Stoch Environ Res Risk Assess (2009) 23:1071–1087
DOI 10.1007/s00477-008-0278-7
ORIGINAL PAPER
Spatio-temporal changes of hydrological processes and underlying
driving forces in Guizhou region, Southwest China
Tao Yang Æ Xi Chen Æ Chong-Yu Xu Æ
Zhi-Cai Zhang
Published online: 2 October 2008
Springer-Verlag 2008
Abstract Understanding the changes in streamflow and
associated driving forces is crucial for formulating a sustainable regional water resources management strategy in
the environmentally fragile karst area of the southwest
China. This study investigates the spatio-temporal changes
in streamflow of the Guizhou region and their linkage with
meteorological influences using the Mann–Kendall trend
analysis, singular-spectrum analysis (SSA), Lepage test,
and flow duration curves (FDCs). The results demonstrate
that: (1) the streamflow in the flood-season (June–August)
during 1956–2000 increased significantly (confidence level
C95%) in most catchments, closely consistent with the
distinct increasing trend of annual rainfall over wet-seasons. The timings of abrupt change for streamflow in most
catchments are found to occur at 1986; (2) streamflow in
the Guizhou region experiences significant seasonal changes prior/posterior to 1986, and in most catchments the
coefficient of variation of monthly streamflow increases;
(3) spatial changes in streamflow indicate that monthly
streamflow in the north-west decreases but increases in
other parts; (4) the spatial high- and low-flow map (Q5 and
Q95) reveals an increase in the extremely large streamflow
in the five eastern catchments but a decrease in the
T. Yang (&) X. Chen Z.-C. Zhang
State Key Laboratory of Hydrology,
Water Resources and Hydraulics Engineering,
Hohai University, 210098 Nanjing, People’s Republic of China
e-mail: enigama2000@hhu.edu.cn; tfrank.yang@gmail.com
C.-Y. Xu
Department of Geosciences, University of Oslo,
Oslo, Norway
T. Yang
Yellow River Institute of Hydraulic Research,
450003 Zhengzhou, China
extremely low streamflow in the four eastern catchments
and three western catchments during 1987–2000. An
increase in streamflow, particularly extreme flows, during
the flood season would increase the risk of extreme flood
events, while a decrease in streamflow in the dry season is
not beneficial to vegetation restoration in this ecologically
fragile region.
Keywords Spatio-temporal Changes Hydrological processes The Guizhou karst region Trend test FDCs Detection of change-point
1 Introduction
Understanding the underlying behavior of interaction
between the hydrologic regime, climate factors, and
anthropogenic effects is important for formulating a sustainable regional management strategy (Zheng et al. 2007;
Kim et al. 2007; Yang et al. 2008). Changes in the
hydrologic regime of a catchment may indicate the impact
of climate change on stream flow (Aguado et al. 1992).
Since the publication of the Third Assessment Report of
the Intergovernmental Panel on Climate Change (McCarthy et al. 2001), considerable efforts have been made to
detect trends of hydrological variables and shifts in stream
flow around the world (Zhang et al. 2001, 2006, 2007a;
Burn and Elnur 2002; Lin et al. 2005; Becker et al. 2006).
The results of these efforts have shown distinct trends in
stream flow (McCarthy et al. 2001). However, these trends
cannot all be definitively ascribed to changes in regional
temperature and/or precipitation. The continuing land-use
change in many catchments has exercised considerable
impacts on the hydrological processes (e.g., Huang and
Zhang 2004; Zheng et al. 2007).
123
1072
With the intensifying human activities and climate
change in the region, Southwestern China experiences
more frequent natural hazards, such as flash floods, debris
flows, landslides and droughts, which have led to a number
of regional social and environmental issues, e.g., economic
and life losses as well as eco-environmental deterioration
(Zhang et al. 2002, 2006, 2007a). The Guizhou Province,
located in the eastern part of the Yunnan-Guizhou Plateau,
China, is one of the largest and continuous karst areas in
the world. The southwest karst area is featured by an
extremely fragile environment resulting from serious land
degradation, termed ‘‘karst rocky desertification’’ (Song
et al. 1983; Yang 1988; Wang et al. 2004a, b). Attempts
have been made to understand the driving forces of the
changes in streamflow in this region (Chen et al. 2005;
Zhang et al. 2007b; Sen 2008). Intensive water utilization
in the region has been identified as the main force leading
to serious environmental problems (GPDWR 2004). It is
also recognized that other factors, such as climate change
or variability and land-use change, together may have
contributed to these changes in the flow regime (GPDWR
2004). However, most of the previous studies were
conducted in one or a limited number of catchments for
the evaluation of eco-environmental impacts of karst
rocky desertification in the Guizhou karst area (Chen et al.
2005). Meanwhile, literature addressing the potential
impacts of climate changes on the regional hydrological
processes in this region is not available, and no study
concerning the quantification of hydrological changes has
been found covering all major catchments in the Guizhou
karst area to assess the governing behavior regarding the
hydrological changes in a regional perspective. Furthermore, adequate concerns have not been addressed as
regards the prospective consequences resulting from these
changes.
With this consideration in mind, this paper attempts to
detect and assess the spatio-temporal changes in the
regional hydrological processes of the Guizhou karst area
under dual-interferences of the climate change and human
activities during the second half of the twentieth century.
The specific purposes of this study are therefore: (1) to
identify trends in annual stream flow since the 1950s; (2) to
quantify interannual and intra-annual variability in stream
flow; and (3) to examine the spatio-temporal changes in the
streamflow regime by the flow durations curve (FDC)
method. The underlying driving forces leading to these
changes of hydrological regimes are also addressed. The
results of this investigation can provide important insights
into the key hydrological processes in supporting ecoenvironmental restoration, management and natural disaster mitigation in the mountainous regions surrounded with
vulnerable environment.
123
Stoch Environ Res Risk Assess (2009) 23:1071–1087
2 Study region
Guizhou Province, located in southwestern China (Fig. 1),
has one of the largest, continuous karst areas in the world.
It covers 17,600 km2 with a population of 32.4 million.
About 73% of Guizhou is karst, which is underlain by up to
10,000 m of soluble carbonate rocks. Eighty-seven percent
of the province is a mountainous plateau, 10% is hilly and
only 3% is classified as flat (Zeng 1994; GPDWR 2004).
The study area has a subtropical wet monsoon climate. The
mean annual temperature is 20.1C, of which the highest
average monthly temperature is in July, and the lowest is in
January. Annual precipitation is 1,300 mm, with a distinct
summer wet season and a winter dry season. The annual
average runoff ranges from 200 to 1,200 mm.
Monthly streamflow records (1956–2000) are selected
from ten hydrological gauges at eight major rivers (namely,
the Wu, Beipan, Hongshu, Liu, Qinshui, Wuyang, Jin, and
Furong River) in the Guizhou Province. Streamflow
changes in these eight catchments can represent the basic
hydrological regimes of the whole Guizhou Province.
Monthly precipitation, temperature, pan evaporation, and
hours of solar radiation records (1956–2000) for 19 sites in
Guizhou were utilized in this investigation (Fig. 1). More
detailed information concerning hydrological records of
these gauging stations is listed in Table 1.
3 Methodology
The methods used in calculating the actual evapotranspiration, trend detection, change point identification, spatial
mapping are presented in the following subsections.
