research article recent trends in temperature and...

Research ArticleRecent Trends in Temperature and Precipitation inthe Langat River Basin Malaysia

Mahdi Amirabadizadeh1 Yuk Feng Huang2 and Teang Shui Lee1

1Faculty of Engineering Universiti Putra Malaysia 43300 Serdang Selangor Malaysia2Faculty of Engineering and Science Universiti Tunku Abdul Rahman 53300 Kuala Lumpur Malaysia

Correspondence should be addressed to Mahdi Amirabadizadeh mamir692gmailcom

Received 10 September 2014 Revised 7 November 2014 Accepted 20 November 2014

Academic Editor Lin Wang

Copyright copy 2015 Mahdi Amirabadizadeh et alThis is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in anymedium provided the originalwork is properly cited

A study was undertaken to detect long-term trends in the annual and seasonal series of maximum and minimum temperaturesMeasurements were taken at 11 meteorological stations located in the Langat River Basin in Malaysia The rainfall and maximumand minimum temperature data were obtained from the Malaysia Meteorological Department (MMD) and the Department ofIrrigation and Drainage (DID) Malaysia The procedures used included the Mann-Kendall test the Mann-Kendall rank statistictest and the Theil-Senrsquos slope method The analytical results indicated that when there were increasing and decreasing trends inthe annual and seasonal precipitation and temperature only the increasing trends were significant at the 95 confidence level TheTheil-Senrsquos slope method showed that the rate of increment in the annual precipitation is greater than the seasonal precipitationA bootstrap technique was applied to explore uncertainty about significant slope values for rainfall as well as the maximum andminimum temperatures The Mann-Kendall rank statistics test indicated that most of the trends in the annual and seasonal timeseries started in the year 2000 All of the annual and seasonal significant trends were obtained at the stations located in the northeast and northeast portions of the Langat River Basin

1 Introduction

Quantitatively speaking climate change refers to the statisti-cally significant variations of the mean state of the climate orthat of its variability for decades or even over a longer period[1] Although climate change happens globally its effectshowever can be deterministic dependent on the region ofstudy Temperature and precipitation variables are the mostimportant measures that indicate the signs of climate changeThe Intergovernmental Panel on Climate Change (IPCC)AR4 specified that the average global surface temperaturehas increased by 0074∘C per year from 1906 to 2000 [2]Furthermore the IPCC AR5 report indicated that increasesin mean temperature will continue for the 2016ndash2035 period[1] The reports from IPCC also indicated that the frequencyof extreme rainfall events have increased over most landareas consistent with growing temperatures and atmosphericwater vapour [3] An analysis of the long-term changes inclimate variables is very crucial for water resource planning

and management as well as to put things into their properperspective and context [4]

Many trend analyses have been carried out on cli-mate variables especially with temperature and precipita-tion Many of researchers applied the nonparametric Mann-Kendall method in the detection of trend in hydrologicvariables [5 6] The Mann-Kendall rank statistics test andTheil-Senrsquos slope method are other methods that are widelyused to declare trends and magnitude changes in the climatevariables in annual and seasonal time scales [7 8]

The comparison between the trend detection methods isthe main subject of many studies Yue et al [9] compared thepower of theMann-Kendall and Spearman tests for detectingthemonotonic trend in time seriesThey stated that the powerof the test depends on the slope of trend level of significancesample size and properties of the datasets such as skewnessand variation The test results also demonstrated that both ofthe tests provided the same consequences in the absence ofautocorrelation

Hindawi Publishing CorporationAdvances in MeteorologyVolume 2015 Article ID 579437 16 pageshttpdxdoiorg1011552015579437

2 Advances in Meteorology

N

W E

S

101∘20

998400E 101∘30

998400E 101∘40

998400E 101∘50

998400E 102∘E

3∘10

998400N

3∘N

2∘50

998400N

2∘40

998400N

2∘30

998400N

2∘20

998400N

Langat River Basin

Temperature stationRainfall stationRiver network

1

2

3

4

5

6

7

8

9

10

11

Peninsular Malaysia

0 5 10 20

(km)

Thailand

Straitof Malacca

SouthChina

sea

Singapore

Figure 1 Geographic location and spatial distribution of stations in the Langat River Basin

