆天山碳储量时空演变研究[J]. 干旱区研究, 2022, 39(6): 1896-1906. DOI: 10.13866/j.azr.2022.06.20.
[8]	胡珂, 韩念龙, 于淼, 等. 基于遥感生态指数的三亚市土地利用变化模拟[J]. 中国水土保持科学(中英文), 2023, 21(1): 101-109. DOI: 10.16843/j.sswc.2023.01.012.
[9]	YU X R, XIAO J T, HUANG K, et al. Simulation of land use based on multiple models in the western Sichuan Plateau[J]. Remote Sensing, 2023, 15(14): 3629. DOI: 10.3390/rs15143629.
[10]	NABOUREH A, REZAEI MOGHADDAM M H, FEIZIZADEH B, et al. An integrated object-based image analysis and CA-Markov model approach for modeling land use/land cover trends in the Sarab plain[J]. Arabian Journal of Geosciences, 2017, 10(12): 259. DOI: 10.1007/s12517-017-3012-2.
[11]	MATLHODI B, KENABATHO P K, PARIDA B P, et al. Analysis of the future land use land cover changes in the Gaborone Dam Catchment using CA-Markov model: Implications on water resources[J]. Remote Sensing, 2021, 13(13): 2427. DOI: 10.3390/rs13132427.
[12]	余洲, 李明玉, 钱雨扬, 等. 基于CA_Markov模型多情景模拟的三峡库区土地利用变化及其生态环境效应[J]. 水土保持研究, 2024, 31(3): 363-372. DOI: 10.13869/j.cnki.rswc.2024.03.023.
[13]	吕霜霜. 三峡库区土地利用变化及生态服务价值研究[D]. 重庆: 西南大学, 2019.
[14]	胡碧松, 张涵玥. 基于CA-Markov模型的鄱阳湖区土地利用变化模拟研究[J]. 长江流域资源与环境, 2018, 27(6):1207-1219. DOI:10.11870/cjlyzyyhj201806004.
[15]	SONG Y Z, WANG J F, GE Y, et al. An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: Cases with different types of spatial data[J]. GIScience & Remote Sensing, 2020, 57(5): 593-610. DOI: 10.1080/15481603.2020.1760434.
[16]	刘敏超, 李迪强, 温琰茂, 等. 三江源地区土壤保持功能空间分析及其价值评估[J]. 中国环境科学, 2005, 25(5): 627-631. DOI:10.3321/j.issn:1000-6923.2005.05.028.
[17]	SANG L L, ZHANG C, YANG J Y, et al. Simulation of land use spatial pattern of towns and villages based on CA-Markov model[J]. Mathematical and Computer Modelling, 2011, 54(3/4): 938-943. DOI: 10.1016/j.mcm.2010.11.019.
[18]	WANG Q H, KALANTAR-ZADEH K, KIS A, et al. Electronics and optoelectronics of two-dimensional transition metal dichalcogenides[J]. Nature Nanotechnology, 2012, 7(11): 699-712. DOI: 10.1038/nnano.2012.193. pmid: 23132225
[19]	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), 2003(1):72-80. DOI:10.3969/j.issn.1674-7240.2003.01.008.
[20]	方精云, 刘国华, 徐嵩龄. 我国森林植被的生物量和净生产量[J]. 生态学报, 1996, 16(5): 497-508.
[21]	黄玫, 季劲钧, 曹明奎, 等. 中国区域植被地上与地下生物量模拟[J]. 生态学报, 2006, 26(12): 4156-4163. DOI:10.3321/j.issn:1000-0933.2006.12.031.
[22]	朴世龙, 方精云, 贺金生, 等. 中国草地植被生物量及其空间分布格局[J]. 植物生态学报, 2004, 28(4): 491-498. DOI:10.3321/j.issn:1004-5759.2001.02.015.
[23]	揣小伟, 黄贤金, 郑泽庆, 等. 江苏省土地利用变化对陆地生态系统碳储量的影响[J]. 资源科学, 2011, 33(10):1932-1939. DOI:10.1007/s11442-011-0845-6.
