Human and Natural Impacts on the Water Resources in the Syr Darya River Basin, Central Asia

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Article Human and Natural Impacts on the Water Resources in the Syr Darya River Basin, Central Asia

Shan Zou 1,2,3,4,5,6, Abuduwaili Jilili 1,2,3,*, Weili Duan 1,2,* , Philippe De Maeyer 4,5,6 and Tim Van de Voorde 4,5,6 1 State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011 China; [email protected] 2 University of Chinese Academy of Sciences, No. 19A, Yuquan Road, Shijingshan District, Beijing 10049, China 3 Chinese Academy of Sciences Research Centre for Ecology and Environment of Central Asia, Urumqi 830011, China 4 Department of Geography, Ghent University, 9000 Ghent, Belgium; [email protected] (P.D.M.); [email protected] (T.V.d.V.) 5 Sino-Belgian Joint Laboratory of Geo-Information, Ghent University, 9000 Gent, Belgium 6 Sino-Belgian Joint Laboratory of Geo-Information, Xinjiang Institute of Ecology and Geography, Urumqi 830011, China * Correspondence: [email protected] (A.J.); [email protected] (W.D.)

Received: 14 March 2019; Accepted: 24 May 2019; Published: 31 May 2019

Abstract: Water resources are increasingly under stress in Central Asia because downstream countries are highly dependent on upstream countries. Water is essential for irrigation and is becoming scarcer due to climate change and human activities. Based on 20 hydrological stations, this study firstly analyzed the annual and seasonal spatial–temporal changes of the river discharges, precipitation, and temperature in the Syr Darya River Basin and then the possible relationships between these factors were detected. Finally, the potential reasons for the river discharge variations have been discussed. The results show that the river discharges in the upper stream of the basin had significantly risen from 1930 to 2006, mainly due to the increase in temperature (approximately 0.3 ◦C per decade), which accelerated the melting of glaciers, while it decreased in the middle and lower regions due to the rising irrigation. In the middle of the basin, the expansion of the construction land (128.83 km2/year) and agricultural land (66.68 km2/year) from 1992 to 2015 has significantly augmented the water consumption. The operations of reservoirs and irrigation canals significantly intercepted the river discharge from the upper streams, causing a sharp decline in the river discharges in the middle and lower reaches of the Syr Darya River in 1973. The outcomes obtained from this study allowed us to understand the changes in the river discharges and provided essential information for effective water resource management in the Syr Darya River Basin.

Keywords: Syr Darya River Basin; river discharge; climate change; trend analysis; land use

1. Introduction Due to the arid climate, water has become a priority in the socio-economic development of Central Asia. Along with the rapid economic growth and population rise in this region, the future demand for water resources will certainly continue to increase and the water shortage will likely become more and more serious [1,2]. A population growth of 20 million people in this region is expected by 2040 [3], which will require additional irrigated areas in response to a larger food production. For example, the irrigated area in Uzbekistan is expected to rise by 5–11% by 2020 [4,5], eventually causing a 5–19% increase in the water demand [6]. Also, water resource management will encounter additional

Sustainability 2019, 11, 3084; doi:10.3390/su11113084 www.mdpi.com/journal/sustainability Sustainability 2019, 11, 3084 2 of 18 difficulties from climate change, which will increase the vulnerability of the water supplies [7]. On the one hand, the projected temperature augmentations will lead to a longer growing period and a higher actual evapotranspiration, causing a 5% rise in the irrigation water consumption by 2030 [8,9]. On the other hand, the glacier loss will augment the discharge at first and will have little effect on the annual discharge once the glaciers have disappeared. Therefore, the glacier loss in the Pamir, Tien-Shan, and Alay mountains has been accelerating because of the higher temperature, which will potentially cause the annual river discharges in Central Asia to become as low as 50% by 2050 [10,11]. Identifying the links between the water resources, human activities, and climate change in Central Asia could offer the potential to improve the water resource management for human well-being and environmental sustainability. In addition, due to the transboundary water conflict, water resource allocation in Central Asia is a big and complicated problem [12]. After the fall of the Soviet Union in 1991, the region’s major rivers (including the Syr Darya River and the Amu Darya River) became transboundary rivers, which triggered a series of water conflicts between upstream and downstream countries over the past few years [13]. The Syr Darya River is the second longest river in Central Asia, which is a typical transboundary river and shared by Kyrgyzstan, Kazakhstan, Tajikistan, and Uzbekistan. It originates from the glacial meltwater and precipitation of the Tien Shan Mountains in Kyrgyzstan and eastern Uzbekistan (the upper streams are the Naryn River and the Kara Darya) and further meanders its way for about 2212 km west and northwest through Uzbekistan and southern Kazakhstan to the North Aral Sea. The river provides an abundance of water for agricultural production, industrial, and household activities and discharges huge amounts of sediments and freshwater into the North Aral Sea, thus adjusting the sea water level and the biogeochemical cycles [14,15]. With the rapid economic development and population explosion during recent years, the amount of water flowing from the Syr Darya River into the North Aral Sea has decreased over the past sixty years, directly causing a decrease in the sea water level [16]. Typically, human activities including change in land use (especially the expansion of agricultural land) and dam construction (e.g., the Karkidon, Kasansai, and Andijan dams) have significantly affected the water supply and water demand patterns, while simultaneously being exacerbated by the increased pollutants [17,18]. Also, climate change is likely to exacerbate the water stress in the Syr Darya River Basin [19]. As the water resources of the Syr Darya River mainly come from the glaciers and snowmelting in the high mountain ranges of the Tien Shan Mountains, the rising temperatures cause melting glaciers and ice sheets and disturb the hydrological dynamics as a result (e.g., the total and seasonal streamflow) [20]. These changes eventually trigger a lower and lower runoff [21], which causes negative implications for the water availability in the Syr Darya River Basin (and for the conflicting water demands between agriculture and hydropower). Efforts in research have led to significant improvements in the evaluation of the water environment and resources in the Syr Darya River Basin [22–24]. For example, using satellite data, Crétaux et al. [25] estimated the total water storage of reservoirs in the Syr Darya River Basin; Wegerich et al. [15] discussed the critical role of the water supply in achieving a sustainable water security and provided some measures for irrigated agriculture. Although all these studies focused on the changes in water resources in the Syr Darya River Basin, the majority of previous studies mainly discussed the water resources in a regional sub-basin and only a few researchers analyzed the water resources for the whole basin [22]. Moreover, these studies only investigated the complete basin changes based on the limited hydrological and meteorological stations and remain unclear in some fields (e.g., the seasonal changes in the river discharges) [17]. For example, Savoskul et al. [14] comprehensively investigated the water modifications and other natural resources (climate, topography, land cover, and so on) and the related socio-economic aspects, but did not show the detailed seasonal changes between the upper, middle, and lower regions of the Syr Darya River. Therefore, the annual and seasonal changes in the river discharges, precipitation, and temperature and their correlations were investigated for the whole Syr Darya River Basin in this study. We also explored the potential for the land use impact on the water resources. Specifically, this study addresses: Sustainability 2019, 11, 3084 3 of 18

