Changing properties of precipitation concentration in the Pearl River basin, China
Stoch Environ Res Risk Assess (2009) 23:377–385
DOI 10.1007/s00477-008-0225-7
O R I G I N A L P A P E R
Changing properties of precipitation concentration in the Pearl
River basin, China
Qiang Zhang Æ Chong-yu Xu Æ Marco Gemmer Æ
Yongqin David Chen Æ Chunling Liu
Published online: 12 March 2008
Ó Springer-Verlag 2008
Abstract In this paper, precipitation concentrations across the Pearl River basin and the associated spatial patterns are analyzed based on daily precipitation data of
42 rain gauging stations during the period 1960–2005.
Regions characterized by the different changing properties of precipitation concentration index (CI) are identified. The southwest and northeast parts of the Pearl River basin are characterized by lower and decreasing precipitation CI; the northwest and south parts of the study river basin show higher and increasing precipitation CI. Higher but decreasing precipitations CI are found in the West and East
River basin. Comparison of precipitation CI trends before and after 1990 shows that most parts of the Pearl River basin are characterized by increasing precipitation CI after
1990. Decreasing precipitation CI after 1990 (compared to precipitation CI changes before 1990) is observed only in a few stations located in the lower Gui River and the lower
Yu River. Significant increasing precipitation CI after 1990 is detected in the West River, lower North River and upper
Beipan River. These changes of precipitation CI in the
Pearl River basin are likely to be associated with the consequences of the well-evidenced global warming. These findings can contribute to basin-scale water resource management and conservation of ecological environment in the Pearl River basin.
Keywords Precipitation variability
Concentration index (CI) Climate trend
Spatial distribution Pearl River
1 Introduction
Q. Zhang (
&
) Y. D. Chen
Institute of Space and Earth Information Science,
The Chinese University of Hong Kong, Shatin, NT,
Hong Kong, China e-mail: zhangqnj@gmail.com
Q. Zhang
Nanjing Institute of Geography and Limnology,
Chinese Academy of Sciences, 210008 Nanjing, China
C. Xu
Department of Geosciences, University of Oslo,
PO Box 1047, Blindern, 0316 Oslo, Norway
M. Gemmer
National Climate Center, China Meteorological Administration,
100081 Beijing, China
Y. D. Chen C. Liu
Department of Geography and Resource Management,
The Chinese University of Hong Kong, Shatin,
Hong Kong, China
Under the influence of the well-evidenced global warming situation, precipitation patterns are expected to change and extreme weather events (e.g. floods, droughts, and rainstorms) are likely to occur more frequently. A significantly decreasing number of rain days and significantly increasing precipitation intensity values have been identified in many places of the world such as China (Zhai et al.
; Gong and Ho
2000 ) and the USA (Karl et al.
). Precipitation extremes such as rainstorms and particularly precipitation events accounting for high percentages of the yearly total in a few very rainy days are directly responsible for flood occurrences. Higher precipitation concentration, represented by higher percentages of the yearly total precipitation in a few very rainy days, has the potential to cause floods and droughts, which is expected to put considerable pressure on water resources.
This situation is worsening by the sharp increase in water
123
378 Stoch Environ Res Risk Assess (2009) 23:377–385 consumption owing to the population explosion, unprecedented rise in standards of living, and enormous economic development (Xu and Singh
; Xu
precipitation amounts and intensity may influence the soil’s vulnerability to erosion, which will change plant growth conditions and agricultural practice, and can cause altered land-use management strategies (Scholz et al.
to its considerable scientific and practical merits, it is a subject of great interest to analyze the statistical structure of precipitation rates based on daily precipitation dataset.
) suggested that precipitation information with daily resolution in Spain is highly important and presented a precipitation concentration index (CI) to evaluate the contribution of the days of greatest rainfall to the total amount. However, to the best of our knowledge, no such study has been conducted in the Pearl River basin, the second largest river in terms of streamflow magnitude in China. Given its considerable importance for the environment and the society, we used CI to analyze the statistical structure of precipitation rates in the Pearl River basin.
