ORIGINAL PAPER

Observed changes of drought/wetness episodes in the PearlRiver basin, China, using the standardized precipitationindex 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 overthe Pearl River basin for 1960–2005 were analyzed toclassify anomalously wet and dry conditions by using thestandardized precipitation index (SPI) and aridity index (I) forthe rainy season (April–September) and winter (December–February). Trends of the number of wet and dry monthsdecided by SPI were detected with Mann-Kendall technique.Furthermore, we also investigated possible causes behindwet and dry variations by analyzing NCAR/NCEP reanalysisdataset. The results indicate that: (1) the Pearl River basintends to be dryer in the rainy season and comes to be wetter

in winter. However, different wetting and drying propertiescan be identified across the basin: west parts of the basintend to be dryer; and southeast parts tend to be wetter; (2) thePearl River basin is dominated by dry tendency in the rainyseason and is further substantiated by aridity index (I)variations; and (3) water vapor flux, moisture contentchanges in the rainy season and winter indicate differentinfluences of moisture changes on wet and dry conditionsacross the Pearl River basin. Increasing moisture contentgives rise to an increasing number of wet months in winter.However, no fixed relationships can be observed betweenmoisture content changes and number of wet months in therainy season, indicating that more than one factor caninfluence the dry or wet conditions of the study region.The results of this paper will be helpful for basin-scale waterresource management under the changing climate.

1 Introduction

Assessment of water resource variations is a pre-requisite tounderstand and adopt appropriate management strategieswith the aim to avoid adverse environmental effects andreconcile conflicts between users (Xu and Singh 2004).Climate changes, particularly the current well-evidencedglobal warming and its impacts on hydrological regimes,especially hydrological extremes, e.g. droughts and floods,have become a priority area both for process research andfor water management practices (Xu et al. 2005). It wasbelieved that projected global climate changes have thepotential to accelerate the global hydrological cycle. Manystudies indicated that global warming alters precipitationpatterns and results in more frequent extreme weatherevents, e.g. floods, droughts, and rainstorms (Zhang et al.2008a; WMO 2003). These results are undoubtedly usefulfor the better understanding of increasing flood/drought

Theor Appl Climatol (2009) 98:89–99DOI 10.1007/s00704-008-0095-4

Q. ZhangInstitute of Space and Earth Information Science,The Chinese University of Hong Kong,Shatin, Hong Kong, China

Q. ZhangState 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. XuDepartment of Geosciences, University of Oslo,P.O. Box 1047, Blindern,0316 Oslo, Norway

Z. ZhangJiangsu 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, Chinae-mail: zhangqnj@gmail.com

hazards over the world (e.g. Herschy 2002; Mirza 2002).Furthermore, public awareness of extreme climatic eventshas risen sharply in recent years partly due to thecatastrophic nature of floods, droughts, storms and otherclimatic extremes (e.g. Beniston and Stephenson 2004;Zhang et al. 2006a, b, 2008b). Therefore, it is of scientificand practical merit to better understand changing character-istics of dryness and wetness variations for improvingintegrated water resource management at the basin scaleand human mitigation to hydrological alterations.

Environmental droughts generally include: (1) meteoro-logical drought, (2) hydrological drought; and (3) agricul-tural drought (e.g. Livada and Assimakopoulos 2007). Thisstudy focuses on the meteorological drought which isdefined as a lack of precipitation over a region for a periodof time. The standardized precipitation index (SPI) (McKeeet al. 1993, 1995; Hayes et al. 1999) was widely used toreveal meteorological drought (e.g. Silva et al. 2007; Bordi etal. 2004a; Moreira et al. 2006) and was proven to be a usefultool in the estimation of the intensity and duration of droughtevents (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 ontime scales of 3, 6, 12, 24 and 48 months. In this study, theSPI is calculated based on 1 and 3-month precipitation timeseries.

