Temporalandspati_省略_emihumidEastAsia_HUA
? The Author(s) 2013. This article is published with open access at https://www.wendangku.net/doc/0817954906.html,
https://www.wendangku.net/doc/0817954906.html,
*Corresponding author (email: hactgexin@https://www.wendangku.net/doc/0817954906.html,)
Article
Geography
November 2013 Vol.58 No.33: 4143-4152
doi: 10.1007/s11434-013-5959-z
Temporal and spatial variations in the Palmer Drought Severity Index over the past four centuries in arid, semiarid, and semihumid East Asia
HUA Ting *, WANG XunMing, ZHANG CaiXia & LANG LiLi
Key Laboratory of Desert and Desertification, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China
Received January 26, 2013; accepted May 23, 2013; published online July 5, 2013
Based on a database of 106 annually resolved tree-ring chronologies and 244 Palmer Drought Severity Index (PDSI) grid data, we attempted to reconstruct gridded spatial drought patterns in each year over the past four centuries in the arid, semiarid, and semi-humid East Asia. The results showed that these regions mainly experienced drought events during the periods from AD 1601 to AD 1652, AD 1680 to AD 1718, AD 1779 to AD 1791, AD 1807 to AD 1824, AD 1846 to AD 1885, and AD 1961 to AD 1999. In the middle of the 16th century, severe droughts occurred mainly in North China; during the period from AD 1876 to AD 1878, droughts occurred in most parts of northern China; and from the 1920s to 1940s, catastrophic drought events spread across almost all of northern China and Mongolia. These historical drought events caused severe ecological and environmental problems and substantially affected the development of human society. In these regions, temperature and summer monsoon precipitation are the main factors influencing drought events. In western areas, PDSI and temperature exhibit a close relationship, whereas in eastern areas, summer monsoon rainfall is the dominant factor influencing variations in PDSI.
East Asia, Palmer Drought Severity Index (PDSI), dendrochronology, drought event, forcing factors
Citation: Hua T, Wang X M, Zhang C X, et al. Temporal and spatial variations in the Palmer Drought Severity Index over the past four centuries in arid, semiarid,
and semihumid East Asia. Chin Sci Bull, 2013, 58: 4143-4152, doi: 10.1007/s11434-013-5959-z
Although recently Sheffield et al. [1] suggested that drought events on a global scale actually showed little change over the past 60 years, it is widely believed that in arid, semiarid, and parts of semihumid regions, increasingly frequent and intensified drought events have been triggered by recent climate change [2–6]. The arid, semiarid, and semihumid East Asia (including northeastern China, part of North Chi-na, Mongolia, northwestern China, and part of central Asia) lies in the eastern portion of the Afro-Asian Arid Zone, and climate changes in this region have profound impacts on global climate system through the atmosphere general cir-culations. This region has high annual evaporation and low annual precipitation (mainly less than 450 mm with the hu-midity indices ranging from 0.05 to 0.65) with great inter-annual variability [7], and the ecosystem is so fragile that frequent historical occurrences of drought events have cau- sed tremendous problems such as desertification [8–10], agriculture failures [11], and sandstorms [12–14], as well as many other environmental issues [15]; therefore, recon-structing and researching historical spatiotemporal varia-tions in drought events in this region is of great interest to researchers studying climatology, ecology, history, and en-vironmental issues.
Because of the limited length and distributed range of observational records in arid, semiarid, and semihumid re-gions, many current studies of the history of drought in parts of Mongolia [16], northern China [8,17,18], and west-ern East Asia [19,20] are based on evidences of tree-ring series [21–24], multi-indicators of lake sediments [19,25], and historical documentary records [26–28]. These studies
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either analyze the time series of dryness and wetness in a single location [16,29,30] or use areal average/principal pattern to represent changes in dryness of the entire region [20], however, very few studies focus on a highly resolved spatiotemporal reconstruction and comprehensive integra-tion of droughts. Therefore, in the present study, we used high-resolution tree-ring width chronologies and Palmer Drought Severity Index (PDSI) grid data to reconstruct the spatial patterns of drought and their evolutionary histories in the arid, semiarid, and semihumid regions of East Asia from AD 1601 to AD 1999 and to explore the possible influences of potential forcing factor on droughts.
1 Data sources and reconstruction methods
1.1 PDSI grid data
PDSI data were obtained from calculations of surface ob-servational temperature and precipitation records to measure variations in soil moisture content that depend on surface water supply and demand. PDSI has been widely used to indicate meteorological and agricultural droughts [31,32], regional changes in dryness and wetness, and drought events in different historical periods. We used the 2.5°×2.5° spatial resolved PDSI grid data developed by Dai et al. [33], which cover the arid, semiarid, and semihumid regions of East Asia (Figure 1), as the main data sources. All of the 244 PDSI grid data span the common interval from AD 1953 to AD 1999. Since our study area includes some higher-latitude regions, where the monthly mean temperature in spring is very low, and the surface is always covered by snow and ice, which will affect the indications of PDSI because of their deficiencies in considering the effect of thawing snow and ice in spring and in representing the moisture condition of snowcapped surfaces [33]. In addition, since the precipita-tion in summer may play the most important role in crop and grass growths and the drought events occurred in this season had more impacts on ecological environments, we thus only used the PDSI series for the warm season (June to August). Generally, the PDSI decreases as aridity intensifies and increases as climate gets wet. This index is a standard-ized measurement with a specific range from 10 to 10; therefore, PDSI values sampled from a wide spatial distri-bution could be compared to obtain a reliable spatial pattern in a given year as well as variations in these spatial patterns [34].