3.1 Actual evapotranspiration estimation model
Granger (1989) showed that an equation similar to Penman
could also be derived following the approach of Bouchet’s
(1963) complementary relationship. Granger and Gray
(1989) derived a modified form of Penman’s equation for
estimating the actual evapotranspiration from different
non/saturated land covers:
ETa ¼
DG
cG
Rn =k þ
Ea
DG þ c
DG þ c
ð1Þ
where G is a dimensionless relative evapotranspiration
parameter, defined as the ratio of actual to potential
evapotranspiration, Rn is the net radiation near the surface,
D is the slope of the saturation vapour pressure curve at the
air temperature, c is the psychrometic constant, k is the
latent heat, and Ea is the drying power of the air (Penman
1948):
Stoch Environ Res Risk Assess (2009) 23:1071–1087
0
103 E
0
104 E
0
0
105 E
0
0
107 E
106 E
0
108 E
109 E
0
29 N
0
29 N
0
28 N
0
27 N
0
28 N
0
27 N
Latitudeo(N)
Fig. 1 The map demonstrates
the study region in Guizhou
Karst area (Bordered in darkblack shaded area), China. It is
composed of eight major
catchments, whose names
together with their
representative hydrological
gauges are listed as following:
(1) Beipan River/Zhedong
Station; (2) Hongshui River/
Tian’e station; (3) Liu River/
Shihuichang station; (4) Qinshui
River/Jinping station; (5)
Wuyang River/Chongtan
station; (6) Jin River/Lujiadong
station; (7) Wu River/Yachihe
station (Upper-stream),
Jiangjiehe station (Middlestream), and Sinan station
(Lower-stream); (8) Furong
River/Changba station
1073
0
0
26 N
26 N
0
0
25 N
25 N
0
103 E
0
104 E
0
0
105 E
0
106 E
0
107 E
0
108 E
109 E
o
Longtitude (E)
Table 1 Detailed information of the hydrological outlet-gauges for eight major catchments of the Guizhou province (1956–2000)
Cv of streamflow
Catchment
Outlet station
Abbreviation
Location
1
Upper Wu River
Yachihe
YCH
106.12E
26.82N
18,187
333.4
0.20
Middle Wu River
Jiangjiehe
JJH
107.41E
27.33N
42,306
694.5
0.21
Lower Wu River
Sinan
SN
108.25E
27.93N
51,270
872.3
0.19
2
Beipan River
Zhedong
ZD
105.98E
25.04N
20,372
387.8
0.26
3
Hongshui River
Tian’e
TE
107.15E
24.99N
105,830
1571.7
0.22
4
Liu River
Shihuichang
SHC
108.49E
25.89N
6,554
141.7
0.23
5
6
Qinshui River
Wuyang River
Jinping
Chongtan
JP
CT
109.20E
108.92E
26.68N
27.25N
13,483
5,055
271.1
87.3
0.20
0.22
7
Jin River
Lujiadong
LJD
109.23E
27.72N
3,346
89.2
0.21
8
Furong River
Changba
CB
107.68E
28.80N
5,454
108.1
0.21
Ea ¼ 0:0026ð1 þ 0:54U2 Þðes ea Þ
ð2Þ
In which, U2 is wind speeds at 2-m elevation, and es and
ea are saturated and actual vapor pressure, respectively.
Granger and Gray (1989) showed that there exists a unique
relationship between G and a parameter which they called
the relative drying power, D, given as
Ea
D¼
ð3Þ
Ea þ Rn
and
G¼
1
1 þ 0:028e8:045D
ð4Þ
Drainage
area (km2)
Mean streamflow
(m3/s)
No
Later on, Granger (1998) modified Eq. 4 to:
G¼
1
þ 0:006D
0:793 þ 0:20e4:902D
ð5Þ
Xu and Singh (2005) reported that the performance of
this model (GG) in temperate humid region is encouraging,
thus is hereby applied for the estimation of actual evapotranspiration for the Guizhou province. The model
parameters for each site are calibrated by the annual
evapotranspiration estimated by water-balance equation as
addressed by Xu and Singh (2005).
123
1074
Stoch Environ Res Risk Assess (2009) 23:1071–1087
3.2 Mann–Kendall trend analysis
Sen’s T and the Mann–Kendall (MK) trend test are regarded as powerful tools in exploring trends of hydrological
series (Yu et al. 1993; Van Belle and Hughes 1984; Zhang
et al. 2006, 2007a). The rank-based MK method (MK)
(Mann 1945; Kendall 1975) is highly recommended by the
World Meteorological Organization to assess the significance of monotonic trends in streamflow series (Mitchell
et al. 1966), for it has an advantage of not assuming any
distribution form for the data and has the same power as its
parametric competitors. In the test, the null hypothesis H0
is that the deseasonalized data (x1,…., xn) are a sample of n
independent and identically distributed random variables
(Yu et al. 1993). The alternative hypothesis H1 of a twosided test is that the distribution of xk and xj are not identical for all k, j B n with k = j (Kahya and Kalayci 2004).
The test statistic S is computed with Eqs. 6 and 7 as:
S¼
n1 X
n
X
sgnðxj xk Þ
ð6Þ
k¼1 j¼kþ1
8
< þ1
0
sgnðxj xk Þ ¼
:
1
if ðxj xk Þ [ 0
if ðxj xk Þ ¼ 0
if ðxj xk Þ\0
ð7Þ
The statistics S is approximately normally distributed
when n C 8, with the mean and the variance as follows:
EðSÞ ¼ 0
ð8Þ
VarðSÞ ¼
nðn 1Þð2n þ 5Þ Pn
i¼1 ti iði
1Þð2i þ 5Þ
ð9Þ
18
where ti is the number of ties of extent i.
The standardized statistics (Z) is formulated as:
8 S1
pffiffiffiffiffiffiffiffiffiffi if S [ 0
>
< VarðSÞ
0
if S ¼ 0
Z¼
>
: pSþ1
ffiffiffiffiffiffiffiffiffiffi if S\0
ð10Þ
VarðSÞ
In a two-sided test for trend, the H0 of no trend should
be rejected if |z| [ Za/2 at the a level of significance. A
positive Z indicates an upward trend and vice versa (Kahya
and Kalayci 2004). The effect of the serial correlation on
the MK test was eliminated using a pre-whitening
technique (e.g., Yue and Wang 2002).
3.3 Change-point detection based on singular-spectrum
analysis
Singular-spectrum analysis (SSA) is recognized as a useful
method for a change-point detection (Moskvina 2001,
Moskvina and Zhigljavsky 2003) and it will be used in
current study to investigate the change points of streamflow
in Guizhou province. For the sake of reading, the method
123
used in the study is abstracted from (Moskvina 2001,
Moskvina and Zhigljavsky 2003) and is briefly described as
follows. Let x1, x2,… be a time series, M and N be two
integers (M B N/2), and set K = N – M ? 1. Define the
vectors Xj = (xj,…, xj?M-1)T (j = 1, 2,…) and the matrix:
X ¼ ðxiþj1 ÞM;K
i;j¼1 ¼ ðX1 ; . . .; XK Þ
ð11Þ
which is called the trajectory matrix. We consider X as
multivariate data with M characteristics and K observations. The columns Xj of X, considered as vectors, lie in the
M-dimensional space RM. The singular value decomposition (SVD) of the so-called lag-covariance matrix R = XXT
(and of the trajectory matrix X itself) provides us with a
collection of M eigenvalues and eigenvectors. A particular
combination of a certain number l \ M of these eigenvectors determines an l-dimensional hyperplane in RM.
According to the SSA algorithm, the M-dimensional data is
projected onto this l-dimensional subspace and the subsequent averaging over the diagonals gives us an
approximation to the original series.
One of the features of the SSA algorithm is that the
distance between the vectors Xj (j = 1,…,K) and the ldimensional hyperplane is controlled by the choice of l and
can be reduced to a rather small value. If the time series
{xt}Nt=1 is continued for t [ N and there is no change in the
mechanism which generates values xt; then this distance
should stay reasonably small for Xj,j C K (for testing, we
take L such vectors). However, if at a certain time N ? s the
mechanism generating xt (t C N ? s) has changed, then an
increase in the distance between the l-dimensional hyperplane and the vectors Xj for j C K ? s is to be expected.
The SSA expansion tends to pick up the main structure
of the time series, if there is one (This happens when the ldimensional subspace approximates well the M-dimensional vectors X1,…, XK). If this structure is being found
and there are no structural changes, then the SSA continuation of the time series should agree with the continued
series (that is, the L vectors Xj for j C K should stay close
to the l-dimensional subspace). A change in the structure of
the time series should force the corresponding vectors Xj
out of the subspace. SSA performs the analysis of the time
series structure in a nonsequential (off-line) manner.
However, a change-point detection is typically a sequential
(on-line) problem, and we aim to develop an algorithm that
can be used in the on-line regime. This can be achieved by
sequentially applying the SVD to the lag-covariance
matrices computed in a sequence of time intervals, either
[n ? 1,n ? N] or [1, n ? N]. Here n = 0,1,…, is the iteration number and N is the length of the time interval where
the trajectory matrix is computed. The following presents a
reasonable choice of the key parameters (N, M, p, q) in
detecting the significant changes of noisy series using the
SSA approach.
