Theor Appl Climatol (2009) 98:89–99
DOI 10.1007/s00704-008-0095-4
ORIGINAL PAPER
Observed changes of drought/wetness episodes in the Pearl
River basin, China, using the standardized precipitation
index and aridity index
Qiang Zhang & Chong-Yu Xu & Zengxin Zhang
Received: 24 April 2008 / Accepted: 29 November 2008 / Published online: 16 January 2009
# Springer-Verlag 2009
Abstract Monthly precipitation data of 42 rain stations over
the Pearl River basin for 1960–2005 were analyzed to
classify anomalously wet and dry conditions by using the
standardized precipitation index (SPI) and aridity index (I) for
the rainy season (April–September) and winter (December–
February). Trends of the number of wet and dry months
decided by SPI were detected with Mann-Kendall technique.
Furthermore, we also investigated possible causes behind
wet and dry variations by analyzing NCAR/NCEP reanalysis
dataset. The results indicate that: (1) the Pearl River basin
tends to be dryer in the rainy season and comes to be wetter
Q. Zhang
Institute of Space and Earth Information Science,
The Chinese University of Hong Kong,
Shatin, Hong Kong, China
Q. Zhang
State Key Laboratory of Lake Science and Environment,
Nanjing Institute of Geography and Limnology,
Chinese Academy of Sciences,
73 East Beijing Road,
Nanjing 210008, China
C.-Y. Xu
Department of Geosciences, University of Oslo,
P.O. Box 1047, Blindern,
0316 Oslo, Norway
Z. Zhang
Jiangsu Key Laboratory of Forestry Ecological Engineering,
Nanjing Forestry University,
73 East Beijing Road,
Nanjing 210008, China
Q. Zhang (*)
Nanjing Institute of Geography and Limnology,
Chinese Academy of Science,
73 East Beijing Road,
Nanjing 210008, China
e-mail: zhangqnj@gmail.com
in winter. However, different wetting and drying properties
can be identified across the basin: west parts of the basin
tend to be dryer; and southeast parts tend to be wetter; (2) the
Pearl River basin is dominated by dry tendency in the rainy
season and is further substantiated by aridity index (I)
variations; and (3) water vapor flux, moisture content
changes in the rainy season and winter indicate different
influences of moisture changes on wet and dry conditions
across the Pearl River basin. Increasing moisture content
gives rise to an increasing number of wet months in winter.
However, no fixed relationships can be observed between
moisture content changes and number of wet months in the
rainy season, indicating that more than one factor can
influence the dry or wet conditions of the study region.
The results of this paper will be helpful for basin-scale water
resource management under the changing climate.
1 Introduction
Assessment of water resource variations is a pre-requisite to
understand and adopt appropriate management strategies
with the aim to avoid adverse environmental effects and
reconcile conflicts between users (Xu and Singh 2004).
Climate changes, particularly the current well-evidenced
global warming and its impacts on hydrological regimes,
especially hydrological extremes, e.g. droughts and floods,
have become a priority area both for process research and
for water management practices (Xu et al. 2005). It was
believed that projected global climate changes have the
potential to accelerate the global hydrological cycle. Many
studies indicated that global warming alters precipitation
patterns and results in more frequent extreme weather
events, e.g. floods, droughts, and rainstorms (Zhang et al.
2008a; WMO 2003). These results are undoubtedly useful
for the better understanding of increasing flood/drought
90
hazards over the world (e.g. Herschy 2002; Mirza 2002).
Furthermore, public awareness of extreme climatic events
has risen sharply in recent years partly due to the
catastrophic nature of floods, droughts, storms and other
climatic extremes (e.g. Beniston and Stephenson 2004;
Zhang et al. 2006a, b, 2008b). Therefore, it is of scientific
and practical merit to better understand changing characteristics of dryness and wetness variations for improving
integrated water resource management at the basin scale
and human mitigation to hydrological alterations.
