Please wait a minute...
Journal of Arid Land
Journal of Arid Land  2024, Vol. 16 Issue (1): 46-70    DOI: 10.1007/s40333-024-0090-3     CSTR: 32276.14.s40333-024-0090-3
Research article     
Spatiotemporal changes of gross primary productivity and its response to drought in the Mongolian Plateau under climate change
ZHAO Xuqin1,2, LUO Min1,2,*(), MENG Fanhao1,2, SA Chula1,2, BAO Shanhu1,2, BAO Yuhai1,2
1College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China
2Inner Mongolia Key Laboratory of Remote Sensing and Geographic Information Systems, Hohhot 010022, China
Download: HTML     PDF(5095KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

Gross primary productivity (GPP) of vegetation is an important constituent of the terrestrial carbon sinks and is significantly influenced by drought. Understanding the impact of droughts on different types of vegetation GPP provides insight into the spatiotemporal variation of terrestrial carbon sinks, aiding efforts to mitigate the detrimental effects of climate change. In this study, we utilized the precipitation and temperature data from the Climatic Research Unit, the standardized precipitation evapotranspiration index (SPEI), the standardized precipitation index (SPI), and the simulated vegetation GPP using the eddy covariance-light use efficiency (EC-LUE) model to analyze the spatiotemporal change of GPP and its response to different drought indices in the Mongolian Plateau during 1982-2018. The main findings indicated that vegetation GPP decreased in 50.53% of the plateau, mainly in its northern and northeastern parts, while it increased in the remaining 49.47% area. Specifically, meadow steppe (78.92%) and deciduous forest (79.46%) witnessed a significant decrease in vegetation GPP, while alpine steppe (75.08%), cropland (76.27%), and sandy vegetation (87.88%) recovered well. Warming aridification areas accounted for 71.39% of the affected areas, while 28.53% of the areas underwent severe aridification, mainly located in the south and central regions. Notably, the warming aridification areas of desert steppe (92.68%) and sandy vegetation (90.24%) were significant. Climate warming was found to amplify the sensitivity of coniferous forest, deciduous forest, meadow steppe, and alpine steppe GPP to drought. Additionally, the drought sensitivity of vegetation GPP in the Mongolian Plateau gradually decreased as altitude increased. The cumulative effect of drought on vegetation GPP persisted for 3.00-8.00 months. The findings of this study will improve the understanding of how drought influences vegetation in arid and semi-arid areas.

Key words： gross primary productivity (GPP)      climate change      warming aridification areas      drought sensitivity      cumulative effect duration (CED)      Mongolian Plateau     
Received: 25 July 2023      Published: 31 January 2024
Corresponding Authors: *LUO Min (E-mail: luomin@imnu.edu.cn)
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
ZHAO Xuqin
LUO Min
MENG Fanhao
SA Chula
BAO Shanhu
BAO Yuhai
Cite this article:

ZHAO Xuqin, LUO Min, MENG Fanhao, SA Chula, BAO Shanhu, BAO Yuhai. Spatiotemporal changes of gross primary productivity and its response to drought in the Mongolian Plateau under climate change. Journal of Arid Land, 2024, 16(1): 46-70.

URL:

http://jal.xjegi.com/10.1007/s40333-024-0090-3     OR     http://jal.xjegi.com/Y2024/V16/I1/46

Fig. 1 Digital elevation model (DEM; a) and vegetation type (b) of the Mongolian Plateau
Fig. 2 Trends in gross primary productivity (GPP) changes and their persistence in the Mongolian Plateau. (a), spatial distribution of GPP changing rate; (b), classification of the significance of GPP changing trends; (c), GPP changing trends in different vegetation types; (d), spatial distribution of Hurst index; (e), persistence of GPP changing trends; (f), persistence of GPP changing trends in different vegetation types. NV, non-vegetation area; SI, significant increase; SDe, significant decrease; NSI, no significant increase; NSDe, no significant decrease; NCI, non-continuous increase; NCD, non-continuous decrease; CSI, continued significant increase; CSD, continued significant decrease; CNSI, continued non-significant increase; CNSD, continued non-significant decrease; CF, coniferous forest; MS, meadow steppe; SH, shrub; TS, typical steppe; DF, deciduous forest; AS, alpine steppe; CR, cropland; DS, desert steppe; SV, sandy vegetation.