Stoch Environ Res Risk Assess (2009) 23:1071–1087
1075
Window width (N): The choice of N depends on the
kind of structural changes we are looking for. A general
rule is to choose N reasonably large. However, if we
allow small gradual changes in the time series then we
could not take N very large. Also, structural changes
should not happen too often; ideally, at most one change
may occur in any subseries of length N. If N is too large,
then we can either miss or smooth out the effects of
changes in the time series.
Lag (M): If N is not very large, which should be
regarded as the most interesting case in practice, by default
we choose M = N/2 and I = {1,…, l}, where l is such that
the first l components describe well the signal and the
lower M - l components correspond to noise.
Length and location of the test sample (p, q): a general
recommendation is to choose p C K, this makes columns of the base and test matrices different. If
p C K = M ? K - 1, then the base and test matrices
consist of different elements. This choice of p is reasonable
if the delay time between the change-point and the moment
of its detection permits such a choice.
Herein, only summarized description of the computation
procedure is provided, more detailed information of the
algorithm and choice of key parameters can be referred to
Moskvina (2001), Moskvina and Zhigljavsky (2003).
The terms in Eq. 12 can be derived based on the following equations:
3.4 Lepage change-point test
Statistical features of the segments divided by change
points are detected by the mean and coefficient of variation
(Cv). The mean, lx, of a random variable, X, is its expected
value. Thus,
The Lepage test is a non-parametric, two-sample test for
location and dispersion (Lepage 1971) which has been
widely used to detect changes such as long-term trends,
cyclic variations and step-like changes for rainfall (Yonetani 1993; Benjamin and Roger 2005; Matsuyama et al.
2002). The Lepage assumes that the size of the studied
series is equal to or greater than ten and the Lepage statistic
(HK) follows the Chi-square (v2) distribution with two
degrees of freedom. The Lepage statistic (HK) is a sum of
the squares of the standardized Wilcoxon’s and Ansari–
Bradley’s statistics, i.e.,
HK ¼
½W EðWÞ2 ½A EðAÞ2
þ
VðWÞ
VðAÞ
ð12Þ
If HK exceeds 5.99 the difference between two sample
means is judged as significant at the 95% confidence level
(i.e., 5% significance level). HK is calculated as follows.
Let x = (x1, x2,…, xn1) and y = (y1, y2,…, yn2) be two
independent samples of size n1 and n2. Assume that
ui = 1 if the ith smallest observation in a combined
sample of the size (n1 ? n2) belongs to x and ui = 0 if it
belongs to y.
W¼
nX
1 þn2
ð13Þ
iui
i¼1
n1 ðn1 þ n2 þ 1Þ
2
EðWÞ ¼
n2 n1 ðn1 þ n2 þ 1Þ
2
n
n
þn
1
1
X
X2
A¼
iui þ
ðn1 þ n2 i þ 1Þui
VðWÞ ¼
i¼1
ð14Þ
ð15Þ
ð16Þ
i¼n1 þ1
If n1 ? n2 is even, E(A) and V(A) will be estimated as:
EðAÞ ¼
n1 ðn1 þ n2 þ 2Þ
4
ð17Þ
VðAÞ ¼
n1 n2 ðn1 þ n2 2Þðn1 þ n2 þ 2Þ
48ðn1 þ n2 1Þ
ð18Þ
If n1 ? n2 is odd, E(A) and V(A) will be estimated as:
EðAÞ ¼
VðAÞ ¼
n1 ðn1 þ n2 þ 1Þ2
4ðn1 þ n2 Þ
n1 n2 ðn1 þ n2 þ 1Þ½ðn1 þ n2 Þ2 þ 3
48ðn1 þ n2 Þ2
lx ¼ EðXÞ ¼ l01
ð19Þ
ð20Þ
ð21Þ
A sample estimate of the population mean is the
calculated as
arithmetic average, X;
n
X
xi
X ¼
ð22Þ
n
i¼1
A dimensionless measure of dispersion is the coefficient
of variation, defined as the standard deviation divided by
the mean. The coefficient of variation, Cv, is estimated as:
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Pk
Þ2
sx
i¼1 ðxi x
ð23Þ
cv ¼ ; where sx ¼
x
n1
3.5 Flow duration curve
A flow duration curve (FDC) is a simple and effective
method of summarizing the distribution of stream flow for
a given catchment (Zheng et al. 2007). The shape of the
FDC is determined by rainfall pattern, catchment size and
123
1076
Stoch Environ Res Risk Assess (2009) 23:1071–1087
physiographic characteristics of the catchment. The shape
of the FDC can also be influenced by water resources
development and land use (Smakhtin 1999). An FDC is
widely used as a measure of the flow regime as it provides
an easy way of displaying the complete range of flow and it
can also be used to assess changes in the flow regime
following land use and climate change, by considering flow
changes in percentile (Smakhtin 1999; Brown et al. 2005;
Lane et al. 2005; Mu et al. 2007; Zheng et al. 2007). FDCs
are constructed from stream flow data over a time interval
of interest, such as daily, weekly, monthly or annually, and
provide a measure of the percentage of time a given stream
flow is equaled or exceeded over that interval. Each value
of discharge Q has a corresponding exceedance probability
p; and an FDC is simply a plot of Qp, the pth quantile or
percentile of stream flow versus exceedance probability p,
where p is defined by
p ¼ 1 p Qp q
ð24Þ
The quantile Qp is a function of observed stream flow,
and since this function depends upon observations, it is
often termed the empirical quantile function (Vogel and
Fennessey 1994).
3.6 Spatial interpolation
To understand the spatial patterns of statistical characteristics of hydrologic alterations across the Guizhou region,
the geostatistical or stochastic methods are used because
they capitalize the spatial correlation between neighboring
observations to predict attributed values at unsampled
locations (e.g., Goovaerts 1999; Sauquet 2006; Hartkamp
et al. 1999). Goovaerts (1999) indicated that the major
advantage of the Kriging method over other simple interpolation methods is that sparsely sampled observations of
the primary attribute can be complemented by secondary
attributes that are more densely sampled. Therefore, the
Kriging interpolation method was used to demonstrate the
spatial patterns of hydrologic changes within the study
region.
4 Results
4.1 Interannual variability of streamflow
Interannual variability of streamflow, which is mainly
serving as a catchment response to climate variability and
human activity, is one of the most important aspects of
hydrological regime for a catchment (Zheng et al. 2007).
The MK and linear trend analyses were conducted for
streamflow of annual-average, maximum, minimum, and
season (i.e., flood season and non-flood season) over the
entire period (1956–2000). The results are shown in
Table 2. For illustrative purposes, the time series and the
linear trend of mean annual stream flow are shown in
Fig. 2. It is seen from Table 2 and Fig. 2 that for mean
annual stream flow, 9 out of 10 stations have shown an
increasing trend, of which 2 are significant at the 5% significant level (Lower Wu River and Liu River). Similar
results are obtained for annual maximum (9 out of 10
stations have increasing trend and 2 are significant at the
5% significant level) and annual minimum (8 out of 10
stations have increasing trend and 2 are significant at the
5% significant level) series. In all the three cases, the Upper
Wu River is the only exception, where an insignificant
decreasing trend is detected. Of all the parameters tested,
streamflow in the flood-season demonstrates the most
obviously upward trends (all 10 stations show increasing
trends and 4 of them, i.e., SN, ZD, JP and LJD are significant at the 5% significant level). While downward
trends are detected in streamflow in the non-flood season
Table 2 Trend test (P-values) of the streamflow for eight major catchments of the Guizhou province using MK test
No
Catchment
Outlet gauges
Annual average
Annual maximum
Annual minimum
Flood season
Non-flood season
1.
Upper Wu River
YCH
0.72 (-)
0.77 (-)
0.21 (-)
0.76 (?)
0.70 (-)
Middle Wu River
JJH
0.39 (?)
0.12 (?)
0.01 (?)*
0.20 (?)
0.01 (?)*
2.
Lower Wu River
Beipan River
SN
ZD
0.02 (?)*
0.12 (?)
0.28 (?)
