Please wait a minute...
Journal of Arid Land
Journal of Arid Land  2022, Vol. 14 Issue (5): 521-536    DOI: 10.1007/s40333-022-0094-9     CSTR: 32276.14.s40333-022-0094-9
Research article     
Projection of hydrothermal condition in Central Asia under four SSP-RCP scenarios
YAO Linlin1,2,3, ZHOU Hongfei1,2,*(), YAN Yingjie1,2,3, LI Lanhai1,3,4,5,6, SU Yuan1,2,3
1State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
2Fukang National Field Scientific Observation and Research Station for Desert Ecosystems, Chinese Academy of Sciences, Fukang 831505, China
3University of Chinese Academy of Sciences, Beijing 100049, China
4Ili Station for Watershed Ecosystem Research, Chinese Academy of Sciences, Xinyuan 835800, China
5CAS Research Center for Ecology and Environment of Central Asia, Urumqi 830011, China
6Xinjiang Key Laboratory of Water Cycle and Utilization in Arid Zone, Urumqi 830011, China
Download: HTML     PDF(1721KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

Hydrothermal condition is mismatched in arid and semi-arid regions, particularly in Central Asia (including Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan, and Turkmenistan), resulting many environmental limitations. In this study, we projected hydrothermal condition in Central Asia based on bias-corrected multi-model ensembles (MMEs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under four Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios (SSP126 (SSP1-RCP2.6), SSP245 (SSP2-RCP4.5), SSP460 (SSP4-RCP6.0), and SSP585 (SSP5-RCP8.5)) during 2015-2100. The bias correction and spatial disaggregation, water-thermal product index, and sensitivity analysis were used in this study. The results showed that the hydrothermal condition is mismatched in the central and southern deserts, whereas the region of Pamir Mountains and Tianshan Mountains as well as the northern plains of Kazakhstan showed a matched hydrothermal condition. Compared with the historical period, the matched degree of hydrothermal condition improves during 2046-2075, but degenerates during 2015-2044 and 2076-2100. The change of hydrothermal condition is sensitive to precipitation in the northern regions and the maximum temperatures in the southern regions. The result suggests that the optimal scenario in Central Asia is SSP126 scenario, while SSP585 scenario brings further hydrothermal contradictions. This study provides scientific information for the development and sustainable utilization of hydrothermal resources in arid and semi-arid regions under climate change.

Key words： hydrothermal condition      water-thermal product index      bias correction and spatial disaggregation      SSP-RCP scenarios      Central Asia     
Received: 22 December 2021      Published: 31 May 2022
Fund:  the Strategic Priority Research Program of Chinese Academy of Sciences, Pan-Third Pole Environment Study for a Green Silk Road (Pan-TPE) of China(XDA2004030202);Shanghai Cooperation and the Organization Science and Technology Partnership of China(2021E01019)
Corresponding Authors: *: ZHOU Hongfei (E-mail: zhouhongfei_ucas@163.com)
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
YAO Linlin
ZHOU Hongfei
YAN Yingjie
LI Lanhai
SU Yuan
Cite this article:

YAO Linlin, ZHOU Hongfei, YAN Yingjie, LI Lanhai, SU Yuan. Projection of hydrothermal condition in Central Asia under four SSP-RCP scenarios. Journal of Arid Land, 2022, 14(5): 521-536.

URL:

http://jal.xjegi.com/10.1007/s40333-022-0094-9     OR     http://jal.xjegi.com/Y2022/V14/I5/521

Fig. 1 Overview of the study area. Note that this map is based on the standard map (No. GS (2016) 1666) of the Map Service System (http://bzdt.ch.mnr.gov.cn/) marked by the Ministry of Natural Resources of the People's Republic of China, and the base map has not been modified.
