Article The Radial Growth of Schrenk (Picea schrenkiana Fisch. et Mey.) Records the Hydroclimatic Changes in the Chu River Basin over the Past 175 Years

Ruibo Zhang 1,2,3,*, Bakytbek Ermenbaev 4, Tongwen Zhang 1, Mamtimin Ali 1 , Li Qin 1 and Rysbek Satylkanov 4 1 Institute of Desert Meteorology, Meteorological Administration, Key Laboratory of -ring Physical and Chemical Research of China Meteorological Administration, Key Laboratory of Tree-ring Ecology of Uigur Autonomous Region, Urumqi 830002, China; [email protected] (T.Z.); [email protected] (M.A.); [email protected] (L.Q.) 2 Climate Change Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China 3 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Joint International Research Laboratory of Climate and Environment Change/Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China 4 Tien-Shan Scientific Center, Institute of Water Problem and Hydropower of National Academy of Sciences of Kyrgyz Republic, Bishkek 720033, Kyrgyzstan; [email protected] (B.E.); [email protected] (R.S.) * Correspondence: [email protected]; Tel.: +86-991-2662971

 Received: 5 January 2019; Accepted: 26 February 2019; Published: 2 March 2019 

Abstract: The Chu River is one of the most important rivers in arid Central . Its discharge is affected by climate change. Here, we establish a tree-ring chronology for the upper Chu River Basin and analyze the relationships between radial growth, climate, and discharge. The results show that the radial growth of Schrenk spruce (Picea schrenkiana Fisch. et Mey.) is controlled by moisture. We also reconstruct a 175-year standardized precipitation-evapotranspiration index (SPEI) for the Chu River Basin. A comparison of the reconstructed and observed indices reveal that 39.5% of the variance occurred during the calibration period of 1952–2014. The SPEI reconstruction and discharge variability of the Chu River show consistent long-term change. They also show that the Chu River Basin became increasingly dry between the 1840s and the 1960s, with a significant drought during the 1970s. A long and rapid wetting period occurred between the 1970s and the 2000s, and was followed by increasing drought since 2004. The change in the SPEI in the Chu River Basin is consistent with records of long-term precipitation, SPEI and Palmer Drought Severity Indices (PDSI) in other proximate regions of the western Tianshan Mountains. The hydroclimatic change of the Chu River Basin may be associated with westerly wind. This study is helpful for disaster prevention and water resource management in arid central Asia.

Keywords: tree rings; Schrenk spruce (Picea schrenkiana Fisch. et Mey.); hydroclimatology; Chu River; Tianshan Mountains; climate change; Central Asia

1. Introduction It is widely recognized that global warming has occurred since the mid-19th century [1]. However, corresponding hydroclimatic changes demonstrate significant regional variations [2]. Arid Central Asia (ACA) covers 5 × 106 km2, and includes , Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan, and Xinjiang in northwest China. Drought is a major climate disaster in ACA and the

Forests 2019, 10, 223; doi:10.3390/f10030223 www.mdpi.com/journal/forests Forests 2019, 10, 223 2 of 11 cause of considerable agricultural, economic and environmental damage. The Tianshan Mountains, which extend across the region, are known as the “water towers” of Central Asia, and are the largest and most important mountain system in ACA. The region is especially sensitive to climate change [3,4]. Obvious warming in the Tianshan Mountains has been detected at a rate of 0.3 ◦C/10 years [5], and persistent warming is exacerbating droughts and water shortages. The Chu River, which originates in the Tianshan Mountains, is one of the longest rivers in ACA. Its river basin is shared by Kyrgyzstan (where the river originates) and Kazakhstan, and its waters feed millions of people and support the social development and economic prosperity of both countries. Hence, it is particularly important to understand long-term hydroclimatic changes in the Chu River Basin. However, the sparse and unevenly distributed meteorological and hydrological stations in the region provide limited data for understanding the region’s climate and water resource variations [6]. As proxy data is also limited, long-term climate change research for the region is lacking. However, Schrenk spruce (Picea schrenkiana Fisch. et Mey.) is distributed throughout the Tianshan Mountains, and its radial growth is an ideal proxy for past climate change [7]. Tree-ring proxies are an important source of high-resolution, absolutely dated information about the hydroclimate of the Common Era (C.E.). They are widespread and well replicated, and they can be statistically calibrated against overlapping instrumental records to produce validated reconstructions and associated estimates of uncertainty in past climate variability at an annual resolution [8]. Recent studies have increasingly shed light on the historical moisture variability of arid Central Asia [7,9,10]. However, because moisture availability is especially variable in mountains, localized hydroclimatic reconstructions are needed. The history of hydroclimatic change in the Chu River Basin is still unclear, despite the river’s importance. In this study, we established a tree-ring-width chronology using tree-ring samples collected in the Chu River Basin in 2014. We analyzed the radial growth of Schrenk spruce and its response to climate, and reconstructed the standardized precipitation-evapotranspiration index (SPEI) to understand past changes in moisture availability. We also examined the relationship between the hydroclimatic changes over the last 175 years and large-scale oscillations in the climate system.

