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Upper Irtysh River Flow Since AD 1500 As Reconstructed by Tree Rings, Reveals the Hydroclimatic Signal of Inner Asia

Upper Irtysh River Flow Since AD 1500 As Reconstructed by Tree Rings, Reveals the Hydroclimatic Signal of Inner Asia

Climatic Change DOI 10.1007/s10584-016-1814-y

Upper flow since AD 1500 as reconstructed by tree rings, reveals the hydroclimatic signal of inner

Feng Chen1 & Yujiang Yuan1 & Nicole Davi2,3 & Tongwen Zhang1

Received: 30 March 2016 /Accepted: 18 September 2016 # Springer Science+Business Media Dordrecht 2016

Abstract In a warming world, is one of the main concerns for sustainable development and human well-being in . Due to the lack of instrumental streamflow records, the natural variability of the water supply from inner Asian is not well understood from a long-term perspective. Here, we have reconstructed the streamflow of Upper Irtysh River from AD 1500 to 2010, based on the tree-ring width indices of spruce (Picea obovata)andlarch(Larix sibirica) from the Altay Mountains. The reconstruction explains 48.4 % of the recorded streamflow variance over the common period 1958–2008. This streamflow reconstruction is representative of regional moisture conditions over the Irtysh River basin area. Some significant spectral peaks are identified, and suggest the influence of natural forcing on the streamflow of the Upper Irtysh River, such as ENSO and solar activity. The linkages of our reconstruction with sea surface temperature in the northern , eastern equatorial , and equatorial Atlantic Ocean suggest the connection of regional streamflow variations to large-scale atmospheric circulation. We also find that there is the relationship between regional /streamflow variations in inner Asia and the interac- tion of the mid-latitude Westerlies and Asian summer . Our 511-year streamflow reconstruction provides a long-term perspective on current and twentieth century wet and dry events in the Irtysh River basin, is useful to guide predictions of future variability, and aids future water resource management.

Electronic supplementary material The online version of this article (doi:10.1007/s10584-016-1814-y) contains supplementary material, which is available to authorized users.

* Feng [email protected]

1 Key Laboratory of Tree-ring Physical and Chemic Research of Meteorological Administration/ Key Laboratory of Tree-ring Ecology of Uigur Autonomous , China Meteorological Administration, No 46 Jianguo Road, Urumqi, China 2 Department of Environmental Science, William Paterson University, Wayne, NJ, USA 3 Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USA Climatic Change

Keywords Irtysh River. Tree rings . Streamflow reconstruction . Sea surface temperature . Atmospheric circulation

