Abstract

The Xinjiang province of China is vulnerable to drought, but the occurrence of drought varies substantially among different sub-areas. This study investigated drought characteristics in Xinjiang province and its sub-area using the Mann–Kendall trend test, cluster analysis and Morlet wavelet analysis. The results show that drought in Xinjiang is generally becoming less severe, and there is a non-uniform spatial variation of drought, which is especially pronounced for stations in northern Xinjiang. There is a unique spatiotemporal distribution trend of drought in Xinjiang, and the inter-decadal variation of drought shows a gradual shift from the east to the west and then back to the east again over the past 55 years. Northern Xinjiang is becoming wetter at a faster rate compared with that of southern Xinjiang, and it also has a higher occurrence of change point sites (70%). The historical drought situation in Xinjiang is better characterized by three clusters. Cluster 1 is the driest, cluster 2 has a clear alleviating tendency of drought, while cluster 3 shows late occurrence of change point. A broader view of the accumulated variation of drought is formulated in this study, which may help to identify potential droughts to support drought disaster management and mitigation.

INTRODUCTION

Drought is characterized by a negative water balance originating from a deficiency of precipitation or a lack of available water resources for an extended period of time (Wilhite & Glantz 1985). It has become an increasingly serious problem with detrimental environmental and socioeconomic consequences. However, drought patterns have become more complicated in recent years due to climate change, and thus there is a need to better understand drought patterns and characteristics.

The spatiotemporal variability of droughts has received considerable attention due to their serious detrimental impacts. The Intergovernmental Panel on Climate Change (IPCC) documented large uncertainties about the ‘future vulnerability, exposure, and responses of interlinked human and natural systems’ (IPCC 2014). China's IPCC indicated that the average warming rate in China (0.9–1.5 °C) was higher than the global average with an obvious regional difference in the annual precipitation over the last century (http://www.cma.gov.cn). An increase in frequency and severity of drought is expected in the future due to climate changes, and thus the spatiotemporal variability of drought at global and regional scales has become a topic of considerable research interest. Sheffield et al. (2002) conducted a global assessment of drought under the warming climate over the past 60 years. Numerous regional studies have also been conducted to analyze the spatiotemporal variability of drought (Lewis et al. 2011; Gosling 2014).

The Xinjiang Uygur Autonomous Region (hereafter referred to as Xinjiang for short) is a key eco-environmental zone in China with a semi-arid climate and the hottest temperature nationwide. Yan et al. (2016) investigated the combined impacts of future climate and land use changes on the streamflow in Xinjiang. Zhang et al. (2015) also investigated the impacts of climate changes on spatiotemporal properties of drought extremes and local agriculture. Zhang et al. (2012c) found that northern Xinjiang was wetter than southern Xinjiang, and the whole region has exhibited a wetting tendency with a higher frequency of heavy precipitation extremes since 1980. Among the indexes used in monitoring drought (Zhang et al. 2012a; Wang et al. 2015), the Standardized Precipitation Index (SPI) is suitable for drought reconstruction in Xinjiang (Zhai & Feng 2011). Previous indexes can be classified into two groups: (1) indexes related to the physical processes of droughts, such as the Palmer Drought Severity Index (Palmer 1965) and Surface Water Supply Index (Shafer & Dezman 1982); (2) indexes based only on precipitation, such as Standard Precipitation Index SPI (McKee et al. 1993). The SPI has been commonly used in recent studies due to its simplicity and temporal flexibility (Li et al. 2012; Belayneh et al. 2014). However, the impacts of droughts appear to be multifaceted and often difficult to quantify over large areas and long time scales. Clearly, a better understanding of the spatiotemporal changes in droughts is essential in the development of drought contingency plans (Heim 2002).

This is especially important in Xinjiang with its towering mountains and large low-altitude plateaus or basins. In this study, we investigated the spatiotemporal variation of droughts in Xinjiang and its sub-areas over the past 55 years. We focused on regionalization using a clustering algorithm and drought properties for partitioning meteorology stations of interest. Subsequently, change point analysis was performed for homogeneous regions, and the relationship between SPI-based drought and its affecting factors discussed.

The rest of this paper is structured as follows: immediately below, the study area and data set are introduced, as well as the statistical technique used in the study. This is followed by the results for the whole of Xinjiang as well as for different geographic sub-areas. The next section discusses the temporal trend, especially the occurrence of change point in some stations and regions. Finally, the conclusions and recommendations for future research are presented.

