Please wait a minute...
Journal of Arid Land
Journal of Arid Land  2023, Vol. 15 Issue (7): 757-778    DOI: 10.1007/s40333-023-0062-z     CSTR: 32276.14.s40333-023-0062-z
Research article     
Correlation analysis between the Aral Sea shrinkage and the Amu Darya River
WANG Min1,2,3,4,5, CHEN Xi1,2,4,5,6,*(), CAO Liangzhong7, KURBAN Alishir1,4,5, SHI Haiyang8, WU Nannan1, EZIZ Anwar1, YUAN Xiuliang1, Philippe DE MAEYER1,2,3,4,5
1State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
2University of Chinese Academy of Sciences, Beijing 100049, China
3Department of Geography, Ghent University, Ghent 9000, Belgium
4Sino-Belgian Joint Laboratory of Geo-Information, Urumqi 830011, China
5Sino-Belgian Joint Laboratory of Geo-Information, Ghent 9000, Belgium
6Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, China
7Jiujiang University, Jiujiang 332000, China
8Hohai University, Nanjing 211100, China
Download: HTML     PDF(1917KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

The shrinkage of the Aral Sea, which is closely related to the Amu Darya River, strongly affects the sustainability of the local natural ecosystem, agricultural production, and human well-being. In this study, we used the Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST) model to detect the historical change points in the variation of the Aral Sea and the Amu Darya River and analyse the causes of the Aral Sea shrinkage during the 1950-2016 period. Further, we applied multifractal detrend cross-correlation analysis (MF-DCCA) and quantitative analysis to investigate the responses of the Aral Sea to the runoff in the Amu Darya River, which is the main source of recharge to the Aral Sea. Our results showed that two significant trend change points in the water volume change of the Aral Sea occurred, in 1961 and 1974. Before 1961, the water volume in the Aral Sea was stable, after which it began to shrink, with a shrinkage rate fluctuating around 15.21 km3/a. After 1974, the water volume of the Aral Sea decreased substantially at a rate of up to 48.97 km3/a, which was the highest value recorded in this study. In addition, although the response of the Aral Sea's water volume to its recharge runoff demonstrated a complex non-linear relationship, the replenishment of the Aral Sea by the runoff in the lower reaches of the Amu Darya River was identified as the dominant factor affecting the Aral Sea shrinkage. Based on the scenario analyses, we concluded that it is possible to slow down the retreat of the Aral Sea and restore its ecosystem by increasing the efficiency of agricultural water use, decreasing agricultural water use in the middle and lower reaches, reducing ineffective evaporation from reservoirs and wetlands, and increasing the water coming from the lower reaches of the Amu Darya River to the 1961-1973 level. These measures would maintain and stabilise the water area and water volume of the Aral Sea in a state of ecological restoration. Therefore, this study focuses on how human consumption of recharge runoff affects the Aral Sea and provides scientific perspective on its ecological conservation and sustainable development.

Key words： Aral Sea shrinkage      recharge runoff      Amu Darya River      Syr Darya River      multifractal detrend cross-correlation analysis (MF-DCCA)      Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST)      Central Asia     
Received: 30 January 2023      Published: 31 July 2023
Corresponding Authors: *CHEN Xi (E-mail: chenxi@ms.xjb.ac.cn)
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
WANG Min
CHEN Xi
CAO Liangzhong
KURBAN Alishir
SHI Haiyang
WU Nannan
EZIZ Anwar
YUAN Xiuliang
Philippe DE MAEYER
Cite this article:

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. Journal of Arid Land, 2023, 15(7): 757-778.

URL:

http://jal.xjegi.com/10.1007/s40333-023-0062-z     OR     http://jal.xjegi.com/Y2023/V15/I7/757

Fig. 1 Overview of the Aral Sea region in Central Asia (a) and MODIS images showing the lake area variations of the Aral Sea in 1977 (b), 2010 (c), 2020 (d). MODIS images are derived from National Aeronautics and Space Administration (NASA) (https://www.earthdata.nasa.gov/sensors/modis).