1 Introduction

Global warming is exacerbating immediate and negative effects on the hydroclimatic variations and of Asia, and it is threatening the livelihood of billions of people (Cook et al. 2010;Dai2011; Huang et al. 2015a). The accelerated pace of glacier retreats in the high altitude mountain areas of inner Asia with global warming raises concerns about the sustained supply of fresh water to meet increasing water consumption (Yao et al. 2004; Niederer et al. 2008; Oberhänsli et al. 2011; Siegfried et al. 2012;KulkarniandKaryakarte2014). Since many international rivers of China provide fresh water for adjacent countries of central and , fluctuations in China’s climate, glacier and streamflow can have wide-ranging geopolitical consequences. However, hydroclimatical records that provide information on hydroclimatical variability are short for China (eg. Perhaps just give an estimate of years here) , especially in river basins that cross political boundaries, such as Irtysh, , Brahmaputra and Rivers. Extended hydroclimatical records for international rivers basin are thus critically important for evaluating the variability of water resource and developing suitable water resource management policies for China and neighboring countries. Tree rings play an important role in our understanding of past hydroclimatical variability in Asia. An important development is the reconstruction of the hydroclimate in Monsoonal Asia, the so-called Monsoon Asia Drought Atlas (MADA) developed by Cook et al. (2010). Careful and prudent water resource management requires detailed and reliable knowledge about streamflow variations on annual to centennial time-scales, in addition to standardized values of PDSI. Several tree-ring chronologies also have been developed from inner Asia in recent decades that capture long-term streamflow variation (Davi et al. 2006, 2013;Yangetal.2012; Gou et al. 2010;Cooketal.2013;Pedersonetal.2013a, b). These streamflow reconstructions make it possible to describe the long-term streamflow history across inner Asia that was not possible using the recorded data alone, however, for the huge area of inner Asia, the number of tree-ring based streamflow reconstructions is low and international rivers basin in China have yet to be developed. The Irtysh River is one of the main international rivers in China and it drains an extensive area from 47° N to 61° N (Fig. 1). From its origins as the Black Irtysh in the Altay Mountains in , China, the Irtysh flows northwest through in , where it meets the and rivers before merging with the River near Khanty-Mansiysk in western , after 4, 248 km. The Ob-Irtysh River makes up the seventh largest river in the world, and is the only international river in China flowing into the Ocean. Reservoirs and the Irtysh––Ürümqi have made extensive irrigated possible in the arid region of north Xinjiang, and have provided an improved water supply to more than 4 million people (Tan and Feng 2003). The Upper Irtysh River Basin has a continuous gauging station record beginning in January 1958, however, streamflow has not been reconstructed to date. In this study we develop a tree-ring based streamflow reconstruction for the Upper Irtysh River Basin. We analyze the temporal variations in the streamflow and put recent streamflow variations and trends in the context of the past six centuries. To facilitate the use of the reconstruction for water resource management, we also analyze the frequency, intensity, and Climatic Change

Fig. 1 Map of the sample sites, weather stations, and hydrological station (Kuwei) in the study area

duration of drought and pluvial events and compare the reconstructed values to those observed during the instrumental period. Finally, to identify the major climatic forcings influencing streamflow, we compare the Irtysh River reconstruction with atmospheric circulation.

2Dataandmethods

2.1 Geographical settings

The headwater area of the Irtysh River is located in the Altay Mountains in China. In this region trees were sampled between 1130 and 2145 m a.s.l., mountain peaks are up to 4374 m a.s.l. and small alpine glaciers can be found at high elevation. The mean annual precipitation from the Fuyun meteorological station (46°59′N, 89°31′E, 826.6 m a.s.l.) during the period 1962–2010 is 189.7 mm and the mean annual temperature for the same period is 3.0 °C. Snowfall usually lasts 6 months from October to March (Fig. 2a). July is the hottest month (average temperature 22.2 °C) while January is the coldest month (average temperature − 20.5 °C). This area is one of the coldest places in China during the winter. The mean annual streamflow from the Kuwei hydrological station (47°20′N, 89°41′E, 1200 m a.s.l.) in the Upper Irtysh River Basin is 307.1 m3/s during the period 1958–2008. The seasonal distributions of precipitation and streamflow differ somewhat, but both increase rapidly from April to June (Fig. 2b). However, there is only one peak (in June) in the monthly distribution of streamflow, while there are two peaks (in July and November) in precipitation. The streamflow peak is directly related to the meltwater input from snowpack in the higher elevations of the watershed during the warm season. Both temperature and precipitation showed the significant upward trends (Fig. 2c), and no significant upward trends was found in the annual streamflow (Fig. 2d). The dominant vegetation type in low altitude areas of the Altay Mountains is semi-arid with scattered tree cover. The tree species used in our study is spruce (Picea obovata) Climatic Change

Fig. 2 a Monthly total precipitation and monthly mean temperature at the Fuyun meteorological station. b Monthly streamflow data at Kuwei hydrological station and monthly total precipitation at the Fuyun meteoro- logical station. c Comparison between observed annual precipitation and temperature from 1962 to 2010. d Observed annual streamflow from 1957 to 2008 and larch (Larix sibirica). They typically occupy the low altitude areas of the Altay Mountains and grow on thin, rocky with limited water-holding capacity. Shrubs, grasses, and herbs are scattered in the understory of the forests.