Fig. 3 Variation trends of drought indices in the Mongolian Plateau. (a), spatial distribution of Sen's slope of the standardized precipitation index (SPI); (b), classification of changing trends of SPI; (c), SPI changes in different vegetation types; (d), spatial distribution of Sen's slope of the standardized precipitation evapotranspiration index (SPEI); (e), classification of changing trends of SPEI; (f), SPEI changes in different vegetation types. SH, significant humidification; SDr, significant dryness; NSH, non-significant humidification; NSDr, non-significant dryness.
Fig. 4 Spatial distribution of warming aridification areas in the Mongolian Plateau (a) and the percentage of warming aridification areas in different vegetation types (b)
Fig. 5 Spatial distribution and classification of correlation coefficients between GPP and SPI (a and b), GPP and SPEI (d and e), and GPP and temperature (g and h), and the percentage of the classification of correlation coefficients in different vegetation types (c, f, and i). SPC, significantly positive correlation; SNC, significantly negative correlation; NSP, non-significantly positive correlation; NSN, non-significantly negative correlation.
Fig. 6 Spatial distribution and classification of multiple correlation coefficients of GPP with SPI and temperature (a and b), and GPP with SPEI and temperature (d and e), and the percentage of the classification of multiple correlation coefficients in different vegetation types (c and f). SC, significant correlation; IC, insignificant correlation.
Fig. 7 Sensitivity of GPP to drought indices and temperature and spatial distribution of main control factors. (a), spatial distribution of the sensitivity of GPP to SPI; (b), spatial distribution of the sensitivity of GPP to temperature with SPI and temperature as influencing variables; (c), spatial distribution of main control factors to GPP with SPI and temperature as influencing variables; (d), the percentage of main control factors to GPP in different vegetation types with SPI and temperature as influencing variables; (e), spatial distribution of the sensitivity of GPP to SPEI; (f), spatial distribution of the sensitivity of GPP to temperature with SPEI and temperature as influencing variables; (g), spatial distribution of main control factors to GPP with SPEI and temperature as influencing variables; (h), the percentage of main control factors to GPP in different vegetation types with SPEI and temperature as influencing variables.
Fig. 8 Effects of temperature rise on the sensitivity of GPP to drought in different vegetation types. (a), SPI; (b), SPEI.
Fig. 9 Spatial distribution of cumulative effect duration of drought obtained using SPI (a) and SPEI (c), and variation of cumulative effect duration of drought obtained using SPI (a) and SPEI (c) for different vegetation types. The boxes represent the range from the lower quantile (Q25) to the upper quantile (Q75). The cross symbols and horizontal lines inside the boxes represent the means and medians, respectively. The upper and lower whiskers indicate the maximum and minimum values, respectively.
Fig. S1 Altitude dependence of gross primary productivity (GPP) on drought sensitivity and drought accumulation. SPI, standardized precipitation index; SPEI, standardized precipitation evapotranspiration index. The boxes represent the range from the lower quantile (Q25) to the upper quantile (Q75). The cross symbols and horizontal lines inside the boxes represent the means and medians, respectively. The upper and lower whiskers indicate the maximum and minimum values, respectively.
[1]   Abramopoulos F, Rosenzweig C, Choudhury B. 1988. Improved ground hydrology calculations for global climate models (GCMs): Soil water movement and evapotranspiration. Journal of Climate, 1(9): 921-941.
doi: 10.1175/1520-0442(1988)001<0921:IGHCFG>2.0.CO;2
[2]   Agarwal S, Suchithra A S, Singh S P. 2021. Analysis and interpretation of rainfall trend using Mann-Kendall's and Sen's slope method. Indian Journal of Ecology, 48(2): 453-457.
[3]   Akhalkatsi M. 2017. Climate global change on reproduction and diversity of agricultural plants in semi-arid regions of Georgia (Caucasus Ecoregion). Agricultural Research & Technology: Open Access Journal, 3(4): 555619, doi: 10.19080/artoaj. 2017.03.555619.