Environmental droughts generally include: (1) meteorological drought, (2) hydrological drought; and (3) agricultural drought (e.g. Livada and Assimakopoulos 2007). This
study focuses on the meteorological drought which is
defined as a lack of precipitation over a region for a period
of time. The standardized precipitation index (SPI) (McKee
et al. 1993, 1995; Hayes et al. 1999) was widely used to
reveal meteorological drought (e.g. Silva et al. 2007; Bordi et
al. 2004a; Moreira et al. 2006) and was proven to be a useful
tool in the estimation of the intensity and duration of drought
events (Bordi et al. 2004a). Livada and Assimakopoulos
(2007) used the SPI to analyze drought events in Greece.
Wilhite and Glantz (1985) applied the SPI in Nebraska on
time scales of 3, 6, 12, 24 and 48 months. In this study, the
SPI is calculated based on 1 and 3-month precipitation time
series.
The Pearl River is the third largest river in drainage
basin area and the second largest river in terms of
streamflow in China. However, uneven spatial and temporal
distribution of water resources, with 80% of the total
discharge occurring in the flooding seasons, i.e. April–
September, negatively affects the effective human use of
water resource. With booming economic development in
the Pearl River basin, pollution-induced water shortage is
threatening the security of regional water resources. The
East River, a tributary of the Pearl River, bears the heavy
responsibility of water supply for Shenzhen and Hong
Kong, and about 80% of Hong Kong’s annual water
demands come from the East River. Due to the significant
role of water resource in regional economic development
and conservation of ecological environment in the region,
precipitation changes and possible underlying causes have
drawn increasing concerns. Wang et al. (2008) explored
changing properties of precipitation extremes and streamflow extremes in the East River, one tributary of the Pearl
River basin. Dong (2006) indicated close relations between
extreme precipitation changes and spatial and temporal
distribution of floods in the region. Luo et al. (2008)
analyzed precipitation trends in North River basin by using
Mann-Kendall trend test and Sen’s T test. In terms of dry
and wet changes in China, Bordi et al. (2004b) investigated
time–space variations of dry and wet periods in the east
China. However, to our best knowledge, no such reports are
Q. Zhang et al.
available in the Pearl River basin so far despite numerous
studies of this topic available in the literatures. With this in
mind, the objectives of this paper are: (1) to detect changing
properties of dry and wet episodes defined by SPI; (2) to
analyze trends of frequency and intensity of dry and wet
events by using Mann-Kendall trend test; and (3) to explore
possible causes behind wet and drought variations in the
Pearl River basin with NCAR/NCEP reanalysis dataset.
2 Study region and data
The Pearl River (97°39′E–117°18′E; 3°41′N–29°15′N)
(Fig. 1) is the second largest river in terms of streamflow
in China with a drainage area of 4.42×105 km2 (PRWRC
1991). The Pearl River basin involves three major tributaries:
West River, North River and East River. The West River is
the largest tributary accounting for 77.8% of the total
drainage area of the basin. The North River is the second
largest tributary with a drainage area of 46,710 km2. The
East River accounts for 6.6% of the total area of the Pearl
River. The Pearl River basin is located in the tropical and
sub-tropical climate zone with the annual mean temperature
ranging between 14–22°C. The precipitation mainly concentrates during April–September (Zhang et al. 2008a), accounting for 72–88% of the annual precipitation (PRWRC 1991).
The daily precipitation dataset covering 1 January 1960–
31 December 2005 was collected from 42 rain stations in
the Pearl River basin. Location of the rain gauging stations
can be referred to Fig. 1. Data quality control was made in
an earlier study (Zhang et al. 2008a). The NCAR/NCEP
reanalysis dataset was used to detect changes of water
vapor flux and moisture content in the Pearl River basin
with the intention to understand possible causes behind wet
and drought changes over the basin.
3 Methodology
3.1 Standardized precipitation index (SPI)
The Standardized precipitation index (SPI) (McKee et al.
1993) was used to quantify the precipitation deficit on
multiple time scales. According to McKee et al. (1993), the
SPI was defined on each of the time scales as the difference
between precipitation on the time series (xi) and the mean
value ðxÞ, divided by the standard deviation (s), i.e.