DATA AND METHODS

Study area and dataset

Xinjiang (73°40′–96°18′E and 34°25′–48°10′N) is located in Central Asia with a total area of 1.6 million km2 and an elevation of −158 m to 7,390 m. It plays an important strategic role in the ‘One Belt and One Road Initiative’, and shares borders with eight countries: India, Russia, Kazakhstan, Kyrgyzstan, Tajikistan, Pakistan, Mongolia, and Afghanistan (Figure 1). Xinjiang can be geographically divided into two regions by the Tianshan Mountain, one of the seven largest mountains in the world. The extensive development of inland rivers results in the formation of unique ‘mountain-oasis-desert’ ecosystems in the arid region. Generally, it has a temperate continental climate with limited precipitation. The daily meteorological data collected at 51 meteorological stations from 1 January 1961 to 31 December 2015 were used in this study, and were downloaded from the official Chinese meteorological website (http://data.cma.cn). The sea surface temperature (SST) data over the North Atlantic Ocean (20–70°N) with a resolution of 5° × 5° were provided by the National Oceanic and Atmospheric Administration (NOAA). Data quality control and homogeneity assessment were attained using the RclimDex software package (Zhang & Yang 2004). The process consists of two steps: (1) missing records were interpolated using data of nearby stations in the same year; and (2) data quality control was performed and unreasonable data were handled in accordance with the missing data.

Figure 1

Location of the study area and the meteorological stations for data collection.

Figure 1

Location of the study area and the meteorological stations for data collection.

Drought indexes

The SPI is a meteorological drought index based solely on precipitation data (Mckee et al. 1993). Three main advantages may arise from the use of SPI: (1) the calculation is relatively easy as this index is based on precipitation alone; (2) it can characterize drought intensity and duration well; and (3) it has a multi-temporal scale application dimension. The SPI-12 is a good indicator of the long-term drought conditions, which is calculated based on the total precipitation amount of the past 12 months. In this paper, the gamma distribution function is found to fit the precipitation data well, because the precipitation does not follow a normal distribution. Detailed computation of the SPI can be found in Cacciamani et al. (2007).

Proposed methodology

The variation of SPI-12 from the meteorological stations was first examined using k-means analysis, which would enable the grouping of stations with similar temporal trends. The Mann–Kendall (MK) method (Kendall 1975) and correlation analysis were used for trend and change point analysis. The Morlet wavelet (Weng & Lau 1994) was used to locate vital temporal statistics. The overall methodology is shown in Figure 2.

Figure 2

The steps of the methodology.

Figure 2

The steps of the methodology.

Statistical methods

The non-parametric statistical MK test (Kendall 1975) has wide applications in climate modeling, trend detection, environmental studies, and hydrology. It is a non-parametric test requiring no specific assumption of the joint distribution of the data, and thus is generally not affected by departures from normality.

The Morlet wavelet transform method converts the initial signal into a compact description (Weng & Lau 1994), which has two major advantages over other wavelet functions (i.e., Mexican hat wavelet, Daubechies wavelet, and Morlet wavelet) in hydrological analysis: (1) continuity of the results; and (2) arbitrary selection of time scales.

The heterogeneity measures (Hosking & Wallis 1997) shown in Table 1 are used to evaluate the homogeneity of the areas by comparing the dispersion of sample L-moments among sites with the L-moment for the group of sites. The heterogeneity test should follow the following principles: the region is treated as acceptably homogeneous if H < 1; possibly heterogeneous if 1 < H < 2; and definitely heterogeneous if H > 2, respectively. The heterogeneity H statistic and V statistic for the sample and simulated regions can be represented as follows:  
formula
(1)
 
formula
(2)
where uv is the average of simulated V values, σv is the standard deviation of simulated V values, τi is the sample L-moment at each site, τR is the averaged sample L-moment at a regional scale, and ni is the length of the time series at site i, respectively. The H(1), H(2), and H(3) are calculated by the L-coefficient of variation, L-skewness, and L-kurtosis used as the L-moment, respectively.
Table 1

