Carbon sequestration in typical sandy lands of China from 1982 to 2024： Patterns，evolution，and driving forces

doi:10.7522/j.issn.1000-694X.2025.00016

Journal of Desert Research ›› 2025, Vol. 45 ›› Issue (6): 59-69.DOI: 10.7522/j.issn.1000-694X.2025.00016

Previous Articles Next Articles

Carbon sequestration in typical sandy lands of China from 1982 to 2024： Patterns，evolution，and driving forces

Huishi Du¹(), Wei Qu¹, Hasi Eerdun²()

^1.College of Geographical Science and Tourism，Jilin Normal University，Siping 136000，Jilin，China
^2.School of Natural Resources，Faculty of Geographical Science，Beijing Normal University，Beijing 100875，China

Received:2024-12-13 Revised:2025-04-01 Online:2025-11-20 Published:2025-11-26
Contact: Hasi Eerdun

Abstract

Abstract:

The potential of carbon sequestration in typical sandy regions of China has long been overlooked，with a lack of systematic analysis over long time series and quantitative assessments of the driving mechanisms of climate change and human activities. In this study，we employed the CASA and Thornthwaite Memorial models to analyze the spatiotemporal variations in net ecosystem productivity （NEP） across typical sandy areas，including the Mu Us Sandy Land，the Hunshandake Sandy Land，the Horqin Sandy Land，the Hulun Buir Sandy Land，and the Songnen Sandy Land，from 1982 to 2024. We also explored the driving mechanisms of meteorological and human factors on the evolution of NEP. The results show that，by 2024，the average annual NEP of the typical sandy regions was 166.67 g·m^-2·a^-1，with significant spatial differentiation. The carbon sequestration capacity in the eastern regions was notably higher than in the western regions. Among these areas，the Songnen Sandy Land exhibited the strongest carbon sequestration capacity，with an annual NEP of 211.08 g·m^-2·a^-1，while the Mu Us Sandy Land had a weaker carbon sequestration capacity，with an annual NEP of only 122.68 g·m^-2·a^-1. From 1982 to 2024，the overall NEP of these regions showed a decline followed by a recovery，reaching a minimum of 130.68 g·m^-2·a^-1 in 2000 and subsequently rising to 166.67 g·m^-2·a^-1 by 2024. In terms of spatial changes，the areas with increased carbon sequestration accounted for 59.96% （10.03×10⁴ km²） of the total study area，with significant increases covering 57.79% （96 682.67 km²），primarily distributed in the Hulun Buir Sandy Land and the Songnen Sandy Land. In contrast，areas with significant decreases accounted for 35.85% （59 977.05 km²），mostly in the Mu Us Sandy Land and the Hunshandake Sandy Land. Human activities were the dominant factor contributing to the improvement of carbon sequestration，with a relative contribution rate of 49.33%. This study provides scientific evidence for carbon neutrality and ecological conservation in sandy regions.

Key words: sandy land ecosystems, carbon sequestration, net ecosystem productivity, meteorological factors, human factors

CLC Number:

P951

Huishi Du, Wei Qu, Hasi Eerdun. Carbon sequestration in typical sandy lands of China from 1982 to 2024： Patterns，evolution，and driving forces[J]. Journal of Desert Research, 2025, 45(6): 59-69.

Figures/Tables 9

Fig.1 Land use status of typical sandy land

Table 1 Identification of the relative roles of climate change and human activities in actual，potential and human-induced NEP changes

斜率_气象	斜率_潜在	斜率_人为	NEP变化的原因
+	+	+	气象因素增加NEP
-	-	-	气象因素减少NEP
+	-	-	人为因素增加NEP
-	+	+	人为因素减少NEP
+	+	-	二者共同作用增加NEP
-	-	+	二者共同作用减少NEP

Fig.2 Spatial distribution of net ecosystem productivity in typical sandy land in 2024

Fig.3 Average net ecosystem productivity （NEP） and NEP changes of different ecosystems in typical sandy land during 1982-2024

Fig.4 Spatial pattern of NEP in typical sandy land during 1982-2024

Fig.5 Change trend of regional carbon sink in typical sandy land during 1982-2024

Fig.6 The meteorological factors in typical sandy land and their correlation with NEP