Index Description Equation Optimum value
Bias Bias between the historical MMEs and reference data $Bias=\frac{\sum^{i=1}_{n}MME_{hist,i}-obs_{hist,i}}{n}$ 0.0
Relative bias
(RB)		 The ratio of bias to historical reference data $RB=\frac{Bias}{obs_{hist}}$ 0.0
Time correlation (TC) Time correlation between the historical MMEs and reference data $TC=\frac{\sum^{n}_{i=1}(MME_{hist,i}-\bar{MME}_{hist})×\sum^{n}_{i=1}(obs_{hist,i}-\bar{obs}_{hist})}
{\sqrt{\sum^{n}_{i=1}(MME_{hist,i}-\bar{MME}_{hist})^{2}}×\sqrt{\sum^{n}_{i=1}(obs_{hist,i}-\bar{obs}_{hist})^{2}}}$		 1.0
Spatial correlation (SC) Spatial correlation between the historical MMEs and reference data $SC=\frac{\sum^{m}_{j=1}(MME_{hist,j}-\bar{MME}_{hist})×\sum^{m}_{j=1}(obs_{hist,j}-\bar{obs}_{hist})}
{\sum^{m}_{j=1}(MME_{hist,j}-\bar{MME}_{hist})^{2}×\sqrt{\sum^{m}_{j=1}(obs_{hist}-\bar{obs}_{hist})^{2}}}$		 1.0
Normalized standard
deviation (NSTD)		 Normalized standard deviation between the historical MMEs and reference data $NSTD=\frac{\sum^{i=1}_{n}(MME_{hist,i}-\bar{MME}_{hist,i})^{2}}
{\sum^{i=1}_{n}(obs_{hist,i}-\bar{obs}_{hist,i})^{2}}$		 0.8-1.2
Mean absolute error (MAE) Mean absolute error of the historical MMEs $MAE=\frac{\sum^{i=1}_{n}|MME_{hist,i}-obs_{hist,i}|}{n}$ 0.0
Table 1 Evaluation indices of bias correction
Fig. 2 Spatial distribution of relative bias (RB) of precipitation based on five Global Circulation Models (GCMs) (CanESM5 (a), IPSL-CM6A-LR (b), MIROC6 (c), MRI-ESM2-0 (d), and FGOALS-g3 (e)) and multi-model ensembles (MMEs, f) in Central Asia during 1975-2014. Note that this map is based on the standard map (No. GS (2016) 1666) of the Map Service System (http://bzdt.ch.mnr.gov.cn/) marked by the Ministry of Natural Resources of the People's Republic of China, and the base map has not been modified.
Fig. 3 Spatial distribution of RB of temperature based on five GCMs (CanESM5 (a), IPSL-CM6A-LR (b), MIROC6 (c), MRI-ESM2-0 (d), and FGOALS-g3 (e)) and MMEs (f) in Central Asia during 1975-2014. Note that this map is based on the standard map (No. GS (2016) 1666) of the Map Service System (http://bzdt.ch.mnr.gov.cn/) marked by the Ministry of Natural Resources of the People's Republic of China, and the base map has not been modified.
Fig. 4 Spatial distribution of RB of potential evapotranspiration (PET) based on five GCMs (CanESM5 (a), IPSL-CM6A-LR (b), MIROC6 (c), MRI-ESM2-0 (d), and FGOALS-g3 (e)) and MMEs (f) in Central Asia during 1975-2014. Note that this map is based on the standard map (No. GS (2016) 1666) of the Map Service System (http://bzdt.ch.mnr.gov.cn/) marked by the Ministry of Natural Resources of the People's Republic of China, and the base map has not been modified.
Fig. 5 Taylor diagram of precipitation
(a), temperature (b), and PET (c) in Central Asia during 1975-2014. The green lines represent the reference of mean square error.
Index Precipitation Temperature PET Tmax Tmin
BC AC BC AC BC AC BC AC BC AC
Bias 0.27 0.01 1.20 0.08 -0.37 0.01 0.44 0.00 1.00 0.04
RB 0.39 0.01 0.18 0.01 -0.15 0.00 0.03 0.00 0.63 0.02
TC 0.36 0.66 0.96 0.96 0.96 0.97 0.97 0.97 0.97 0.96
SC 0.62 0.63 0.88 0.96 0.85 0.91 0.91 0.96 0.88 0.96
NSTD 0.86 0.98 1.09 1.00 1.28 1.01 1.15 1.01 1.10 1.01
MAE 0.47 0.39 2.97 3.15 0.74 0.41 2.99 2.32 2.87 2.03
Table 2 Statistical evaluation of bias correction for climate variables
Fig. 6 Annual RB of precipitation (a), temperature (b), PET (c), maximum temperature (Tmax, d), and minimum temperature (Tmin, e) before and after correction in Central Asia during 1975-2014
Fig. 7 Temporal variation of water-thermal product index (k index) at annual (a) and seasonal (b-e) scales during different periods. The boxes represent the range from the lower quantile (Q25) to the upper quantile (Q75). The black horizontal lines represent the means. The upper and lower whiskers extent to the maximum and minimum value within the 1.5 interquartile range of the upper and lower quartile, respectively. SSP126, SSP1-RCP2.6; SSP245, SSP2-RCP4.5; SSP460, SSP4-RCP6.0; SSP585, SSP5-RCP8.5. SSP, Shared Socioeconomic Pathway; RCP, Representative Concentration Pathway.
