Front. Earth Sci. China 2009, 3(4): 381–388
DOI 10.1007/s11707-009-0050-4
RESEARCH ARTICLE
Variability and stability of water resource in the arid regions
of China: a case study of the Tarim River basin
Qiang ZHANG (✉)1, Chong-Yu XU2, Hui TAO3
1 Department of Water Resources and Environment, Sun Yat-sen University, Guangzhou 510275, China
2 Department of Geosciences, University of Oslo, Oslo N-0316, Norway
3 State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing
210008, China
© Higher Education Press and Springer-Verlag 2009
Abstract In this study, we analyzed two long term
streamflow records of the Aksu River basin, the main water
source of the Tarim River basin, using multiscale t-test and
F-test with the aim to understand the changing characteristics of hydrological regimes in terms of the first moment
(mean or average) and the second moment (variance). The
results indicate the following: 1) In general, increasing
streamflow was observed in two periods: 1965–1970 and
1980–1985. Since 1986, the streamflow of the Aksu River
has been persistently increasing. 2) Synchronous variations
can be found between the subseries variance and the
subseries mean, i.e., an increase in the subseries mean is
usually consistent with an increase in the subseries
variance, and vice versa. Therefore, streamflow changes
of the Aksu River tend to be unsteady, although streamflow
is persistently increasing since 1986. 3) The streamflow
changes of the Aksu River are heavily dependent on
precipitation and ice melting. Increase of precipitation and
more ice melting in recent decades, particularly after the
1980s, are the major causes of streamflow changes of the
Aksu River basin.
Keywords multiscale variations, streamflow variations,
scanning t-test, scanning F-test, Aksu River, Tarim River
basin
1
Introduction
Global warming has the potential to accelerate hydrological cycle and increase hydrological variability spatially
and temporally (Zhang et al., 2008a). As a result of altered
Received May 13, 2009; accepted June 26, 2009
E-mail: zhangq68@mail.sysu.edu.cn
river flow regimes, e.g., duration, changing magnitude, and
frequency of high or low flows would also be changed.
Climate change, resulting from global warming, might
increase and intensify extreme events (WMO, 2003).
Many studies indicate that the global warming has led to
the changes in global hydrological cycle and to the
increase in global and continental runoff (e.g., Semenov
and Bengtsson, 2002; Labat et al., 2004). Garcia and
Mechoso (2005) have observed increasing trends in
streamflow of the South American rivers starting during
the 1970s. Higher frequency of high and low flows
occurring during certain periods has also been detected in
Swedish rivers (Lindström and Bergström, 2004).
In the Yangtze River basin, more hydrological extremes,
represented by the increasing trend of annual maximum
water level and streamflow have been observed (Zhang et
al., 2006). On the other hand, in northern China, serious
water problems, such as streamflow cessation and water
curtailment, have become more frequent in some rivers,
such as the Yellow River, in recent decades (Xu, 2001,
2002; Xu et al., 2002; Zhang et al., 2008b). This indicates
that the availability of water resources is different from
region to region (Xu, 2000). More attention, therefore,
should be paid to the variability and availability of regional
water resources under current climate change (e.g., Xu,
2002; Kundzewicz, 2004). This is because rational water
resource management hinges on better understanding of
changing properties of hydrological regimes.
This study examines streamflow of the Tarim River, the
longest inland river in China with an annual flow of 4–6
billion cubic meters. The past couple of decades have
witnessed considerable regulation of river flows, with an
aim of satisfying the water needs of the booming economy
in western China, particularly the development of
agriculture. However, hydrological regulation gives rise
to detrimental effects, such as desertification and soil
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Front. Earth Sci. China 2009, 3(4): 381–388
salinity, particularly the water desiccation (dry up) in the
lower Tarim River basin, which, in turn, have inflicted
significant negative effects on the basin’s ecosystem. This
increases the current concerns to water resource problems
linking to climate change in the Tarim River basin (e.g., Xu
et al., 2004; Chen et al., 2006).