2.2 Tree-ring network

Spruce (Picea obovata) trees were sampled at seven sites (QBL, TLD, XSK, SEE, XTK, KYS and DEN) in the western slope of the Altay Mountains for the analyses performed herein (Fig. 1). At least two increment cores were taken from each living tree that was sampled, and cores sections were taken from available dead wood. All sampling was performed in open stands growing on shallow or rocky . In total, these seven spruce sites provide 356 samples taken from 189 trees. Additional tree-ring width data from larch trees from three sites on the eastern slope of the Altay Mountains () were obtained from the National Climatic Data Center (http://www.ncdc.noaa.gov/): Ankhny Khoton (AK), Khovd Golgi (KG), and Khoton Nuur (KN) (Davi et al. 2009). This tree-ring network covered most of the headwater area of the Irtysh River. Site information, including latitude and longitude, slope and cores/trees is listed in Table 1. The cores were mounted and prepared following standard procedures (Stokes and Smiley, 1968). Annual ring widths were measured to a precision of 0.001 mm with a TA Unislide Measurement System (Velmex Inc., Bloomfield, New York). The program COFECHA was used to test the accuracy of visual crossdating and measurement of ring widths (Holmes 1983). We used negative exponential curve in the ARSTAN program to remove age-related growth trends from individual series (Cook and Kairiukstis 1990). The detrended data from individual tree cores were combined into site chronologies using a bi-weight robust mean (Cook and Kairiukstis 1990)(TableS1). Due to high mean correlation with the master series (r =0.58) Climatic Change

Table 1 Site information including; location, elevation, aspect and slope, number of trees and cores, tree species, and source of data. Locations of the corresponding meteorological and hydrological stations in the Altay Mountains are found in Fig. 1

Site Lat. (N) Long. (E) Elevation (m) Aspect Slope Cores/Trees Species Source

TLD 47°49′ 89°00′ 1260–1280 W 30–40° 57/29 Picea obovata 1 XSK 47°42′ 88°59′ 1130–1280 EN 5–40° 46/26 Picea obovata 1 SEE 47°35′ 88°48′ 1155–1167 NW 0–15° 48/25 Picea obovata 1 XTK 47°41′ 89°06′ 1667–1700 S 30–45° 51/28 Picea obovata 1 KYS 47°31′ 89°39′ 1590–1660 E 10–40° 51/27 Picea obovata 1 DEN 47°25′ 89°38′ 1430–1460 E 10–40° 62/34 Picea obovata 1 QBL 48°00′ 87°36′ 1204–1215 S 0–10° 41/20 Picea obovata 2 KN 48°30′ 88°30′ 2145 Larix sibirica 3 KG 48°30′ 87°48′ 2021 Larix sibirica 3 AK 48°36′ 88°22′ 2121 Larix sibirica 3 Fuyun 46°59′ 89°31′ 826.6 Kuwei 47°20′ 89°41′ 1200

1 = Chen et al. 2014. 2 = This work. 3 = Davi et al. 2009 and high correlations among site chronologies (Table S2), all detrended ring-width series from the ten sites were combined into the regional chronology (RC) using a bi-weight robust mean. The variance in chronologies was stabilized in the chronology compilation process with the Briffa Rbar-weighted method (Osborn et al. 1997). The statistic of expressed population signal (EPS, Wigley et al. 1984) was used with a threshold value of 0.85 to determine the reliable period for which the chronology is sufficiently replicated. We use the standard version of the regional chronology for our final analyses.