1.2 Tree-ring dataset
We developed a proxy network consisting of 150 annually resolved tree-ring width chronologies over arid, semiarid, and parts of semihumid East Asia from the International Tree-Ring Data Bank (ITRDB) (https://www.wendangku.net/doc/0817954906.html,/ paleo/treering.html) and other literature (Figure 1, Table S1); the details of detrending methods are provided in the source publications (Table S1). To reconstruct historical dryness (or PDSI) based only on variations in tree-ring chronologies, a close relationship should be established between tree-ring records and PDSI. Here, we performed one-tailed Pearson correlation tests for each tree-ring chronology and its near-est PDSI grid data to check the relationships between them. Chronologies with statistically significant positive correla-tions remained unchanged and were used directly in the study; those showing significant negative correlations were also acceptable because we could use the opposite series of the original chronologies to keep identical trends between the adjusted chronologies and PDSI in at least the common period. However, chronologies that failed to pass the corre-lation tests were eliminated from further calculations. We found that most chronologies were statistically significant at the 0.01 level but that some chronologies were significant at the 0.05 level (Table S2). Eventually, 106 chronologies that had passed the correlation significance tests were selected for the study.
The source tree-ring data were of varying time lengths, ranging from 130 to 800 years, and the number of chronol-ogies declined backward in time (Figure 2). Therefore, dur-ing the reconstruction procedure, the time-varying tree-ring
Figure 1Arid, semiarid, and semihumid East Asia and distributions of PDSI grid data (gray squares). Locations of tree-ring chronologies are shown as symbols in terms of the year they extend back to (at a minimum) AD 1867 (stars), AD 1801 (triangles), AD 1750 (squares), AD 1697 (diamonds), AD 1651 (crosses), and AD 1601 (circles).
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Figure 2 Variations in sample size of the true tree-ring chronologies (dashed line) and extended chronologies in the reconstruction procedure (solid line) and the seven reconstruction intervals (T 1 to T 7, blue blocks; widths and heights of the blocks indicate time spans and number of chronologies used in each reconstruction interval, respectively).
dataset was divided into seven reconstruction intervals in terms of the number of ring-width chronologies available in each year. Within an interval, chronologies that their time span exceeded the common span were removed the extra part; consequently, the time spans of the same interval were equal for a regular stepwise calculation (see Section 1.3 for details). Because more than half of the 106 chronologies ended at or after AD 1999, we used the Regularized Expec-tation Maximization (RegEM) [35] method to extend the missing data. Eventually, all of the 106 chronologies cov-ered a common period of time from AD 1953 to AD 1999. The period from AD 1953 to AD 1999 was used as the cali-bration period because it also overlapped the common peri-od of all 244 PDSI grid data; this period was also used for the verification test of the reconstructions. 1.3 Reconstruction method
The reconstruction procedure generally follows the methods described by Mann et al. [36], except for some simplifica-tions. As suggested by Mann, both two fundamental hy-potheses (linear relationships between tree-ring chronolo-gies and PDSI data, and the applicability of leading drought variation patterns derived from modern PDSI data for the past four centuries) are proved to be tenable in our study regions [37]. The reconstruction procedures include several steps, including (1) decomposition of modern (from AD 1953 to AD 1999) monthly averaged (from June to August) PDSI grid data into several principal patterns and their evo-lutionary series by means of conventional Principal Com-ponent Analysis (PCA), (2) calibration of each selected PDSI principal time series against the proxy network during the overlapped period from AD 1953 to AD 1999, (3) PDSI reconstruction in each year during the pre-1953 period using relationships from calculations in the calibration, and (4) cross-validation of the reconstructed PDSI against raw grid data (i.e. verification). During the reconstruction procedure, all of the instrumental PDSI grid data and chronologies spanned the common period from AD 1953 to AD 1999; thus, we tested the reliability of the reconstructed PDSI us-ing the raw PDSI from this period.
Specifically, from AD 1953 to AD 1999, the 244-point monthly mean PDSI grid data from June to August were
decomposed by means of PCA to generate several spatial patterns (derived from empirical orthogonal functions, EOFs) and their corresponding temporal scores (principal components, PCs). Since the first 20 pairs of EOFs and PCs accounted for the majority (87.05%) of the total resolved variance, they could represent temporal and spatial varia-tions in PDSI for the entire region. Therefore, from the first 20 pairs, we selected a few PCs that were statistically sig-nificantly correlated with each chronology and eventually used eight PCs (No. 1 to No. 8) and their corresponding EOFs in the reconstruction. We established a relationship matrix W (8, 106) between the eight PCs matrix U (47, 8) and the scattered tree ring-width sequences P (47, 106) in the overlapped time span from AD 1953 to AD 1999 (T 1= 47), which could be expressed as an over-determined equa-tions U (47, 8) W (8, 106)=P (47, 106). Then, the relation-ship matrix W (8, 106) served as the bridge from the tree-ring series to reconstructed PCs (RPCs) in each year before AD 1952, and the magnitude of the matrix W (8, N i ) decreased in each time interval (T i ) as time went back be-cause the tree-ring sample size (N i ) decreased from 106 (N 2) in the second interval from AD 1867 to AD 1952 (T 2) to 43 (N 7) in the final interval from AD 1601 to AD 1650 (T 7) (see Supplementary Material Table S3 for details of T i and N i ). These calculations could be expressed as another over-
determined equations ?U
(T i , 8) W (8, N i )=P (T i , N i ). In the process of solving seven sets of over-determined equations,
we used singular value decomposition to evaluate the least- squares solutions. Although all sets of equations had the same number of unknowns, the number of equations in each set (i.e. each time span) lessened as the number of available chronologies decreased, which would lead to different am-plitudes of change among each reconstructed PC ?U
(T i , 8) as well as the original PC U (47, 8). Thus, we scaled ?U
(T i , 8) with the ratio of standard deviations for the original PC and for RPCs in each time span so that they had an identical range of change amplitudes. These calculations worked well as long as the number of equations was larger than the num-ber of unknowns, which was always realized in every time span as suggested by Mann et al. [36]. Finally, the PDSI values of 244 grids were reconstructed in every year from AD 1601 to AD 1952 by linearly combining the eight EOFs
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and RPCs.