About 80% of the total streamflow of Pearl River occurs during the flooding season, i.e. April–September. Therefore, uneven temporal distribution of water resource has a negative influence on the effective human use of water resources. Increasing water consumption, due to thriving socio-economy within the Pearl River basin, and qualityinduced water shortage will further deteriorate regional water security in the Pearl River basin. The East River, one of the major tributaries of Pearl River, is responsible for
80% of Hong Kong’s annual water demand. Spatial and temporal variations of water resource are closely related with precipitation changes (Zhang et al.
ular, precipitation concentration directly impacts water resources variability. Therefore, an improved understanding of the statistical structure of precipitation rates across the Pearl River basin will be of great importance in fluvial water resource management. Numerous studies of precipitation variability have been undertaken all over the world using various statistical procedures. Becker et al. (
and Gemmer et al. (
2004 ) used the Mann–Kendall trend
test and detected significant positive and negative trends in monthly precipitation totals in the Yangtze basin. Wang
) studied temporal trends in annual and seasonal mean precipitation totals and extreme precipitation events in China during 1961–2001 using linear regression method. They showed that annual mean precipitation has increased significantly in Southwestern,
Northwestern, and Eastern China, whereas it decreased significantly in Central, Northern and Northeastern China.
Zhang et al. (
2007 ) analyzed the changing characteristics
of precipitation maxima using the Mann–Kendall trend test and explored the possible causes for the changes by using
123 the NCEP/NCAR reanalysis dataset (Zhang et al.
Wang et al. (
2008 ) applied the Mann–Kendall trend test to
study the trends of precipitation maxima at the East River, one tributary of the Pearl River. They found little change in annual extreme precipitation in terms of various indices, but some significant changes were detected in the monthly
precipitation series. Wang et al. ( 2006
) used the Mann–
Kendall trend test to explore precipitation trends across the
Pearl River basin based on monthly precipitation data
) analyzed the possible correlation between extreme precipitation and occurrence of flood hazards across the Pearl River basin, showing tremendous impacts of extreme precipitation on the spatial and temporal distribution of floods. However, some scientific questions still remain unanswered, such as: (a) precipitation intensity changes represented by precipitation concentration variability; and (b) precipitation concentration trends across the study river basin and possible implication for water resource management and human mitigation to flood/drought hazards. Therefore, the objective of this paper is twofold: (1) to explore the spatial and temporal patterns of precipitation CI based on the daily precipitation datasets available; and (2) to analyze precipitation CI trends across the river basin using the Mann–
Kendall trend test.
2 Study region and data
The Pearl River (97 ° 39
0
E–117 ° 18
0
E; 3 ° 41
0
N–29 ° 15
0
N),
Fig.
1 , is the second largest river (in terms of streamflow
magnitude) in China with drainage area of 4.42
9 10
5 km
2
(PRWRC
1991 ). The Pearl River basin has three major
Fig. 1 Study region and rain gauging stations
Stoch Environ Res Risk Assess (2009) 23:377–385 379 tributaries: West River, North River and East River. The West
River is the largest one, involving Nanpan River, Hongshui
River, Qian River and West River. The main tributaries are:
Beipan River, Liu River, Yu River and Gui River (Fig.
). Its length is about 2,075 km with a drainage area of 353,120 km
2
, accounting for 77.8% of the total drainage area of the Pearl
River basin. The North River is the second largest tributary, having length of 468 km and drainage area of 46,710 km
2
.
The East River is about 520 km long with a drainage area of
27,000 km
2
, accounting for 6.6% of the total area of the Pearl
River (PRWRC
). Pearl River basin is located in the tropical and sub-tropical climate zones. The annual mean temperature ranges from 14 to 22 ° C. The multi-annual average humidity is between 71 and 80% (PRWRC
).
Precipitation during April–September accounts for 72–88% of the yearly total. The Pearl River basin is economically developed and is of great importance in the socio-economic development of China. However, frequent floods have a negative impact on the regional economic development.
Therefore, it is of great importance to conduct a thorough investigation of precipitation intensity and its changing characteristics across the Pearl River basin based on highresolution daily precipitation datasets. Daily precipitation data for the 1960–2005 period were collected from 42 national standard rain stations located in the Pearl River basin (Fig.