2. Data and Methods

2.1. Study Area The Chu River is one of the longest rivers in Central Asia (73◦240–77◦040 E, 41◦450–43◦110 N), with a length of approximately 1067 km and a drainage area of 62,500 km2 (Figure1). The river starts in Kyrgyzstan and runs through the country for 115 km before becoming the border between Kyrgyzstan and Kazakhstan for 221 km. The last 731 km are in Kazakhstan. It is one of the longest rivers in both Kyrgyzstan and in Kazakhstan, and is fed mainly by glaciers and melting snow; rainfall is of secondary importance. Like most rivers in arid regions, the Chu River is an inland river, originating in the middle ranges of the Tianshan Mountains and disappearing in the desert. After passing through the narrow Boom Gorge, the river enters the comparatively flat Chu Valley, within which the Kyrgyz capital of Bishkek and the Kazakh city of Chu are located. Much of the Chu’s water is diverted into a network of canals to irrigate the fertile black soils of the Chu Valley for farming, both on the Kyrgyzstan and Kazakhstan sides of the river. Finally, the Chu River disappears in the Moyynqunm Desert. The study area is located in the headwater region of Chu River Basin [11]. Forests 2019, 10, 223 3 of 11 Forests 2018, 9, x FOR PEER REVIEW 3 of 11

FigureFigure 1. Map 1. Map of tree-ring of tree-ring sample sample sites sites information. information. ( (AA)) Map Map of of arid arid Central Central Asia; Asia; (B) Sketch (B) Sketch map mapof of tree-ring sampling site, meteorological station and SPEI grid. tree-ring sampling site, meteorological station and SPEI grid.

2.2. Tree-Ring2.2. Tree-Ring Data Data We collected tree-ring samples on the southwestern Zailiy Alatau Range in northern We collected tree-ring samples on the southwestern Zailiy Alatau Range in northern Kyrgyzstan. Kyrgyzstan. The sampling site was located near the Chong Kemin River (76°23′ E, 42°48′ N, The sampling site was located near the Chong Kemin River (76◦230 E, 42◦480 N, elevation 2400 m, elevation 2400 m, designated the “CKM” group). Forty-one increment cores were collected from 21 designatedtrees in the virgin “CKM” forest. group). The pure Forty-one Schrenk spruce increment forests cores distributed were collected in the shady from slopes 21 of mountains. in virgin forest. The pureWe chose Schrenk trees sprucewith larger forests slopes, distributed thinner soil in thelayers, shady less slopescompetition of mountains. and less interference, We chose which trees with largerradial slopes, growth thinner is limited soil layers, by the less climate. competition The trees and with less no interference,injury and disease which were radial sampled growth in order is limited by theto climate.minimize The the treessignalwith of non-climatic no injury effects and disease on tree weregrowth. sampled in order to minimize the signal of non-climaticFollowing effects standard on tree growth.dendrochronological methods, all tree-ring cores were brought to the Key FollowingLaboratory standardof Tree-ring dendrochronological Physical and Chemical methods, Research allof tree-ringChina Meteorological cores were Administration, brought to the Key Laboratorydried naturally, of Tree-ring mounted, Physical and sanded and Chemical with progressiv Researchely finer of China grains Meteorologicalsizes until the ring Administration, structures were clear. The samples were then cross-dated with skeleton plots and measured with 0.001 mm dried naturally, mounted, and sanded with progressively finer grains sizes until the ring structures precision using a Velmex measuring system [12]. The quality of the cross-dating was checked with werethe clear. COFECHA The samples program were [13] then to cross-datedensure exact withdating. skeleton We developed plots and a measuredsite chronology with using 0.001 mm precisionestablished using astandardisation Velmex measuring techniques system with [12 ].th Thee program quality ARSTAN of the cross-dating [14]. We chose was checkedthe negative with the COFECHAexponential program curve[ 13(NEC)] to ensuremethod exact to de-trend dating. th Wee developedgrowth trend. a siteWe chronologyused a subsample using establishedsignal standardisation techniques with the program ARSTAN [14]. We chose the negative exponential curve (NEC) method to de-trend the growth trend. We used a subsample signal strength (SSS) of 0.85 as an appropriate cut-off criterion for climate reconstructions [15] (Figure2). The SSS is a measure of Forests 2019, 10, 223 4 of 11 Forests 2018, 9, x FOR PEER REVIEW 4 of 11 decreasingstrengthForests 2018 predictive (SSS), 9, x FOR of 0.85PEER power as REVIEW an of appropriate transfer functions cut-off criterion due to reductionsfor climate reconstructions in the sample size[15] (Figure of underlying4 of 2). 11 The SSS is a measure of decreasing predictive power of transfer functions due to reductions in the tree-ring series back in time [16]. samplestrength size (SSS) of underlyingof 0.85 as an tree-ring appropriate series cut-off back in criterion time [16]. for climate reconstructions [15] (Figure 2). The SSS is a measure of decreasing predictive power of transfer functions due to reductions in the sample2.0 size of underlying tree-ring series back in time [16]. SSS>0.85 1.52.0 40 SSS>0.85 40 1.01.5 20

0.51.0 Sample depth

Tree-ring index 20

0.00.5 0 Sample depth

Tree-ring index 1750 1800 1850 1900 1950 2000 0.0 Year (C.E.) 0 1750 1800 1850 1900 1950 2000 Figure 2. Tree-ring-width chronology (STD) in the Chu River Basin. Figure 2. Tree-ring-width chronologyYear (C.E.) (STD) in the Chu River Basin.