In addition to reconstructing the PDSI in each year be-fore AD 1953, we also recalculated the PDSI in each year from AD 1953 to AD 1999 so that we could compare origi-nal and recalculated PDSI values. Here, we computed re-solved variance for each grid to estimate the homogeneity between actual and reconstructed PDSI values [36]. The results showed that during the period from AD 1953 to AD 1999, 238 of the 244 grids passed the test on at least a 99% significance level, and the other six grids passed the test on at least a 95% significance level (Figure 3), which suggests high reliability of the reconstructions. Currently, based on the evidence of tree-ring chronologies, Cook et al. [23] used the point-to-point method to reconstruct yearly drought pat-terns of Asian monsoon regions over the past millennium; by comparison, our tree-ring dataset not only includes chro-nologies similar to those reported by Cook et al. [23] in the overlapped study area but also contains 20 chronologies sampled from northeastern China and North China, which makes our reconstruction more representative in the dryness and wetness of the arid, semiarid, and semihumid East Asia.
2 Results and discussion
2.1 The principal components analysis
The process of PCA enables us to decompose a two-dimen- sional dataset into a range of leading spatial patterns and their corresponding time series, which is an approach to reducing the dimension of the original data and is widely applied in meteorological research. Here, we decomposed the regular instrumental PDSI dataset to generate the prin-cipal spatial patterns (or EOFs) and the principal compo-nents (PCs). Both of the first two eigenvectors account for high fractions of variations (20.74% and 13.29% of the total variance, respectively); thus, these two pairs of EOFs and PCs could be used to represent the dominant spatiotemporal variations in the arid and semiarid East Asia (Figure 4). By
combining the first eigenvector and its time series, we found that the Qinghai-Tibetan Plateau and parts of higher lati-tudes showed opposite trends compared to the other regions. The former regions transferred from wetness to dryness in AD 1964 (having passed the Mann-Kendall test at a 95% significance level), which was also recorded in other litera-ture [38], whereas the latter regions transferred from dry-ness to wetness. The second eigenvector showed opposite trends in change between two sides of the meridional line around 105°E. The western side includes eastern central Asia and the Xinjiang regions as well as the western part of the Hexi Corridor, and the eastern side includes most of Mongolia, North China, and northeastern China. This mode is probably correlated with the fact that the eastern area is under influence of the East Asia summer monsoon, whereas the western area is influenced by the westerly winds [39]. PC No. 2 suggested that the western area had a significant moistening trend after AD 1980 (having passed the Mann- Kendall test at a 95% significance level) and that the eastern area had a severe drying trend at the same time; this infor-mation was also reported and analyzed in other literature [40,41].
2.2 Annual spatial patterns and history of drought
By calculating the linear combinations of selected principal spatial patterns and their reconstructed PCs, we obtained the spatial pattern of PDSI in each year over the past 400 years, which helped to depict the spatiotemporal evolution of dry-ness and wetness in the arid, semiarid, and semihumid East Asia. In particular, three well-known severe drought events
of the past four centuries, which occurred in the late Ming
Dynasty (AD 1625 to AD 1644), AD 1876 to AD 1878, and 1920s to 1940s, were all identified in our reconstructed results.
During the period from the 1630s to the 1640s, severe
Figure 3 Resolved variance (r 2) of reconstructed PDSI values to original PDSI values during the period from AD 1953 to AD 1999; colors denote values significant at the 95% (yellow), 97.5% (yellowish green), 99.5% (green), and 99.9% (blue) levels.
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Figure 4 EOFs (upper) for the first two eigenvectors and their corresponding PCs (lower) of the PDSI in arid, semiarid, and semihumid regions of East Asia during the period from AD 1953 to AD 1999.
drought events occurred in North China. These successive years of droughts [23,42,43] caused a large-scale famine [44,45], which was believed to be one of the primary con-tributors to the collapse and eventual demise of the Ming Dynasty [46]. From our reconstructions, we also found that North China, especially the Hebei Province and its sur-rounding area, suffered intensive droughts from AD 1638 to AD 1643 (Figure 5). Additionally, from AD 1876 to AD1878, most of northern China, especially Shaanxi, Shanxi, and Henan provinces [18], underwent serious droughts because of the strongest El Ni?o events of past 150 years [47]. When El Ni?o event occurred, the negative anomaly of sea surface temperature (SST) in west Pacific made the subtropical high move southward, and the consequent southward rain belt may result in occurrences of drought in the Yellow River drainage and other regions of northern China [11,48,49]. The drought event created sharp reductions in agricultural yields and caused a surge in the death toll [50,51]. Our re-construction results also revealed that severe drought events occurred in most parts of northern China during the period from AD 1876 to 1878 (Figure 6).
In addition, the great drought that occurred during the 1920s and 1940s over most of northern China and Mongolia continued for about 20 years and was regarded as one of the
Figure 5 Reconstructed PDSI patterns in the years during the late Ming Dynasty (only reconstruction of some representative years are shown due to lim-ited space).
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Figure 6Reconstructed PDSI patterns in each year from AD 1876 to AD 1878.
most severe natural hazards over the past 200 years [52,53]. This catastrophic drought caused widespread crop failures [53] and millions of deaths during the famines that followed [54]. Our reconstruction results also showed that most parts of northern China and Mongolia suffered from successive serious droughts from 1928 to AD 1930 and from AD 1937 to AD 1943 (Figure 7), which was highly consistent with historical records. Specifically, the catastrophic drought in Henan Province that occurred after AD 1941, which reached its peak in AD 1942, spread throughout the entire province. This severe drought was accompanied by the locust plague in the next year (AD 1943) [55], which altogether had a destructive impact on local lives and caused tens of thou-sands of people to die of starvation.
Although there are some uncertainties in our reconstruc-tions due to biases during the data integration process be-cause of the unevenly distributed tree-ring sites and rapid reduction in sample size going back in time, these historical, well-known drought events could be strong evidence to support our reconstruction results.