While there were interests in the examination of theexistence of trends for many climatic variables in Malaysiaa limited number of studies have been done in this countryMeng et al [10] investigated the trend of regional warmingin Malaysia using a mean annual temperature time seriesof approximately 50 years Their findings showed completeagreement with the temperature increments reported by theIPCC for Malaysia which are 099 to 344∘C per 100 yearsTangang et al [11] applied the Mann-Kendall test for 32stations over whole peninsula of Malaysia for the period of1974ndash2004TheMK test for mean variability and persistenceof wet spells revealed positive and negative trends in thestations east of the peninsula during the northeast monsoonand southwest monsoon seasons respectively They alsoconcluded that there is a wide gap in the knowledge of climatechange throughout Malaysia

Suhaila et al [12] investigated the existing trends in dailyrainfall in peninsular Malaysia based on seasonal rainfallduring 1975ndash2004 The results of the Mann-Kendall testindicated a decreasing trend in the total amount of rainfalland frequency of wet days as well as the increasing trendin rainfall intensity during the southwest monsoon On thecontrary the results of the test showed reverse trends in theparameters for northeast monsoon flow

The comparison associated with preceding studies inclimate change in Malaysia indicated that there is a gap inmany climate parameters such as maximum temperatureminimum temperature and annual precipitationmdashone of themost important factor Furthermore the beginning of trendis actually another concern that was not addressed in the pastresearches

In this study we analysed and examined the signs ofchange in the annual precipitation as well as the maximumand minimum temperature regime specific to Langat RiverBasin (a strategic basin) located in Malaysia (Figure 1) TheLangat River Basin is the most urbanised river basin inMalaysia located in the southern part of KlangValley Duringthe last decade this watershed has experienced rapid devel-opment in urbanisation agriculture and industrialisationThis watershed supplies water to two-thirds of the state ofSelangor for its domestic hydroelectric plant industry andagricultural consumption As Selangor State is currently fac-ing a water shortage problem especially during the southwestmonsoon season analysing the trends of rainfall and themaximum and minimum temperatures is a vital activityin management of the water resources for the future Theobjectives of this research were achieved using the Mann-Kendall and Mann-Kendal rank statistic methods The ratesof changes in these variables were then determined using theTheil-Senrsquos slope method

2 Materials and Methods

21 Study Area The total area of the Langat River Basin isapproximately 2352 km2 It lies between latitudes 2∘4010158401510158401015840to 3∘1610158401510158401015840N and longitudes 101∘1710158402010158401015840 to 101∘5510158401010158401015840 E inthe southern part of the Klang Valley region The northernpart of the basin is a mountainous area while its southernpart consists of a flat area The mean areal annual rainfall ofthe Langat River Basin is 19941mm The highest recordedmonthly rainfall is about 3271mm occurring in Novemberwhile the lowest is 976mm in JuneThese twomaximum and

Advances in Meteorology 3

Table 1 Geographic characteristics of stations for recorded precipitation data

Station ID Number Longitude (E) Latitude (N) Altitude (m) Period2815001 1 101∘321015840 2∘491015840 3 1971ndash20112818110 2 101∘521015840 2∘531015840 36 1971ndash20112917001 3 101∘471015840 2∘591015840 39 1976ndash20113118102 4 101∘521015840 3∘101015840 91 1971ndash20112913001 5 101∘231015840 2∘551015840 3 1974ndash20112719001 6 101∘561015840 2∘451015840 93 1971ndash201144256 7 101∘301015840 2∘491015840 8 1974ndash201145241 8 101∘561015840 2∘431015840 641 1974ndash2011Ampangan 9 101∘531015840 3∘131015840 2333 1985ndash2011Hospital Seremban 10 101∘5610158403710158401015840 2∘4210158403310158401015840 641 1971ndash2011Petaling Jaya 11 101∘391015840 3∘61015840 608 1974ndash2011

minimum precipitations occurred in the northeast monsoonand the southwest monsoon periods respectively

Besides global warming the climate of Malaysia is asso-ciated with many global and regional phenomena such asmonsoons El NinoLa Nina and the Indian Ocean Dipole(IOD)These phenomena could affect the severity of extremeevents in the country Scientists state that global warmingwill directly affect systems such as hydrological cycles andextreme events such as El NinoLa Nina [13]