;
Table
). There are a few missing data in the daily precipitation datasets. Of the 42 stations, 7 stations have some missing data; in total, the missing data values consist less than
0.01% of the total number of data. The missing precipitation values are estimated by averaging precipitation values during neighboring days. We assumed that this gap filling method will have no influence on the long-term temporal trend.
Furthermore, the data consistency was checked by the double-mass method and the result showed that all the data series used in the study were consistent.
3 Methodology
The study methodology used in this work is based on the fact that the contribution of the days of a given rainfall to the total amount is generally adjustable by a negative exponential distribution (Brooks and Carruthers
;
Martin-vide
). Given a geographical and a time-period, the probability of small daily amounts of precipitation is higher than that of large daily amounts of precipitation.
Therefore, starting with the lowest class, the absolute frequencies will decrease exponentially (Martin-vide
). To evaluate the impact of the different daily precipitation values (and, in particular, to assess the contribution of the largest precipitation value on the total precipitation) we studied the accumulated precipitation %
( Y ) contributed by the accumulated day % ( X ) during Y ’s occurrence. Based on the work of Riehl (
) and Martin-vide (
2004 ) introduced the following
computational procedure:
(1) the precipitation class limits are classified (in this paper 1 mm precipitation was used as class interval);
(2) the number of days with precipitation range falling into each class intervals are counted and the associated amount of precipitation is computed;
(3) the cumulative summation of output items of Step 2 is calculated; and
(4) based on the results of Step 3, the cumulative % of rainy days and the associated amount of precipitation are obtained.
Following Step 4, an exponential curve X (cumulative % of rainy days) versus Y (cumulative % of precipitation) is derived. Martin-vide (
2004 ) recommended the exponential
model Y = aX exp( bX ), where a and b are regression coefficients. In the present work, we also used this model to simulate the observed rainfall data. The Gini concentration index 2 S /10,000 is used as a concentration measure, where
S is the area enclosed by the bisector of the quadrant and the polygonal line or Lorenz curve. The precipitation concentration assembles the Gini coefficient, which is the area circled by the perfect distribution (45 ° ) line and the
Lorenz curve. The Lorenz curve is then described by the model Y = aX exp( bX ), where the coefficients a and b are estimated by the least-squares method (Martin-vide
After the a and b have been determined, the definite integral of the exponential curve between 0 and 100 is the area under the curve
A
0 ¼ a b e bx x
1 b
100
:
0
ð 1 Þ
Based on A
0
, the area S
0 compressed by the curve, the equidistribution line and X = 100 is the difference between
5,000 and the value of A
0
. From this value the precipitation concentration, which resembles that of Gini, can be defined as
CI = S
0
= 5 ; 000 ð 2 Þ
Therefore, the CI value is the fraction of S
0 and the surface area of the lower triangle delimited by the equidistribution line (Martin-vide
adopt the precipitation CI to study the statistical structure of daily precipitation of the Pearl River basin due to the implication that it can display some exceptional information of precipitation extremes. The CI shows the contribution of extreme precipitation of certain time durations, say several days, to the total precipitation amount of the defined time interval, e.g.
1 year.
