Please wait a minute...
Journal of Arid Land
Journal of Arid Land  2021, Vol. 13 Issue (5): 516-533    DOI: 10.1007/s40333-021-0005-5     CSTR: 32276.14.s40333-021-0005-5
Research article     
Potential responses of vegetation to atmospheric aerosols in arid and semi-arid regions of Asia
JIAO Linlin1,2,3, WANG Xunming2,3,*(), CAI Diwen2,3, HUA Ting4
1College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China
2Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3University of Chinese Academy of Sciences, Beijing 100049, China
4Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China
Download: HTML     PDF(3026KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

Changes in atmospheric aerosols have profound effects on ecosystem productivity, vegetation growth and activity by directly and indirectly influencing climate and environment conditions. However, few studies have focused on the effects of atmospheric aerosols on vegetation growth and activity in the vulnerable arid and semi-arid regions, which are also the source areas of aerosols. Using the datasets of aerosol optical depth (AOD), normalized difference vegetation index (NDVI) and multiple climatic variables including photosynthetically active radiation (PAR), surface solar radiation (SSR), surface air temperature (TEM) and total precipitation (PRE), we analyzed the potential responses of vegetation activity to atmospheric aerosols and their associated climatic factors in arid and semi-arid regions of Asia from 2005 to 2015. Our results suggested that areas with decreasing growing-season NDVI were mainly observed in regions with relatively sparse vegetation coverage, while AOD tended to increase as NDVI decreased in these regions. Upon further analysis, we found that aerosols might exert a negative influence on vegetation activity by reducing SSR, PAR and TEM, as well as suppressing PRE in most arid and semi-arid regions of Asia. Moreover, the responses of atmospheric aerosols on vegetation activity varied among different growing stages. At the early growing stage, higher concentration of aerosol was accompanied with suppressed vegetation growth by enhancing cooling effects and reducing SSR and PAR. At the middle growing stage, aerosols tended to alter microphysical properties of clouds with suppressed PRE, thereby restricting vegetation growth. At the late growing stage, aerosols exerted significantly positive influences on vegetation activity by increasing SSR, PAR and TEM in regions with high anthropogenic aerosols. Overall, at different growing stages, aerosols could influence vegetation activity by changing different climatic factors including SSR, PAR, TEM and PRE in arid and semi-arid regions of Asia. This study not only clarifies the impacts of aerosols on vegetation activity in source areas, but also explains the roles of aerosols in climate.

Key words： aerosol optical depth      climatic factors      normalized difference vegetation index      spatiotemporal distribution      indirect effect     
Received: 17 September 2019      Published: 10 May 2021
Corresponding Authors:
About author: *WANG Xunming (E-mail: xunming@igsnrr.ac.cn)
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Linlin JIAO
Xunming WANG
Diwen CAI
Ting HUA
Cite this article:

JIAO Linlin, WANG Xunming, CAI Diwen, HUA Ting. Potential responses of vegetation to atmospheric aerosols in arid and semi-arid regions of Asia. Journal of Arid Land, 2021, 13(5): 516-533.

URL:

http://jal.xjegi.com/10.1007/s40333-021-0005-5     OR     http://jal.xjegi.com/Y2021/V13/I5/516

Fig. 1 Arid and semi-arid regions and land uses in Asia. Land uses are referenced from NASA (National Aeronautics and Space Administration; https://lpdaac.usgs.gov). AERONET, aerosol robotic network. 1, Dushanbe; 2, Issyk-Kul; 3, Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL); 4, Dalanzadgad.
Fig. 2 Scatter plot of MODIS (moderate-resolution imaging spectroradiometer) retrieved AOD (aerosol optical depth) against AERONET (aerosol robotic network) AOD
Fig. 3 Spatial pattern (a) and linear trend (b) of NDVI (normalized difference vegetation index) at growing stages from 2005 to 2015 in arid and semi-arid regions of Asia. Areas passing the significance test (P<0.05) are shown in a gray square grid in Figure 3b.
