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Human activities rather than climate change dominate the growth of carbon fluxes in the Hexi Corridor oasis area, China

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Abstract

Carbon fluxes are essential indicators assessing vegetation carbon cycle functions. However, the extent and mechanisms by which climate change and human activities influence the spatiotemporal dynamics of carbon fluxes in arid oasis and non-oasis area remains unclear. Here, we assessed and predicted the future effects of climate change and human activities on carbon fluxes in the Hexi Corridor. The results showed that the annual average gross primary productivity (GPP), net ecosystem productivity (NEP), and ecosystem respiration (Reco) in the Hexi Corridor oasis increased by 263.91 g C·m−2·yr−1, 118.45 g C·m−2·yr−1 and 122.46 g C·m−2·yr−1, respectively, due to the expansion of the oasis area by 3424.84 km2 caused by human activities from 2000 to 2022. Both oasis and non-oasis arid ecosystems in the Hexi Corridor acted as carbon sinks. Compared to the non-oasis area, the carbon fluxes contributions of oasis area increased, ranging from 10.21% to 13.99% for GPP, 8.50% to 11.68% for NEP, and 13.34% to 17.13% for Reco. The contribution of the carbon flux from the oasis expansion area to the total carbon flux change in the Hexi Corridor was 30.96% (7.09 Tg C yr−1) for GPP, 29.57% (3.39 Tg C yr−1) for NEP and 32.40% (3.58 Tg C yr−1) for Reco. The changes in carbon fluxes in the oasis area were mainly attributed to human activities (oasis expansion) and temperature, whereas non-oasis area was mainly due to climate factors. Moreover, the future increasing trends were observed for GPP (64.99%), NEP (66.29%) and Reco (82.08%) in the Hexi Corridor. This study provides new insights into the regulatory mechanisms of carbon cycle in the arid oasis and non-oasis area.

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References

  • Bao S, Wutzler T, Koirala S et al., 2022. Environment-sensitivity functions for gross primary productivity in light use efficiency models. Agricultural and Forest Meteorology, 312: 108708.

    Article  Google Scholar 

  • Bernacchi C J, VanLoocke A, 2015. Terrestrial ecosystems in a changing environment: A dominant role for water. Annual Review of Plant Biology, 66(1): 599–622.

    Article  CAS  Google Scholar 

  • Bie Q, Xie Y, 2020. The constraints and driving forces of oasis development in arid region: A case study of the Hexi Corridor in northwest China. Scientific Reports, 10(1): 17708.

    Article  CAS  Google Scholar 

  • Chang X, Xing Y, Gong W et al., 2023. Evaluating gross primary productivity over 9 ChinaFlux sites based on random forest regression models, remote sensing, and eddy covariance data. Science of The Total Environment, 875: 162601.

    Article  CAS  Google Scholar 

  • Chen Y, Cao R, Chen J et al., 2021. A practical approach to reconstruct high-quality Landsat NDVI time-series data by gap filling and the Savitzky-Golay filter. ISPRS Journal of Photogrammetry and Remote Sensing, 180: 174–190.

    Article  Google Scholar 

  • Davies J, Poulsen L, Schulte-Herbrüggen B et al., 2012. Conserving dryland biodiversity. Nairobi: International Union for the Conservation of Nature Drylands Initiative, xii+84p.

    Google Scholar 

  • Duan Z, Yang Y, Zhou S et al., 2021. Estimating gross primary productivity (GPP) over rice-wheat-rotation croplands by using the random forest model and eddy covariance measurements: Upscaling and comparison with the MODIS product. Remote Sensing, 13(21): 4229.

    Article  Google Scholar 

  • Esters L, Rutgersson A, Nilsson E et al., 2020. Non-local impacts on eddy-covariance air-lake CO2 fluxes. Boundary-Layer Meteorology, 178(2): 283–300.

    Article  Google Scholar 

  • Flanagan L B, Syed K H, 2011. Stimulation of both photosynthesis and respiration in response to warmer and drier conditions in a boreal peatland ecosystem. Global Change Biology, 17(7): 2271–2287.

    Article  Google Scholar 

  • Fonseca L D M, Dalagnol R, Malhi Y et al., 2019. Phenology and seasonal ecosystem productivity in an Amazonian floodplain forest. Remote Sensing, 11(13): 1530.

    Article  Google Scholar 

  • Gilmanov T G, Soussana J F, Aires L et al., 2007. Partitioning European grassland net ecosystem CO2 exchange into gross primary productivity and ecosystem respiration using light response function analysis. Agriculture, Ecosystems & Environment, 121(1/2): 93–120.