0.01 (?)*
0.01 (?)*
0.07 (?)
0.05 (?)*
0.01 (?)*
0.01 (?)*
0.52 (?)
3.
Hongshui River
TE
0.51 (?)
0.67 (?)
0.21 (?)
0.41 (?)
0.62 (?)
4.
Liu River
SHC
0.03 (?)*
0.09 (?)
0.15 (?)
0.33 (?)
0.20 (?)
5.
Qinshui River
JP
0.12 (?)
0.01 (?)*
0.49 (?)
0.02 (?)*
0.96 ()
6.
Wuyang River
CT
0.84 (?)
0.24 (?)
0.42 (?)
0.14 (?)
0.41 (-)
7.
Jin River
LJD
0.84 (?)
0.38 (?)
0.83 ()
0.05 (?)*
0.37 (-)
8.
Furong River
CB
0.22 (?)
0.43 (?)
0.38 (?)
0.45 (?)
0.99 ()
The ‘(?)’ sign means an upward trend, the ‘(-)’ sign means a downward trend, ‘()’ means no trend, and ‘*’ denotes trend are statistically
significant at 5% significance level
123
Stoch Environ Res Risk Assess (2009) 23:1071–1087
500
1200
Y=883.1-0.28X
Y= -4321+2.53X
Stream flow (m /s)
400
350
300
250
200
1000
3
3
Stream flow (m /s)
450
(a) YCH
800
600
400
(b) JJH
200
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
Year
Year
1400
600
Y= -4799+2.62X
Y= -7049+4.0X
1000
800
600
400
300
200
(c) SN
(d) ZD
400
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
Year
Year
2600
2400
250
Y= -4476+3.06X
Y= -1254+0.71X
2200
Stream flow (m /s)
2000
1800
1600
1400
1200
1000
800
600
1955
200
3
Stream flow (m3/s)
500
3
Stream flow (m /s)
Stream flow (m3/s)
1200
150
100
(e) TE
(f) SHC
50
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
1960 1965 1970 1975 1980 1985 1990 1995 2000
Year
Year
450
400
150
Y= -255+0.17X
Y= -1951+1.12X
Stream flow (m /s)
350
3
Stream flow (m3/s)
125
300
250
200
150
100
75
50
(g) JP
(h) CT
100
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
25
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
Year
150
Year
200
Y= -2.93+0.04X
Y= -69.8+0.08X
125
3
Stream flow (m /s)
Stream flow (m3/s)
Fig. 2 Linear trend test of the
annual average streamflow at
ten outlet-gauges of eight major
catchments in Guizhou province
(1956–2000)
1077
100
75
50
150
100
(j) CB
(i) LJD
25
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
50
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
Year
Year
123
Y=-9.00+0.60X (P=0.64)
1400
Precipitation (mm)
Fig. 3 Linear trend test of the
annual precipitation,
temperature, pan evaporation,
actual evaporation and hours of
annual solar radiation in
Guizhou province (1956–2000)
Stoch Environ Res Risk Assess (2009) 23:1071–1087
1300
1200
1100
1000
(a)
Precipitation in flood-season (mm)
1078
900
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
800
Y=-3308.1+1.95X (P=0.07)
700
600
500
400
300
(b)
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
Year
Year
1500
17.0
Pan evaporation (mm)
Average temperature (°C)
1450
Y=4788-1.81X (P=0.04)
Y=3.23+0.63X (P=0.08)
16.5
16.0
15.5
15.0
1400
1350
1300
1250
1200
1150
1100
(c)
14.5
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
(d)
1050
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
Year
Year
1700
Y=2332-0.89X (P=0.01)
600
550
(e)
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
Year
for three gauges (i.e., YCH, CT and LJD, not significant at
the 95% confidence level) and no trend in two gauges (i.e.,
JP and CB).
To identify the underlying driving forces of streamflow
changes in Guizhou province, changes in other primary
hydro-meteorological components (i.e., annual precipitation, temperature, pan evaporation, actual evapotranspiration and hours of solar radiation) over the period
(1956–2000) are examined and shown in Fig. 3. It is seen
that the annual total precipitation (Fig. 3a) in the region is
slightly increasing, and the precipitation in flood-seasons
(Fig. 3b) (June–August) is increasing at the significant level
of 10%. Figure 3c shows that the annual mean air temperature is increasing at the significant level of 10%. Figure 3c–f
show that both pan evaporation (Fig. 3d) and actual evapotranspiration (Fig. 3e) are significantly decreasing at the
significant level of 5% primarily resulting from a remarkable
decrease in solar radiation (Fig. 3f), although the annual
mean air temperature (Fig. 3c) is increasing (significant at
the 10% level). This result is consistent with an earlier study
in the Yangtze River basin (Xu et al. 2006). It is anticipated
123
Hours of annual solar radiation (h)
Actual evapotranspiration (mm)
650
1600
Y=9838-4.32X (P<0.01)
1500
1400
1300
1200
1100
(f)
1000
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
Year
that the increase in precipitation during flood-seasons and the
decrease in actual evapotranspiration due to decreasing solar
radiation are jointly responsible for the increase in streamflow in Guizhou province. More quantitative investigation in
this aspect is needed in future studies.
The SSA and Lepage test are collectively utilized for
the change-point detection of annual streamflow in these
catchments in order to guarantee the validity of detection. For illustrative purposes, the procedure for changepoint analysis for Hongshui River at TE is exemplified.
Figure 4a, b demonstrates the initial, reconstruction and
residuals series by the SSA approach for change-point
analysis. Figure 4c shows two change points (1981 and
1986) detected using the SSA test with different combinations of N, M, p, and q (Moskvina 2001, Moskvina
and Zhigljavsky 2003). Figure 4d shows different change
points (1986 and 1992) detected using the Lepage test
with different lengths of the segments (Benjamin and
Roger 2005). Figure 4d indicates that the change point of
1986 is closer to the line denoted as the 99% confidence
level than 1992. Altogether, 1986 is identified as the
Stoch Environ Res Risk Assess (2009) 23:1071–1087
Fig. 4 Demonstration of
change-points detection in
annual average streamflow
series at TE with dual-approach
(Singular-spectrum
test ? Lepage test).
a Initial ? reconstruction
series; b Residuals produced by
the singular-spectrum test;
c Test statistics by the singularspectrum test; d Test statistics
by the Lepage test. The timing
of the change point, after
assessment of the results of the
singular-spectrum test and
Lepage joint-test, is 1986. The
change point is confident at
[95% confidence level during
all change points
1079
2600
2400
(a) Initial + reconstruction
600
Initial
Reconstruction
(b) Residuals
400
2200
200
2000
0
1800
1600
1400
-200 -400
1200
-600
1000
-800
800
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
10
(c)
9
(d)
8
7
6
5
4
3
2
1
0
-1
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Table 3 List of change-point detection results of the Guizhou
province using the Singular-spectrum test and Lepage test
No Catchment
Outlet Potential change points
gauges
Singular-spectrum test Lepage test
1.
YCH
Upper Wu River
1987
1986, 1991
Middle Wu River JJH
1977
–
2.
Lower Wu River
Beipan River
SN
ZD
1986, 1989
1986
1986
1974, 1980
3.
Hongshui River
TE
1981, 1986
1986, 1992
4.
Liu River
SHC
1986
–
5.
Qinshui River
JP
1989
1986, 1989
6.
Wuyang River
CT
1986, 1989
1986
7.
Jin River
LJD
1986
1989
8.
Furong River
CB
1986
1986, 1989
‘‘–’’ Means failure in the change point detection
most important change point in the tests by both SSA
and Lepage approaches. Similar results are found for
most of the other sites as well (Table 3). Therefore, the
timing of 1986 is accepted as the change point of
streamflow in all sites of Guizhou province to facilitate
the further quantification of spatio-temporal changes in
hydrological regimes for the study region hereafter. The
results also suggest that the SSA used for identifying
change points is better than the Lepage test, while the
Lepage test has an advantage of determining the degree
to which the change point is statistically significant.
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
Hydrological changes in annual mean streamflow and
the coefficient of variation (Cv) of the selected 10 catchments in Guizhou province prior/posterior to the change
point (i.e., 1986) are shown in Table 4 (the 4th and 5th
column). It shows that both Q and Cv posterior to 1986
significantly increase for most catchments in Guizhou
province, indicating that an increase in the annual
streamflow amount after 1986 results in large variability of
the annual streamflow series.