The Tarim River basin is located in the arid area of the
northwestern China. Little is known about decadal changes
of hydrological characteristics in the river basins, particularly the water resources of the headstream. Also, little has
been given to the analysis of long-term variations in
hydrologic regimes, including abrupt shifts of means (the
first central moment) and variances (the second central
moment) of streamflow (Aizen et al., 1997). This
constitutes the main objectives of the present study: 1) to
investigate shifts of the first and the second central moment
of the streamflow series at different time scales and 2) to
discuss implications of streamflow changes at different
time scales.
2
Study region: Tarim River basin
Located in the arid region of NW China, the Tarim River is
1321 km long, running from west to east along the northern
edge of the Taklimakan Desert. The drainage area of the
Tarim River is 1.02106 km2, being the largest continental
river in China and highly dependent on the meltwater
supplied by the Tien Shan, Kunlun, Eastern Pamir, and
Karakorum high mountains that surround the basin.
Altogether, there are 114 rivers in the Tarim River basin,
which form nine drainage systems, i.e., Aksu, Hotan,
Yarkant, Qarqan, Keriya, Dina, Kaxgar, Kaidu–Konqi, and
Cheerchen rivers (Fig. 1). The Tarim River is formed by
the confluence of the Kaxgar and Yarkand rivers in the far
west, flowing northeastward, and then converge the Aksu
and the Hotan rivers. Only the Aksu River is the eternal
river, being the Tarim’s most important tributary, supplying 70%–80% of the total water discharge of the Tarim
River (Yang and He, 2003). Due to its exceptional role in
the ecological environment and the economic development, the Aksu River is of great importance for
investigating the changing properties of the hydrological
series of the Tarim basin.
3
Data and methodology
Long series covering 1956–2006 of monthly streamflow
from two hydrological stations, i.e., Shaliguilanke and
Xiehela, as shown in Fig. 1, were used in this study.
Drainage areas of Shaliguilanke station and Xiehela station
are 19166 km2 and 12816 km2, respectively. These two
hydrological stations are located in the upper Aksu River.
Just as what is aforementioned, Aksu River supplies 70%–
80% of the total water discharge of the Tarim River, being
the most important tributary of the Tarim River basin.
These two hydrological stations can well represent the
water variation of the Aksu River in history and, hence, can
help address the variability and availability of water
resources of the Tarim River basin.
Jiang et al. (2002) grafted the wavelet technique (Kumar
and Foufoula-Georgiou, 1994) onto the Student t-test and
the F-test (Cramer, 1946) to develop algorithms for
scanning t-test and scanning F-test, respectively. Wavelet
transform technique mainly aims at detecting periodicity
properties of the time series at different time scale.
Hydrological processes are usually influenced by various
factors at different time scales. Therefore, the methods
used in the present study have the potential to differentiate
external factors influencing the hydrological system. The
scanning t-test attempts to detect significant changes in the
Fig. 1 Location of the Tarim River basin and hydrological stations
Qiang ZHANG et al. Variability and stability of water resource in the arid regions of China
first moment (subseries means or averages) for each time
period at different time scales within a long time series,
while the scanning F-test helps examine significant
changes in the subseries variance (the second moment)
(Jiang et al., 2007)..
The scanning t-test defines the statistic t(n, j) as the
difference of subsample averages between every two
adjoining subseries with equal subseries size (n), i.e.,
tðn, jÞ ¼ ðxj2 – xj1 Þ n1=2 ðs2j2 þ s2j1 Þ – 1=2 ,
(1)
where
xj1 ¼
j–1
jþn – 1
1 X
1 X
xðiÞ, xj2 ¼
xðiÞ,
n i¼j – n
n i¼j
s2j1 ¼
j–1
1 X
xðiÞ – xj1 Þ2 ,
n – 1 i¼j – n
s2j2 ¼
jþn – 1
1 X xðiÞ – xj2 Þ2 ,
n – 1 i¼j
where subsample size n may vary as n = 2, 3,..., < N/2, or
may be selected at suitable intervals. j = n + 1, n + 2...,
N – n + 1 are the reference time point.
