Download Spatial and temporal variability of daily CHU Jianting

J. Geogr. Sci. 2010, 20(2): 248-260
DOI: 10.1007/s11442-010-0248-0
© 2010
Science in China Press
Springer-Verlag
Spatial and temporal variability of daily
precipitation in Haihe River basin, 1958–2007
CHU Jianting1,2,3, XIA Jun1, XU Chongyu4, LI Lu1,2, WANG Zhonggen1
1. Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and
Natural Resources Research, CAS, Beijing 100101, China;
2. Graduate University of Chinese Academy of Sciences, Beijing 100039, China;
3. Institute of Physics & Meteorology, University of Hohenheim, Garbenstr. 30, D-70955 Stuttgart, Germany;
4. Department of Geosciences, University of Oslo, P O BOX 1047 Blindern, N-0316 Oslo, Norway
Abstract: The seasonal variability and spatial distribution of precipitation are the main cause
of flood and drought events. The study of spatial distribution and temporal trend of precipitation in river basins has been paid more and more attention. However, in China, the precipitation data are measured by weather stations (WS) of China Meteorological Administration and
hydrological rain gauges (RG) of national and local hydrology bureau. The WS data usually
have long record with fewer stations, while the RG data usually have short record with more
stations. The consistency and correlation of these two data sets have not been well understood. In this paper, the precipitation data from 30 weather stations for 1958–2007 and 248
rain gauges for 1995–2004 in the Haihe River basin are examined and compared using linear
regression, 5-year moving average, Mann-Kendall trend analysis, Kolmogorov-Smirnov test,
Z test and F test methods. The results show that the annual precipitation from both WS and
RG records are normally distributed with minor difference in the mean value and variance. It
is statistically feasible to extend the precipitation of RG by WS data sets. Using the extended
precipitation data, the detailed spatial distribution of the annual and seasonal precipitation
amounts as well as their temporal trends are calculated and mapped. The various distribution
maps produced in the study show that for the whole basin the precipitation of 1958–2007 has
been decreasing except for spring season. The decline trend is significant in summer, and
this trend is stronger after the 1980s. The annual and seasonal precipitation amounts and
changing trends are different in different regions and seasons. The precipitation is decreasing
from south to north, from coastal zone to inland area.
Keywords: climate change; spatial and temporal variability of precipitation; Mann-Kendall method; Kolmogorov-Smirnov test; Z test; F test; Haihe River basin
1
Introduction
With the global warming, the local hydrological pattern will change, or has been changing.
Received: 2009-08-18 Accepted: 2009-10-12
Foundation: National Basic Research Program of China, No.2010CB428406; The Key Knowledge Innovation Project of
the CAS, No.KZCX2-YW-126; Key Project of National Natural Science Foundation of China, No.40730632
Author: Chu Jianting, Ph.D, specialized in climate change, land surface processes and water resources research
*
Corresponding author: Xia Jun, Professor, E-mail: [email protected]
www.scichina.com
www.springerlink.com
CHU Jianting et al.: Spatial and temporal variability of daily precipitation in Haihe River basin, 1958–2007
249
One of the import aspects of this change is the spatial and temporal variability of precipitation. In addition, the abnormal seasonal variability and uneven spatial-temporal distribution
of precipitation are often the direct reasons for the extreme flood and draught events. In
China, spatial-temporal variability of precipitation in watershed scale, as well as the impact
of precipitation on runoff, has been paid more and more attention.
The research results from Zhang et al. (2009b) and Ren (2007) show that great differences exist in the temporal trend & spatial distribution of precipitation over different river
basins in China. Zheng (2001) studied the temperature, precipitation and runoff of the Yellow River basin for 1951–1998. The results showed that the discharge in the Yellow River
basin has been decreasing continuously since the 1950s, however, this is not true as for precipitation; violent human activity is one of the main reasons for the decrease of discharge.
Jiang (2005) analyzed the reasons for the discharge decrease in the Tarim River basin and
pointed out that the precipitation in the 1970s is less than in the 1960s, while tends to increase after the 1970s. Zhang et al. (2008b) analyzed the spatial-temporal trend of extreme
precipitation in the Yangtze River basin, and they found that the variability of extreme precipitation is milder before the 1970s, while has obvious increasing or decreasing trend after
the 1970s depending on the regions and seasons; the wet days decreased, whereas, the intensity increased, especially in the middle and lower reaches. Furthermore, all of these variabilities caused the frequency of flood events increased in these regions.
