sdge

Accepted 31 December 2008

This manuscript was handled byK. Georgakakos, Editor-in-chief

enc

the patterns of change in seasonal extremes at the local level. Kerala, the south-western state of theIndian peninsula, comprising of a total of six gridded areas, was chosen for this study focusing on the var-

periods has been reported occurring in Kerala, the south-westernstate of Indian peninsula (Pal and Al-Tabbaa, 2007). Kerala is a veryimportant region for India since a large part of Indias agro-econ-omy is concentrated there. Although Kerala is one of very few high-est monsoon rainfall regions in India, along with the North-EasternIndian states, and receives the rst monsoon showers every year,signicant amount of precipitation in the other seasons is alsoimportant from an agricultural point of view. Furthermore,

study. The authors have found that spring precipitation makes avery important contribution to annual rainfall variabilities andchanges in India and that extreme rainfall plays a dominant rolein deciding the seasonal rainfall strength (Pal and Al-Tabbaa,submitted for publication). Therefore, in addition to the winter sea-son (DecemberFebruary), spring and autumn seasonal precipita-tions are also considered in this study to investigate whetherthere have been any signicant changes in the extreme rainfall inKerala over the second half of the 20th century. Previous analysesby the authors found that the average autumn and winter rainfallin Kerala does not seem to show any signicant trends but that the

* Corresponding author. Tel.: +44 (0) 1223 766683; fax: +44 (0) 1223 339713.

Journal of Hydrology 367 (2009) 6269

Contents lists availab

H

elsE-mail addresses: ip250@cam.ac.uk (I. Pal), aa22@cam.ac.uk (A. Al-Tabbaa).Temporal changes in discrete random extreme events arebecoming important in climate change scenario studies becauseof their socio-economic impacts. The risk of extreme events is dif-cult to predict but their impacts could be severe. To outline thechange in rainfall extremes in a certain region, it is necessary tolook at the historical trends of statistical properties of seasonalrainfall extremes. An increase of extreme events has been reportedin the monsoon season over central India (Goswami et al., 2006).Also, rainfall with more than 10 years and up to 50 years of return

taka in peninsular India (De et al., 2005), make it important to lookat the change in extreme rainfalls in the other seasons as well.

Assessment of other seasonal extreme rainfall changes in Indianregion is scarce in the literature. Only Revadekar and Kulkarnis(2007) recently published work on winter monsoon extremesand their relation with ENSO in SouthEast peninsular India inthe months of OctoberDecember provides some information.However, they did not consider other important seasons such asspring (MarchMay) and autumn (OctoberNovember) in theirKeywords:Climate changeTime series analysisRainfall extremeSeasonal rainfallTrendKerala

Introduction0022-1694/$ - see front matter 2009 Elsevier B.V. Adoi:10.1016/j.jhydrol.2008.12.025iability and changes in rainfall extremes in the different seasons. Since other studies by the authors havefocused on the monsoon season, this paper considers the winter, spring and autumn seasons only. A set ofindices derived from the daily rainfall time series is dened and used to examine the changes in extremerainfall through assessing long-term trends by non-parametric MannKendall technique. The trends aredetermined over the period of 19542003, which are also tested for signicance. The results show thatthere are large intra-regional differences in the trends in different seasons. Local changes were found dif-ferent from the large spatial scale averages in Kerala. Winter and autumn extreme rainfall were foundhaving an increasing tendency with statistically signicant changes in some regions indicating moreoccurrences of winter and autumn oods. On the other hand the spring seasonal extreme rainfall showeddecreasing trends, which together with increasing frequency of the dry days is mainly affecting the totalseasonal precipitation, which mainly point towards the vulnerability of Kerala to increasing probability ofwater scarcity in the pre-monsoon time and a delaying monsoon onset. Overall, the results of this studyare good indicators of local climate changes over the ve decades that will assist in seasonal forecastingand risk management.