Fig. 2 Flow chart of the lake data correction in this study. WV, WL, and WA represent the water volume, water level, and water area, respectively; WV_2 and WV_3 represent the transformed water volume under the two transformation methods, respectively; WA_c, WL_c, and WVC_c represent the corrected water area, water level, and water volume change, respectively; H-W process and S-W process represent the inversion of the water level-water volume relation and the inversion of the water area-water volume relation, respectively.
Fig. 3 Results of the change point detection for the study variables based on the Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST) model. (a), water volume change; (b), Samanbay_R (runoff from the downstream of the Amu Darya River); (c), K-S, water consumption in the middle-lower reaches of the Amu Darya River; (d), K-K, water consumption in the middle-upper reaches of the Amu Darya River; (e), Kelif_R, upstream-originating flow of the Amu Darya River; (f), Syr_Down_R, runoff from the downstream of the Syr Darya River. Dashed lines represent the location of change points, and grey envelopes indicate 95% credible intervals for the fitted trend signals.
Variable 1950-1960 1961-1973 1974-2016
Trend Slope P Trend Slope P Trend Slope P
WVC → - - → - - ↓ -1.032 ***
Syr_Down_R → - - → - - → - -
Samanbay_R → - - → - - ↓ -0.175 **
K-S → - - → - - ↑ 0.175 **
K-K → - - → - - → - -
Kelif_R → - - → - - → - -
Variable 1950-1958 1959-1990 1991-2016
Trend Slope P Trend Slope P Trend Slope P
K-K ↓ -1.746 ** ↑ 0.341 *** → - -
Table 1 Trend analysis of the variables by different periods and their slope values
Variable 1950-1960 1961-1973 1974-2016
Mean Min Max Mean Min Max Mean Min Max
WVC (km3/a) -3.103 -14.076 6.724 15.212 -6.112 30.570 17.719 -6.033 48.968
Syr_Down_R (km3) 16.127 9.500 21.100 6.400 3.200 10.600 0.908 0.200 2.100
Samanbay_R (km3) 45.994 31.005 55.400 35.059 21.818 71.067 7.469 0.117 24.196
K-S (km3) 12.550 3.145 27.539 23.486 -12.523 36.727 51.076 34.348 58.427
K-K (km3) 0.630 -8.200 9.756 7.948 3.527 14.836 16.295 10.224 26.723
Kelif_R (km3) 67.182 49.600 80.000 63.485 50.000 96.300 59.837 35.300 83.500
Variable 1950-1958 1959-1990 1991-2016
Mean Min Max Mean Min Max Mean Min Max
K-K (km3) 0.026 -8.200 9.756 11.709 2.375 22.005 16.973 11.750 26.723
Table 2 Statistical characteristics of the variables by different periods
Fig. 4 Long-range cross-correlations of the water volume change (WVC) with different variables. q-value is the order of the fluctuation function that can be changed to examine different characteristics of the data.
Model I (1950-1985) Model II (1986-2016)
Formula $WVC={{\alpha }_{1}}\times WA-{{\beta }_{11}}\times {{R}_{1}}-{{\beta }_{2}}\times {{R}_{2}}$ $WVC={{\alpha }_{2}}\times WA-{{\beta }_{12}}\times {{R}_{1}}$
$WA=\left\{ \begin{matrix} 0.08\times WV-20.86,\text{ }WV\in (961,1006] \\ \begin{align} & -1.68e-05\times W{{V}^{2}}+0.05\times WV+ \\ & 22.52,\text{ }WV\in (422,961] \\ \end{align} \\ \end{matrix} \right.$ $\text{WA}=\begin{cases}2.68\text{e}-9\times\text{WV}^4+3.28\times\text{WV}^3+1.59\text{e}-3\times\\\text{WV}^2-0.41\times\text{WV}+11.54,\\\text{WV}\in(79,422]\\3.97\text{e}-6\times\text{WV}^4-1.04e-3\times\text{WV}^3+0.10\times\\\text{WV}^2-3.75\times\text{WV}+51.31,\\\text{WV}\in[43,79]\end{cases}$
