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Vegetation Index Reconstruction and Linkage with Drought for the Source Region of the Yangtze River Based on Tree-ring Data

Jinjian LI Shu WANG Ningsheng QIN Xisheng LIU Liya JIN

LI Jinjian, WANG Shu, QIN Ningsheng, LIU Xisheng, JIN Liya, 2021. Vegetation Index Reconstruction and Linkage with Drought for the Source Region of the Yangtze River Based on Tree-ring Data. Chinese Geographical Science, 31(4): 684−695 doi:  10.1007/s11769-021-1217-5
Citation: LI Jinjian, WANG Shu, QIN Ningsheng, LIU Xisheng, JIN Liya, 2021. Vegetation Index Reconstruction and Linkage with Drought for the Source Region of the Yangtze River Based on Tree-ring Data. Chinese Geographical Science, 31(4): 684−695 doi:  10.1007/s11769-021-1217-5

doi: 10.1007/s11769-021-1217-5

Vegetation Index Reconstruction and Linkage with Drought for the Source Region of the Yangtze River Based on Tree-ring Data

Funds: Under the auspices of the Second Tibetan Plateau Scientific Expedition and Research (STEP) Program (No. 2019QZKK0103), National Natural Science Foundation of China (No. 41772173, 41405077), the Thousand Talents Program for High-end Innovation of Qinghai Province, the Applied Basic Research Project of Qinghai Province (No. 2019-zj-7045)
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  • Figure  1.  Location of the tree-ring sample sites at the source region of the Yangtze River. AG, XRS, YG, BG represent the tree-ring sites’ ID

    Figure  2.  Tree-ring width Residual (RES) chronologies of the source region of the Yangtze River, and number of cores. The year with Subsample Signal Strength (SSS) > 0.85 is marked by an arrow. AG, XRS, YG, BG represent the tree-ring sites’ ID

    Figure  3.  Correlation analysis between the four tree-ring chronologies and climatic factors from October of the previous year to September of the current year during 1962–2013 in the source region of the Yangtze River. AG, XRS, YG, BG represent the tree-ring sites’ ID

    Figure  4.  The changes of reconstructed Normalized Difference Vegetation Index (NDVI) during 1665–2013 and comparison between the observed and reconstructed NDVI during 1982–2013. The period with grey (white) rectangle was degradation (growth) periods

    Figure  5.  The wavelet power spectrum (a) and wavelet variance (b) of the wavelet analysis of reconstructed Normalized Difference Vegetation Index (NDVI). The dotted areas suggest significance at a level of 0.05 and the areas with grid line indicate the cone of influence

    Figure  6.  The correlation coefficient between Normalized difference Vegetation Index (NDVI) and Palmer Drought Severity Index (PDSI) from 1665–2005 around the source region of the Yangtze River

    Figure  7.  The wavelet coherence and phase between Normalized Difference Vegetation Index (NDVI) and Palmer Drought Severity Index (PDSI). The vectors indicate the phase difference between NDVI and PDSI; a horizontal arrow pointing from left to right signifies their being in phase. The thick black line is the 95% significance level and the thin black line indicates the cone of influence

    Table  1.   Information of the four tree-ring sampling sites in the source region of the Yangtze River

    IDLatitude and
    Longitude
    Altitude / m Mean sensitivity (MS)Period with Subsample Signal Strength
    (SSS) > 0.85 (minimum number of trees)
    Signal to Noise
    Ratio (SNR)
    Expressed Population
    Signal (EPS)
    AS32.9°N, 95.8°E40420.331665–2013 (11)11.980.94
    XRS33.8°N, 96.3°E39790.311617–2013 (9)18.890.95
    YG33.8°N, 96.2°E44050.311568–2013 (13)13.110.93
    BG33.8°N, 96.4°E40830.291553–2013 (8)7.190.88
    下载: 导出CSV

    Table  2.   Correlation between monthly average or growth season average normalized difference vegetation index and climate factors during 1982–2015 in the source region of the Yangtze River