[4]   Bai Y, Li S G. 2022. Growth peak of vegetation and its response to drought on the Mongolian Plateau. Ecological Indicators, 141: 109150, doi: 10.1016/j.ecolind.2022.109150.
[5]   Bo Y, Li X K, Liu K, et al. 2022. Three decades of gross primary production (GPP) in China: Variations, trends, attributions, and prediction inferred from multiple datasets and time series modeling. Remote Sensing, 14(11): 2564, doi: 10.3390/ rs14112564.
[6]   Charlton C, Stephenson T, Taylor M A, et al. 2022. Evaluating skill of the Keetch-Byram drought index, vapour pressure deficit and water potential for determining bushfire potential in Jamaica. Atmosphere, 13(8): 1276, doi: 10.3390/atmos13081267.
[7]   Chen S L, Huang Y F, Wang G Q. 2021. Detecting drought-induced GPP spatiotemporal variabilities with sun-induced chlorophyll fluorescence during the 2009/2010 droughts in China. Ecological Indicators, 121: 107092, doi: 10.1016/j.ecolind.2020.107092.
[8]   Chen X N, Tao X, Yang Y P. 2022. Distribution and attribution of gross primary productivity increase over the Mongolian Plateau, 2001-2018. IEEE Access, 10: 25125-25134.
doi: 10.1109/ACCESS.2022.3155722
[9]   Chu D, Shen H F, Guan X B, et al. 2021. Long time-series NDVI reconstruction in cloud-prone regions via spatio-temporal tensor completion. Remote Sensing of Environment, 264: 112632, doi: 10.1016/j.rse.2021.112632.
[10]   Cui T X, Wang Y J, Sun R, et al. 2017. Estimating vegetation primary production in the Heihe river basin of China with multi-source and multi-scale data. PLoS ONE, 11(4): e0153971, doi: 10.1371/journal.pone.0153971.
[11]   Dannenberg M P, Yan D, Barnes M L, et al. 2022. Exceptional heat and atmospheric dryness amplified losses of primary production during the 2020 U.S. Southwest hot drought. Global Change Biology, 28(16): 4794-4806.
doi: 10.1111/gcb.v28.16
[12]   Deng H Y, Yin Y H, Han X. 2020. Vulnerability of vegetation activities to drought in Central Asia. Environmental Research Letters, 15(8): 084005, doi: 10.1088/1748-9326/ab93fa.
[13]   Du J, He Z B, Piatek B K, et al. 2019. Interacting effects of temperature and precipitation on climatic sensitivity of spring vegetation green-up in arid mountains of China. Agricultural and Forest Meteorology, 269-270: 71-77.
doi: 10.1016/j.agrformet.2019.02.008
[14]   Feng S, Fu Q. 2013. Expansion of global drylands under a warming climate. Atmospheric Chemistry and Physics, 13(19): 10081-10094.
[15]   Green J K, Berry J, Ciais P, et al. 2020. Amazon rainforest photosynthesis increases in response to atmospheric dryness. Science Advances, 6(47): eabb7232, doi: 10.1126/sciadv.abb7232.
[16]   Gu X L, Guo E L, Yin S, et al. 2022. Differentiating cumulative and lagged effects of drought on vegetation growth over the Mongolian Plateau. Ecosphere, 13(12): e4289, doi: 10.1002/ecs2.4289.
[17]   Guan X B, Chen J M, Shen H F, et al. 2022. Comparison of big-leaf and two-leaf light use efficiency models for GPP simulation after considering a radiation scalar. Agricultural and Forest Meteorology, 313: 108761, doi: 10.1016/j.agrformet.2021. 108761.
[18]   Guo E L, Wang Y F, Wang C L, et al. 2021. NDVI indicates long-term dynamics of vegetation and its driving forces from climatic and anthropogenic factors in Mongolian Plateau. Remote Sensing, 13(4): 688, doi: 10.3390/rs13040688.
[19]   Hang J, Guan X, Ji F. 2012. Enhanced cold-season warming in semi-arid regions. Atmospheric Chemistry and Physics, 12(12): 5391-5398.