GOF R2=0.9212 Adjusted R2=0.9164 R2=0.9326 Adjusted R2=0.9303
Model I: parameter estimate Model II: parameter estimate
α1 β11 β2 α2 β12
Coef. 0.837*** -0.772*** -1.217*** 0.864*** -0.964***
Std coef. 0.170** -0.741*** -0.401** 0.647*** -0.690***
Table 3 Specific structure and parameter estimates of Model I and Model II
Model III (1986-2016) Model IV (1986-2016)
Formula $WVC={{\alpha }_{3}}\times WA-{{\gamma }_{1}}\times {{C}_{1}}-{{\gamma }_{2}}\times {{C}_{2}}-{{\gamma }_{3}}\times U-{{\gamma }_{4}}\times RV$ $WVC={{\alpha }_{3}}\times WA-{{\gamma }_{2}}\times {{C}_{2}}-{{\gamma }_{3}}\times U-{{\gamma }_{4}}\times RV$
$\text{WA}=\begin{cases}2.68\text{e}-9\times\text{WV}^4+3.28\times\text{WV}^3+1.59\text{e}-3\times\\\text{WV}^2-0.41\times\text{WV}+11.54,\\\text{WV}\in(79,422]\\3.97\text{e}-6\times\text{WV}^4-1.04\text{e}-3\times\text{WV}^3+0.10\times\\\text{WV}^2-3.75\times\text{WV}+51.31,\\\text{WV}\in[43,79]\end{cases}$ $\text{WA}=\begin{cases}2.68\text{e}-9\times\text{WV}^4+3.28\times\text{WV}^3+1.59\text{e}-3\times\\\text{WV}^2-0.41\times\text{WV}+11.54,\\\text{WV}\in(79,422]\\3.97\text{e}-6\times\text{WV}^4-1.04\text{e}-3\times\text{WV}^3+0.10\times\\\text{WV}^2-3.75\times\text{WV}+51.31,\\\text{WV}\in[43,79]\end{cases}$
GOF R2=0.9074 Adjusted R2=0.8931 R2=0.9041 Adjusted R2=0.8934
Model III: parameter estimate Model IV: parameter estimate
α3 γ1 γ2 γ3 γ4 α3 γ2 γ3 γ4
Coef. 0.765*** 0.201 0.360*** -0.348*** 0.204** 0.768*** 0.382*** -0.323*** 0.246***
Std coef. 0.934*** 0.010 0.705*** -0.054 0.446*** 0.936*** 0.713*** -0.045 0.454***
Table 4 Specific structure and parameter estimates of Model III and Model IV
1950-1960 1961-1973 1974-1991 1992-2016
Events (1) The Soviet government ordered the large-scale cotton production in the
late 1950s.
(2) The construction of the Karakum Canal began in 1954, and was put into operation in 1956.		 (1) From 1960 to 1970, the irrigated area in the Aral Sea region experienced an approximate increase
of 6.400×103 km2.		 (1) The Karshi Canal began
its operation in 1973.
(2) The Amu-Bukhara Canal was put into use in 1974.
(3) The cotton-growing area
in the Aral Sea region reached a stable state in the 1980s.		 (1) The Soviet Union dissolved in 1991.
(2) Around 1992, significant changes occurred in the cropping structure, with the cotton area decreasing and the area of grain crops increasing.
Table 5 List of the background events in the Area Sea region during 1950-2016 period
Fig. 5 Comparison between historical runoff values in the downstream of the Amu Darya River and simulated runoff values under different scenarios
Fig. 6 Comparison between historical water volume values in the Aral Sea and simulated water volume values under different scenarios
[1]   Abdullaev I. 2004. The analysis of water management in Bukhara oasis of Uzbekistan: historical and territorial trends. Water International, 29(1): 20-26.
doi: 10.1080/02508060408691744
[2]   Abdullaev I, Giordano M, Rasulov A. 2005. Cotton in Uzbekistan: Water and Welfare. In: Proceedings of Conference on "Cotton Sector in Central Asia: Economic Policy and Development Challenges". 3-4 November, 2005. The School of Oriental and African Studies, University of London, London.