2.3 Hydrometeorological data and analysis methods

The streamflow record from the Kuwei hydrological station (47°20′ N, 89°41′ E, 1200 m a.s.l.) reflects a relatively natural flow as there are no upstream of this station and there are no farms or industry in the headwater area of the Irtysh River (Fig. 1). There are a small number of Kazakh herdsmen living in the surrounding mountain areas, thus, the influence of human activities on the streamflow record at the gauging location is very limited. Correlation analysis indicates that annual (August–July) streamflow of the Irtysh River at Kuwei correlates well (r = 0.73, p < 0.001) with August–July total precipitation of Fuyun. The result shows that the Irtysh River streamflow is closely related to local precipitation in its headwater region. We first screened the tree-ring data in correlation analysis with the hydrometeorological records from prior July to current September. Correlations between the seasonal combinations of monthly hydrometeorological factors (precipitation and streamflow) and tree-ring series were also evaluated in order to determine the most appropriate season to develop for the hydrometeorological reconstructions. Simple linear regression models were used to reconstruct the hydrometeorological variations of the Upper Irtysh River. A split calibration-verification scheme was employed to test the reliability of the streamlow reconstruction (Cook and Kairiukstis 1990). There are 50 years of instrumental streamflow data; the calibration and Climatic Change verification periods were split into 30 (1959–1978 and 1979–2008) and 20 (1959–1978 and 1989–2008) years. Thirty years of data were used for calibration, and 20 years data were used for verification. Verification statistics used included the reduction of error (RE) and coefficient of efficiency (CE) and the sign test (ST) (Cook and Kairiukstis 1990). We determined periods of high and low flow for the Irtysh River reconstruction using 20-year low-pass values, where reconstructed periods were noted that were higher/lower than the long-term recon- structed average from AD 1500 to 2010 continuously for more than 10 years. The multi- taper spectral (Mann and Lees 1996) and wavelet analysis (Torrence and Compo 1998) was employed to examine the characteristics of reconstructed series in the frequency domain. To demonstrate that our reconstruction represents regional drought variations and explore teleconnections, we conducted spatial correlations of our streamflow reconstruction with the gridded self-calibrating Palmer Drought Severity Index (scPDSI, van der Schrier et al. 2013) and sea surface temperature (SST) dataset of HadISST1 (Rayner et al. 2003) for the period 1950–2010 by the use of the KNMI climate explorer (http://climexp.knmi. nl). To establish whether our streamflow reconstructions exhibited links with large-scale atmospheric circulations, composites of May–September 500 hPa vector wind anomalies from the 1981–2010 mean were created for the highest and lowest deciles of reconstructed streamflow (n = 10) during the period 1948–2010. In order to further reveal the large- scale hydrometeorological characteristics in inner Asia, we extracted the first principal component of the streamflow and drought reconstructions of Xinjiang and Mongolia (Davi et al. 2006, 2013;Pedersonetal.2001, 2013a, b) using principal component analyses (Jolliffe 2002).

3Results

3.1 Streamflow reconstruction

As shown in Fig. 3a, the temperature in prior September and current May–June is negatively correlated with tree growth. In contrast, the precipitation during prior July–August, prior December, and current May–July is positively correlated with tree growth (Fig. 3d). Clearly, both temperature and precipitation have some significant impacts on tree growth in the study area. The tree-ring index significantly correlates (p < 0.01) with river streamflow of July– December of the previous year. Much higher positive correlations were seen between tree rings and current June–September (Fig. 3c), particularly from June to July, and highest positive correlation (r = 0.696, p < 0.001) was seen between tree ring index and August–July streamflow. The growth of the trees in our study area is also clearly limited by climate variation, especially those related to annual (August–July) precipitation (r =0.659, p < 0.001). Given the high correlation between streamflow and precipitation in our study area, it is not surprising that tree growth is well correlated with streamflow. Based on the correlation analyses, the reconstruction was developed by calibrating tree rings with total August–July streamflow. RE (0.608 and 0.282) and CE (0.524 and 0.126) are − − both positive. The results of sign test (19+/1 ) and (15+/5 ) are both at the 0.05 confidence level (Table S3). The statistics of calibration and verification test results showed a good model fit. The reconstruction explained 48.4 % of the actual streamflow variance during 1958–2008 (Fig. 3d). Meanwhile, as can be seen in Fig. 3e, the reconstructed precipitation fits very well with the original precipitation curve, except for some extraordinary high value points. Based Climatic Change