2.3 Historical drought events and possible contributors We calculated the area average of time series at all grids to represent the holistic PDSI series of the arid, semiarid, and semihumid areas of East Asia over the past 400 years (Fig-ure 8). Although all of the grids spanned several different latitudes, they were still assigned with equal weight because the area differences in the small range of latitudes are not so obvious that the grid area would be affected. The total re-constructed PDSI sequences were smoothed using a 10-year low-pass filter and are shown in Figure 8. The results showed that the arid, semiarid, and semihumid East Asia experi-enced multiple dryness and wetness fluctuations during the
Figure 7Reconstructed PDSI in the years from AD 1920s to AD 1940s (only reconstruction of some representative years are shown due to limited space).
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Figure 8 Variations in total reconstructed PDSI (solid line) and its 10-year smoothed sequence (dashed line) in arid, semiarid, and semihumid East Asia over the past four centuries. Key phases of drought are shown in gray shades.
period from AD 1601 to AD 1999: these regions suffered severe droughts from AD1601 to AD1652, AD 1680 to AD 1718, AD 1779 to AD 1791, AD 1807 to AD 1824, AD 1846 to AD 1885, and AD 1961 to AD 1999 and witnessed relatively humid climate in the periods between droughts. Since our study area covers a large extent of land with multiple climate modes, the influences of temperature and summer monsoon on dryness are complicated and may vary among the different regions, especially between the western non-monsoon regions and the eastern monsoon regions, where the climate and environment are very different. There-fore, variations in forcing factors would have more compli-cated influences on trends in PDSI over regions with and without the impact of the East Asia summer monsoon, we thus divided the study region at the meridian at 105°E (as suggested from EOF No. 2) and calculated area averages on both sides of the dividing line (Figure 9(a) and (b)).
Although several external forcing factors, such as solar irradiance variations, volcanic explosions, and changes in atmospheric greenhouse gases, could have fundamental in-fluences on variations in dryness and wetness [56–58], temperature and Asian summer monsoon would have more straightforward influences [23,59,60]. The temperature and summer monsoon history for the arid, semiarid, and semi-humid regions of East Asia has been reconstructed by other scientists [23,42,61]. Here, we used reconstructed Northern Hemisphere temperatures based on the worldwide tree-ring series by Mann et al. [36] and 18O sequence from Wan-xiang Cave to indicate Asian summer monsoon [42] (Figure 9(c) and (d)), and a running correlation test with a window width of 100 years was used to check the influences of these factors on droughts in western and eastern areas. The tem-perature series were derived by an independent tree-ring network in the present study; thus, the reliability of the test results would not change. The running window contained 100 years. The central year started at AD 1650, which cal-culated the correlation during the period from AD 1601 to AD 1700. Then, the central year moved to AD 1651, which calculated the correlation during the period from AD 1602 to AD 1701. Calculations continued until the central year
ended at AD 1931, which calculated the period from AD 1881 to AD 1980. Finally, time series of the correlation coefficients between temperature and PDSI in both western and eastern areas were obtained (Figure 9 (e)).
Figure 9(a) and (b) shows differences between the his-torical PDSI in the western and eastern areas over the past four centuries. Divergences exist between the correlation of reconstructed PDSI with temperature and with summer monsoon in both areas (Figure 9(e)): PDSI in the western area had a higher positive correlation with temperature but a lower relationship with monsoon (correlation coefficients fluctuated around zero), while PDSI in the eastern area had a close correlation with Asian summer monsoon but little correlation with temperature (correlation coefficients fluc-tuated around zero). These differences may be related to distinct regional climate characteristics. In the western area, Asian summer monsoon had little influence on dryness and wetness because this area lies beyond the scope of the Asian summer monsoon; by comparison, a rise in temperature and accompanying increase in precipitation [62] are the main reasons for variations in regional droughts and floods. As a result, PDSI in this area had a relatively higher positive correlation with temperature. In contrast, in the eastern area, although an increase in temperature would lead to relative dryness because of massive water loss accompanied by evaporation, since the eastern area falls within the scope of the summer monsoon, the strength of the summer monsoon and its associated precipitation would play a dominant role in regional warm-season dryness and wetness [63]. There-fore, the PDSI in the eastern area had a higher positive cor-relation with the summer monsoon. In addition, over around 200 years (1670s to 1880s), both temperature and summer monsoon have a significant correlation (P <0.05) with PDSI in the western and eastern areas, respectively. During the time before and after this 200-year period, both correlation coefficients failed to pass the significance test (P <0.05) at the same time, probably because PDSI during these two pe-riods are under the influences of special climate conditions. During the period from the 1600s to 1670s, being recog-nized as the Little Ice Age Maximum [64,65], the weakened
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Figure 9Variations in the average reconstructed PDSI in the (a) western area and (b) eastern area; (c) reconstructed Northern Hemisphere temperatures [36]; (d) δ 18O of Wanxiang Cave [42]; and (e) 100-year running correlation coefficients (Correl coeff) between reconstructed PDSI and temperature series (dashed lines) and δ 18O sequence (solid lines) in both areas over the past four centuries. Two pairs of light gray lines suggest thresholds at 0.1 and 0.05 sig-nificance levels.
Asia summer monsoon and low mean temperature (Figure 9(c) and (d)) may have less effective impacts on PDSI in western and eastern areas, respectively. During the latter period from the 1880s to 2000s, with the end of the Little Ice Age and the increased greenhouse effect, temperature had increased greatly, triggering enhancement of evapora-tion with massive moisture loss [66]. As a result, in the western areas, increased temperature had both positive and negative impacts on variations in PDSI, and thus there were no significant correlations between temperature and PDSI during this period; in the eastern areas, although the streng- thened summer monsoon accompanying increased precipi-tation promoted wetting trend, rises in temperature with enhanced evaporation acted reversely to dryness and wet-ness, therefore, insignificant correlations were found be-tween Asia summer monsoon and PDSI in this period. However, we list here are only conjectural explanations, and further investigative studies are still needed. 3 Conclusion
(1) The PCA shows the existence of two opposite chang-es in drought patterns in the arid, semiarid, and semihumid East Asia, including a reversed-phase change between the Qinghai-Tibetan Plateau and the other regions and between western nonmonsoon regions and eastern monsoon regions.