[3]   Abduraupov R, Akhmadjanova G, Ibragimov A, et al. 2022. Modeling of water management for cotton production in Uzbekistan. Agricultural Water Management, 265: 107535, doi: 10.1016/j.agwat.2022.107535.
doi: 10.1016/j.agwat.2022.107535
[4]   Aldaya M M, Muñoz G, Hoekstra A Y. 2010. Water Footprint of Cotton, Wheat and Rice Production in Central Asia. Research Report Series No. 41. Delft, The Netherlands: UNESCO-IHE Institute for Water Education.
[5]   Alkan Ç, Konukcu F. 2022. An investigation on the climate change and drought types in the Porsuk Stream Watershed, west of Turkey. Algerian Journal of Engineering and Technology, 7: 37-46.
[6]   Bai J, Chen X, Yang L, et al. 2012. Monitoring variations of inland lakes in the arid region of Central Asia. Frontiers of Earth Science, 6: 147-156.
doi: 10.1007/s11707-012-0316-0
[7]   Banakara K B, Sharma N, Sahoo S, et al. 2023. Evaluation of weather parameter-based pre-harvest yield forecast models for wheat crop: a case study in Saurashtra region of Gujarat. Environmental Monitoring and Assessment, 195: 51, doi: 10.1007/s10661-022-10552-4.
doi: 10.1007/s10661-022-10552-4
[8]   Bekchanov M, Lamers J P A, Karimov A, et al. 2012. Estimation of spatial and temporal variability of crop water productivity with incomplete data. In: MartiusC, RudenkoI, LamersJ P A, et al. Cotton, Water, Salts and Soums. Dordrecht: Springer, 329-344.
[9]   Berdugo M, Gaitán J J, Delgado-Baquerizo M, et al. 2022. Prevalence and drivers of abrupt vegetation shifts in global drylands. Proceedings of the National Academy of Sciences, 119(43): e2123393119, doi: 10.1073/pnas.2123393119.
doi: 10.1073/pnas.2123393119
[10]   Cai D L, Yu L J, Zhu J F, et al. 2022. The shrinkage of Lake Lop Nur in the twentieth Century: A comprehensive ecohydrological analysis. Journal of Hydrometeorology, 23(8): 1245-1255.
doi: 10.1175/JHM-D-21-0217.1
[11]   Chen S A, Michaelides K, Grieve S W D, et al. 2019. Aridity is expressed in river topography globally. Nature, 573: 573-577.
doi: 10.1038/s41586-019-1558-8
[12]   de Beurs K M, Henebry G M, Owsley B C, et al. 2015. Using multiple remote sensing perspectives to identify and attribute land surface dynamics in Central Asia 2001-2013. Remote Sensing of Environment, 170: 48-61.
doi: 10.1016/j.rse.2015.08.018
[13]   Dukhovny V, Umarov P, Yakubov H, et al. 2007. Drainage in the Aral Sea basin. Irrigation and Drainage, 56(S1): S91-S100.
[14]   FAO (Food and Agriculture Organization of the United Nations). 2020. FAOSTAT data. [2023-01-30]. http://faostat.fao.org.
[15]   Feng Y H, Zhang H, Tao S L, et al. Decadal lake volume changes (2003-2020) and driving forces at a global scale. Remote Sensing, 14(4): 1032, doi: 10.3390/rs14041032.
doi: 10.3390/rs14041032
[16]   Foster S. 2018. Is UN Sustainable Development Goal 15 relevant to governing the intimate land-use/groundwater linkage? Hydrogeology Journal, 26(4): 979-982.
[17]   Froebrich J, Kayumov O. 2004. Water management aspects of Amu Darya: Options for future strategies. In: Nihoul J C J, Zavialov P O, Micklin P P. Dying and Dead Seas Climatic Versus Anthropic Causes. NATO Science Series: IV: Earth and Environmental Sciences. Dordrecht: Springer, 49-76.
