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Article A 422-Year Reconstruction of the Kaiken River Streamflow, , Northwest

Heli Zhang 1,2, Huaming Shang 1, Feng Chen 2,*, Youping Chen 2, Shulong Yu 1 and Tongwen Zhang 1

1 Key Laboratory of Tree-Ring Physical and Chemical Research of China Meteorological Administration/ Key Laboratory of Tree-Ring Ecology of Xinjiang Uigur Autonomous , Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China; [email protected] (H.Z.); [email protected] (H.S.); [email protected] (S.Y.); [email protected] (T.Z.) 2 Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security, Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650031, China; [email protected] * Correspondence: [email protected]

 Received: 5 October 2020; Accepted: 11 October 2020; Published: 14 October 2020 

Abstract: Our understanding of Central Asian historical streamflow variability is still limited because of short instrumental hydrologcial records. Based on tree-ring cores collected from three sampling sites in Kaiken River basin near Tien Shan, a regional tree-ring width chronology were developed. The correlation analysis showed that the runoff of Kaiken River from previous August to current June was significantly correlated with the regional chronology, and the high correlation coefficient was 0.661 (p < 0.01). Based on the regional chronology, the August-June runoff of Kaiken River has been reconstructed over the past 422 year, and it accounted for 43.7% of actual runoff variance during the common period 1983–2013. The reconstruction model is reliable, and the trend of observed and reconstructed data is relatively consistent. The results of multi-taper spectral analysis for the runoff reconstruction indicated some remarkable cycles for the past 422 years; the 11.5-year cycles correspond to the solar cycle and is found widely in runoff reconstructions in Central . This may imply a solar influence on the hydroclimate variations of Tien Shan. The runoff reconstruction of Kaiken River compares well with runoff reconstructions the Urumqi River and Manas River, and implies that there is a common driving factor for the runoff in central Tien Shan, China. The analysis of linkages between climate variation and the runoff reconstruction of Kaiken River shows that there is a relationship between extremes in runoff variation and abnormal atmospheric circulations. Our 422-year steamflow reconstruction provides long-term perspective on current and 20th century hydrological events in central Tien Shan, is useful for aids sustainable water management and addresses regional climate change challenges.

Keywords: tree rings; central Tien Shan; Kaiken River; streamflow variation

1. Introduction Economic development, as well as urbanization and the increasing population, raises doubts over the supply of water resources to meet the growing needs of society in semiarid and arid areas around the world [1–4]. Understanding of temporal-spatial hydrological variations is thus vital to improve our knowledge about water resources in drylands [5–8]. However, instrumental hydrological records over are short and sparse in spatial distribution, especially in Tien Shan, China. High-resolution hydrology proxy records, such as history records and tree rings, are needed to interpret the Central Asian hydrological changes. The Kaiken River basin is located in the northern slope of Tien Shan, Central Asia. It originates from the Bogda peak in central Tien Shan. It is the largest

Atmosphere 2020, 11, 1100; doi:10.3390/atmos11101100 www.mdpi.com/journal/atmosphere Atmosphere 2020, 11, 1100 2 of 11 river in Qitai, Xinjiang. The average annual runoff of Kaiken River is 1.6 108 m3. As a basic of × national commodity grain, Qitai plays an important role in Xinjiang’s food security. During the historical period, the Kaiken River is always the most important water source for irrigation for the Qitai Oasis agriculture, and until 2004, the irrigation water still accounted for more than 97.6% of the utilization of water resources, which affected on agricultural and animal husbandry production in Qitai [9]. At the same time, the Kaiken River and the neighboring Manas and Urumqi rivers are the main sources of fresh water for the Central Asian largest urban agglomeration, included and Urumqi. Adequate runoff into the oasis cities is important to the social and economic wellbeing of the local people. Thus, to address the challenge of climate change, especially drought, the prudent water resource planning requires the reliable hydrological knowledge on different time-scales. Tree rings are playing an increasingly vital role in revealing past climatic and environmental change [10–15]. Todate, there are tree-ring based runoff reconstructions based on for Central Asia [16–23], and the tree-ring widths of Picea schrenkiana from Tien Shan are potentially sensitive to hydrological changes [16,24]. Nevertheless, runoff reconstructions are still insufficient in the vast areas of Central Asia for interpreting the recent hydrological variability in a long-term perspective. However, it is still an open question whether the long-term runoff variation in different areas of Tien Shan is synchronous. In this research, we developed a new regional tree-ring width chronology of Picea schrenkiana from the Kaiken River basin, central Tien Shan, China. We analyzed the relationships between the radial growth of Picea schrenkiana and hydroclimate factors. Based on tree-ring width data, we developed August–June runoff reconstruction for Kaiken River over the period 1592–2013 CE. This new runoff record is compared with existing runoff reconstructions from other parts of central Tien Shan. Such long-term runoff records have important socioeconomic implications for agriculture and the animal husbandry of Tien Shan, China.