(2) The reconstruction results show the spatiotemporal variations of drought over the past 400 years and the extent of three notable drought event. For example, during the late Ming Dynasty, continued severe droughts occurred in North China; drought events from 1876 to 1878 took place mainly in North China and northeastern China; and the disastrous drought of the 1920s to 1940s spread throughout northern China and the Mongolian Plateau.
(3) Our results also suggest that the study regions expe-rienced dryness from AD1601 to AD1652, AD 1680 to AD 1718, AD 1779 to AD 1791, AD 1807 to AD 1824, AD
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1846 to AD 1885, and AD 1961 to AD 1999. Temperature and summer monsoon precipitation are the main contribu-tors to those variations; in the western area, temperature had a close relationship with PDSI, but in the eastern area, summer monsoon precipitation may play a dominant role in regional dryness and wetness.
Although previous studies reconstructed high-resolution temporal and spatial variations in droughts over the Asian summer monsoon region, our reconstruction may help pro-vide valuable information on historical drought in northern China because our tree-ring database consists of many chronologies sampled from northeastern China and North China, which are absent in previous literature. Our recon-struction results show that under modern global change, most of arid, semiarid, and semi-humid East Asia has expe-rienced drying trend since the 1960s, which is consistent with the increased evaporation under the global greenhouse gas- induced warming.
This work was supported by the National Natural Science Foundation of China (41225001) and the National Key Project of Scientific and Technical Supporting Program (2012BAC19B09).
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Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
Supporting Information
Table S1Descriptions of tree-ring chronologies
Table S2Correlation coefficients between each tree-ring chronology and its nearest PDSI
Table S3Details of each of seven reconstruction intervals
The supporting information is available online at https://www.wendangku.net/doc/0817954906.html, and https://www.wendangku.net/doc/0817954906.html,. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains en-tirely with the authors.
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Geography
Table S1 Descriptions of tree-ring chronologies Code
Long. (°E)
Lat. (°N)
Cores
Alt. (m a.s.l.)
Duration M.S.
Detrending
Ref. agl 120.9 51.47 47 760–825 1707–2007 0.151 & [1] ak 88.37 48.6 38 & 1469–2004 0.195
Negative exponential or linear regression
ITRDB
akt 87.58 50.42 30 2000 1601–1994 & & ITRDB altay a 87.85 48.335 51 2310–2312 1571–2000 & & [2] baga 74.58 36.03 31 3100 1593–1993 0.2298 Negative exponential or linear regression ITRDB bagb 74.58 36.03 38 3300 1369–1993 0.1786 Negative exponential or linear regression ITRDB bagc 74.933 35.9 37 3050 1438–1999 0.2730 Negative exponential or linear regression ITRDB bagd 74.933 35.9
25 3750
1240–1999
0.1827 Negative exponential or linear regression ITRDB bg 91 49.9667 22 & 1350–2005 0.2422 Negative exponential or linear regression ITRDB bgol 90.97 47.1 41 & 1375–2004 0.1618 Negative exponential or linear regression ITRDB blka 93.3 43.85 91 2810 1571–2002 0.1741 Negative exponential or linear regression ITRDB blkb 93.38 43.83 51 2840 1608–2002 0.2046 Negative exponential or linear regression ITRDB blkd 93.3 43.82 44 2380
1807–2002
0.2025 Negative exponential or linear regression ITRDB bng 100.17 38.13 41 3350–3450 1800–2003 & Negative exponential or linear regression [3] bsg b 106.08 39.08 50 1600–2000 1742–1997 0.4278 &
[4] bu 90.98 49.97 24 & 1519–2005 0.1331 Negative exponential or linear regression ITRDB byab 117.13
43.5
38 1329
1842–2004
0.27 Negative exponential or linear regression [5] byv 88.15 43.93 40 1815 1867–2004 0.2746
Negative exponential or linear regression
ITRDB bzl 124.58 51.95 57 500–900 1738–2004 0.1249 60-yr spline [6] cbc c 128.03 41.99 40 738 1764–2005 0.105 50-yr spline [7] cbpi c 128.07 42.08 34 1700 1654–1993 0.134 spline [8] cp 85.63 51 28 1450 1611–1994 & & ITRDB dqh 100.7 35 50 3755 1433–2004 0.241 Negative exponential regression [9] dqs 111.28 40.8
30 1300 1600–1996 0.34 55-yr spline
[10] dul 98
36
& 3800
159–1993 & Negative exponential or linear regression ITRDB dxal 121.43 49.93 76 1058 1776–2000 0.32 Negative exponential or linear regression [11] fst 86.45 42.75 34 2570–2650 1576–2005 0.32 &
[12] gqns 116.39
43.22
43 1363
1847–2004
0.35 Negative exponential or linear regression [5] hlbr 119.85 48.25 86 720–870 1806–2007 & cubic smoothing spline
[13] hls 108.55 35.45 45 1000–1350 1743–2003 0.27 Negative exponential or linear regression [14] hnn 86.98 48.8 58 1770–1940 1660–2002 0.1782 & [15] hrb 94.88 49.37 43 & 1516–1998 0.0994 Negative exponential or linear regression ITRDB hsg 83.6 43.67
2135 1671–2000 0.093 &
[16] htm 107.47 48.35 63
&
996–2002 0.1463 Negative exponential or linear regression ITRDB hvr 100.2
50.77 13 2300
1550–1994
0.274 Negative exponential or linear regression ITRDB ir 111.67 48.83 34 & 1265–1997 0.3323
Negative exponential or linear regression
ITRDB
jea 85.23 50.87 30 1450 1636–1994 & &
ITRDB jpk 82.92 44.1 69 2555 1669–2000 & 69-yr spline [17] jwa 85.23 50.87 30 1400 1761–1994 & & ITRDB kbl 81.72 42.8 41 2593 1686–2004
0.1156
Negative exponential or linear regression
ITRDB
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et al. Chin Sci Bull November (2013) Vol.58 No.33
T,
kedn 82.87 43.15 46 1499 1634–2004 0.1552 Negative exponential or linear regression ITRDB