In China, the Haihe River basin possesses special important status in politics, economy,
and culture. In recent years, the contradiction between water demand and water supply becomes more and more serious, which is partly caused by the global warming and superfluous
groundwater exploitation. It is very urgent to study how to maintain the sustainable utilities
of water resources under the climate change. For this need, the research has not been done
enough in Haihe River basin partly due to the limited access to data and research instruments (Liu, 1999). In addition, the existed studies mainly focused on the analysis of specific
weather condition causing flood and drought events (Ping et al., 2003); the analysis of the
weather condition and water vapor transfer over the Haihe River basin (Fan and Liu, 1992;
Zhang et al., 2008); the analysis of the historic trend and forecast of the future trend of runoff and water resources, etc. (Yuan et al., 2005; Liu et al., 2004; Shi, 1995). The study by
Ren (2007) showed that the decreased amount of precipitation in the Haihe River basin is
the largest among ten major river basins in China. The results of Zhang and Wang (2007)
gave the similar conclusion. Liu et al. (2004) pointed out that the relationship between precipitation and runoff is different in different regions of the Haihe River basin, and we need
to pay more attention to the influence of human activities.
In China, large-scale precipitation observation data often come from two sources: the
standard meteorological stations of China Meteorological Administration and the rain
gauges of hydrological bureaus. The former has longer measuring period, and it is good to
analyze the historical trend of precipitation; the latter has more stations but shorter observation periods, which is better to be used to calibrate and validate the hydrological models. In
addition, precipitation data are managed by these two departments separately, which will
impede the detailed description of their spatial-temporal distribution.
Recognizing the above concerns, the objective of this paper is two-folds: (1) to compare
the consistency and correlativity between the two precipitation data sets, i.e., from weather
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stations (WS) and from rain gauges (RG), and to explore the feasibility of extending precipitation data of RG with the help of WS data; and (2) to obtain the detailed spatial–temporal variation of precipitation making good use of the advantages of these two data
sets. The research will be useful to both the hydrology sectors and the meteorological sectors,
and will provide some references to the related research in other river basins.
2
Data and methods
2.1
Data
The Haihe River basin is located between 112°–120°E and 35°–43°N, and has an area of
about 31.8×104 km2, of which, hills and plateau occupy nearly 60% of the total area, and the
remaining 40% belongs to the plain area.
To the north of the watershed is the Yanshan Mountain, to the west is the Taihang
Mountain, and to the east is the vast North
China Plain. From north to south, the Haihe
River basin can be divided into three parts:
Haihe, Luanhe and Tuhaimajiahe, and their
areas are 2.32×104 km2, 4.45×104 km2 and
3.18×104 km, respectively. For the convenience of study, we divide the whole catchment into 283 sub-basins (Figure 1).
In the basin, there are 30 weather stations with 50 years (1958–2007) of data,
which are provided by the China Meteorological Administration, and 248 rain gauges
for 10 years (1995–2004) obtained from the
hydrological bureaus.
2.2
Methods
In this paper, 5-year moving average and Figure 1 The location of weather stations, rain gauges
linear regression methods were used to de- and 283 sub-basins in the Haihe River basin
tect the linear trend pattern of time series (Zhang et al., 2008a, 2009a); Mann-Kendall
(Zheng, 2001; Jiang, 2005; Zhang et al., 2008a, 2009a; Wang, 2008) method was used to do
the significance test of the non-linear changing pattern; Kolmogorov-Smirnov test was applied to check the distribution pattern (Xu, 2001), and Z and F test were used to check the
equality of mean value and variance between the two data sets, respectively. In addition,
some other statistical indicators were calculated and compared which are presented in the
Results section.
Given the time series {xi}, i = 1, 2,…, n, some of the used methods are explained as follows.
2.2.1
Mann-Kendall trend test
The study of Yue and Wang (2002) show that the higher the auto-correlation in the time se-
CHU Jianting et al.: Spatial and temporal variability of daily precipitation in Haihe River basin, 1958–2007
251
ries, the larger the error one may expect in using Mann-Kendall test. Generally, the
auto-correlation in time series needs to be removed, which is done in the following procedure.