The Table-Look-Up Test (Von Storch and Zwiers, 1999)
was adopted to correct the significance criterion of statistic
t(n, j) based on lag-1 autocorrelation coefficients of the
pooled subsample and the subsample of size n since
hydrological series are usually autocorrelated. Criterion
t0.05 for the correction of the dependence is adopted to
determine significant changes at time scales longer than 30
years. For shorter subsample sizes, the critical values
usually are overly restrictive. Since the significance level
varies with n and j, to make values comparable, the test
statistic was normalized as
tr ðn,jÞ ¼ tðn,jÞ=t0:05
(2)
When |tr(n, j)| > 1.0, the change is significant at the 95%
confidence level. tr(n, j) < – 1.0 denotes a significant
decrease, and tr(n, j) > 1.0 denotes a significant increase.
Significant changes of subseries variances can be
defined as
8
– ðSj12 =Sj22 Þ=Fα , f or Sj2 < Sj1
>
>
<
Fr ðn, jÞ ¼ 0, f or Sj2 ¼ Sj1 or Sj1 ¼ 0, Sj2 ¼ 0, (3)
>
>
: 2 2
ðSj2 =Sj1 Þ=Fα , f or Sj2 > Sj1
where the subsample standard deviations Sj1 and Sj2 are
calculated in the same way as in Eq.(1). Fα is a threshold
value for the effective degree of freedom after the
correction of dependence and in a normalized distribution
for the time series. The effective degree of freedom for the
correction of dependence can be estimated as (Hammersley, 1946):
Ef ðnÞ ¼ f ðnÞ hXk
i–1
2
r
ðτÞ
, rðkÞ ! 0
τ¼0
383
(4)
where f(n) is the degree of freedom listed in the F form.
A local minimum in Fr(n, j) < – 1.0 denotes a
significant change toward a smaller variance, i.e., the
record becomes much steadier, whereas a local maximum
in Fr (n, j) > 1.0 indicates a significant change towards a
larger variance, i.e., the records become more unsteady
(Jiang et al., 2007). The computation procedure was
performed by writing computer program with Matlab
software package. The results are three columns of data.
The first column denotes time interval, i.e., from 1950 to
2005; the second column is the time scales; and the third
column is tr(n, j) values. The contours were made by using
Surfer software package.
4
Results
4.1
Detection of stream flow change with scanning t-test
The result of the scanning t-test for the streamflow series at
the Shaliguilanke station was illustrated in Fig. 2 and that
of the Xiehela station was shown in Fig. 3. In terms of
changes in streamflow at the Shaliguilanke station, based
on the local maxima and minima of t-test values, six
positive (significant increase in streamflow) and three
negative (significant decrease in streamflow) centers were
identified in the streamflow series (at 95% confidence
level) Fig. 2(a). The first statistically significant change
point toward a decrease in the streamflow was detected
with a negative center of the contours in 1962 at a 38month time scale. This negative center was followed by the
positive center in 1966 at a 53-month time scale. This
increase in streamflow extended to 1978, and then, the
streamflow declined. After 1981, another increase in
streamflow occurred and was followed by a reduction
between 1981 and 1983 (Fig. 2(b)). This reduction
occurred at a time scale of 152 months (Fig. 2(a)). After
1985, three significant changes in increasing streamflow
were identified at all time scales, i.e., from 32 months to
256 months (Fig. 2(a)). The Fig. 2(a) also indicates that
two decreases of streamflow can also be detected at a time
scale of 32 months, centered in 1990 and 1997. In general,
there were two episodes of increase and one episode of
decrease detected at time scales longer than 64 months;
five episodes of decrease and five episodes of increase
were identified at time scales shorter than 45 months.
Fig. 2(b) further illustrates significant changes in the mean
streamflow.