4.2 Intra-annual variability
The intra-annual variability, or seasonality of streamflow,
is influenced mostly by the seasonal cycle of precipitation,
temperature and catchment management schemes, e.g.,
flow regulation (Zheng et al. 2007; Mu et al. 2007).
Figure 5 shows considerable changes in intra-annual
streamflow for most catchments. Compared with that of
1956–1986, average monthly streamflow during 1987–
2000 decreases in April–June but increases in July–
September. The phenomena are extremely remarkable in
May and July, respectively.
The monthly coefficient of variation can be used to
describe intra-annual variability of stream flow (Zheng
et al. 2007). The multiyear mean coefficient of variation for
monthly flow prior and posterior to 1986 is listed in
Table 4. Except for SHC and CB, monthly Cv in all
catchments increases significantly, indicating a large intraannual variation of streamflow after 1986.
123
1080
Stoch Environ Res Risk Assess (2009) 23:1071–1087
Table 4 Hydrological changes of the selected catchments prior/posterior to the change point
No
1
Catchment
Outlet
gauges
Q ðm3 =sÞ
Annual Cv
Monthly Cv
Change (%)
Q5/Q50
Prior
Prior
Post
Prior
Post
Q5
Q95
Prior
Post
Change
(%)
Prior
Post
Change
(%)
Post
Q95/Q50
Upper Wu River
YCH
343
313
4.47
5.00
0.88
0.98
-0.7
-3.9
4.47
5.00
11.9
0.30
0.33
10.0
Middle Wu River
JJH
686
710
4.03
4.25
0.81
0.84
0.1
9.6
4.03
4.25
5.5
0.31
0.37
19.4
2
Lower Wu River
Beipan River
SN
ZD
851
376
920
413
3.47
5.28
3.74
6.27
0.79
0.93
0.80
1.08
4.8
12.5
5.2
0.1
3.47
5.28
3.74
6.27
7.8
18.8
0.31
0.30
0.33
0.31
6.5
3.3
3
Hongshui River
TE
1,507
1,601
4.45
5.04
0.89
0.95
-1.8
0.1
4.45
5.04
13.3
0.27
0.31
14.8
4
Liu River
SHC
137
152
6.21
5.63
1.03
1.02
2.2
0.2
6.21
5.63
-9.3
0.30
0.27
-10.0
5
Qinshui River
JP
264
282
4.54
4.63
0.86
0.95
5.6
-3.4
4.54
4.63
2.0
0.35
0.38
8.6
6
Wuyang River
CT
86
91
3.74
4.10
0.76
0.83
8.8
-3.8
3.74
4.10
9.6
0.47
0.45
-4.3
7
Jin River
LJD
89
89
4.19
4.22
0.84
0.92
0.8
-6.7
4.19
4.22
0.7
0.27
0.25
-7.4
8
Furong River
CB
106
109
4.50
4.66
0.90
0.89
3.5
4.8
4.50
4.66
3.6
0.29
0.31
6.9
The annual coefficient of variation Cv is obtained from the two subseries of annual stream flow before and after the change point (1986). Monthly
Cv is the mean monthly Cv for the two series before or after the change point (Zheng et al. 2007). Prior periods (1956–1986), Posterior periods
(1987–2000)
4.3 Spatio-temporal changes of streamflow regime
by FDCs
Figure 6 and Table 4 (last eight columns) show monthly
FDCs, high and low flows (Q5, Q95), and high and low flow
indices (Q5/Q50, Q95/Q50) in the two periods. Obviously,
the high flow (Q5) usually occurring during the flood season is increased at most gauges (except YCH and TE). But
the low flow (Q95) shows a 3.4–6.7% decrease for the four
catchments in the eastern side and one in western side,
compared with a 4.8–9.6% increase in the middle area. The
high flow index (Q5/Q50), defined as the ratio between
monthly streamflow exceeds 5% of the time (Q5) and
monthly streamflow exceeds 50% of the time (Q50), and is
increased by 0.7–18.8% in the posterior period for all
gauges, except SHC. The low flow index (Q95/Q50), defined
as the ratio between monthly stream flow exceeds 95% of
the time (Q95) and monthly streamflow exceeds 50% of the
time (Q50), and is increased in seven catchments and is
decreased in the remaining catchments.
To better understand the spatial patterns of statistical
characteristics of hydrologic alterations across the Guizhou
region, we utilize the Kriging method to describe the
Cv, Q5, and Q95 changes prior/
spatial variations of Q;
posterior to 1986. Figure 7a shows a significant decrease in
Q in the north-western part (i.e., the upper and middle Wu
River) and an increase in the north-eastern part (i.e., the
lower Wu, Wuyang, Qinshui, and Liujiang Rivers) and the
south-eastern part (i.e., the Beipan and Hongshui Rivers) of
Guizhou province. However, annual Cv shows an opposing
spatial variation patterns, i.e., significant decrease in the
south-eastern part and increase in the northeastern part
(Fig. 7b). Both Fig. 7a, b sever as a spatial index-map of
123
streamflow variability in the region in support of the
regional sustainable water-resources management and ecoenvironment restoration. Figure 7c, d show spatial changes
in high- and low-flow indices (Q5, and Q95) prior/posterior
to 1986 in the Guizhou region. In the eastern part, over 5%
increase in extremely large streamflow (Q5) (Fig. 7c) during 1987–2000 could significantly increase flood hazard,
possibly increasing debris-flow and land-slide events; furthermore, over 5% decrease in low flow (Q95) (Fig. 7d)
implies less water resources are available for irrigation and
ecological utilization during the drought season.
To identify the spatial changes in the streamflow
associated with spatial patterns of precipitation and evaporation, we further describe the spatial changes in
meteorological factors prior/posterior to 1986. Figure 8a
demonstrates that the precipitation decrease dominates in
the western part compared with the increase in the eastern
and south-eastern parts. Actual evapotranspiration calculated from the GG model demonstrates an opposite spatial
distribution, i.e., increase in the western part and decrease
in the eastern part (Fig. 8b). These spatial variations of the
precipitation and actual evapotranspiration are in high
agreement with the spatial pattern of streamflow (Fig. 7a),
the available water resources (precipitation minus actual
evapotranspiration). Therefore, the climatic factor is
regarded as the primary driving force which influences the
streamflow change in the Guizhou Province.