Heterogeneity test

Name SPI-based drought
 
H(1) H(2) H(3) 
Cluster 1 −0.7966 −0.8820 −1.2298 
Cluster 2 1.4103 −0.5163 0.0101 
Cluster 3 −0.6137 1.3984 1.2197 
Name SPI-based drought
 
H(1) H(2) H(3) 
Cluster 1 −0.7966 −0.8820 −1.2298 
Cluster 2 1.4103 −0.5163 0.0101 
Cluster 3 −0.6137 1.3984 1.2197 

K-means method

Different basins and hydrological factors can have different impacts on local water resources and precipitation, and thus the frequency and severity of drought can vary significantly in different sub-areas. The sites of interest are partitioned by the clustering algorithms based on the K-means method widely used in geoscience studies, whose advantage is the simple implementation in the search of clusters. It is a non-hierarchical clustering method which begins with the calculation of the centroids for each cluster, followed by the calculation of the distances between the current data vector and each of the centroids, and finally assigning vectors to the cluster whose centroid is the closest. The iterative steps involved in the K-means clustering include (Sönmez 2009):

  • (1)

    All samples are selected randomly into the k clusters, each of which is represented by a centroid.

  • (2)

    Computing the centroid of each cluster by averaging its sample vectors.

  • (3)

    All samples are redistributed to the nearest cluster according to the distance between the cluster centroid and sample, and then the centroid of the cluster is recalculated.

Steps 2 and 3 are repeated until the clusters become stable.

RESULTS

Cluster analysis

The frequency of droughts at different spatial scales changes, largely depending on geographical locations, basin size, and local hydrological factors. Three main clusters can be obtained using the K-means clustering based on drought, which are largely associated with the topography and climate of the region. The heterogeneity test in Table 1 shows that these three clusters are possibly homogeneous regions. Figure 3 shows the geographical location of the three clusters (cluster 1, 2, and 3), corresponding roughly to south Xinjiang, north Xinjiang, and mountainous area (Tianshan Mountain, Altai Mountain, and Pamirs), respectively.

Figure 3

Regionalization of meteorological stations for drought analysis.

Figure 3

Regionalization of meteorological stations for drought analysis.

Trend analysis of Xinjiang province

The inter-decadal variation (1961–2015) of drought shows an obvious decreasing tendency in 1961–2015 (Figure 4). Severe drought is observed in north Xinjiang in the period 1971–1980, which might be related to the most severe drought that occurred in 1974. However, further study is needed to fully elucidate the causes of this phenomenon. The inter-decadal variation of drought shows a gradual shift from the east to the west and then back to the east again over a period of 55 years. The most severe drought occurs in the period 1961–1970 and the more recent period 2001–2015 in the east region, whereas the period 1971–2000 shows the reverse phenomenon.

Figure 4

Spatial distribution of drought in different periods (10 years in all plots except for the last one (5 years)).

Figure 4

Spatial distribution of drought in different periods (10 years in all plots except for the last one (5 years)).

Figure 5(a) shows the inter-annual and inter-decadal variation of drought in the study area based on SPI-12, and Figure 5(b) shows the changing trend of drought obtained by the MK method. Figure 5(a) shows that this region experiences widespread drought from 1961 to the mid-1980s, and subsequently there is a relative humid period with higher SPI-12 values. The average SPI-12 value is −0.365, −0.312, −0.028, 0.211, 0.308, and 0.354 in the period 1961–1970, 1971–1980, 1981–1990, 1991–2000, 2001–2010, and 2010–2015, respectively, thereby indicating that this region becomes increasingly humid over time.

Figure 5

Inter-annual and inter-decadal variations based on SPI-12 (a), changing trend obtained by the MK method (b), and changing trends of different drought grades (c) in Xinjiang during 1961–2015. In Figure 5(a) the portions in red and green represent the six periods of 1961–1970, 1971–1980, 1981–1990, 1991–2000, 2001–2010, and 2010–2015. Please refer to the online version of this paper to see this figure in color: http://dx.doi.org/10.2166/nh.2018.105.

Figure 5

Inter-annual and inter-decadal variations based on SPI-12 (a), changing trend obtained by the MK method (b), and changing trends of different drought grades (c) in Xinjiang during 1961–2015. In Figure 5(a) the portions in red and green represent the six periods of 1961–1970, 1971–1980, 1981–1990, 1991–2000, 2001–2010, and 2010–2015. Please refer to the online version of this paper to see this figure in color: http://dx.doi.org/10.2166/nh.2018.105.