    Climate factorMar.Apr.MayJun.Jul.Aug.Sep.Growth season (May to Sep.)
    Average maximum temperature 0.509** 0.379* 0.385* 0.425* 0.473** 0.180 0.438* 0.355*
    Average minimum temperature 0.210 –0.054 0.161 0.486** 0.322 0.102 0.389* 0.449*
    Average temperature 0.422* 0.269 0.384* 0.484** 0.482** 0.171 0.451** 0.425*
    Precipitation –0.336 –0.207 –0.363* 0.050 –0.173 0.084 –0.098 0.125
    Notes: *, ** suggested the significance at a level of 0.05 and 0.01, respectively
    下载: 导出CSV

    Table  3.   The relationship between the four tree-ring chronologies and monthly normalized difference vegetation index during 1982–2013 in the source region of the Yangtze River

    IDJan.Feb.Mar.Apr.MayJun.Jul.Aug.Sep.Oct.Nov.Dec.Jul.–Aug.
    AS–0.110–0.135–0.028–0.114–0.399*0.2920.490**0.510**0.1250.082–0.0100.0260.548**
    XRS–0.035–0.029–0.030–0.149–0.2410.445**0.472**0.549**0.2120.244–0.0280.0720.568**
    YG–0.074–0.0670.013–0.212–0.1930.2750.462**0.568**0.2610.2680.0580.0450.567**
    BG–0.020–0.053–0.099–0.119–0.340*0.2870.474**0.612**0.1610.018–0.1500.0270.594**
    Notes: *, ** suggests significance at a level of 0.05 and 0.01, respectively
    下载: 导出CSV

    Table  4.   Statistical parameters of the transfer function and cross-validation for the Normalized Difference Vegetation Index (NDVI) reconstruction during 1982–2013 in source region of the Yangtze River

    Correlation coefficientR2Reduced errorThe average value of the productSign testFirst-order sign test
    0.663**0.4390.5213.797**19 (20a, 21b)21 (20a, 22b)
    Notes: ** the significance at a level of 0.01; a, b: Numbers of identical signs required to reach significance at a level of 0.05 and 0.01, respectively
    下载: 导出CSV

    Table  5.   The standard of wetness and dryness according to the mean and Standard Deviation (SD)

    ValuesGrade of wet/drought
    PDSI<mean–1.5SD Extreme drought
    Mean–1.5SD<PDSI<mean–SD Drought
    Mean–SD<PDSI<mean+SD Normal
    Mean+SD<PDSI<mean+1.5SD Wet
    Mean+1.5SD<PDSI Extreme wet
    Note: Palmer Drought Severity Index (PDSI)
    下载: 导出CSV

    Table  6.   Statistics for graded drought and wet years, and correlations between Palmer Drought Severity Index (PDSI) of drought/wet years and the corresponding Normalized Difference Vegetation Index (NDVI) during 1665–2005 in source region of the Yangtze River

    Extreme drought yearsDrought yearsNormal yearsWet yearsExtreme wet years
    Number of years/Percentage 25/7.3% 20/5.9% 252/73.9% 27/7.9% 17/5.0%
    Correlation coefficient 0.429* 0.229 0.465** 0.136 –0.681**
    Notes: *, ** suggests significance at a level of 0.05 and 0.01, respectively
    下载: 导出CSV
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  • 收稿日期:  2020-12-04
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Vegetation Index Reconstruction and Linkage with Drought for the Source Region of the Yangtze River Based on Tree-ring Data

doi: 10.1007/s11769-021-1217-5
    基金项目:  Under the auspices of the Second Tibetan Plateau Scientific Expedition and Research (STEP) Program (No. 2019QZKK0103), National Natural Science Foundation of China (No. 41772173, 41405077), the Thousand Talents Program for High-end Innovation of Qinghai Province, the Applied Basic Research Project of Qinghai Province (No. 2019-zj-7045)
    通讯作者: LI Jinjian. E-mail: ljj@cuit.edu.cn