Fig. 4 Spatial pattern (a) and linear trend (b) of AOD (aerosol optical depth) at growing stages from 2005 to 2015 in arid and semi-arid regions of Asia. Areas passing the significance test (P<0.05) are shown in a gray square grid in Figure 4b.
Region Early growing stage Middle growing stage Late growing stage
Scenario a Scenario b Scenario a-b Scenario a Scenario b Scenario a-b Scenario a Scenario b Scenario a-b
A 46.3 71.1 -24.8 82.0 87.6 -5.6 57.6 70.1 -12.5
A1 69.5 85.3 -15.9 95.5 91.2 4.3 55.9 80.6 -24.7
A2 68.8 93.8 -25.0 49.3 92.0 -42.7 67.0 87.7 -20.7
A3 19.3 49.8 -30.5 65.7 95.0 -29.3 47.1 63.0 -15.9
A4 36.3 46.4 -10.1 61.1 89.4 -28.3 48.6 57.6 -9.0
A5 32.2 58.4 -26.3 93.9 92.0 1.9 48.3 57.4 -9.1
Table 1 Area percentage (%) with significantly positive correlations from two scenarios of partial correlation between PAR (photosynthetically active radiation) and NDVI (normalized difference vegetation index) in different regions at growing stages
Fig. 5 Spatial pattern of partial correlation between NDVI (normalized difference vegetation index) at growing stages and PAR (photosynthetically active radiation) when considering AOD (aerosol optical depth) as the control variable (right panel) or not (left panel) in arid and semi-arid regions of Asia during three selected 16-d periods of growing stages: 97-112 d (a and b), 193-208 d (c and d), and 209-224 d (e and f). Areas with insignificant (P>0.05) correlations and those with missing AOD values are not shown.
Fig. S1 Spatial pattern in the number of 16-d period with a significantly positive correlation between NDVI (normalized difference vegetation index) and PAR (photosynthetically active radiation) when considering AOD (aerosol optical depth) as the control variable (right panel) or not (left panel) over the arid and semi-arid regions of Asia during growing stages (each having four or six 16-d periods). Areas with insignificant (P>0.05) correlations and those with missing AOD values are not shown. Abbreviations are the same as in Figures S2-S4.
Fig. 6 Spatial pattern of partial correlation between NDVI (normalized difference vegetation index) at growing stages and PRE (total precipitation) when considering AOD (aerosol optical depth) as the control variable (right panel) or not (left panel) in arid and semi-arid regions of Asia during three selected 16-d periods of growing stages: 97-112 d (a and b), 193-208 d (c and d), and 209-224 d (e and f). Areas with insignificant (P>0.05) correlations and those with missing AOD values are not shown.
Fig. S2 Spatial pattern in the number of 16-d period with a significantly positive correlation between NDVI and PRE (total precipitation) when considering AOD as the control variable (right panel) or not (left panel) over the arid and semi-arid regions of Asia during growing stages (each having four or six 16-d periods). Areas with insignificant (P>0.05) correlations and those with missing AOD values are not shown.
Region Early growing stage Middle growing stage Late growing stage
Scenario a Scenario b Scenario a-b Scenario a Scenario b Scenario a-b Scenario a Scenario b Scenario a-b
A 62.5 91.9 -29.4 50.9 86.3 -35.4 60.3 82.3 -22.0
A1 50.3 87.8 -37.5 36.2 80.6 -44.5 87.3 92.1 -4.8
A2 65.9 70.4 -4.5 0.7 91.3 -90.5 40.6 58.8 -18.2
A3 63.7 93.6 -29.9 50.3 90.3 -40.0 40.2 85.2 -45.0
A4 26.7 76.7 -50.0 65.0 95.6 -30.6 76.6 92.6 -16.0
A5 82.6 92.6 -10.0 37.3 81.7 -44.4 40.6 61.1 -20.6
Table 2 Area percentage (%) with significantly positive correlations from two scenarios of partial correlation between PRE (total precipitation) and NDVI (normalized difference vegetation index) in different regions at growing stages
Fig. 7 Spatial pattern of partial correlation between NDVI (normalized difference vegetation index) and SSR (surface solar radiation) when considering AOD (aerosol optical depth) as the control variable (right panel) or not (left panel) in arid and semi-arid regions of Asia during three selected 16-d periods of growing stages: 97-112 d (a and b), 193-208 d (c and d), and 209-224 d (e and f). Areas with insignificant (P>0.05) correlations and those with missing AOD values are not shown.