In the seasonal time scale the surface climate inMalaysiais affected by two monsoon regimes namely the southwest(SW) monsoon and northeast (NE) monsoon patterns TheSWmonsoon season that is dominated by the low level south-westerly winds begins in May and lasts through August Onthe other hand the NE monsoon season that is controlledby the northeast wind commences in November and ends inFebruary of the following year [14]

The recorded data for rainfall (subdaily measurements)and maximum and minimum temperature (daily mea-surements) were obtained from the Malaysia Meteorolog-ical Department (MMD) and Department of Irrigationand Drainage (DID) The daily data for precipitation wasextracted from subdaily data at each station The series ofannual andmonsoon seasonal recorded data which includedthe maximum temperature (119879Max) the minimum tempera-ture (119879Min) and total precipitation (119875) were analysed for theexistence of monotonic trends

The seasonal and annual values of 119879Max and 119879Min werecomputed as the average values over the season and yearrespectively The study periods were matched to all theavailable records at each station which varied between 27and 41 years The recorded daily data were available atthree temperature stations and eight precipitation stationsMonthly values were averaged to obtain the NE monsoon(winter) and SW monsoon (summer) temperature for eachof the three stations Spatial distribution and geographiccharacteristics of the precipitation and temperature stationsare shown in Figure 1 and Table 1 respectively

These stations were chosen as the database due to thegood quality datasets suitable length (close to 30 years)of data recorded which is considered to be long enoughfor studying the changes in climate and good spatiality

distribution in the mountainous and flat regions to cover thewhole area of the river basin

In the analysis of climate variables it is primarily impor-tant to investigate the quality of the recorded climatic datasuch as looking for missing values analysing the outliersand also administering the consistency test In the case ofmissing observations the daily missed values were repairedusing the Ameliaview11 package in R software [15] whichis based on the bootstrap method Also for the longermissed observations the recorded values in neighbouringstations that had high correlations were used to complete thetemperature and precipitation time series

Outliers in the data sets can lead to false trend results andinaccurate statistical properties [16] Bremer [17] found thatthe interquartile ratio (IQR) method reduces biases in thedataset caused by outliers while also keeping the informationfor extreme events as well In the IQR method the higherand lower thresholds are described by Q75 + 5lowastIQR andQ25 minus 5lowastIQR respectively [18] Then each value greater orsmaller than the higher and lower bands respectively is setas an outlier and eliminated from the data sets

The homogeneity of the time series has been investigatedby many researchers [19] In this study the double-masscurve procedure [20] was applied to test the consistencyof climate variables The double-mass curve method is agraphical procedure to identify the inconsistency of stationrecords by comparing the time trend in one station withother relatively stable records at other stations or the averageof several nearby stations [6] Results of the double mass-curves for all of the stations are straight lines and no apparentbreakpoints are detected in the time series

The mean value of precipitation (119875) at the observedstations varied between 16583 in Station 1 and 23516mm inStation 8 which are located in the flat andmountainous areasrespectively This pattern was repeated for the mean of theseasonal precipitation in the SWmonsoon and NE monsoonseasons This is shown in Table 2 and Figure 2

As shown in Figure 2 the ranges of variation at Stations 9and 11 are the smallest and greatest values in the temperaturetime series respectively These differences in 119879Max and 119879Minbetween stations were more related to the land use andaltitude of gauging stations Station 9 located in forest and


Table 2 Statistical properties of annual and seasonal rainfall and temperature in the Langat River Basin

Annual SeasonalStation Variable NE Monsoon SWMonsoon

Mean SD Skew Mean SD Skew Mean SD Skew1

Precipitation

16583 3049 minus014 5835 18237 003 4688 14364 0062 18838 3663 035 5945 18172 073 5175 14483 0683 21912 4173 minus031 7733 24515 013 5628 17561 0154 21287 3795 036 6621 19853 minus014 6380 17820 0395 17503 3966 minus016 6879 21940 045 4604 12203 0686 19519 3631 001 6205 18853 024 5397 14100 017 19656 4709 243 6772 20692 135 6067 18801 1458 23516 3454 minus038 7861 22475 minus009 6486 14017 058