    Article  CAS  Google Scholar 

  • Gocic M, Trajkovic S, 2013. Analysis of changes in meteorological variables using Mann-Kendall and Sen’s slope estimator statistical tests in Serbia. Global and Planetary Change, 100: 172–182.

    Article  Google Scholar 

  • Gu Q, Wei J, Luo S et al., 2018. Potential and environmental control of carbon sequestration in major ecosystems across arid and semi-arid regions in China. Science of the Total Environment, 645: 796–805.

    Article  CAS  Google Scholar 

  • Guo N, Wang Xiaoping, Cai Dihua et al., 2010. Analyses on the vegetation index variation and its formation causes in the oases in Northwest China in recent 22 years. Arid Zone Research, 27(1): 75–82. (in Chinese)

    Article  Google Scholar 

  • Han L, Chen Y, Wang Y et al., 2024. Divergent responses of subtropical evergreen and deciduous forest carbon cycles to the summer 2022 drought. Environmental Research Letters, 19(5): 054043.

    Article  CAS  Google Scholar 

  • Huang F, Ochoa C G, Chen X et al., 2021a. Modeling oasis dynamics driven by ecological water diversion and implications for oasis restoration in arid endorheic basins. Journal of Hydrology, 593: 125774.

    Article  Google Scholar 

  • Huang N, Wang L, Zhang Y et al., 2021b. Estimating the net ecosystem exchange at global FLUXNET sites using a random forest model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 9826–9836.

    Article  Google Scholar 

  • Jiang P, Yuan Y, 2023. Responses of vegetation gross primary production to vapor pressure deficit in Xinjiang. Arid Land Geography, 47(3): 403–412. (in Chinese)

    Google Scholar 

  • Jiang W, Yuan L, Wang W et al., 2015. Spatio-temporal analysis of vegetation variation in the Yellow River Basin. Ecological Indicators, 51: 117–126.

    Article  Google Scholar 

  • Jiang Y, Du W, Chen J et al., 2022. Climatic and topographical effects on the spatiotemporal variations of vegetation in Hexi Corridor, northwestern China. Diversity, 14(5): 370.

    Article  Google Scholar 

  • Jung M, Schwalm C, Migliavacca M et al., 2020. Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach. Biogeosciences, 17(5): 1343–1365.

    Article  CAS  Google Scholar 

  • Kannenberg S A, Anderegg W R L, Barnes M L et al., 2024. Dominant role of soil moisture in mediating carbon and water fluxes in dryland ecosystems. Nature Geoscience, 17(1): 38–43.

    Article  CAS  Google Scholar 

  • Li C, Liu Y, Zhu T et al., 2023a. Considering time-lag effects can improve the accuracy of NPP simulation using a light use efficiency model. Journal of Geographical Sciences, 33(5): 961–979.

    Article  Google Scholar 

  • Li C, Zhu T, Zhou M et al., 2021a. Temporal and spatial change of net primary productivity of vegetation and its determinants in Hexi Corridor. Acta Ecologica Sinica, 41(5): 1931–1943. (in Chinese)

    Google Scholar 

  • Li J, Zhou F, Jiao L et al., 2024. Variation of main climatic elements and climate production potential in Hexi Corridor during 1960–2022. Journal of Desert Research, 44(6): 14–25. (in Chinese)

    Google Scholar 

  • Li M, Wang J, Li K et al., 2023b. Spatial-temporal pattern analysis of grassland yield in Mongolian Plateau based on artificial neural network. Remote Sensing, 15(16): 3968.

    Article  Google Scholar 

  • Li Q, Yang D, Feng L et al., 2021b. Dynamics of vegetation NDVI in Chengdu-Chongqing Economic Circle from 2000 to 2018. Chinese Journal of Ecology, 40(9): 2967–2977. (in Chinese)

    Google Scholar 

  • Liu J, Kuang W, Zhang Z et al., 2014. Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s. Journal of Geographical Sciences, 24(2): 195–210.

    Article  Google Scholar 

  • Liu S M, Xu Z W, Wang W Z et al., 2011. A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem. Hydrology and Earth System Sciences, 15(4): 1291–1306.

    Article  Google Scholar 

  • Liu W, He H, Wu X et al., 2022. Spatiotemporal changes and driver analysis of ecosystem respiration in the Tibetan and Inner Mongolian grasslands. Remote Sensing, 14(15): 3563.

    Article  Google Scholar 

  • Lloyd J, Taylor J A, 1994. On the temperature dependence of soil respiration. Functional Ecology, 8(3): 315–323.