(1) The manner in which climate change is affecting the high mountains and plains of the Syr Darya River Basin;Sustainability (2) the2019 di, 11ff, erencesx FOR PEER in REVIEW the upstream and middle and lower regions of the Syr Darya3 Riverof 18 Basin underof the Syr global Darya warming River Basin; and human(2) the differences activities; in and the (3) upstream the manner and middle in which and the lower land regions use changes of the and theirSyr Darya impact River on theBasin water under resources global warming could beand evaluated. human activities; Previous and (3) research the manner hardly in which addressed the these issues.land use In changes particular, and ourtheir paper impact adds on the insights water byresources analyzing could the be changes evaluated. in waterPrevious resources research in the Syrhardly Darya addressed River Basin these based issues. on In 20 particular, hydrological our stationspaper adds from insights 1930 toby 2006analyzing (the obtained the changes results in providewater essential resources information in the Syr for Darya effective River water Basin resourcebased on 20 management hydrological in stations this basin) from and 1930 exploring to 2006 (the the annualobtained and seasonal results changesprovide essential in the river information discharges for effective and their water influencing resource factorsmanagement (e.g., in precipitation, this basin) temperature,and exploring and land the useannual changes). and seasonal These changes were explored in the river and discharges discussed and and their could influencing offer important factors information(e.g., precipitation, for effective watertemperature, resource and management land use changes). in the SyrThese Darya were River explored Basin. and discussed and Thiscould study offer isimportant structured information as follows: for effective The study water area, resource datasets, management and methodology in the Syr Darya are River briefly Basin. described in the next section. The annual and seasonal trend results and possible influencing factors This study is structured as follows: The study area, datasets, and methodology are briefly will be presented in Section3, followed by the conclusions and future work (Section4). described in the next section. The annual and seasonal trend results and possible influencing factors will be presented in Section 3, followed by the conclusions and future work (Section 4). 2. Datasets and Methods 2. Datasets and Methods 2.1. Study Area The2.1. SyrStudy Darya Area River, the second longest river in Central Asia, originates from the Tien Shan MountainsThe in KyrgyzstanSyr Darya River, and easternthe second Uzbekistan longest river and in flows Central into Asia, the originates North Aral from Sea, the withTien aShan total 2 lengthMountains of 2212 km in (Figure Kyrgyzstan1). The and river eastern drains Uzbekistan an area ofand about flows 402,760 into the km North, which Aral isSea, shared with a by total four countrieslength including of 2212 km Kyrgyzstan, (Figure 1). Uzbekistan,The river drains Tajikistan, an area of and about Kazakhstan 402,760 km (Figure2, which1 ).is Theshared Syr by Darya four River Basincountries has including a hot and Kyrgyzstan, arid climate Uzbekistan, in the downstream Tajikistan, plains and Kazakhstan but a cool (Figure and humid 1). The climate Syr Darya in the mountainsRiver [ 26Basin]. Due has toa hot the and snow arid and climate glacier in the melting downstream in the mountains,plains but a aboutcool and 80% humid of water climate resources in the flow betweenmountains March [26]. andDue Septemberto the snow [and27]. glacier The annual melting rainfall in the mountains, in the basin about approximately 80% of water amounts resources to 350 mmflow/year, between with aMarch huge and difference September between [27]. The the annual Tien Shan rainfall (500–1000 in the basin mm/ year)approximately and the downstreamamounts to plain (100–200350 mm/year, mm/ year)with a [ huge27]. About difference 20 million between people the Tien live Shan in the(500–1000 basin andmm/year) 80% of and the the population downstream live plain (100–200 mm/year) [27]. About 20 million people live in the basin and 80% of the population in rural areas and consume about 90% of the water resources for irrigation [14,26]. Due to the overall live in rural areas and consume about 90% of the water resources for irrigation [14,26]. Due to the overexploitation of the water resources, the amount of water flowing into the Aral Sea has significantly overall overexploitation of the water resources, the amount of water flowing into the Aral Sea has decreasedsignificantly since the decreased 1960s, especially since the during1960s, especially the decadal during periods the decadal 1971–1980 periods and 1971–1980 1981–1990. and With 1981– the collapse1990. of theWith Soviet the collapse Union of in the 1991, Soviet more Union river in 1991, flows more have river reached flows have the Aralreached Sea the during Aral Sea 1991–2000 during because1991–2000 of the decrease because inof waterthe decrease demand in water (but hasdemand been (b constantlyut has been decreasing constantly duringdecreasing the subsequentduring the decade,subsequent contributing decade, to the contributing unprecedented to the loweringunprecedented of the lowering Aral Sea of levels the Aral and Sea an environmentallevels and an disasterenvironmental in the region) disaster [14]. in the region) [14].

Figure 1. Map of the location, river networks, and hydrological stations of the Syr Darya River Basin, Figure 1. Map of the location, river networks, and hydrological stations of the Syr Darya River Basin, Central Asia. Central Asia.

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2.2. Datasets In this study, a total of 20 hydrological stations were selected so as to obtain the river flow data, in which nine stations (number 1–9) are located along the main Syr Darya River and eleven stations (number 10–20) in the major tributaries (Figure1). The annual discharge was collected at most stations for the period 1930–2006 and the remaining sites demonstrated at least 35 years of data. Therefore, it is enough to capture the trend of the river flow in all of these stations. Due to the lack of ground observation data, the monthly, seasonal, and yearly precipitations and temperatures in the basin from 1931 to 2015 were calculated by using the Climatic Research Unit (CRU, TS v.4.01), which is a gridded dataset with a 0.5◦ resolution and has been confirmed to be reasonable for Central Asia [28,29]. In order to better understand the impacts of human activities on water resources, data on human population growth and land use change have also been collected. The Gridded Population of the World, Version 4 (GPWv4) was downloaded from the NASA Social Data and Application Centre (SEDAC) (http://sedac.ciesin.columbia.edu) to extract the population density for 2000, 2005, 2010, and 2015, the spatial resolution of which approximately amounts to 1 km [30]. The yearly landcover data from 1992 to 2015 have been obtained from the European Space Agency (ESA) climate change initiative (CCI), which has 37 categories with a 300 m spatial resolution and confirmed to be reliable in Central Asia [31]. Based on the categories defined by the Intergovernmental Panel on Climate Change (IPCC) [32], we merged the 37 categories into 7 types (agricultural land, forest, grassland, water body, construction land, and bare land).

2.3. Methodology

2.3.1. Trend Analysis The Mann–Kendall (MK) test, which is a popular non-parametric statistical test [33,34], was used to detect the changes of the hydro-meteorological time series in this study. For a given time series X X , i = 1, 2, ... , n , the Mann–Kendall S Statistics could be calculated as follows: i{ i } n 1 n X− X = sign(T T ), (1) j − i i=1 j=i+1

  1 i f Tj Ti > 0  − sign Tj Ti =  0 i f Tj Ti = 0 , (2) −  −  1 i f T T < 0 − j − i where the sign function is an odd mathematical function that extracts the sign of a real number and Tj and Ti represent the hydro-meteorological variability on multiple time scales j and i, where j > i, respectively. The statistics S show the approximate normal distribution when n 10. The mean and ≥ variance are calculated as [33]: E[S] = 0, (3) Xn σ2 = n(n 1)(2n + 5) t t 1 2t + 5 /18. (4) { − − j j − j } j=1

Then, the standard test statistics ZS are given by:

 S 1  − , f or S > 0  σ Z =  0, f or S = 0 . (5) s   S+1 σ , f or S < 0

ZS is used to measure the significance of the trend [35]. A positive value of ZS suggests an increasing trend and a negative value of Z illustrates a decreasing trend. If Z is greater than 1.96 S | S| Sustainability 2019, 11, 3084 5 of 18 on the 5% significance level, the null hypothesis of no trend is invalid, which indicates that the trend is significant.

2.3.2. Change-Point Detection The multiple structural change detection was applied so as to detect the abrupt changes in river discharges in the Syr Darya River Basin [36]. The annual river discharge in the hydrological stations was modeled by the linear structural change model with m breaks (m + 1 segment): yi = ajxi + bj + εi, i = ij 1 + 1, ... , ij; j = 1, ... , m + 1 , (6) − where y and x represent the hydro-meteorological variability on multiple time scales j and i, j illustrates the index of the segment, (i1, ... , im) show the set of the break positions, and, by convention, i0 = 0, im+1 = n (n is the size of the time series), a and b stand for the regression coeﬃcients estimated by the ordinary least square approach, and ε demonstrates the residue. The minimum of the sum of the squared residues is the key factor to determine the optimal set of break positions. Detailed methodological information can be found in [36].

2.3.3. Correlation Analysis (1) Pearson’s Correlation

The Pearson’s correlation coefficient was exploited to measure the relationship between the precipitation, temperature, and river discharges, which indicates the covariance of the two variables divided by the product of their standard deviations. Its correlation coefficient (r) could be computed as [37]: P (x mz) y my r = q − − , (7) P 2 P 2 (x mz) y my − − where mz and my illustrate the means of the x and y variables. Then, the correlation values presented for the analysis of the precipitation, temperature, and river discharge were tested on a confidence level of 95%, which is used to calculate the significance of the correlation.

(2) Multiple Linear Regression

In order to quantitatively assess the eﬀects of the precipitation and temperature on the river discharge, a multiple linear regression analysis was performed with the river discharge as a dependent variable and the precipitation and temperature as the independent variables.

y = β0 + β1x1 + β2x2 + ε, (8) where y represents the river discharge of the Syr Darya River, β0 stands for the y-interception (constant term), x1 shows the precipitation (mm), x2 is the temperature (◦C), ε is the model’s error term, and β1 and β2 illustrate the slope coeﬃcients for the precipitation and temperature, respectively.

2.3.4. Land Use Change Analysis Land use is an important factor that affects a river’s discharge. Here, the land use variance amplitude and land use transition matrix were applied so as to measure the changes in land use from 1992 to 2015 in the Syr Darya River Basin. The land use variation amplitude can be computed by the following equation: Landi1 Landi0 Vi = − 100%, (9) Landi0 × where i represents the seven land cover types including the agricultural land, forest, grassland, water body, construction land, and bare land; Landi0 and Landi1 demonstrate the area at the beginning Sustainability 2019, 11, 3084 6 of 18 and end of the year (of the i land use type), respectively. The transition probability matrix can be Sustainability 2019, 11, x FOR PEERth REVIEW 6 of 18 calculated by: 𝐴  n  where represents the area of different typesA11 ofA land12 use, A 1then number of land use types (here n is  ···  seven), and i and j in 𝐴 demonstrate the A typeA of land useA at the beginning and end of the year,  21 22 2n  respectively. The final result indicatesA = the. transfer. ring··· direction. , of the land use types during the (10)  . . .. .   . . . .  period 1992–2015.   A An Ann n1 2 ··· where3. ResultsA represents and Discussion the area of different types of land use, n the number of land use types (here n is seven), and i and j in A demonstrate the type of land use at the beginning and end of the year, 3.1. Trend Analysis of the ijRiver Discharge respectively. The final result indicates the transferring direction of the land use types during the period 1992–2015.As shown in Figure 2a, the stations with a higher annual average river discharge are located mainstream, especially in the middle of the Syr Darya River. Generally, the river flow exhibited an 3. Resultsincreasing and trend Discussion in the upper region, while a decreasing move in the middle and lower regions. The highest value was up to 738.5 m3/s at the station in Kazakhstan, which decreased to 284.8 m3/s before 3.1.flowing Trend Analysis into theof Aral the Sea. River Discharge Figure 2b shows the results of the Mann–Kendall trend test (Z) for 20 hydrological stations in As shown in Figure2a, the stations with a higher annual average river discharge are located the Syr Darya River Basin, suggesting that the river discharges rose at the upper rivers, while they mainstream, especially in the middle of the Syr Darya River. Generally, the river flow exhibited an fell in the middle and lower regions. Concretely, there were more stations with significant positive increasingtrends (the trend red in circle) the upper in the region, Tien-Shan while Mounta a decreasingin and movethe biggest in the Z middle value amounted and lower to regions. 5.12. By The 3 3 highestcontrast, value stations was up with to 738.5important m /s negative at the station trends in (the Kazakhstan, blue circle) whichwere mostly decreased scattered to 284.8 in the m middle/s before flowingand lower into the reaches Aral and Sea. the lowest Z value amounted to −4.67.