English Abstract

LI Jinjian, WANG Shu, QIN Ningsheng, LIU Xisheng, JIN Liya, 2021. Vegetation Index Reconstruction and Linkage with Drought for the Source Region of the Yangtze River Based on Tree-ring Data. Chinese Geographical Science, 31(4): 684−695 doi:  10.1007/s11769-021-1217-5
Citation: LI Jinjian, WANG Shu, QIN Ningsheng, LIU Xisheng, JIN Liya, 2021. Vegetation Index Reconstruction and Linkage with Drought for the Source Region of the Yangtze River Based on Tree-ring Data. Chinese Geographical Science, 31(4): 684−695 doi:  10.1007/s11769-021-1217-5
    • The source region of the Yangtze River (Fig. 1) in the southwestern Qinghai Province is approximately 738 km from the east to west, and 406 km from the south to north, with an area of approximately 26.2 × 104 km2 (Qian et al., 2006). The landform is dominated by high plains and hills, with an average elevation of more than 4000 m above sea level (a.s.l.). The monthly average temperature is above 0℃ only from June to September, with the lowest temperature in January (–16.5℃) and the highest temperature in July (6.0℃). The total monthly precipitation ranges from 2 mm (December) to 94 mm (July). Precipitation from May to September (months with relatively high temperatures) accounts for 88.7% of the annual precipitation, and the concurrent warm and wet climatic conditions are favorable for the growth and development of vegetation. The ecosystems in the source region of the Yangtze River principally include alpine steppe, alpine meadow, alpine marsh and wetland, localized and sparsely distributed alpine shrubs in some river valleys, and cushion plants and sparse vegetation on the rock slopes of the high mountains. The NDVI exhibits unimodal pattern, changes only slightly from January to March, and in November and December; therefore, almost no vegetation grows during these periods. It begins to increase in April, when the vegetation enters the rapid growth season, and peaks from July to August (0.37–0.38), when vegetation growth is most active. In September, the NDVI starts to decrease as the vegetation withers. It is evident that the vegetation growth in SRYR is concentrated from May to September, and this period is the main vegetation growth season in this region.

      Figure 1.  Location of the tree-ring sample sites at the source region of the Yangtze River. AG, XRS, YG, BG represent the tree-ring sites’ ID

    • We sampled tree-rings at four sites in SRYR in September 2013 (Fig. 1 and Table 1). All the trees were living in remote areas that are rarely influenced by human activities. We used growth cores with a diameter of 5.15 mm and obtained two or three cores from each tree. A total of 203 cores from four sites were used in this study. The COFECHA program (Holmes, 1983) was used after the collected samples were processed by drying, pasting, polishing, and measuring. Cross-dating and verification of the results were performed to ensure accurate dating results. The data were detrended by a negative exponential curve using ARSTAN (Cook, 1985; http://www.ldeo.columbia.edu/tree-ring-laboratory/resources/software). The autoregressive model was then used to eliminate low-frequency variation caused by competition among trees within the forest. Eventually, we obtained three types of chronologies: Standard (STD) chronologies, Residual (RES) chronologies, and ARSTAN (ARS) chronologies. The RES chronologies remove the continuous influence of the special early physiological conditions on the following growth years, and therefore retain more high-frequency signals (Fritts, 1976). Meanwhile, NDVI is sensitive to climate conditions (Hlásny et al., 2015; Mariano et al., 2018; Butterfield et al., 2020), which leads to significant high-frequency variation. Therefore, the RES chronologies were used for reconstruction in this study.

      Table 1.  Information of the four tree-ring sampling sites in the source region of the Yangtze River

      IDLatitude and
      Longitude
      Altitude / m Mean sensitivity (MS)Period with Subsample Signal Strength
      (SSS) > 0.85 (minimum number of trees)
      Signal to Noise
      Ratio (SNR)
      Expressed Population
      Signal (EPS)
      AS32.9°N, 95.8°E40420.331665–2013 (11)11.980.94
      XRS33.8°N, 96.3°E39790.311617–2013 (9)18.890.95
      YG33.8°N, 96.2°E44050.311568–2013 (13)13.110.93
      BG33.8°N, 96.4°E40830.291553–2013 (8)7.190.88