2. Materials and Methods

2.1. Study Area Description The Kaiken River is located at the southern margin of Zhungeer Basin in Qitai, Xinjiang, and originated from the Bogda peak in central Tien Shan, China. With a drainage area of 280 km2 and a length of 64 km. The tree-ring width data that formed the base for the runoff reconstruction came from central Tien Shan which are situated between 43◦300 to 44◦ N, 88◦300 to 90◦500 E (Figure1). The sampling sites information is shown in Table1. The dominant tree species in central Tien Shan is Schrenk spruce (Picea schrenkiana). At sampling sites, spruce trees grew in open stands with thin and rocky soil in the understory of the forests.

2.2. Tree-Ring Chronology All tree-ring samples were selected from isolated living trees with relatively homogeneous environment. Tree-ring samples were chosen from three different sites in central Tien Shan in August 2008 and July 2014. Two cores per tree were obtained from the relatively old spruce trees at breast height. Tree-ring sampling activities were mainly concentrated in the spruce forests to arrange 1500 to 2500 m a.l.s. Three different sampling sites are hardly affected by human activities and provided 144 cores from 72 trees in total. After dried, mounted on wooden holders, and polished with 400 grit sandpaper to enhance tree-ring bound, annual ring widths of all cores were measured by a manual Henson micrometer (LINTAB™, Rinntech, Heidelberg, Germany) with a 0.01 mm precision. The computer program COFECHA [25,26] was applied to check cross-dated results of tree-ring series. The regional tree-ring width chronology (STD) were developed by the software ARSTAN. Due to the similar growth environment and average high correlation with the master series (r = 0.59), detrended data from all cores were combined into a regional chronology after cross-dated again. Each individual ring-width series was detrended by fitting a negative exponential curve. More than the 0.85 threshold value of the express population signal (EPS) is considered acceptable of chronology quality [27]. Atmosphere 2020, 11, x FOR PEER REVIEW 3 of 11

Table 1. Information about the sampling sites in the Kaiken River Basin.

Site Code Latitude (N) Longitude (E) Elevation (m) BYX 43°36′920″ 90°27′755″ 2360 AtmosphereXDY2020 , 11, 1100 43°47′48.21″ 88°51′41.30″ 2000 3 of 11 KGY 43°32′19.14″ 89°36′51.11″ 2230

Figure 1. MapMap of of sampling sampling sites, sites, meteorological meteorological station, hydrological station and main rivers in central Tien Shan,Shan, China.China.

2.3. Hydroclimate DataTable 1. Information about the sampling sites in the Kaiken River Basin. Several observedSite data Code were used Latitude in this (N) research, Longitude including (E) runoff Elevation data (m)from the Kaiken River