3 FigureFigure 2. Map 2. Map of (ofa) (a the) the mean mean annual annual river river discharge discharge (m3/s),/s), ( (bb) )the the Mann–Kendall Mann–Kendall trend trend test test (Z), (Z), and and 3 (c) the(c) trendthe trend for for each each decade decade (m (m/(s3/(s*decade))*decade)) forfor 2020 hydrological statio stationsns in in the the Syr Syr Darya Darya River River Basin. Basin.

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Figure2b shows the results of the Mann–Kendall trend test (Z) for 20 hydrological stations in the Syr Darya River Basin, suggesting that the river discharges rose at the upper rivers, while they fell in the middle and lower regions. Concretely, there were more stations with significant positive trends (the red circle) in the Tien-Shan Mountain and the biggest Z value amounted to 5.12. By contrast, stations with important negative trends (the blue circle) were mostly scattered in the middle and lower reaches and the lowest Z value amounted to 4.67. − Figure2c clearly illustrates the decadal trend in the annual river discharge for each station. Like the results of the Mann–Kendall trend test, most stations with positive growth rates were located at the upper rivers, whereas the stations with negative growth rates tended to be clustered in the middle and lower regions. During the study period, the largest positive growth rate was up to 20.6 m3/(s decade) · at station 4, followed by 8.9 m3/(s decade) at station 17; on the other hand, the largest negative growth · rate measured up to 103.2 m3/(s decade) at station 7, followed by 59.3 m3/(s decade) at station 8. − · − · In order to further understand the detailed changes of the river discharges, six typical stations (including stations 1, 6–8, 10, and 13) were selected to detect the abrupt points and the results are shown in Figure3. From this figure, we can conclude that the river discharges at stations 6–8 tended to fall significantly at a 95% confidence level with Z values at 3.02, 4.67, and 2.92, respectively, and − − − increased significantly at stations 1, 10, and 13 with the Z values at 2.50, 3.90, and 4.50, respectively. The results indicate that an augmentation was noticed at the upper streams of the Syr Darya Basin, especially in the tributaries of the Tien-Shan Mountain, but a decreasing trend could be seen in the middle and lower regions during the period 1930–2006. These results are in line with the findings shown in Figure2. Also, Figure3 clearly illustrates that an abrupt point (in 1973) was detected for stations 6–8, dividing the record into two time periods, including 1930–1973 and 1974–2006. The main reason is that in 1973, the largest dam (Toktogul Dam) was finished in order to control the river discharge to provide sufficient irrigation water. The annual average river discharge of these two intervals at station 8 were 565.7 and 355.3 m3/s, respectively, a decrease of 210.5 m3/s between these two time periods. In addition, a significant increasing trend was found from 1974 to 2006 for stations 6–8, with the Z values at 5.62, 4.85, and 4.80, respectively. As in the case of the annual data, the river discharges tended to rise during four seasons in station 1 (see Figure4a). Among them, a significant increase was found from 1930 to 2006 for both summer and autumn, with the Z values at 2.36 and 3.48, respectively (see Table1 and Figure4a), suggesting that the increases of the river discharges during the year mainly came from summer and autumn. Also, summer had the largest value of mean annual river discharges during the period 1930–2006, up to 219.4 m3/s, followed by spring (63.9 m3/s), autumn (57.3 m3/s), and winter (26.5 m3/s). Figure4b shows the seasonal changes of the river discharges at station 8, which indicates that the river discharges tended to decrease from 1930 to 2006 for all four seasons. Winter had the largest decreasing trend (Z = 2.41), − followed by summer (Z = 2.15), spring (Z= 2.12), and autumn (Z = 1.32). Also, Figure4b shows − − − that the river discharges at station 8 exhibited a significant rising trend for all four seasons from 1974 to 2006. During the period 1974–2006, the blue linear trend line of Figure4b indicates that winter showed the highest increasing trend (Z = 5.16), followed by autumn (Z = 5.04), spring (Z = 3.67), and summer (Z = 2.68), eventually leading to the rise in the annual river discharges from 1974 to 2006 (Figure3c). Sustainability 2019, 11, x FOR PEER REVIEW 7 of 18

Figure 2c clearly illustrates the decadal trend in the annual river discharge for each station. Like the results of the Mann–Kendall trend test, most stations with positive growth rates were located at the upper rivers, whereas the stations with negative growth rates tended to be clustered in the middle

Sustainabilityand lower2019 regions., 11, 3084 During the study period, the largest positive growth rate was up to8 20.6 of 18 m3/(s•decade) at station 4, followed by 8.9 m3/(s•decade) at station 17; on the other hand, the largest negative growth rate measured up to −103.2 m3/(s•decade) at station 7, followed by −59.3 3.2.m3/(s Trend•decade) Analysis at station of the Precipitation8. and Temperature FigureIn order5 shows to further the spatialunderstand distribution the detailed of the chan meanges values of the and river trends discharges, of the annual six typical precipitation stations and(including temperature stations during 1, 6–8, the 10, period and 13) 1930–2015. were selected As shown to detect in Figure the 5abrupta, the mean points annual and the precipitation, results are whichshown rangedin Figure from 3. From 100 to this 700 figure, mm across we can the conclude basin, was that less the in river the lowerdischarges area of at thestations Syr Darya 6–8 tended Basin comparedto fall significantly with the at other a 95% regions. confidence Also, level the highestwith Z values precipitation at −3.02, was −4.67, found and in −2.92, the mountains respectively, in theand middleincreased of the significantly basin, which at measuredstations 1, up 10, to 700and mm13 /withyear. the However, Z values an inverseat 2.50, distribution 3.90, and 4.50, was detectedrespectively. in the The precipitation results indicate trends that (Figure an augmentation5c), in which was the noticed middle at region the upper illustrated streams the of largestthe Syr decrease,Darya Basin, up toespecially 40 mm/decade. in the tributaries Also, an increasingof the Tien-Shan trend wasMountain, found inbut the a de lowercreasing and trend upper could regions be ofseen the in basin, the middle up to 20and mm lower/decade. regions However, during the the peri trendod of1930–2006. the annual These precipitation results are in in each line gridwith was the notfindings signiﬁcant. shown in Figure 2.

600 60 (a ) S ta tion 6 (Middle) y=13.10x – 31.18 (d ) S ta tion 1 (Upper) 400 Z=5.62, P<0.01 40 /s ) /s ) 3 200 3 20

y (m y 0 (m y 0 -200 -20

Anom al 3 Anom al 3 -400 Trend: -33.6 m /(s*decade) -40 Trend: 3.1 m /(s*decade) y = -3.36x + 134.53, Z=-3.02, P<0.01 y = 0.31x – 12.644, Z=2.5, P<0.01 -600 -60 1973 900 15 (b ) S ta tion 7 (Middle) y=13.604x – 544.11 (e ) S ta tion 10 (Upper) 600 Z=4.85, P<0.01 10 /s ) /s ) 3 300 3 5

y (m y 0 (m y 0

-300 -5 Anomal Anomal 3 3 -600 Trend: -103.2 m /(s*decade) -10 Trend: 1.2 m /(s*decade) y = -10.32x + 402.48, Z=-4.67, P<0.01 y = 0.12x - 5.39, Z=3.90, P<0.01 -15 -900 1973 600 20 (c ) S ta tion 8 (Lower) y=12.393x – 330.94 (f) S ta tion 13 (Upper) 400 Z=4.80, P<0.01 10 /s ) /s )

3 200 3

y (m y 0 (m y 0 -200

Anom al Anom al -10 3 3 -400 Trend: -25.3 m /(s*decade) Trend: 1.3 m /(s*decade) y = -2.53x + 98.82, Z=-2.92, P<0.01 y = 0.13x - 5.02, Z=4.50, P<0.01 -600 -20 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year Year

Figure 3. Time series, trends, and abrupt changes of the annual river discharge anomaly (m3/s) at (a) Figure 3. Time series, trends, and abrupt changes of the annual river discharge anomaly (m3/s) at (a) station 6, (b) station 7, (c) station 8, (d) station 1, (e) station 10, and (f) station 13 from 1930 to 2006. In station 6, (b) station 7, (c) station 8, (d) station 1, (e) station 10, and (f) station 13 from 1930 to 2006. In the trendline equation, x is the independent variable and represents the number of years relative to the the trendline equation, x is the independent variable and represents the number of years relative to base period and y is the dependent variable and represents the annual river discharge anomaly (m3/s). the base period and y is the dependent variable and represents the annual river discharge anomaly Stations 6 and 7 are located at the middle stream of the Syr Darya River, station 8 at the lower stream of (m3/s). Stations 6 and 7 are located at the middle stream of the Syr Darya River, station 8 at the lower the Syr Darya River, and stations 1, 10, and 13 are situated at the upper stream of the Syr Darya River. stream of the Syr Darya River, and stations 1, 10, and 13 are situated at the upper stream of the Syr Darya River.