At first, calculate the 1st order auto-correlation coefficient:
1 n −1
∑ ( xt − x )( xi +1 − x )
Cov( xi , xi +1 ) n − 2 i =1
ρ1 =
=
Var ( xi )
1 n
( xi − x ) 2
∑
n − 1 i =1
Then, remove the auto-correlation from the original time series:
xi′ = xi − ρ1 xi −1
(1)
(2)
Simply, the transferred series ( xi′), i = 1, 2, ..., n is still noted as ( xi ), i = 1, 2, ..., n .
Second, calculate Kendall indicator, τ, variance, σ τ2 , as well as normalized variable U
(Zheng, 2001):
τ=
4p
−1
n(n − 1)
(3)
2(2n + 5)
9n(n − 1)
(4)
σ τ2 =
U = τ / στ
(5)
where p is the number of occurrence of dual observation in precipitation time series.
U can be used to reflect the trend in hydrological or meteorological time series. The larger
the |U|, the more obvious the changing trend is. If U > 0, there is an increasing trend, and
vice versa. Given the significance level α, the critical value Uα/2 can be obtained from the
standard normal-distribution table; if |U| > Uα/2, reject the hypothesis of no trend, and suppose the changing trend is significant. For example, given α = 0.05, then, Uα/2 = U0.025 = 1.96;
if U > 1.96, the increasing trend is significant, if U < –1.96, the decreasing trend will be significant.
2.2.2
Kolmogorov-Smirnov (K-S) test
The objective of this test is to statistically verify whether the distribution of the observation
data is similar to some known distribution patterns. This test is based on the difference between cumulative frequency curve of observation and theoretical frequency curve of expectation (Xu, 2001).
At first, calculate the cumulative frequency of the theoretical distribution, i.e., Fe(x), and
calculate the cumulative frequency based on the data, i.e., Fo(x):
Fo(x) = k/n
(6)
where k is the number of observations less than or equal to x, and n is the total number of
observations.
Then, calculate the maximum deviation D:
D = max|Fe(x)–Fo(x)|
(7)
Given the hypothesis to be tested as:
H0: Fe(x)=Fo(x), Ha: Fe(x)≠Fo(x)
The hypothesis of the observation following the decided distribution is rejected if
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D≥Dα(n), in which, Dα(n) is the critical value and α is the significance level.
2.2.3
Z test
Given the hypothesis to be tested as:
H0: μ1–μ2 = δ, H1: μ1–μ2 ≠ δ
where, δ is set to be zero for testing the equality of the two means μ1 and μ2.
Calculate the statistics:
x − y −δ
z=
s12 s22
+
n1 n2
(8)
The hypothesis of the two means are identical is rejected if |z| ≥ zα/2. Given α = 0.05, then,
zα/2 = z0.025 = 1.96. In which, s12 , s22 , n1 , n2 are variances and lengths of the two data sets,
respectively.
2.2.4
F test
Given the hypothesis to be tested as:
H 0 : σ 12 = σ 22 , H1 : σ 12 ≠ σ 22
Calculate the statistics:
F=
s12
s22
where s12 > s22
(9)
The hypotheses of the two variances are the same is rejected if F≥Fα(n1–1,n2–1). Given
the significance level α = 0.05, and n1=n2=120, then, Fα(n1–1,n2–1)=F0.05(119,119)=1.35 will
be found from the F-distribution table.
3
3.1
Results
Temporal trend of precipitation in the Haihe River basin
For the daily precipitation of 30 weather stations from 1958 to 2007, the area average annual
and seasonal precipitation are obtained by simple averaging method; then, the temporal
trends of seasonal and annual area-average precipitation are calculated (Figure 2).
It is seen that in winter (a), the precipitation is decreasing with the rate around 0.5
mm/10a, which is not significant at the 5% significance level; in spring (b), there is a minor
increase, and the increasing trend is not significant at 5% significance level; in summer (c),
the decreasing trend in precipitation is significant at the 5% significance level, and the average decreasing rate is about 22 mm/10a; in autumn (d), the precipitation is decreasing with
the rate about 2.7 mm/10a, which is not significant at 5% significance level. In total, the
annual precipitation (e) is decreasing with a rate of 23 mm/10a, and this trend is significant
at 5% significance level.