Figure 5(c) shows the changing trends of different drought grades. It shows that the number of sites with ‘normal drought’ shows an increasing trend (17%) since the early 1990s, whereas that with ‘special drought’ decreases dramatically and approaches 0% over the last 4 years. The variation tendency of ‘normal drought’ shows an obvious increasing trend at the rate of 0.33/a, followed by that of ‘medium drought’, ‘mild drought’, ‘special drought’, and ‘severe drought’ with a rate of −0.09/a, −0.06/a, −0.05/a, and −0.04/a, respectively. These results are largely in agreement with those of Li et al. (2012), which also show a decreasing tendency of drought in Xinjiang with additional differentiation of drought grades.

Trend analysis of each cluster

Table 2 shows that the maximum and medium SPI-12 values reach a maximum in cluster 3, followed by those in cluster 2 and cluster 1, indicating the high vulnerability of cluster 1 where water scarcity is a tangible constraint. Figure 6 shows an obvious decreasing tendency of drought in all clusters. Both cluster 2 and cluster 3 show the same changing trend after the change point, and drought decreases significantly with few negative values. However, drought occurs for some time in cluster 1.

Table 2

Basic statistics of regional droughts

Name Numbers of sites Spatial distribution of sites SPI-12 value
 
Medium Max 
Cluster 1 23 Among them 17 sites located in South Xinjiang (74%) −0.024 0.996 
Cluster 2 18 Among them 14 sites located in North Xinjiang (78%) 0.019 1.176 
Cluster 3 10 All of the sites with high altitude in mountainous area 0.033 1.296 
Name Numbers of sites Spatial distribution of sites SPI-12 value
 
Medium Max 
Cluster 1 23 Among them 17 sites located in South Xinjiang (74%) −0.024 0.996 
Cluster 2 18 Among them 14 sites located in North Xinjiang (78%) 0.019 1.176 
Cluster 3 10 All of the sites with high altitude in mountainous area 0.033 1.296 
Figure 6

Drought patterns in each cluster according to SPI-12 annual variations.

Figure 6

Drought patterns in each cluster according to SPI-12 annual variations.

Presence of periodicity over time

The wavelet transform coefficients, which indicate how the energy of the strain signals is divided in the time-frequency domain (Darpe 2007), are shown in the left part of Figure 7, where the x-axis denotes the time parameter and the y-axis denotes the scale parameter that is inversely related to the frequency. The scalogram surface in the right part of Figure 7 shows the position of the dominant energy. The plots of wavelet power spectrum for the SPI-12 data illustrate the periodic oscillation of droughts. A periodicity of 1.5–3.5 year is clearly observed in the wavelet spectrum of cluster 1 and cluster 2 with a cycle of more than 30 years. However, the long-scale patterns (more than 30 years) are relatively weak. Cluster 3 shows a periodicity of 1.5–3.5 years, 5–7 years, 8–10 years and the cycle of more than 30 years. However, the 5–7 year component is the strongest and can affect the entire study period. In summary, there is a strong short-scale periodicity (1.5–10 years) in Xinjiang, which may be related to the atmospheric circulation, one of the main factors affecting climate change. For example, Dai et al. (2012) found that precipitation was greatly affected by the North Atlantic Oscillation.

Figure 7

Dominant periodicities of SPI-12 based drought by Morlet-wavelet analysis in each cluster.

Figure 7

Dominant periodicities of SPI-12 based drought by Morlet-wavelet analysis in each cluster.

Change points

Drought shows an obvious decreasing tendency in the periods before and after the change point (Figures 8(a) and 8(b)), especially in northern Xinjiang, which is in good agreement with that of precipitation and precipitation extremes (Zhang et al. 2012b, 2012c). The significance is calculated by the MK method (Figure 8(c)). It shows that the changing trend is enhanced from the south to the north throughout the study area, and about 55% of sites have a significant alleviating tendency of drought at the 0.01 significance level (|Zs| greater than 2.32). However, droughts at some sites are more, yet not significantly, severe.

Figure 8

Spatial patterns of droughts before (a) and after (b) the change point (shown in Figure 5); (c) the significance test represented by Z values.

Figure 8

Spatial patterns of droughts before (a) and after (b) the change point (shown in Figure 5); (c) the significance test represented by Z values.