9 119879Max 317 046 007 311 062 minus008 320 042 067119879Min 216 047 minus088 215 044 minus079 217 050 minus123

10 119879Max 319 036 048 319 054 061 318 028 052119879Min 235 055 minus018 234 058 minus108 236 055 minus008

11 119879Max 328 042 004 324 066 049 329 04 013119879Min 240 057 031 237 061 027 242 058 023

mountainous regions demonstrated less variation in themaximum and minimum temperatures than did the twoother stations near the urban area and lower altitudes The5-year moving average indicated an approximate monotonicincreasing trend for the annual maximum temperature inStation 9 and the annual minimum temperature in Stations10 and 11

As shown in Figure 3(b) the stations that are locatednorth and east of the Langat River Basin receive more rainfallthan do those stations west of the basin during the studyperiod These results are consistent with those obtained byDeni et al [21] The mean annual 119879Max and 119879Min range from316 to 33∘C and 216 to 242∘C respectively

22 Trend Analysis There are two main ways to declare thesignificant trends in the climatic data sets parametric andnonparametric trend methods In the parametric trend anal-ysis data should be independent and normally distributedwhile in the nonparametric method the only requirement isto be independent In this study the nonparametric Mann-Kendall was used for the detection of significant trends theMann-Kendall rank statistic test was used for determining thebeginning of trend and the Theil-Senrsquos slope procedure wasapplied to measure the quantity of change in the climatologictime series

221 Mann-Kendal Test The Mann-Kendall (MK) test is arobust method of detecting a monotonic trend in hydrocli-mate data The MK method is a rank-based procedure witha strength being the application in skewed data Anothersuitable property of this test is that it has a low sensitivity toabrupt breaks whichmay be caused by inhomogeneous data

For an observed time series 119909 = 1199091 1199092 119909119899 the trend test

statistic 119878 is given by

119878 =

119899minus1

sum

119894=1

119899

sum

119895=119894+1

Sgn (119909119895minus 119909119894) (1)

34

33

32

31

25

24

23

22

21

1985 1990 1995 2000 2005 2010 2015

Year

Station 9

Station 10

Station 11

5-year moving average5-year moving average5-year moving average

Tm

in(∘

C)T

max

(∘C)

Figure 2 Annual maximum and minimum temperatures and their5-year moving average in the Langat River Basin

where

Sgn (119909) =

1 119909 gt 0

0 119909 = 0

minus1 119909 lt 0

(2)

And 119894 and 119895 are the rank of observation of the 119909119894 119909119895of time

series


101∘30

9984000998400998400E 101

∘50

9984000998400998400E

3∘10

9984000998400998400N

3∘09984000998400998400N

2∘50

9984000998400998400N

2∘40

9984000998400998400N

2∘30

9984000998400998400N

2∘20

9984000998400998400N

CategoryWater bodyUrban areaPermanent cropsSwamps marshland

Short-term crops

OthersIdle grasslandHorticultural landsForest landAnimal husbandryand wetland forestareas

N

W E

S

(a)

Annual rainfall (mm)N

Rainfall (mm)1813

1885

1955

2023

2087

2149

2214

0 5 10 20

(km)

(b)

Maximum temperature

316ndash318318ndash32320ndash322322ndash324

324ndash326326ndash328328ndash33

N

0 5 10 20 30 40

(km)

(c)

lt218



0 5 10 20 30 40

(km)

Minimum temperatureN

(d)

Figure 3 Spatial characteristics of the Langat River Basin include (a) land use map (b) annual precipitation (c) minimum temperature and(d) maximum temperature


The variance is computed as

119881 =119899 (119899 minus 1) (2119899 + 5)

18minus

119892

sum

119894=1

119905119894(119905119894minus 1) (2119905

119894+ 5)

18 (3)

where 119892 is the number of groups of tied rank and 119905119894is number

of ties in group 119894 For a sample size of 30 or larger the 119878statistic is normally distributed and the value of the standarddeviation is computed by

119885 =

119904 minus 1

11988105119878 gt 0

0 119878 = 0

119904 + 1

11988105119878 lt 0

(4)

When |119885| gt 1198851minus1205722

or the 119875 value is smaller than thesignificance level (120572) the null hypothesis is rejected and thereis a significance trend in the time series The 119885