2009 Elsevier B.V. All rights reserved.

increasing off-monsoon seasonal oods and thunderstorms insome parts of Kerala, and also in the neighbouring state of Karna-Received 17 October 2008Received in revised form 4 December 2008

monsoon ooding in India and particularly in the peninsular region, highlight the urgent need to look atTrends in seasonal precipitation extremeof climate change in Kerala, India

Indrani Pal *, Abir Al-TabbaaDepartment of Engineering (GRO), University of Cambridge, Trumpington Street, Cambri

a r t i c l e i n f o

Article history:

s u m m a r y

Recent news on the occurr

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journal homepage: www.ll rights reserved. An indicator

CB2 1PZ, United Kingdom

e of off-seasonal natural disasters, such as pre-monsoon drought and post-

le at ScienceDirect

ydrology

evier .com/locate / jhydrol

spring rainfall does (unpublished work). Since the changes inshort-duration extreme events do have the potential to indicatelong-term seasonal climatic changes (Keim and Cruise, 1998), win-ter and autumn extremes are also examined here. Six separategridded regions comprising of the whole state of Kerala are stud-ied. Fig. 1 shows the position of the Kerala state in India and thegridded regions in Kerala (orange colour) considered in this study.Gridded regional analysis will help us to identify the spatialchanges at very small scales, which will provide the local informa-tion on the changing climate that is not usually extracted from theaggregated spatial mean (Bardossy and Hundecha, 2003). Collec-tively, this work aims at characterising the singular events thathave the potential to help in dening and assessing the associatedrisks, and developing mitigation and adaptation strategies in thestate of Kerala.

Database and methodology

Lack of long-term daily precipitation time series restricts theempirical analysis of the extreme precipitation changes in the pre-cipitation distribution. High resolution (1 1 Lat/Long) griddeddaily rainfall datasets comprising of 53 years of data (19512003) covering the whole Indian region was collected from the Na-tional Climate Center, Indian Meteorological Department, Pune(http://www.imdpune.gov.in/). The daily time series for the sixgrids of Kerala was then extracted from this database for the50 years (19542003) of analysis done here. Gridded rainfall datasets are useful for regional studies on the climate variability andchanges. The high resolution gridded daily rainfall data base for In-dia was developed based on interpolation of the daily rainfall data

from 1803 stations in India having at least 90% data availabilityduring the period of 19512003 (Rajeevan et al., 2005,2006). Stan-dard quality control was performed in order to minimise the risk ofgenerating spatial or temporal inhomogeneities in the griddeddata. A comparison with similar global gridded rainfall revealedthat this database is better in accurate representation of spatialand temporal rainfall variations in India (Rajeevan et al., 2006).

The analysis of the precipitation extremes was based on theindices developed under the World Climate Research Programmeon Climate Variability and Predictability Working Group on Cli-mate Change Detection (Peterson et al., 2002; Peterson, 2005).Some of these indices have been previously used in the analysesof the trends in global and regional climates (Bardossy andHundecha, 2003; Alexander et al., 2006). Selective indices usedhere for the seasonal analyses are demonstrated in Table 1. Sea-sonal extreme indices were calculated on a yearly basis for theentire 50 years (19542003) of study for all the areas underinvestigation in Kerala. The base period considered here was19611990. Before moving onto the extremes, changes in sea-sonal total rainfall in the wet days and changes in the frequencyof the dry days are also examined and discussed in the rst twosub-sections below.

All the trends for each index were determined using non-para-metric MannKendall test, which were also tested for signicanceat 95% level (Wilks, 1995; Onoz and Bayazit, 2003; Luo et al., inpress). Since there are chances of outliers to be present as the ex-treme rainfall events, non-parametric MannKendall test is usefulbecause its statistic is based on the sign of differences, not directlyon the values of the random variable and therefore the trendsdetermined is less affected by the outliers. The MannKendall test

I. Pal, A. Al-Tabbaa / Journal of Hydrology 367 (2009) 6269 63Fig. 1. Location of Kerala state in India (orange colour grids mark the study regions consi1 = 11.5N, 75.5E; 2 = 11.5N, 76.5E; 3 = 10.5N, 75.5E; 4 = 10.5N, 76.5E; 5 = 9.5N, 76.5E andcolour in this gure legend, the reader is referred to the web version of this article.)dered). The numerical numbers in the grid points represent 1 1 grids as follows,6 = 8.5N, 76.5E [from Rajeevan et al., 2006]. (For interpretation of the references to

is applicable to the detection of a monotonic trend in a time serieswith no seasonal or other cycle. The test is based on the statistic S,which is calculated using the formula

S Xn1

k1

Xnjk1sgnxj xk 1

sgnxj xk 1 if xj xk > 00 if xj xk 01 if x x < 0

8>:

9>=>; 2

Results

The results of the trend assessments are summarised in the fol-lowing headings. The gures discussed in the sections below showthree-panel map of trends in various gridded regions in Kerala forthe three seasons analysed. Only the grids under study are shownin those gures. Symbolic colours are used to indicate the trends,which are mentioned in the respective captions.

Table 1Extreme rainfall indices (DP = daily precipitation amount).