Fig. S3 Spatial pattern in the number of 16-d period with a significantly positive correlation between NDVI and SSR (surface solar radiation) when considering AOD as the control variable (right panel) or not (left panel) over the arid and semi-arid regions of Asia during growing stages (each having four or six 16-d periods). Areas with insignificant (P>0.05) correlations and those with missing AOD values are not shown.
Region Early growing stage Middle growing stage Late growing stage
Scenario a Scenario b Scenario a-b Scenario a Scenario b Scenario a-b Scenario a Scenario b Scenario a-b
A 50.2 76.1 -26.0 17.9 44.9 -27.0 43.7 53.7 -10.0
A1 48.1 82.5 -34.4 4.7 42.6 -37.9 43.6 48.5 -4.9
A2 35.3 22.4 13.0 51.3 85.4 -34.1 36.4 57.4 -21.0
A3 70.3 79.0 -8.7 22.2 85.5 -63.3 14.3 53.6 -39.3
A4 41.6 54.4 -12.8 35.6 84.3 -48.7 60.8 79.4 -18.7
A5 53.4 70.0 -16.6 5.6 23.4 -17.8 46.1 46.8 -0.7
Table 3 Area percentage (%) of significantly positive correlations from two scenarios of partial correlation between SSR (surface solar radiation) and NDVI (normalized difference vegetation index) in different regions at growing stages
Region Early growing stage Middle growing stage Late growing stage
Scenario a Scenario b Scenario a-b Scenario a Scenario b Scenario a-b Scenario a Scenario b Scenario a-b
A 71.8 73.9 -2.1 19.0 40.3 -21.2 35.7 54.9 -19.3
A1 49.9 80.5 -30.6 4.3 40.0 -35.6 51.7 64.6 -12.9
A2 0.0 13.1 -13.1 19.8 49.2 -29.4 0.0 24.9 -24.9
A3 75.6 91.9 -16.4 16.7 75.9 -59.3 29.8 65.0 -35.1
A4 79.0 75.3 3.7 10.4 69.7 -59.3 4.1 30.2 -26.1
A5 80.5 84.1 -3.6 4.0 14.4 -10.5 68.8 73.7 -4.9
Table 4 Area percentage (%) of significantly positive correlations from two scenarios of partial correlation between TEM (surface air temperature) and NDVI (normalized difference vegetation index) in different regions at growing stages
Fig. 8 Spatial patterns of partial correlation between NDVI (normalized difference vegetation index) at growing stages and TEM (surface air temperature) when considering AOD (aerosol optical depth) as the control variable (right panel) or not (left panel) in arid and semi-arid regions of Asia of three selected 16-d periods at growing stages: 97-112 d (a and b), 193-208 d (c and d), and 209-224 d (e and f). Areas with insignificant (P>0.05) correlations and those with missing AOD values are not shown.
Fig. S4 Spatial pattern in the number of 16-d period with a significantly positive correlation between NDVI and TEM (surface air temperature) when considering AOD as the control variable (right panel) or not (left panel) over the arid and semi-arid regions of Asia during growing stages (each having four or six 16-d periods). Areas with insignificant (P>0.05) correlations and those with missing AOD values are not shown.