DISCUSSION

Occurrence of change points

Table 3 shows a summary of change point analysis of each site. Change point is detected in 65% of sites over the past 55 years. Of the 18 sites with no change point, 14 sites (78%) are located to the south of Tianshan Mountain; whereas of the 33 sites with a change point, 23 sites (70%) are distributed north of Tianshan Mountain. Table 3 also shows the number of change points in the period 1961–1970, 1971–1980, 1981–1990, 1991–2000, and 2001–2010, respectively. It shows that about 4% of change points occur in the period 1971–1980, 74% in the period 1981–1990, and 22% in the period 1990–2000, respectively. This is in line with the occurrence duration shown in Figure 8. However, some abnormal phenomena are also noted. For instance, 30% of sites south of Tianshan Mountain have drought change points in some years (detailed in the table). It can be concluded that the most significant change occurs in the 1980s where there is an abrupt change from warm-dry to warm-wet in northwest China.

Table 3

Change-point analysis of the whole of Xinjiang by MK test

Change point Numbers of sites Distribution of sites Period of change points (number of sites) Significance 
Detected 33 North of Tianshan (23) 1971–1980 (1) Z ≥ 2.32 
1981–1990 (17) 1.9 ≤ Z ≤ 4.92 
1991–2000 (5) 2.22 ≤ Z ≤ 3.22 
South of Tianshan (10) 1971–1980 (4) 2.44 ≤ Z ≤ 2.76 
1981–1990 (2) 2.03 ≤ Z ≤ 3.09 
1991–2000 (2) 2.56 ≤ Z ≤ 3.55 
2000–2010 (2) 1.47 ≤ Z ≤ 2,63 
Not detected 18 North of Tianshan (4) – – 
South of Tianshan (14) – – 
Change point Numbers of sites Distribution of sites Period of change points (number of sites) Significance 
Detected 33 North of Tianshan (23) 1971–1980 (1) Z ≥ 2.32 
1981–1990 (17) 1.9 ≤ Z ≤ 4.92 
1991–2000 (5) 2.22 ≤ Z ≤ 3.22 
South of Tianshan (10) 1971–1980 (4) 2.44 ≤ Z ≤ 2.76 
1981–1990 (2) 2.03 ≤ Z ≤ 3.09 
1991–2000 (2) 2.56 ≤ Z ≤ 3.55 
2000–2010 (2) 1.47 ≤ Z ≤ 2,63 
Not detected 18 North of Tianshan (4) – – 
South of Tianshan (14) – – 

Based on the cluster analysis, the diverse changing trends of droughts are presented in Figure 9. In cluster 1, the change point occurs over a period of time from 1974 to 1986, which differs from that in clusters 2 and 3 in which the change point is distinctly clear and later in time. Figure 9 shows the occurrence of change point in 1986 in cluster 2 and in 1991 in cluster 3, respectively. All clusters show a significant increasing trend at the 0.001 significance level, with a |Zs| value greater than 2.56 (3.48 in cluster 1, 4.78 in cluster 2, and 2.66 in cluster 3, respectively). Future studies are needed to account for the strongest variation in cluster 2, which may be due to the terrain and local microclimate identified during clustering. An example is that the movement of water vapor from the Arctic Ocean can be affected by local terrain and climate, thus causing an increase in precipitation in North Xinjiang. These findings are also supported by previous studies on the effect of climate change on precipitation in Xinjiang (Shi et al. 2002; Feng et al. 2006). It has been suggested that: (1) the global warming effect may result in greater circulation of water masses over the Tibetan plateau and consequently more precipitation in Xinjiang; and (2) the atmospheric humidity and water content increase significantly. Shi et al. (2002) found a significant change in snowmelt in the west Tianshan region since 1987. In addition, we also found a continuous alleviating tendency of drought since 2002, with an average changing rate of 0.016 per decade.

Figure 9

Change point in each cluster.

Figure 9

Change point in each cluster.