Index Description Units

PREP_ST Seasonal total precipitation from wet days (DPP 1 mm) mmTDD Seasonal total number of dry days (DP < 1 mm) DaysR95p Seasonal total precipitation from DP > 95th percentile of the wet days (based on the period 19611990) mmR99p Seasonal total precipitation from DP > 99th percentile of the wet days (based on the period 19611990) mmRX1 day Seasonal maximum precipitation in 1 day mmRX5 day Seasonal maximum precipitation in ve consecutive days mmRXF Extreme frequency, i.e. number of days with rainfall >95 percentile in the season daysRXP Extreme percent, i.e. proportion of total seasonal rainfall from all events above the average long-term 95th percentile %

64 I. Pal, A. Al-Tabbaa / Journal of Hydrology 367 (2009) 6269j k

where n is the number of observed data series, xj and xk are the val-ues in periods j and k, respectively, j > k. For nP 10, the samplingdistribution of S is as follows. Z follows the standard normaldistribution:

Z

S1VARS

p if S > 00 if S 0

S1VARS

p if S < 0

8>>>>>:

9>>>=>>>;

3

whereVAR(S) is determined as

VARS 118

nn 12n 5 Xqp1

tptp 12tp 5" #

4

where q is the number of tied groups and tp is the number of datavalues in the pth group. If |Z| > Z1a/2, null hypothesis is rejectedand a signicant trend exists in the time series. Z1a/2 is the criticalvalue of Z from the Standard Normal Table, for 95% condence thevalue of Z1a/2 is 1.96. A positive value of Z indicates an upwardtrend and a negative value of Z indicates a downward trend.Fig. 2. Trends of seasonal total precipitations from the wet days (Long-term changes of seasonal total precipitation (PREP_ST)

The trends in total precipitation from the wet days (the dayswith precipitation P1 mm) show seasonal variabilities, as seenin Fig. 2. The spatially averaged trends in different seasons overthe whole of Kerala are shown in Fig. 3. The most apparent featureof the results in Fig. 2 is that trends in seasonal precipitations overthe study period are not consistent throughout the state (exceptspring season) even though the mean and standard deviation ofdaily rainfall over these regions is more or less homogeneous(see Table 2). This intra-regional variability for the precipitationindices is possibly because of small spatial correlations for precip-itation because of topographical differences (Fig. 4). Fig. 2 alsoshows that two extreme northern regions of Kerala show positivetendencies in winter seasonal precipitation and, four locationsshow negative tendencies, while none are signicant. On the otherhand, spatially averaged winter seasonal rainfall show positivetendency in Kerala, spring precipitation has statistically signicantdecreasing trends throughout the state, as in Fig. 3. Furthermore,although positive tendencies were noticed everywhere in KeralaPREP_ST) in various gridded regions in Kerala in 19542003.

of HI. Pal, A. Al-Tabbaa / Journal(except grid 4), no region has statistically signicant trend for theautumn precipitation (Fig. 2). The spatially averaged trends inPREP_ST in autumn season in Kerala also show positive tendencies(Fig. 3). Hence the above results indicate that the trends corre-sponding to large scaled spatial averages are not representativeof the local regional changes, and therefore are not recommendedto use in local projects.

Long-term changes of seasonal total number of dry days (TDD)

The winter season has the maximum number of dry days in Ker-ala. Spatially averaged trends of TDD in various seasons are shownin Fig. 3. The gure indicates whether possible increase or decreasein water stress is experienced in different seasons in Kerala, whichis important for natural vegetation and crop growth. It could be no-ticed in Fig. 3 that the number of dry days are signicantly increas-ing in spring season, which is a clear indication of severe waterstress in this season. The increasing frequency of dry days in springseason could be another indication of delayed monsoon onset inKerala. Spatially averaged trends of total number of dry days inwinter and autumn seasons have decreasing tendency, which arepossibly contributing to increase in total precipitation from thewet days (PREP_ST), as in Figs. 2 and 3.

Fig. 3. Trends of spatially averaged extreme indice

Table 2Mean and standard deviation of seasonal precipitations.