[18]   Fróna D, Szenderák J, Harangi-Rákos M. 2021. Economic effects of climate change on global agricultural production. Nature Conservation, 44: 117-139.
doi: 10.3897/natureconservation.44.64296
[19]   Grant L, Vanderkelen I, Gudmundsson L, et al. 2021. Attribution of global lake systems change to anthropogenic forcing. Nature Geoscience, 14(11): 849-854.
doi: 10.1038/s41561-021-00833-x
[20]   He H L, Hamdi R, Luo G P, et al. 2022. Numerical study on the climatic effect of the Aral Sea. Atmospheric Research, 268: 105977, doi: 10.1016/j.atmosres.2021.105977.
doi: 10.1016/j.atmosres.2021.105977
[21]   Hrvatin M, Zorn M. 2022. Climate change impacts on hydrology in the Mediterranean part of Slovenia. In: LealFilho W,Manolas E. Climate Change in the Mediterranean and Middle Eastern Region. Cham: Springer, 85-118.
[22]   Hu Z Y, Chen X, Zhou Q M, et al. 2022. Dynamical variations of the terrestrial water cycle components and the influences of the climate factors over the Aral Sea Basin through multiple datasets. Journal of Hydrology, 604: 127270, doi: 10.1016/j.jhydrol.2021.127270.
doi: 10.1016/j.jhydrol.2021.127270
[23]   Huang J P, Yu H P, Guan X D, et al. 2016. Accelerated dryland expansion under climate change. Nature Climate Change, 6(2): 166-171.
doi: 10.1038/NCLIMATE2837
[24]   Huang W J, Duan W L, Chen Y N. 2022. Unravelling lake water storage change in Central Asia: Rapid decrease in tail-end lakes and increasing risks to water supply. Journal of Hydrology, 614: 128546, doi: 10.1016/j.jhydrol.2022.128546.
doi: 10.1016/j.jhydrol.2022.128546
[25]   Kariyeva J, van Leeuwen W J D. 2012. Phenological dynamics of irrigated and natural drylands in Central Asia before and after the USSR collapse. Agriculture, Ecosystems & Environment, 162: 77-89.
doi: 10.1016/j.agee.2012.08.006
[26]   Kojić M, Schlüter S, Mitić P, et al. 2022. Economy-environment nexus in developed European countries: Evidence from multifractal and wavelet analysis. Chaos, Solitons and Fractals, 160: 112189, doi: 10.1016/j.chaos.2022.112189.
doi: 10.1016/j.chaos.2022.112189
[27]   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: 3044, doi: 10.1038/s41467-020-16757-w.
doi: 10.1038/s41467-020-16757-w pmid: 32576822
[28]   Li Q, Li X, Ran Y H, et al. 2021. Investigate the relationships between the Aral Sea shrinkage and the expansion of cropland and reservoir in its drainage basins between 2000 and 2020. International Journal of Digital Earth, 14(6): 661-677.
doi: 10.1080/17538947.2020.1865466
[29]   Li Y, Qi S, Li J J. 2022. Research on ecological restoration technology in arid or semi-arid areas from the perspective of the Belt and Road Initiative. Journal of Resources and Ecology, 13(6): 964-976.
doi: 10.5814/j.issn.1674-764x.2022.06.002
[30]   Liu H J, Chen Y N, Ye Z X, et al. 2019. Recent lake area changes in Central Asia. Scientific Reports, 9: 16277, doi: 10.1038/s41598-019-52396-y.
doi: 10.1038/s41598-019-52396-y pmid: 31700019
[31]   Liu Z B, Liu T, Huang Y, et al. 2022. Comparison of crop evapotranspiration and water productivity of typical delta irrigation areas in Aral Sea Basin. Remote Sensing, 14(2): 249, doi: 10.3390/rs14020249.
doi: 10.3390/rs14020249
[32]   Micklin P P. 1988. Desiccation of the Aral Sea: a water management disaster in the Soviet Union. Science, 241(4870): 1170-1176.
pmid: 17740781
[33]   Micklin P. 2007. The Aral Sea disaster. Annual Review of Earth and Planetary Sciences, 35: 47-72.
doi: 10.1146/earth.2007.35.issue-1
[34]   Micklin P, Aladin N V. 2008. Reclaiming the Aral Sea. Scientific American, 298(4): 64-71.
pmid: 18380143
[35]   Micklin P. 2010. The past, present, and future Aral Sea. Lakes & Reservoirs: Research and Management, 15(3): 193-213.