5 Conclusions and discussions
Climate variability and change have led to significant
impacts on hydrological regimes in the Guizhou province,
Stoch Environ Res Risk Assess (2009) 23:1071–1087
Fig. 5 Comparison of monthly
streamflow between the prior
(White bar, 1956–1986) and
posterior (Black bar,
1987–2000) periods
1000
1081
2000
(a) YCH
800
(b) JJH
1500
600
1000
400
500
200
0
2500
Jan Feb Mar Apr May Jun
Jul Aug Sep Oct Nov Dec
0
1400
(c) SN
1200
Jan Feb Mar Apr May Jun
Jul Aug Sep Oct Nov Dec
(d) ZD
2000
1000
1500
800
600
1000
400
500
0
5000
200
Jan Feb Mar Apr May Jun
Jul Aug Sep Oct Nov Dec
0
500
(e) TE
4000
400
3000
300
2000
200
1000
100
0
800
700
Jan Feb Mar Apr May Jun
Jul Aug Sep Oct Nov Dec
0
Jan Feb Mar Apr May Jun
Jul Aug Sep Oct Nov Dec
(f) SHC
Jan Feb Mar Apr May Jun
Jul Aug Sep Oct Nov Dec
250
(g) JP
(h) CT
200
600
500
150
400
100
300
200
50
100
0
Jan Feb Ma
A
Ma
Ju
Ju
Au
Se
Oc
Nov Dec
250
0
300
(i) LJD
200
Jan Feb Mar Apr Ma
Jun
Ju
Au
Se
Oct Nov Dec
(j) CB
250
200
150
150
100
100
50
50
0
Jan Feb Mar Apr May Jun
Jul Aug Sep Oct Nov Dec
southwestern China over 1956–2000. The results of this
investigation demonstrate that the variation in annual
runoff is primarily dominated by climate variability, i.e.,
precipitation and evapotranspirations variations. Meanwhile, human activities (e.g., deforestation, dam
construction and operation, etc.) also exert considerable
interference on runoff processes and result in more complexity in hydrological changes in the region (e.g., Song
0
Jan Feb Mar Apr May Jun
Jul Aug Sep Oct Nov Dec
et al. 1983; Yang 1988; Wang et al. 2004a, b; GPDWR
2004). Some interesting conclusions can be presented and
discussed as follows:
Interannual variability of stream flow is mostly controlled by precipitation and evapotranspiration variability
in the Guizhou Karst region. Streamflow in flood-seasons
during 1956–2000 increased significantly (confidence level
C95%) in four of the eight catchments, closely associated
123
-12
3
Relative reduction
-10
-8
100
-6
-4
-2
30
1956-1986
1987-2000
10
1000
0
JJH
0
0
10
20
30
40
50
60
70
80
90
-10
100
0
100
10
10
0
20
30
40
50
60
3
1000
70
80
90
10
0
100
-10
-20
0
10
1000
30
40
50
60
70
80
90
100
0
-10
30
1000
1956-1986
1987-2000
20
Relative reduction
3
10
Monthly flow (m /s)
Relative reduction
3
20
10
100
0
-10
Relative reduction (%)
1956-1986
1987-2000
(f)
Relative reduction (%)
Monthly flow (m /s)
30
100
SHC
-20
20
30
40
50
60
70
80
90
-20
10
100
0
10
Percentage of exceedance(%)
20
10
100
0
-10
JP
10
30
40
50
60
70
50
60
70
80
90
100
80
90
40
1000
1956-1986
1987-2000
30
Relative reduction
20
100
10
0
CT
-10
10
-20
100
0
10
Percentage of exceedance(%)
20
30
40
50
60
70
80
90
100
Percentage of exceedance(%)
(j)
10
0
-10
LJD
1956-1986
1987-2000
Relative reduction
5
100
0
10
CB
Relative reduction (%)
100
20
Relative reduction (%)
Relative reduction
10
1000
3
30
1956-1986
1987-2000
Monthly flow (m /s)
1000
3
40
3
30
(h)
Monthly flow (m /s)
3
Relative reduction
20
30
Relative reduction (%)
40
Relative reduction (%)
1956-1986
1987-2000
1000
20
Percentage of exceedance(%)
50
(g)
Monthly flow (m /s)
40
Percentage of exceedance(%)
100
Monthly flow (m /s)
100
10
20
10
90
20
TE
(i)
80
Relative reduction
Percentage of exceedance(%)
(e) 10000
10
70
ZD
-10
0
60
50
SN
100
10
50
1956-1986
1987-2000
Monthly flow (m /s)
3
1000
0
40
Relative reduction (%)
20
10
30
(d)
30
Relative reduction (%)
Monthly flow (m /s)
10000
0
20
Percentage of exceedance(%)
Percentage of exceedance(%)
(c)
20
Relative reduction
YCH
10
Relative reduction (%)
Monthly flow (m /s)
(b) 10000
-14
1956-1986
1987-2000
1000
3
(a)
Relative reduction (%)
Fig. 6 Changes of FDCs in the
eight catchments of Guizhou
Region
Stoch Environ Res Risk Assess (2009) 23:1071–1087
Monthly flow (m /s)
1082
-5
0
10
20
30
40
50
60
70
80
Percentage of exceedance(%)
with the distinctly increasing trends observed in rainfall
records and decreasing trends in evapotranspiration over
the flood-seasons (June–August) in the study catchments.
At the same time, the decreasing annual total evapotranspiration (significant at the 95% confidence level) is
identified to be consistent with the decreasing hours of
annual solar radiation (significant at the 95% confidence
123
90
100
0
10
20
30
40
50
60
70
80
90
100
Percentage of exceedance(%)
level). These climatic factors jointly influence the change
in annual streamflow in flood seasons and lead to streamflow increasing in the study region. The results of SSA and
the Lepage test indicate that 1986 is a significant change
point at above the 95% confidence level.
The Guizhou region experiences significant changes in
the seasonal streamflow distribution during the posterior
Stoch Environ Res Risk Assess (2009) 23:1071–1087
0
0
105 E
0
0
109 E
120 E
C
Ri han
ve gb
r
a
.
ng
Guizhou Prov.
0
Sina
ro
30 N
0
29 N
0
St
he
jie
ng
r
ive
Jin River
Cho
Jia
R
Wu
an
Wu y
0
27 N
Yachihe St.
0
28 N
Lujia dong St.
.
28 N
g Riv
ngta
n St
.
er
Jinping St.
0
27 N
Qi ns hu i Ri ve r
Guiyang
0
26 N
0
ip
an
Ri
ve
Liu River
Hongshui River
r
on
gS
W
t.
Tian ’e St.
Streamflow gauges
100
Streams
0
N
0
25 N
E
S
Provincial capital
0
103 E
26 N
Shihuichang St.
ed
0
25 N
Be
Zh
-9 % - -7 %
-7 % - -5 %
-5 % - -3 %
-3 % - -1 %
-1 % - 1 %
1%- 3%
3%- 6%
6%- 8%
8 % - 10 %
10 % - 12 %
L a titu d e o(N)
Beijing
n St
29 N
0
108 E
0
100 E
0
50 N
0
0
107 E
106 E
St
80 E
Chishui
St.
0
0
104 E
.
0
(a) 103 E
Fu
Fig. 7 Spatial hydrological
changes of the Guizhou region
prior/posterior to the change
point. a Mean streamflow ðQÞ;
b Coefficient of variance (Cv);
c High-flow (Q5); d Lowflow(Q95)
1083
0
104 E
0
105 E
200
0
106 E
400 Km
0
107 E
0
108 E
109 E
o
Longtitude (E)
0
0
0
103 E
100 0 E
0
106 E
50 N
Ri han
ve gb
r a
Chishui St.
.
ng
Sina
ro
Fu
0
St
he
jie
ng
Ri
ver
Jin River
Cho
Jia
Wu
Wu y
0
27 N
Yachihe St.
0
28 N
Lujiad
Lujia dong
ong St.
.
28 N
ang
Rive
nn S t
ngta
.
r
Jinping St.
0
27 N
Qi ns hu i Ri ver
Guiyang
Latitude o(N)
Guizhou Pr ov.
30 0 N
0
29 N
C
Beijing
n St
29 N
109 E
108 E
120 0 E
0
0
0
0
107 E
St
80 0 E
0
105 E
104 E
.
(b)
-32.0 % - -25.5 %
-25.5 % - -18.9 %
0
-18.9 % - -12.4 %
26 N
Be
-12.4 % - - 5.9 %
-5.9 % -
5.9 %
5.9 % -
7.1 %
0
ip
an
Ri
Hongshui River
ve
r
Zh
7.1 % - 13.6 %
ed
on
13.6 % - 20.1 %
0
gS
25 N
20.1 % - 26.6 %
W
t.
Tian’ e St.
Streamflow gauges
100
Streams
0
0
104 E
N
0
25 N
E
S
Provincial capital
103 E
26 N
Shihuichang St.
Liu River
0
105 E
0
0
106 E
107 E
200
400 Km
0
108 E
0
109 E
o
Longtitude (E)
record (1987–2000) compared with those in the prior period
(1956–1986). In most catchments, obvious reduction occurs
in spring (i.e., April–June) and increases in summer (i.e.,
July–September) in average monthly streamflow during
1987–2000. Meanwhile, all the catchments show increasing
values of the coefficient of variation ðCv Þ for monthly stream
flow except for SHC and CB, which show decreases in
the coefficient of variation. The increasing coefficient of
123
1084
0
0
0
104 E
103 E
0
100 E
120 E
Fu
Sina
ro
ng
Guizhou Pr ov.
0
30 N
0
29 N
.
Beijing
0
St
he
R
r
ive
Jin River
Cho
Jia
ng
jie
Wu
Wu y
0
27 N
Ya chihe St.
Yachihe
0
28 N
Lujiad
Lujiad ong
ong St.
.
28 N
ang
Rive
t
ann S
n g tta
.
r
Jinping St.