[24]	陈光水, 杨玉盛, 谢锦升, 等. 中国森林的地下碳分配[J]. 生态学报, 2007, 27(12): 5148-5157.
[25]	GIARDINA C P, RYAN M G. Evidence that decomposition rates of organic carbon in mineral soil do not vary with temperature[J]. Nature, 2000, 404(6780): 858-861. DOI: 10.1038/35009076.
[26]	ALAM S A, STARR M, CLARK B J F. Tree biomass and soil organic carbon densities across the Sudanese woodland savannah: A regional carbon sequestration study[J]. Journal of Arid Environments, 2013, 89: 67-76. DOI: 10.1016/j.jaridenv.2012.10.002.
[27]	邵壮, 陈然, 赵晶, 等. 基于FLUS与InVEST模型的北京市生态系统碳储量时空演变与预测[J]. 生态学报, 2022, 42(23): 9456-9469.
[28]	CONGALTON R G. Accuracy assessment and validation of remotely sensed and other spatial information[J]. International Journal of Wildland Fire, 2001, 10(4): 321. DOI: 10.1071/wf01031.
[29]	COHEN J. A coefficient of agreement for nominal scales[J]. Educational and Psychological Measurement, 1960, 20(1): 37-46. DOI: 10.1177/001316446002000104.
[30]	CHANG X Q, XING Y Q, WANG J Q, et al. Effects of land use and cover change (LUCC) on terrestrial carbon stocks in China between 2000 and 2018[J]. Resources, Conservation and Recycling, 2022, 182: 106333. DOI: 10.1016/j.resconrec.2022.106333.
[31]	LIU W, LIU D F, LIU Y. Spatially heterogeneous response of carbon storage to land use changes in Pearl River Delta urban agglomeration, China[J]. Chinese Geographical Science, 2023, 33(2): 271-286. DOI: 10.1007/s11769-023-1343-3.
[32]	ROY S, MAJUMDER S, BOSE A, et al. Does geographical heterogeneity influence urban quality of life? a case of a densely populated Indian City[J]. Papers in Applied Geography, 2023, 9(4): 395-424. DOI: 10.1080/23754931.2023.2225541.
[33]	李曼, 吴东丽, 何昊, 等. 1990—2020年黄河流域碳储量时空演变及驱动因素研究[J]. 生态环境学报, 2025, 34(3): 333-344. DOI: 10.16258/j.cnki.1674-5906.2025.03.001.
[34]	王成武, 罗俊杰, 唐鸿湖. 基于InVEST模型的太行山沿线地区生态系统碳储量时空分异驱动力分析[J]. 生态环境学报, 2023, 32(2): 215-225. DOI: 10.16258/j.cnki.1674-5906.2023.02.001.
[35]	YANG X T, BAI X, YAO W Q, et al. Spatioemporal dynamics and driving forces of soil organic carbon changes in an arid coal mining area of China investigated based on remote sensing techniques[J]. Ecological Indicators, 2024, 158: 111453. DOI: 10.1016/j.ecolind.2023.111453.
[36]	万路通, 刘小丹, 郭汉清, 等. 基于InVEST-PLUS模型的首都西部生态涵养区碳储量时空演替及预测[J]. 地质通报, 2025, 44(7): 1272-1284. DOI: 10.12097/gbc.2024.10.049.
[37]	张爽, 高启晨, 张戎, 等. 基于PLUS-InVEST模型碳储量时空演变及驱动因素分析:以纳帕海流域为例[J]. 中国环境科学, 2024, 44(9): 5192-5201. DOI: 10.19674/j.cnki.issn1000-6923.2024.0168.
[38]	XI H J, LI T H. Unveiling the spatiotemporal dynamics and influencing factors of carbon stocks in the Yangtze River basin over the past two decades[J]. Science of The Total Environment, 2024, 954: 176261. DOI: 10.1016/j.scitotenv.2024.176261.
[39]	李文婕, 黄草, 夏丹, 等. 2003—2022年洞庭湖区碳储量变化趋势及驱动因素分析[J]. 水利水电技术(中英文), 2025, 56(8):32-48. DOI: 10.13968/j.cnki.wrahe.2025.08.003.