The streamflow series of Xiehela station showed a
significant increase in the year of 1966, 1978, 1990, and
2002. At time scales longer than 128 months, streamflow at
Xiehela station increased greatly with the change point in
1978 (Fig. 3(a)). At time scales of 46–128 months, four
episodes of increase and two episodes of decrease were
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Front. Earth Sci. China 2009, 3(4): 381–388
Fig. 2 (a) Contours of the normalized scanning t-test standardized by the “Table-Look-up” critical value t0.05 for the standardized
streamflow series at the Shaliguilanke station (Contour interval is 0.2, and the zero-contour is hidden; solid lines denote positive values,
and dashed lines negative values.); (b) Change points and episode averages (thick solid line) resulted from (a) (The thin solid line indicates
Gaussian filtering of the standardized streamflow series.)
detected. At time scales shorter than 46 months, however,
six episodes of increase and six episodes of decrease were
identified. The first change point was characterized by a
decrease in streamflow in 1962, and this decrease extended
till 1966. A significant increase of streamflow was
distinguished by a positive center in 1966 at a time scale
of 32–45 months. The period between 1966 and 2006 was
divided into four periods characterized by an increase in
streamflow, and three episodes dominated by a decrease in
streamflow.
It can be seen in Fig. 3(b) that the mean streamflow of
the decrease episode between 1999 and 2002 is larger than
that of the decrease episode between 1984 and 1990.
4.2 Detection of streamflow variation change with scanning
F-test
The scanning F-test of the streamflow series of the
Shaliguilanke station was calculated at the same time
scales, as in Fig. 2. At time scales longer than 64 months,
two positive (increases of subseries variances) and one
negative (decrease in subseries variances) significant
changes were detected with local maxima and minima in
the contours (Fig. 4). More variations of significant
changes were detected at shorter time scales. At time
scales shorter than 64 months, six positive significant
changes were identified in the streamflow series of the
Shaliguilanke station. Comparison between Fig. 4 and
Fig. 2(a) shows that the time when the changes of the
second moment (subseries variances) start is similar to
those of the first moment (subseries means), particularly at
longer time scales. Substantial differences between change
years of subseries variances and subseries means can be
distinguished at shorter time scales.
More frequent variations of significant changes in
subseries variances can be observed in the streamflow
series of the Xiehela station (Fig. 5) than those in the
streamflow series of the Shaliguilanke station (Fig. 4).
Taking time scales of 64 months as a threshold value, six
episodes of decrease and five episodes of increase of
subseries variances were observed at time scales longer
than 64 months; six episodes of decrease and five episodes
of increase in subseries variances were detected at time
scales shorter than 64 months. Comparison between Fig. 5
and Fig. 3(a) indicates that the years with significant
change in subseries means are similar to those of subseries
Qiang ZHANG et al. Variability and stability of water resource in the arid regions of China
385
Fig. 3 (a) Contours of the normalized scanning t-test for the standardized streamflow series at the Xiehela station at 95% confidence
level (Contour interval is 0.2, and the zero-contour is hidden; solid lines denote positive values, and dashed lines negative values.); (b)
change points and episode averages (thick solid line) indicated from (a) (The thin solid line indicates Gaussian filtering of the standardized
streamflow series.)
Fig. 4 Contours of the scanning F-test on standardized streamflow series at the Shaliguilanke station at 95% confidence level (Contour
interval is 0.2 with the zero-contour lines hidden, solid lines denote positive values, and dashed lines negative values)
variances. A smaller standard deviation means a steadier
hydrological changes, and vice versa. These results imply
synchronous variability of significant changes of subseries
means and variances. To further clarify these assumptions,
changes in subseries variances and subseries means were
placed in the same plot for studying connections between
changes in subseries variances and subseries means
(Figs. 6 and 7). It can be observed that a decrease in the
mean streamflow is usually accompanied by a decrease in
subseries variance. Therefore, an increase in streamflow
usually means unsteady changes in streamflow.