2.3. Meteorological2.3. Meteorological and an HydrologicalFigured Hydrological 2. Tree-ring-width Data Data chronology (STD) in the Chu River Basin. We collected the monthly mean temperature (1896–1988) and precipitation (1895–2004) data We2.3. collected Meteorological the monthlyand Hydrological mean temperatureData (1896–1988) and precipitation (1895–2004) data from from the Frunze meteorological station (43.82 N, 74.58 E, 756.0 m) as a climate background analysis, the Frunze meteorological station (43.82 N, 74.58 E, 756.0 m) as a climate background analysis, because becauseWe collectedit is thelocated monthly in meanthe temperatureChu River (1 896–1988)Basin. andWe precipitationalso used (1895–2004) standardized data it is locatedprecipitation-evapotranspirationfrom the in Frunze the Chu meteorological River Basin. station index We (43.82(SPEI) also usedN, da 74.58ta standardized(1901–2014) E, 756.0 m) from as precipitation-evapotranspiration a climatethe nearest background grid (42.75° analysis, N, ◦ ◦ index76.25°because (SPEI) E). dataTheit SPEI (1901–2014)is locatedis based on fromin monthlythe the nearestChu precipitation River grid (42.75andBasin. potential N,We 76.25 evapotranspirationalsoE). used The SPEIstandardized data is basedfrom on monthlytheprecipitation-evapotranspiration Climatic precipitation Research and Unit potential of the Universityindex evapotranspiration (SPEI) of Eadastta Anglia (1901–2014) dataand can from from represent the the Climaticnearest drought grid Researchintensity. (42.75° The UnitN, of the UniversityGlobal76.25° E).SPEI The of database East SPEI Anglia is offersbased and long on canmonthly-term, represent robust precipitation information drought and intensity. aboutpotential drought The evapotranspiration Global conditions SPEI at database thedata global from offers long-term,scale,the Climatic and robust has Research informationa 0.5 degree Unit of aboutspatial the University droughtresolution. of conditions Ea Thest Anglia main atadvantageand the can global represent of the scale, droughtSPEI and Global hasintensity. aDrought 0.5 The degree Global SPEI database offers long-term, robust information about drought conditions at the global spatialMonitor resolution. is thus The its near main real-time advantage character, of the a SPEI characteristic Global Drought best suited Monitor for drought is thus monitoring its near real-time and scale, and has a 0.5 degree spatial resolution. The main advantage of the SPEI Global Drought character,early awarning characteristic purposes best [17]. suited The foraverage drought annual monitoring total discharge and early of the warning Chu River purposes is derived [17]. The primarilyMonitor is from thus itsrunoff near fromreal-time the Chucharacter, Valley a char(notacteristic including best the suited Cochkor for drought Valleys). monitoring We used andthe average annual total discharge of the Chu River is derived primarily from runoff from the Chu Valley averageearly warning annual totalpurposes discharge [17]. fromThe 1970–1999average annual [18]. total discharge of the Chu River is derived (not includingprimarilyThe mean thefrom Cochkor temperature runoff Valleys).from from the We1925Chu used toValley 1988 the (notand average theincluding total annual precipitation the total Cochkor discharge from Valleys). 1950 from toWe 1970–19992000 used were the [18]. Theanalyzedaverage mean annual because temperature total the discharge meteorologic from from 1925al 1970–1999 data to 1988 were and[18]. discontinuous. the total precipitation The mean temperature from 1950 was to 10.3 2000 °C were ◦ analyzedand becauseaverageThe mean annual the temperature meteorological precipitation from datawas1925 429.0 wereto 1988 mm discontinuous. and over the their total respective Theprecipitation mean periods. temperature from An 1950 analysis to was 2000 10.3 of were theC and averageclimateanalyzed annual data because precipitation indicates the meteorologicthat wasboth 429.0 theal temperature mmdata overwere their discontinuous.and the respective precipitation The periods. mean of the temperature An Chu analysis River was Basin of the10.3 have climate°C data indicatesincreasedand average that(Figure annual both 3A). theprecipitation The temperature amplitudes was and 429.0are the0.1 mm °C/10a precipitation over andtheir 6 respectivemm/10a, of the Chu respectively.periods. River An Basin Monthlyanalysis have ofmean increased the (Figuretemperatureclimate3A). The data amplitudes peaksindicates in summer, that are both 0.1 whereas the◦C/10a temperature the and average 6 mm/10a,and monthly the precipitation respectively. precipitation of Monthlytheof theChu Chu River mean River Basin temperature Basin have is increased (Figure 3A). The amplitudes are 0.1 °C/10a and 6 mm/10a, respectively. Monthly mean peaksbimodal in summer, and peaks whereas in spring the average (March to monthly May) and precipitation winter (October of theto December) Chu River (Figure Basin 3B). is bimodal and temperature peaks in summer, whereas the average monthly precipitation of the Chu River Basin is peaksbimodal in spring and (March peaks in to spring May) and(March winter to May) (October and winter to December) (October to (Figure December)3B). (Figure 3B). A15 Temperature Precipitation B 30 Temperature Precipitation 120 700 A Temperature Precipitation BC) 20 Temperature Precipitation