1minus1205722and 119875

value are obtained from the standard normal distributiontable In this study the significance level is 120572 = 005 andtherefore the null hypothesis of no trend is rejected while|119885| gt 196

TheMK test analysis techniquewas widely used in detect-ing trends in climate variables [5 8 22ndash26]The prewhiteningprocedure was carried out before completing the MK test toeliminate the influence of autocorrelation on the results of thetest The prewhitening procedure is described in Section 23

222 Mann-Kendall Rank Statistic Test According to Sney-ers [27] the Mann-Kendall rank statistic (MKRS) test is usedto determine the approximate beginning year of significanttrends (eg [6 14 28]) Two sequential values 119880

119905and 1198801015840

119905

are determined from progressive and backward time seriesrespectivelyThis method uses relative values of each elementin the time series (119909

1 1199092 119909

119899) The following steps are

utilised in four sequences

(1) The magnitudes of 119909119894annual or seasonal time series

(119894 = 1 119899) are compared with 119909119895(119895 = 1 119894 minus 1)

At each comparison the number of cases 119909119894gt 119909119895is

counted and indicated by 119899119894

(2) The test statistic 119905119894variable is computed using

119905119894=

119894

sum

119894=1

119899119894 (5)

(3) As the 119905119894distribution is asymptotically normal the

mean and variance of 119905119894are calculated as follows

119864 (119905) =119899 (119899 minus 1)

4

Var (119905119894) =

1

72[119894 (119894 minus 1) (2119895 + 5)]

(6)

(4) Finally the sequential values of the 119880119905are computed

by

119880119905=119905119894minus 119864 (119905)

radicVar (119905119894)

(7)

Similarly these four steps are followed to compute the1198801015840119905

in backward direction from the end of the time series If thetime series of 119880

119905and 1198801015840

119905

intersect each other and at least oneof them is greater than a chosen level of significance thenthere is a statistically significant trendThe point where the119880

119905

and1198801015840119905

cross each other indicates the approximate beginningyear of a developing trendwithin the time series In this studyas the level of significance 120572 = 005 then the absolute valuesof calculating 119880

119905and 1198801015840

119905

are compared with the Gaussiandistribution value l96

223 Theil-Senrsquos Slope Method TheTheil-Senrsquos slope method[29] is used to estimate the rate of change the rate of change

120573 = Median(119909119895minus 119909119894

119895 minus 1) 119894 lt 119895 (8)

where 119909119895and 119909

119894are the data values in times 119895 and 119894

respectivelyThe120573 sign represents the direction of change andits value indicates the steepness of change [7 25] Accordingto Yue et al [9] the slope estimated byTheil-Senrsquos slope (TSS)estimator is a robust estimation of themagnitude of the trendIn order to determine the magnitude of TSS an algorithm inR programme that corresponds to an extension of originalTSS test was applied This technique is frequently used indetermining the slope of trend in climate variable time series(eg [5 30ndash33])

23 Influence of Serial Correlation In time series analysisit is essential that the data sets used for trend analysis befree of serial correlation in the time series von Storch andNavarra [34] found that the presence of serial correlation inthe time series can complicate the results of the identificationof the trend detection for the Mann-Kendall test since thepositive first-order autocorrelation can increase the expectednumber of false positive outcomes In this study the trend-free prewhitening (TFPW) approach that was modified byBurn et al [35] was used prior to the application of theMann-Kendall and Theil-Senrsquos slope procedures to removethe significant serial correlation from the time series Thisprocedure can be followed in five steps

(1) Compute the TSS 120573 using (8)(2) Remove the monotonic trend from the original data

sets using

119884119905= 119883119905minus 120573119905 (9)

where119883119905is the time series value at time 119905 and119884

119905is the

detrended series(3) Compute 119903

1 the lag-1 serial correlation of the new

series If the value of 1199031is not statistically significant at

the 5 level the estimated 120573 is accepted Otherwisethe detrended series is prewhitened through

1198841015840

119905

= 119884119905minus 1199031119884119905minus1 (10)

where 1198841015840119905

is the prewhitened series


3200

3000

2800

2600

2400

2200

2000

1800

1600

1400

1200

1000

Ann

ual r

ainf

all (

mm

)Station 3

1980 1990 2000 2010

Year

Y = 121X minus 2194292600

2400

2200

2000

1800

1600

1400

1200

1000

Ann

ual r

ainf

all (

mm

)