Fig. 8 Projected spatial distribution of k index at annual (a) and seasonal (b-e) scales under SSP126 scenario in Central Asia during 2015-2100. Note that this map is based on the standard map (No. GS (2016) 1666) of the Map Service System (http://bzdt.ch.mnr.gov.cn/) marked by the Ministry of Natural Resources of the People's Republic of China, and the base map has not been modified.
Fig. 9 Projected spatial distribution of the difference value of k index at annual (a1-a3) and seasonal (b1-e3) scales under SSP245, SSP460, and SSP585 scenarios in Central Asia during 2015-2100. (b1-b3), spring; (c1-c3), summer; (d1-d3), autumn; (e1-e3), winter. Note that this map is based on the standard map (No. GS (2016) 1666) of the Map Service System (http://bzdt.ch.mnr.gov.cn/) marked by the Ministry of Natural Resources of the People's Republic of China, and the base map has not been modified.
Fig. 10 Spatial distribution of dominant climate variable at annual (a1-a4) and seasonal (b1-e4) scales under SSP126, SSP245, SSP460, and SSP585 scenarios in Central Asia during 2015-2100. (b1-b4), spring; (c1-c4), summer; (d1-d4), autumn; (e1-e4), winter. Note that this map is based on the standard map (No. GS (2016) 1666) of the Map Service System (http://bzdt.ch.mnr.gov.cn/) marked by the Ministry of Natural Resources of the People's Republic of China, and the base map has not been modified.
[1]   Abd-Elmabod S K, Muñoz-Rojas M, Jordán A, et al. 2020. Climate change impacts on agricultural suitability and yield reduction in a Mediterranean region. Geoderma, 374: 114453, doi: 10.1016/j.geoderma.2020.114453.
doi: 10.1016/j.geoderma.2020.114453
[2]   Ahmed K F, Wang G L, Silander J, et al. 2013. Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the U.S. northeast. Global and Planetary Change, 100: 320-332.
doi: 10.1016/j.gloplacha.2012.11.003
[3]   Alley W M. 1984. The Palmer drought severity index: limitations and assumptions. Journal of Applied Meteorology and Climatology, 23(7): 1100-1109.
[4]   Bannayan M, Sanjani S, Alizadeh A, et al. 2010. Association between climate indices, aridity index, and rainfed crop yield in northeast of Iran. Field Crops Research, 118(2): 105-114.
doi: 10.1016/j.fcr.2010.04.011
[5]   Beven K. 1979. A sensitivity analysis of the Penman-Monteith actual evapotranspiration estimates. Journal of Hydrology, 44(3-4): 169-190.
doi: 10.1016/0022-1694(79)90130-6
[6]   Cardoso A S, Alonso J, Rodrigues A S, et al. 2019. Agro-ecological terroir units in the North West Iberian Peninsula wine regions. Applied Geography, 107: 51-62.
doi: 10.1016/j.apgeog.2019.03.011
[7]   Carli C, Yuldashev F, Khalikov D, et al. 2014. Effect of different irrigation regimes on yield, water use efficiency and quality of potato (Solanum tuberosum L.) in the lowlands of Tashkent, Uzbekistan: A field and modeling perspective. Field Crops Research, 163: 90-99.
doi: 10.1016/j.fcr.2014.03.021
[8]   Chernozhukov V, Galichon A, Hallin M, et al. 2017. Monge-kantorovich depth, quantiles, ranks and signs. The Annals of Statistics, 45(1): 223-256.
[9]   Christensen J H, Boberg F, Christensen O B, et al. 2008. On the need for bias correction of regional climate change projections of temperature and precipitation. Geophysical Research Letters, 35(20): 6.
[10]   Deng H Y, Yin Y H, Han X. 2020. Vulnerability of vegetation activities to drought in Central Asia. Environmental Research Letters, 15(8): 12.
[11]   Dubovyk O, Ghazaryan G, Gonzalez J, et al. 2019. Drought hazard in Kazakhstan in 2000-2016: a remote sensing perspective. Environmental Monitoring and Assessment, 191(8): 1-17.