      The Mean Sensitivity (MS) and Signal-to-Noise Ratios (SNR) were employed to test whether the chronologies could reveal regional climate and environmental changes (Wang et al., 2016), the higher values suggest the more climatic signals in tree-ring width. In general, MS > 0.1 and SNR > 5, which suggest the chronology could be used to reconstruct. Meanwhile, the Subsample Signal Strength (SSS) was used to evaluate the reliable starting year, an SSS > 0.85 was used to ensure a reliable chronology length (Liu et al., 2010; Bao et al., 2012). According to Table 1, the MS values of the four chronologies were 0.33, 0.31, 0.31, and 0.29, and the SNR values were 11.98, 18.89, 13.11, and 7.19, suggesting that the radial growth was sensitive to climate and environmental changes. And the reliable period of the four chronologies were 1665–2013, 1617–2013, 1568–2013 and 1553–2013, respectively. As shown in Fig. 2, although the longest distance between the sample sites is nearly 150 km, their variation characteristics are still highly consistent. The correlation coefficients between the chronologies in the common period (1665–2013, n = 349) were 0.395 (AS and BG, P < 0.0001), 0.437 (AS and YG, P < 0.0001), 0.518 (AS and XRS, P < 0.0001), 0.593 (BG and YG, P < 0.0001), 0.674 (BG and XRS, P < 0.0001), and 0.682 (YG and XRS, P < 0.0001).

      Figure 2.  Tree-ring width Residual (RES) chronologies of the source region of the Yangtze River, and number of cores. The year with Subsample Signal Strength (SSS) > 0.85 is marked by an arrow. AG, XRS, YG, BG represent the tree-ring sites’ ID

    • Meteorological data, including the mean maximum temperature, mean minimum temperature, mean temperature, precipitation, were provided by China Meteorological Administration, which is a monthly gridded dataset with a spatial resolution of 0.25° × 0.25°. The dataset is based on the observed data from over 2400 weather stations in China. The method of ‘anomaly approach’, which considering the change of climate factors with altitude, was applied in the dataset production. The details could be found in the reference Xu et al. (2009) and Wu and Gao (2013). The dataset covers the period 1961 to 2018.

      The Palmer Drought Severity Index (PDSI) data were from the Monsoon Asia Drought Atlas (MADA), which was reconstructed by Cook et al. (2010) using tree-ring data. The dataset covers the period 1300 to 2005, and has a total of 534 grid points and a spatial resolution of 2.5° × 2.5°.

      The NDVI dataset provided by Global Inventory Modeling and Mapping Studies (GIMMS) is currently one of the most commonly used vegetation indices. This dataset eliminates the influence of temporal changes on volcanic eruptions, solar elevation angle, and sensor sensitivity (Fensholt et al., 2009), and can effectively represent regional changes in vegetation growth. GIMMS NDVI dataset 3 g.v1 (https://ecocast.arc.nasa.gov/data/pub/gimms/3g.v1/) is a 15 d synthetic dataset from the National Aeronautics and Space Administration’s (NASA’s) Goddard Space Flight Center, with a spatial resolution of 1/12°, which is about 8 km. It is the longest NDVI dataset, spanning from 1981 to 2015. Monthly NDVI data were obtained using the maximum value composite (MVC) method (Holben, 1986). To exclude the effect of non-vegetation factors, pixels with a multiyear average NDVI smaller than 0.1 were excluded. The NDVI variation dataset of the SRYR can be obtained through preprocessing, such as format conversion, projection transformation, subset extraction, and cropping.

    • The relationships between tree-ring width, climate and then NDVI were analyzed using the Pearson correlations in the SPSS, and then a multivarate linear regression was employed to reconstruct the NDVI. The leave-one-out method (Michaelsen, 1987) was used to test the model’s stability and reliability. The testing statistics mainly include Correlation Coefficient (r), Reduced Error (RE), Average Value of the Product (t), and the sign test. To analyze the characteristics of NDVI changes, we also used some mathematical statistical diagnosis methods including wavelet analysis (Torrence, 1998) and cross-wavelet analysis (Grinsted et al., 2004).

    • According to Fig. 3, the relationship between the tree-ring width chronology and climatic factors exhibits good consistency at the four sampling points, and the correlation between the tree-ring and precipitation was most significantly positive in May (r = 0.580; P < 0.05). In terms of temperature, it exhibited significant negative correlations with the mean maximum temperature in May (the highest correlation coefficient was –0.543, P < 0.05), meanwhile, the chronology of AS and YG was significant negative correlated with the mean temperature in May as well. And, chronologies had a significant positive correlation with both the mean minimum temperature (the highest correlation coefficient was 0.435, P < 0.05) and mean temperature (the highest correlation coefficient was 0.436; P < 0.05) in July (except the tree-ring of AS).