800

600

Station 4

Y = 153X minus 290641

1980 1990 2000 2010

Year1970

2400

2200

2000

1800

1600

1400

1200

1000

Ann

ual r

ainf

all (

mm

)

800

600

400

1980 1990 2000 2010

Year1970

Station 6

2200

2000

1800

1600

1400

1200

1000Ann

ual r

ainf

all (

mm

)

800

600

900

800

700

600

500

400

300

200

Seas

onal

rain

fall

(mm

)

Annual or seasonal rainfallSignificant trend5-year moving average

1980 1990 2000 2010

Year1980 1990 2000 2010

Year1970

Station 6Station 8 SW monsoon

Y = 171X minus 325595

Y = 134X minus 251079 Y = 48X minus 90252





Figure 4 The plot of significant trends for annual and seasonal precipitation in the Langat River Basin at the 95 confidence level

(4) Add the monotonic trend to the prewhitened seriesthrough

11988410158401015840

119905

= 1198841015840

119905

+ 120573119905 (11)

where 11988410158401015840119905

is the trend-free prewhitened series(5) Calculate the MK statistic on 11988410158401015840

119905

series and examinethe local significance of the calculated statistics

3 Results and Discussion

31 Analyses of Lag-1 Autocorrelation and the Prewhiten-ing Process The results of lag-1 autocorrelations and theprewhitening process of the annual and seasonal precipi-tation and temperature in the related situations are shownin Tables 3 and 4 As shown while the serial correlationsfor the annual 119875 119879Min 119879Max before the TFPW processwere all positive positive and negative serial correlationswere obtained at the seasonal time series of precipitationand temperature While the temperature variables required

the TFPW process before the MK test the majority ofthe precipitation variables did not require any prewhiteningprocedure The strongest and weakest autocorrelation werefound in the annual 119879Min (Station 9) and SW monsoon 119879Min(Station 11) respectively Generally the temperature variableshave a higher autocorrelation than did the precipitationvariables The majority of the seasonal precipitation timeseries is free of significant lag-1 serial correlation

32 Trend in Precipitation and Temperature Variables Theresults of theMK test the TSS estimator andMKRS test for119875119879Max and 119879Min are presented in Tables 5 6 and 7 The resultsof theMK test for the annual and seasonal time series are alsopresented in Figures 3 and 4

321 Analysis of Precipitation The results of the MK test ofthe annual and seasonal precipitation time series are shownin Table 5 and the significant trends at the 95 confidencelevel are illustrated in Figure 4 The results of the MK test


Table 3 Lag-1 serial correlation values for temperature variables before and after prewhitening

Station Critical1199031

119879Max 119879Min NE 119879Max SW 119879Max NE 119879Min SW 119879Min1199031

1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840

9 037 048lowast minus001 058lowast minus002 036 mdash minus003 mdash 055lowast minus005 045lowast 03410 031 037lowast 008 002 mdash 012 mdash 017 mdash minus009 mdash 042lowast 00411 030 039lowast 002 075lowast 007 015 mdash 016 mdash 016 mdash 002 mdashlowastSignificant at the 5 significance levelmdashNo need to follow TFPW process

Table 4 Lag-1 serial correlation values for annual and seasonal precipitation before and after TFPW process (1199031

1015840)

Station Critical 1199031

P (mm) NE monsoon SWmonsoon1199031

1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840

1 031 011 mdash minus018 mdash minus005 mdash2 031 006 mdash minus007 mdash 01 mdash3 033 027 mdash 006 mdash minus003 mdash4 031 036lowast 005 minus003 mdash 02 mdash5 032 021 mdash minus016 mdash 009 mdash6 031 029 mdash minus013 mdash minus023 mdash7 032 015 mdash 007 mdash 003 mdash8 032 036lowast 027 minus008 mdash 010 mdashlowastSignificant at the 5 level of significancemdashNo need to follow TFPW process

Table 5 The results of the MK and TSS methods for annual and seasonal precipitation in the Langat River Basin