[12]   Eyring V, Bony S, Meehl G A, et al. 2016. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5): 1937-1958.
doi: 10.5194/gmd-9-1937-2016
[13]   Fang W, Huang S, Huang Q, et al. 2019. Probabilistic assessment of remote sensing-based terrestrial vegetation vulnerability to drought stress of the Loess Plateau in China. Remote Sensing of Environment, 232: 111290, doi: 10.1016/j.rse.2019.111290.
doi: 10.1016/j.rse.2019.111290
[14]   Ge F, Zhu S P, Luo H L, et al. 2021. Future changes in precipitation extremes over Southeast Asia: insights from CMIP 6 multi-model ensemble. Environmental Research Letters, 16(2): 024013, doi: 10.1088/1748-9326/abd7ad.
doi: 10.1088/1748-9326/abd7ad
[15]   Geng H, Pan B, Huang B, et al. 2017. The spatial distribution of precipitation and topography in the Qilian Shan Mountains, northeastern Tibetan Plateau. Geomorphology, 297: 43-54.
doi: 10.1016/j.geomorph.2017.08.050
[16]   Gidden M J, Riahi K, Smith S J, et al. 2019. Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century. Geoscientific Model Development, 12(4): 1443-1475.
doi: 10.5194/gmd-12-1443-2019
[17]   Guo H, Bao A M,Chen T, et al. 2021. Assessment of CMIP6 in simulating precipitation over arid Central Asia. Atmospheric Research, 252: 105451, doi: 10.1016/j.atmosres.2021.105451.
doi: 10.1016/j.atmosres.2021.105451
[18]   Harris I, Jones P D, Osborn T J, et al. 2014. Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset. International Journal of Climatology, 34(3): 623-642.
doi: 10.1002/joc.3711
[19]   Horion S, Prishchepov A V, Verbesselt J, et al. 2016. Revealing turning points in ecosystem functioning over the Northern Eurasian agricultural frontier. Global Change Biology, 22(8): 2801-2817.
doi: 10.1111/gcb.13267
[20]   Ji X, Li Y, Luo X, et al. 2020. Evaluation of bias correction methods for APHRODITE data to improve hydrologic simulation in a large Himalayan basin. Atmospheric Research, 242: 104964, doi: 10.1016/j.atmosres.2020.104964.
doi: 10.1016/j.atmosres.2020.104964
[21]   Jiang L L, Jiapaer G, Bao A M, et al. 2019. Monitoring the long-term desertification process and assessing the relative roles of its drivers in Central Asia. Ecological Indicators, 104: 195-208.
doi: 10.1016/j.ecolind.2019.04.067
[22]   Jiang L L, Bao A M,Jiapaer G, et al. 2022. Monitoring land degradation and assessing its drivers to support sustainable development goal 15.3 in Central Asia. Science of the Total Environment, 807: 150868, doi: 10.1016/j.scitotenv.2021.150868.
doi: 10.1016/j.scitotenv.2021.150868
[23]   Kienzler K M, Lamers J P A, McDonald A, et al. 2012. Conservation agriculture in Central Asia-What do we know and where do we go from here? Field Crops Research, 132: 95-105.
doi: 10.1016/j.fcr.2011.12.008
[24]   Konapala G, Mishra A K, Wada Y, et al. 2020. Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation. Nature Communications, 11(1): 3044, doi: 10.1038/s41467-020-16 757-w.
doi: 10.1038/s41467-020-16757-w pmid: 32576822
[25]   Lacombe G, Hoanh C T, Smakhtin V. 2012. Multi-year variability or unidirectional trends? Mapping long-term precipitation and temperature changes in continental Southeast Asia using PRECIS regional climate model. Climatic Change, 113: 285-299.
doi: 10.1007/s10584-011-0359-3
[26]   Li J, Fei L, Li S, et al. 2020. Development of "water-suitable" agriculture based on a statistical analysis of factors affecting irrigation water demand. Science of the Total Environment, 744: 140986, doi: 10.1016/j.scitotenv.2020.140986.
doi: 10.1016/j.scitotenv.2020.140986
[27]   Li M X, Ma Z G. 2018. Decadal changes in summer precipitation over arid northwest China and associated atmospheric circulations. International Journal of Climatology, 38(12): 4496-4508.