      Figure 3.  Correlation analysis between the four tree-ring chronologies and climatic factors from October of the previous year to September of the current year during 1962–2013 in the source region of the Yangtze River. AG, XRS, YG, BG represent the tree-ring sites’ ID

      Rather than a single climatic factor, the NDVI is jointly affected by various factors, such as precipitation, temperature, and evaporation (Peng et al., 2010; Sun et al., 2013; Wang et al., 2015). Table 2 shows that there were significant correlations (P < 0.05) between NDVI and climatic factors (mainly include the monthly average maximum temperature in March to July and September, the monthly average minimum temperature in June and September, the monthly average temperature in March, May to July and September, monthly precipitation in May) in the SRYR. Thus, it exhibited a better correlation with tree-ring, especially during the growth season. Table 3 shows the correlation between the tree-ring and NDVI for each month from 1982 to 2013. The tree-ring was significantly positively correlated with the NDVI of July and August at all four sampling sites with the highest correlation coefficient 0.612. The correlations reached significance at a level of 0.01, indicating that the tree-ring mirrored changes in the NDVI. Therefore, it is feasible to restore the NDVI data using the tree-ring chronology.

      Table 2.  Correlation between monthly average or growth season average normalized difference vegetation index and climate factors during 1982–2015 in the source region of the Yangtze River

      Climate factorMar.Apr.MayJun.Jul.Aug.Sep.Growth season (May to Sep.)
      Average maximum temperature 0.509** 0.379* 0.385* 0.425* 0.473** 0.180 0.438* 0.355*
      Average minimum temperature 0.210 –0.054 0.161 0.486** 0.322 0.102 0.389* 0.449*
      Average temperature 0.422* 0.269 0.384* 0.484** 0.482** 0.171 0.451** 0.425*
      Precipitation –0.336 –0.207 –0.363* 0.050 –0.173 0.084 –0.098 0.125
      Notes: *, ** suggested the significance at a level of 0.05 and 0.01, respectively

      Table 3.  The relationship between the four tree-ring chronologies and monthly normalized difference vegetation index during 1982–2013 in the source region of the Yangtze River

      IDJan.Feb.Mar.Apr.MayJun.Jul.Aug.Sep.Oct.Nov.Dec.Jul.–Aug.
      AS–0.110–0.135–0.028–0.114–0.399*0.2920.490**0.510**0.1250.082–0.0100.0260.548**
      XRS–0.035–0.029–0.030–0.149–0.2410.445**0.472**0.549**0.2120.244–0.0280.0720.568**
      YG–0.074–0.0670.013–0.212–0.1930.2750.462**0.568**0.2610.2680.0580.0450.567**
      BG–0.020–0.053–0.099–0.119–0.340*0.2870.474**0.612**0.1610.018–0.1500.0270.594**
      Notes: *, ** suggests significance at a level of 0.05 and 0.01, respectively
    • Based on the above results, because of the significant correlation between tree-ring width and the NDVI of July to August, the RES chronology established from the AS, XRS, YG, and BG tree-ring sampling sites over the period 1982–2013 was selected as the independent variable, while the average NDVI from July to August in SRYR was used as the dependent variable, to establish the following multiple regression equation:

      $$ \begin{split} {P}_{NDVI}=&0.326+0.011\times {C}_{\mathrm{A}\mathrm{S}}+0.027\times {C}_{\mathrm{B}\mathrm{G}}+0.013\times\\ &{C}_{\mathrm{Y}\mathrm{G}}+0.006\times {C}_{\mathrm{X}\mathrm{R}\mathrm{S}} \end{split} $$ (1)

      where PNDVI represents the regional average of the NDVI from July to August in SRYR, CAS, CBG, CYG and CXRS represents RES chronology from tree-ring sites AS, BG, YG, and XRS, respectively. The explained variance (R2) in the reconstruction equation was 43.9%, and the adjusted explained variance was 35.9%. The correlation coefficient between the reconstructed sequence and the original measured sequence was 0.663 (n = 33, P < 0.01).