15 ° 30 120 °C) 600700 90 10 10

C) 20 ° °C) 500600 6090 10 0 5 10 400500 60 -10 30 Temperature ( Temperature Precipitation (mm) Temperature ( Temperature 0 5 300400 Precipitation (mm) 0 -20-10 030 Temperature ( Temperature Precipitation (mm) Temperature ( Temperature

1940 1960 1980 2000300 Precipitation (mm) JFMAMJJASOND 0 Year (C.E.) -20 Monthly 0 1940 1960 1980 2000 JFMAMJJASOND Figure 3. Annual (A) and monthly (B) changes in mean temperature (1925–1988) and total Year (C.E.) Monthly precipitation (1950–2000) at the Frunze meteorological station. FigureFigure 3. Annual 3. Annual (A) and (A monthly) and monthly (B) changes (B) changes in mean in temperature mean temperature (1925–1988) (1925–1988) and total and precipitation total (1950–2000)2.4. Methodsprecipitation at the (1950–2000) Frunze meteorological at the Frunze station.meteorological station.

2.4. Methods2.4. Methods We used the Pearson correlation coefficient to analyze the relationship between the tree-ring chronologies and SPEI, and a linear regression model to perform the reconstruction [19]. The SPEI Forests 2019, 10, 223 5 of 11 reconstruction was conducted on the basis of a split calibration-verification procedure that was designedForests to 2018 test, 9,the x FOR reliability PEER REVIEW of the model [20]. The length of the final reconstruction5 of equals 11 the longest nestedWe used regression the Pearson model correlation that still coefficient has good to analyze calibration the relationship and verification between the results. tree-ring Spectral propertieschronologies of both reconstructionsand SPEI, and a linear were regression investigated model using to perform a multi-taper the reconstruction method [19]. (MTM), The SPEI a powerful tool in spectralreconstruction estimation was conducted that is particularly on the basis effectiveof a split forcalibration-verification short time series [procedure21]. that was designed to test the reliability of the model [20]. The length of the final reconstruction equals the 3. Resultslongest nested regression model that still has good calibration and verification results. Spectral properties of both reconstructions were investigated using a multi-taper method (MTM), a powerful 3.1. Tree-Ringtool in spectral Response estimation to Climate that and is particularly the Reconstructed effective SPEIfor short time series [21].

Correlation3. Results and response analysis revealed significant correlations between the SPEI and tree growth during both the previous and current growing seasons. The greatest single correlation between the CKM3.1. tree-ring-width Tree-ring Response standardto Climate and chronology the Reconstructed (Figure SPEI2) and SPEI from the previous July to current June was 0.629Correlation (n = 64, andp < response 0.001). analysis Moisture revealed from thesign previousificant correlations July to currentbetween Junethe SPEI was and the tree dominant climaticgrowth factor during for tree both growth the previous in the Chu and River current Basin. growing seasons. The greatest single correlation Basedbetween on thethe resultsCKM tree-ring-width of the correlation standard analysis, chronolo wegy (Figure reconstructed 2) and SPEI SPEI from from the theprevious previous July July to to current June was 0.629 (n = 64, p < 0.001). Moisture from the previous July to current June was the currentdominant June for climatic the Chu factor River for Basin. tree growth The transferin the Chu function River Basin. was Based on the results of the correlation analysis, we reconstructed SPEI from the previous July to current June for the Chu River Basin.SPEIp7c6 The =transfer 0.911 function× CKM was− 0.753 (1)

SPEIp7c6 ＝ 0.911 × CKM − 0.753 (1) where SPEIp7c6 is the SPEI from the previous July to current June, and CKM is the Chro-Kemin chronologywhere detrended SPEIp7c6 is the by SPEI the from negative the previous exponential July to curve current fitting June, withand CKM and is without the Chro-Kemin application of an adaptivechronology power detrended transformation. by the negati Forve exponential function (1) curve during fitting the with calibration and without period application (1951–2014), of an the adaptive power transformation. For function (1) during the calibration period (1951–2014), the reconstruction explained 39.5% of the variance (38.6% after adjustment for loss of degrees of freedom) reconstruction explained 39.5% of the variance (38.6% after adjustment for loss of degrees of in the SPEIfreedom) data, in with the SPEIn = data, 64, r with= 0.629, n = 64, F 1,r = 62 0.629,= 40.53, F1, 62 = and 40.53,p

A 1.0 Reconstructed Observed B 1.0 Reconstructed Observed

0.5 0.5

0.0 0.0 SPEI index -0.5 -0.5 First order difference First order -1.0 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 Year (C.E.) Year (A.D.) C SPEI reconstruction Mean value Low-pass filtering (20-yrs) 1999 2003 0.5 Mean+2SD Mean+SD SPEI 0.0 Mean-SD

Mean-2SD 1974 1984 1840 1860 1880 1900 1920 1940 1960 1980 2000 Year (C.E.)