Fig. 3 a-c Correlation coefficients from the regional chronology with monthly total precipitation (1962–2010), mean monthly temperature (1962–2010), and monthly streamflow (1958–2008). The coefficients were calculated from the previous July to the current September. The dotted lines indicate significant variables (p<0.05). d Scatter plot of observed and reconstructed streamflow of the Upper Irtysh River showing the linear relationship. e Scatter plot of observed and reconstructed annual (August–July) precipitation of Fuyun showing the linear relationship on the EPS threshold value (0.85), the streamflow reconstruction used in further analyses was truncated prior to AD 1500 (Fig. 4a).

3.2 The characteristics of the streamflow reconstruction

Mean August–July streamflow of the Upper Irtysh River over the period AD 1500–2010 is 302.58 m3/s. According to the streamflow reconstruction, the low and high streamflow periods were showed in Table 2. Extremely low streamflow years (≤2standard deviation) occurred in AD 1506, 1570, 1602, 1645, 1646, 1811, 1812, 1885, 1945 and 1951 (Table 2). As shown in Fig. 4c, low-frequency peaks were found at 22.8 yr. (95 % level) and 13.8-yr. (95 %). Other significant peaks were found at 7.4-yr. (95 %), 5.4-yr. (95 %) and 2.1–3.0-yr. (95 %). Temporal characteristic of the different cycles is illustrated in Fig. 4d. The wavelet indicates significant power at 13–20 years for three periods centered AD 1630, 1800 and 1870, and significant (though less robust) power at 50–60 years is also detected. As shown in Fig. 5a, the reconstructed streamflow significantly correlate with the gridded August–July scPDSI data in most part of the Irtysh River Basin over the period AD 1950–2010, and the higher correlations still focus on the Altay Mountains. These results indicated that the streamflow reconstruction also represents moisture variations for a large territory in the Irtysh River Basin. Significant (p < 0.05) negative correlations with June–August temperature (Mitchell and Jones 2005) were also found in the Irtysh River Basin (Fig. 5b). Over the common period of 1950– 2010, distinct features include the significant (p < 0.05) correlations between the streamflow reconstruction and sea surface temperature (SST, Rayner et al. 2003) in the eastern equatorial Pacific Ocean, the northern Indian Ocean, and equatorial Atlantic Ocean (Fig. 5c). Climatic Change

Fig. 4 a Reconstructed streamflow of the Upper Irtysh River. The bold line was smoothed with a 20-year low pass filter. Central horizontal line shows the mean of the estimated values; inner horizontal lines (dotted lines) show one standard deviation, and outer horizontal lines show two standard deviations. The date 1500 indicates the EPS cut-off of 0.85 (tree number ≥ 6). b Plot of the running expressed population signal (EPS) and the running series of average correlation (Rbar). c MTM spectral density of reconstructed streamflow for the the Irtysh River. The bold line indicates the null hypothesis; the dash and dotted lines indicate the 90 and 95 % significance level respectively. d Wavelet (Morlet 6.0/6) power spectrum of the streamflow reconstruction (1500– 2010), with contour levels chosen to be at 75, 50, 25, and 5 % of the wavelet power above each level. Black contour is the 10 % significance level using a red-noise (autoregressive lag-1) background spectrum

4 Discussion

4.1 Influence of natural forcing on the upper Irtysh River streamflow

In the low elevation forests of the Altay Mountains, the relationship between tree growth and precipitation/streamflow can be described as a linear relationship, with a strong common drought signals captured by tree rings from larch and spruce (Davi et al. 2009;Chenetal. 2014). Negative correlation with temperature and positive correlation with rainfall during the growing season relate to the output (evapotranspiration) and input (precipitation) processes that determine soil water availability and streamflow. When temperature rises in spring and summer (May–July), the larch and spruce trees need more water for earlywood growth, so Climatic Change