5
Discussion and conclusions
In this study, the statistical properties, particularly the
changes in subseries means and variances, were analyzed
using multiscale t-test and F-test techniques. Two long
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Front. Earth Sci. China 2009, 3(4): 381–388
Fig. 5 Contours of the scanning F-test on standardized streamflow series at the Xiehela station at 95% confidence level (Contour interval
is 0.2 with the zero-contour lines hidden, solid lines denote positive values, and dashed lines negative values)
Fig. 6 Episode averages (b) and standard deviations (a) of standardized streamflow series at the Shaliguilanke station
Fig. 7
Episode averages (b) and standard deviations (a) of standardized streamflow series at the Xiehela station
Qiang ZHANG et al. Variability and stability of water resource in the arid regions of China
monthly streamflow series derived from two hydrological
stations located in the main headstream, the Aksu River,
were analyzed. The Aksu River provides more than 70% of
streamflow for the Tarim River basin; therefore, statistical
properties of the streamflow series of the Aksu River
reflect the main changing properties of the surface water
resources of the Tarim River basin.
Multiscale t-test indicates that the streamflow changes of
Shaliguilanke and Xiehela station present similar statistical
properties. The streamflow increased before 1960s, which
was followed by a decreasing trend. Increased streamflow
also occurred in the year of 1965, 1970, 1980, and 1985.
After 1985, the streamflow of the Aksu River persistently
increased. Comparison of changing properties of streamflow of the Shaliguilanke station and the Xiehela station
indicates subtle differences in that there are two episodes
dominated by decreasing mean streamflow at the Xiehela
station. Actually, increasing streamflow can reflect major
changing properties of streamflow of the Xiehela station
after 1978, although two episodes of decrease in streamflow can be identified after 1978. These two decreases in
mean streamflow of the Shaliguilanke station are not
obvious when compared to those of the Xiehela station.
Multiscale F-test indicates that more frequent variations
in subseries variances can be observed in the streamflow
series at the time scales of < 64 months at the Xiehela
station when compared to those at the Shaliguilanke
station. This result evidences more frequent shifts in
subseries variances from one time interval to another,
reflecting unsteady streamflow changes at the Xiehela
station than those at the Shaliguilanke station with time
scales of < 64 months. Comparison of multiscale t-test and
F-test results illustrates nearly synchronous occurrence of
subseries means and variances, implying that increasing
streamflow usually gives rise to unsteady streamflow
variability.
In the Tarim River basin, precipitation experienced a
significant decrease in the 1970s, followed by an increase
in the 1980s and the 1990s (Chen et al., 2006; Zhang et al.,
2009). Changing properties of streamflow were in good
agreement with those of precipitation changes, i.e., a
decrease in streamflow in the 1970s and an increase in
streamflow in the 1980s, particularly a persistent increase
in streamflow after the 1990s. The results on the changing
properties of streamflow in the Tarim River basin achieved
in this study is of practical significance in regional water
resource management and ecological environmental conservations under changing climate in the Tarim River
basin.
Acknowledgements The research was financially supported by the ‘985’
project (No. 37000-3171315), innovative project from Nanjing Institute of
Geography and Limnology, CAS (No. CXNIGLAS200814; 08SL141001280052) and by the 111 Project under B08048, Ministry of Education and
State Administration of Foreign Experts Affairs of China. Prof. Zhongyuan
Chen kindly helped to improve the language of this paper. Thanks should be
extended to two anonymous reviewers and the editor, Prof. Dr. Zhanghua
387
Wang, for their invaluable comments and suggestions which greatly help to
improve the quality of this paper.