(89°50′ E, 43°36′ N, 1520BYX m a.s.l.) and 43 ◦regional36092000 climate90 ◦data270755 (Qitai,00 89°27′2360 E, 44°03′ N, 1061.2 m a.s.l.) (Figure 1). The monthlyXDY total precipitation 43◦47048.21 and00 mean88◦51 temperature041.3000 from2000 1983 to 2013 at the Qitai meteorological stationKGY were showed 43◦32 0 in19.14 Figure00 2A.89◦ 36 The051.11 annual00 total2230 precipitation and mean temperature are 296.8 mm and 5.4 °C. July is the hottest and wettest month (mean temperature 22.8°C 2.3.and Hydroclimatetotal precipitation Data 36 mm) while January is the coldest month (mean temperature −17.4°C). The mean annual runoff of the Kaiken River is 5.1 m3/s during the period 1983–2013. Figure 2B shows Several observed data were used in this research, including runoff data from the Kaiken River monthly mean runoff of the Kaiken River from 1983 to 2013. The seasonal distributions of runoff and (89◦500 E, 43◦360 N, 1520 m a.s.l.) and regional climate data (Qitai, 89◦270 E, 44◦030 N, 1061.2 m a.s.l.) precipitation and runoff is very different (Figure 2B). There is only one peak (in July) in the monthly (Figure1). The monthly total precipitation and mean temperature from 1983 to 2013 at the Qitai precipitation distribution, while there are one peak with high runoff months (May–July). The runoff meteorological station were showed in Figure2A. The annual total precipitation and mean temperature peak is directly linked to the meltwater input from snowpack of Tien Shan during the warm season. are 296.8 mm and 5.4 ◦C. July is the hottest and wettest month (mean temperature 22.8◦C and total precipitation 36 mm) while January is the coldest month (mean temperature 17.4 C). The mean annual 2.4. Statistical Analysis − ◦ runoff of the Kaiken River is 5.1 m3/s during the period 1983–2013. Figure2B shows monthly mean runoffToof understand the Kaiken Riverhistorical from runoff 1983 to changes, 2013. The correlation seasonal distributions analysis was of runoappliedff and to precipitation identify the andresponse runo ffofis tree very-ring diff widthserent (Figure to hydroclimate.2B). There isMonthly only one total peak precipitation (in July) in, themonthly monthly mean precipitation runoff and distribution,monthly mean while temperature there are onewere peak used with for highcorrelation runoff monthsanalysis, (May–July). over a span The of 14 runo monthsff peak from is directly prior linkedAugust to to the current meltwater September. input fromA linear snowpack regression of Tien model Shan was during computed the warm between season. the predictors (tree- ring width series) and the predict runoff for the calibration period 1983–2013. Because the actual 2.4.runoff Statistical record Analysisis relatively short, the ‘leave-one-out’ method was used to assess the reliability of the modeTo [2 understand8]. Verification historical statistical runo parametersff changes, correlationused included analysis the reduction was applied of toerror identify (RE), thethe responsesign test of(ST), tree-ring sign test widths of the to first hydroclimate. difference (FST) Monthly and the total product precipitation, means monthlytest (PMT) mean [29]. runo ff and monthly mean temperature were used for correlation analysis, over a span of 14 months from prior August to current September. A linear regression model was computed between the predictors (tree-ring width series) and the predict runoff for the calibration period 1983–2013. Because the actual runoff record is relatively short, the ‘leave-one-out’ method was used to assess the reliability of the mode [28]. Verification statistical parameters used included the reduction of error (RE), the sign test (ST), sign test of the first difference (FST) and the product means test (PMT) [29]. AtmosphereAtmosphere2020 2020, 11, 1,1 1100, x FOR PEER REVIEW 4 ofof 1111 Atmosphere 2020, 11, x FOR PEER REVIEW 4 of 11

FigureFigure 2. 2. ((A)) MonthlyMonthly total total precipitation precipitation and and monthly monthly mean mean temperature temperature recordsrecords atat the the Qitai Qitai Figure 2. (A) Monthly total precipitation and monthly mean temperature records at the Qitai meteorologicalmeteorological station station fromfrom 1983 1983 to to 2013. 2013. (B(B)) The The monthly monthly average average streamflow streamflow of of Kaiken Kaiken River River from from meteorological19831983 to to 2013. 2013. station from 1983 to 2013. (B) The monthly average streamflow of Kaiken River from 1983 to 2013. 3.3. ResultsResults andand DiscussionDiscussion 3. Results and Discussion 3.1.3.1. Hydroclimate-GrowthHydroclimate-Growth AnalysisAnalysis 3.1. Hydroclimate-Growth Analysis FigureFigure3 3shows shows the the correlations correlations between between tree-ring tree-ring width width series series and and monthly monthly total total precipitation precipitation fromfromFigure prior prior August 3August shows to tothe current current correlations September, September, between and and tree indicates indicates-ring width significant significant series positive positiveand monthly correlations correlations total pr ( p(ecipitationp< <0.05) 0.05) inin frompriorprior prior September September August and to and current current current April. September, April. Based Based onand various indicates on various multi-month significant multi combination,- monthpositive combination, correlations the highest (p the correlation < 0.05) highest in prior(rcorrelation= 0.53, Septemberp < (r0.01) = 0.53, and was p current found< 0.01) between April. was found Based tree-ring between on width various tree series -ring multi and width-month total series August–June combination, and total precipitation. Augustthe highest–June correlationSignificantprecipitation. positive(r = Significant 0.53, correlations p < positive 0.01) was (p correlations< found0.05) between between (p < 0.05) monthly tree between-ring runo width monthlyff and series tree-ring runoff and totaland width tree August series-ring– width wereJune precipitation.occurseries at were prior occur Significant September, at prior positive October September correlations and, October December, (p and< 0.05) and December between current, January,monthlyand current May runoff January, and and June. tree May The-ring and highest width June. seriescorrelationThe highest were betweenoccur correlation at tree-ringprior between September width tree series,- ringOctober andwidth seasonaland series December runoand seasonalff,combinations and current runoff January,combinations were 0.661 May (p