[1]   Bennie J J, Wiltshire A J, Joyce A N, et al. 2010. Characterizing inter-annual variation in the spatial pattern of thermal microclimate in a UK upland using a combined empirical-physical model. Agricultural and Forest Meteorology, 150(1):12-19.
doi: 10.1016/j.agrformet.2009.07.014
[2]   Bi J R, Huang J P, Shi J S, et al. 2017. Measurement of scattering and absorption properties of dust aerosol in a Gobi farmland region of northwestern China-a potential anthropogenic influence. Atmospheric Chemistry and Physics, 17:7775-7792.
doi: 10.5194/acp-17-7775-2017
[3]   Bokhorst S, Bjerke J W, Bowles, F W, et al. 2008. Impacts of extreme winter warming in the sub-Arctic: growing season responses of dwarf shrub heathland. Global Change Biology, 14(11):2603-2612.
doi: 10.1111/j.1365-2486.2008.01689.x
[4]   Chameides W L, Yu H, Liu S C, et al. 1999. Case study of the effects of atmospheric aerosols and regional haze on agriculture: An opportunity to enhance crop yields in China through emission controls. Proceedings of the National Academy of Sciences of the United States of America, 96(24):13626-13633.
[5]   Cheng S J, Bohrer G, Steiner A L, et al. 2015. Variations in the influence of diffuse light on gross primary productivity in temperate ecosystems. Agricultural and Forest Meteorology, 201:98-110.
doi: 10.1016/j.agrformet.2014.11.002
[6]   Chuai X W, Huang X J, Wang W J, et al. 2013. NDVI, temperature and precipitation changes and their relationships with different vegetation types during 1998-2007 in Inner Mongolia, China. International Journal of Climatology, 33(7):1696-1706.
doi: 10.1002/joc.3543
[7]   Dey S, Tripathi S N, Singh R P. 2004. Influence of dust storms on the aerosol optical properties over the Indo-Gangetic Basin. Journal of Geophysical Research, 109:D20211.
doi: 10.1029/2004JD004924
[8]   Dong B, Wilcox L J, Highwood E J, et al. 2019. Impacts of recent decadal changes in Asian aerosols on the East Asian summer monsoon: roles of aerosol-radiation and aerosol-cloud interactions. Climate Dynamics, 53(5):3235-3256.
doi: 10.1007/s00382-019-04698-0
[9]   Du J, He Z B, Yang J J, et al. 2014. Detecting the effects of climate change on canopy phenology in coniferous forests in semi-arid mountain regions of China. International Journal of Remote Sensing, 35(17):6490-6507.
doi: 10.1080/01431161.2014.955146
[10]   Dubovik O, King M D. 2000. A flexible inversion algorithm for retrieval of aerosol optical properties from sun and sky radiance measurements. Journal of Geophysical Research, 105(D16):20673-20696.
doi: 10.1029/2000JD900282
[11]   Engström A, Ekman A M L. 2010. Impact of meteorological factors on the correlation between aerosol optical depth and cloud fraction. Geophysical Research Letters, 37(18):1480-1493.
[12]   Ezhova E, Ylivinkka I, Kuusk J, et al. 2018. Direct effect of aerosols on solar radiation and gross primary production in boreal and hemiboreal forests. Atmospheric Chemistry and Physics, 18:17863-17881.
doi: 10.5194/acp-18-17863-2018
[13]   Fang J Y, Piao S L, He J S, et al. 2004. Increasing terrestrial vegetation activity in China, 1982-1999. Science China Series C, 47(3):229-240.
[14]   Gessner U, Naeimi V, Klein I, et al. 2013. The relationship between precipitation anomalies and satellite-derived vegetation activity in Central Asia. Global Planetary Change, 110:74-87.
doi: 10.1016/j.gloplacha.2012.09.007
[15]   Ginoux P, Prospero J M, Gill T E, et al. 2012. Global-scale attribution of anthropogenic and natural dust sources and their emission rates based on MODIS deep blue aerosol products. Review of Geophysics, 50(3): RG3005.