3.2
Spatial and temporal relationship of precipitation in the Haihe River basin
As mentioned above, the long-term precipitation data from WS can describe the temporal
changing trend of precipitation in the whole river basin, however, it can not picture the de-
CHU Jianting et al.: Spatial and temporal variability of daily precipitation in Haihe River basin, 1958–2007
253
Figure 2 Temporal trend of the area-average precipitation in Haihe River basin from 1958 to 2007: a) winter; b)
spring; c) summer; d) autumn; e) annual. The p value is the significance level of the linear regression equation.
tailed local characters because the number of stations is limited. At the same time, the number of RG is much more than WS in the same catchment, which can reflect the local distribution features. However, the time span is always shorter, which will limit the accuracy of
the analysis for the temporal trend of time series. Consequently, it is important to study how
to couple the advantages of the two data sets, and how to obtain the detailed information
about precipitation in the Haihe River basin adequately.
At first, the distribution patterns of precipitation of the two data sets are tested to see if
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Journal of Geographical Sciences
they both follow the normal distribution. For illustrative purpose, the test of RG data of the
Xingtai station is presented here and the results are shown in Figure 3, in which, the mean
value is x =
1 n
1 n
xi = 529.11 , and the standard deviation is s =
∑
∑ ( xi − x )2 = 191.72.
n i =1
n − 1 i =1
Given α=0.05, n=10, then, Dα(n)=0.409 can be read from the K-S Table. From Figure 3
we get the maxD(x)=0.1808<0.409, the hypothesis of the data belonging to normal distribution can not be rejected at 95% confidence level.
Similarly, five pairs of data from weather stations (WS) and rain gauges (RG) are randomly chosen, which have the same name and location, the maximum D values of the K-S
test are shown in Table 1.
Figure 3
K-S test diagram of Xingtai weather station for 1995–2004
Table 1 Maximum deviation (D) values of the weather stations and rain gauges for normal distribution in the
Haihe River basin (1995–2004)
Station
Weather stations (WS)
Station number
Year
Rain gauges (RG)
Maximum error D
Station number
Year
Maximum error D
Xingtai
53798
1998
0.1808
242
1998
0.1808
Weichang
54311
2001
0.2454
62
1998
0.1539
Zunhua
54429
2003
0.2120
284
2003
0.1920
Yueting
54539
1999
0.1264
265
1997
0.1596
Raoyang
54606
1998
0.2088
161
2001
0.1675
From Table 1, it can be found that all of the maximum D are less than Dα(n), i.e., 0.409.
So, their distribution patterns satisfy the normal distribution at 95% confidence level. Since
all of the data are chosen randomly, we can suppose that the two sets of data of annual precipitation largely satisfy the normal distribution in the whole Haihe River basin.
In the same way, the statistics of the monthly precipitation are calculated and tested by
using the Z and F tests, and the results are shown in Table 2.
It is seen that: all of the Z values are less than 1.96, and F values are less than
F0.05(119,119)=1.35, therefore, the mean values and variances of each pair of data are statistically equal at 95% confidence level. In addition, the correlation coefficients are all greater
than 0.98, the slopes are around 1, and the intercepts are near zero, which means the two
CHU Jianting et al.: Spatial and temporal variability of daily precipitation in Haihe River basin, 1958–2007
255
Table 2 Comparison of statistical indices of monthly precipitation between weather stations and rain gauges in
the Haihe River basin (1995–2004)
Station
μ (mm)
WS
Xingtai
RG
σ (mm)
WS RG
Cv
WS
Cs
RG
WS
RG
Slope Intercept (mm)
ρ
R2
Z
F
44.1
44.3 73.2 73.7
1.66
1.67
3.12
3.11 1.004
–0.031
0.998
0.995
0.02
1.01
Weichang 37.4
38.2 47.0 49.0
1.26
1.28
1.73
1.71 1.028
–0.268
0.986
0.971
0.13
1.09
Zunhua
52.5
52.5 76.6 76.5
1.46
1.46
2.40
2.30 0.995
0.239
0.996
0.993
0.01
1.00
Yueting
44.3
44.8 61.6 62.5
1.39
1.39
2.40
2.28 1.001
0.490
0.989
0.979
0.06
1.02
Raoyang
35.0
35.0 50.6 50.7
1.44
1.45
2.58
2.46 0.990
0.380
0.982
0.964
0.00
1.02
Note: 1) WS means weather stations, while RG means rain gauges; 2) μ, σ, Cv, and Cs stand for mean value, standard
deviation, coefficient of variation and coefficient of skew of the two time series respectively; ρ and R2express the correlation coefficient and determination coefficient of the two time series; while Z and F are the Z index and F index respectively; the slope and y-intercept in the table come from the regression equation which takes the precipitation from
weather stations as independent variables, and from rain gauges as dependent variables.