FigureFigure 3. 3.Correlation Correlation coe ffi coefficientscients between between radial radialgrowth growth of spruce of trees spruce and total trees monthly and total precipitation monthly Figuremonthlyprecipitation 3. meanCorrelation monthly temperature, mean coefficients temperature, and monthly between and mean radialmonthly runo growth meanff in runoffof the spruce Kaiken in the treesKaiken River. and Ri Correlations ver. total Correlations monthly are precipitationcomputedare computed from monthly from the previous the mean previous temperature, August August to current andto current monthly September September mean over runoff over 1983–2013. in 1983 the –Kaiken2013. Dashed Dashed River. lines Correlationslines indicate indicate the are95%the computed 95% confidence confidence from level. the level. previous August to current September over 1983–2013. Dashed lines indicate the 95% confidence level. Atmosphere 2020, 11, x FOR PEER REVIEW 5 of 11

AtmosphereTree2020-ring, 11 width, 1100 series and runoff correlate to variations of August–June precipitation and5 June of 11 temperature, revealing that corresponding signals from climate factors were contained within tree- ring Tree-ring records and width runoff. series The and strong runoff linkcorrelate between to variations runoff and of August–Junetree-ring width precipitation series supports and June this temperature,conclusion. Recognizing revealing that this corresponding interlinkage, signals it was fromassumed climate that factors the were physiol containedogical linkage within tree-ring between recordsclimate and factors runo andff. The tree strong-ring growth link between provides runo someff and usefultree-ring explanations width series for supports revealing this the conclusion. linkage Recognizingbetween the thisclimate interlinkage,-integrated it variations was assumed of tree that-ring the physiologicalgrowth and the linkage runoff between [30]. Our climate research factors has andshowed tree-ring the physiological growth provides linkage some bet usefulween explanations tree-ring growth for revealing and fluctuations the linkage in Augustbetween–June the climate-integratedprecipitation and June variations temperatures. of tree-ring Seasonal growth distribution and the runo of runoffff [30]. (with Our researchMay–July has peak) showed and thethe physiologicalsignificant positive linkage between correlation tree-ring detected growth between and fluctuations August–June in August–June precipitation precipitation and tree- andring Junegrowth temperatures./runoff in this Seasonal study distribution likely reflect of highruno ff precipitation(with May–July, especially peak) and for the the significant winter snowpack positive correlationsynchronized detected with betweenabove mean August–June runoff years. precipitation Good moisture and tree-ring conditions growth at/runo the ffendin thisof the study previous likely reflectgrowth high season precipitation, and heavy especiallysnow in winter for the can winter provide snowpack enough synchronized water for the withnext aboveyear’s meantree growth, runoff and more water for high mountains and spruce forests is converted into runoff [31–34]. Thus, high years. Good moisture conditions at the end of the previous growth season and heavy snow in winter can August–June precipitation is indicative of both increased runoff and luxuriant spruce tree growth. provide enough water for the next year’s tree growth, and more water for high mountains and spruce High June temperatures enhance evapotranspiration and decrease soil moisture content, and lead to forests is converted into runoff [31–34]. Thus, high August–June precipitation is indicative of both reduced earlywood growth and runoff [35,36]. increased runoff and luxuriant spruce tree growth. High June temperatures enhance evapotranspiration and decrease soil moisture content, and lead to reduced earlywood growth and runoff [35,36]. 3.2. Runoff Reconstruction of the Kaiken River 3.2. RunoBasedff Reconstruction on the above of correlation the Kaiken Riveranalysis results, a linear regression model was developed to indicBasedate the on August the above-June correlationrunoff variations analysis of the results, Kaiken a linear River. regression The transfer model model was was developed designed toas indicatefollows: the August-June runoff variations of the Kaiken River. The transfer model was designed as follows: Y = 4.411X − 0.027 (1) Y = 4.411X 0.027 (1) − TheThe dependent dependent variable variableY isY August–Juneis August–June runo runoffff of the of Kaiken the Kaiken River, and River, the and independent the independent variable Xvariableis the regional X is the tree-ringregional chronology.tree-ring chronology. During the During calibration the calibration period 1983–2013, period 1983 the- regional2013, the tree-ringregional chronologytree-ring chronology accounts for accounts 43.7% of for total 43.7% variance of total of the variance actual runoof theff data actual (41.7% runoff after data adjustment (41.7% after for lossadjustment of degrees for ofloss freedom). of degrees Figure of freedom).4 shows Figure a comparison 4 shows of a actualcompa andrison reconstructed of actual and August–Junereconstructed runoAugustff of–June the Kaiken runoffRiver, of the and Kaiken reveals River, that and the reveal reconstructeds that the runo reconstructedff is consistent runoff with is the consistent actual runo withff duringthe actual the runoff calibration during period the calibration 1983–2013. period The results 1983– of2013. reduction The results of error of (RE)reduction the sign of error test (ST), (RE) sign the testsign of test the (ST), first sig diffnerence test of (FST)the first and difference product mean(FST) testand (PMT)product are mean showed test in(PMT) Table are2. showed in Table 2.