[16]   Gu L, Pallardy S G, Hosman K P, et al. 2016. Impacts of precipitation variability on plant species and community water stress in a temperate deciduous forest in the central US. Agricultural and Forest Meteorology, 217:120-136.
doi: 10.1016/j.agrformet.2015.11.014
[17]   He B, Chen A F, Wang H L, et al. 2015. Dynamic response of satellite-derived vegetation growth to climate change in the Three North Shelter Forest Region in China. Remote Sensing, 7(8):9998-10016.
doi: 10.3390/rs70809998
[18]   Hsu N C, Gautam R, Sayer A M, et al. 2017. Global and regional trends of aerosol optical depth over land and ocean using SeaWiFS measurements from 1997 to 2010. Atmospheric Chemistry and Physics, 17:8037-8053.
[19]   Huang J P, Minnis B, Lin T, et al. 2006. Possible influences of Asian dust aerosols on cloud properties and radiative forcing observed from MODIS and CERES. Geophysical Research Letters, 33(6):L06824.
[20]   Huang J P, Fu Q, Su J, et al. 2009. Taklimakan dust aerosol radiative heating derived from CALIPSO observations using the Fu-Liou radiation model with CERES constraints. Atmospheric Chemistry and Physics, 9:4011-4021.
doi: 10.5194/acp-9-4011-2009
[21]   Huang J P, Wang T H, Wang W C, et al. 2014. Climate effects of dust aerosols over East Asian arid and semiarid regions. Journal of Geophysical Research, 119:11398-11416.
[22]   Huang J P, Li Y, Fu C, et al. 2017. Dryland climate change: Recent progress and challenges. Review of Geophysics, 55(3):719-778.
doi: 10.1002/rog.v55.3
[23]   Hui W J, Cook B I, Ravi S, et al. 2008. Dust-rainfall feedbacks in the West African Sahel. Water Resources Research, 44(5):W05202.
[24]   IPCC. 2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. New York: Cambridge University Press, 1-12.
[25]   Justice C O, Vermote E, Townshend J R G, et al. 1998. The moderate resolution imaging spectroradiometer (MODIS): Land remote sensing for global change research. IEEE Transactions on Geoscience and Remote Sensing, 36(4):1228-1249.
doi: 10.1109/36.701075
[26]   Kim Y, Kimball J S, Didan K, et al. 2014. Response of vegetation growth and productivity to spring climate indicators in the conterminous United States derived from satellite remote sensing data fusion. Agricultural and Forest Meteorology, 194:132-143.
doi: 10.1016/j.agrformet.2014.04.001
[27]   Latha R, Vinayak B, Murthy B S. 2018. Response of heterogeneous vegetation to aerosol radiative forcing over a northeast Indian station. Journal of Environmental Management, 206:1224-1232.
doi: S0301-4797(17)30916-7 pmid: 29066023
[28]   Lau K M, Kim M K, Kim K M, 2006. Asian summer monsoon anomalies induced by aerosol direct forcing: The role of the Tibetan Plateau. Climate Dynamics, 26:855-864.
doi: 10.1007/s00382-006-0114-z
[29]   Levy R C, Mattoo S, Munchak L A, et al. 2013. The collection 6 MODIS aerosol products over land and ocean. Atmospheric Measurement Techniques, 6:2989-3034.
doi: 10.5194/amt-6-2989-2013
[30]   Li X Q, Ting M F, Lee D E. 2018. Fast adjustments of the Asian summer monsoon to anthropogenic aerosols. Geophyscial Research Letters, 45(2):1001-1010.
[31]   Li Z Q, Niu F, Fan J W, et al. 2011. Long-term impacts of aerosols on the vertical development of clouds and precipitation. Natural Geoscience, 4:888-894.
[32]   Liu X F, Zhu X F, Li S S, et al. 2015. Changes in growing season vegetation and their associated driving forces in China during 2001-2012. Remote Sensing, 7(11):15517-15535.
doi: 10.3390/rs71115517
[33]   Mi W, Li Z Q, Xia X G, et al. 2007. Evaluation of the Moderate Resolution Imaging Spectroradiometer aerosol products at two aerosol robotic network stations in China. Journal of Geophysical Research, 112: D22S08.
[34]   Mike B. 2002. The Influence of Aerosols on Plant Growth. Honolulu: Georgia Institute of Technology, 5-10.