Possible factors affecting change point

The occurrence of meteorological drought of interest in this study depends mainly on changing climate, especially the precipitation. The regional variation of dry-wet climate is intimately linked with precipitation and evaporation, and thus climate factors, such as sunshine, temperature, and relative humidity, are potential factors affecting the change point. In addition, the variation of global SST patterns and large-scale atmospheric circulation is also associated with regional changes in precipitation and temperature due to tele-connections (Alexander et al. 2009). Thus, SST is also considered in this study. Correlation analysis was conducted for the early, middle, and latter stages of the change period (1981–1990) and shown in Table 4. The results show a significant relationship between precipitation and the SPI-12 value, and an increase in precipitation can result in a reduction in the severity of drought. However, the relationship between drought and relative humidity is not stable, with the maximum value observed in the middle stage. The SPI-12 value is negatively correlated with temperature, drought, and sunshine, probably because an increase in sunshine can result in high temperature and a large amount of evaporation, and consequently the aggravation of drought. However, SST shows a weak relationship probably due to the location of the study area, because Xinjiang is in the hinterland of China, and the moist air from the ocean can be affected by the Tibetan plateau with an average altitude of above 4,000 m.

Table 4

Correlation analysis of drought-related factors during the earlier, middle and latter stage of change period

  Precipitation Temperature Sunshine Relative humidity SST 
Earlier stage 0.97 −0.28 −0.18 0.76 0.13 
Middle stage 0.95 0.08 −0.20 0.82 0.17 
Latter stage 0.97 −0.17 −0.62 0.45 0.19 
  Precipitation Temperature Sunshine Relative humidity SST 
Earlier stage 0.97 −0.28 −0.18 0.76 0.13 
Middle stage 0.95 0.08 −0.20 0.82 0.17 
Latter stage 0.97 −0.17 −0.62 0.45 0.19 

CONCLUSIONS

This study investigated drought characteristics in Xinjiang province and its sub-areas using MK test, cluster analysis, and Morlet wavelet analysis. The results show that drought in the Xinjiang area is generally becoming less severe, and there is non-uniform spatial variation of drought, which is especially pronounced in northern Xinjiang. Change point analysis shows multiple change point incidents over the past 55 years. Some salient conclusions can be drawn from this study:

  • (1)

    There is a unique spatiotemporal distribution trend of drought in Xinjiang, and the inter-decadal variation of drought shows a gradual spatial shift from the east to the west and then back to the east again in the period 1971–2015.

  • (2)

    North Xinjiang is becoming wetter at a faster rate compared to south Xinjiang, and it has a higher occurrence of change point sites (70%) than south Xinjiang (30%).

  • (3)

    The historical drought situation in Xinjiang is better represented by three clusters rather than a homogeneous region. The periodicity of drought in Xinjiang is stronger in short time scale. Cluster 1 is the driest, cluster 2 has a clear alleviating tendency of drought, and cluster 3 shows late occurrence of change point.

ACKNOWLEDGEMENTS

This research was supported by the China Clean Development Mechanism Fund (2014092, 2014108), the National Science and Technology Support Program (2012BAC19B0305), and the National Natural Science Foundation of China (41671331, 11231010, 11471223). We are grateful to and thank anonymous referees for their valuable comments and constructive suggestions on the manuscript. The authors declare that there is no conflict of interests.