Regions Mean

Winter Spring

1 11.5N,75.5E 0.4 3.82 11.5N,76.5E 0.4 2.33 10.5N,75.5E 0.5 4.34 10.5N,76.5E 0.4 2.75 9.5N,76.5E 1 5.276 8.5N,76.5E 1 4.5ydrology 367 (2009) 6269 65Results corresponding to every individual grid in Kerala aredifferent from the spatial average results above. The trends forevery individual region are displayed in Fig. 5. The gure showsthat the number of winter dry days has no signicant trend inmost places except only the region at the highest elevation (grid2) that exhibits signicantly decreasing number of dry days inKerala. Also that, the other north most region (grid 1) showsdecreasing tendency in the number of dry days. This could beone of several reasons for the increase in winter precipitation inthese two northern regions, as discussed in the previous section.The spatial distribution of the regions indicates that the famousport and agricultural district Kozhikode/Calicut, is situated in grid1 (see Fig. 4). In spring, most of the areas have been undergoingwater stresses since they have statistically signicant increasingtrends of the number of dry days; whereas, interestingly, no sig-nicant trends are noticed in the north most regions (grids 1 and2) in Kerala. In autumn, around 33% of the total area under inves-tigation shows a signicant decrease in the number of dry days,the areas are located at the highest elevation (grid 2) and at thesouthern coastal part of Kerala (grid 6). Grid 1, the other northernpart of Kerala also shows decreasing tendency of TDD in autumn,like winter. Exactly opposite results were noticed for the totalnumber of wet days in the various seasons (not shown) meaning

s in various seasons in Kerala in 19542003.

SD

Autumn Winter Spring Autumn

6.0 2.2 5.0 5.24.8 2.3 4.0 6.87.1 2.9 5.7 5.26.1 2.1 2.9 4.58.4 2.5 4.8 5.67.6 2.5 4.1 5.9

of H66 I. Pal, A. Al-Tabbaa / Journalthat there is an increasing tendency of rainfall occurring morefrequently in the winter and autumn seasons and less frequentlyin spring in Kerala.

Trends of extreme precipitations

Indices R95p and R99pThe remaining six indices in Table 1 give a direct measure of

extreme precipitation in the various seasons. Figs. 6 and 7 showthe trends corresponding to the total precipitation above 95thand 99th percentile. The percentile rainfall values were averagedover 19611990, i.e. the base line set by WMO to separate 24-hdaily rainfall extremes in every year of the 50 years of study. Allthe extreme rainfall amounts in a year are then summed up toget R95p and R99p for every year. Fig. 3 displays the spatiallyaveraged trends and variabilities of the various seasonal R95pand R99p in Kerala. It is noted in the gure that, both the cases

Fig. 4. Topographicaydrology 367 (2009) 6269exhibit no trends in the winter season. A decreasing tendency ofR95p index in spring season is noticed while no trends werefound in R99p index. In autumn season, however, total amountof extreme precipitation above 95 percentile show increasingtendency for both the cases although none was statisticallysignicant.

The results corresponding to the gridded regions were variableand sometimes different from that of spatial average. While in win-ter, trends corresponding to both the indices show no tendencies,decreasing tendencies in R95p index are observed in spring butno trend in R99p index was found in spring season. Therefore, itis noted that, together with statistically signicant increases inthe number of dry days and decrease in extreme precipitations,the spring season tends to go through severe water stresses, whichis a familiar phenomenon these days in Kerala. Furthermore, whileincreasing tendency in R95p in autumn season is almost every-where in Kerala, only a single region (grid 5) shows an increasing

l map of Kerala.

of HI. Pal, A. Al-Tabbaa / Journaltendency and other grids have no trends for the case of R99p index.Hence, increase in extreme rainfall tends to exhibit a higher num-ber of ash oods in the autumn season, which is also an emergingscenario in some parts of Kerala (De et al., 2005); for example, theplace Kochi (see Fig. 4) and its near by regions, which fall under thegrid 5.

Indices RX1 day and RX5 dayThese indices indicate whether there are changes in the amount

of precipitation received in the day with the highest precipitationand the amount of precipitation received in a 5-day scenario peryear with the highest precipitation, respectively. These indices givean indication of the trends in precipitation amounts usually com-ing from extreme weather occurrences. Trends in spatially aver-aged RX1 day and RX5 day indices for various seasons are shown

Fig. 5. Trends of total number of dry days (TDD) in various sea

Fig. 6. Trends of seasonal total precipitation from daily rainfall >95th percentile from the19542003.ydrology 367 (2009) 6269 67in Fig. 3. Fig. 3 displays that winter and autumn extremes areincreasing in Kerala and spring extremes are decreasing, whichare affecting the seasonal totals, as also mentioned before.