Figure 4.FigureReconstructed 4. Reconstructed SPEI variabilitySPEI variabilit of they of Chu the River Chu Basin.River (Basin.A) Comparison (A) Comparison of the reconstructedof the SPEI (bluereconstructed line) and SPEI the (blue SPEI line) recorded and the in SPEI grid recorded from CRU in grid (black from line)CRU during(black line) the during common the period 1951–2014.common (B) Comparisonperiod 1951–2014. of the (B) firstComparison differences of the (year-to-year first differences changes) (year-to-year between changes) the between reconstruction (blue line)the andreconstruction the SPEI grid(blue(black line) and line). the ( CSPEI) Reconstructed grid (black line). SPEI (C from) Reconstructed the previous SPEI July from to thethe current June for the Chu River Basin since 1840 C.E. (gray line). The bold black line shows the data smoothed with a 20-years low-pass filter to emphasize the long-term fluctuations. The solid horizontal line represents the long-term mean for the period 1840–2014; the dashed horizontal lines represent the mean value ± 1σ and the dotted horizontal lines represent the mean value ± 2σ. Forests 2019, 10, 223 6 of 11

A leave-one-out cross-validation test indicated that the regression model passed all verification tests (Table1). We also verified the reliability and stability of the model using split-sample calibration-verification tests. The explained variances are relatively high during the two calibration periods. All of the correlation coefficient (r), first order difference correlation (rd), explained variance (R2), F-test (F), product mean test (t), sign test (ST) and first order difference sign test (ST1) achieved or surpassed the 95% significance level. The reduction of error (RE) and coefficient of efficiency (CE) which are particularly rigorous indicators of reconstructed reliability, were both positive, suggest that the linear regression equation was statistically validated. Finally, we compared the first differences with the SPEI grid and obtained a correlation coefficient of 0.514 (p < 0.001, n = 63) (Figure4B). This indicates that there is good consistency in the high frequency changes between the reconstruction and the observed. Equation (1) was therefore used successfully to reconstruct SPEI from the previous July to current June in the Chu River Basin for the period 1840–2014 (Figure4C).

Table 1. Statistics of the leave-one-out cross-validation and the split-sample calibration-verification test model for the SPEI reconstruction.

Leave-one-out Cross-validation Test

r rd ST ST1 t RE SPEI 0.599 ** 0.470 ** 47+/17− ** 42+/21− * 4.803 ** 0.357 Split-sample Calibration-verification Test Calibration Verification Period r R2 F Period r RE CE ST ST1 1951–1982 0.653 0.426 22.33 1983–2014 0.594 0.409 0.263 22+/10− 22+/9− * 1983–2014 0.594 0.353 16.33 1951–1982 0.653 0.494 0.235 25+/7− ** 19+/12− 1951–2014 0.629 0.395 40.53 * indicate significance at the 95% level of confidence. ** indicate significance at the 99% level of confidence.

3.2. Changes in Moisture over the Past 175 Years As shown in Figure4C, the number of drought years and wet years are consistent: 15% (27a) of the years exceeded the mean + 1σ (standard deviation), and 16% (28a) were lower than the mean—1σ. Extreme drought years occurred in 1974 and 1984, when the SPEI was lower than the mean—2σ. Conversely, 1999 and 2003 were extremely moist years with SPEI exceeding the mean + 2σ. The reconstruction was subjected to 20-year low-pass filtering in order to further understand the low-frequency change in SPEI over the past 175-years (Figure4C). The results revealed five drying periods and four wetting periods. The drying periods occurred in 1840–1873, 1904–1917, 1934–1945, 1956–1974, and 2004–2014; wetting prevailed in 1874–1903, 1918–1833, 1946–1955, and 1975–2003. Notably, the climate in the Chu river basin over the past 175-years presents a slow process of drought from the 1840s to the 1960s, and a significant drought in the 1970s. Then, the SPEI change exhibited a long period of rapid wetting from the 1970s to 2000s. Since 2004, however, there has again been a strong drying trend, even dropping lower than the average value in the last 3 years.