Table 2 Summary characteristics of reconstructed streamflow of the Upper Irtysh River

Lowest m3/s Highest m3/s 5-year low m3/s 5-year high m3/s Low High streamflow streamflow streamflow streamflow streamflow streamflow year year event event period period

1506 164.1 2000 442.1 1883–1887 224.4 1802–1806 400.1 1512–1526 1500–1511 1945 180.5 1960 428.1 1566–1570 226.8 1958–1962 378.6 1545–1606 1527–1544 1812 181.6 1806 420.9 1810–1814 228.7 1613–1617 371.1 1641–1653 1607–1640 1570 190.6 1804 418.8 1974–1978 232.9 1608–1612 364.2 1713–1722 1654–1677 1885 191.6 1784 411.7 1643–1647 233.1 1743–1747 363.7 1735–1737 1685–1712 1602 191.8 1776 416.0 1601–1605 233.3 1773–1777 363.5 1751–1768 1723–1734 1646 193.1 1616 418.8 1947–1951 242.7 1998–2002 360.8 1785–1796 1738–1750 1645 193.9 1609 416.2 1756–1760 243.9 1669–1673 360.6 1809–1829 1769–1784 1811 195.5 1510 425.4 1520–1524 247.6 1869–1873 357.0 1876–1889 1797–1808 1951 195.9 1504 411.9 1579–1583 248.7 1527–1531 353.0 1900–1910 1830–1875 1943–1954 1911–1942 1966–1985 1955–1965 1986–2005

higher precipitation would be expected to contribute to a wider ring, low temperature and high streamflow. On the other hand, if soil water availability decreases under warming conditions, tree growth could slow down, producing a narrow ring and low streamflow. As both PDSI and streamflow combine the influence of precipitation and temperature (evapotranspiration losses), high correlations between tree-ring width data and PDSI/streamflow are not surprising. Several temperature-sensitive tree-ring density series have been developed from upper treeline (head- waters area) in the Altay Mountains (Chen et al. 2012). The significant negative correlations (r = −0.213, p <0.001,n = 375) of tree-ring density series with the reconstructed streamflow support the connection temperature and streamflow. However, this relationship was weak in the Altay Mountains during the (Büntgen et al. 2016), and the impacts of temperature on regional drought and streamflow have been enhanced during current warm period (Fig. 5d). This means that the strength of this relationship is influenced by climate change. However, the uncertainties in the degree of climate warming (Loaiciga et al. 1996; IPCC 2007) would increase the difficulty of hydroclimatic prediction. During the wettest years, mean 500 hPa winds exhibit strong northerly and northwesterly flow over inner Asia. Temperature and precipitation composites suggest that extremely high reconstructed streamflow years are characterized by wet and slightly cooler than average conditions. This is consistent with high-level transport of cool and wet air from , which should provide adequate moisture for inner Asia. Meanwhile, this atmospheric circulation pattern generates an anomalous southerly over east China and, and increases gain water vapor from the low-mid latitude oceans (Fig. 5e). During the driest years, the opposite pattern occurs (Fig. 5f). The decadal cycles (22.8 yr. and 13.8-yr) suggests the influence of solar activities on the Irtysh River streamflow (Fig. 6a), and resembles other findings in surrounding areas (Hale 1924;Davietal.2006). The 2.1–5.4 year peak cycle falls within the overall bandwidth of natural climate oscillations, such as El Niño–Southern Oscillation (ENSO) (Li et al. 2011), suggesting a possible connection between regional streamflow and large-scale atmospheric Climatic Change