1660–2000 0.1758 & [15]
kem 87.4 48.6 63 1730–1910
kg 88.8 48.7 21 & 1215–2004 0.2125 Negative exponential or linear regression ITRDB
khb 72.58 40.17 33 2900 1346–1995 0.1812 Negative exponential or linear regression ITRDB
kk 91.57 49.92 38 2500 1326–1998 0.271 Negative exponential or linear regression ITRDB
2060 1340–2000 0.358 Negative exponential or linear regression ITRDB
kl 99.87 48.17 66
kla 72.58 40.17 51 2600 1839–1995 0.2354 Negative exponential or linear regression ITRDB
spline [18]
kt 106.51 35.54 52 & 1616–2009 & 100-yr
ku 94.58 49.5 27 & 1692–1996 0.4084 Negative exponential or linear regression ITRDB
1836–2000 0.38 Negative exponential or linear regression [19]
lls 111.35 37.77 45 1740–1900
lsh 128.89 47.18 31 427 1724–2008 0.17 Negative exponential or linear regression [20]
lx 117.73 43.64 22 1197 1836–2004 0.37 Negative exponential or linear regression [5]
manz 107 47.77 35 1755 1505–1994 0.342 Negative exponential or linear regression ITRDB
mek 101.92 31.78 40 2500 1575–2007 0.2399 Negative exponential or linear regression ITRDB
mhm 100.12 46.82 20 & 1565–2004 0.3086 Negative exponential or linear regression ITRDB
miqa 87.92 43.77 52 1970 1653–2002 0.1313 Negative exponential or linear regression ITRDB
miqb 88.02 43.8 60 2080 1653–2002 0.2090 Negative exponential or linear regression ITRDB
mul 130.25 43.08 48 710 1636–2005 0.15 Negative exponential or linear regression [21]
mula 90.22 43.6 89 2250 1715–2002 0.1168 Negative exponential or linear regression ITRDB
mulb 90.1 43.6 56 2170 1829–2002 0.1327 Negative exponential or linear regression ITRDB
ndb 101.32 46.32 21 & 1411–2002 0.2085 Negative exponential or linear regression ITRDB
og 111.68 48.83 23 & 1599–2001 0.2569 Negative exponential or linear regression ITRDB
ovt 91.43 49.8667 36 & 1576–2001 0.2547 Negative exponential or linear regression ITRDB
pr 94.88 49.38 39 & 1249–1998 0.2404 Negative exponential or linear regression ITRDB
1777–2000 0.25 Negative exponential or linear regression [22]
ql 108.77 33.85 44 2600–2750
qlhw 99.95 38.45 36 2750 1767–1999 & & [23] qlmyk 99.95 38.44 46 3450 1730–1999 & & [23] qltlc 99.95 38.75 36 3100 1829–1999 & & [23]
1770–2004 0.2181 Negative exponential or linear regression [24]
qs 123.13 41.18 53 300–326
qx 82.68 43.08 42 1710 1721–2004 0.1048 Negative exponential or linear regression ITRDB
sb 100.03 47.27 33 2500 1363–1999 0.239 Negative exponential or linear regression ITRDB
1770–2000 0.16 & [25]
sd 102.22 37.95 56 300–3100
sev 86.72 43.72 46 1865 1785–2004 0.1702 Negative exponential or linear regression ITRDB
1660–2004 0.2095 & [15]
slh 86.83 48.65 51 2220–2320
sm 100.83 49.48 29 1800 1557–2002 0.574 Negative exponential or linear regression ITRDB
sxnw 112.08 38.83 27 1600–2787 1686–2003 0.25 Negative exponential or linear regression [26]
syfl 123.58 41.82 72 & 1657–2004 0.364 Negative exponential or linear regression [27]
1766–1999 0.36 Negative exponential or linear regression [28]
syk 105.87 38.62 78 2400–2600
1841 1638–1998 0.278 Negative exponential or linear regression ITRDB
tb 97.12 48.77 28
2190–2400 0.089 RCS [29]
tbk 85.1 46.95 59 2100–2400
tbl 83.7 43.63 54 2310 1671–2000 0.115 & [16]
1740–2004 & & [30] tbs 107.37 33.57 40 3400–3500
tch 88.12 43.88 49 1913 1694–2004 0.2324 Negative exponential or linear regression ITRDB
tcm 91.55 49.7 2000 1570–1995 Negative exponential or linear regression ITRDB
terel 107.45 47.9 1798–1994 & & ITRDB
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tp 98.93 48.3 90 2420 1475–1999 0.136
Negative exponential or linear regression
ITRDB ttg 86.78 43.35 50 2425 1557–2000 0.121 & [31] tyn 89.58 54.23 30 650 1613–1994 & & ITRDB ukh 85.37 50.15 24 1700 1581–1994 & & ITRDB unbst 110.55
48.57
33 1070
1651–1996
0.356 Negative exponential or linear regression ITRDB uu 103.23 48.98 27 1400 1511–2002 0.439
Negative exponential or linear regression
ITRDB
uub 87.68 50.5 24 1950 1697–1994 & & ITRDB uul 87.65 50.48 24 2150 1581–1994 & & ITRDB wuy 129.23 48.12 30 351 1738–2008 0.37 Negative exponential or linear regression [20] xc 100.27 30.23 44 4050 1306–2007 0.1283 Negative exponential or linear regression ITRDB xinl 100.28 30.87 42 3300 1663–2007
0.1860 Negative exponential or linear regression ITRDB xls 104.03 35.67 93 2370–2570 1774–2003
&
Negative exponential or linear regression
[32] xnq 120.8
51.38 51 945–980 1866–2007
0.12 & [1] xqz 87.08 43.43 51 2050–2610 1563–2000 0.09 & [31] xxv 86.3 43.78 44 1878 1706–2004 0.2454 Negative exponential or linear regression ITRDB ys a 118.66 36.28 23 860 1750–1992 0.245 48-80yr spline
[33] ysns 116.61 43.09 27 1331 1821–2004
0.29 Negative exponential or linear regression [5] yych 100.3
34.8
32 3750–3845 1614–2001
0.136 Negative exponential or linear regression [34] zm 107.5 47.78 57 1415 1582–1996 0.4 Negative exponential or linear regression ITRDB zs 100.28 48.15 36 1900 1513–2001 0.329 Negative exponential or linear regression ITRDB zt 100.95 46.52 46 & 1216–2002
0.2084
Negative exponential or linear regression
ITRDB
Note: the default chronologies are derived from full rings, those with superscript a indicate sequences being linearly reconstructed from tree-ring chro-nologies, and the superscript b denote late wood chronologies; the default types of tree ring-width chronologies are standard chronologies, those with super-script c are residual chronologies. & denotes that the data are unavailable; the “code” means the title of chronology used in present study, which is mainly given by the source literatures or ITRDB, and the term ITRDB denotes International Tree Ring Data Bank.