[35]   Min Q L, Li R, Lin B, et al. 2009. Evidence of mineral dust altering cloud microphysics and precipitation. Atmospheric Chemistry and Physics, 9(9):3223-3231.
doi: 10.5194/acp-9-3223-2009
[36]   Mohammat A, Wang X H, Xu X T, et al. 2013. Drought and spring cooling induced recent decrease in vegetation growth in Inner Asia. Agricultural and Forest Meteorology, 178-179:21-30.
doi: 10.1016/j.agrformet.2012.09.014
[37]   Nair U S, Mcnider R T, Patadia F, et al. 2011. Sensitivity of nocturnal boundary layer temperature to tropospheric aerosol surface radiative forcing under clear-sky conditions. Journal of Geophysical Research, 116(D2):D02205.
[38]   Nemani R R, Keeling C D, Hashimoto H, et al. 2003. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 300(5625):1560-1563.
doi: 10.1126/science.1082750
[39]   Niyogi D, Chang H I, Saxena V K, et al. 2004. Direct observations of the effects of aerosol loading on net ecosystem CO2 exchanges over different landscapes. Geophysical Research Letters, 31(20):L20506.
doi: 10.1029/2004GL020915
[40]   Park H S, Sohn B J. 2010. Recent trends in changes of vegetation over East Asia coupled with temperature and rainfall variations. Journal of Geophysical Research, 115:D14101.
doi: 10.1029/2009JD012752
[41]   Pauli H, Gottfried M, Dullinger S, et al. 2012. Recent plant diversity changes on Europe's mountain summits. Science, 336(6079):353-355.
doi: 10.1126/science.1219033
[42]   Peng S, Chen A, Xu L, et al. 2011. Recent change of vegetation growth trend in China. Environmental Research Letters, 6(4):044027.
doi: 10.1088/1748-9326/6/4/044027
[43]   Piao S L, Mohammat A, 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, 16:340-348.
doi: 10.1016/j.gloenvcha.2006.02.002
[44]   Qian Y, Gong D Y, Fan J W, et al. 2009. Heavy pollution suppresses light rain in China: Observations and modeling. Journal of Geophysical Research, 114: D00K02.
[45]   Ramanathan V, Crutzen P, Kiehl J, et al. 2001. Aerosols, climate, and the hydrological cycle. Science, 294(5549):2119-2124.
doi: 10.1126/science.1064034
[46]   Roderick M L, Farquhar G D, Berry S L, et al. 2001. On the direct effect of clouds and atmospheric particles on the productivity and structure of vegetation. Oecologia, 129(1):21-30.
doi: 10.1007/s004420100760 pmid: 28547064
[47]   Rosenfeld D, Rudich Y, Lahav R, et al. 2001. Desert dust suppressing precipitation: A possible desertification feedback loop. Proceedings of the National Academy of Sciences of the United States of America, 98(11):5975-5980.
[48]   Rosenfeld D, Wood R, Donner L J, et al. 2013. Aerosol cloud-mediated radiative forcing: Highly uncertain and opposite effects from shallow and deep clouds. New York: Springer Science+Business Media, 105-149.
[49]   Rosenfeld D, Andreae M O, Asmi A, et al. 2014. Global observations of aerosol-cloud-precipitation-climate interactions. Reviews of Geophysics, 52(4):750-808.
doi: 10.1002/2013RG000441
[50]   Running S W, Justice C O, Salomonson V, et al. 1994. Terrestrial remote sensing science and algorithms planned for EOS/MODIS. International Journal of Remote Sensing, 15(17):3587-3620.
doi: 10.1080/01431169408954346
[51]   Schimel D S, House J I, Hibbard K A, et al. 2001. Recent patterns and mechanisms of carbon exchange by terrestrial ecosystems. Nature, 414:169-172.
pmid: 11700548
[52]   Shao Y P, Wyrwoll K H, Chappell A, et al. 2011. Dust cycle: An emerging core theme in Earth system science. Aeolian Research, 2(4):181-204.
doi: 10.1016/j.aeolia.2011.02.001
[53]   Solano R, Didan K, Jacobson A, et al 2010. MODIS Vegetation Index Users' Guide (MOD13 Series) Collection 5. Tucsn: University of Arizona, 35-42.