The Pearl River is the third largest river in drainagebasin area and the second largest river in terms ofstreamflow in China. However, uneven spatial and temporaldistribution of water resources, with 80% of the totaldischarge occurring in the flooding seasons, i.e. April–September, negatively affects the effective human use ofwater resource. With booming economic development inthe Pearl River basin, pollution-induced water shortage isthreatening the security of regional water resources. TheEast River, a tributary of the Pearl River, bears the heavyresponsibility of water supply for Shenzhen and HongKong, and about 80% of Hong Kong’s annual waterdemands come from the East River. Due to the significantrole of water resource in regional economic developmentand conservation of ecological environment in the region,precipitation changes and possible underlying causes havedrawn increasing concerns. Wang et al. (2008) exploredchanging properties of precipitation extremes and stream-flow extremes in the East River, one tributary of the PearlRiver basin. Dong (2006) indicated close relations betweenextreme precipitation changes and spatial and temporaldistribution of floods in the region. Luo et al. (2008)analyzed precipitation trends in North River basin by usingMann-Kendall trend test and Sen’s T test. In terms of dryand wet changes in China, Bordi et al. (2004b) investigatedtime–space variations of dry and wet periods in the eastChina. However, to our best knowledge, no such reports are

available in the Pearl River basin so far despite numerousstudies of this topic available in the literatures. With this inmind, the objectives of this paper are: (1) to detect changingproperties of dry and wet episodes defined by SPI; (2) toanalyze trends of frequency and intensity of dry and wetevents by using Mann-Kendall trend test; and (3) to explorepossible causes behind wet and drought variations in thePearl 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 streamflowin China with a drainage area of 4.42×105 km2 (PRWRC1991). The Pearl River basin involves three major tributaries:West River, North River and East River. The West River isthe largest tributary accounting for 77.8% of the totaldrainage area of the basin. The North River is the secondlargest tributary with a drainage area of 46,710 km2. TheEast River accounts for 6.6% of the total area of the PearlRiver. The Pearl River basin is located in the tropical andsub-tropical climate zone with the annual mean temperatureranging between 14–22°C. The precipitation mainly concen-trates during April–September (Zhang et al. 2008a), account-ing 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 inthe Pearl River basin. Location of the rain gauging stationscan be referred to Fig. 1. Data quality control was made inan earlier study (Zhang et al. 2008a). The NCAR/NCEPreanalysis dataset was used to detect changes of watervapor flux and moisture content in the Pearl River basinwith the intention to understand possible causes behind wetand 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 onmultiple time scales. According to McKee et al. (1993), theSPI was defined on each of the time scales as the differencebetween precipitation on the time series (xi) and the meanvalue xð Þ, divided by the standard deviation (s), i.e.

SPI ¼ xi � x

sð1Þ

The same definition is widely used in the literature (e.g.,McKee et al. 1993; Livada and Assimakopoulos 2007). It iswell known that very seldom the monthly precipitation time

90 Q. Zhang et al.

series fits a normal distribution, thus, an initial transforma-tion of the data series is usually done to make SPI astandard normal distribution variable. In this study, differ-ent probability distributions have been used to fit precipi-tation time series on various time scales; gamma, normaland log-normal distributions were selected as candidatedistributions; and the Kolmogorov-Smirnov test was per-formed to evaluate the goodness-of-fit. The results (notshown) indicated that normal distribution had the worstperformance. Gamma distribution and log-normal distribu-tion performed equally well for the monthly precipitationseries in the basin. Particularly, log-normal distribution hadbetter performance in describing precipitation properties inthe rainy season. Gamma distribution performed better inwinter (January, February and December) when comparedto log-normal distribution. Even so, log-normal distributionalso fit the precipitation changes in winter at >95%confidence level well. Therefore, we chose log-normalprobability distribution for the monthly precipitation seriesin the basin. After logarithmic transformation of the dataset,the sample mean and variance of the transformed data willbe my and sy, then the SPI becomes

SPI ¼ Z ¼ ln xð Þ � my

syð2Þ

The drought and wetness severity adopted in this studyis defined in Table 1. Due to the fact that precipitationmainly 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 responsibil-ity of water supply for Hong Kong and the Pearl RiverDelta, the SPI changes in winter (January, February andDecember) were also studied. Moreover, the number ofmonths characterized by drought and wetness categories inthe rainy season was also analyzed.