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Table S2Correlation coefficients between each tree-ring chronology and its nearest PDSI
Code Alt. (m asl) Duration (A.D.) Long. (°E) Lat. (°N)
Freedom PDSI_x PDSI_y Correl
Coeff
agl 760–825 1707–2007 120.9 51.47 105 121.25 51.25 0.173223776 ak 1469–2004 88.37 48.6 57 88.75 48.75 0.221813324 akt 2000 1601–1994 87.58 50.42 57 88.75 48.75 -0.293628368 altay 2310–2312 1571–2000 87.85 48.335 63 88.75 48.75 -0.3324026 baga 3100 1593–1993 74.58 36.03 82 73.75 36.25 0.364156988 bagb 3300 1369–1993 74.58 36.03 82 73.75 36.25 0.237021322 bagc 3050 1438–1999 74.933 35.9 88 73.75 36.25 0.274041297 bagd 3750 1240–1999 74.933 35.9 54 76.25 36.25 0.268067819 bg 1350–2005 91 49.9667 55 91.25 48.75 0.23257092 BGOL 1375–2004 90.97 47.1 63 88.75 46.25 0.219408885 blka 2810 1571–2002 93.3 43.85 49 91.25 43.75 -0.323991055 blkb 2840 1608–2002 93.38 43.83 49 91.25 43.75 -0.290674243 blkd 2380 1807–2002 93.3 43.82 56 98.75 36.25 0.225822793 bng 3350–3450 1800–2003 100.17 38.13 50 101.25 38.75 -0.234910058 bsg a1600–2000 1742–1997 106.08 39.08 60 106.25 38.75 0.329548689 bu 1519–2005 90.98 49.97 55 91.25 48.75 -0.258651695 byab 1363 1842–2004 117.13 43.5 63 116.25 43.75 0.419052765
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5
byv 1815 1867–2004 88.15 43.93 58 88.75 43.75 0.438681084 bzl 500–900 1738–2004 124.58 51.95 88 123.75 51.25 0.250949995 cbc a 738 1764–2005 128.03 41.99 93 128.75 41.25 0.282079277 cbpi a 1700 1654–1993 128.07 42.08 89 128.75 41.25 0.249945514 cp 1450 1611–1994 85.63 51 82 86.25 51.25 -0.184024331 dqh 3755 1433–2004 100.7 35 66 101.25 36.25 0.237045221 dqs 1300 1600–1996 111.28 40.8 77 111.25 38.75 0.325455491 dul 3100–3800 326BC–2000 98 36.17 64 98.75 36.25 0.369529804 dxal 1058 1776–2000 121.43 49.93 104 121.25 48.75 -0.302355901 fst 2570–2650 1576–2005 86.45 42.75 58 88.75 43.75 0.38078525 gqns 1331 1847–2004 116.39 43.22 63 116.25 43.75 0.366928186 hlbr
119.75 49.22 97 118.75 48.75 0.382623832 hls 1000–1350 1743–2003 108.55 35.45 83 108.75 36.25 0.425969738 hnn 1770–1940 1660–2002 86.98 48.8 87 88.75 48.75 0.319660023 hrb 1516–1998 94.88 49.37 48 96.25 48.75 0.249178699 hsg 2135 1671–2000 83.6 43.67 55 83.75 41.25 -0.244212178 htm 996–2002 107.47 48.35 66 106.25 48.75 0.282454576 hvr 2300 1550–1994 100.2 50.77 89 98.75 51.25 -0.34655129 ir 1265–1997 111.67 48.83 99 111.25 48.75 0.421409952 jea 1450 1636–1994 85.23 50.87 80 86.25 48.75 -0.197099589 jpk 2555 1669–2000 82.92 44.1 70 81.25 43.75 0.239330891 jwa 1400 1761–1994 85.23 50.87 80 86.25 48.75 -0.235367068 kbl 2593 1686–2004 81.72 42.8 67 83.75 43.75 0.246844772 kedn 1499 1634–2004 82.87 43.15 71 83.75 43.75 0.338775684 kem 1730–1910 1660–2000 87.4 48.6 53 88.75 46.25 0.311117483 kg 1215–2004 88.8 48.7 63 88.75 48.75 0.267584992 khb 2900 1346–1995 72.58 40.17 115 73.75 38.75 -0.290247919 kk 2500 1326–1998 91.57 49.92 53 91.25 48.75 -0.221409618 kl 2060 1340–2000 99.87 48.17 51 101.25 46.25 0.382440282 kla 2600 1839–1995 72.58 40.17 115 73.75 41.25 0.418914613 kt 1616–2009 106.51 35.54 65 106.25 36.75 0.662309138 ku
1692–1996 94.58 49.5 53 93.75 48.75 0.37019029 lls 1740–1900