[20]   Hayes M, Svoboda M, Wall N, et al. 2011. The Lincoln Declaration on drought indices: Universal meteorological drought index recommended. Bulletin of the American Meteorological Society, 92(4): 485-488.
doi: 10.1175/2010BAMS3103.1
[21]   He B, Tuya W, Qinchaoketu S, et al. 2022a. Climate change characteristics of typical grassland in the Mongolian Plateau from 1978 to 2020. Sustainability, 14(24): 16529, doi: 10.3390/su142416529.
[22]   He P X, Ma X L, Meng X Y, et al. 2022b. Spatiotemporal evolutionary and mechanism analysis of grassland GPP in China. Ecological Indicators, 143: 109323, doi: 10.1016/j.ecolind.2022.109323.
[23]   Huang Y C, Liu B W, Zhao H G, et al. 2022. Spatial and temporal variation of droughts in the Mongolian Plateau during 1959-2018 based on the gridded self-calibrating palmer drought severity index. Water, 14(2): 230, doi: 10.3390/w14020230.
[24]   Ji J Y, Lin H. 2022. Evaluating regional carbon inequality and its dependence with carbon efficiency: implications for carbon neutrality. Energies, 15(19): 7022, doi: 10.3390/en15197022.
[25]   Jiang C Y, Ryu Y. 2016. Multi-scale evaluation of global gross primary productivity and evapotranspiration products derived from Breathing Earth System Simulator (BESS). Remote Sensing of Environment, 186: 528-547.
doi: 10.1016/j.rse.2016.08.030
[26]   Kang Y, Guo E L, Wang Y F, et al. 2021. Application of temperature vegetation dryness index for drought monitoring in Mongolian Plateau. Chinese Journal of Applied Ecology, 32(7): 2534-2544.
doi: 10.13287/j.1001-9332.202107.018
[27]   Kocsis T, Kovács-Székely I, Anda A. 2020. Homogeneity tests and non-parametric analyses of tendencies in precipitation time series in Keszthely, Western Hungary. Theoretical and Applied Climatology, 139(3): 849-859.
doi: 10.1007/s00704-019-03014-4
[28]   Li C L, Filho L W, Yin J, et al. 2018. Assessing vegetation response to multi-time-scale drought across Inner Mongolia Plateau. Journal of Cleaner Production, 179: 210-216.
doi: 10.1016/j.jclepro.2018.01.113
[29]   Li G S, Yu L X, Liu T X, et al. 2023. Spatial and temporal variations of grassland vegetation on the Mongolian Plateau and its response to climate change. Frontiers in Ecology and Evolution, 11: 1067209, doi: 10.3389/fevo.2023.1067209.
[30]   Li S J, Wang J M, Zhang M, et al. 2021. Characterizing and attributing the vegetation coverage changes in North Shanxi coal base of China from 1987 to 2020. Resources Policy, 74: 102331, doi: 10.1016/j.resourpol.2021.102331.
[31]   Luo M, Meng F H, Sa C L, et al. 2021. Response of vegetation phenology to soil moisture dynamics in the Mongolian Plateau. Catena, 206: 105505, doi: 10.1016/j.catena.2021.105505.
[32]   McKee T B, Doesken N J, Kleist J. 1993. The relationship of drought frequency and duration to time scales. Eighth Conference on Applied Climatology, 17-22.
[33]   Meng F H, Luo M, Wang Y Q, et al. 2023. Revisiting the main driving factors influencing the dynamics of gross primary productivity in the Mongolian Plateau. Agricultural and Forest Meteorology, 341: 109689, doi: 10.1016/j.agrformet.2023.109689.
[34]   Na R S, Na L, Du H B, et al. 2021. Vegetation greenness variations and response to climate change in the arid and semi-arid transition zone of the Mongo-Lian Plateau during 1982-2015. Remote Sensing, 13(20): 4066, doi: 10.3390/rs13204066.
[35]   Nandintsetseg B, Shinoda M. 2011. Seasonal change of soil moisture in Mongolia: its climatology and modelling. International Journal of Climatology, 31(8): 1143-1152.
doi: 10.1002/joc.v31.8
[36]   Neda K, Hossein R, Javad B. 2022. Investigation of drought trend on the basis of the best obtained drought index. Water Resources Management, 36(4): 1355-1375.
doi: 10.1007/s11269-022-03086-4
[37]   Nie T, Dong G T, Jiang X H, et al. 2021. Spatio-temporal changes and driving forces of vegetation coverage on the loess Plateau of Northern Shaanxi. Remote Sensing, 13(4): 613, doi: 10.3390/rs13040613.