Fig.7 The human factors in typical sandy land and their correlation with NEP

Fig.8 Attribution of carbon sink driving force in typical sandy land

References 53

[1]	Anav A， Friedlingstein P， Beer C，et al.Spatiotemporal patterns of terrestrial gross primary production：a review［J］.Reviews of Geophysics，2015，53（3）：785-818.
[2]	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.
[3]	李洁，张远东，顾峰雪，等.中国东北地区近50年净生态系统生产力的时空动态［J］.生态学报，2014，34（6）：1490-1502.
[4]	侯金龙，马志强，杨澄，等.京津冀地区植被碳源/汇的时空变化特征及影响因素分析［J］.生态环境学报，2024，33（9）：1329-1338.
[5]	Piao S L， He Y， Wang X H，et al.Estimation of China's terrestrial ecosystem carbon sink： methods，progress and prospects［J］.Science China Earth Sciences，2022，65（4）：1-11.
[6]	张梅，黄贤金，揣小伟，等.中国净生态系统生产力空间分布及变化趋势研究［J］.地理与地理信息科学，2020，36（2）：69-74.
[7]	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.
[8]	Ganatsas P， Tsakaldimi M， Karydopoulos T，et al.Carbon pools in a 77-year-old oak forest under conversion from coppice to high forest［J］.Sustainability，2022，14（21）：13764.
[9]	Mo L， Zohner C M， Reich P B，et al.Integrated global assessment of the natural forest carbon potential［J］.Nature，2023，624（7990）：92-101.
[10]	Hovenden M J， Newton P C D， Wills K E.Seasonal not annual rainfall determines grassland biomass response to carbon dioxide［J］.Nature，2014，511（7511）：583-586.
[11]	Evans J， Geerken R.Discrimination between climate and human-induced dryland degradation［J］.Journal of Arid Environments，2004，57（4）：535-554.
[12]	He Y， Piao S， Li Xet al.Global patterns of vegetation carbon use efficiency and their climate drivers deduced from MODIS satellite data and process based models［J］.Agricultural and Forest Meteorology，2018，256/257（6）：150-158.
[13]	Teng M J， Zeng L X， Hu W J，et al.The impacts of climate changes and human activities on net primary productivity vary across an ecotone zone in Northwest China［J］.Science of the Total Environment，2020，714（1）：136691.
[14]	赵选，张向阳，刘国发，等.秦岭（陕西段）植被碳汇时空变化与气候关联性研究［J］.测绘通报，2024，29（9）：55-61.
[15]	崔珍珍，马超，陈登魁.1982-2015年科尔沁沙地植被时空变化及气候响应［J］.干旱区研究，2021，38（2）：536-544.
[16]	韩其飞，陆研，李超凡.气候变化对中亚草地生态系统碳循环的影响研究［J］.干旱区地理，2018，41（6）：1351-1357.
[17]	贺军奇，魏燕，高万德，等.毛乌素沙地东南缘植被NDVI时空变化及其对气候因子的响应［J］.干旱区地理，2022，45（5）：1523-1533.
[18]	傅伯杰，刘焱序，王帅，等.科学改善荒漠化地区人与自然关系［J］.中国科学院院刊，2024，39（12）：2027-2036.
[19]	Zhou W， Gang C C， Zhou F C，et al.Quantitative assessment of the individual contribution of climate and human factors to desertification in Northwest China using net primary productivity as an indicator［J］.Ecological Indicators，2015，48（9）：560-569.
[20]	Wang C D， Li W Q， Sun M X，et al.Exploring the formulation of ecological management policies by quantifying interregional primary ecosystem service flows in Yangtze River Delta Region，China［J］.Journal of Environmental Management，2021，284（8）：112042.
[21]	Schönbach P， Wan H， Gierus M，et al.Grassland responses to grazing： effects of grazing intensity and management system in an Inner Mongolian steppe ecosystem［J］.Plant and Soil，2011，340（8）：103-115.
[22]	周怡婷，严俊霞，刘菊，等.2000-2021年黄土高原生态分区NEP时空变化及其驱动因子［J］.环境科学，2024，45（5）：2806-2816.
[23]	张怡，王志慧，卢小平，等.黄土高原生态系统碳汇时空变化及其影响因素［J］.水土保持研究，2025，32（1）：266-274.
[24]	Dai E F， Huang Y， Wu Z.Analysis of spatio-temporal features of a carbon source/sink and its relationship to climatic factors in the Inner Mongolia grassland ecosystem［J］.Journal of Geographical Sciences，2016，26（3）：297-312.
[25]	王江，曹生奎，刘振梅，等.青海湖流域生态系统碳汇时空格局动态研究［J］.草地学报，2025，33（3）：936-947.