1836–2000 111.35 37.77 83 111.25 38.75 -0.341999696 lsh 427 1724–2008 128.89 47.18 87 128.75 48.75 0.212834449 lx 1329 1836–2004 117.73 43.64 58 118.75 43.75 0.375948006 manz 1755 1505–1994 107 47.77 93 106.25 48.75 0.268624874 mek 2500 1575–2007 101.92 31.78 65 101.25 31.25 0.258104087 mhm 1565–2004 100.12 46.82 51 101.25 46.25 0.39630752 miqa 1970 1653–2002 87.92 43.77 58 88.75 43.75 0.36233583 miqb 2080 1653–2002 88.02 43.8 58 88.75 43.75 0.452706509 mul 710 1636–2005 130.25 43.08 90 128.75 43.75 0.338437453 mula 2250 1715–2002 90.22 43.6 58 88.75 43.75 0.340403026 mulb 2170 1829–2002 90.1 43.6 58 88.75 43.75 0.270126594 ndb 1411–2002 101.32 46.32 116 101.25 48.75 0.198500921 og 1599–2001 111.68 48.83 103 111.25 48.75 0.449353118 ovt 1576–2001 91.43 49.8667 53 91.25 48.75 0.250762187 pr
1249–1998
94.88
49.38
70
93.75
51.25
0.270225734
6 Hua
et al. Chin Sci Bull November (2013) Vol.58 No.33
T,
ql 2600–2750 1777–2000 108.77 33.85 82 108.75 36.25 -0.606312447 qlhw 2750 1767–1999 99.95 38.45 65 101.25 36.25 0.219283542 qlmyk 3450 1730–1999 99.95 38.44 63 98.75 38.75 -0.240449659 qltlc 3100 1829–1999 99.95 38.75 63 98.75 38.75 0.202481462 qs 300–326 1770–2004 123.13 41.18 92 123.75 41.25 0.309533337 qx 1710 1721–2004 82.68 43.08 71 83.75 43.75 0.324967083 sb 2500 1363–1999 100.03 47.27 50 101.25 46.25 0.241180986 sd 1770–2000 102.22 37.95 50 103.75 41.75 0.249158319 sev 1865 1785–2004 86.72 43.72 54 86.25 43.75 0.227318282 slh 2220–2320 1660–2004 86.83 48.65 63 88.75 48.75 -0.238445756 sm 1800 1557–2002 100.83 49.48 51 101.25 48.75 0.23252687 sxnw 1600–2787 1686–2003 112.08 38.83 83 111.25 38.75 0.552113861 syfl & 1657–2004 123.58 41.82 90 121.25 41.25 0.266005729 syk 2400–2600 1766–1999 105.87 38.62 62 106.25 38.75 0.387336786 tb 1841 1638–1998 97.12 48.77 49 96.25 48.75 0.329341033 tbk 2100–2400 85.1 46.95 79 86.25 46.25 -0.327116648 tbl 2310 1671–2000 83.7 43.63 55 83.75 41.25 -0.23010121 tbs 3400–3500 1740–2004 107.37 33.57 113 108.75 31.25 -0.220440047 tch 1913 1694–2004 88.12 43.88 58 88.75 43.75 0.321845237 tcm 2000 1570–1995 91.55 49.7 50 91.25 48.75 0.32144439 terel 1798–1994 107.45 47.9 56 106.25 46.25 0.367309061 tp 2420 1475–1999 98.93 48.3 79 98.75 48.75 0.207559915 ttg 2425 1557–2000 86.78 43.35 54 86.25 43.75 0.450758351 tyn 650 1613–1994 89.58 54.23 102 88.75 53.75 0.289820573 ukh 1700 1581–1994 85.37 50.15 83 86.25 51.25 -0.310672537 unbst 1070 1651–1996 110.55 48.57 100 111.25 48.75 0.462506265 uu 1400 1511–2002 103.23 48.98 116 103.75 48.75 0.286066313 uub 1950 1697–1994 87.68 50.5 88 88.75 48.75 -0.493423014 uul 2150 1581–1994 87.65 50.48 88 88.75 51.25 -0.184637267 wuy 351 1738–2008 129.23 48.12 87 128.75 48.75 -0.231253188 xc 4050 1306–2007 100.27 30.23 51 98.75 31.25 0.260246079 xinl 3300 1663–2007 100.28 30.87 66 98.75 31.25 0.3197894 xls 2370–2570 1774–2003 104.03 35.67 66 103.75 36.25 0.302975141 xnq 945–980 1866–2007 120.8 51.38 93 118.25 53.75 0.225758484 xqz 2610 1563–2000 87.08 43.43 51 88.75 41.25 -0.383881497 xxv 1878 1706–2004 86.3 43.78 58 88.75 43.75 0.237653186 ys 860 1750–1992 118.66 36.28 105 118.75 36.25 0.239494737 ysns 1197 1821–2004 116.61 43.09 63 116.25 43.75 0.479159374 yych 3750–3845 1614–2001 100.3 34.8 66 101.25 33.75 -0.273655697 zm 1415 1582–1996 107.5 47.78 57 108.75 48.75 0.533335101 zs 1900 1513–2001 100.28 48.15 51 101.25 48.75 0.30426165 zt 1216–2002 100.95 46.52 51 101.25 46.25 0.256582332 Positive Correlation a=0.05 a=0.025 a=0.01
Negative Correlation a=0.05 a=0.025 a=0.01
Hua T, et al . Chin Sci Bull November (2013) Vol.58 No.33
7
Table S3 Details of each of seven reconstruction intervals
No.
Time span (A.D.)
Time length (T i )
Number of chronologies (N i )
1 1953–1999 47 106
2 1867–1952 86 106
3 1801–1866 66 9
4 4 1750–1800 51 82
5 1697–1749 53 70
6 1651–1696 46 54
7 1601–1650
50
43