[36]   Micklin P. 2016. The future Aral Sea: hope and despair. Environmental Earth Sciences, 75: 844, doi: 10.1007/s12665-016-5614-5.
doi: 10.1007/s12665-016-5614-5
[37]   Pettitt A N. 1979. A non-parametric approach to the change-point problem. Journal of the Royal Statistical Society: Series C (Applied Statistics), 28(2): 126-135.
[38]   Pham-Duc B, Sylvestre F, Papa F, et al. 2020. The Lake Chad hydrology under current climate change. Nature Scientific Reports, 10: 5498, doi: 10.1038/s41598-020-62417-w.
doi: 10.1038/s41598-020-62417-w
[39]   Pohlert T, 2016. Non-parametric trend tests and change-point detection. CRAN Repository. [2022-09-18]. https://CRAN.R-project.org/package=trend.
[40]   Rahmani F, Fattahi M H. 2021. A multifractal cross-correlation investigation into sensitivity and dependence of meteorological and hydrological droughts on precipitation and temperature. Natural Hazards, 109(3): 2197-2219.
doi: 10.1007/s11069-021-04916-1
[41]   Rufin P, Peña-Guerrero M D, Umirbekov A, et al. 2022. Post-Soviet changes in cropping practices in the irrigated drylands of the Aral Sea basin. Environmental Research Letters, 17(9): 095013, doi: 10.1088/1748-9326/ac8daa.
doi: 10.1088/1748-9326/ac8daa
[42]   Schlüter M, Savitsky A G, McKinney D C, et al. 2005. Optimizing long-term water allocation in the Amudarya River delta: a water management model for ecological impact assessment. Environmental Modelling & Software, 20(5): 529-545.
[43]   Sharma A, Huang H P, Zavialov P, et al. 2018. Impact of desiccation of Aral Sea on the regional climate of Central Asia using WRF model. Pure and Applied Geophysics, 175(1): 465-478.
doi: 10.1007/s00024-017-1675-y
[44]   Shi J C, Guo Q Z, Zhao S, et al. 2022. The effect of farmland on the surface water of the Aral Sea Region using Multi-source Satellite Data. PeerJ, 10: e12920, doi: 10.7717/peerj.12920.
doi: 10.7717/peerj.12920
[45]   Spoor M. 1993. Transition to market economies in former Soviet Central Asia: Dependency, cotton and water. The European Journal of Development Research, 5(2): 142-158.
doi: 10.1080/09578819308426591
[46]   Spoor M. 1998. The Aral Sea Basin crisis: transition and environment in former Soviet Central Asia. Development and Change, 29(3): 409-435.
doi: 10.1111/dech.1998.29.issue-3
[47]   Su Y N, Li X, Feng M, et al. 2021. High agricultural water consumption led to the continued shrinkage of the Aral Sea during 1992-2015. Science of The Total Environment, 777: 145993, doi: 10.1016/j.scitotenv.2021.145993.
doi: 10.1016/j.scitotenv.2021.145993
[48]   Sun J, Li Y P, Suo C, et al. 2019. Impacts of irrigation efficiency on agricultural water-land nexus system management under multiple uncertainties—A case study in Amu Darya River basin, Central Asia. Agricultural Water Management, 216: 76-88.
doi: 10.1016/j.agwat.2019.01.025
[49]   Tischbein B, Awan U K, Abdullaev I, et al. 2012. Water management in Khorezm: Current situation and options for improvement (hydrological perspective). In: Martius C, Rudenko I, Lamers J P A, et al. Cotton, Water, Salts and Soums. Dordrecht: Springer,69-92.
[50]   Vinushree R, Ashalatha K V, Mohammed Rizwan Saif, et al. 2022. Trend analysis of groundwater level using Mann-Kendall test in Dharwad district. The Pharma Innovation Journal, 11(5S): 596-600.
[51]   Wang J D, Song C Q, Reager J T, et al. 2018. Recent global decline in endorheic basin water storages. Nature Geoscience, 11(12): 926-932.
doi: 10.1038/s41561-018-0265-7
[52]   Wang X X, Chen Y N, Li Z, et al. 2020. The impact of climate change and human activities on the Aral Sea Basin over the past 50 years. Atmospheric Research, 245: 105125, doi: 10.1016/j.atmosres.2020.105125.
doi: 10.1016/j.atmosres.2020.105125
[53]   Woolway R I, Kraemer B M, Lenters J D, et al. 2020. Global lake responses to climate change. Nature Reviews Earth & Environment, 1(8): 388-403.