doi: 10.1002/joc.5682
[28]   Li W, Li C, Liu X, et al. 2018. Analysis of spatial-temporal variation in NPP based on hydrothermal conditions in the Lancang-Mekong River Basin from 2000 to 2014. Environmental Monitoring and Assessment, 190(6): 321, doi: 10.1007/s10661-018-6690-7.
doi: 10.1007/s10661-018-6690-7
[29]   Li Z, Fang G, Chen Y, et al. 2020. Agricultural water demands in Central Asia under 1.5 degrees C and 2.0 degrees C global warming. Agricultural Water Management, 231: 106020, doi: 10.1016/j.agwat.2020.106020.
doi: 10.1016/j.agwat.2020.106020
[30]   Lioubimtseva E, Henebry G M. 2009. Climate and environmental change in arid Central Asia: Impacts, vulnerability, and adaptations. Journal of Arid Environments, 73(11): 963-977.
doi: 10.1016/j.jaridenv.2009.04.022
[31]   Luo M, Liu T, Meng F H, et al. 2019. Spatiotemporal characteristics of future changes in precipitation and temperature in Central Asia. International Journal of Climatology, 39(3): 1571-1588.
doi: 10.1002/joc.5901
[32]   Mannig B, Muller M, Starke E, et al. 2013. Dynamical downscaling of climate change in Central Asia. Global and Planetary Change, 110: 26-39.
doi: 10.1016/j.gloplacha.2013.05.008
[33]   Martonne E D. 1926. A new ciimatological function: the aridity index. La Météorologie, 2: 449-458. (in French)
[34]   McCain C M, Colwell R K. 2011. Assessing the threat to montane biodiversity from discordant shifts in temperature and precipitation in a changing climate. Ecology Letters, 14(12): 1236-1245.
doi: 10.1111/j.1461-0248.2011.01695.x
[35]   Meng M, Ni J, Zhang Z G. 2004. Aridity index and its applications in geo-ecological study. Acta Phytoecologica Sinica, 28: 853-861. (in Chinese)
[36]   Mondal S K, Huang J, Wang Y, et al. 2021. Doubling of the population exposed to drought over South Asia: CMIP6 multi-model-based analysis. Science of the Total Environment, 771: 145186, doi: 10.1016/j.scitotenv.2021.145186.
doi: 10.1016/j.scitotenv.2021.145186
[37]   Ni J, Zhang X S. 1997. Estimation of water and thermal product index and its application to the study of vegetation-climate interaction in China. Acta Botanica Sinica, 12: 1147-1159. (in Chinese)
[38]   Reshmidevi T V, Eldho T I, Jana R. 2009. A GIS-integrated fuzzy rule-based inference system for land suitability evaluation in agricultural watersheds. Agricultural Systems, 101(1-2): 101-109.
doi: 10.1016/j.agsy.2009.04.001
[39]   Rivera J A,Arnould G. 2020. Evaluation of the ability of CMIP 6 models to simulate precipitation over Southwestern South America: Climatic features and long-term trends (1901-2014). Atmospheric Research, 241: 104953, doi: 10.1016/j.atmosres.2020.104953.
doi: 10.1016/j.atmosres.2020.104953
[40]   Schierhorn F, Hofmann M, Adrian I, et al. 2020. Spatially varying impacts of climate change on wheat and barley yields in Kazakhstan. Journal of Arid Environments, 178: 104164, doi: 10.1016/j.jaridenv.2020.104164.
doi: 10.1016/j.jaridenv.2020.104164
[41]   Seljaninov G T. 1966. Agroclimatic Map of the World. Leningrad: Hydrometeoizdat Publishing House.
[42]   Seo K H, Ok J. 2013. Assessing future changes in the East Asian summer monsoon using CMIP 3 models: results from the best model ensemble. Journal of Climate, 26(5): 1807-1817.
doi: 10.1175/JCLI-D-12-00109.1
[43]   Su B, Huang J, Mondal S K, et al. 2021. Insight from CMIP6 SSP-RCP scenarios for future drought characteristics in China. Atmospheric Research, 250: 105375, doi: 10.1016/j.atmosres.2020.105375.
doi: 10.1016/j.atmosres.2020.105375
[44]   Sun F Y, Mejia A, Zeng P, et al. 2019. Projecting meteorological, hydrological and agricultural droughts for the Yangtze River basin. Science of the Total Environment, 696: 134076, doi: 10.1016/j.scitotenv.2019.134076.
doi: 10.1016/j.scitotenv.2019.134076
[45]   Taylor K E. 2001. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research-Atmospheres, 106(D7): 7183-7192.
doi: 10.1029/2000JD900719
[46]   Teutschbein C, Seibert J. 2012. Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. Journal of Hydrology, 456: 12-29.