4. Discussion Many previous studies have confirmed that the radial growth of Schrenk spruce growing at lower elevations is limited by moisture conditions prior to the growing season [7,9,10,22–25]. Zhang et al. [26] analyzed intra-annual radial growth based on data from continuously monitored dendrometers and suggested that moisture between late May to late June is a limiting factor for the radial growth of Schrenk spruce in the Tianshan Mountains. Studies of tree-ring widths and their relation to climate for in arid and semiarid sites iteratively demonstrate that ring-width growth is influenced not only by the climate during the growing season, but also by climatic conditions in the autumn, winter, and spring prior to the growing season [12]. Changes in the SPEI in the Chu River Basin over the past 175-years are consistent with the dry/wet changes in the western Tianshan Mountains (Figure5). Numerous studies have shown a Forests 2018, 9, x FOR PEER REVIEW 7 of 11 Forests 2018, 9, x FOR PEER REVIEW 7 of 11 Many previous studies have confirmed that the radial growth of Schrenk spruce growing at lowerMany elevations previous is limitedstudies byhave moisture confirmed conditions that the prior radial to the growth growing of Schrenk season [7,9,10,22–25].spruce growing Zhang at loweret al. elevations [26] analyzed is limited intra-annual by moisture radial conditions growth prior based to the on growing data from season continuously [7,9,10,22–25]. monitored Zhang etdendrometers al. [26] analyzed and suggested intra-annual that moistureradial growth between based late Mayon data to late from June continuously is a limiting factormonitored for the dendrometersradial growth and of suggestedSchrenk spruce that moisture in the Tianshan between Mountains. late May to Studies late June of is tree-ring a limiting widths factor and for thetheir radialrelation growth to climate of Schrenk for conifers spruce inin thearid Tianshan and semiarid Mountains. sites iteratively Studies of demonstrate tree-ring widths that andring-width their relationgrowth to is climate influenced for conifersnot only in byarid the and climate semiarid during sites the iteratively growing demonstrate season, but thatalso ring-width by climatic Forestsgrowthconditions2019 , 10is , 223influenced in the autumn, not only winter, by andthe springclimate prior during to the the growing growing season season, [12]. but also by climatic7 of 11 conditionsChanges in the in autumn, the SPEI winter, in the and Chu spring River prior Basin to overthe growing the past season 175-years [12]. are consistent with the Changes in the SPEI in the Chu River Basin over the past 175-years are consistent with the trenddry/wet of increasing changes precipitation in the western from Tianshan the 1980s Mounta [27]. Shiins et al.(Figure [28] further 5). Numerous suggested studies that the have climate shown in a dry/wet changes in the western Tianshan Mountains (Figure 5). Numerous studies have shown a Xinjiangtrendshifted of increasing from warm-dry precipitation to warm-wet from the in1980s the 1980s.[27]. Shi Several et al. recent[28] further studies suggested have also that shown the thatclimate trend of increasing precipitation from the 1980s [27]. Shi et al. [28] further suggested that the climate precipitationin Xinjiang and shifted the PDSI from have warm-dry decreased to since warm-wet 2004 [9 ,10in ].the This 1980s. study Several further recent confirms studies the moisture have also in Xinjiang shifted from warm-dry to warm-wet in the 1980s. Several recent studies have also fluctuationshown that phenomenon precipitation in recent and the years. PDSI have decreased since 2004 [9,10]. This study further confirms shown that precipitation and the PDSI have decreased since 2004 [9,10]. This study further confirms theWe moisture compared fluctuation the consistency phenomenon of the SPEI in recent reconstruction years. with other studies in the western Tianshan the moisture fluctuation phenomenon in recent years. MountainsWe (Figurecompared5) and the found consistency that past of moisturethe SPEI changesreconstruction in the regionwith other are very studies consistent. in the western The We compared the consistency of the SPEI reconstruction with other studies in the western correlationTianshan coefficient Mountains between (Figure the 5) SPEIand reconstructionfound that past and moisture southern changes Kazakhstan in the [9], region Dzungarian are very Tianshan Mountains (Figure 5) and found that past moisture changes in the region are very Alatauconsistent. [10], and The Issyk correlation Lake [7] coefficient are 0.596 (Pearson, between nthe= 175,SPEIp reconstruction< 0.0001), 0.482 and (Pearson, southernn = Kazakhstan 175, p < consistent. The correlation coefficient between the SPEI reconstruction and southern Kazakhstan 0.0001),[9], Dzungarian and 0.399 (Pearson, Alatau n[10],= 131, andp

Z-scores -1

Z-scores -1 -2 -2 -3 -3 1780 1800 1820 1840 1860 1880 1900 1920 1940 1960 1980 2000 1780 1800 1820 1840 1860 1880Year (C.E.) 1900 1920 1940 1960 1980 2000 Year (C.E.) FigureFigure 5. Comparison 5. Comparison of SPEI of reconstructionSPEI reconstruction from this from study this and study other and studies other in thestudies western in Tianshanthe western Mountains.FigureTianshan 5. (Comparison AMountains.) Reconstructed of(A )SPEI Reconstructed SPEI reconstruction in Chu River SPEI Basinfrom in Chu (thisthis River study study); Basin and (B) Reconstructedother(this study);studies ( Bin precipitation) Reconstructedthe western inTianshan southernprecipitation Mountains. Kazakhstan in southern ( [A9];) Reconstructed ( CKazakhstan) Reconstructed [9]; SPEI (C PDSI )in Reconstructed Chu in Dzungarian River Basin PDSI Alatau (this in Dzungarianstudy); [10]; (D ()B Reconstructed) AlatauReconstructed [10]; (D ) precipitationprecipitationReconstructed in in Issyk-Kul, southernprecipitation Kyrgyzstan Kazakhstan in Issyk-Kul, [7 ].[9]; (KyrgyzstanC) Reconstructed [7]. PDSI in Dzungarian Alatau [10]; (D) Reconstructed precipitation in Issyk-Kul, Kyrgyzstan [7].