Figure 4. Comparison of the actual and reconstructed August-June runoff of the Kaiken River for the Figure 4. Comparison of the actual and reconstructed August-June runoff of the Kaiken River for the common period 1983–2013. common period 1983–2013. All evaluative statistics exceed the 0.01 confidence level, and demonstrate that the credibility All evaluative statistics exceed the 0.01 confidence level, and demonstrate that the credibility of of regression model. We further compared the runoff reconstruction with drought reconstruction of regression model. We further compared the runoff reconstruction with drought reconstruction of eastern Tien Shan [8], and high correlation (r = 0.76, p < 0.01) as found between the series during the Atmosphere 2020, 11, x FOR PEER REVIEW 6 of 11 Atmosphere 2020, 11, 1100 6 of 11 eastern Tien Shan [8], and high correlation (r = 0.76, p < 0.01) as found between the series during the commoncommon period period 1725–2013.1725–2013. Therefore,Therefore, based based on on this this model model and and the the 0.85 0.85 EPS EPS threshold threshold value, value the runo, theff runoffvariations variations of the of Kaiken the Kaiken River River have beenhave reconstructedbeen reconstructed for the for period the period 1592–2013 1592– CE2013 (Figure CE (Figure5). 5).

TableTable 2. 2. StatisticalStatistical characteristics characteristics of of the the verification verification of of the the streamflow streamflow reconstruction. reconstruction.

R R2 R2adj F ST FST t RE R R2 R2 F ST FST t RE 0.645 0.417 0.396 adj 20.00 23+/7− * 22+/6− * 2.76 * 0.32 0.645 0.417 0.396 20.00 23+/7 * 22+/6 * 2.76 * 0.32 R: multiple correlation coefficient; R2: explained variance−; R2adj: Adjusted− explanatory variance; FST: 2 2 signR: multiple test of correlationthe first difference; coefficient; RST:: explained sign test; variance; t: productR adj mean;: Adjusted RE: explanatory reduction variance;of error; FST: * Signi signfi testcant of at the first difference; ST: sign test; t: product mean; RE: reduction of error; * Significant at the 99% confidence level. the 99% confidence level.

FigureFigure 5. 5. (A(A) )The The sample sample depth, depth, running running series series of of mean mean correlation correlation (Rbar) (Rbar) and and expr expressedessed population population signalsignal (EPS) (EPS) of of the the regional regional tree tree-ring-ring chronology. chronology. (B (B) )Reconstructed Reconstructed (black (black line) line) and and 21 21-year-year low low-pass-pass filteredfiltered (red (red line) line) runoff runoff ofof the the Kaiken Kaiken River River from from 1592 1592 to to 2013. 2013. Central Central horizontal horizontal line line (dotted (dotted line) line) showsshows average average value value of of reconstru reconstructedcted runoff runoff series.series.