Moreover, precipitation extremes are in close relation
123
380 Stoch Environ Res Risk Assess (2009) 23:377–385
Table 1 Locations of gauging stations; precipitation mean, maximum, interquantile range (IQR) at each station; and precipitation % contributed by 25% of the rainiest days for 42 rain stations
Station name Longitude Latitude Altitude (m) Mean Maximum IQR Rainy days (%)
Guilin
Nanxiong
Fengshan
Hechi
Duan
Liuzhou
Mengshan
Huozhou
Lianzhou
Shaoguan
Fougang
Lianping
Xunwu
Napo
Baise
Xianning
Zhanyi
Yuxi
Luxi
Mengzi
Anshun
Xingren
Wangmo
Luodian
Dushan
Rongjiang
Rongan
Jingxi
Laibin
Guiping
Wuzhou
Guangning
Gaoyao
Guangzhou
Heyuan
Zengcheng
Huiyang
Longzhou
Nanning
Luoding
Taishan
Shenzhen
25 ° 58 0 N
25 ° 13 0 N
25 ° 19
0
N
25 ° 08
0
N
24 ° 33
0
N
24 ° 42
0
N
23 ° 56 0 N
24 ° 21 0 N
24 ° 12 0 N
24 ° 25
0
N
24 ° 47
0
N
24 ° 41
0
N
23 ° 52
0
N
24 ° 22
0
N
26 ° 52 0 N
25 ° 35 0 N
24 ° 20 0 N
24 ° 32
0
N
23 ° 23
0
N
26 ° 15
0
N
25 ° 26
0
N
25 ° 11
0
N
25 ° 26 0 N
25 ° 50 0 N
24 ° 57 0 N
23 ° 25 0 N
23 ° 54 0 N
23 ° 08
0
N
23 ° 45
0
N
23 ° 24
0
N
23 ° 29
0
N
23 ° 38 0 N
23 ° 02 0 N
23 ° 10 0 N
23 ° 44 0 N
23 ° 20
0
N
23 ° 05
0
N
22 ° 20
0
N
22 ° 38
0
N
22 ° 46
0
N
22 ° 15 0 N
22 ° 33 0 N
108 ° 32 0 E
109 ° 24 0 E
110 ° 18
0
E
114 ° 19
0
E
107 ° 02
0
E
108 ° 03
0
E
108 ° 06 0 E
109 ° 24 0 E
110 ° 31 0 E
111 ° 32
0
E
112 ° 23
0
E
113 ° 36
0
E
113 ° 32
0
E
114 ° 29
0
E
104 ° 17 0 E
103 ° 50 0 E
102 ° 33 0 E
103 ° 46
0
E
103 ° 23
0
E
105 ° 54
0
E
105 ° 11
0
E
106 ° 05
0
E
106 ° 46 0 E
107 ° 33 0 E
115 ° 39 0 E
105 ° 50 0 E
106 ° 36 0 E
106 ° 25
0
E
109 ° 14
0
E
110 ° 05
0
E
111 ° 18
0
E
112 ° 26 0 E
112 ° 27 0 E
113 ° 20 0 E
114 ° 41 0 E
113 ° 50
0
E
114 ° 25
0
E
106 ° 51
0
E
108 ° 13
0
E
111 ° 34
0
E
112 ° 47 0 E
114 ° 06 0 E
2,237.5
1,898.7
1,716.9
1,704.3
1,300.7
1,431.1
1,378.5
566.8
440.3
1,013.3
285.7
121.3
164.4
133.8
484.6
211.0
170.8
96.8
145.7
108.8
98.3
61.0
68.6
214.8
303.9
794.1
173.5
739.9
84.9
42.5
114.8
57.3
41.0
41.0
40.6
38.9
22.4
128.8
121.6
53.3
32.7
18.2
2.48
2.75
2.53
2.56
2.34
3.71
3.68
3.39
3.14
3.63
3.29
5.27
5.19
4.17
4.19
4.11
4.74
3.95
4.77
4.25
4.44
4.26
5.93
4.83
4.43
3.83
2.99
4.40
3.74
4.69
4.04
4.68
4.52
4.73
5.32
52.14
4.72
3.57
3.60
3.70
5.39
5.30
105.9
155.1
98.0
149.2
122.7
193.1
207.6
222.4
336.7
161.8
212.1
367.9
255.9
152.7
221.0
242.4
295.5
181.0
213.1
222.6
192.3
208.8
294.9
200.6
213.3
166.5
169.8
186.6
311.8
330.1
334.5
202.6
216.3
253.6
264.8
253.5
405.3
216.5
229.9
182.9
282.2
344.0
3.1
2.8
1.8
0.6
2.5
2.9
2.5
3.6
3.2
2.5
1.8
2.0
2.4
1.8
3.2
1.3
1.1
0.9
1.2
1.1
2.1
1.6
3.0
1.1
1.0
1.9
1.9
23.0
1.8
1.4
1.4
1.8
1.9
1.4
2.9
2.4
2.0
2.7
2.4
1.6
3.0
2.1
61.93
60.78
57.11
59.27
58.08
57.45
66.70
50.96
61.31
55.05
62.09
58.97
61.46
58.35
65.02
72.87
53.37
52.83
53.79
50.46
69.65
68.45
55.87
58.09
66.83
62.39
64.00
55.26
54.02
59.52
58.96
55.62
55.67
55.40
66.63
58.17
60.87
57.86
60.27
57.26
53.89
55.18
with flood events. Therefore, study of precipitation CI is of scientific and practical merits in better understanding of flood events in the Pearl River basin. It is also useful for basin-scale water resource management.