Fig. 1 Study region and raingauging stations

Table 1 The standardized precipitation index (SPI) categories basedon the initial classification of SPI values

Category SPI Probability (%)

Extremely wet 2.00 and above 2.3Severely wet 1.50–1.99 4.4Moderately wet 1.00–1.49 9.2Near normal −0.99–0.99 68.2Moderate drought −1.00 to −1.49 9.2Severe drought −1.50 to −1.99 4.4Extreme drought −2.00 and less 2.3

Changes of drought/wetness episodes in the Pearl River basin, China 91

3.2 Aridity index

To further understand dry and wet variations in terms ofagriculture demand, we also calculated the aridity index andcompared it with SPI in this study. Aridity index is definedby de Martonne (1926) who used it to study irrigationdemands (WMO 1975) and is computed as:

Ii ¼ 12Pi

Ti þ 10ð3Þ

where Pi is the monthly precipitation amount; Ti is themonthly mean air temperature. The aridity index aims toidentify the months when irrigation is necessary. Generally,irrigation is necessary when Ii<20. Research results byLivada and Assimakopoulos (2007) indicated that, on amonthly basis, there is a statistically significant exponentialcorrelation between SPI and aridity index (I = Ii/12) andsignificant linear correlation between aridity index andrainfall departures (RD). RD is defined as the differencebetween the monthly precipitation and its mean values, andthen is divided by its standard deviation. They also statedthat the exponential relationship between I and SPI should bedue to the normalization of the monthly precipitationdata prior to SPI estimation (Livada and Assimakopoulos2007).

3.3 Trend test

There are many statistical methods available for trenddetection and each method has its own strength andweakness in trend detection (Zhang et al. 2008a). In thispaper, the Mann-Kendall (MK) test (Kendall 1975; Mann1945), recommended by the World Meteorological Organi-zation (Mitchell et al. 1966), is used to study trends of SPI,intensity of wet and dry months and also the number ofmonths when irrigation is necessary. For the sake ofcompletion and understanding of the results, the procedureof MK trend test performed in the study is brieflyintroduced as follows.

First the MK test statistic is calculated as

S ¼Xn�1

i¼1

Xn

j¼iþ1

sgn xj � xi� � ð4Þ

where sgn xj � xi� � ¼

þ1; xj > xi0; xj ¼ xi

�1; xj < xi:

8<

: and n is the sample

size. The statistics S is approximately normally distributedwhen n≥8, with the mean and the variance as follows:

E sð Þ ¼ 0 ð5Þ

V Sð Þ ¼n n� 1ð Þ 2nþ 5ð Þ �Pn

i¼1tii i� 1ð Þ 2iþ 5ð Þ

18ð6Þ

where ti is the number of ties of extent i.The standardized statistics (Z) for one-tailed test is

formulated as:

Z ¼

S � 1ffiffiffiffiffiffiffiffiffiffiffiffiffiffiVar Sð Þp S > 0

0 S ¼ 0

S þ 1ffiffiffiffiffiffiffiffiffiffiffiffiffiffiVar Sð Þp S < 0

8>>>>>><

>>>>>>:

ð7Þ

At the 5% significance level, the null hypothesis of no trendis rejected if |Z| > 1.96.

3.4 Calculation of frequency of wet and dry monthsin the rainy season

The number of months fell in each categories (frequency ofwet and dry months), i.e. from extreme dry to extreme wet(see Table 1 for division) in the rainy season of each year isa useful indicator for the dryness/wetness of the catchmentsince the rainy season captures 80% of the yearlyprecipitation total. The trends of these numbers arecalculated via the following steps: (1) calculate the SPIfor each month in the rainy season and count the number ofmonths falling in each categories within each year, (2) takethe numbers in each categories of each year as a time seriesand calculate the trend of the time series, and (3) spatiallyinterpolate the trend calculated for each station. Due to thefact that the number of extreme dry or wet months is scarce,only, e.g., 75 extreme dry months can be identified in thePearl River basin for past 46 years account for 4% of totalmonths in the rainy season. For the sake of comparison, thenumber of extreme dry months will not be analyzed,rather We will only be analyzing the trends of the numberof severe (moderate) dry (wet) months in the rainyseason.

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0

50

100

150

200

250

300P

reci

pita

tion

(mm

)

Fig. 2 Areal monthly mean precipitation in the Pearl River basin(mm/month, average of 42 stations) for the period 1960–2005

92 Q. Zhang et al.

4 Results and discussion

4.1 Spatial and temporal patterns of SPI

Figure 3 maps the spatial distribution of SPI trends acrossthe Pearl River basin for the rainy season. It can be seenthat the major parts of the basin are characterized bydecreasing SPI, indicating that drying tendency dominatesmajor parts of the Pearl River basin. However, only a fewplaces are characterized by the decreasing trends of SPIsignificant at >95% confidence level. A slight increasingtrend (not significant at 95% level) of SPI can be identifiedin a few locations. Thus, generally, wet tendency is mainlyidentified in southeast parts of the Pearl River basin in therainy season. As mentioned earlier, the Pearl River basinbears the heavy responsibility of water supply for the PearlRiver Delta and Hong Kong—and that is particularly thecase for the East River. Figure 4 shows that the entire PearlRiver basin is characterized by increasing SPI in the winterseason. Thus, the wet tendency prevails over the basin inthe winter season, although the increasing trends are notsignificant at >95% confidence level.