[47]   Vicente-Serrano S M, Begueria S, Lopez-Moreno J I. 2010. 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
[48]   Wang H, Zang F, Zhao C, et al. 2022. A GWR downscaling method to reconstruct high-resolution precipitation dataset based on GSMaP-Gauge data: A case study in the Qilian Mountains, Northwest China. Science of the Total Environment, 810: 1522066, doi: 10.1016/j.scitotenv.2021.152066.
doi: 10.1016/j.scitotenv.2021.152066
[49]   Wang J S, Chen F H, Jin L Y, et al. 2010. Characteristics of the dry/wet trend over arid central Asia over the past 100 years. Climate Research, 41: 51-59.
doi: 10.3354/cr00837
[50]   Wang T, Tu X, Singh V P, et al. 2021. Global data assessment and analysis of drought characteristics based on CMIP6. Journal of Hydrology, 596: 126091, doi: 10.1016/j.jhydrol.2021.126091.
doi: 10.1016/j.jhydrol.2021.126091
[51]   Weiland F C S, van Beek L P H, Weerts A H, et al. 2012. Extracting information from an ensemble of GCMs to reliably assess future global runoff change. Journal of Hydrology, 412: 66-75.
[52]   Weltzin J F, Loik M E, Schwinning S, et al. 2003. Assessing the response of terrestrial ecosystems to potential changes in precipitation. Bioscience, 53(10): 941-952.
doi: 10.1641/0006-3568(2003)053[0941:ATROTE]2.0.CO;2
[53]   Wood A W, Maurer E P, Kumar A, et al. 2002. Long-range experimental hydrologic forecasting for the eastern United States. Journal of Geophysical Research-Atmospheres, 107(D20): 15, doi: 10.1029/2001jd000659.
doi: 10.1029/2001jd000659
[54]   Wood A W, Leung L R, Sridhar V, et al. 2004. Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Climatic Change, 62(1-3): 189-216.
doi: 10.1023/B:CLIM.0000013685.99609.9e
[55]   Wu H, Huang A, He Q, et al. 2013. Projection of the spatial and temporal variation characteristics of precipitation over Central Asia of 10 CMIP5 models in the next 50years. Arid Land Geography, 36(4): 669-679. (in Chinese)
[56]   Wu Z T, Dijkstra P, Koch G W, et al. 2011. Responses of terrestrial ecosystems to temperature and precipitation change: a meta-analysis of experimental manipulation. Global Change Biology, 17(2): 927-942.
doi: 10.1111/j.1365-2486.2010.02302.x
[57]   Xu H j, Wang X P, Zhang X X. 2016. Decreased vegetation growth in response to summer drought in Central Asia from 2000 to 2012. International Journal of Applied Earth Observation and Geoinformation, 52: 390-402.
doi: 10.1016/j.jag.2016.07.010
[58]   Yu Y, Chen X, Disse M, et al. 2020. Climate change in Central Asia: Sino-German cooperative research findings. Science Bulletin, 65(9): 689-692.
doi: 10.1016/j.scib.2020.02.008
[59]   Yuan Y, Bao A, Jiang P, et al. 2022. Probabilistic assessment of vegetation vulnerability to drought stress in Central Asia. Journal of Environmental Management, 310: 114504, doi: 10.1016/j.jenvman.2022.114504.
doi: 10.1016/j.jenvman.2022.114504
[60]   Zeng P, Sun F,Liu Y, et al. 2021. Mapping future droughts under global warming across China: A combined multi-timescale meteorological drought index and SOM-Kmeans approach. Weather and Climate Extremes, 31: 100304, doi: 10.1016/j.wace.2021.100304.
doi: 10.1016/j.wace.2021.100304
[61]   Zhan Y J, Ren G Y, Yang S. 2018. Change in precipitation over the Asian continent from 1901-2016 based on a new multi-source dataset. Climate Research, 76(1): 41-57.
doi: 10.3354/cr01523
[62]   Zhang J, Su Y, Wu J, et al. 2015. GIS based land suitability assessment for tobacco production using AHP and fuzzy set in Shandong province of China. Computers and Electronics in Agriculture, 114: 202-211.
doi: 10.1016/j.compag.2015.04.004
[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] BAO Anming, YU Tao, XU Wenqiang, LEI Jiaqiang, JIAPAER Guli, CHEN Xi, Tojibaev KOMILJON, Shomurodov KHABIBULLO, Xabibullaev B SAGIDULLAEVICH, Idirisov KAMALATDIN. Ecological problems and ecological restoration zoning of the Aral Sea[J]. Journal of Arid Land, 2024, 16(3): 315-330.