SPI ¼
xi x
s
ð1Þ
The same definition is widely used in the literature (e.g.,
McKee et al. 1993; Livada and Assimakopoulos 2007). It is
well known that very seldom the monthly precipitation time
Changes of drought/wetness episodes in the Pearl River basin, China
91
Fig. 1 Study region and rain
gauging stations
series fits a normal distribution, thus, an initial transformation of the data series is usually done to make SPI a
standard normal distribution variable. In this study, different probability distributions have been used to fit precipitation time series on various time scales; gamma, normal
and log-normal distributions were selected as candidate
distributions; and the Kolmogorov-Smirnov test was performed to evaluate the goodness-of-fit. The results (not
shown) indicated that normal distribution had the worst
performance. Gamma distribution and log-normal distribution performed equally well for the monthly precipitation
series in the basin. Particularly, log-normal distribution had
better performance in describing precipitation properties in
the rainy season. Gamma distribution performed better in
winter (January, February and December) when compared
to log-normal distribution. Even so, log-normal distribution
also fit the precipitation changes in winter at >95%
confidence level well. Therefore, we chose log-normal
probability distribution for the monthly precipitation series
in the basin. After logarithmic transformation of the dataset,
the sample mean and variance of the transformed data will
^ y and s^ y , then the SPI becomes
be m
SPI ¼ Z ¼
^y
lnð xÞ m
s^ y
ð2Þ
The drought and wetness severity adopted in this study
is defined in Table 1. Due to the fact that precipitation
mainly concentrated in the rainy season (April–September;
Fig. 2), we studied the changes of SPI for the rainy season.
Because the Pearl River basin bears the heavy responsibility of water supply for Hong Kong and the Pearl River
Delta, the SPI changes in winter (January, February and
December) were also studied. Moreover, the number of
months characterized by drought and wetness categories in
the rainy season was also analyzed.
Table 1 The standardized precipitation index (SPI) categories based
on the initial classification of SPI values
Category
SPI
Probability (%)
Extremely wet
Severely wet
Moderately wet
Near normal
Moderate drought
Severe drought
Extreme drought
2.00 and above
1.50–1.99
1.00–1.49
−0.99–0.99
−1.00 to −1.49
−1.50 to −1.99
−2.00 and less
2.3
4.4
9.2
68.2
9.2
4.4
2.3
92
Q. Zhang et al.
300
First the MK test statistic is calculated as
Precipitation (mm)
250
S¼
200
n1 X
n
X
sgn xj xi
ð4Þ
i¼1 j¼iþ1
150
100
50
0
8
< þ1; xj > xi
0; xj ¼ xi : and n is the sample
where sgn xj xi ¼
:
1; xj < xi
size. The statistics S is approximately normally distributed
when n≥8, with the mean and the variance as follows:
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Fig. 2 Areal monthly mean precipitation in the Pearl River basin
(mm/month, average of 42 stations) for the period 1960–2005
E ðsÞ ¼ 0
ð5Þ
nðn 1Þð2n þ 5Þ 3.2 Aridity index
To further understand dry and wet variations in terms of
agriculture demand, we also calculated the aridity index and
compared it with SPI in this study. Aridity index is defined
by de Martonne (1926) who used it to study irrigation
demands (WMO 1975) and is computed as:
12Pi
Ii ¼
Ti þ 10
ð3Þ
where Pi is the monthly precipitation amount; Ti is the
monthly mean air temperature. The aridity index aims to
identify the months when irrigation is necessary. Generally,
irrigation is necessary when Ii <20. Research results by
Livada and Assimakopoulos (2007) indicated that, on a
monthly basis, there is a statistically significant exponential
correlation between SPI and aridity index (I = Ii/12) and
significant linear correlation between aridity index and
rainfall departures (RD). RD is defined as the difference
between the monthly precipitation and its mean values, and
then is divided by its standard deviation. They also stated
that the exponential relationship between I and SPI should be
due to the normalization of the monthly precipitation
data prior to SPI estimation (Livada and Assimakopoulos
2007).