Fig. 5 a Spatial correlation fields of reconstructed streamflow of the Upper Irtysh River with regional gridded August–July scPDSI for the period 1950–2010. The numbers 1, 2, 3, 4, 5, 6 and 7 denote the locations of the Upper Irtysh River (this study), western Tien Shan (Chen et al. 2013), Hutubi (Chen et al. 2015), Urumqi (Chen et al. 2016), Yeruu River (Pederson et al. 2013a, b), (Davi et al. 2013) and Selenge River (Davi et al. 2006). b Spatial correlation fields of reconstructed streamflow with regional gridded May–July temperature for the period 1950–2010. c Correlation patterns of reconstructed streamflow with the concurrent Indo-Pacific SSTs over their overlapping periods (1950–2010). d Running 51-year correlation between the reconstructed streamflow and temperature-sensitive tree-ring density series (Chen et al. 2012). The dotted lines indicate significant variables (p<0.05). Composite anomaly maps of 500-hPa vector wind for the 10 wettest (e)and10 driest (f) years for reconstructed streamflow during the period 1948–2008 systems (Fig. 6b). Using tree-ring records from Tien Shan, a possible mechanism was proposed for the correlations of hydroclimatical variability in northern Xinjiang with SST in the eastern equatorial Pacific Ocean and the northern Indian Ocean (Li et al. 2010). During the drought period, the SST anomaly is negative in the tropical eastern Pacific and the northern Indian Ocean but positive in the extratropical North Pacific Ocean, indicating a cool phase of ENSO (La Niña), and vice versa (Li et al. 2010). The significant correlations of the reconstructed

Fig. 6 a Cross-wavelet transform between the reconstructed streamflow of the Upper Irtysh River and sunspot b number series. b Cross-wavelet transform of the reconstructed streamflow and ENSO index (Li et al. 2010). c Cross-wavelet transform between the reconstructed AMO index (Gray et al. 2004) and Xinjiang series. d Cross- wavelet transform between the Mongolia and Xinjiang series. e Cross-wavelet transform between the reconstructed APO (Zhou et al. 2009) and AMO index. The 5 % significance level against red noise is shown as a thick contour. The relative phase relationship is shown as arrows (with in-phase pointing right, anti-phase pointing left) Climatic Change Climatic Change streamflow with SSTs in the Pacific Ocean, the Atlantic Ocean and the Indian Ocean support such a connection (Fig. 5c).