[38]   Pan L, Xia H M, Zhao X Y, et al. 2021. Mapping winter crops using a phenology algorithm, time-series sentinel-2 and landsat-7/ 8 images, and google earth engine. Remote Sensing, 13(13): 2510, doi: 10.3390/ rs13132510.
[39]   Pei Y Y, Dong J W, Zhang Y, et al. 2022. Evolution of light use efficiency models: improvement, uncertainties, and implications. Agricultural and Forest Meteorology, 317: 108905, doi: 10.1016/j.agrformet.2022.108905.
[40]   Peng J, Wu C Y, Wang X Y, et al. 2019. Satellite detection of cumulative and lagged effects of drought on autumn leaf senescence over the Northern Hemisphere. Global Change Biology, 25(6): 2174-2188.
doi: 10.1111/gcb.14627 pmid: 30897264
[41]   Piao S L, Amwar M, Fang J Y, et al. 2006. NDVI-based increase in growth of temperate grasslands and its responses to climate changes in China. Global Environmental Change, 16(4): 340-348.
doi: 10.1016/j.gloenvcha.2006.02.002
[42]   Piao S L, Sitch S, Ciais P, et al. 2013. Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends. Global Change Biology, 19(7): 2117-2132.
doi: 10.1111/gcb.2013.19.issue-7
[43]   Piao S L, He Y, Wang X H, et al. 2022. Estimation of China's terrestrial ecosystem carbon sink: methods, progress and prospects. Science China (Earth Sciences), 65(4): 641-651.
doi: 10.1007/s11430-021-9892-6
[44]   Qu S, Wang L C, Lin A W, et al. 2020. Distinguishing the impacts of climate change and anthropogenic factors on vegetation dynamics in the Yangtze River Basin, China. Ecological Indicators, 108: 105724, doi: 10.1016/j.ecolind.2019.105724.
[45]   Reddy A R, Chaitanya K V, Vivekanandan M. 2004. Drought-induced responses of photosynthesis and antioxidant metabolism in higher plants. Journal of Plant Physiology, 161(11): 1189-1202.
doi: 10.1016/j.jplph.2004.01.013 pmid: 15602811
[46]   Ritter F, Berkelhammer M, Garcia-Eidell C. 2020. Distinct response of gross primary productivity in five terrestrial biomes to precipitation variability. Communications Earth & Environment, 1(1): 34, doi: 10.1038/s43247-020-00034-1.
[47]   Sun S L, Sun G, Peter C, et al. 2015. Drought impacts on ecosystem functions of the U.S. National Forests and Grasslands: Part II assessment results and management implications. Forest Ecology and Management, 353: 269-279.
doi: 10.1016/j.foreco.2015.04.002
[48]   Vanikiotis T, Stagakis S, Kyparissis A. 2021. MODIS PRI performance to track light use efficiency of a Mediterranean coniferous forest: Determinants, restrictions and the role of LUE range. Agricultural and Forest Meteorology, 307: 108518, doi: 10.1016/j.agrformet.2021.108518.
[49]   Vicente-Serrano S M, Beguería S, López-Moreno J. 2010a. A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. Journal of Climate, 23(7): 1696-1718.
doi: 10.1175/2009JCLI2909.1
[50]   Vicente-Serrano S M, Beguería S, López-Moreno J, et al. 2010b. A new global 0.5° gridded dataset (1901-2006) of a multiscale drought index: Comparison with current drought index datasets based on the Palmer drought severity index. Journal of Hydrometeorology, 11(4): 1033-1043.
doi: 10.1175/2010JHM1224.1
[51]   Wang K, Bastos A, Ciais P, et al. 2022a. Regional and seasonal partitioning of water and temperature controls on global land carbon uptake variability. Nature Communications, 13(1): 3469, doi: 10.1038/s41467-022-31175-w.
[52]   Wang M J, Sun R, Zhu A R, et al. 2020. Evaluation and comparison of light use efficiency and gross primary productivity using three different approaches. Remote Sensing, 12(6): 1003, doi: 10.3390/rs12061003.