[54]   Wu Q Y, Yue H, Liu Y, et al. 2022. Geospatial quantitative analysis of the Aral Sea Shoreline changes using RS and GIS techniques. Earth Science Informatics, 15(1): 137-149.
doi: 10.1007/s12145-021-00714-2
[55]   Xiang C H, Hao X Z, Wang W H, et al. 2019. Asymmetric MF-DCCA method based on fluctuation conduction and its application in air pollution in Hangzhou. Journal of Advanced Computational Intelligence and Intelligent Informatics, 23(5): 823-830.
doi: 10.20965/jaciii.2019.p0823
[56]   Yan X, Li L H. 2023. Spatiotemporal characteristics and influencing factors of ecosystem services in Central Asia. Journal of Arid Land, 15(1): 1-19.
doi: 10.1007/s40333-022-0074-0
[57]   Yang X C, Tian S Y, You W, et al. 2021. Reconstruction of continuous GRACE/GRACE-FO terrestrial water storage anomalies based on time series decomposition. Journal of Hydrology, 603: 127018, doi: 10.1016/j.jhydrol.2021.127018.
doi: 10.1016/j.jhydrol.2021.127018
[58]   Yang X W, Wang N L, Chen A A, et al. 2020a. The relationship between area variation of the Aral Sea in the arid Central Asia and human activities and climate change. Journal of Glaciology and Geocryology, 42(2): 681-692. (in Chinese)
[59]   Yang X W, Wang N L, CHEN A A, et al. 2020b. Changes in area and water volume of the Aral Sea in the arid Central Asia over the period of 1960-2018 and their causes. CATENA, 191: 104566, doi: 10.1016/j.catena.2020.104566.
doi: 10.1016/j.catena.2020.104566
[60]   Yuldashev N K, Nabokov V I, Nekrasov K V, et al. 2020. Modernization and intensification of agriculture in the republic of Uzbekistan. E3S Web of Conferences, 222: 06033, doi: 10.1051/e3sconf/202022206033.
doi: 10.1051/e3sconf/202022206033
[61]   Zhang M, Chen Y N, Shen Y J, et al. 2019. Tracking climate change in Central Asia through temperature and precipitation extremes. Journal of Geographical Sciences, 29(1): 3-28.
doi: 10.1007/s11442-019-1581-6
[62]   Zhang Q F, Chen Y N, Li Z, et al. 2022. Controls on alpine lake dynamics, Tien Shan, Central Asia. Remote Sensing, 14(19): 4698, doi: 10.3390/rs14194698.
doi: 10.3390/rs14194698
[63]   Zhao J S, Wang Z J, Weng W B. 2004. Study on the holistic model for water resources system. Science in China Series E: Technological Sciences, 47(1): 72-89.
doi: 10.1360/04ez0007
[64]   Zhao K G, Wulder M A, Hu T X, et al. 2019. Detecting change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics: A Bayesian ensemble algorithm. Remote Sensing of Environment, 232: 111181, doi: 10.1016/j.rse.2019.04.034.
doi: 10.1016/j.rse.2019.04.034
[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] 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.
[4] 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.
[5] YAO Linlin, ZHOU Hongfei, YAN Yingjie, LI Lanhai, SU Yuan. Projection of hydrothermal condition in Central Asia under four SSP-RCP scenarios[J]. Journal of Arid Land, 2022, 14(5): 521-536.
[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] Durdiev KHAYDAR, CHEN Xi, HUANG Yue, Makhmudov ILKHOM, LIU Tie, Ochege FRIDAY, Abdullaev FARKHOD, Gafforov KHUSEN, Omarakunova GULKAIYR. Investigation of crop evapotranspiration and irrigation water requirement in the lower Amu Darya River Basin, Central Asia[J]. Journal of Arid Land, 2021, 13(1): 23-39.
[12] 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.
[13] 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.
[14] 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.
[15] 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.