REFERENCES

REFERENCES
Alexander
L. V.
,
Uotila
P.
&
Nicholls
N.
2009
Influence of sea surface temperature variability on global temperature and precipitation extremes
.
Journal of Geophysical Research
114
(
D18
),
3151
3157
.
Cacciamani
C.
,
Morgillo
A.
,
Marchesi
S.
,
Pavan
V.
2007
Monitoring and forecasting drought on a regional scale: Emilia-Romagna region
. In:
Methods and Tools for Drought Analysis and Management
(
Rosso
G.
,
Vega
T.
&
Bonaccorso
B.
, eds).
Springer
,
The Netherlands
, pp.
29
48
.
Dai
X. G.
,
Wang
P.
&
Zhang
K. J.
2012
A study on precipitation trend and fluctuation mechanism in northwestern China over the past 60 years
.
Acta Physica Sinica
62
(
12
),
9201
9211
.
Darpe
A. K.
2007
A novel way to detect transverse surface crack in a rotating shaft
.
Journal of Sound Vibration
305
(
1–2
),
151
171
.
Feng
S.
,
Huang
Y.
&
Xu
Y. P.
2006
Impact of global warming on the water cycle in Xinjiang region
.
Journal of Glaciology and Geocryology
28
(
4
),
523
532
.
Heim
R. R.
2002
A review of twentieth-century drought indices used in the United States
.
Bulletin of the American Meteorological Society
83
(
8
),
1149
1165
.
Hosking
J. R. M.
&
Wallis
J. R.
1997
Regional Frequency Analysis: An Approach Based on L-Moments
.
Cambridge University Press
,
New York
.
IPCC
2014
Summary for Policymakers. Climate Change 2014: Impacts, Adaptation and Vulnerability
.
Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change
.
Kendall
M. G.
1975
Rank Correlation Methods
.
Griffin
,
London
,
UK
.
Lewis
S. L.
,
Brando
P. M.
,
Phillips
O. L.
,
van der Heijden
G. M. F.
&
Nepstad
D.
2011
The 2010 Amazon drought
.
Science
331
(
6071
),
554
.
Li
J.
,
Zhang
Q.
,
Chen
X.
&
Bai
Y.
2012
SPI-based drought variations in Xinjiang, China
.
Journal of Applied Meteorological Science
23
(
3
),
322
330
.
McKee
T. B.
,
Doesken
N. J.
&
Kleist
J.
1993
The relationship of drought frequency and duration to time scales
. In:
Proceedings of Eighth Conference on Applied Climatology
,
Anaheim, CA
,
American Meteorological Society
, pp.
179
184
.
Palmer
W. C.
1965
Meteorological Drought
.
Research Paper No. 45
.
US Weather Bureau
,
Washington, DC
.
Shafer
B. A.
&
Dezman
L. E.
1982
Development of a Surface Water Supply Index (SWSI) to assess the severity of drought conditions in snowpack runoff areas
. In:
Proceedings of the Western Snow Conference
,
Reno, Nevada
, pp.
164
175
.
Sheffield
J.
,
Wood
E. F.
&
Roderick
M. L.
2002
Little change in global drought over the past 60 years
.
Nature
491
(
7424
),
435
438
.
Shi
Y. F.
,
Shen
Y. P.
&
Hu
R. J.
2002
The preliminary discussion of signal, effects and prospects about northwest climate transformation from dry and warm to wet
.
Journal of Glaciology and Geocryology
24
,
19
226
.
Sönmez
İ.
2009
Determination of the Proper Site Spacing Density: Correlation, Power Spectrum, and True Field Error Variance Approaches
.
VDM Verlag Dr. Müller Aktiengesellschaft & Co. KG Publishing
,
Saarbrücken
,
Germany
,
126
.
Wilhite
D. A.
&
Glantz
M. H.
1985
Understanding the drought phenomenon: the role of definitions
.
Water International
10
,
111
120
.
Yan
R. H.
,
Huang
J. C.
,
Wang
Y.
,
Gao
J. F.
&
Qi
L. Y.
2016
Modeling the combined impact of future climate and land use changes on the stream of Xinjiang Basin, China
.
Hydrology Research
47
(
2
),
356
372
.
Zhai
L.
&
Feng
Q.
2011
Dryness wetness climate variation based on Standardized Precipitation Index in Northwest China
.
Journal of Natural Resources
26
(
5
),
847
857
.
Zhang
X. B.
&
Yang
F.
2004
RClimDex (1.0) User Manual
.
Climate Research Branch Environment Canada
,
Downs View
,
Ontario
,
1
23
.
Zhang
Q.
,
Li
J.
,
Singh
V. P.
&
Bai
Y.
2012a
SPI-based evaluation of drought events in Xinjiang, China
.
Natural Hazards
64
(
1
),
481
492
.
Zhang
Q.
,
Li
J.
,
Singh
V. P.
,
Xu
C. Y.
&
Bai
Y. G.
2012b
Changing structure of the precipitation process during 1960-2005 in Xinjiang
.
China. Theoretical & Applied Climatology
110
(
1–2
),
229
244
.
Zhang
Q.
,
Singh
V. P.
,
Li
J. F.
,
Jiang
F.
&
Bai
Y. G.
2012c
Spatio-temporal variations of precipitation extremes in Xinjiang, China
.
Journal of Hydrology
434
,
7
18
.
Zhang
Q.
,
Sun
P.
,
Li
J.
,
Singh
V. P.
&
Liu
J.
2015
Spatiotemporal properties of droughts and related impacts on agriculture in Xinjiang, China
.
International Journal of Climatology
35
(
7
),
1254
1266
.