Fig. 8 depicts the trends corresponding to these two indices invarious seasons and a number of gridded regions in Kerala. Trendsin both the indices showed coherent tendencies; therefore the re-sults corresponding to both are displayed in a single gure (Fig. 8).Winter extremes have increasing trends in northern most regionsin Kerala (grids 1 and 2), which must have affected the seasonal to-tal, as discussed in Long-term changes of seasonal total precipita-tion (PREP_ST). For the spring, the extreme precipitation isdecreasing everywhere while the trend corresponding to grid 4 isstatistically signicant. In addition, the autumn extremes showincreasing trends except a decrease in southern coastal tip ofKerala.

sons in different gridded regions in Kerala in 19542003.

wet days (based on the period 19611990) in different gridded regions in Kerala in

of H68 I. Pal, A. Al-Tabbaa / JournalIndices RXF and RXPThe extreme frequency index (RXF) examines changes in the

number of extreme events and was calculated by counting thenumber of events in a year with intensities above a threshold.The extreme percent (RXP), on the other hand, is the proportionof total seasonal rainfall from all events above the same threshold.A long-term (19611990) 95th percentile of the daily rainfallintensity in the monsoon months was used as the threshold toseparate the extreme events, which varies signicantly from yearto year and also spatially in Kerala. The trends of extreme rainfallfrequencies corresponding to different regions in Kerala in all theseasons showed no signicant trends but only one region (grid3) in spring season that showed signicantly negative trend in ex-treme rainfall frequency (not displayed); whereas, trends corre-sponding to extreme rainfall percent (RXP) are different from

Fig. 7. Trends of seasonal total precipitation from daily rainfall >95th percentile from the19542003.

Fig. 8. Trends of seasonal maximum precipitation in 1 day andydrology 367 (2009) 6269that of RXF, as shown in Fig. 9. Fig. 9 also shows that, while winterextreme rainfall proportion has no trend like RXF, around 50% ofthe spatial area of Kerala is undergoing decreasing trends in RXPin the spring season, which is also true for the averages (notshown). Furthermore, autumn extreme rainfall proportion hasincreasing trends and they are statistically signicant in two grid-ded regions in eastern Kerala (grids 2 and 5), which are at theproximity of western ghat mountains. Therefore, in addition togrid 5, as discussed in Indices R95p and R99p, signicant increas-ing trend in total contribution from the extreme autumn rainfall insome parts of Kerala is an indication of more ooding in the au-tumn season.

Collectively, although not always statistically signicant, winterand autumn rainfall extremes are always showing increasing ten-dencies of oods and spring showing increasing tendency of water

wet days (based on the period 19611990) in different gridded regions in Kerala in

5 day in different gridded regions in Kerala in 19542003.

all

I. Pal, A. Al-Tabbaa / Journal of Hydrology 367 (2009) 6269 69Conclusions

Although the mean and standard deviation of daily rainfall overthe Kerala regions in India are comparatively homogeneous, trendsin seasonal extremes are not regular throughout the state. Achange in the regional circulation pattern because of temperaturestress. In addition, it can now be seen that extremes plays one ofthe most important roles in the seasonal rainfall changes whereintendencies of the changes have high local variabilities in Kerala,which implies that the regional assessment of climatological vari-ables is needed for the local developments.

Fig. 9. Trends of extreme percent (RXP), i.e. proportion of total seasonal rainfall fromKerala in 19542003.changes might be the cause of differences in spatial variations. Ker-ala has positive trends in the winter total precipitation, extremeamount, and intensity, and negative trends in the frequency ofthe dry days, which are always true especially for the higher eleva-tion and north most regions. In contrast, Kerala is undergoing a sig-nicant decrease in the spring precipitation in all the regions. Theindex related to dry days, TDD, showed a more signicant increas-ing trend in the spring season in almost the entire state of Keralaand negative trends in the winter and autumn in small specicparts. As for the extremes, the spring season shows negative signif-icant trends in amount, intensity and frequency. The state of Keralaas a whole has a tendency to increase in autumn rainfall, while per-cent contribution from the extremes is signicant in two griddedregions of interest. This entire work, together with the authorsprevious work for the monsoon season are expected to help to de-ne and assess the risk of ooding and draughts, agricultural plan-ning and other developments associating with the mitigation andadaptation strategies for climate change in the state of Kerala.References

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events above the average long-term 95th percentile in different gridded regions inBeijiang River Basin, Guangdong province, China. Hydrol. Process. doi:10.1002/hyp.6801.

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Trends in seasonal precipitation extremes An indicator of climate change in Kerala, India Introduction Database and methodology Results Long-term changes of seasonal total precipitation (PREP_ST) Long-term changes of seasonal total number of dry days (TDD) Trends of extreme precipitations Indices R95p and R99p Indices RX1day and RX5day Indices RXF and RXP

ConclusionsReferences