4.2 Frequency of wet and dry months in the rainy season

Figure 5 demonstrates the spatial patterns of trends in thenumber of dry or wet months in the rainy season, i.e.moderate (severe) wet and moderate (severe) dry. Here wedecide the number of moderate (severe) wet and moderate(severe) dry months in the following way: (1) the monthwith SPI>1.5 (or SPI<−1.5) is defined as severe wet (ordry) month; (2) the month with SPI>1 (or SPI<−1) isdefined as moderate wet (dry) month. Different spatialpatterns can be identified for wet or dry episodes ofdifferent categories (Fig. 5). Figure 5a indicates a decreas-ing number of severe wet months in large parts of the PearlRiver basin, though this decreasing trend is not statisticallysignificant. Stations with an increasing number of severewet months distribute sporadically across the basin. Similarphenomenon can be identified in changes of moderate wetmonths (Fig. 5b). A large area of the Pearl River basin ischaracterized by a decreasing number of moderate wetmonths and increasing trends can be observed mainly inthe lower East River, lower North River, lower West Riverand lower Beipan River. Different spatial patterns can be

Fig. 3 Spatial distribution pattern of trends of SPI in the rainy seasons (April–September) across the Pearl River basin. The numbers in the figureare Z values. Positive values indicate increasing trend and vice versa. If |Z| ≥ 1.96, then the trend is 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 thefigure are Z values. Positive values indicate increasing trend and vice versa. If |Z| ≥ 1.96, then the trend is significant at >95% confidence level

Changes of drought/wetness episodes in the Pearl River basin, China 93

observed in the changes of severe and moderate drymonths (Fig. 5c,d). Major parts of the Pearl River basinare dominated by increasing severe and moderate drymonths. Similarly, these trends are not statistically signif-icant. It can be seen from Fig. 5c that the increasingnumber of severe dry months is mainly observed in BeipanRiver, Nanpan River and Hongshui River and decreasingtrends are mainly found in the east parts of the Pearl Riverbasin. 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, YuRiver, Qian River and Liu River. Decreasing trends in thenumber of moderate dry months are identified mainly inthe east parts of the Pearl River basin (Fig. 5d). Figure 6illustrates areal average SPI across the Pearl River basin. Itcan be observed from Fig. 6 that SPI of the basin isincreasing before 1980s, and is decreasing after 1980s. Anobvious decreasing tendency is found during 1985–1998.SPI in winter, however, is consistently increasing, partic-ularly during 1970–2005.

• Rain gauging station Rain gauging station

• Rain gauging station Rain gauging station • Rain gauging station Rain gauging station

• Rain gauging station Rain gauging station

Fig. 5 Spatial distribution pattern of trends of the number of wet anddry 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 indicateincreasing trend and vice versa. If |Z| ≥ 1.96, then the trend issignificant at >95% confidence level

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 –1

–0.5

0

0.5

SPI in rainy season

SP

I

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 –1

–0.5

0

0.5

1

1.5

SPI in winter

SP

I

Time (year)

Fig. 6 Areal average SPIchanges in the rainy season(April–September) and winter(DJF) for the Pearl River basin.Dashed lines denote quadraticfit

94 Q. Zhang et al.

Figure 7 shows trends of frequency of months whenirrigation is necessary, i.e. I<20; hereafter, we will simplycall months with I<20 dry months. It can be seen fromFig. 7 that an increasing number of dry months can bedetected mainly in western parts of the Pearl River basin, i.e. Nanpan River, Beipan River, Hongshui River, lower LiuRiver and upper Zuo River. Major regions in the easternparts of the basin are characterized by a decreasingfrequency of dry months. Figure 7 demonstrates that placescharacterized by increasing number of dry months largelymatch with those characterized by an increasing number ofsevere dry months defined by SPI (Fig. 5c,d). These resultsimply that, at least in the Pearl River basin, SPI and I indexseem to perform similarly well in reflecting wet and dryconditions in the basin. To verify the results of Fig. 5, wefurther analyzed relationships between I, SPI and rainfalldeparture (RD), and for illustrative purpose the results for arandomly selected station are shown in Fig. 8a for the rainyseason and Fig. 8b for winter. Similar results have beenobtained for the relationships between I, SPI and RD whencompared to those by Livada and Assimakopoulos (2007)over Greece. Excellent linear relations can be fit between Iand RD, and exponential relationships between I and SPI.The exponential relationship between I and SPI can beattributed to the transformation of original monthly precip-itation data by using log-normal distribution function.