3.3 Trend test
There are many statistical methods available for trend
detection and each method has its own strength and
weakness in trend detection (Zhang et al. 2008a). In this
paper, the Mann-Kendall (MK) test (Kendall 1975; Mann
1945), recommended by the World Meteorological Organization (Mitchell et al. 1966), is used to study trends of SPI,
intensity of wet and dry months and also the number of
months when irrigation is necessary. For the sake of
completion and understanding of the results, the procedure
of MK trend test performed in the study is briefly
introduced as follows.
n
P
ti iði 1Þð2i þ 5Þ
i¼1
V ðS Þ ¼
18
ð6Þ
where ti is the number of ties of extent i.
The standardized statistics (Z) for one-tailed test is
formulated as:
8
S1
>
>
pffiffiffiffiffiffiffiffiffiffiffiffiffiffi
>
>
>
>
< VarðS Þ
Z¼
0
>
>
>
Sþ1
>
>
>
: pffiffiffiffiffiffiffiffiffiffiffiffiffiffi
VarðS Þ
S>0
S¼0
ð7Þ
S<0
At the 5% significance level, the null hypothesis of no trend
is rejected if |Z| > 1.96.
3.4 Calculation of frequency of wet and dry months
in the rainy season
The number of months fell in each categories (frequency of
wet and dry months), i.e. from extreme dry to extreme wet
(see Table 1 for division) in the rainy season of each year is
a useful indicator for the dryness/wetness of the catchment
since the rainy season captures 80% of the yearly
precipitation total. The trends of these numbers are
calculated via the following steps: (1) calculate the SPI
for each month in the rainy season and count the number of
months falling in each categories within each year, (2) take
the numbers in each categories of each year as a time series
and calculate the trend of the time series, and (3) spatially
interpolate the trend calculated for each station. Due to the
fact that the number of extreme dry or wet months is scarce,
only, e.g., 75 extreme dry months can be identified in the
Pearl River basin for past 46 years account for 4% of total
months in the rainy season. For the sake of comparison, the
number of extreme dry months will not be analyzed,
rather We will only be analyzing the trends of the number
of severe (moderate) dry (wet) months in the rainy
season.
Changes of drought/wetness episodes in the Pearl River basin, China
93
Fig. 3 Spatial distribution pattern of trends of SPI in the rainy seasons (April–September) across the Pearl River basin. The numbers in the figure
are Z values. Positive values indicate increasing trend and vice versa. If |Z| ≥ 1.96, then the trend is significant at >95% confidence level
4 Results and discussion
4.2 Frequency of wet and dry months in the rainy season
4.1 Spatial and temporal patterns of SPI
Figure 5 demonstrates the spatial patterns of trends in the
number of dry or wet months in the rainy season, i.e.
moderate (severe) wet and moderate (severe) dry. Here we
decide the number of moderate (severe) wet and moderate
(severe) dry months in the following way: (1) the month
with SPI>1.5 (or SPI<−1.5) is defined as severe wet (or
dry) month; (2) the month with SPI>1 (or SPI<−1) is
defined as moderate wet (dry) month. Different spatial
patterns can be identified for wet or dry episodes of
different categories (Fig. 5). Figure 5a indicates a decreasing number of severe wet months in large parts of the Pearl
River basin, though this decreasing trend is not statistically
significant. Stations with an increasing number of severe
wet months distribute sporadically across the basin. Similar
phenomenon can be identified in changes of moderate wet
months (Fig. 5b). A large area of the Pearl River basin is
characterized by a decreasing number of moderate wet
months and increasing trends can be observed mainly in
the lower East River, lower North River, lower West River
and lower Beipan River. Different spatial patterns can be
Figure 3 maps the spatial distribution of SPI trends across
the Pearl River basin for the rainy season. It can be seen
that the major parts of the basin are characterized by
decreasing SPI, indicating that drying tendency dominates
major parts of the Pearl River basin. However, only a few
places are characterized by the decreasing trends of SPI
significant at >95% confidence level. A slight increasing
trend (not significant at 95% level) of SPI can be identified
in a few locations. Thus, generally, wet tendency is mainly
identified in southeast parts of the Pearl River basin in the
rainy season. As mentioned earlier, the Pearl River basin
bears the heavy responsibility of water supply for the Pearl
River Delta and Hong Kong—and that is particularly the
case for the East River. Figure 4 shows that the entire Pearl
River basin is characterized by increasing SPI in the winter
season. Thus, the wet tendency prevails over the basin in
the winter season, although the increasing trends are not
significant at >95% confidence level.