0
27 N
Qi ns hu i Ri ver
Guiyang
Latitude o(N)
50 N
C
Ri han
ve gb
r a
29 N
109 E
St
80 E
0
108 E
107 E
106 E
0
0
0
0
0
0
105 E
0
n St
(c)
.
Fig. 7 continued
Stoch Environ Res Risk Assess (2009) 23:1071–1087
-1.2 % ~ -0.1 %
-0.1 % ~ 1.0 %
1.0 %~ 2.1 %
0
26 N
Be
2.1 %~ 3.2 %
3.2 %~ 4.3 %
0
ip
an
4.3 %~ 5.4 %
Ri
Liu River
ve
Hongshui River
r
ed
6.5 %~ 7.6 %
Zh
5.4 %~ 6.5 %
on
7.6 %~ 8.7 %
0
gS
25 N
t.
W
Tian’ e St.
Streamflow gauges
100
Streams
0
0
104 E
0
107 E
109 E
0
108 0E
109 0E
108 E
0
106 0E
400 Km
0
107 E
0
120 E
St
100 E
v e ng
r ba
n St
.
Ri
Fu
Sina
ro
ng
Guizhou Pr ov .
0
30 N
29 0N
ha
C
Beijing
Wu
er
Riv
t
ann S
nnggtta
Cho
Wu y
0
27 N
Ya ch ih e St.
Yachihe
0
28 N
Lujiad
L u jiadong
on g S t.
Jin R iver
Ji
an
gj
ieh
eS
t.
28 0N
ang
Rive
.
r
Jinping St.
0
27 N
Qin shu i Riv er
Guiyang
Latitudeo(N)
0
50 N
0
29 N
200
0
106 E
o
Longtitude (E)
105 0E
0
80 E
0
105 E
104 0E
0
(d) 103 E
0
25 N
E
.
103 E
N
S
Provincial capital
0
26 N
Shihuichang St.
-6.9%~ -5.1%
-5.1%~ -3.3%
-3.3%~ -1.4%
26 0N
Be
-1.4%~ -0.4 %
0.4%~ 2.2%
0
ip
an
2.2%~ 4.1%
Ri
Liu River
Hongshui River
r
ed
5.9% ~ 7.7%
ve
Zh
4.1%~ 5.9%
on
7.7% ~ 9.6%
gS
25 0N
t.
W
Tian’ e St.
Str eamflow gauges
100
Str eams
103 E
0
104 E
0
105 E
v Þ in monthly stream flow during 1987–2000
variation ðC
supposedly results from increased stream flow in the flood
season.
Spatial maps of changes in streamflow regime prior/posterior to the change point show reductions of monthly flow
in the north-western part and increases in other parts of
ðQÞ
the Guizhou region. The spatial variation of coefficient of
variation (Cv) shows an opposite pattern as compared with
123
N
25 0N
E
S
Provincial capital
0
26 N
Shihuichang S
St.
t.
0
106 E
Longtitude o(E)
0
107 E
200
400 Km
0
108 E
0
109 E
that of mean streamflow. The spatial variations of high- and
low-flow index (Q5, and Q95) in the Guizhou area prior/
posterior to the change point indicate high natural-hazard
risks of flood, debris-flow, and land-slide events in the five
low-land catchments of eastern Guizhou region after the
change point in 1986 as compared with before.
The results of this investigation will provide important
insights into the key hydrological processes for supporting
Stoch Environ Res Risk Assess (2009) 23:1071–1087
0
0
0
107 E
106 E
109 E
108 E
0
120 E
St
100 E
Beijing
Guizhou Prov.
.
n St
Fu
Sina
ro
30 0 N
0
29 N
0
St
he
r
ive
Jin River
Cho
Jia
ng
jie
R
Wu
an
Wu y
0
27 N
Yachihe St.
0
28 N
Lujia dong St.
.
28 N
g Riv
ngta
n St
.
er
Jinping St.
0
27 N
Qi ns hu i Ri ve r
Guiyang
L a titu d e o(N)
50 0 N
C
Ri han
ve gb
r
a
29 N
0
0
105 E
0
80 E
0
0
0
104 E
.
0
103 E
(a)
ng
Fig. 8 Spatial changes of the
Guizhou region prior/posterior
to the change point for.
a Precipitation; b Actual
evapotranspiration
1085
-57.6 % - -14.7 %
0
26 N
-14.6 % -
-9.3 %
-9.3 % -
-4.1 %
-4.1 % -
-1.1 %
0%-
1.1 %
1.1 % -
6.4 %
Be
0
ip
an
Ri
ve
57.1 %
gS
16.8 %
16.8 % -
on
11.7 % -
ed
0
Hongshui River
r
Zh
6.5 % - 11.6 %
25 N
W
t.
Tian ’e St.
Streamflow gauges
100
Streams
0
N
0
25 N
E
S
Provincial capital
0
103 E
26 N
Shihuichang St.
Liu River
0
104 E
0
105 E
200
0
106 E
400 Km
0
107 E
0
108 E
109 E
o
Longtitude (E)
(b) 103 E
0
0
0
106 E
109 E
108 E
107 E
120 0 E
St
0
29 N
Ri
C
Beijing
0
St
he
jie
ng
R
r
ive
Jin River
Cho
Jia
Wu
Wu y
0
27 N
Yachihe St.
0
28 N
Lujia dong St.
.
28 N
ang
Rive
ngta
n St
.
r
Jinping St.
0
27 N
Qi ns hu i Ri ve r
Guiyang
0
Be
26 N
0
ip
an
-18.1 % - -14.8 %
Ri
on
gS
- 4.9 % - - 1.6 %
1.7 % -
5.1 %
5.1 % -
8.4 %
W
Tian’ e St.
t.
1.7 %
Hongshui River
r
ed
- 8.2 % - - 4.9 %
- 1.6 % -
ve
Liu River
Zh
-11.5 % - - 8.2 %
0
0
N
0
25 N
E
S
100
200
400 Km
8.4 % - 11.7 %
103 E
26 N
Shihuichang St.
-14.8 % - -11.5 %
25 N
Latitude o(N)
Fu
Sina
ro
n St
.
ng
G uizhou P rov.
30 0 N
ve ngb
r a
50 0 N
ha
29 N
100 0 E
0
105 E
104 E
80 0 E
0
0
0
.
0
0
104 E
0
105 E
0
0
106 E
107 E
0
108 E
0
109 E
o
Longtitude (E)
eco-environment restoration, management and natural
disasters mitigation in mountainous regions surrounded
with vulnerable environment. However, the impacts of
human activity (i.e., deforestation, afforestation, terrace
and trapped dams) on hydrological changes in the study
area are yet to be investigated and analyzed in a multidisciplinary perspective.
Acknowledgments The work was financially supported by a
National Basic Research Program (‘‘973 Program’’, 2006CB403200),
123
1086
open Research Grant from the Key Sediment Lab of the Ministry for
Water Resources (2008001), key grant from the National Natural
Science Foundation of China (40830639), key Research Grant
from Chinese Ministry of Education (308012), and a National Key
Technology R&D Program (2007BAC03A060301). Cordial thanks
should also be extended to two reviewers and the editor for their
constructive comments and suggestions which greatly improved the
quality of this paper. Prof. V.P. Singh of Texas A&M University
kindly offered helps to improve the quality of the final version of
the paper.
References
Aguado E, Cayan DR, Riddle LG, Roos M (1992) Climatic
fluctuations and the timing of West Coast stream-flow. J Clim
5:1468–1483
Benjamin V, Roger NJ (2005) Detection of abrupt changes in
Australian decadal rainfall (1890–1989). CSIRO Atmospheric
Research Technical Paper No. 73
Bouchet RJ (1963) Evapotranspiration réelle et potentielle, signification climatique. GeneralAssemblyBerkeley, Int. Assoc. Sci.