data sets are highly correlated, regression equations have small interception values and
slopes are close to one. Furthermore, when comparing some other statistical indices, such as
μ, σ, Cv and Cs, the differences between two pairs of data are also minor, therefore, there is
very close relationship between these two precipitation data sets. The WS data are used to
extend the RG data, the procedure and the results are discussed as follows.
First, in order to determine the error when replacing the area-average precipitation with
point precipitation, the relationship between each WS and its corresponding area-average
precipitation calculated from the RGs in the area represented by the WS needs to be studied.
Thiessen polygons are partitioned with WS stations as the target stations (Figure 4); then,
the area-averages of precipitation from RGs in each polygon are calculated.
Second, the precipitation data of WS and the area-average of RG represented by the WS
for the 10 years (1995–2004) are simultaneously selected and the above mentioned statistical
analysis methods are performed to examine the difference of the two data sets. It is found
that all of the Z values are less than 1.96 (results not shown), which means that the mean
values from point observation of WS and area-average calculation are statistically equal.
From the range of F values, it is deduced that the variances between two data sets are also
largely similar except for few stations (results not shown). In all, the statistical indicators are
similar; the correlation coefficients and determination coefficients are higher: both of them
will range from 0.88 to 0.98. Therefore, the correlation between these two data sets is very
close.
Third, an adjusting ratio is defined:
multi − annual area − average rainfall μ RG
(10)
=
rcf =
multi − annual point rainfall
μWS
then,
′ ,i = rcf ⋅ xWS ,i
xRG
(11)
′ ,i is extended precipitation for rain gauges, while xWS,i is the original observation
where xRG
data from weather stations.
And finally, the spatial distribution of multi-annual average precipitation (1995–2004)
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Journal of Geographical Sciences
with data from 30 weather stations
and 248 rain gauges are compared
(Figure 5) (for illustrative purpose,
only annual distribution maps are
shown). It is seen that the precipitation from rain gauges can reflect
more details in spatial pattern. The
precipitation distribution pattern in
summer is much similar to the annual values. Figure 5 reveals that (1)
along the Yanshan and Taihang
mountains (in the north and west
regions of the map), there are
rain-rich regions in the windward
and less precipitation in the leeward and plain in the piedmont; (2)
precipitation is always decreasing
from south to north, from coastal
zone to inland. The omitted seasonal maps also show that winter
has least precipitation, which is
decreasing from south to north, and
the spatial distributions in spring
and autumn are much similar.
Figure 4 Thiessen polygon in the Haihe River basin according
to the altitude of weather stations
Figure 5 Comparison of spatial distribution of annual precipitation in the Haihe River basin between weather
stations and rain gauges (1995–2004)
CHU Jianting et al.: Spatial and temporal variability of daily precipitation in Haihe River basin, 1958–2007
257
The range of difference in precipitation
from WS and RG can be seen from Figure
6 (maps of seasonal precipitation have been
omitted).
In summary, the difference between
these two data sets is in general less than
100 mm, however, the values from rain
gauges seem always larger than that from
weather stations in the piedmont of Yanshan and Taihang mountains (e.g. in the
north and west regions), but the opposite is
true in Wutaishan and some other regions
in the rear of the mountains (in far west
regions). This condition may be caused by
two reasons: (1) due to the limited number
of weather stations in the whole watershed,
the local spatial features can not be clearly
described. The interpolated precipitation by
WS stations will be underestimated in the
piedmont of mountains, and overestimated
in the rear of the mountains; (2) the spatial
Figure 6 The variability of annual precipitation differinterpolation by the Inverse Distance
ence between rain gauges and weather stations in 283
Weighted (IDW) method may also intro- sub-basins of the Haihe River for 1995–2004
duce some biases. From the above, it can be
seen that the difference of the spatial patterns represented by the two data sets is not large at
most of the regions, however, the difference is remarkable in few regions, which indicated
that more stations need to be used in order to represent the details of the spatial patterns.