3.3.3.3. The The C Characteristicsharacteristics of of the the R Runounoffff RReconstructioneconstruction TheThe August August–June–June runoff runoff reconstructionreconstruction of of the the Kaiken Kaiken River River spanned spanned 422 422 years; years; the the average average 3 3 runoffrunoff isis 4.33 4.33 m m3/s/s and and the the standard standard deviation deviation (σ) (σ )is is 0.82 0.82 m m3/s./s. The The thick thick line line is is the the 21 21-year-year low low-pass-pass filteredfiltered values values.. The The reconstruction reconstruction series series includes includes seven seven high high runoff runoff periodsperiods (the (the 21 21-year-year low low-pass-pass 3 valuesvalues > 4.334.33 m m3/s/ scontinuously continuously for for more more than than 10 10 years), years), including including 1592 1592 to to 1609 1609 CE, CE, 1628 1628 to to 1662 1662 CE, CE, 16721672 to to 1709 1709 CE, CE, 1739 1739–1748–1748 CE, CE, 1774 1774–1804–1804 CE, CE, 1836 1836–1853–1853 CE, CE,1934 1934-2005-2005 CE, and CE, four and fourdry periods dry periods (the 3 21(the-year 21-year low-pass low-pass values values  4.33 m< 34.33/s continuously m /s continuously for more for than more 10 years), than 10 including years),including 1610 to 1627 1610 CE, to 16631627 to CE, 1671 1663 CE, to 1710 1671 to CE, 1738 1710 CE, to 17381749– CE,1773 1749–1773 CE, 1805 CE,–1835 1805–1835 CE, 1854 CE,–1868 1854–1868 CE, 1876 CE,–1888 1876–1888 CE, 189 CE,5– 19331895–1933 CE (Figure CE (Figure 5B). 5B). SeveralSeveral tree tree-ring-ring based based runoff runoff reconstructionsreconstructions for for surrounding surrounding river river basin basin have have recently recently been been developed.developed. Yuan Yuan et etal. al. [16 [16] developed] developed an anOctober October–September–September runoff runo reconstructionff reconstruction since since 1629 1629 CE for CE thefor Manasi the Manasi River, River, capturing capturing 51% of 51% the total of the variance total variance in the calibration in the calibration period (1956 period–2000). (1956–2000). Yuan et al.Yuan [28] et developed al. [28] developed 15 Schrenk 15 Schrenk spruce spruce tree-ring tree-ring width width chronologies chronologies and and reconstructed reconstructed annual annual (January(January–December)–December) runoff runoff ofof the the Urumqi Urumqi River River for for the the period period 1633 1633–1989.–1989. Th Thee calibration calibration model model explainedexplained 69.4% 69.4% of of the the observed observed runo runoffff variances variances for thefor period the period from 1958 from to 1958 1989. to Correlations 1989. Correlations between betweenthe Kaiken the reconstructionKaiken reconstruction and other and runo otherff reconstructions runoff reconstructions by Yuan by et Yuan al. [16 et,37 al.], computed[16,37], computed over the overcommon the common periods, periods, are 0.25 are and 0.25 0.33, and respectively, 0.33, respectively, and increase and increase to 0.38 and to 0.38 0.43 and after 0.43 21-year after low-pass21-year lowfiltering.-pass Ourfiltering. runo ff Ourreconstruction runoff reconstruction reflects similar reflects high/ low similar runo high/lowff intervals runoff in the neighboring intervals in areas the neighboringas runoff reconstructions areas as runoff (Figurereconstructions6). As showed (Figure by 6). first As showed principal by componentfirst principal (PC1), component regional (PC1), low regionalrunoff conditions low runoff during conditions the 1641–1644, during the 1704–1727, 1641–1644, 1753–1777, 1704–1727, 1804–1832, 1753–1777, 1853–1869 1804–1832, and 1853 1905–1932–1869 andfound 1905 in–1932 this studyfound occurredin this study synchronously occurred synchronously in the Urumqi in and the ManasiUrumqi River and Manasi basin. TheRiver common basin. The common runoff signals found in the three runoff reconstructions suggest that our reconstruction Atmosphere 2020, 11, 1100 7 of 11

Atmosphere 2020, 11, x FOR PEER REVIEW 7 of 11 runoff signals found in the three runoff reconstructions suggest that our reconstruction represents broad-scalerepresents broad regional-scale runo regionalff variations. runoff variations. The low The runo lowff periodrunoff period 1641–1644 1641– and1644 1753–1777 and 1753– is1777 also is twoalso well-documented two well-documented historical historical droughts droughts the Ming the Dynasty Ming Dynasty drought drought [38] and [ the38] Strangeand the Parallels Strange droughtParallels [39 drought], and demonstrated[39], and demonstrated that large-scale that largedrought-scale extreme drought events extreme in Central events and in eastern Central Asia and caneastern effectively Asia can a ffeffectivelyect the runo affectff variation the runoff of variation arid areas of in arid Asian areas inland. in Asian Many inland. studies Many also studies reported also thatreported the 1917–1919 that the 1917 drought–1919 drought was most was severe most [ 40severe–42]. [40 Of– particular42]. Of particular interest in isterest the downwardis the downward trend duringtrend during the 1950s–2010s. the 1950s– Although2010s. Although the regional the regional climate isclimate still warm is still and warm wet [and43], wet evaporation [43], evaporation increases withincreases the risewith in the temperatures rise in temperatures lead to thelead decline to the decline of runo offf ,runoff, especially especially in the in eastern the eastern Xinjiang Xinjiang and Mongoliaand [18,44 [1,458,44]. ,45]. Multi-taperMulti-taper method method spectral spectral analysis analysis [46 [46] was] was employed employed to reveal to reveal the characteristics the characteristics of the runoff of the runovariationff variation of the of Kaiken the Kaiken in the in frequencythe frequency domain. domain. The The analysis analysis over over the the range range of of our our runorunoffff reconstruction indicatedindicated low- low and- and high-frequency high-frequency periodicities periodicities (Figure (Figure7). The 7). low-frequency The low-frequency (60.2 years) (60.2 andyears) high-frequency and high-frequency peaks peaks (5.6, 3.2, (5.6, and 3.2 2.2, and years) 2.2 years) exceed exceed the 0.01 the confidence0.01 confidence level level based based on a on red a noisered noise null null continuum. continuum. Other Other significant significant high-frequency high-frequency periodicities periodicities were were found found at 11.5 at year,11.5 year, 9.5 year, 9.5 3.7year, year, 3.7 andyear 2.0, and year 2.0 (95%) year (95%) (Figure (Figure7). 7).