Hydrol., Gentbrugge, Belgium, Publ. No. 62, pp 134–142
Brown A, Zhang L, McMahon T, Western A, Vertessy R (2005) A
review of paired catchment studies with reference to the seasonal
flows. J Hydrol 310:28–61
Burn DH, Elnur MAH (2002) Detection of hydrologic trends and
variability. J Hydrol 255:107–122
Chen HY, Chen BY, Chen B (2005) Lithologic characteristics of
Houzhai Karst small valley Puding, Guizhou Province. Guizhou
Geol 22(4):284–288 (in Chinese with English abstract)
Goovaerts P (1999) Performance Comparison of Geostatistical
Algorithms for Incorporating Elevation into the Mapping of
Precipitation. The IV International Conference on GeoComputation was hosted by Mary Washington College in
Fredericksburg, VA, USA, on 25–28 July 1999
Granger RJ (1989) An examination of the concept of potential
evaporation. J Hydrol 111:9–19
Granger RJ (1998) 5–7 March partitioning of energy during the snowfree season at the Wolf Creek Research Basin, In: Pomeroy JW,
Granger RJ (eds) Proceedings of a Workshop held in Whitehorse, Yukon, pp 33–43
Granger RJ, Gray DM (1989) Evaporation from natural nonsaturated
surfaces. J Hydrol 111:21–29
Hartkamp AD, De Beurs D, Stein A, White JW (1999) Interpolation
Techniques for Climate Variables. NRG-GIS Series 99–01.
Mexico, D.F.: CIMMYT
Huang M, Zhang L (2004) Hydrological responses to conservation
practices in a catchment of the Loess Plateau, China. Hydrological Process 18:1885–1898
Kahya E, Kalayci S (2004) Trend analysis of streamflow in Turkey. J
Hydrol 289:128–144
Kendall MG (1975) Rank correlation methods. Griffin, London
Kim BS, Kim HS, Seoh BH, Kim NW (2007) Impact of climate
change on water resources in Yongdam Dam Basin, Korea.
Stoch Environ Res Risk Assess 21(4):1436–3240
Lane P, Hickel K, Best A, Zhang L (2005) The effect of afforestation
on flow duration curves. J Hydrol 310:253–265
Lepage Y (1971) A combination of Wilcoxon’s and Ansari-Bradley’s
statistics. Biometrika 58:213–217
Lin Z, Levy JK, Xu X, Zhao S, Hartmann J (2005) Weather and
seasonal climate prediction for flood planning in the Yangtze
River Basin. Stoch Environ Res Risk Assess 19(6):428–437
Mann HB (1945) Nonparametric tests against trend. Econometrica
13:245–259
123
Stoch Environ Res Risk Assess (2009) 23:1071–1087
Matsuyama H, Marengo JA, Obregon GO, Nobre CA (2002) Spatial
and temporal variability of rainfall in tropical south America as
derived from climate prediction center merged analysis of
precipitation. Int J Climatol 22:175–195
McCarthy JJ, Canziani OF, Leary NA, Dokken DJ, White KS (eds)
(2001) Climate Change 2001: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Third
Assessment Report of the Intergovernmental Panel on Climate
Change. Cambridge University Press, Cambridge
Mitchell JM, Dzerdzeevskii B, Flohn H, Hofmeyr WL, Lamb HH,
Rao KN, Wallen CC (1966) Climate Change, WMO Technical
Note No. 79, World Meteorological Organization, 79
Moskvina V (2001) Application of the singular spectrum analysis for
change-point detection in time series. Ph.D. thesis, Cardi
University
Moskvina V, Zhigljavsky AA (2003) An algorithm based on singularspectrum analysis for change-point detection, communication in
statistics. Stat Simul 32:319–352
Mu XM, Zhang L, McVicar TR, Chille B, Gau P (2007) Analysis of
the impact of conservation measures on stream flow regime in
catchments of the Loess Plateau, China. Hydrological Process
21:2124–2134
Penman HL (1948) Natural evaporation from open water, bare and
grass. Proc R Soc Lond Ser A 193:120–145
Sauquet E (2006) Mapping mean annual river discharges: geostatistical developments for incorporating river network
dependencies. J Hydrol 331:300–314
Sen AK (2008) Complexity analysis of riverflow time series. Stoch
Environ Res Risk Assess. doi:10.1007/s00477-008-0222-x
Smakhtin VU (1999) Restoration of natural daily flow time-series in
regulated rivers using non-linear spatial interpolation technique.
Regulated Rivers Res Manage 15:311–323
Song LH, Zhang YG, Fang JF, Gu ZX (1983) Karst development and
the distribution of karst drainage systems in Dejiang, Guizhou
Province, China. J Hydrol 61(1–3):3–17
Stefan Becker, Marco Gemmer and Tong Jiang (2006) Spatiotemporal analysis of precipitation trends in the Yangtze River
catchment. Stoch Environ Res Risk Assess 20(6):1436–3240
The Guizhou Provincial Department of Water Resources (GPDWR)
(2004) Report of investigation and assessment on current
situation of water resources development and utilization in
Guizhou province
Van Belle G, Hughes JP (1984) Nonparametric tests for trend in water
quality. Water Resour Res 20(1):127–136
Vogel RM, Fennessey NM (1994) Flow-duration curves. I: new
interpretation and confidence intervals. J Water Resour Plann
Manage 120:485–504
Wang SJ, Li RL, Sun CX, Zhang DF, Li FQ, Zhou DQ, Xiong KN,
Zhou ZF (2004a) How types of carbonate Rock Assemblages
constrain the distribution of Karst Rocky Desertified land in
Guizhou Province, PR China: Phenomena and Mechanisms.
Land Degrad Dev 15:123–131
Wang SJ, Liu QM, Zhang DF (2004b) Karst Rocky Desertification in
southwestern China: Geomorphology, landuse, impact and
rehabilitation. Land Degrad Dev 15:115–121
Xu C-Y, Singh VP (2005) Evaluation of three complementary
relationship evapotranspiration models by water balance
approach to estimate actual regional evapotranspiration in
different climatic regions. J Hydrol 308:105–121
Xu C-Y, Gong L, Jiang T, Chen D, Singh VP (2006) Analysis of
spatial distribution and temporal trend of reference evapotranspiration in Changjiang (Yangtze River) catchment. J Hydrol
327:81–93
Yang H (1988) The fragile karst environment. In: Guizhou Society of
Environmental Science: A Study on the Karst Environment in
Guizhou, vol. 17. Guizhou People’s Publishing Press
Stoch Environ Res Risk Assess (2009) 23:1071–1087
Yang T, Zhang Q, Chen YD, Tao X, Xu C-Y, Chen X (2008) A
spatial assessment of hydrologic alternation caused by dam
construction in the middle and lower Yellow River, China,
Hydrological Processes. doi:10.1002/hyp.6993
Yonetani T (1993) Detection of long term trend, cyclic variation and
step-like change by the Lepage test. J Meteorological Soc Jpn
71:415–418
Yu YS, Zou S, Whittemore D (1993) Non-parametric trend analysis
of water quality data of rivers in Kansas. J Hydrol 150:61–80
Yue S, Wang CY (2002) Applicability of prewhitening to eliminate
the influence of serial correlation on the Mann-Kendall test.
Water Resour Res 38(6):1068
Zeng Z (1994) Suggestion on poverty-deviation in the karst mountain
areas in south China. In: Xie Y, Yang M (eds) Human activity
and karst environment. Beijing Science and Technology Press,
Beijing, pp 15–19
Zhang L, Dawes WR, Walker GR (2001) The response of mean
annual evapotranspiration to vegetation changes at catchment
scale. Water Resour Res 37:701–708
1087
Zhang JQ, Zhou CH, Xu KQ, Masataka W (2002) Flood disaster
monitoring and evaluation in China. Environmental Hazards
4:33–43
Zhang Q, Xu CY, Becker S, Jiang T (2006) Sediment and runoff
changes in the Yangtze River basin during past 50 years. J
Hydrol 331:511–523
Zhang Q, Xu CY, Jiang T, Wu YJ (2007a) Possible influence of
ENSO on annual maximum streamflow of Yangtze River, China.
J Hydrol 333:265–274
Zhang ZC, Chen X, Wang W, Shi P (2007b) Analysis of rainfall trend
and extreme events in Guizhou. Earth Environ 35(4):351–356 (in
Chinese with English abstract)
Zheng HX, Zhang L, Liu CM, Shao QX, Yoshihiro FKS (2007)
Changes in stream flow regime in headwater catchments of the
Yellow River basin since the 1950s. Hydrological Process
21:886–893
123