      To verify the stability and reliability of the reconstruction equation, it was validated using the leave-one-out method, and the relevant statistics are listed in Table 4. The statistical characteristics, including the correlation coefficient (r = 0.663), and average value of the product (t = 3.797), reached significance at a level of 0.01. Reduced Error (RE = 0.521) is positive, which indicates that the reconstruction equation is stable and reliable. The first-order sign test reached significance at a level of 0.05, while the sign test failed to reach significance at a level of 0.05, indicating that the reconstruction equation performed better in the reconstruction of high-frequency variations than low-frequency variations.

      Table 4.  Statistical parameters of the transfer function and cross-validation for the Normalized Difference Vegetation Index (NDVI) reconstruction during 1982–2013 in source region of the Yangtze River

      Correlation coefficientR2Reduced errorThe average value of the productSign testFirst-order sign test
      0.663**0.4390.5213.797**19 (20a, 21b)21 (20a, 22b)
      Notes: ** the significance at a level of 0.01; a, b: Numbers of identical signs required to reach significance at a level of 0.05 and 0.01, respectively
    • Using the reconstruction equation, the summer (July and August) NDVI of SRYR from 1665 to 2013 were reconstructed (Fig. 4). The five highest value years were 1718 (value: 0.407), 1873 (0.404), 1673 (0.404), 1780 (0.403) and 1773 (0.401), and the five lowest value years were 1824 (value: 0.353), 1872 (0.353), 1684 (0.354), 1995 (0.355) and 1717 (0.356). To reveal the decadal variations in the growth and degradation periods, an 11-yr moving average was calculated. The growth periods (or degradation period) were defined as periods in which the NDVI was consecutively greater (or lower) than the average of the reconstruction (0.382) for at least 10 yr. There were eight growth stages (1754–1764, 1766–1783, 1794–1811, 1828–1838, 1843–1855, 1862–1873, 1897–1909, and 1932–1945) and four degradation stages (1679–1698, 1726–1753, 1910–1923, and 1988–2000) during the past 349 yr. The longest growth periods were 1766–1783 and 1794–1811, with durations of 18 yr. The longest degradation period was 1726–1753, with a duration of 28 yr.

      Figure 4.  The changes of reconstructed Normalized Difference Vegetation Index (NDVI) during 1665–2013 and comparison between the observed and reconstructed NDVI during 1982–2013. The period with grey (white) rectangle was degradation (growth) periods

      It is evident from the wavelet analysis (Fig. 5) that there were significant cycles in the NDVI variations, with periods of 2–3 yr and 3–8 yr. The 2–3 yr cycle is consistent with the ‘Quasi-Biennial Oscillation’, which is a common periodicity in meteorological systems (Mukherjee et al., 1985; Zhou et al., 2009; Shi et al., 2014). The 3–8 yr cycle coincides with the oscillation period of the El Niño-Southern Oscillation (ENSO) (Li et al., 2013), and mainly occurred in the late seventeenth century, late 18 century, and early 19 century. These results are consistent with those of existing studies on the periodic analysis of reconstructed temperature (Liang et al., 2008; Shi et al., 2010), precipitation (Gou et al., 2013; Yang et al., 2014), and runoff (Xiao et al., 2017; Li et al., 2018) in SRYR, indicating that the climatic factors there are affected by large-scale ocean-atmospheric circulations such as the Pacific Decadal Oscillation (PDO) and ENSO, and that atmospheric circulation may affect vegetation growth in SRYR by affecting its climate.

      Figure 5.  The wavelet power spectrum (a) and wavelet variance (b) of the wavelet analysis of reconstructed Normalized Difference Vegetation Index (NDVI). The dotted areas suggest significance at a level of 0.05 and the areas with grid line indicate the cone of influence