Figure 6.6. The firstfirst principal component (PC1) of the runorunoffff reconstructionsreconstructions of Kaiken, Urimqi and Manasi rivers. rivers. Graphical Graphical comparison comparison of various of various runo runoffff reconstructions reconstructions for Kaiken, for Kaiken, Urimqi Urimqi and Manasi and riversManasi derived rivers derived from tree from rings. tree All rings. runo ffAllreconstructions runoff reconstructions were smoothed were smoothed with a 21-year with a low-pass 21-year filterlow- topass emphasize filter to emphasize long-term fluctuations.long-term fluctuations. The August–June runoff reconstruction of the Kaiken River presented in this article are The August–June runoff reconstruction of the Kaiken River presented in this article are characterized by cyclical fluctuations of above and below mean runoff. The 11.5-year cycle resembles characterized by cyclical fluctuations of above and below mean runoff. The 11.5-year cycle resembles other findings in neighboring areas and implies the impact of solar activity on Central Asian runoff other findings in neighboring areas and implies the impact of solar activity on Central Asian runoff there [17,18,21]. The appearance of these low and high frequency cycles (60.2, 5.6, 3.7, 3.2, 2.2, there [17,18,21]. The appearance of these low and high frequency cycles (60.2, 5.6, 3.7, 3.2, 2.2, and 2.0 and 2.0 years) suggests that the oscillations of the ocean-atmosphere system have impacted on the years) suggests that the oscillations of the ocean-atmosphere system have impacted on the hydrologic hydrologic cycle of the Kaiken River basin for the past 422 years, such as North Atlantic Oscillation cycle of the Kaiken River basin for the past 422 years, such as North Atlantic Oscillation (NAO) and (NAO) and Oscillation (AO) [44,47,48]. Over the historical period, runoff reconstruction for Arctic Oscillation (AO) [44,47,48]. Over the historical period, runoff reconstruction for the Kaiken the Kaiken River show significant correlation with the January–June NAO index (r = 0.23, p < 0.01) River show significant correlation with the January–June NAO index (r = −0.23, p <− 0.01) and the and the January–February AO index (r = 0.21, p < 0.01) [49] during the common period 1850–2013. January–February AO index (r = −0.21, p −< 0.01) [49] during the common period 1850–2013. Some Some studies have revealed that the positive NAO and AO phase could lead to the precipitation studies have revealed that the positive NAO and AO phase could lead to the precipitation decrease decrease in Central Asia, include Xinjiang, owing to decrease in eastward water-vapor transport from in Central Asia, include Xinjiang, owing to decrease in eastward water-vapor transport from to Central Asia, which causes an increase in column water vapor content in Central Asia, and vice versa [48–50]. Atmosphere 2020, 11, 1100 8 of 11

Europe to Central Asia, which causes an increase in column water vapor content in Central Asia, Atmosphere 2020, 11, x FOR PEER REVIEW 8 of 11 Atmosphereand vice versa2020, 11 [,48 x FOR–50]. PEER REVIEW 8 of 11

FigureFigure 7. 7. MTMMTM spectral spectral density density of of the the runoff runoff reconstructionreconstruction of the the Kaiken Kaiken River. River. The The dash dash and and dotted dotted Figure 7. MTM spectral density of the runoff reconstruction of the Kaiken River. The dash and dotted lineslines indicate indicate the the 0.05 0.05 and and 0.01 confidence confidence level, respectively. lines indicate the 0.05 and 0.01 confidence level, respectively. TheThe 500 500-hPa-hPa vector vector wind wind composite composite anomalies anomalies of the of highest the highest and lowest and reconstructed lowest reconstructed runoff (n The 500-hPa vector wind composite anomalies of the highest and lowest reconstructed runoff (n =runo 10)ff in(n the= 10) period in the 1948 period–2013 1948–2013 support supportthe connection the connection with AO with and AO NAO and (Figure NAO (Figure8). High8). = 10) in the period 1948–2013 support the connection with AO and NAO (Figure 8). High reconstructedHigh reconstructed runoff runo yearff meanyear mean500 hPa 500 winds hPa winds exhibit exhibit strong strong westerly westerly and northwesterly and northwesterly flow over flow reconstructed runoff year mean 500 hPa winds exhibit strong westerly and northwesterly flow over Coverentral Central Asia Asia(Figure (Figure 8A), 8indicatingA), indicating a strong a strong phase phase of the of the westerlies. westerlies. This This is consistent is consistent with with the Central Asia (Figure 8A), indicating a strong phase of the westerlies. This is consistent with the enhancedthe enhanced westerly westerly flow flow of cold of cold air air from from the the high high latitude latitude areas, areas, which which should should lead lead to to increased enhanced westerly flow of cold air from the high latitude areas, which should lead to increased snsnowfallowfall in in Central Central Asia, Asia, and and providing providing sufficient sufficient water water tree tree growth growth and and runoff runoff formation in in the snowfall in Central Asia, and providing sufficient water tree growth and runoff formation in the followingfollowing year. year. During During the the lowest lowest runo runoffff years, years, the theopposite opposite pattern pattern occurs. occurs. As discussed As discussed above, above, runoff following year. During the lowest runoff years, the opposite pattern occurs. As discussed above, runoffvariation variation in our studyin our areastudy is area related is related to theintensification to the intensifica oftion the of mid-latitude the mid-latitude westerly westerly circulation. circulation. runoff variation in our study area is related to the intensification of the mid-latitude westerly circulation.