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Also, Figure 3 clearly illustrates that an abrupt point (in 1973) was detected for stations 6–8, dividing the record into two time periods, including 1930–1973 and 1974–2006. The main reason is that in 1973, the largest dam (Toktogul Dam) was finished in order to control the river discharge to provide sufficient irrigation water. The annual average river discharge of these two intervals at station 8 were 565.7 and 355.3 m3/s, respectively, a decrease of 210.5 m3/s between these two time periods. In addition, a significant increasing trend was found from 1974 to 2006 for stations 6–8, with the Z values at 5.62, 4.85, and 4.80, respectively. As in the case of the annual data, the river discharges tended to rise during four seasons in station 1 (see Figure 4a). Among them, a significant increase was found from 1930 to 2006 for both summer and autumn, with the Z values at 2.36 and 3.48, respectively (see Table 1 and Figure 4a), suggesting that the increases of the river discharges during the year mainly came from summer and autumn. Also, summer had the largest value of mean annual river discharges during the period 1930– 2006, up to 219.4 m3/s, followed by spring (63.9 m3/s), autumn (57.3 m3/s), and winter (26.5 m3/s). Figure 4b shows the seasonal changes of the river discharges at station 8, which indicates that the river discharges tended to decrease from 1930 to 2006 for all four seasons. Winter had the largest decreasing trend (Z = −2.41), followed by summer (Z = −2.15), spring (Z= −2.12), and autumn (Z = −1.32). Also, Figure 4b shows that the river discharges at station 8 exhibited a significant rising trend for all four seasons from 1974 to 2006. During the period 1974–2006, the blue linear trend line of Figure 4b indicates that winter showed the highest increasing trend (Z = 5.16), followed by autumn (Z = Sustainability5.04),2019 spring, 11, 3084(Z = 3.67), and summer (Z = 2.68), eventually leading to the rise in the annual river9 of 18 discharges from 1974 to 2006 (Figure 3c).

(a ) S ta tion 1 (b) Station 8 50 800 Spring Spring y=11.58x – 346.62

25 /s ) 400 Z=3.67, P<0.01 /s ) 3 3

0 (m y 0 y (m y

-25 Trend: 1.8 m 3/(s*decade) -400 Trend: -33 m 3 /(s*decade) Anom al Anom al y = 0.18x – 6.77, Z=1.61, P>0.01 y = -3.30x + 128.8, Z=-2.12, P<0.01 -50 -800 150 1000 Summer Summer

/s ) y=6.70x – 275.14 /s ) 3 3 500 Z=2.68, P<0.01 50 y (m y y (m y 0 -50 Anomal Anomal Trend: 6.6 m 3 /(s*decade) -500 Trend: -40.2 m 3 /(s*decade) y = 0.66x – 26.54, Z=2.36, P<0.01 y = -4.02x + 156.93, Z=-2.15, P<0.01 -150 -1000 500 40 y=13.51x – 301.28 Autumn Autumn /s ) /s ) 3 3 20 250 Z=5.04, P<0.01 y (m y y (m y 0 0 Anomal

Anomal -20 -250 3 Trend: 2.5 m 3 /(s*decade) Trend: -8.3 m /(s*decade) y = 0.25x – 9.70, Z=3.48, P<0.01 y = -0.83x + 32.54, Z=-1.32, P>0.01 -40 -500 10 400 Winter Winter y=17.83x – 397 /s )

/s ) 200 5 3 Z=5.16, P<0.01 3 y (m y y (m y 0 0

-5 3 -200 3

Trend: 0.5 m (s*decade) Anom al Trend: -18.2 m /(s*decade) Anom al y = -1.82x + 70.89, Z=-2.41, P<0.01 -10 y = 0.05x – 1.83, Z=1.54, P>0.01 -400 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year Year

3 FigureFigure 4. Time 4. Time series, series, trends, trends, and and abrupt abrupt changes changes of of the the seasonal seasonal river river discharge discharge anomaly anomaly (m3/s) (mat (a/)s) at (a) stationstation 1 and1 and (b )(b station) station 8 8 during during the the studystudy period.period. In In th thee trendline trendline equation, equation, x is x the is the independent independent variablevariable and represents and represents the numberthe number of yearsof years relative relative to to the the basebase period; yy demonstrates demonstrates the the dependent dependent variablevariable and stands and stands for the for annualthe annual river river discharge discharge anomaly anomaly (m(m33/s)./s).

3 Table 1. Changes of the seasonal discharges (m /s) at station 1 and station 8 and the precipitation (mm/year) and temperature (◦C) changes in the Syr Darya River Basin.

Variable Season Mean Z Decadal Trend Annual 92.7 2.50 * 3.1 Spring 63.9 1.61 1.8 Station 1 Summer 219.4 2.36 * 6.6 Autumn 57.3 3.48 * 2.5 Winter 26.5 1.54 0.5 Annual 475.6 2.92 * 33.6 − − Spring 599.7 2.12 * 33.0 − − Station 8 Summer 594.3 2.15 * 40.2 − − Autumn 319.7 1.32 * 8.3 − − Winter 385.0 2.41 * 18.2 − − Annual 307.2 1.60 4.3 Spring 124.6 0.69 1.3 Precipitation (1930–2015) Summer 32.7 0.25 0.2 Autumn 56.8 1.07 1.4 Winter 93.1 1.40 1.5 Annual 8.6 6.83 * 0.3 Spring 9.5 4.29 * 0.3 Temperature (1930–2015) Summer 22.2 5.64 * 0.2 Autumn 8.7 5.51 * 0.3 Winter 5.8 3.24 * 0.3 − Note: * indicates that changes are signiﬁcant on the 95% conﬁdence level. Sustainability 2019, 11, x FOR PEER REVIEW 9 of 18

3.2. Trend Analysis of the Precipitation and Temperature Figure 5 shows the spatial distribution of the mean values and trends of the annual precipitation and temperature during the period 1930–2015. As shown in Figure 5a, the mean annual precipitation, which ranged from 100 to 700 mm across the basin, was less in the lower area of the Syr Darya Basin compared with the other regions. Also, the highest precipitation was found in the mountains in the middle of the basin, which measured up to 700 mm/year. However, an inverse distribution was detected in the precipitation trends (Figure 5c), in which the middle region illustrated the largest decrease, up to 40 mm/decade. Also, an increasing trend was found in the lower and upper regions ofSustainability the basin,2019 up, 11 to, 3084 20 mm/decade. However, the trend of the annual precipitation in each grid10 was of 18 not significant.

FigureFigure 5. 5. MapsMaps of of the the mean mean annual annual values values in in the the ( (aa)) precipitation precipitation (mm) (mm) and and ( (bb)) temperature temperature (°C) (◦C) and and c d linearlinear decadal decadal trends trends in in the the ( (c)) precipitation (mm/decade) (mm/decade) and ( d) temperature (°C/decade) (◦C/decade) during 1930–2015 in the Syr Darya River Basin. Red pluses indicate the grid points with changes that are 1930–2015 in the Syr Darya River Basin. Red pluses indicate the grid points with changes that are significant at a 95% significance level. significant at a 95% significance level. As shown in Figure5b, the annual mean temperature was much higher in the middle and lower As shown in Figure 5b, the annual mean temperature was much higher in the middle and lower regions of the Syr Darya Basin compared with the high mountainous regions, ranging from around regions of the Syr Darya Basin compared with the high mountainous regions, ranging from around 15 C to around 5 C. Figure5d clearly indicates that a rising trend was detected for all grid cells of 15 °C◦ to around −5 °C.◦ Figure 5d clearly indicates that a rising trend was detected for all grid cells of the basin, ranging from approximately 0.4 C/decade to 0.6 C/decade. The lower region of the basin the basin, ranging from approximately 0.4 °C/decade◦ to 0.6 °C/decade.◦ The lower region of the basin showed a larger increase compared with the other regions, up to 0.6 C/decade, which reveals that the showed a larger increase compared with the other regions, up to 0.6◦ °C/decade, which reveals that plain was more sensitive to the global warming. The augmenting temperature has accelerated the the plain was more sensitive to the global warming. The augmenting temperature has accelerated the glacier melting in the Tien Shan Mountains. glacier melting in the Tien Shan Mountains. Table1 also shows the changes in precipitation and temperature on both the seasonal and annual scales, suggesting that a rising trend was found for all seasons from 1930 to 2015. The mean regional annual precipitation measured 307.2 mm with an increasing trend (Figure6a). Spring had the largest value in mean precipitation, up to 124.6 mm/year, followed by winter (93.1 mm/year), autumn (56.8 mm/year), and summer (32.7 mm/year). Overall, the precipitation trend was not significant for all

seasons. The average regional annual temperature was 8.6 ◦C with a rise on the 95% confidence level (Z = 6.83) (Figure6b). Summer demonstrated the highest mean annual temperature, up to 22.2 ◦C, followed by spring (9.5 C), autumn (8.7 C), and winter ( 5.8 C). Also, the annual temperature in ◦ ◦ − ◦ summer, autumn, spring, and winter tended to augment significantly on the 95% confidence level with the Z values at 5.64, 5.51, 4.29, and 3.24, respectively. Sustainability 2019, 11, 3084 11 of 18 Sustainability 2019, 11, x FOR PEER REVIEW 10 of 18