123
Martin-vide (
2004 ) computed the CI of precipitation
based on entire time interval of each rainfall series for each station and then explored the spatial patterns of precipitation CI. In this paper, we analyze precipitation CI based on
Stoch Environ Res Risk Assess (2009) 23:377–385 381 yearly rainfall series, meaning that one precipitation CI for
1 year and then detect possible trends of precipitation CI for each station. Spatial patterns of precipitation CI and associated trends are all presented. There are many statistical techniques available to detect trends within the time series including moving average, linear regression, Mann–
Kendall trend test, filtering technology, etc. Each method has its own strength and weakness in trend detection.
However, non-parametric trend detection methods are less sensitive to outliers than are parametric statistics such as
Pearson’s correlation coefficient. Moreover, the rank-based nonparametric Mann–Kendall test (Kendall
; Mann
) can test trends in a time series without requiring normality or linearity (Wang et al.
), and is therefore highly recommended for general use by the World Meteorological Organization (Mitchell et al.
used in detection of trends in hydrological series (e.g.
Zhang et al.
). This paper also uses the Mann–Kendall
(MK) test method to analyze trends within the precipitation
CI series across the Pearl River basin. The procedure of
MK trend test adopted in this study is as follows:
First, the MK test statistic is calculated as
S ¼ j ¼ i þ 1 j ¼ i þ 1 where sgn ð x j sgn ð x j x i
Þ ¼
8
< x i
Þ ;
þ 1 ;
0 ;
:
1 ;
ð 3 x j
[ x i x j
¼ x x j
\ x i i
; and n is the sample
Þ size. The statistics S is approximately normally distributed when n C 8, with the mean and the variance, respectively,
E ð S Þ ¼ 0 ; ð 4 Þ
V ð S Þ ¼ n ð n 1 Þð 2 n þ 5 Þ
P n i ¼ 1
18 t i i ð i 1 Þð 2 i þ 5 Þ
; ð 5 Þ where t i is the number of ties of extent i . The standardized statistics ( Z ) for one-tailed test is formulated as
Z ¼
8
< p ffiffiffiffiffiffiffiffiffiffiffiffi
0
ð S Þ
S [ 0
S ¼ 0 : p ffiffiffiffiffiffiffiffiffiffiffiffi
ð S Þ
S \ 0
ð 6 Þ
At the 5% significance level, the null hypothesis of no trend is rejected if | Z | [ 1.96.
4 Results
4.1 Spatial patterns of precipitation CI
For illustrative purposes, in this study we considered precipitation changes at Xianning (26 ° 52
0
N, 104 ° 17
0
E).
Similar results are obtained for other stations in the study
100
90
80
70
60
50
40
30
20
Xianning, Guizhou
10
0
0 10 20 30 40 50 60 70 80 90 100
Sum (Ni) %
Fig. 2 Concentration or Lorenz curves for precipitation observatories in the Xianning station. The stars denote observed precipitation and the circles denote simulated precipitation data.
Pi means precipitation during day i = 1,..., n .