[26]	Ge W Y， Deng L Q， Wang F，et al.Quantifying the contributions of human activities and climate change to vegetation net primary productivity dynamics in China from 2001 to 2016［J］.Science of the Total Environment，2021，773（12）：145648.
[27]	罗嘉艳，张靖，徐梦冉，等.浑善达克沙地植被变化定量归因及多情景预测［J］.干旱区地理，2023，46（4）：614-624.
[28]	Du H， Hasi E.Sandy Land-lake-vegetation landscape of Songnen sandy land of China： pattern，process and mechanism［J］.Chinese Geographical Science，2022，32（4）：580-591.
[29]	Wood D， Lenne J M.Revised wisdom in agriculture land use policy： 10 years on from Rio［J］.Land Use Policy，2005，2005（22）：75-93.
[30]	赵哈林，赵学勇，张铜会，等.我国西北干旱区的荒漠化过程及其空间分异规律［J］.中国沙漠，2011，31（1）：1-8.
[31]	袁静芳，周海丽，张星烁，等.京津风沙源治理区植被固碳能力估算及归因分析［J］.生态学报，2024，44（15）：6731-6743.
[32]	杜海波，杨山，李振亚，等.碳中和目标下长三角碳源/汇时空特征及其影响因素［J］.环境科学，2024，45（12）：6848-6857.
[33]	Mao D H， Wang Z M， Luo L，et al.Integrating AVHRR and MODIS data to monitor NDVI changes and their relationships with climatic parameters in Northeast China［J］.International Journal of Applied Earth Observation and Geoinformation，2011，27（1）：77-85.
[34]	朱文泉，潘耀忠，何浩，等.中国典型植被最大光利用率模拟［J］.科学通报，2006，56（6）：700-706.
[35]	Yin L， Dai E F， Zheng D，et al.What drives the vegetation dynamics in the Hengduan Mountain region，southwest China： climate change or human activity？［J］.Ecological Indicators，2020，112（5）：106013.
[36]	Geerken R， Ilaiwi M.Assessment of rangeland degradation and development of a strategy for rehabilitation［J］.Remote Sensing of Environment，2004，90（4）：490-504.
[37]	Pei Z Y， Ouyang H， Zhou C P，et al.Carbon balance in an alpine steppe in the Qinghai‐Tibet Plateau［J］.Journal of Integrative Plant Biology，2009，51（5）：521-526.
[38]	Chen B X， Zhang X Z， Tao J，et al.The impact of climate change and anthropogenic activities on alpine grassland over the Qinghai-Tibet Plateau［J］.Agricultural and Forest Meteorology，2014，189（3）：11-18.
[39]	He H L， Wang S Q， Zhang L，et al.Altered trends in carbon uptake in China's terrestrial ecosystems under the enhanced summer monsoon and warming hiatus［J］.National Science Review，2019，6（3）：505-514.
[40]	Liu D， Li Y， Wang T，et al.Contrasting responses of grassland water and carbon exchanges to climate change between Tibetan Plateau and Inner Mongolia［J］.Agricultural and Forest Meteorology，2018，249（2）：163-175.
[41]	李彦娥，王化齐，刘江，等.西北地区生态系统碳汇时空分布特征及相关驱动因子分析［J］.西北地质，2023，56（4）：185-195.
[42]	Tu H， Jiapaer G， Yu T，et al.Effects of land cover change on vegetation carbon source/sink in arid terrestrial ecosystems of Northwest China，2001-2018［J］.Remote Sensing，2023，15（9）：2471.
[43]	王丽霞，史园莉，张宏伟，等.2000-2020年北方农牧交错区植被生态功能变化及驱动因子分析［J］.生态环境学报，2021，30（10）：1990-1998.
[44]	郭紫晨，刘树林，康文平，等.2000-2015年毛乌素沙区植被覆盖度变化趋势［J］.中国沙漠，2018，38（5）：1099-1107.
[45]	涂海洋，古丽·加帕尔，于涛，等.中国陆地生态系统净初级生产力时空变化特征及影响因素［J］.生态学报，2023，43（3）：1219-1233.
[46]	Knapp A K， Ciais P， Smith M D.Reconciling inconsistencies in precipitation-productivity relationships：implications for climate change［J］.New Phytologist，2017，214（1）：41-47.
[47]	石智宇，王雅婷，赵清，等.2001-2020年中国植被净初级生产力时空变化及其驱动机制分析［J］.生态环境学报，2022，31（11）：2111-2123.
[48]	尹振良，冯起，王凌阁，等.2000-2019年中国西北地区植被覆盖变化及其影响因子［J］.中国沙漠，2022，42（4）：11-21.
[49]	侯丽丽，银山，都瓦拉，等.基于CASA模型的浑善达克沙地植被NPP模拟及时空分析［J］.水土保持研究，2020，27（2）：165-171.
[50]	马启民，贾晓鹏，王海兵，等.气候和人为因素对植被变化影响的评价方法综述［J］.中国沙漠，2019，39（6）：48-55.
[51]	Zhao M S， Running S W.Drought-induced reduction in global terrestrial net primary production from 2000 through 2009［J］.Science，2010，329（5994）：940-943.
[52]	汤李琛，李洁，陈慧，等.西部大开发地区种植业净碳汇时空格局及驱动因素［J/OL］.环境科学，2025..
[53]	冯筱，屈建军，丁新辉，等.沙漠化逆转过程中榆林市植被净初级生产力时空格局及其影响因素［J］.中国沙漠，2024，44（1）：22-32.