[53]   Wang M M, Wang S Q, Wang J B, et al. 2018. Detection of positive gross primary production extremes in terrestrial ecosystems of China during 1982-2015 and analysis of climate contribution. Journal of Geophysical Research: Biogeosciences, 123(9): 2807-2823.
doi: 10.1029/2018JG004489
[54]   Wang S H, Zhang Y G, Ju W M, et al. 2021a. Tracking the seasonal and inter-annual variations of global gross primary production during last four decades using satellite near-infrared reflectance data. Science of the Total Environment, 755(P2): 142569, doi: 10.1016/j.scitotenv.2020.142569.
[55]   Wang S P, Wang J S, Zhang Q, et al. 2016. Cumulative effect of precipitation deficit preceding severe droughts in southwestern and southern China. Discrete Dynamics in Nature and Society, 2016: 2890852, doi: 10.1155/2016/2890852.
[56]   Wang Y H, Fu Z, Hu Z M, et al. 2022b. Tracking global patterns of drought-induced productivity loss along severity gradient. Journal of Geophysical Research: Biogeosciences, 127(6): e2021JG006753, doi: 10.1029/2021jg006753.
[57]   Wang Z, Liu S G, Wang Y P, et al. 2021b. Tighten the bolts and nuts on GPP estimations from sites to the globe: An assessment of remote sensing based LUE models and supporting data fields. Remote Sensing, 13(2): 168, doi: 10.3390/rs13020168.
[58]   Wang Z, Zhang T L, Pei C Y, et al. 2022c. Multisource remote sensing monitoring and analysis of the driving forces of vegetation restoration in the Mu Us sandy land. Land, 11(9): 1553, doi: 10.3390/land11091553.
[59]   Wei X N, He W, Zhou Y L, et al. 2022. Global assessment of lagged and cumulative effects of drought on grassland gross primary production. Ecological Indicators, 136: 108646, doi: 10.1016/j.ecolind.2022.108646.
[60]   Wei X N, He W, Zhou Y L, et al. 2023. Increased sensitivity of global vegetation productivity to drought over the recent three decades. Journal of Geophysical Research: Atmospheres, 128(7): e2022JD037504, doi: 10.1029/2022JD037504.
[61]   Wolf S, Eugster W, Ammann C, et al. 2013. Contrasting response of grassland versus forest carbon and water fluxes to spring drought in Switzerland. Environmental Research Letters, 8(3): 035007, doi: 10.1088/1748-9326/8/3/035007.
[62]   Wu C L, Wang T J. 2022. Evaluating cumulative drought effect on global vegetation photosynthesis using numerous GPP products. Frontiers in Environmental Science, 10: 908875, doi: 10.3389/fenvs.2022.908875.
[63]   Wu X P, Zhang R R, Bento V A, et al. 2022. The effect of drought on vegetation gross primary productivity under different vegetation types across China from 2001 to 2020. Remote Sensing, 14(18): 4658, doi: 10.3390/rs14184658.
[64]   Xiao J F, Chevallier F, Gomez C, et al. 2019. Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years. Remote Sensing of Environment, 233: 111383, doi: 10.1016/j.rse.2019.111383.
[65]   Xie X Y, Li A N. 2020. Development of a topographic-corrected temperature and greenness model (TG) for improving GPP estimation over mountainous areas. Agricultural and Forest Meteorology, 295: 108193, doi: 10.1016/j.agrformet. 2020.108193.
[66]   Xie Z Y, Zhao C L, Zhu W Q, et al. 2023. A radiation-regulated dynamic maximum light use efficiency for improving gross primary productivity estimation. Remote Sensing, 15(5): 1176, doi: 10.3390/rs15051176.
[67]   Xiong Q, Sun Z Y, Cui W, et al. 2022. A study on sensitivities of tropical forest GPP responding to the characteristics of drought—A case study in Xishuangbanna, China. Water, 14(2): 157, doi: 10.3390/w14020157.
[68]   Xu H L, Chen Y N, Li W H. 2003. Multiple regression analysis of the relationship between environmental factors and desertification in the lower Tarim River. Arid Zone Research, 20(1): 39-43. (in Chinese)
[69]   Yin C H, Luo M, Meng F H, et al. 2022. Contributions of climatic and anthropogenic drivers to net primary productivity of vegetation in the Mongolian Plateau. Remote Sensing, 14(14): 3383, doi: 10.3390/rs14143383.