(a) Precipitation (mm/year) (b) Temperature ( ℃） 600 11 y = 0.43x + 288.42, Z=1.60 y = 0.03x + 7.56, Z=6.83 500 ） 10 ℃ 400 9

on (m mon /year) 300 8 ta ti pi 200 7 Trend: 4.3 mm/decade Temperature ( Temperature Trend: 0.3 ℃/decade Preci 100 6 1930 1936 1942 1948 1954 1960 1966 1972 1978 1984 1990 1996 2002 2008 2014 1930 1936 1942 1948 1954 1960 1966 1972 1978 1984 1990 1996 2002 2008 2014 Year Year

Figure 6. Time series and trends in the precipitation (mm) and temperature ( C) in the whole basin Figure 6. Time series and trends in the precipitation (mm) and temperature (°C)◦ in the whole basin from 1930 to 2015. The red straight line in each sub-figure is the trend line. In the trendline equation, x from 1930 to 2015. The red straight line in each sub-figure is the trend line. In the trendline equation, stands for the independent variable and represents the number of years relative to the base period; y is x stands for the independent variable and represents the number of years relative to the base period; the dependent variable and illustrates the annual river discharge anomaly (m3/s). y is the dependent variable and illustrates the annual river discharge anomaly (m3/s). 3.3. Relation of the Climatic Factors and River Discharges Table 1. Changes of the seasonal discharges (m3/s) at station 1 and station 8 and the precipitation Table(mm/year)2 shows and the temperature Pearson’s (°C) correlation changes betweenin the Syr the Darya climatic River factorsBasin. (precipitation and temperature) and river discharges at stations 1, 6–7, 10, and 13 in the Syr Darya River Basin. Positive correlations were detected betweenVariable the precipitation andSeason river dischargesMean for allZ stations fromDecadal 1930 to Trend 2015. A positive Annual 92.7 2.50 * 3.1 correlation on a 95% statistical significance level was found at stations 1, 6, 8, 10, and 13 with a Spring 63.9 1.61 1.8 corresponding value of 0.32, 0.31, 0.39, 0.50, and 0.23 respectively, suggesting that the increasing annual Station 1 Summer 219.4 2.36 * 6.6 precipitation caused augmenting annualAutumn river discharges57.3 during 3.48 the * period 1930–2015. 2.5 The correlation coefficient measured 0.14 at station 7 butWinter was not important.26.5 Moreover, 1.54 according to the 0.5 interpretation of the correlation coefficients (Table3), weAnnual found that475.6 a moderate − correlation2.92 * strength was−33.6 only discovered at station 10 and that a weak correlationSpring strength 599.7 was detected−2.12 at stations* 1, 6, and−33.0 8. Station 8 Summer 594.3 −2.15 * −40.2 Table 2. Results of the correlation analysisAutumn between 319.7 the climatic− factors1.32 * and discharges− in8.3 the Syr Darya River Basin. Winter 385.0 −2.41 * −18.2 Annual 307.2 1.60 4.3 Station Station 1 StationSpring 6 Station124.6 7 Station 0.69 8 Station 10 1.3 Station 13 PrecipitationPrecipitation (1930–2015) 0.32 * Summer 0.31 * 32.7 0.14 0.390.25 * 0.50 * 0.2 0.23 * Temperature 0.29 * Autumn0.38 * 56.80.45 * 1.070.32 * 0.29 * 1.4 0.55 * −Winter −93.1 − 1.40 1.5 Note: * Correlation on a 95% confidence level. Annual 8.6 6.83 * 0.3 Spring 9.5 4.29 * 0.3 Temperature (1930–2015)Table 3. InterpretationSummer of the22.2 correlation 5.64 coeffi * cients [38]. 0.2 Autumn 8.7 5.51 * 0.3 Correlation CoeWinterfficient (R)−5.8 Correlation3.24 * Strength 0.3 Note: * indicates0.0–0.3 that changes are significant on the Very 95% weak confidence level. 0.3–0.5 Weak Table 1 also shows the changes0.5–0.7 in precipitation and temperature Moderate on both the seasonal and annual scales, suggesting that a rising trend0.7–0.9 was found for all seasons Strongfrom 1930 to 2015. The mean regional 0.9–1.0 Very strong annual precipitation measured 307.2 mm with an increasing trend (Figure 6a). Spring had the largest value in mean precipitation,Note: This up descriptor to 124.6 applies mm/year, to both thefollowed positive by and winter negative (93. relations.1 mm/year), autumn (56.8 mm/year), and summer (32.7 mm/year). Overall, the precipitation trend was not significant for all seasons. The average regional annual temperature was 8.6 °C with a rise on the 95% confidence level A significant correlation was noticed between the temperature and river discharges for all stations (Z = 6.83) (Figure 6b). Summer demonstrated the highest mean annual temperature, up to 22.2 °C, from 1930 to 2015, which could be divided into two groups. On the one hand, a positive correlation followed by spring (9.5 °C), autumn (8.7 °C), and winter (−5.8 °C). Also, the annual temperature in was detected at stations 1, 10, and 13, with a corresponding value of 0.29, 0.29, and 0.55, respectively, summer, autumn, spring, and winter tended to augment significantly on the 95% confidence level which indicates that the rising temperature caused the glaciers to melt more quickly, probably leading with the Z values at 5.64, 5.51, 4.29, and 3.24, respectively. to increasing river discharges during the period 1930–2015. On the other hand, a negative correlation was found at stations 6–8, with corresponding values of 0.38, 0.45, and 0.32, respectively. This − − −

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Sustainability 2019, 11, x FOR PEER REVIEW 12 of 18 difference reveals that the increasing discharges from the upper rivers (see stations 1, 10, and 13) thewere interannual consumed invariability the middle in andprecipitation lower rivers on ofthe the river Syr Daryadischarge River are Basin, the same possibly as othoseffsetting of the the interannualpositive effects variability of the rising in temperature. temperature. However, only station 10 demonstrated a moderate positive correlation strength between the river discharges and temperature, while other stations showed a weak Table 4. Linear regression of the river discharge (m3/s) against the precipitation (mm) and negative (or positive) correlation. temperature (°C) in the Syr Darya River Basin. In order to fully understand the combining effects of the precipitation and temperature on the river dischargeStation of the SyrParameter Darya River, a multipleInterception linear regressionPrecipitation analysis wasTemperature performed with the river discharge as the dependentCoefficients variable (and0.55 the precipitation 0.11 and temperature 6.85 as independent variables, the results ofStandard which are error shown in Table 19.784 and Figure 0.037). The regression 1.97 t values for the Station 1 precipitation and temperaturet value at station 1 were 0.02 3.56 (p < 0. 001) 3.56 and 3.48 (p < 0.001), 3.48 respectively, reflecting a positive correlationp value between the climatic >0.05 factors (both <0.001 the temperature <0.001 and precipitation) and the river discharge.Coefficients The regression t values684.42 for the precipitation 1.40 and temperature−74.95 at station 8 were 3.60 (p < 0. 001) andStandard2.93 error (p < 0.005), 257.13 respectively, revealing 0.39 a positive correlation 25.57 between Station 8 − the precipitation and rivert discharge value (and a negative 2.66 correlation 3.60 between the temperature−2.93 and river discharge). Additionally, thep magnitudevalue of the <0.001t values at stations <0.001 1 and 8 indicates <0.005 that the effects of the interannual variability in precipitation on the river discharge are the same as those of the interannual variability in temperature.

FigureFigure 7. 7. CorrelationsCorrelations between between the annual precipitation, temperature,temperature, andandriver river discharges discharges at at stations stations 1 1and and 8 in8 in the the Syr Syr Darya Darya River River Basin. Basin. The The straight straight line line represents represents the linearthe linear regression regression lines lines and theand black the blackshade shade is a 95% is a conﬁdence 95% confidence band. band.

To conclude, evidence for the climate change impact (on the water resources of the Syr Darya To conclude, evidence for the climate change impact (on the water resources of the Syr Darya River Basin) is plentiful. Over the past 85 years, temperatures have signiﬁcantly increased in all River Basin) is plentiful. Over the past 85 years, temperatures have significantly increased in all parts parts of the basin, which probably caused glaciers in the high mountains to melt faster than before, of the basin, which probably caused glaciers in the high mountains to melt faster than before, shaping/creating more ice runoﬀ [3,39]. Precipitation has increased in both the upper and lower parts shaping/creating more ice runoff [3,39]. Precipitation has increased in both the upper and lower parts of the basin and decreased in the middle of the basin (Figure5c), which is in line with the results from of the basin and decreased in the middle of the basin (Figure 5c), which is in line with the results from SorgSorg et et al. al. [21]. [21]. Also, Also, substantial substantial climatic climatic changes changes are are expected expected to to continue continue occurring occurring in in the the pattern pattern of of precipitation andand temperaturestemperatures [ 40[40],], which which are are likely likely to to augment augment the the uncertainties uncertainties in waterin water supply supply and andin water in water resource resource management. management.