Figure 6. Cont. Forests 2019, 10, 223 8 of 11 ForestsForests 2018 2018, ,9 9, ,x x FOR FOR PEER PEER REVIEW REVIEW 88 ofof 1111

FigureFigureFigure 6. Spatial6. 6. Spatial Spatial correlation correlation correlation between between between the the the reconstruction reconstruction reconstruction and an an previousdd previous previous July July July to to currentto current current June June June grid grid grid data data data (1951–2012).(1951–2012).(1951–2012). (A ( )A(A Precipitation) )Precipitation Precipitation data data data from from from the the the CRU-TS CRU-TS CRU-TS 3.22; 3.22; 3.22; (B () B( PrecipitationB) )Precipitation Precipitation data data data from from from the the the Global Global Global PrecipitationPrecipitationPrecipitation Climatology Climatology Climatology Centre Centre Centre (GPCC) (GPCC) (GPCC) V7; V7; V7; (C )( C SPEI;(C) )SPEI; SPEI; (D )( D( scPDSI.D) )scPDSI. scPDSI.

AA comparisonA comparison comparison of of theof the the SPEI SPEI SPEI reconstruction reconstruction reconstruction and and and discharge di dischargescharge variability variability variability of of theof the the Chu Chu Chu River River River indicates indicates indicates consistentconsistentconsistent long-term long-term long-term change change change (Figure (Figure (Figure7). 7). The7). The The correlation correla correlationtion coefficient coefficient coefficient between between between the the the reconstruction reconstruction reconstruction and and and dischargedischargedischarge is is 0.540is 0.540 0.540 (n ( n=(n = 30,= 30, 30,p p

140 Streamflow (m Streamflow (m 0.40.4 0.20.2 120120 SPEI SPEI 0.00.0 100100 3 3 /s) -0.2-0.2 /s) -0.4-0.4 8080 19701970 1975 1975 1980 1980 1985 1985 1990 1990 1995 1995 2000 2000 YearYear (C.E.) (C.E.)

Figure 7. Comparison of the SPEI reconstruction and discharge of the Chu River. FigureFigure 7. 7. Comparison Comparison of of the the SPEI SPEI reconstructi reconstructionon and and discharge discharge of of the the Chu Chu River. River. Forests 2019, 10, 223 9 of 11 Forests 2018, 9, x FOR PEER REVIEW 9 of 11 Forests 2018, 9, x FOR PEER REVIEW 9 of 11 30 30 95% SL 95% SL 20 r 20 2.8-2.9-yr r 2.8-2.9-yr 4.4-yr 2.6-yr Powe 4.4-yr 2.6-yr 10Powe 2.0-yr 10 2.0-yr

0 0.00 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 Frequecy Frequecy

FigureFigure 8.8. Multi-TaperMulti-Taper Power Power spectra spectra for for the the reconstructed reconstructed SPEI SPEI (AD (AD 1840–2014). 1840–2014). 95% 95% SL representsSL represents the Figure 8. Multi-Taper Power spectra for the reconstructed SPEI (AD 1840–2014). 95% SL represents 95%the 95% significance significance level. level. the 95% significance level.