Ni denotes the time (days) river basin. Figure
indicates that the simulated and observed cumulative percentage of rainy days matched well. The values of the regression coefficients a and b are shown in Table
. Statistical analysis results show that these coefficients are significantly different from 0 at
[ 95% confidence level. Thus, the equation Y = aX exp( bX ) can be used in the simulation of precipitation changes in the Pearl River basin. Comparison between precipitation concentrations at the Pearl River basin and at peninsular Spain (Martin-Vide
) indicates that the concentrations at the Pearl River basin were higher than those at the peninsular Spain, with larger precipitation CI values. The CI of the peninsular Spain precipitation series varies between 0.55 and 0.7 and that of the Pearl River basin ranges between 0.74 and 0.80. The percentage of precipitation contributed by 25% of the rainiest days also corroborates this conclusion. The result may be due to the different climatic systems of peninsular Spain and the Pearl
River basin. Peninsular Spain experiences three climatic types: continental, maritime, and Mediterranean. The locally generated continental climate covers the majority of peninsular Spain, which is characterized by wide diurnal and seasonal variations in temperature and by low and irregular rainfall with high rates of evaporation that leave the land arid. On the other hand, the Pearl River basin is characterized by monsoon climate with extremely uneven temporal and spatial distribution of precipitation. Moreover the precipitation in the Pearl River basin is usually the result of typhoon and convective precipitation. This may be the cause of the higher precipitation CI values when compared with those of peninsular Spain. Figure
demonstrates precipitation CI variations across the Pearl River basin. We define the region with CI B 0.75 as the region
123
382 Stoch Environ Res Risk Assess (2009) 23:377–385
Table 2 Coefficients of a and b for exponential curves given by Eq.
(
); CI and precipitation % contributed by 25% of the rainiest days for
42 gauging stations during 1960–2005 in the Pearl River basin
Station name a b CI Precipitation %
0.00096
0.00126
0.00082
0.00059
0.00034
0.00033
0.00114
0.00040
0.00019
0.00156
0.00139
0.00189
0.00193
0.00026
0.00025
0.00053
0.00051
0.00141
0.00088
0.00125
0.00117
0.00081
0.00046
0.00064
0.00117
0.00204
0.00036
0.00068
0.00056
0.00064
0.00118
0.00113
0.00148
0.00029
0.00090
0.00093
0.00122
0.00153
0.00038
0.00055
0.00036
0.00032
Shaoguan
Fougang
Lianping
Xunwu
Napo
Baise
Jingxi
Laibin
Guiping
Wuzhou
Guangning
Gaoyao
Guangzhou
Heyuan
Zengcheng
Huiyang
Longzhou
Nanning
Luoding
Taishan
Shenzhen
Xianing
Zhanyi
Yuxi
Luxi
Mengzi
Anshun
Xingren
Wangmo
Luodian
Dushan
Rongjiang
Rongan
Guilin
Nanxiong
Fengshan
Hechi
Duan
Liuzhou
Mengshan
Huozhou
Lianzhou
The coefficient of determination of the regression, R
2 xx% at all stations is bigger than
0.069
0.066
0.071
0.074
0.079
0.080
0.067
0.078
0.085
0.075
0.076
0.065
0.070
0.067
0.067
0.064
0.065
0.062
0.062
0.082
0.082
0.073
0.074
0.073
0.067
0.068
0.065
0.071
0.076
0.073
0.067
0.062
0.079
0.081
0.070
0.069
0.067
0.064
0.078
0.074
0.079
0.080
0.762
0.755
0.764
0.774
0.789
0.790
0.755
0.787
0.805
0.780
0.778
0.750
0.762
0.752
0.755
0.745
0.751
0.739
0.737
0.794
0.799
0.773
0.780
0.778
0.758
0.756
0.747
0.771
0.780
0.776
0.758
0.737
0.790
0.795
0.765
0.764
0.754
0.748
0.792
0.779
0.790
0.788
74.83
75.05
77.53
76.13
78.31
79.34
74.30
78.08
78.93
77.97
78.15
75.72
75.08
74.67
74.17
72.97
74.86
73.14
72.19
75.59
77.35
78.82
78.79
77.98
75.47
74.85
73.57
78.89
76.71
78.86
75.97
71.90
79.25
78.88
74.80
73.71
73.38
72.78
81.14
79.70
80.35
77.99
characterized by high precipitation concentrations and the region with CI C 0.78 as the region dominated by low precipitation concentrations.
Precipitation CI values
123
Fig. 3 Spatial distribution of precipitation concentration index, CI
(1960–2005) in the Pearl River basin. The contour interval is 0.01.