[70]   Yin C H, Chen X Q, Luo M, et al. 2023. Quantifying the contribution of driving factors on distribution and change of net primary productivity of vegetation in the Mongolian Plateau. Remote Sensing, 15(8): 1986, doi: 10.3390/rs15081986.
[71]   Yu T, Sun R, Xiao Z Q, et al. 2018. Estimation of global vegetation productivity from global land surface satellite data. Remote Sensing, 10(2): 327, doi: 10.3390/rs10020327.
[72]   Yuan W P, Liu S G, Yu G R, et al. 2010. Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data. Remote Sensing of Environment, 114(7): 1416-1431.
doi: 10.1016/j.rse.2010.01.022
[73]   Zhan C, Liang C, Zhao L, et al. 2022. Drought-related cumulative and time-lag effects on vegetation dynamics across the Yellow River Basin, China. Ecological Indicators, 143: 109409, doi: 10.1016/j.ecolind.2022.109409.
[74]   Zhang B Q, Wu P T, Zhao X N, et al. 2012. Changes in vegetation condition in areas with different gradients (1980-2010) on the Loess Plateau, China. Environmental Earth Sciences, 68(8): 2427-2438.
doi: 10.1007/s12665-012-1927-1
[75]   Zhang M, Yuan X, Otkin J A. 2020. Remote sensing of the impact of flash drought events on terrestrial carbon dynamics over China. Carbon Balance and Management, 15(1): 20, doi: 10.1186/s13021-020-00156-1.
pmid: 32964321
[76]   Zhang S Z, Zhu X F, Liu T T, et al. 2022a. Response of gross primary production to drought under climate change in different vegetation regions of China. Acta Ecologica Sinica, 42(8): 3429-3440. (in Chinese)
[77]   Zhang X, Sa C L, Hai Q S, et al. 2023. Quantifying the effects of snow on the beginning of vegetation growth in the Mongolian Plateau. Remote Sensing, 15(5): 1245, doi: 10.3390/rs15051245.
[78]   Zhang Y, Xiao X M, Wu X C, et al. 2017. A global moderate resolution dataset of gross primary production of vegetation for 2000-2016. Scientific Data, 4(1): 170165, doi: 10.1038/sdata.2017.165.
[79]   Zhang Y Z, Wang Z Q, Wang Q, et al. 2022b. Comparative assessment of grassland dynamic and its response to drought based on multi-index in the Mongolian Plateau. Plants, 11(3): 310, doi: 10.3390/plants11030310.
[80]   Zhang Z Y, Ju W M, Zhou Y L, et al. 2022c. Revisiting the cumulative effects of drought on global gross primary productivity based on new long-term series data (1982-2018). Global Change Biology, 28(11): 3620-3635.
doi: 10.1111/gcb.v28.11
[81]   Zhao X, Ma X W, Chen B Y, et al. 2022. Challenges toward carbon neutrality in China: Strategies and countermeasures. Resources, Conservation & Recycling, 176: 105959, doi: 10.1016/J.RESCONREC.2021.105959.
[82]   Zhen Z, Flurin B, Valentin B, et al. 2018. Converging climate sensitivities of European forests between observed radial tree growth and vegetation models. Ecosystems, 21(3): 410-425.
doi: 10.1007/s10021-017-0157-5
[83]   Zheng Y, Shen R Q, Wang Y W, et al. 2020. Improved estimate of global gross primary production for reproducing its long-term variation, 1982-2017. Earth System Science Data, 12(4): 2725-2746.
doi: 10.5194/essd-12-2725-2020
[1] CHEN Zhuo, SHAO Minghao, HU Zihao, GAO Xin, LEI Jiaqiang. Potential distribution of Haloxylon ammodendron in Central Asia under climate change[J]. Journal of Arid Land, 2024, 16(9): 1255-1269.
[2] SUN Chao, BAI Xuelian, WANG Xinping, ZHAO Wenzhi, WEI Lemin. Response of vegetation variation to climate change and human activities in the Shiyang River Basin of China during 2001-2022[J]. Journal of Arid Land, 2024, 16(8): 1044-1061.