    Article  Google Scholar 

  • Meng Y, Jiang P, Yuan F, 2020. Contrasting impacts of vapor pressure deficit on gross primary productivity in two typical grassland ecosystems in China. Chinese Journal of Ecology, 39(11): 3633–3642. (in Chinese)

    Google Scholar 

  • Raich, Nadelhoffer K J, 1989. Belowground carbon allocation in forest ecosystems: Global trends. Ecology, 70(5): 1346–1354.

    Article  Google Scholar 

  • Schild J E M, Vermaat JE, de Groot RS et al., 2018. A global meta-analysis on the monetary valuation of dryland ecosystem services: The role of socio-economic, environmental and methodological indicators. Ecosystem Services, 32: 78–89.

    Article  Google Scholar 

  • Song Y, Jiao W, Wang J et al., 2022. Increased global vegetation productivity despite rising atmospheric dryness over the last two decades. Earth’s Future, 10(7): e2021EF002634.

    Article  Google Scholar 

  • Su F, Wang F, Li Z et al., 2020. Predominant role of soil moisture in regulating the response of ecosystem carbon fluxes to global change factors in a semi-arid grassland on the Loess Plateau. Science of The Total Environment, 738: 139746.

    Article  CAS  Google Scholar 

  • Sun Q, Meyer W S, Koerber G R et al., 2016. A wildfire event influences ecosystem carbon fluxes but not soil respiration in a semi-arid woodland. Agricultural and Forest Meteorology, 226/227: 57–66.

    Article  Google Scholar 

  • Sun S, Du W, Song Z et al., 2021. Response of gross primary productivity to drought time-scales across China. Journal of Geophysical Research: Biogeosciences, 126(4): e2020JG005953.

    Article  Google Scholar 

  • Tang X, Xiao J, Ma M et al., 2022. Satellite evidence for China’s leading role in restoring vegetation productivity over global karst ecosystems. Forest Ecology and Management, 507: 120000.

    Article  Google Scholar 

  • Tarin T, Nolan RH, Eamus D et al., 2020. Carbon and water fluxes in two adjacent Australian semi-arid ecosystems. Agricultural and Forest Meteorology, 281: 107853.

    Article  Google Scholar 

  • Vivaldo G, Magnani M, Baneschi I et al., 2023. Carbon dioxide exchanges in an alpine tundra ecosystem (Gran Paradiso National Park, Italy): A comparison of results from different measurement and modelling approaches. Atmospheric Environment, 305: 119758.

    Article  CAS  Google Scholar 

  • Wang H, Li X, Xiao J et al., 2019. Carbon fluxes across alpine, oasis, and desert ecosystems in northwestern China: The importance of water availability. Science of the Total Environment, 697: 133978.

    Article  CAS  Google Scholar 

  • Wang T, Wang X, Zhang S et al., 2024. Interannual change control mechanism of carbon flux in inland river basins in cold and arid regions. Earth Science, 49(5): 1907–1919. (in Chinese)

    Google Scholar 

  • Wu X, Fan Y, Kang D et al., 2022. Responses of vegetation water and carbon fluxes to climate change and human activities in Hexi Corridor. Journal of China Agricultural University, 27(10): 212–225. (in Chinese)

    Google Scholar 

  • Wutzler T, Lucas-Moffat A, Migliavacca M et al., 2018. Basic and extensible post-processing of eddy covariance flux data with REddyProc. Biogeosciences, 15(16): 5015–5030.

    Article  CAS  Google Scholar 

  • Xie Y, Zhang X, Liu Y et al., 2022. Oasis dataset of Hexi Corridor based on landsat data (1986–2020). In: National Tibetan Plateau Data Center ed. National Tibetan Plateau Data Center.

    Google Scholar 

  • Xu T, Guo Z, Liu S et al., 2018. Evaluating different machine learning methods for upscaling evapotranspiration from flux towers to the regional scale. Journal of Geophysical Research: Atmospheres, 123(16): 8674–8690.

    Article  Google Scholar 

  • Xue S, Wu G, 2023. Sensitivities of vegetation gross primary production to precipitation frequency in the Northern Hemisphere from 1982 to 2015. Remote Sensing, 16(1): 21.

    Article  Google Scholar 

  • Yang P, Wang N, Zhao L et al., 2022. Responses of grassland ecosystem carbon fluxes to precipitation and their environmental factors in the Badain Jaran Desert. Environmental Science and Pollution Research, 29(50): 75805–75821.

    Article  CAS  Google Scholar 

  • Yao Y, Liu J, Zhang M et al., 2020. Impact of climatic change on the agriculture in Hexi Oasis and countermeasures. Ecology and Environmental Sciences, 29(8): 1499–1506. (in Chinese)

    Google Scholar 

  • You C, Wang Y, Tan X et al., 2023. Inner Mongolia grasslands act as a weak regional carbon sink: A new estimation based on upscaling eddy covariance observations. Agricultural and Forest Meteorology, 342: 109719.