Site Annual P (mm) NE monsoon SWmonsoon119878 119885 119875 value 120573 (mmyr) 119878 119885 119875 value 120573 (mmyr) 119878 119885 119875 120573 (mmyr)

1 110 122 022 +45 28 030 076 +08 104 116 024 +252 59 065 054 +30 72 080 043 +17 minus44 minus048 063 minus113 146 203 004lowast +126 110 143 014 +58 26 033 073 +104 252 292 0004lowast +153 138 154 012 +54 122 136 017 +325 minus83 minus10 030 minus58 minus37 minus045 065 minus15 minus83 minus103 030 minus166 280 313 0002lowast +171 136 152 013 +41 198 221 002lowast +487 73 091 037 +45 43 053 059 +07 89 111 027 +258 188 245 0014lowast +134 91 113 026 +39 77 096 034 +22lowastStatistically significant at the 95 confidence level

Table 6 Results of the MK and TSS methods for annual temperature variables in the Langat River Basin

Station 119879Max (∘C) 119879Min (

∘C)119878 119885 119875 value 120573 (∘Cyr) 119878 119885 119875 value 120573 (∘Cyr)

9 145 300 0003lowast +0035 52 106 0287 +00110 135 168 009 +0008 370 483 0000lowast +00311 minus29 minus034 075 minus0002 401 484 0000lowast +004lowastStatistically significant at 95 confidence level

Table 7 The results of the MK and TSS methods for seasonal temperature variables in the Langat River Basin

Station Variable NE Monsoon SWMonsoon119878 119885 119875 value 120573 (∘Cyr) 119878 119885 119875 value 120573 (∘Cyr)

9 119879Max 117 242 0015lowast +004 149 309 0002lowast +003119879Min minus3 minus004 098 +00001 minus29 minus062 054 minus0007

10 119879Max 71 088 038 +0007 87 108 028 +0005119879Min 411 515 0000lowast +0046 420 548 0000lowast +004

11 119879Max minus114 minus127 0204 minus0012 12 012 0902 +0001119879Min 558 626 0000lowast +0043 568 639 0000lowast +0041

lowastStatistically significant at the 95 confidence level


for prewhitened annual precipitation showed that there aresignificant positive trends at Stations 3 4 6 and 8 whichrepresents 50 of the precipitation stations As shown inFigure 3(b) by considering the spatiality of these stations thesignificance trends were obtained in these stations which arelocated east and north of the basin In addition as illustratedby the altitude and DEM (Table 1 and Figure 1) of thesestations the positive significant trends are obtained in higheraltitude areas The land use of the area around Stations 3 and4 are urban and forest respectively

The score values of this variable conformed to the119875 valuesat these stations In addition the largest 119878 value obtained atStation 6 adapted to the most powerful significance trend testat that station On the contrary among these four stationswith significant trends Station 3 has the smallest 119878 value andthe largest 119875 value This indicates the smallest rate of changeat this station Nevertheless the majority of trends for theseasonal precipitation time series were insignificant at the95 confidence level The only significant trend at the 95confidence level was detected at Station 6 for SW monsoonprecipitation

The results of Theil-Senrsquos test of annual and seasonalprecipitation time series are shown inTable 5 and significancetrends at the 95 confidence level are illustrated in Figure 4As indicated in Table 5 the largest increasing slope of sig-nificant annual precipitation occurred at Station 6 while thesmallest rates were obtained at Station 3 which were 171 and126mmyear respectively Based on the results of theMK testfor the annual and seasonal precipitations the test could notdetect any significant negative trends in the time series

The consequences of theMKRS test for significant annualand seasonal precipitation and temperatures are shown inFigure 5 The sequential values of the 119880(119905) 1198801015840(119905) and theconfidence limits are depicted by solid red blue and dashedlines respectively As shown in Figure 5(a) the intersectionpoint of the 119880(119905) and 1198801015840(119905) curves has changed the regimeof the downward trend to the increasing trend around year1990 which became significant in the year 2002 In spite ofthe existence of significant positive trends at Stations 4 6and 8 the MKRS test was not capable of determining theapproximate beginning year of the trends During the studyperiod of these stations the 119880(119905) and 119880

1015840

(119905) have becomesignificant especially around the year 2000 and after

322 Analysis of 119879Max The outputs of the trend detectionanalysis for annual and seasonal maximum temperature inthe Langat River Basin are presented in Tables 6 and 7 andalso in Figures 5 and 6

