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Journal of Arid Land  2023, Vol. 15 Issue (7): 757-778    DOI: 10.1007/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
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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 wordsAral 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:
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.

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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) (
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
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