[54]   Simmons A, Uppala S, Dee D, et al. 2006. ERA-interim: New ECMWF reanalysis products from 1989 onwards, ECMWF News, 110:25-35.
[55]   Steiner A L, Chameides W L. 2005. Aerosol-induced thermal effects increase modelled terrestrial photosynthesis and transpiration. Tellus Series B: Chemical and Physical Meteorology, 57(5):404-411.
doi: 10.3402/tellusb.v57i5.16559
[56]   Strada S, Unger N, Yue X. 2015. Observed aerosol-induced radiative effect on plant productivity in the eastern United States. Atmospheric Environment, 122:463-476.
doi: 10.1016/j.atmosenv.2015.09.051
[57]   Tie X, Huang R J, Dai W, et al. 2016. Effect of heavy haze and aerosol pollution on rice and wheat productions in China. Scientific Reports, 6(1):29612.
doi: 10.1038/srep29612
[58]   Wang X, Wu J, Chen M, et al. 2018. Field evidences for the positive effects of aerosols on tree growth. Global Change Biology, 24:498-499.
[59]   Wang X M, Ma W Y, Hua T, et al. 2017. Variation in vegetation greenness along China's land border. Science China Earth Sciences, 60:1-8.
doi: 10.1007/s11430-016-0278-3
[60]   Wild M. 2009. Global dimming and brightening: A review. Journal of Geophysical Research, 114: D00D16.
[61]   Xie J, Huang J B, Wang G Y, et al. 2010. The effects of clouds and aerosols on net ecosystem CO2 exchange over semi-arid Loess Plateau of Northwest China. Atmospheric Chemistry and Physics, 10:8205-8218.
doi: 10.5194/acp-10-8205-2010
[62]   Yan Z B, Lin Z H, Zhang H. 2014. The relationship between the East Asian subtropical westerly jet and summer precipitation over East Asia as simulated by the IAP AGCM4.0. Atmospheric Oceanic Science Letters, 7(6):487-492.
doi: 10.1080/16742834.2014.11447212
[63]   Yin Y, Chen L. 2007. The effects of heating by transported dust layers on cloud and precipitation: A numerical study. Atmospheric Chemistry and Physics, 7:3497-3505.
doi: 10.5194/acp-7-3497-2007
[64]   Yoon J, Burrows J P, Vountas M, et al. 2014. Changes in atmospheric aerosol loading retrieved from space-based measurements during the past decade. Atmospheric Chemistry and Physics, 14:26001-26041.
[65]   Yu H B, Chin M, West J J, et al. 2013. A multi-model assessment of the influence of regional anthropogenic emission reductions on aerosol direct radiative forcing and the role of intercontinental transport. Journal of Geophysical Research, 118(2):700-720.
[66]   Yue X, Unger N, Harper K, et al. 2017. Ozone and haze pollution weakens net primary productivity in China. Atmospheric Chemistry and Physics, 17(9):6073-6089.
doi: 10.5194/acp-17-6073-2017
[67]   Zhang D F, Zakey A S, Gao X J, et al. 2009. Simulation of dust aerosol and its regional feedbacks over East Asia using a regional climate model. Atmospheric Chemistry and Physics, 9:1095-1110.
doi: 10.5194/acp-9-1095-2009
[68]   Zhang R, Ouyang Z T, Xie X, et al. 2016. Impact of climate change on vegetation growth in arid Northwest of China from 1982 to 2011. Remote Sensing, 8(5):364.
doi: 10.3390/rs8050364
[69]   Zhang X Q, Lei Y C, Pang Y, et al. 2014. Tree mortality in response to climate change induced drought across Beijing, China. Climatic Change, 124(1):179-190.
doi: 10.1007/s10584-014-1089-0
[70]   Zhang X Y, Arimoto R, An Z S. 1997. Dust emission from Chinese desert sources linked to variations in atmospheric circulation. Journal of Geophysical Research, 102:28-41.