    • The change of NDVI is controlled by many factors, and climate is one of the main factors. Most studies show that temperature and precipitation was the most important factors (Ding et al., 2007; Wang et al., 2015). However, in different regions, the effects of temperature and precipitation are different. The results of previous indicated that the temperature played more important roles (Tang et al., 2006) in three-river source region. In our research, it also exhibited a better correlation between NDVI and temperature. This may be related to the vegetation, climate and landform of the study area (Tang et al., 2006). Our samples were taken from SRYR, an area located in the eastern part of the Tibetan Plateau. During the early growth season, the significant positive correlation between tree radial growth and precipitation, and the significant negative correlation between tree radial growth and temperature, may indicate that an increase in temperature can result in enhanced evaporation and plant transpiration, thereby exacerbating the water stress effect and inhibiting the growth of trees, while an increase in precipitation could be conducive to tree growth. Similar conclusions have been confirmed in previous studies (Qin et al., 2003; Gou et al., 2008; Shi et al., 2010). However, during July, the positive correlation between tree radial growth, precipitation, and temperature, suggests that concurrent rain and heat are conducive to photosynthesis, resulting in wide rings (Liang et al., 2008). There is a high statistical correlation between the tree ring width chronology and NDVI. The radial growth of trees is related to the climatic conditions in the growing season. NDVI reflects the growth status of vegetation and is also related to the climatic and environmental conditions, indicating that there is consistency in the response of the two to the limiting factors.

    • The growth conditions of vegetation are constrained by a variety of factors, among which climate is one of the most important. Among all climatic factors, drought is considered to be of key influence on vegetation. The PDSI was calculated based on precipitation, temperature, soil moisture, etc., and is representative of the regional extent of drought and wet conditions (Palmer, 1965; Dai et al., 2004). Increasing evidence suggests that NDVI series could capture the signals of drought events, especially extreme drought events (Wang et al., 2017; Zhang et al., 2018). As shown in Fig. 6, there were significantly positively correlated in SRYR and its vicinity between the reconstructed NDVI and PDSI during the period 1665–2005, with the correlation coefficient between the mean PDSI and the reconstructed NDVI of the SRYR was 0.611, which reached significance at a level of 0.001. The comparison with other hydro-climatic reconstructions suggests low NDVI period often matched drought period. For example, the low NDVI period 1910–1923 was matched the 1910s–1920s drought periods which widely occurred in central and western China (Bao et al., 2012; Yang et al., 2014; Li et al., 2018). To specifically examine the relationship between drought and NDVI, cross-wavelet analysis was used to show the coherence and phase lag between the NDVI and PDSI (Fig. 7). The result indicates significant resonance between the NDVI and PDSI at scales of quasi 4 yr, quasi 8 yr, and quasi 32 yr. The resonance of quasi 4 yr was found in 1650–1700, 1750–1800, 1860–1980, and 1950–1970. The resonance of quasi 8 yr occurred in 1740–1760, 1830–1850, 1940–1960, and 1980–2000. There was a quasi 32 yr resonance in 1900–1950. Furthermore, a nearly horizontal rightward arrow was in resonance of quasi 4 yr and quasi 8 yr, suggesting that the NDVI and PDSI signals were in phase. A right-downward arrow with an approximately 30° phase angle in the quasi 32 yr resonance indicated that the PDSI led the NDVI change by 1/8 cycle, which is approximately 4 yr.

      Figure 6.  The correlation coefficient between Normalized difference Vegetation Index (NDVI) and Palmer Drought Severity Index (PDSI) from 1665–2005 around the source region of the Yangtze River

      Figure 7.  The wavelet coherence and phase between Normalized Difference Vegetation Index (NDVI) and Palmer Drought Severity Index (PDSI). The vectors indicate the phase difference between NDVI and PDSI; a horizontal arrow pointing from left to right signifies their being in phase. The thick black line is the 95% significance level and the thin black line indicates the cone of influence