Fig. 4 Spatial distribution pattern of trends of SPI in winter (January, February and December) across the Pearl River basin. The numbers in the
figure are Z values. Positive values indicate increasing trend and vice versa. If |Z| ≥ 1.96, then the trend is significant at >95% confidence level
94
Q. Zhang et al.
• Rain gauging station
• Rain gauging station
• Rain gauging station
• Rain gauging station
Fig. 5 Spatial distribution pattern of trends of the number of wet and
dry months of different categories in the rainy season (April–
September). a severe wet; b moderate wet; c severe dry; d moderate
dry. The numbers in the figure are Z values. Positive values indicate
increasing trend and vice versa. If |Z| ≥ 1.96, then the trend is
significant at >95% confidence level
observed in the changes of severe and moderate dry
months (Fig. 5c,d). Major parts of the Pearl River basin
are dominated by increasing severe and moderate dry
months. Similarly, these trends are not statistically significant. It can be seen from Fig. 5c that the increasing
number of severe dry months is mainly observed in Beipan
River, Nanpan River and Hongshui River and decreasing
trends are mainly found in the east parts of the Pearl River
basin. In terms of the number of moderate dry months,
increasing trends are observed in west and south parts of
the basin, e.g. Nanpan River, Beipan River, Zuo River, Yu
River, Qian River and Liu River. Decreasing trends in the
number of moderate dry months are identified mainly in
the east parts of the Pearl River basin (Fig. 5d). Figure 6
illustrates areal average SPI across the Pearl River basin. It
can be observed from Fig. 6 that SPI of the basin is
increasing before 1980s, and is decreasing after 1980s. An
obvious decreasing tendency is found during 1985–1998.
SPI in winter, however, is consistently increasing, particularly during 1970–2005.
0.5
SPI
0
–0.5
SPI in rainy season
–1
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
1970
1975
1980
1985
Time (year)
1990
1995
2000
2005
1.5
1
SPI
Fig. 6 Areal average SPI
changes in the rainy season
(April–September) and winter
(DJF) for the Pearl River basin.
Dashed lines denote quadratic
fit
SPI in winter
0.5
0
–0.5
–1
1960
1965
Changes of drought/wetness episodes in the Pearl River basin, China
95
Fig. 7 Trends of the number of months when agricultural irrigation is necessary, i.e. I<20. The numbers in the figure are Z values. Positive values
indicate increasing trend and vice versa. If |Z| ≥ 1.96, then the trend is significant at >95% confidence level
Figure 7 shows trends of frequency of months when
irrigation is necessary, i.e. I<20; hereafter, we will simply
call months with I<20 dry months. It can be seen from
Fig. 7 that an increasing number of dry months can be
detected mainly in western parts of the Pearl River basin, i.
e. Nanpan River, Beipan River, Hongshui River, lower Liu
River and upper Zuo River. Major regions in the eastern
parts of the basin are characterized by a decreasing
frequency of dry months. Figure 7 demonstrates that places
characterized by increasing number of dry months largely
match with those characterized by an increasing number of
severe dry months defined by SPI (Fig. 5c,d). These results
imply that, at least in the Pearl River basin, SPI and I index
seem to perform similarly well in reflecting wet and dry
conditions in the basin. To verify the results of Fig. 5, we
further analyzed relationships between I, SPI and rainfall
departure (RD), and for illustrative purpose the results for a
randomly selected station are shown in Fig. 8a for the rainy
season and Fig. 8b for winter. Similar results have been
obtained for the relationships between I, SPI and RD when
compared to those by Livada and Assimakopoulos (2007)
over Greece. Excellent linear relations can be fit between I
and RD, and exponential relationships between I and SPI.
The exponential relationship between I and SPI can be
attributed to the transformation of original monthly precipitation data by using log-normal distribution function.