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Sustainability 2019, 11, x FOR PEER REVIEW 13 of 18 Table 4. Linear regression of the river discharge (m3/s) against the precipitation (mm) and temperature 3.4. Impact(◦C) in of the the Syr Land Darya Use River on the Basin. River Discharges The dischargeStation from a river Parameter basin depends Interception on the precipitation, Precipitation evapotranspiration, Temperature and storage factors [41,42]. The results mentionedCoefficients above show 0.55 the changes of the 0.11 river discharges, 6.85 climate factors, Standard error 19.78 0.03 1.97 (precipitationStation and temperature) 1 and their correlations in the Syr Darya River Basin. These results indicate that the river dischargest value increased in the 0.02 upper streams, 3.56 mainly due to the 3.48 rise in melting p value >0.05 <0.001 <0.001 ice, while they decreased in the middle and lower streams because of human activities. Here, we Coefficients 684.42 1.40 74.95 would like to further discuss the potential reasons from the two aspects of land− use and reservoir Standard error 257.13 0.39 25.57 Station 8 construction. t value 2.66 3.60 2.93 − p value <0.001 <0.001 <0.005 Table 5. Changes of the main land cover types in the Syr Darya River Basin during the period 1992– 2015. 3.4. Impact of the Land Use on the River Discharges Agricultural Construction Bare TheLand discharge Use from a river basinForest depends Grassland on the precipitation, Water Body evapotranspiration, and storage Land Land Land factorsChange [41,42 rate]. The results mentioned above show the changes of the river discharges, climate factors, 66.68 * 5.93 29.61 22.01 * 127.83 * −252.06 * (precipitation(km2/year) and temperature) and their correlations in the Syr Darya River Basin. These results indicate thatZ the river discharges1.96 increased1.27 in the upper1.91 streams,5.21 mainly due to the6.82 rise in melting−6.82 ice, while theyNote: decreased * indicates in the the land middle cover and with lower changes streams that are because significant of humanon the 95% activities. significance Here, level. we would like to further discuss the potential reasons from the two aspects of land use and reservoir construction. Figure8 8 andand Table5 5 showshow the changes changes in in the the main main types types of ofland land use use in the in theSyr SyrDarya Darya River River Basin Basinfrom 1992 from to 1992 2015. to 2015. Grassland Grassland is the is thelargest largest land land cover, cover, occupying occupying about about 48% 48% of of the the entire entire basin, followed by agriculturalagricultural land (29%) and barebare landland (19%).(19%). Three types of land cover, includingincluding thethe agricultural land,land, water water body, body, and and construction construction land, land, exhibited exhibited a noticeable a noticeable increase increase on a 95% on statistical a 95% significancestatistical significance level during level 1992 during to 2015. 1992 Among to 2015. them, Among the them, change the rate change of construction rate of construction land was land the highest,was the highest, up to 127.83 up to km 127.832/year, km followed2/year, followed by the agriculturalby the agricultural land (66.68 land km(66.682/year) km2/year) and water and water body (22.01body (22.01 km2/year). km2/year). By contrast, By contrast, the the bare bare land land decreased decreased on on a 95%a 95% statistical statistical significance significance levellevel from 19921992 to 2015, 2015, up up to to 252.06 252.06 km km2/year,2/year, implying implying that that most most regions regions of bare of bare land land hadhad been been converted converted into intoagricultural agricultural land land and andconstruction construction land land [14]. [14 The]. The results results indicate indicate that that the the rapid rapid expansion expansion of the construction landland and and agricultural agricultural land land mainly mainly came came from from the increasesthe increases in the in middle the middle of the of Syr the Darya Syr RiverDarya Basin River over Basin the over past the 24 past years 24 (see years Figure (see8 Figure; Figure 8;9 ).Figure 9).

(a) Agricultural land (103 km2) (b) Forest (103 km2) (c) Grass land (103 km2) 108 10.2 178 ) 2 ) ) 2 2

km 107 177 km km 3 9.9 3 106 3 176 9.6 105 175 104 9.3 174

Land area area (10 Land y = 66.676x + 105245 y = 5.929x + 9662.5 y = 29.61x + 176215 Land area area (10 Land 103 area (10 Land 9 173 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 Year Year Year

(d) Water body (103 km2) (e) Construction land (103 km2) (f) Bare land (103 km2) 72 ) 6 0.4 2 ) ) 2 2 y = 127.83x - 70.512 km km km 3 5.6 0.3 3 3 68 5.2 0.2 64 4.8 0.1 y = 22.005x + 5211.1 Land area area (10 Land y = -252.06x + 69636 Land area area (10 Land Land area area (10 Land 4.4 0 60 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 Year Year Year Figure 8. ChangeChange of of the the different diﬀerent land land covers covers in inthe the (a) ( aagricultural) agricultural land, land, (b) (forest,b) forest, (c) grassland, (c) grassland, (d) (waterd) water body, body, (e) (econstruction) construction land, land, and and (f) ( fbare) bare land land in in the the Syr Syr Darya Darya River River Basin. Basin. In In the trendlinetrendline equation, x is the independent variable and represents the number of years relative to the base period; 3 y is the dependent variablevariable andand representsrepresents thethe annualannual riverriver dischargedischarge anomalyanomaly (m(m3//s).s).

On the one hand, the change in construction land (caused by the increasing population and the rapid economic development) had a demonstrable effect on the hydrological cycle in the Syr Darya

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SustainabilityTable 5. 2019Changes, 11, x FOR of the PEER main REVIEW land cover types in the Syr Darya River Basin during the period 1992–2015.14 of 18

River Basin [17]. TheAgricultural expansion of the urban impermeable surfaces (seeConstruction Figure 9) is likely to reduce Land Use Forest Grassland Water Body Bare Land the ability of land infiltrationLand and resulted in a significant influence on the Landsurface-runoff dynamics [43].Change Also, the rate rapid urbanization could drastically alter the geomorphologic complexity of the river 2 66.68 * 5.93 29.61 22.01 * 127.83 * 252.06 * networks(km / year)[44]. All these changes have demonstrated increases in the total runoff, declines− in the runoff lagZ time, enlargements 1.96 in the 1.27 peak flow, 1.91and augmentations 5.21 in the urban 6.82 flooding 6.82risks [45]. − Therefore, theNote: construction * indicates the land’s land cover expansion with changes generall that arey had signiﬁcant a positive on the effect 95% signiﬁcance on the runoff level. volume and a negative impact on the urban flooding in the Syr Darya River Basin.

FigureFigure 9.9. Maps of the land covers inin thethe SyrSyr DaryaDarya RiverRiver BasinBasin inin 19921992 andand 2015.2015.

OnOn the other one hand, hand, thewith change the agricultural in construction develop landment, (caused a massive by the irrigati increasingon expansion population and river- and thebasin rapid planning economic were development)set up for the Syr had Darya a demonstrable River Basin from effect 1940 on the to 1983 hydrological [46,47]. During cycle inthe the period Syr Daryaof 1940 River to 1983, Basin several [17]. big The reservoirs expansion (see ofFigure the urban 1), including impermeable Kasansai, surfaces Karkidon, (see Andijan, Figure9) Kasansai, is likely toand reduce Tortogul the ability(and small of land reservoirs), infiltration were and construc resultedted in and a significant the total water influence storage on capacity the surface-runo measuredff dynamicsup to 35 km [433].. These Also, reservoirs the rapid urbanization were utilized could to intercept drastically and alter store the the geomorphologic water resources, complexity which were of thethen river transported networks to [44 develop]. All these the agricultural changes have irriga demonstratedtion through increases an immense in the network total runo offf canals., declines Table in the6 shows runoff thelag capacity time, enlargements and length of in theseveral peak main flow, ca andnals. augmentations The capacity inof thethese urban canals flooding ranged risks from [45 40]. Therefore,to 300 m3/s the and construction the length ranged land’s expansionfrom 25 km generally to 344 km. had The a positive Fergana e ffValley,ect on thewhich runo isff onevolume of the and most a negativeancient world impact oases, on the generally urban flooding collects inabout the Syr 4 km Darya3/year River mainly Basin. through the Toktogul and Andijan reservoirOn the operations other hand, and the with Great the agriculturalCanal [48]. The development, largest reservoir a massive (Toktogul) irrigation was finished expansion in 1973 and river-basinand was built planning so as wereto control set up the for river the Syr discharge Darya River to provide Basin fromsufficient 1940 toirrigation 1983 [46 ,water.47]. During All these the periodagricultural of 1940 activities to 1983, probably several bigtriggered reservoirs a significant (see Figure decline1), including in the river Kasansai, discharges Karkidon, in 1973. Andijan, Kasansai, and Tortogul (and small reservoirs), were constructed and the total water storage capacity measured up to 35 kmTable3. These 6. Main reservoirs irrigation were canals utilized of th toe Syr intercept Darya River and store Basin the [48]. water resources, which were then transportedCanal’s to develop Name the agricultural irrigation Capacity (m through3/s) an immenseLength (km) network of canals. Table6 shows the capacity and length of several main canals. The capacity of these canals ranged Great Namangan 61 162 from 40 to 300 m3/s and the length ranged from 25 km to 344 km. The Fergana Valley, which is one of Northern Fergana 110 165 the most ancient world oases, generally collects about 4 km3/year mainly through the Toktogul and Great Fergana 270 344 Andijan reservoir operations and the Great Canal [48]. The largest reservoir (Toktogul) was finished in Great Andijan 200 110 1973 and was built so as to control the river discharge to provide sufficient irrigation water. All these Southern Fergana 130 103 agricultural activities probably triggered a significant decline in the river discharges in 1973. Akhunbabaeva 60 50 Upper Dalverzin 40 30 Lower Dalverzin 78 25 South Golodnaya Steppe 300 127 Kirov 260 120 Kyzylkum 200 115

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Table 6. Main irrigation canals of the Syr Darya River Basin [48].