      This result indicates that overall, the degree of wet and drought in the SRYR will significantly positively impact the growth of local vegetation, with wetter conditions leading to better vegetation and vice versa. However, an ensuing question is what the impact of different levels of drought and wetness on the NDVI is? To answer this question, the wet and dry years were firstly defined according to the mean and Standard Deviation (SD) of the PDSI series (Table 5). During the period 1665–2005, the number of normal years was the highest, 252 yr, accounting for 73.9% of the total; the number of drought (wet) years was 20 yr (27 yr), accounting for 5.9% (7.9%) of the total; the number of extreme drought (extreme wet) years was 25 yr (17 yr), accounting for 7.3% (5.0%) of the total. The frequency of extreme drought years was higher than that of extreme wet years, while the frequency of wet years was higher than that of drought years. Then, the NDVI and PDSI in years corresponding to different drought and wetness grades were extracted for correlation analysis, and the results (Table 6) show that different grades of drought and wet exhibited distinctly different impacts on the NDVI: in normal years, the PDSI and NDVI had significant positive correlations, and the correlation coefficient reached 0.465, indicating that better water conditions resulted in better vegetation, and vice versa. In the dry and wet years, there was still a positive correlation, although it failed to pass the significance test, suggesting that in drought or wet years, the water condition was no longer the key limiting factor. The cause of this phenomenon requires further investigation and discussion. In the extreme drought years, the PDSI and NDVI were positively correlated, with a correlation coefficient of 0.429, and the correlation reached significance at a level of 0.05, indicating that the water condition was one of the most important limiting factors in extreme drought years. However, in extreme wet years, the PDSI and NDVI exhibited significant negative correlations, and the correlation coefficient reached –0.681, reaching significance at a level of 0.01, which was possibly because the study region has a cold plateau climate; the average temperature in summer is less than 10℃, and the temperature difference between sunny days and cloudy days is typically large. Generally, extreme wetness means more precipitation and long-term rainy days, which may lead to lower temperatures and less sunshine, thus limiting vegetation growth. Excessive precipitation can lead to waterlogging, reducing the oxygen content of the soil and thus affecting photosynthesis, causing vegetation to wither (Nakano, 2007; Ashraf and Harris, 2013).

      Table 5.  The standard of wetness and dryness according to the mean and Standard Deviation (SD)

      ValuesGrade of wet/drought
      PDSI<mean–1.5SD Extreme drought
      Mean–1.5SD<PDSI<mean–SD Drought
      Mean–SD<PDSI<mean+SD Normal
      Mean+SD<PDSI<mean+1.5SD Wet
      Mean+1.5SD<PDSI Extreme wet
      Note: Palmer Drought Severity Index (PDSI)

      Table 6.  Statistics for graded drought and wet years, and correlations between Palmer Drought Severity Index (PDSI) of drought/wet years and the corresponding Normalized Difference Vegetation Index (NDVI) during 1665–2005 in source region of the Yangtze River

      Extreme drought yearsDrought yearsNormal yearsWet yearsExtreme wet years
      Number of years/Percentage 25/7.3% 20/5.9% 252/73.9% 27/7.9% 17/5.0%
      Correlation coefficient 0.429* 0.229 0.465** 0.136 –0.681**
      Notes: *, ** suggests significance at a level of 0.05 and 0.01, respectively
    • The analysis of correlations between the tree-ring width chronology, NDVI, and climatic factors of the SRYR, indicates that the tree-ring width chronology is most significantly correlated with the NDVI of July and August. Thus, we developed a stable and reliable multiple regression equation to reconstruct the summer NDVI in the SRYR over the past 349 yr. During this period, there were eight growth stages and four degradation stages. The growth stages were 1754–1764, 1766–1783, 1794–1811, 1828–1838, 1843–1855, 1862–1873, 1897–1909, and 1932–1945; the degradation stages were 1679–1698, 1726–1753, 1910–1923, and 1988–2000. There were significant cycles of 2–3 yr and 3–8 yr, indicating that vegetation growth might be affected by large-scale ocean-atmospheric circulations, such as PDO and ENSO The reconstructed NDVI and the PDSI of the SRYR and its vicinity were significantly positively correlated, but the correlation showed significant asymmetry in extreme drought years and extreme wet years. In extreme drought years, the PDSI and NDVI were positively correlated, whereas in extreme wet years, the PDSI and NDVI had a significant negative correlation.

      This paper provides a long time scale data of local vegetation changes, and reveals that the vegetation growth in SRYR is restricted by the large spatial climate. Also, it was found that there was significant asymmetry in extreme drought years and extreme wet years. This will provide support for future study on the dynamic consistency of vegetation growth. However, this study is still limited because out reconstruction is only based on four sampling sites. Therefore, it is critical to develop a wide tree-ring network in China, even in the whole northern hemisphere. It could help to verify the relative study results and to better understand the impact of climate change on ecological environment.

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