[3] YAN Yujie, CHENG Yiben, XIN Zhiming, ZHOU Junyu, ZHOU Mengyao, WANG Xiaoyu. Impacts of climate change and human activities on vegetation dynamics on the Mongolian Plateau, East Asia from 2000 to 2023[J]. Journal of Arid Land, 2024, 16(8): 1062-1079.
[4] YANG Jianhua, LI Yaqian, ZHOU Lei, ZHANG Zhenqing, ZHOU Hongkui, WU Jianjun. Effects of temperature and precipitation on drought trends in Xinjiang, China[J]. Journal of Arid Land, 2024, 16(8): 1098-1117.
[5] HAN Qifei, XU Wei, LI Chaofan. Effects of nitrogen deposition on the carbon budget and water stress in Central Asia under climate change[J]. Journal of Arid Land, 2024, 16(8): 1118-1129.
[6] WANG Tongxia, CHEN Fulong, LONG Aihua, ZHANG Zhengyong, HE Chaofei, LYU Tingbo, LIU Bo, HUANG Yanhao. Glacier area change and its impact on runoff in the Manas River Basin, Northwest China from 2000 to 2020[J]. Journal of Arid Land, 2024, 16(7): 877-894.
[7] DU Lan, TIAN Shengchuan, ZHAO Nan, ZHANG Bin, MU Xiaohan, TANG Lisong, ZHENG Xinjun, LI Yan. Climate and topography regulate the spatial pattern of soil salinization and its effects on shrub community structure in Northwest China[J]. Journal of Arid Land, 2024, 16(7): 925-942.
[8] Haq S MARIFATUL, Darwish MOHAMMED, Waheed MUHAMMAD, Kumar MANOJ, Siddiqui H MANZER, Bussmann W RAINER. Predicting potential invasion risks of Leucaena leucocephala (Lam.) de Wit in the arid area of Saudi Arabia[J]. Journal of Arid Land, 2024, 16(7): 983-999.
[9] Seyed Morteza MOUSAVI, Hossein BABAZADEH, Mahdi SARAI-TABRIZI, Amir KHOSROJERDI. Assessment of rehabilitation strategies for lakes affected by anthropogenic and climatic changes: A case study of the Urmia Lake, Iran[J]. Journal of Arid Land, 2024, 16(6): 752-767.
[10] LI Chuanhua, ZHANG Liang, WANG Hongjie, PENG Lixiao, YIN Peng, MIAO Peidong. Influence of vapor pressure deficit on vegetation growth in China[J]. Journal of Arid Land, 2024, 16(6): 779-797.
[11] LU Haitian, ZHAO Ruifeng, ZHAO Liu, LIU Jiaxin, LYU Binyang, YANG Xinyue. Impact of climate change and human activities on the spatiotemporal dynamics of surface water area in Gansu Province, China[J]. Journal of Arid Land, 2024, 16(6): 798-815.
[12] YANG Zhiwei, CHEN Rensheng, LIU Zhangwen, ZHAO Yanni, LIU Yiwen, WU Wentong. Spatiotemporal variability of rain-on-snow events in the arid region of Northwest China[J]. Journal of Arid Land, 2024, 16(4): 483-499.
[13] ZHANG Mingyu, CAO Yu, ZHANG Zhengyong, ZHANG Xueying, LIU Lin, CHEN Hongjin, GAO Yu, YU Fengchen, LIU Xinyi. Spatiotemporal variation of land surface temperature and its driving factors in Xinjiang, China[J]. Journal of Arid Land, 2024, 16(3): 373-395.
[14] WANG Baoliang, WANG Hongxiang, JIAO Xuyang, HUANG Lintong, CHEN Hao, GUO Wenxian. Runoff change in the Yellow River Basin of China from 1960 to 2020 and its driving factors[J]. Journal of Arid Land, 2024, 16(2): 168-194.
[15] LIU Xinyu, LI Xuemei, ZHANG Zhengrong, ZHAO Kaixin, LI Lanhai. A CMIP6-based assessment of regional climate change in the Chinese Tianshan Mountains[J]. Journal of Arid Land, 2024, 16(2): 195-219.