Figure 9. Correlation between the SPEI and sea surface temperature (HadISST1). (A) Relationship FigureFigure 9. Correlation9. Correlation between between the the SPEI SPEI and and sea se surfacea surface temperature temperature (HadISST1). (HadISST1). (A )(A Relationship) Relationship between the reconstruction and July-June HadlSST1(1870–2013) p < 10%; (B) Relationship between betweenbetween the the reconstruction reconstruction and an July-Juned July-June HadlSST1(1870–2013) HadlSST1(1870–2013)p < 10%; p < (10%;B) Relationship (B) Relationship between between the the reconstruction and July-June HadlSST1(1981–2010) p < 10%. reconstructionthe reconstruction and July-June and July-June HadlSST1(1981–2010) HadlSST1(1981–2010)p < 10%. p < 10%. 5. Conclusions 5. Conclusions5. Conclusions In previous dendroclimatological studies in the Tianshan Mountains, we found that long-term InIn previous previous dendroclimatological dendroclimatological studies studies in in the th Tianshane Tianshan Mountains, Mountains, we we found found that that long-term long-term changes in moisture have a strong local character [7,9,10,26,38,39]. The consistency of the change changeschanges in in moisture moisture have have a stronga strong local local character character [7, 9[7,9,10,26,38,39].,10,26,38,39]. The The consistency consistency of of the the change change decreases rapidly with increasing distance. It is therefore important to develop a more robust decreasesdecreases rapidly rapidly with with increasing increasing distance. distance. It is therefore It is therefore important important to develop to adevelop more robust a more network robust network of reconstruction if we are to fully understand the hydroclimatic changes in the Tianshan ofnetwork reconstruction of reconstruction if we are to fullyif we understandare to fully theunde hydroclimaticrstand the hydroclimatic changes in the changes Tianshan in Mountains.the Tianshan Mountains. To this end, we developed the first tree-ring chronology for the Chu River Basin, one of ToMountains. this end, we To developed this end, we the developed first tree-ring the first chronology tree-ring for chronology the Chu Riverfor the Basin, Chu River one of Basin, the most one of the most important basins in Central Asia. Climate-growth response results suggest that moisture importantthe most basins important in Central basins Asia. in Central Climate-growth Asia. Climat responsee-growth results response suggest results that moisturesuggest that before moisture and before and early in the growing season is the dominant factor controlling the radial growth of earlybefore in the and growing early in season the growing is the dominant season factoris the controlling dominant thefactor radial controlling growth of the Schrenk radial spruce growth in of Schrenk spruce in the Chu River Basin. Although moisture varies considerably in mountain areas, theSchrenk Chu River spruce Basin. in Althoughthe Chu River moisture Basin. varies Although considerably moisture in mountainvaries considerably areas, our researchin mountain indicates areas, our research indicates that the radial growth of Schrenk spruce has a stable response to moisture in thatour the research radial growth indicates of Schrenkthat the spruceradial growth has a stable of Sc responsehrenk spruce to moisture has a stable in the response Tianshan to mountains. moisture in the Tianshan mountains. theWe Tianshan also developed mountains. a reliable 175-year SPEI reconstruction for the Chu River Basin. We found that We also developed a reliable 175-year SPEI reconstruction for the Chu River Basin. We found past moistureWe also changes developed in the a reliable western 175-year Tianshan SPEI Mountains reconstruction are very for consistent. the Chu River Our reconstructionBasin. We found that past moisture changes in the western Tianshan Mountains are very consistent. Our showedthat past a slow, moisture long-term changes process ofin drought the western from the Tianshan 1840s to theMountains 1960s, followed are very by a longconsistent. and rapid Our reconstruction showed a slow, long-term process of drought from the 1840s to the 1960s, followed wettingreconstruction from the 1970sshowed to thea slow, 2000s. long-term Our reconstruction process of drought also provides from furtherthe 1840s evidence to the for1960s, a drought followed by a long and rapid wetting from the 1970s to the 2000s. Our reconstruction also provides further trendby a since long 2004. and Werapid posit wetting that long-term from the changes1970s to in the the 2000s. SPEI inOur the reconstruction Chu River Basin also may provides be related further to evidence for a drought trend since 2004. We posit that long-term changes in the SPEI in the Chu large-scaleevidence oscillationsfor a drought in the trend climate since system. 2004. We This posit study that further long-term advances changes dendroclimatology in the SPEI in the in the Chu River Basin may be related to large-scale oscillations in the climate system. This study further TianshanRiver Basin Mountains, may be and related is helpful to large-scale for disaster oscillations prevention in and the water climate resource system. management This study in further arid advances dendroclimatology in the Tianshan Mountains, and is helpful for disaster prevention and Centraladvances Asia. dendroclimatology in the Tianshan Mountains, and is helpful for disaster prevention and water resource management in arid Central Asia. Authorwater Contributions: resource managementConceptualization, in arid Central R.Z.; Data Asia. curation, R.Z. and L.Q.; Formal analysis, R.Z. and L.Q.; FundingAuthor Contributions: acquisition, R.Z.; Conceptualization, Investigation, R.Z., R.Z.; B.E., Data T.Z. andcuration, M.A.; R.Z. Methodology, and L.Q.; L.Q.;Formal Resources, analysis, R.S.; R.Z. Validation, and L.Q.; L.Q.;Author Writing—original Contributions: draft, Conceptualization, R.Z.; Writing—review R.Z.; Data & editing, curation, R.Z. R.Z. and L.Q.; Formal analysis, R.Z. and L.Q.; Funding acquisition, R.Z.; Investigation, R.Z., B.E., T.Z. and M.A.; Methodology, L.Q.; Resources, R.S.; Funding acquisition, R.Z.; Investigation, R.Z., B.E., T.Z. and M.A.; Methodology, L.Q.; Resources, R.S.; Validation, L.Q.; Writing—original draft, R.Z.; Writing—review & editing, R.Z. Validation, L.Q.; Writing—original draft, R.Z.; Writing—review & editing, R.Z. Forests 2019, 10, 223 10 of 11

Funding: This study was supported by Basic Research Operating Expenses of the Central-level Non-profit Research Institutes (IDM2016006), the Strategic Priority Research Program of Chinese Academy of Sciences (XDA20100306), National Natural Science Foundation of China Projects (41675152, 41405081) and Young Talent Training Plan of Meteorological Departments of China Meteorological Administration. Acknowledgments: We acknowledge the contribution of many people who helped with field and laboratory work: M. Diushen, F. Chen, S. Yu, H. Shang, S. Jiang, W. Liu, F. Yang and Q. He. Conflicts of Interest: The authors declare no conflict of interest.

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