Canal’s Name Capacity (m3/s) Length (km) Great Namangan 61 162 Northern Fergana 110 165 Great Fergana 270 344 Great Andijan 200 110 Southern Fergana 130 103 Akhunbabaeva 60 50 Upper Dalverzin 40 30 Lower Dalverzin 78 25 South Golodnaya Steppe 300 127 Kirov 260 120 Kyzylkum 200 115

Therefore, the land use changes signiﬁcantly aﬀected the water resources in the Syr Darya River Basin. On the whole, all these changes in land use would likely increase the water consumption, causing the decline of the river discharges in the middle and lower parts of the Syr Darya River [17].

3.5. Impact of the Groundwater Recharge on the River Discharges Recently, the groundwater recharge has been an important source to feed irrigated lands in the Syr Darya River Basin. Generally, the groundwater resources were not widely used for irrigation in Central Asia during the Soviet period because of suﬃcient surface water, a reliable water supply, and the good maintenance of irrigation infrastructure with massive funding from the central government [49]. However, after the collapse of the Soviet Union, the groundwater was gradually extracted to feed irrigated lands in the Syr Darya River Basin, especially in the Fergana Valley [50]. Since 1990, there have been upstream/downstream impacts in the aquifer areas of the Fergana Valley due to the increased irrigated cereal production upstream. On average, about 18% of the total groundwater recharge for the aquifers having transboundary implications came from the subsurface inﬂow to the downstream of the Fergana Valley. The loss in water resources from canals and irrigation is the main source of groundwater in the downstream areas and up to 49% of the groundwater recharge is contributed by irrigation in the Fergana Valley. Currently, 31% of the total recharge originates from the groundwater extraction in the Fergana Valley and it is also needed to manage the groundwater recharge to prevent issues related to the groundwater depletion, degradation of the groundwater quality, and the high extraction cost [51].

4. Conclusions, Management Measures, and Future Work The annual and seasonal changes of the water resources in the Syr Darya River Basin were evaluated based on 20 hydrological stations from 1930 to 2006. Also, the correlations between the river discharges, climate factors, land use changes, and reservoirs were characterized. Some interesting ﬁndings were gathered and summarized as follows: (1) The stations located in the upper streams of the Syr Darya Basin showed an increasing trend in the river discharges from 1930 to 2006, while a decreasing trend was visible in the middle and lower regions; (2) the increased precipitation and melting water led to a rise in the amount of water that ﬂows from the upper to the middle and lower rivers, eventually leading to a rise of the annual river discharges from 1974 to 2006; (3) the expansion of the construction land (128.83 km2/year) and agricultural land (66.68 km2/year) from 1992 to 2015 increased the water consumption, exacerbating the stress of the water resources in the Syr Darya River Sustainability 2019, 11, 3084 16 of 18

Basin; and (4) the establishment of dams (e.g., Kasansai, Karkidon, Andijan, Kasansai, and Tortgul) and irrigation canals has significantly cut off the river discharge, especially from the 1970s onwards. Results could offer useful information that will help to establish effective water resource management in the Syr Darya River Basin. The future progress in the sustainable water resource development in this basin will require (1) an updated, legal, and executable framework for managing the transboundary water resources between Kyrgyzstan, Kazakhstan, Tajikistan, and the Uzbekistan countries, (2) the development of an integrated water management system in the Syr Darya River Basin for the optimal management of hydropower and irrigated agriculture, and (3) the increased investments in national water sectors including agricultural techniques, irrigation networks, and technologies so as to increase the water use efficiency and productivity. Therefore, in future work, more detailed conditions should also be considered for a sustainable water resource development for the whole basin. This study also has a few shortcomings and suggests several areas for future work. Firstly, due to the lack of ground observation data, the CRU dataset was applied to calculate the precipitation and temperature and its course resolution (0.5◦) could not perfectly capture the real precipitation and temperature, probably causing some uncertainties. If possible, more gridded climate datasets will be combined with the existed observed data in order to obtain more reliable climate data. In addition, the outcomes from the Pearson’s correlation could not fully measure the effects of the river discharges because the Syr Darya River was affected by many factors, including the precipitation, temperature, evapotranspiration, and so on. Finally, more complicated hydrological cycles will be considered in order to fully assess the water resources, especially in society-relevant extreme events such as floods, droughts, and so on.

Author Contributions: Conceptualization, S.Z. and W.D.; methodology, S.Z. and W.D.; writing—original draft preparation, S.Z.; writing—review and editing, S.Z., P.D.M., T.V.d.V., and A.J.; funding acquisition, W.D. and A.J. Funding: This study is sponsored by the National Natural Science Foundation of China (No. U1603242), the Pioneer “Hundred Talents Program” of the Chinese Academy of Sciences (Y931161001), the Thousand Youth Talents plan of China (Y971171001), the Science and Technology Service Network Initiative (KFJ-STS-QYZD-071), the Foundation of State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences and the Talent Introduction Project of the Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences. S.Z. was supported by grants from the Chinese Scholarship Council (201804910944). Conﬂicts of Interest: The authors declare no conﬂict of interest regarding the publication of this paper.

References

1. Chatalova, L.; Djanibekov, N.; Gagalyuk, T.; Valentinov, V. The Paradox of water management projects in Central Asia: An institutionalist perspective. Water 2017, 9, 300. [CrossRef] 2. Duan, W.; Chen, Y.; Zou, S.; Nover, D. Managing the water-climate-food nexus for sustainable development in Turkmenistan. J. Clean. Prod. 2019, 220, 212–224. [CrossRef] 3. Siegfried, T.; Bernauer, T.; Guiennet, R.; Sellars, S.; Robertson, A.W.; Mankin, J.; Bauer-Gottwein, P.; Yakovlev, A. Will climate change exacerbate water stress in Central Asia? Clim. Chang. 2012, 112, 881–899. [CrossRef] 4. Abdullaev, I.; Kazbekov, J.; Manthritilake, H.; Jumaboev, K. Participatory water management at the main canal: A case from South Ferghana canal in Uzbekistan. Agric. Water Manag. 2009, 96, 317–329. [CrossRef] 5. Groll, M.; Opp, C.; Kulmatov, R.; Ikramova, M.; Normatov, I. Water quality, potential conﬂicts and solutions—An upstream–downstream analysis of the transnational Zarafshan River (Tajikistan, Uzbekistan). Environ. Earth Sci. 2015, 73, 743–763. [CrossRef] 6. Dukhovny, V.A.; de Schutter, J. Water in Central Asia: Past, Present, Future; Taylor and Francis: London, UK, 2011. 7. Zhupankhan, A.; Tussupova, K.; Berndtsson, R. Water in Kazakhstan, a key in Central Asian water management. Hydrol. Sci. J. 2018, 63, 752–762. [CrossRef] Sustainability 2019, 11, 3084 17 of 18