Fig. 8 Scatter diagram between
I, SPI, (open circles) and I, RD
(closed circles) and correlation
lines for for one station selected
randomly from the dataset. a
Rainy season; b winter. The
example station: Xianning station (26°52′N, 104°17′E)
4.3 Water vapor flux analysis
Water vapor flux plays the key role in changes of drought
and wet conditions; the water vapor is brought to the
Asian continent mainly by the monsoonal flows. Generally, three major low-level monsoonal streams transport
water vapor to China (Simmonds et al. 1999; Tian et al.
2004; Chow et al. 2008): (1) the southwesterly flow
towards the Indian peninsula and Bengal Bay which is
associated with the Indian summer monsoon, and (2) the
southerly flow in the South China Sea and (3) the
southeasterly flow associated with the southern flange of
the north Western Pacific subtropical high (0–20°N, 120–
150°E). Routes of the water vapor transport in the rainy
season (Fig. 9a) clearly demonstrate the sources of the
water vapor: Indian peninsula, Bengal Bay and South China
Sea. However, water vapor in the Pearl River is mainly
from southwesterly flow. Intensity of water vapor flux in
the rainy season is larger than in winter (Fig. 9), indicating
considerable importance of water vapor transport on the
determination of rainy and dry seasons. To further
understand the possible impacts of moisture content on
dry and wet conditions in the rainy season and in winter, we
also calculated and plotted the standardized total number of
wet months (SPI>0) together with standardized moisture
budget and moisture content in the rainy season and in
96
Q. Zhang et al.
(A)
(B)
Fig. 9 Water vapor flux (kg × m−1 × s−1) in the rainy season (a) and in winter (b) for the Pearl River basin
winter (Figs. 10 and 11). It is observed that the relationships
between standardized total number of wet months (SPI>0)
with moisture budget and moisture content are remarkably
better in winter (Fig. 11, dry season) than in the rainy
season (Fig. 10). Furthermore, Fig. 11 indicates an
increasing tendency for the standardized total number of
wet months, moisture budget and moisture content and this
increasing trend is significant at >95% confidence level.
This means that, in winter, increasing moisture budget and
moisture content results in an increasing number of wet
months across the Pearl River basin. This result is in
Fig. 10 Comparison between
standardized areal total amount
of wet months (SPI>0) and
moisture content and moisture
budget in the rainy season
(April–September) across the
Pearl River basin
agreement with earlier result that SPI in winter is in
increasing tendency (Fig. 4), showing considerable influences of moisture variations on wet and dry conditions in
winter. No conclusive relations can be identified between
standardized total number of wet months (SPI>0) and
standardized moisture budget and moisture content in the
rainy season. Thus, we can say that in the winter (dry)
season the wet/dry conditions are mainly influenced by
water vapor and moisture content in the air, while more
factors than moisture transport influence the wet/dry
conditions in the rainy season.
2
1
0
–1
–2
Standardized number of wet rainy season
Standardized moisture content
–3
–4
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2000
2005
2
1
0
–1
–2
Standardized number of wet rainy season
Standardized moisture budget
–3
–4
1960
1965
1970
1975
1980
1985
1990
1995
Changes of drought/wetness episodes in the Pearl River basin, China
Fig. 11 Comparison between
standardized areal total amount
of wet months (SPI>0) and
moisture content and moisture
budget in winter (January, February and December) across the
Pearl River basin
97
3
Standardized number of wet winter
Standardized moisture content
2
1
0
–1
–2
–3
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
1995
2000
2005
3
Standardized number of wet winter
Standardized moisture budget
2
1
0
–1
–2
–3
1960
1965
5 Conclusions
In this study, we quantitatively evaluated dry and wet
conditions by using the standardized precipitation index
(SPI) and aridity index (I) based on monthly precipitation
dataset of 42 rain stations in the Pearl River basin for 1960–
2005. Mann-Kendall trend test was used to detect trends
within the number of months of different dry and wet
categories and SPI values. Furthermore, we also attempted
to explore possible causes behind changing properties of
dry and wet conditions across the Pearl River basin by
using NCAR/NCEP dataset. Some interesting conclusions
are obtained as follows:
1. Different dry or wet tendencies can be identified in the
Pearl River basin in the rainy season and in winter. The
Pearl River basin tends to be dryer in the rainy season
and to be wetter in winter. However, different parts of
the basin show different patterns of dry and wet
conditions— a general dry tendency can be observed
in major parts of the basin in the rainy season, and a
wet tendency in winter can be identified across the
entire basin. Most of the stations show decreasing SPI
index of rainy season but some are not, which may be
due to the extremely inhomogenous spatial distribution
of precipitation as a result of stronger convective
precipitation and typhoon rain storms which are very
common in the rainy season in the Pearl River basin.