4.1.1 Regional- to large-scale hydroclimatic comparisons

Several tree-ring based drought and precipitation reconstructions from Tien Shan (Xinjiang) have recently been developed (Chen et al. 2013, 2015, 2016). Comparisons reveal that regional dry conditions (with low streamflow periods, Table 2) during AD 1566–1570, 1579–1583, 1601–1605, 1643–1647, 1756–1760, 1810–1814, 1883–1887, 1947–1951, 1974–1978 found in this study occurred synchronously in Tien Shan (Fig. 7). The streamflow, drought and precipitation reconstructions from Xinjiang exhibit an upward trend during the 1980–2000s. The principal component analyses (PCA) indicated that the first principal component of the four tree-ring records of Xinjiang have eigenvalues >2.4 and account for 60.89 % of the total variance for the common period AD 1580–2005. The strong common signals suggest that our reconstruction represents large-scale hydroclimatic variations of Xinjiang. Using instrumental records, a possible mechanism was proposed for the significant correlations between precipitation variations in Xinjiang and the atmospheric circulations of mid-latitude (Dai et al. 2013; Huang et al. 2015b). During the positive Atlantic Multidecadal Oscillation (AMO) phase (with negative North Atlantic oscillation (NAO)), precipitation increases in as a result of increased eastward water vapor transport from to central Asia, which causes an increase in water column vapor content in Xinjiang (Dai et al. 2013;Chenetal.2013; Huang et al. 2015b). The in-phase relationship between the reconstructed AMO index (Gray et al. 2004) and Xinjiang series on timescales of 50–100 years supports this connection (Fig. 6c), especially at AD 1750–1830s and 1940– 2000s. Based on tree-ring width data, several streamflow reconstruction also have been developed from Mongolia, and the first principal component of Mongolia streamflow reconstructions (Davi et al. 2006, 2013;Pedersonetal.2001 & 2013a, b) account for 64.29 % of the total variance during the common period AD 1680–1997. Comparison of Xinjiang series with Mongolia series reveals no systematic relationship, likely related to the regional hydroclimatic character of and different natural forcings. The cross-wavelet transform analysis, however, indicate an anti-phase relationship during the full period on timescales of 50–70 years (Fig. 6d). Due to significantly influences of Asian-Pacific Oscillation (APO, Zhou et al. 2009) and AMO on the activities of the Asian summer monsoon and the mid-latitude Westerlies, by using the cross wavelet transform analysis, we analyzed relationships between APO and AMO. We found strong anti-phase relationship between APO and AMO on timescales of 50–70 years (Fig. 6e). Climate proxy records based on pollen and lake levels (reconstructions of much longer time scales) also have shown a contrasting trend in moisture variation between the monsoon-dominated and westerly dominated areas (Chen et al. 2008). Seen together, considering the locations of Xinjiang and Mongolia, the anti-phase relationship between hydroclimatic variations in these is linked with the result of the interactions between the mid-latitude Westerlie and the Asian summer monsoon. However, based on this study and previous studies (Fang et al. 2010), the positive correlations in AD 1750–1830s and 1940–2000s on the decadal scale (Fig. 7e), show that there are positive impacts of the AMO on drought/streamflow of inner Asia, especially for Mongolia (Fig. 5e; Fig. 6c). Therefore, the impacts of mid-latitude Westerlies and the Asian summer monsoon on drought of inner Asia are more complicated than expected, and the complexity might be associated with a number of Climatic Change

Fig. 7 Plots of the discharge reconstruction of the Upper Irtysh River (a), the Urumqi precipitation reconstruc- tion (b,Chenetal.2016), the PDSI record of the central (c,Chenetal.2015)andwesternTienShan(d,Chen et al. 2013); all transformed into z-scores for easy comparison. Shaded areas indicate 5-year lowest and highest streamflow periods of the Upper Irtysh River. e Comparisons between 20-years smoothed Xinjiang and Mongolia series over the common period 1680–1997 unknown physical processes. Further investigation is needed to better understand the associa- tions and the mechanism.

5 Conclusions

Based on the EPS statistic, a regional tree-ring chronology from AD 1500 to 2010 was developed by merging tree rings of ten sampling sites in the Altay Mountains. This record was employed to reconstruct the streamflow of the Upper Irtysh River from previous August to Climatic Change current July. The reconstructed streamflow explained 48.4 % of the actual variance for the common period of 1958–2008 and showed a good model fit. With rising temperatures as a result of global warming, an increasing negative impact on regional streamflow and drought variations is expected. Significant spectral peaks were found, which all fall within the bandwidths for natural climate oscillations, such as ENSO and solar activity. The spatial correlation patterns with Indo-Pacific SSTs suggest linkages of regional streamflow variability with large-scale ocean–atmosphere–land circulation systems. Based on analysis of synoptic climatology associated with some extreme dry and wet years, we think that high streamflow (wet years) is characterized by cool conditions and strong northwesterly and northerly flow over inner Asia; during the dry years, the opposite pattern occurs. Some contrasting trends between Xinjiang and Mongolian reconstructions were found, and suggest interactions between the mid-latitude Westerlies and the Asian summer monsoon.

Acknowledgments This work was supported by the National Science Foundation of China (No. 41275120 and 91547115),ExcellentYouthScienceandTechnologyInnovationPersonnelTrainingProjectofXinjiangUygur Autonomous Region (qn2015yx040), and the Young Talent Training Plan of Meteorological Departments of China Meteorological Administration. Particular thanks are extended to the reviewers for their valuable sugges- tions and comments regarding the revision of the manuscript.

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