8. Aizen, V.B.; Kuzmichenok, V.A.; Surazakov, A.B.; Aizen, E.M. Glacier changes in the central and northern Tien Shan during the last 140 years based on surface and remote-sensing data. Ann. Glaciol. 2006, 43, 202–213. [CrossRef] 9. Ibatullin, S.; Yasinsky, V.; Mironenkov, A. Impacts of Climate Change on Water Resources in Central Asia; Sector Report of the Eurasian Development Bank; Eurasian Development Bank: Almaty, Kazakhstan, 2009; p. 44. 10. Golubtsov, V.; Lineitseva, A.; Merz, B.; Dukhovny, V.; Unger-Shayesteh, K. Receipt of water in the rivers of Northern slope of Jetisu Alatau because of glacier degradation. In Proceedings of the International Scientific Symposium, “Water in Central Asia”, Tashkent, Uzbekistan, 4–26 November 2010; p. 87. 11. Lioubimtseva, E.; Henebry, G.M. Climate and environmental change in arid Central Asia: Impacts, vulnerability, and adaptations. J. Arid Environ. 2009, 73, 963–977. [CrossRef] 12. Bernauer, T.; Siegfried, T. Climate change and international water conflict in Central Asia. J. Peace Res. 2012, 49, 227–239. [CrossRef] 13. Akiyama, T.; Li, J.; Kubota, J.; Konagaya, Y.; Watanabe, M. Perspectives on Sustainability Assessment: An Integral Approach to Historical Changes in Social Systems and Water Environment in the Ili River Basin of Central Eurasia, 1900–2008. World Futures 2012, 68, 595–627. [CrossRef] 14. Savoskul, O.S.; Chevnina, E.V.; Perziger, F.I.; Vasilina, L.Y.; Baburin, V.L.; Danshin, A.I.; Matyakubov, B.; Murakaev, R.R. Water, Climate, Food, and Environment in the Syr Darya Basin. Available online: http: //www.weap21.org/downloads/adaptsyrdarya.pdf (accessed on 21 February 2019). 15. Wegerich, K.; van Rooijen, D.; Soliev, I.; Mukhamedova, N. Water security in the Syr Darya basin. Water 2015, 7, 4657–4684. [CrossRef] 16. Gaybullaev, B.; Chen, S.; Gaybullaev, D. Changes in water volume of the Aral Sea after 1960. Appl. Water Sci. 2012, 2, 285–291. [CrossRef] 17. Soliev, I.; Wegerich, K.; Kazbekov, J. The costs of benefit sharing: Historical and institutional analysis of shared water development in the Ferghana valley, the Syr Darya basin. Water 2015, 7, 2728–2752. [CrossRef] 18. Cai, X.; McKinney, D.C.; Rosegrant, M.W. Sustainability analysis for irrigation water management in the Aral Sea region. Agric. Syst. 2003, 76, 1043–1066. [CrossRef] 19. Shibuo, Y.; Jarsjö, J.; Destouni, G. Hydrological responses to climate change and irrigation in the Aral Sea drainage basin. Geophys. Res. Lett. 2007, 34, L21406. [CrossRef] 20. Farinotti, D.; Longuevergne, L.; Moholdt, G.; Duethmann, D.; Mölg, T.; Bolch, T.; Vorogushyn, S.; Güntner, A. Substantial glacier mass loss in the Tien Shan over the past 50 years. Nat. Geosci. 2015, 8, 716–722. [CrossRef] 21. Sorg, A.; Bolch, T.; Stoffel, M.; Solomina, O.; Beniston, M. Climate change impacts on glaciers and runoff in Tien Shan (Central Asia). Nat. Clim. Chang. 2012, 2, 725–731. [CrossRef] 22. Abdullaev, I.; Molden, D. Spatial and temporal variability of water productivity in the Syr Darya Basin, central Asia. Water Resour. Res. 2004, 40, W08S02. [CrossRef] 23. Bernauer, T.; Siegfried, T. Compliance and performance in international water agreements: The case of the Naryn/Syr Darya Basin. Glob. Gov. 2008, 14, 479–501. [CrossRef] 24. Immerzeel, W.W.; Lutz, A.F.; Droogers, P. Climate Change Impacts on the Upstream Water Resources of the Amu and Syr Darya River Basins; Future Water: Wageningen, The Netherlands, 2012. 25. Crétaux, J.; Biancamaria, S.; Arsen, A.; Bergé-Nguyen, M.; Becker, M. Global surveys of reservoirs and lakes from satellites and regional application to the Syrdarya river basin. Environ. Res. Lett. 2015, 10, 356–357. [CrossRef] 26. Bocchiola, D.; Pelosi, M.G.; Soncini, A. Effects of hydrological changes on cooperation in transnational catchments: The case of the Syr Darya. Water Int. 2017, 42, 852–873. [CrossRef] 27. Cardenal, S.J.P.; Riegels, N.; Bauer-Gottwein, P.; Berry, P.; Smith, R.G.; Yakovlev, A.; Siegfried, T.U. Real-time remote sensing driven river basin modeling using radar altimetry. Hydrol. Earth Syst. Sci. 2011, 15, 241–254. [CrossRef] 28. Harris, I.; Jones, P.D.; Osborn, T.J.; Lister, D.H. Updated high-resolution grids of monthly climatic observations-the CRU TS3. 10 Dataset. Int. J. Climatol. 2014, 34, 623–642. [CrossRef] 29. Deng, H.; Chen, Y. Influences of recent climate change and human activities on water storage variations in Central Asia. J. Hydrol. 2017, 544, 46–57. [CrossRef] 30. Center for International Earth Science Information Network-CIESIN-Columbia University. Gridded Population of the World, Version 4 (GPWv4): Population Density; NASA Socioeconomic Data and Applications Center (SEDAC): Palisades, NY, USA, 2016. Sustainability 2019, 11, 3084 18 of 18

31. Hollmann, R.; Merchant, C.J.; Saunders, R.; Downy, C.; Buchwitz, M.; Cazenave, A.; Chuvieco, E.; Defourny, P.; de Leeuw, G.; Forsberg, R.; et al. The ESA climate change initiative: Satellite data records for essential climate variables. Bull. Am. Meteorol. Soc. 2013, 94, 1541–1552. [CrossRef] 32. Hurtt, G.C.; Chini, L.P.; Frolking, S.; Betts, R.A.; Feddema, J.; Fischer, G.; Klein Goldewijk, K.; Hibbard, K.A.; Janetos, A.C.; Jones, C.; et al. Harmonisation of Global Land-Use Scenarios for the Period 1500–2100 for IPCC-AR5. Available online: http://gel.umd.edu/images/Hurtt_2009_iLEAPS.pdf (accessed on 1 February 2019). 33. Mann, H.B. Nonparametric tests against trend. Econometrica 1945, 13, 245–259. [CrossRef] 34. Kendall, M.G. Rank Correlation Methods, Charles Griffin, London; Griffin: London, UK, 1975. 35. Yue, S.; Pilon, P.; Cavadias, G. Power of the Mann–Kendall and Spearman’s rho tests for detecting monotonic trends in hydrological series. J. Hydrol. 2002, 259, 254–271. [CrossRef] 36. Bai, J.; Perron, P. Computation and analysis of multiple structural change models. J. Appl. Econ. 2003, 18, 1–22. [CrossRef] 37. Lawrence, I.; Lin, K. A concordance correlation coefficient to evaluate reproducibility. Biometrics 1989, 45, 255–268. 38. Hinkle, D.; Wiersma, W.; Jurs, S. Applied Statistics for the Behavioral Sciences, 5th ed.; Rand McNally: Skokie, IL, USA, 1979. 39. Guo, L.; Xia, Z. Temperature and precipitation long-term trends and variations in the Ili-Balkhash Basin. Theor. Appl. Climatol. 2014, 115, 219–229. [CrossRef] 40. Sorg, A.; Mosello, B.; Shalpykova, G.; Allan, A.; Clarvis, M.H.; Stoffel, M. Coping with changing water resources: The case of the Syr Darya river basin in Central Asia. Environ. Sci. Policy 2014, 43, 68–77. [CrossRef] 41. Duan, W.; He, B.; Takara, K.; Luo, P.; Nover, D.; Hu, M. Impacts of climate change on the hydro-climatology of the upper Ishikari river basin, Japan. Environ. Earth Sci. 2017, 76, 490. [CrossRef] 42. Jackson, R.B.; Carpenter, S.R.; Dahm, C.N.; McKnight, D.M.; Naiman, R.J.; Postel, S.L.; Running, S.W. Water in a changing world. Ecol. Appl. 2001, 11, 1027–1045. [CrossRef] 43. Walsh, C.J.; Roy, A.H.; Feminella, J.W.; Cottingham, P.D.; Groffman, P.M.; Morgan, R.P. The urban stream syndrome: Current knowledge and the search for a cure. J. N. Am. Benthol. Soc. 2005, 24, 706–723. [CrossRef] 44. Duan, W.; He, B.; Nover, D.; Fan, J.; Yang, G.; Chen, W.; Meng, H.; Liu, C. Floods and associated socioeconomic damages in China over the last century. Nat. Hazards 2016, 82, 401–413. [CrossRef] 45. McGrane, S.J. Impacts of urbanisation on hydrological and water quality dynamics, and urban water management: A review. Hydrol. Sci. J. 2016, 61, 2295–2311. [CrossRef] 46. Benjaminovich, Z.M.; Tersitskiy, D.K. Irrigation of Uzbekistan II (in Russian); Fan: Tashkent, Uzbekistan, 1975. 47. Forkutsa, I.; Sommer, R.; Shirokova, Y.I.; Lamers, J.; Kienzler, K.; Tischbein, B.; Martius, C.; Vlek, P.L.G. Modeling irrigated cotton with shallow groundwater in the Aral Sea Basin of Uzbekistan: II. Soil salinity dynamics. Irrig. Sci. 2009, 27, 319–330. [CrossRef] 48. Antipova, E.; Zyryanov, A.; McKinney, D.; Savitsky, A. Optimization for Syr Darya Water and Energy Use. Available online: http://www.ce.utexas.edu/prof/mckinney/papers/aral/Naryn(ver3).pdf (accessed on 1 February 2019). 49. Rakhmatullaev, S.; Huneau, F.; Kazbekov, J.; Celle-Jeanton, H.; Motelica-Heino, M.; Coustumer, P.; Jumanov, J. Groundwater resources of Uzbekistan: An environmental and operational overview. Open Geosci. 2012, 4, 67–80. [CrossRef] 50. Heaven, S.; Koloskov, G.B.; Lock, A.C.; Tanton, T.W. Water resources management in the Aral Basin: A river basin management model for the Syr Darya. Irrig. Drain. 2002, 51, 109–118. [CrossRef] 51. Karimov, A.; Smakhtin, V.; Borisov, V.; Jumanov, J. Transboundary aquifers of the Fergana Valley: Challenges and opportunities. In Proceedings of the International UNESCO Conference on “Transboundary Aquifers: Challenges and New Directions”, Paris, France, 6–8 December 2010; pp. 72–73.

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