However, the relatively more homogenous spatial
patterns of SPI index in winter may be attributed to
relatively stable air masses in the season.
1970
1975
1980
1985
1990
2. In terms of the number of dry or wet months in the rainy
season, major parts of the Pearl River basin are
characterized by a decreasing frequency of severe and
moderate wet months. However, an increasing number
of moderate wet months can be observed mainly in
southeast parts of the Pearl River basin. Increasing
frequency of severe dry months can be observed in
regions <108E°, and an increasing number of moderate
wet months can be found in regions <107°E and
between 108–110°E. Results of aridity index analysis
also show a decreasing number of dry months (I<20) in
the rainy season in eastern parts of the Pearl River basin;
adverse trends are found in western parts of the basin.
These results are in good agreement with changes in the
number of dry months in the rainy season. Therefore,
aridity index and SPI perform similarly well in reflecting
dry conditions over the Pearl River basin. Relationships
between I, SPI and RD indicate significant exponential
correlation between I and SPI, and significant linear
correlation between I and RD. The exponential correlation between I and SPI may be due to the logarithmic
transformation of original monthly precipitation data.
3. Moisture flux analysis based on NCAR/NCEP dataset
indicates a stronger intensity of water-vapor transport in
the rainy season than that in winter (dry season),
showing considerable influence of water-vapor flux on
dry and wet conditions of the Pearl River basin. The
source of water vapor lies mainly in the Indian peninsula
and Bengal Bay. Good correlation can be identified
between moisture budget, moisture content and number
of wet months in winter over the Pearl River basin.
98
Increasing moisture budget and moisture content gives
rise to an increasing number of wet months in winter
across the basin. Zhang et al. (2008c) indicated
decreasing summer precipitation and increasing winter
precipitation in the Pearl River basin, and attributed
precipitation variations to changes in the East Asian
summer monsoon system, which is in good agreement
with the results of this study, i.e. drying tendency in the
rainy season and wetting tendency in winter. Wang
(2001) indicated the weakening of the Asian monsoon
circulation after the 1970s which is not beneficial for the
northward propagation of the rain belt, and it can explain
wet and dry variations in the Pearl River basin. It should
be noted here that in comparison with winter, worse
correlation can be detected between moisture budget,
moisture content and number of wet months in the rainy
season, meaning that more factors than moisture budget
and moisture content influence the dry and wet
conditions in the rainy season, which needs further
investigation in the on-going research.
4. To our knowledge, this study is the first that tries to
explore spatial and temporal variations of dryness/
wetness conditions by using more than one indicator, i.
e. SPI and I, over the Pearl River basin and even in
China. Comparison was made between dryness/wetness
indicators by I and SPI with thee aim to show the
applicability of these two techniques and to get a full
picture of dryness/wetness variations in the Pearl River
basin. Particularly, this study attempted to find out the
causes behind the changing properties of dryness/
wetness status in the Pearl River basin by using
NCAR/NCEP reanalysis dataset. This study is of great
scientific and practical merit towards the better understanding of impacts of climate changes, particularly the
global warming, on water resources, agricultural activities and other human activities on river basin scales.
Acknowledgements The work described in this paper was supported by a grant from the Research Grants Council of the Hong Kong
Special Administrative Region, China (Project No. CUHK405308), 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 the
Programme of Introducing Talents of Discipline to Universities—the
111 Project of Hohai University. Cordial thanks should be extended to
two anonymous reviewers and the managing editor, Prof. Dr. Hartmut
Grassl, for their invaluable comments and suggestions which greatly
improved the quality of this paper.
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