The H and L denote regions with high and low precipitation CI, respectively ranging between 0.75 and 0.78 are classified as medium precipitation concentrations. Based on this classification, two areas with low precipitation CI and four areas with high precipitation CI marked with L and H, respectively, are differentiated across the Pearl River basin (Fig.
). The remaining parts of the Pearl River basin are classified as areas dominated by medium precipitation concentration.
Figure
shows that low precipitation concentrations are mainly detected in the Nanpan River, upper North River and upper East River. The upper North River, You River, lower West River and lower East River are characterized by high precipitation concentrations.
4.2 Spatial distribution of precipitation CI trends
Figure
shows the spatial distribution of precipitation CI trends across the Pearl River basin. The numbers marking the contours are Z values. If | Z| [ 1.96, it means that the trend is significant at a [ 95% confidence level. Positive Z values denote upward trend and vice versa. The areas dominated by upward or downward trend are marked with
Fig. 4 Spatial patterns of trends in mean annual precipitation concentration index (CI) in the Pearl River basin. The contour interval is 0.3. The U and D denote regions with upward and downward trends, respectively. The values shown in the figure are Z values. Negative Z values denote downward trend and positive Z values denote upward trend. The trend is significant at the 95% confidence level if | Z | C 1.96, and vice versa. This figure illustrates that the west corner and the east parts of the Pear River basin are dominated by significant CI trends
Stoch Environ Res Risk Assess (2009) 23:377–385 383
U or D, respectively. Figure
indicates that major parts of the Pearl River basin are characterized by no significant trends at a [ 95% confidence level. Increasing precipitation
CI values can be identified in the upper Beipan River, You
River, Liu River and Gui River. Decreasing precipitation
CI values are observed in Nanpan River, Zuo River, Yu
River, West River, North River and East River. Furthermore, the decreasing precipitation CI values in the upper
Nanpan River, Yu River, West River, lower North River and lower East River basin are significant at a [ 95% confidence level. Figures
and
reveal that areas dominated by high precipitation CI values mostly correspond well to areas featured by increasing precipitation CI values and vice versa; with the exception of the lower West River and lower East River, where the precipitation CI values show a significant decreasing trend. Therefore, areas with high precipitation CI tend to have increasing precipitation concentration; areas with low precipitation CI values tend to have decreasing concentrations, implying that the precipitation changes of the Pearl River basin have a potential towards the extreme side. Higher/increasing precipitation concentrations are observed in the northwest part of the
Pearl River basin; lower/decreasing precipitation is observed in the southwest part, north part and northeast part of the Pearl River basin; East part and southeast part of the Pearl River basin are characterized by significantly decreasing but high precipitation CI values.
To further investigate the changing characteristics of precipitation CI over the Pearl River basin before and after 1990, we analyzed the Mann–Kendall trend of precipitation CI values before and after 1990 for each rain gauging stations in the Pearl River basin. Figure
shows the precipitation CI trends before 1990 and
Fig.
5 b displays the precipitation CI trends after 1990. It
can be seen from Fig.
a that most rain gauging stations show decreasing precipitation CI values before 1990.
Furthermore, the precipitation CI values at seven rain gauging stations experience a significant decreasing trend during 1960–1990, and these seven stations are mostly located in the North River basin and lower East River basin. Five out of 42 stations show increasing precipitation CI, but this trend is not significant at a [ 95% confidence level.
Figure
5 b indicates that more stations in the Pearl River
basin are characterized by increasing precipitation CI after
1990. Increasing precipitation CI values were detected in
Nanpan River, upper Beipan River, upper Liu River, West
River and lower North River. Three out of 42 stations showed significantly increasing precipitation CI values after 1990 (Fig.
b). A comparison of Figs.
that most parts of the Pearl River basin experience a transition from decreasing precipitation CI to increasing precipitation CI (see regions marked by U in Fig.
very few stations experience a transition from increasing precipitation CI to decreasing precipitation CI (see regions marked by D in Fig.