Carbon sequestration in typical sandy lands of China from 1982 to 2024： Patterns，evolution，and driving forces

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 53

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Yuqiang Li, Chengzhuo Zheng, Yu Xiang, Yayi Niu, Xuyang Wang, Xiaoming Mou. Carbon cycle in sandy ecosystems： major research advances of the Naiman Desertification Research Station from 1985 to 2025 [J]. Journal of Desert Research, 2025, 45(4): 34-42.
[2]	Liwen Yang, Xiaojun Li, Haotian Yang, Zhishan Zhang, Zhuoqun Shi, Xiao Qin, Dandan Hong, Dayong Wang. Quantitative assessment on the supply-demand balance of carbon sequestration service in artificial sand-binding vegetation ecosystem at the southeastern edge of the Tengger Desert [J]. Journal of Desert Research, 2025, 45(3): 222-232.
[3]	Yue Wang, Nana Yi, Xuegong Jiang, Guicai Ning, Lian Su, Haiyun Xia. Diagnosis of severe dust weather process based on multi-source observational data [J]. Journal of Desert Research, 2024, 44(6): 195-206.
[4]	Yu Xiang, Yuqiang Li, Chengzhuo Zheng, Mengqi Yuan, Weiyuan Liu. Advances in the application of biochar in sandy soil remediation [J]. Journal of Desert Research, 2024, 44(4): 315-326.
[5]	Kang Zhao, Lei Zhang, Kaikai Li, Fei Wang, Bingchang Zhang. A review on autotrophic microorganisms research in dryland soils [J]. Journal of Desert Research, 2022, 42(5): 177-186.
[6]	Yanbin Hu, Qiang Zhang, Guoju Xiao, Zhengji Qiu, Yongping Li, Zhanqiang Guo. Effect of soil carbon， nitrgen and phosphate contents on maize production in semi-arid regions of China [J]. Journal of Desert Research, 2022, 42(3): 261-273.
[7]	Ming Zhao, Chunxiao Zhou, Chong Li, Shuo Liu, Hujia Zhao. Temporal-spatial distribution of sand dust intensity during 1960-2020 in Liaoning [J]. Journal of Desert Research, 2022, 42(2): 113-120.
[8]	Zhao Yu, Qizheng Li, Peiyuan Wang, Qi Jiang. Changes of organic carbon density in desert steppe ecosystem driven by degradation and restoration [J]. Journal of Desert Research, 2022, 42(2): 215-222.
[9]	Xiaopeng Jia, Qimin Ma, Yinping Long, Haibing Wang. Soil evaporation monitored with medium-lysimeter in an artificial forest in the Hobq Desert， China [J]. Journal of Desert Research, 2022, 42(1): 211-222.
[10]	Peixi Su. Review and prospect of the researches on C₄ woody plants and soil inorganic carbon sequestration in deserts of China [J]. Journal of Desert Research, 2022, 42(1): 23-33.
[11]	Jiaoyue Wang, Shugao Qin, Yuqing Zhang. Spatial-temporal patterns of vegetation water use efficiency in the Mu Us Desert [J]. Journal of Desert Research, 2020, 40(5): 120-129.
[12]	Xinyou Wang, Quanlin Ma, Yaolin Wang. Carbon benefits evaluation of the artificial shelter forest in the Shiyanghe River Basin [J]. Journal of Desert Research, 2020, 40(4): 197-205.
[13]	Li Xia, Liu Tingxi, Duan Limin, Wang Guanli, Tong Xin, Zhou Yajun, Yang Xiaojun. Simulation of reference crop evapotranspiration and analysis of the factor effect in Horqin wet meadow [J]. Journal of Desert Research, 2020, 40(2): 134-143.
[14]	Yue Xiyuan, Zuo Xiaoan, Chang Xueli, Xu Chong, Lv Peng, Zhang Jing, Zhao Shenglong, Cheng Qingping. NDVI of Typical Steppe and Desert Steppe in Inner Mongolia in Response to Meteorological Factors [J]. Journal of Desert Research, 2019, 39(3): 25-33.
[15]	Li Fan, Xiao Jianshe, Qi Donglin, Li Lin. Impact Factors of Dust Storms in Qaidam Basin [J]. Journal of Desert Research, 2019, 39(2): 144-150.