References
Aizen V B, Aizen M E, Melack M J, Dozier J (1997). Climatic and
hydrologic changes in the Tien Shan, Central Asia. Journal of
Climate, 10: 1393–1404
Chen Y N, Takeuchi K, Xu C C, Chen Y P, Xu Z X (2006). Regional
climate change and its effects on river runoff in the Tarim Basin,
China. Hydrological Processes, 20: 2207–2216
Cramer H (1946). Mathematical Method of Statistics. Princeton:
Princeton University Press
Garcia N O, Mechoso C R (2005). Variability in the discharge of South
America rivers and in climate. Hydrologic Sciences Journal, 50(3):
459–478
Hammersley J M (1946). Discussion of papers, J Roy Statist Soc,
8, 91
Jiang J M, Gu X Q, Ju J H (2007). Significant changes in subseries
means and variances in an 8000-year precipitation reconstruction
from tree rings in the southwestern USA. Ann Geophys, 25: 1–12
Jiang J M, Mendelssohn R, Schwing F,et al (2002). Coherency detection
of multiscale significant changes in historic Nile flood levels.
Geophys. Res Lett, 29(8): 112-1–112-4
Kumar P, Foufoula-Georgiou E (1994). Wavelet Analysis in Geophysics: An Introduction. In: Foufoula-Georgiou E, Kumar P, eds.
Wavelets in Geophysics. San Diego: Academic Press, 1–43
Kundzewicz Z W (2004). Searching for change in hydrologic data.
Hydrologic Sciences Journal, 49(1): 3–6
Labat D, Goddéris Y, Probst J L, et al (2004). Evidence for global runoff
increase related to climate warming. Advances in Water Resources,
27: 631–642
Lindström G, Bergström S (2004). Streamflow trends in Sweden 1807–
2002. Hydrologic Sciences Journal, 49(1): 69–83
Semenov V, Bengtsson L (2002). Secular trends in daily precipitation
characteristics: greenhouse gas simulation with a coupled AOGCM.
Climate Dynamics, 19: 123–140
Von S H, Zwiers F (1999). Statistical Analysis in Climate Research.
Cambridge: Cambridge University Press, 116
World Meteorological Organization (WMO) (2003). Statement on the
Status of Global Climate in 2003. Geneva: WMO Publication
Xu C Y (2000). Modeling the effects of climate change on water
resources in central Sweden. Water Resources Management, 14:
177–189
Xu J X (2001). High-frequency zone of river desiccation disasters in
China and the influencing factors. Environ Manage, 28: 101–113
Xu J X (2002). River sedimentation and channel adjustment of the lower
Yellow River as influenced by low discharges and seasonal channel
dry-ups. Geomorphology, 43: 151–164
Xu Z X, Chen Y N, Li J Y (2004). Impact of climate change on water
resources in the Tarim River basin. Water Resources Management,
18: 439–458
Xu Z X, Takeuchi H, Ishidaira H, et al (2002). Sustainability analysis for
Yellow River water resources using the system dynamics approach.
Water Resources Management, 16(3): 239–261
388
Front. Earth Sci. China 2009, 3(4): 381–388
Yang Q, He Q (2003). Interrelationship of climate change, runoff and
human activities in Tarim River basin. Journal of Applied
Meteorological Science, 14(3): 309–321 (in Chinese with English
abstract)
Zhang Q, Liu C L, Xu C Y, Xu Y P , Jiang T (2006). Observed trends of
water level and streamflow during past 130 years in the Yangtze
River basin, China. Journal of Hydrology, 324(1–4): 255–265
Zhang Q, Xu C Y, Gemmer M, Chen Y Q (2008a). Changing properties
of precipitation concentration in the Pearl River basin, China.
Stochastic Environmental Research & Risk Assessment, DOI
10.1007/s00477-008-0225-7
Zhang Q, Xu C Y, Yang T (2008b). Variability of water resource of the
Yellow River basin. Water Resources Management, DOI 10.1007/
s11269-008-9320-2
Zhang Q, Xu C Y, Tao H, Jiang T, Cheng Y Q (2009). Climate changes
and their impacts on water resources in the arid regions: a case study
of the Tarim River basin, China. Stochastic Environmental Research
and Risk Assessment (In press)