[71]   Zhou L M, Tucker C J, Kaufmann R K, et al. 2001. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. Journal of Geophysical Research, 106:20069-20083.
doi: 10.1029/2000JD000115
[1] LU Qing, KANG Haili, ZHANG Fuqing, XIA Yuanping, YAN Bing. Impact of climate and human activity on NDVI of various vegetation types in the Three-River Source Region, China[J]. Journal of Arid Land, 2024, 16(8): 1080-1097.
[2] 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.
[3] 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.
[4] CHANG Sen, WEI Yaqi, DAI Zhenzhong, XU Wen, WANG Xing, DUAN Jiajia, ZOU Liang, ZHAO Guorong, REN Xiaoying, FENG Yongzhong. Landscape ecological risk assessment and its driving factors in the Weihe River basin, China[J]. Journal of Arid Land, 2024, 16(5): 603-614.
[5] ZHANG Hui, Giri R KATTEL, WANG Guojie, CHUAI Xiaowei, ZHANG Yuyang, MIAO Lijuan. Enhanced soil moisture improves vegetation growth in an arid grassland of Inner Mongolia Autonomous Region, China[J]. Journal of Arid Land, 2023, 15(7): 871-885.
[6] ZHAO Lili, LI Lusheng, LI Yanbin, ZHONG Huayu, ZHANG Fang, ZHU Junzhen, DING Yibo. Monitoring vegetation drought in the nine major river basins of China based on a new developed Vegetation Drought Condition Index[J]. Journal of Arid Land, 2023, 15(12): 1421-1438.
[7] XU Mengran, ZHANG Jing, LI Zhenghai, MO Yu. Attribution analysis and multi-scenario prediction of NDVI drivers in the Xilin Gol grassland, China[J]. Journal of Arid Land, 2022, 14(9): 941-961.
[8] ZHOU Siyuan, DUAN Yufeng, ZHANG Yuxiu, GUO Jinjin. Vegetation dynamics of coal mining city in an arid desert region of Northwest China from 2000 to 2019[J]. Journal of Arid Land, 2021, 13(5): 534-547.
[9] Ayad M F AL-QURAISHI, Heman A GAZNAYEE, Mattia CRESPI. Drought trend analysis in a semi-arid area of Iraq based on Normalized Difference Vegetation Index, Normalized Difference Water Index and Standardized Precipitation Index[J]. Journal of Arid Land, 2021, 13(4): 413-430.
[10] HAI Yang, LONG Aihua, ZHANG Pei, DENG Xiaoya, LI Junfeng, DENG Mingjiang. Evaluating agricultural water-use efficiency based on water footprint of crop values: a case study in Xinjiang of China[J]. Journal of Arid Land, 2020, 12(4): 580-593.
[11] Yupeng LI, Yaning CHEN, Zhi LI. Effects of land use and cover change on surface wind speed in China[J]. Journal of Arid Land, 2019, 11(3): 345-356.
[12] Wei JIAO, Yaning CHEN, Weihong LI, Chenggang ZHU, Zhi LI. Estimation of net primary productivity and its driving factors in the Ili River Valley, China[J]. Journal of Arid Land, 2018, 10(5): 781-793.
[13] ZHANG Feiyun, BAI Lei, LI Lanhai, WANG Quan. Sensitivity of runoff to climatic variability in the northern and southern slopes of the Middle Tianshan Mountains, China[J]. Journal of Arid Land, 2016, 8(5): 681-693.
[14] JIN Jia, WANG Quan. Assessing ecological vulnerability in western China based on Time-Integrated NDVI data[J]. Journal of Arid Land, 2016, 8(4): 533-545.
[15] ShengChun XIAO, HongLang XIAO, XiaoMei PENG, QuanYan TIAN. Intra-annual stem diameter growth of Tamarix ramosissima and association with hydroclimatic factors in the lower reaches of China’s Heihe River[J]. Journal of Arid Land, 2014, 6(4): 498-510.