4.3 Water vapor flux analysis

Water vapor flux plays the key role in changes of droughtand wet conditions; the water vapor is brought to theAsian continent mainly by the monsoonal flows. Gener-ally, three major low-level monsoonal streams transportwater vapor to China (Simmonds et al. 1999; Tian et al.2004; Chow et al. 2008): (1) the southwesterly flowtowards the Indian peninsula and Bengal Bay which isassociated with the Indian summer monsoon, and (2) thesoutherly flow in the South China Sea and (3) thesoutheasterly flow associated with the southern flange ofthe north Western Pacific subtropical high (0–20°N, 120–150°E). Routes of the water vapor transport in the rainyseason (Fig. 9a) clearly demonstrate the sources of thewater vapor: Indian peninsula, Bengal Bay and South ChinaSea. However, water vapor in the Pearl River is mainlyfrom southwesterly flow. Intensity of water vapor flux inthe rainy season is larger than in winter (Fig. 9), indicatingconsiderable importance of water vapor transport on thedetermination of rainy and dry seasons. To furtherunderstand the possible impacts of moisture content ondry and wet conditions in the rainy season and in winter, wealso calculated and plotted the standardized total number ofwet months (SPI>0) together with standardized moisturebudget and moisture content in the rainy season and in

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 valuesindicate increasing trend and vice versa. If |Z| ≥ 1.96, then the trend is significant at >95% confidence level

Fig. 8 Scatter diagram betweenI, SPI, (open circles) and I, RD(closed circles) and correlationlines for for one station selectedrandomly from the dataset. aRainy season; b winter. Theexample station: Xianning sta-tion (26°52′N, 104°17′E)

Changes of drought/wetness episodes in the Pearl River basin, China 95

winter (Figs. 10 and 11). It is observed that the relationshipsbetween standardized total number of wet months (SPI>0)with moisture budget and moisture content are remarkablybetter in winter (Fig. 11, dry season) than in the rainyseason (Fig. 10). Furthermore, Fig. 11 indicates anincreasing tendency for the standardized total number ofwet months, moisture budget and moisture content and thisincreasing trend is significant at >95% confidence level.This means that, in winter, increasing moisture budget andmoisture content results in an increasing number of wetmonths across the Pearl River basin. This result is in

agreement with earlier result that SPI in winter is inincreasing tendency (Fig. 4), showing considerable influ-ences of moisture variations on wet and dry conditions inwinter. No conclusive relations can be identified betweenstandardized total number of wet months (SPI>0) andstandardized moisture budget and moisture content in therainy season. Thus, we can say that in the winter (dry)season the wet/dry conditions are mainly influenced bywater vapor and moisture content in the air, while morefactors than moisture transport influence the wet/dryconditions in the rainy season.

(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

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 –4

–3

–2

–1

0

1

2

Standardized number of wet rainy seasonStandardized moisture content

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 –4

–3

–2

–1

0

1

2

Standardized number of wet rainy seasonStandardized moisture budget

Fig. 10 Comparison betweenstandardized areal total amountof wet months (SPI>0) andmoisture content and moisturebudget in the rainy season(April–September) across thePearl River basin

96 Q. Zhang et al.

5 Conclusions

In this study, we quantitatively evaluated dry and wetconditions by using the standardized precipitation index(SPI) and aridity index (I) based on monthly precipitationdataset of 42 rain stations in the Pearl River basin for 1960–2005. Mann-Kendall trend test was used to detect trendswithin the number of months of different dry and wetcategories and SPI values. Furthermore, we also attemptedto explore possible causes behind changing properties ofdry and wet conditions across the Pearl River basin byusing NCAR/NCEP dataset. Some interesting conclusionsare obtained as follows:

1. Different dry or wet tendencies can be identified in thePearl River basin in the rainy season and in winter. ThePearl River basin tends to be dryer in the rainy seasonand to be wetter in winter. However, different parts ofthe basin show different patterns of dry and wetconditions— a general dry tendency can be observedin major parts of the basin in the rainy season, and awet tendency in winter can be identified across theentire basin. Most of the stations show decreasing SPIindex of rainy season but some are not, which may bedue to the extremely inhomogenous spatial distributionof precipitation as a result of stronger convectiveprecipitation and typhoon rain storms which are verycommon in the rainy season in the Pearl River basin.However, the relatively more homogenous spatialpatterns of SPI index in winter may be attributed torelatively stable air masses in the season.