3.3
Spatial and temporal variability of precipitation in Haihe River basin
Using the defined adjusting ratio, the precipitation of rain gauges are extended to the length
being equal to that from weather stations, i.e., 1958–2007. The spatial distribution of the
temporal trend can then be evaluated based on the extended data. In Figure 7 the results of
Mann-Kendall trend test to each of the 248 rain gauge stations with extended records are
shown.
It can be seen that in winter (Figure 7a), the precipitation tends to decrease from south to
north, and ranges from 5 to 35 mm. Wutaishan station (in far west region) is a precipitation-rich center for its higher altitude, however, there is obvious decreasing trend around this
station in the past 50 years. In spring (Figure 7b), there are precipitation-rich regions in the
east and west, while less rain in the middle of the watershed, and the amplitude ranges from
45 to 130 mm. In the past 50 years, although there is significant decreasing trend in Wutaishan station, there is a slight increasing trend in part of middle regions, such as Beijing
station, Langfang station, etc, which results in the increasing trend in the whole river basin
on average. In summer (Figure 7c), taking the Yanshan and Taihang mountains (in north and
west regions) as the boundary, there are precipitation-rich regions in the piedmont, and the
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Journal of Geographical Sciences
Figure 7 Spatial distribution of precipitation and
its Mann-Kendall trend in Haihe River basin in (a)
winter; (b) spring; (c) summer; (d) autumn; and (e)
annual for 1958–2007
Note: At each rain gauges, if the M-K value U > 1.96,
the arrowhead is upward, if U < –1.96, the arrowhead
is downward, otherwise, the station is described by a
dot.
CHU Jianting et al.: Spatial and temporal variability of daily precipitation in Haihe River basin, 1958–2007
259
opposite is true in the rear; the total annual precipitation ranges from 175 to 570 mm. In the
past 50 years, there is distinct decreasing trend in most of the regions. In autumn (Figure 7d),
the precipitation pattern is similar to that in spring, and varies between 55 to 195 mm. Annually (Figure 7e), the overall pattern of precipitation in the Haihe River basin shows that
the precipitation decreases from south to north, ranging from 285 to 870 mm; Wutaishan
station and Zunhua-Qinglong regions (in far west and east regions) seem to be the most precipitation-rich regions. In the past 50 years, most of the regions have obvious decreasing
trend. In summary, the extended rain gauges data can describe more details about the spatial
and temporal distribution of precipitation.
4
Conclusions
The abnormal seasonal variability and spatial distribution of precipitation are always the
direct reasons for the flood or draught events. The spatial and temporal variability of precipitation in the river basin has been paid more and more attention. In this study the difference and similarity of precipitation in the Haihe River basin measured by weather stations of
China Meteorological Administration and rain gauges of hydrology bureau are compared.
Based on the comparison results, the short records of rain gauges are extended by using the
data from weather stations, and the extended data are used to study the spatial distribution of
the temporal trend of the precipitation in the region.
The following conclusions are drawn from the study:
(1) In the past 50 years (1958–2007), the precipitation is largely decreasing except for the
spring; the spatial distribution pattern of precipitation in summer is similar to the annual
values.
(2) The annual precipitation from both data sets satisfy the normal distribution; the mean
values and variances are statistically equal to each other at 5% significance level; the correlation between these two data sets is very close, the precipitation from RG can be extended
with the help of WS data.
(3) The extended precipitation can well describe the spatial distribution of precipitation.
Taking the Yanshan and Taihang mountains as boundary, there are precipitation-rich regions
in front of the boundary, while the opposite is true behind the boundary. In addition, the precipitation is decreasing from south to north, from coastal zone to inland.
(4) The annual and summer precipitation in most regions of the Haihe River basin have
been decreasing during the past 50 years, in winter and autumn the precipitation in the west
part of the basin has been decreasing while the majority of the basin has no significant
changing pattern. In spring, precipitation is decreasing in west part of the basin and increasing in the middle part.
(5) This study showed that coupling the advantages of the weather station precipitation
data and the rain gauge data provides good description of spatial-temporal distribution of
precipitation, as well as great help to the study of impact of climate change on water resources in the region.
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