    Article  Google Scholar 

  • Yu T, Zhang Q, Sun R, 2021. Comparison of machine learning methods to up-scale gross primary production. Remote Sensing, 13(13): 2448.

    Article  Google Scholar 

  • Zhang C, Brodylo D, Rahman M et al., 2022. Using an object-based machine learning ensemble approach to upscale evapotranspiration measured from eddy covariance towers in a subtropical wetland. Science of the Total Environment, 831: 154969.

    Article  CAS  Google Scholar 

  • Zhang J, Hao X, Hao H et al., 2021. Climate change decreased net ecosystem productivity in the arid region of Central Asia. Remote Sensing, 13(21): 4449.

    Article  Google Scholar 

  • Zhang K, Wang Y, Mamtimin A et al., 2023. Temporal and spatial variations in carbon flux and their influencing mechanisms on the Middle Tien Shan region grassland ecosystem, China. Remote Sensing, 15(16): 4091.

    Article  Google Scholar 

  • Zhang N, Zhao Y S, Yu G R, 2008. Simulated annual carbon fluxes of grassland ecosystems in extremely arid conditions. Ecological Research, 24(1): 185–206.

    Article  Google Scholar 

  • Zhang Q, Sun R, Jiang G et al., 2016a. Carbon and energy flux from a Phragmites australis wetland in Zhangye oasis-desert area, China. Agricultural and Forest Meteorology, 230/231: 45–57.

    Article  Google Scholar 

  • Zhang Y, Naerkezi N, Zhang Y et al., 2024. Multi-scenario land use/cover change and its impact on carbon storage based on the coupled GMOP-PLUS-InVEST model in the Hexi Corridor, China. Sustainability, 16(4): 1402.

    Article  Google Scholar 

  • Zhang Z, Dong Z, Li J et al., 2016b. Implications of surface properties for dust emission from gravel deserts (gobis) in the Hexi Corridor. Geoderma, 268: 69–77.

    Article  Google Scholar 

  • Zhao B, Chong S L, 2024. NDVI-based vegetation dynamics and its response to precipitation changes in the Hexi Corridor of China from 2000 to 2019. Applied Ecology and Environmental Research, 22(2): 1903–1916.

    Article  Google Scholar 

  • Zhao J, Liu D, Cao Y et al., 2022. An integrated remote sensing and model approach for assessing forest carbon fluxes in China. Science of The Total Environment, 811: 152480.

    Article  CAS  Google Scholar 

  • Zhao W, Yu X, Jiao C et al., 2021. Increased association between climate change and vegetation index variation promotes the coupling of dominant factors and vegetation growth. Science of The Total Environment, 767: 144669.

    Article  CAS  Google Scholar 

  • Zheng J, Zhang Y, Wang X et al., 2023. Estimation of net ecosystem productivity on the Tibetan Plateau grassland from 1982 to 2018 based on random forest model. Remote Sensing, 15(9): 2375.

    Article  Google Scholar 

  • Zheng L, Lu J, Chen X, 2024. Drought offsets the vegetation greenness-induced gross primary productivity from 1982 to 2018 in China. Journal of Hydrology, 632: 130881.

    Article  Google Scholar 

  • Zhong L, Zhang L, 2023. Changes in temperature and precipitation in the plain area of Hexi Corridor from 2000 to 2020. Journal of Desert Research, 43(2): 264–270. (in Chinese)

    Google Scholar 

  • Zhu B, 2022. The recent evolution of dune landforms and its environmental indications in the mid-latitude desert area (Hexi Corridor). Journal of Geographical Sciences, 32(4): 617–644.

    Article  Google Scholar 

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Correspondence to Xufeng Wang.

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Foundation: The Foundation for Distinguished Young Scholars of Gansu Province, No.22JR5RA046; Key Research Program of Gansu Province, No.23ZDKA0004; The Joint Funds of the National Natural Science Foundation of China, No.U22A202690; Interdisciplinary Youth Team Project from the Key Laboratory of Cryospheric Science and Frozen Soil Engineering, No.CSFSE-ZQ-2408; The Youth Innovation Promotion Association CAS to X.W., No.2020422

Author: Zhou Xuqiang (1995–), PhD Candidate, specialized in carbon flux simulation.

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Zhou, X., Wang, X., Ren, Z. et al. Human activities rather than climate change dominate the growth of carbon fluxes in the Hexi Corridor oasis area, China. J. Geogr. Sci. 35, 252–272 (2025). https://doi.org/10.1007/s11442-025-2321-8

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  • DOI: https://doi.org/10.1007/s11442-025-2321-8

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