FigureFigure 8. Composite anomaly mapsmaps ofof 500-hPa500-hPa vector vector wind wind (from (from prior prior August August to currentto current June) June) for for the Figure 8. Composite anomaly maps of 500-hPa vector wind (from prior August to current June) for the10highest 10 highest (A) and(A) and 10 lowest 10 lowest (B) runo (B) ffrunoffyears years for the for runo theff runoffreconstruction reconstruction of the Kaikenof the Kaiken River during River the 10 highest (A) and 10 lowest (B) runoff years for the runoff reconstruction of the Kaiken River duringthe period the 1948–2013.period 1948–2013. during the period 1948–2013. 4. Conclusions 4. Conclusions 4. Conclusions The correlation analyses indicated that the tree-ring width of spruce trees have significant The correlation analyses indicated that the tree-ring width of spruce trees have significant correlationThe correlation with the runo analysesff of the indicated Kaiken River, that the and tree the-ring highest width correlation of spruce between trees thehave seasonalized significant correlation with the runoff of the Kaiken River, and the highest correlation between the seasonalized correlation with the runoff of the Kaiken River, and the highest correlation between the seasonalized runoff and tree rings was found in August–June. The results of the research reveal the feasibility of runoff and tree rings was found in August–June. The results of the research reveal the feasibility of combining actual runoff data and tree-ring width series for runoff reconstruction for the Kaiken River. combining actual runoff data and tree-ring width series for runoff reconstruction for the Kaiken River. The runoff reconstruction has revealed the runoff variations of the Kaiken River during 1592–2013 The runoff reconstruction has revealed the runoff variations of the Kaiken River during 1592–2013 CE. Comparison with runoff reconstructions for the Urimqi and Manasi rivers indicates high CE. Comparison with runoff reconstructions for the Urimqi and Manasi rivers indicates high Atmosphere 2020, 11, 1100 9 of 11 runoff and tree rings was found in August–June. The results of the research reveal the feasibility of combining actual runoff data and tree-ring width series for runoff reconstruction for the Kaiken River. The runoff reconstruction has revealed the runoff variations of the Kaiken River during 1592–2013 CE. Comparison with runoff reconstructions for the Urimqi and Manasi rivers indicates high coherency in the timing of high/low runoff periods across central Tien Shan (China), i.e., low runoff period 1641–1644, 1704–1727, 1753–1777, 1804–1832, 1853–1869, and 1905–1932. In addition, a downward runoff trend occurred in Central Tien Shan since the 1950s. The analysis of synoptic climatology and periodic analysis suggest the runoff variability in the Kaiken River may have strong linkages with large-scale atmospheric circulation variations, such as AO and NAO. Although the runoff reconstructions are preliminary, our reconstruction provides new insights into runoff variations in Tien Shan, Central Asia. More dendrohydrological studies will be needed to enable us to better understand problems such as the forcings responsible for Central Asian runoff variability and re-evaluation of water resources management, and so on.

Author Contributions: Conceived and designed the experiments: F.C. and Y.C. Performed the experiments: F.C. and Y.C. Analyzed the data: H.Z. and F.C. Contributed reagents/ materials/analysis tools: H.Z., F.C., H.S., Y.C., S.Y. and T.Z. Contributed to the writing of the manuscript: H.Z., F.C., H.S., Y.C., S.Y. and T.Z. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region (Grant No. 2017D01B55). Acknowledgments: We thank the Xinjiang Meteorological Bureau for providing climate data. Conflicts of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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