2. In terms of the number of dry or wet months in the rainyseason, major parts of the Pearl River basin arecharacterized by a decreasing frequency of severe andmoderate wet months. However, an increasing numberof moderate wet months can be observed mainly insoutheast parts of the Pearl River basin. Increasingfrequency of severe dry months can be observed inregions <108E°, and an increasing number of moderatewet months can be found in regions <107°E andbetween 108–110°E. Results of aridity index analysisalso show a decreasing number of dry months (I<20) inthe 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 thenumber of dry months in the rainy season. Therefore,aridity index and SPI perform similarly well in reflectingdry conditions over the Pearl River basin. Relationshipsbetween I, SPI and RD indicate significant exponentialcorrelation between I and SPI, and significant linearcorrelation between I and RD. The exponential correla-tion between I and SPI may be due to the logarithmictransformation of original monthly precipitation data.

3. Moisture flux analysis based on NCAR/NCEP datasetindicates a stronger intensity of water-vapor transport inthe rainy season than that in winter (dry season),showing considerable influence of water-vapor flux ondry and wet conditions of the Pearl River basin. Thesource of water vapor lies mainly in the Indian peninsulaand Bengal Bay. Good correlation can be identifiedbetween moisture budget, moisture content and numberof wet months in winter over the Pearl River basin.

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005–3

–2

–1

0

1

2

3

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005–3

–2

–1

0

1

2

3

Standardized number of wet winterStandardized moisture content

Standardized number of wet winterStandardized moisture budget

Fig. 11 Comparison betweenstandardized areal total amountof wet months (SPI>0) andmoisture content and moisturebudget in winter (January, Feb-ruary and December) across thePearl River basin

Changes of drought/wetness episodes in the Pearl River basin, China 97

Increasing moisture budget and moisture content givesrise to an increasing number of wet months in winteracross the basin. Zhang et al. (2008c) indicateddecreasing summer precipitation and increasing winterprecipitation in the Pearl River basin, and attributedprecipitation variations to changes in the East Asiansummer monsoon system, which is in good agreementwith the results of this study, i.e. drying tendency in therainy season and wetting tendency in winter. Wang(2001) indicated the weakening of the Asian monsooncirculation after the 1970s which is not beneficial for thenorthward propagation of the rain belt, and it can explainwet and dry variations in the Pearl River basin. It shouldbe noted here that in comparison with winter, worsecorrelation can be detected between moisture budget,moisture content and number of wet months in the rainyseason, meaning that more factors than moisture budgetand moisture content influence the dry and wetconditions in the rainy season, which needs furtherinvestigation in the on-going research.

4. To our knowledge, this study is the first that tries toexplore 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 inChina. Comparison was made between dryness/wetnessindicators by I and SPI with thee aim to show theapplicability of these two techniques and to get a fullpicture of dryness/wetness variations in the Pearl Riverbasin. Particularly, this study attempted to find out thecauses behind the changing properties of dryness/wetness status in the Pearl River basin by usingNCAR/NCEP reanalysis dataset. This study is of greatscientific and practical merit towards the better under-standing of impacts of climate changes, particularly theglobal warming, on water resources, agricultural activ-ities and other human activities on river basin scales.

Acknowledgements The work described in this paper was sup-ported by a grant from the Research Grants Council of the Hong KongSpecial Administrative Region, China (Project No. CUHK405308), bya Direct Grant from the Faculty of Social Science, The ChineseUniversity of Hong Kong (Project No.: 4450183), National NaturalScience Foundation of China (Grant No.: 40701015), and theProgramme of Introducing Talents of Discipline to Universities—the111 Project of Hohai University. Cordial thanks should be extended totwo anonymous reviewers and the managing editor, Prof. Dr. HartmutGrassl, for their invaluable comments and suggestions which greatlyimproved the quality of this paper.

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