[40]	ZHONG R, PU L J, XIE J Y, et al. Carbon storage in typical ecosystems of coastal wetlands in Jiangsu, China: Spatiotemporal patterns and mechanisms[J]. Catena, 2025, 254: 108882. DOI: 10.1016/j.catena.2025.108882.
[41]	SUN B Y, WANG H J, WU X, et al. Dual impacts of human activities on land cover and carbon storage in the Yellow River Delta (1986—2023)[J]. Ocean & Coastal Management, 2025, 267: 107655. DOI: 10.1016/j.ocecoaman.2025.107655.
[42]	韩玉, 丁素婷, 杨太保. 山西南部中条山生态系统碳储量时空分布及驱动因素[J]. 中国环境科学, 2023, 43(3): 1298-1306. DOI: 10.19674/j.cnki.issn1000-6923.20220915.015.

数据类型	数据名称	数据来源
土地利用数据	2000年、2010年和2020年 CLCD中国逐年土地覆盖数	CLCD中国逐年土地覆盖数据集(https://zenodo.org/record/5816591)
碳库数据	地上植被碳库、地下植被碳库、土壤碳有机碳库和死亡有机碳库	国家青藏高原科学数据中心(https://data.tpdc.ac.cn/)
自然因素数据	高程(X1)	地理空间数据云(https://www.gscloud.cn)
	坡度(X2)	地理空间数据云(https://www.gscloud.cn)
	年均降水量(X3)	国家气象科学数据中心(http://www.nmic.cn/)
	年均气温(X4)	国家气象科学数据中心(http://www.nmic.cn/)
	归一化植被指数(X5)	中国科学院资源环境与科学数据(https://www.resdc.cn)
社会经济数据	人口密度(X6)	中国科学院资源环境与科学数据(https://www.resdc.cn)
社会经济数据	人均经济生产总值(X7)	中国科学院资源环境与科学数据(https://www.resdc.cn)
可达性数据	距省道距离(X8)	全国地理信息资源目录服务系统(https://www.webmap.cn)
可达性数据	距水系距离(X9)	全国地理信息资源目录服务系统(https://www.webmap.cn)

土地利用类型	地上碳密度	地下碳密度	土壤碳密度
耕地	3.71	8.29	75.89
林地	43.15	17.79	100.24
草地	0.72	6.42	87.13
水域	0.36	0	64.09
未利用地	0	0	34.36

土地利用类型	2000年		2010年		2020年
土地利用类型	碳储量/kt	占比/%	碳储量/kt	占比/%	碳储量/kt	占比/%
耕地	3.146 3	0.001 8	2.882 1	0.001 7	2.507 5	0.001 5
林地	2.991 5	0.001 7	2.738 1	0.001 6	2.698 2	0.001 6
草地	163 141.560 8	94.217 6	162 253.763 0	93.775 3	160 809.668 0	93.449 5
水域	8 851.835 5	5.112 1	9 772.438 6	5.648 0	9 773.146 3	5.679 4
未利用地	1 154.482 6	0.666 7	992.171 8	0.573 4	1 493.790 0	0.868 1
总计	173 154.016 7	100	173 023.993 6	100	172 081.810 0	100

土地利用类型	2020年		2030年		2020—2030年碳储量占比变化率/%
土地利用类型	碳储量/kt	占比/%	碳储量/kt	占比/%	2020—2030年碳储量占比变化率/%
耕地	2.507 5	0.001 5	2.663 5	0.001 6	0.000 1
林地	2.698 2	0.001 6	2.618 5	0.001 5	-0.000 1
草地	160 809.668 0	93.449 5	158 472.701 6	92.500 3	-0.949 2
水域	9 773.146 3	5.679 4	11 335.267 3	6.616 4	0.937 0
未利用地	1 493.790 0	0.868 1	1 508.073 0	0.880 3	0.012 2
总计	172 081.810 0	100	171 321.323 9	100