5 Conclusions and discussion
Statistical analysis based on daily rainfall datasets has scientific and practical value in the context of basin-scale water resource management and ecological environment conservation. Precipitation CI, which resembles Gini coefficient, was used in this paper to investigate the statistical structure of precipitation rates based on daily rainfall datasets. Some interesting conclusions were obtained as follows:
Fig. 5 Mann–Kendall trends of precipitation concentration index (CI) before 1990 ( a ) and after 1990 ( b ). The value 1 ( 1 ) denotes decreasing (increasing)
CI but is not significant at the
95% confidence level; the value
2 ( 2 ) denotes decreasing
(increasing) CI but is significant at 95% confidence level. The U and D in ( b ) denote regions characterized, respectively, by increasing and decreasing CI after 1990 (compared to those before 1990)
A
B
123
384 Stoch Environ Res Risk Assess (2009) 23:377–385
The model Y = aX exp( bX ) can adequately represent the statistical structure of precipitation rates over the Pearl
River basin. The precipitation CI defined by Riehl (
Olascoaga ( 1950 ) and Martin-vide ( 2004
) can express quantitatively the precipitation concentration across the
Pearl River basin. Generally, four areas characterized by higher precipitation CI and two areas showing lower precipitation CI were classified in this paper. Higher precipitation CI was observed mainly in the northwest part, south part and southeast part of the Pearl River basin.
Lower precipitation CI was detected mainly in the southwest part and northeast part of the basin.
In addition to the general precipitation CI analysis of the entire time series, we also analyzed precipitation CI at the yearly time-scale and the associated precipitation CI trends. Lower precipitation CI values are expected in the west, south, southeast and east parts of the Pearl River basin. Higher precipitation CI values were detected primarily in the north part of the basin. Mann–Kendall trends of precipitation CI before and after 1990 for each rain gauging stations in the Pearl River basin indicated that higher precipitation CI values (compared to CI values before 1990) can occur in major regions of the Pearl River basin, and particularly in the lower North River, West
River and upper Beipan River.
Spatial heterogeneity was revealed in the spatial pattern of absolute precipitation CI values and the precipitation CI trends across the Pearl River basin. Changing atmospheric moisture, temperature fields, shifts in Asian monsoon strength (due to the well-evidenced global warming) could cause board changes in the precipitation intensity, with no shift in the importance of the various mechanisms (Osborn et al.
). Climatic changes in China are controlled primarily by winter and summer monsoon (Domroes and
Peng
). Rainy seasons in eastern China hinge on the progress and retreat of the East Asian summer monsoon.
Increasing CI values after 1990 in the Pearl River basin may be due to changes in the East Asian summer monsoon system. Some Chinese scholars attributed precipitation anomalies to sea surface temperature anomalies in the equatorial eastern Pacific, subtropical high across the northwest Pacific, monsoon system and snow cover over
Tibet in winter (e.g., Zhao and Xu
; Gong and Ho
). These studies revealed a number of complicated factors with varying influence on precipitation changes in
China. E.g., physical mechanisms causing changes in precipitation concentration are complex and by no means the result of the impact by a single factor; it is rather a combination of Pearl River basin topographical features, evolution of Asian monsoon and perturbation of human activities. Future research would investigate the physical mechanisms underlying the observed statistical structure of daily precipitation rates across the Pearl River basin. It
123 should also be noted that the time series trend depends on the data period considered in the analysis and influenced by the appearance of outliers. Thus, the precipitation concentration trend detected in this study represents temporal of precipitation variations during the study period 1960–
2005. The research findings of this paper are of considerable importance in basin-scale water resource management, flood events in particular, and ecological conservation in the study basin.
Acknowledgments The work described in this paper was fully supported by a Direct Grant from the Faculty of Social Science, The
Chinese University of Hong Kong (Project No. 4450183), National
Natural Science Foundation of China (Grant No.: 40701015), and by the Outstanding Overseas Chinese Scholars Fund from CAS (The
Chinese Academy of Sciences). Cordial thanks should be extended to three anonymous reviewers and Prof. George Christakos, the SERRA editor-in-chief, for their valuable comments and suggestions that greatly improved the quality of this paper.
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