[3] WANG Min, CHEN Xi, CAO Liangzhong, KURBAN Alishir, SHI Haiyang, WU Nannan, EZIZ Anwar, YUAN Xiuliang, Philippe DE MAEYER. Correlation analysis between the Aral Sea shrinkage and the Amu Darya River[J]. Journal of Arid Land, 2023, 15(7): 757-778.
[4] LI Wen, MU Guijin, YE Changsheng, XU Lishuai, LI Gen. Aeolian activity in the southern Gurbantunggut Desert of China during the last 900 years[J]. Journal of Arid Land, 2023, 15(6): 649-666.
[5] YAN Xue, LI Lanhai. Spatiotemporal characteristics and influencing factors of ecosystem services in Central Asia[J]. Journal of Arid Land, 2023, 15(1): 1-19.
[6] SONG Yujia, LIU Xijun, XIAO Wenjiao, ZHANG Zhiguo, LIU Pengde, XIAO Yao, LI Rui, WANG Baohua, LIU Lei, HU Rongguo. Neoproterozoic I-type granites in the Central Tianshan Block (NW China): geochronology, geochemistry, and tectonic implications[J]. Journal of Arid Land, 2022, 14(1): 82-101.
[7] YIN Hanmin, Jiapaer GULI, JIANG Liangliang, YU Tao, Jeanine UMUHOZA, LI Xu. Monitoring fire regimes and assessing their driving factors in Central Asia[J]. Journal of Arid Land, 2021, 13(5): 500-515.
[8] LIU Pengde, LIU Xijun, XIAO Wenjiao, ZHANG Zhiguo, SONG Yujia, XIAO Yao, LIU Lei, HU Rongguo, WANG Baohua. Geochronology, geochemistry, and Sr-Nd isotopes of Early Carboniferous magmatism in southern West Junggar, northwestern China: Implications for Junggar oceanic plate subduction[J]. Journal of Arid Land, 2021, 13(11): 1163-1182.
[9] Sanim BISSENBAYEVA, Jilili ABUDUWAILI, Assel SAPAROVA, Toqeer AHMED. Long-term variations in runoff of the Syr Darya River Basin under climate change and human activities[J]. Journal of Arid Land, 2021, 13(1): 56-70.
[10] WANG Jie, LIU Dongwei, MA Jiali, CHENG Yingnan, WANG Lixin. Development of a large-scale remote sensing ecological index in arid areas and its application in the Aral Sea Basin[J]. Journal of Arid Land, 2021, 13(1): 40-55.
[11] Jiaxiu LI, Yaning CHEN, Zhi LI, Xiaotao HUANG. Low-carbon economic development in Central Asia based on LMDI decomposition and comparative decoupling analyses[J]. Journal of Arid Land, 2019, 11(4): 513-524.
[12] Yang YU, Yuanyue PI, Xiang YU, Zhijie TA, Lingxiao SUN, DISSE Markus, Fanjiang ZENG, Yaoming LI, Xi CHEN, Ruide YU. Climate change, water resources and sustainable development in the arid and semi-arid lands of Central Asia in the past 30 years[J]. Journal of Arid Land, 2019, 11(1): 1-14.
[13] Jinping LIU, Wanchang ZHANG, Tie LIU. Monitoring recent changes in snow cover in Central Asia using improved MODIS snow-cover products[J]. Journal of Arid Land, 2017, 9(5): 763-777.
[14] Chuandong ZHU, Yang LU, Hongling SHI, Zizhan ZHANG. Spatial and temporal patterns of the inter-annual oscillations of glacier mass over Central Asia inferred from Gravity Recovery and Climate Experiment (GRACE) data[J]. Journal of Arid Land, 2017, 9(1): 87-97.
[15] YIN Gang, HU Zengyun, CHEN Xi, TIYIP Tashpolat. Vegetation dynamics and its response to climate change in Central Asia[J]. Journal of Arid Land, 2016, 8(3): 375-388.