Concerning the annual and seasonal analysis of 119879Maxthere were downward and upward trends at the stationsThree out of seven upward trends were statistically significantand none of the decreasing trends were significant at the 5significance level All of the significant trends are related toStation 9 located in the mountainous area and forest landuse categoryThe findings also indicate that the largest 119878 valuefor the annual 119879Max was found at Station 9 The strongestwarming trend in 119879Max was conformed to the lowest 119875 valueat this station The slope estimator test indicated an increase

of 35∘C per century in the maximum temperature at thisstation which is close to the highest rate of temperatureincrements 099ndash344∘C in a century reported by the IPCCin Malaysia The rates of significant seasonal changes in SWmonsoon and NE monsoon seasons for 119879Max were 3 and 4∘Cper century respectively Thus the warming slope of 119879Max inthe annual period is higher than the SWmonsoon season andlower than NE monsoon season These results indicate thatthe monsoonsrsquo winds are active regional phenomena whichinfluence the observed maximum temperature during theseseasons

Regarding the annual significant trend at Station 9 theMKRS test was not able to indicate anymutation point for thispowerful upward trend as shown in Figure 5(c)The series of119880(119905) passed the 95 confidence level in 1990 while theMKRStest detected the beginning years of trend for the annual 119879Maxat Stations 10 and 11 in 1998 and 2003 respectively Howevernone of these trends are significant at the 95 confidencelevel As illustrated in Figure 5(e) the warming trend for theNE monsoon 119879Max at Station 9 began in 1997 and becamesignificant during the same year The MKRS test detectedother intersections between 119880(119905) and 1198801015840(119905) series in the NEmonsoon and SW monsoon seasons at Stations 10 and 11after 1990 and none of them were significant at the 95significance level

323 Analysis of 119879Min The trend analyses for annual andseasonal 119879Min in the three stations Stations 9 10 and 11 ofthe Langat River Basin obtained by the MK MKRS and TSSmethods are given in Tables 6 and 7 and Figures 6 and 7The annual MK trend test results identified the maximum 119878

value at Station 11 and the smallest one at Station 9ThereforeStation 9 which had the most powerful trend in 119879Max wasreplaced by Station 11 for 119879Min The increasing significanttrend of the annual 119879Min at Stations 10 and 11 were significantat the 95 confidence level while it was not as strong as theannual increment in 119879Max at Station 9

According to the results the highest value of the slope forthe annual minimum temperature was identified at Station 11(04∘Cdecade) while the lowest valuewas obtained at Station9 (01∘Cdecade) As shown in Table 6 the probability of thistrend being obtained randomly is close to zero and thus thetest results are significant at the 95 confidence levelTheMKtrend test for the SW monsoon and NW monsoon seasonsindicated significant trends at Stations 10 and 11 The MKtrend analysis of the seasonal119879Min showed a strong significantincreasing trend at Stations 10 and 11 in the seasonal timeperiods The altitudes of Stations 10 and 11 are less than thealtitude of Station 9 The rates of seasonal increasing in 119879Minat these two stations are about 4∘C per century which is nearthe NE monsoon trend in 119879Max at Station 9

The sequential MK test could not detect any beginningpoint for significant seasonal and annual positive trends Theresult of this test showed amutation point only at Station 9 forannual and seasonal trends in 2001 that passed the significantconfidence levels in this year

Therefore the results of the present study indicated aclimate change signal for annual rainfall as well as the


1980 1990 2000 2010

Year

Station 3

2

0

minus2

UtU998400t

(a)

2

0

minus2

minus4

19801970 1990 2000 2010

Year

Station 4

UtU998400t

(b)

minus4

2

0

minus2

4

1980 1990 2000 2010

Year

Station 9

UtU998400t

(c)

2

0

minus2

19801970 1990 2000 2010

Year

Station 6 NE monsoon

UtU998400t

(d)

2

0

minus2

4

1980 1990 2000 2010

Year


UtU998400t

Ut

U998400t

(e)

2

0

minus2

198019701960 1990 2000 2010

Year

Station 6 SW monsoon

UtU998400t

Ut

U998400t

(f)

Figure 5 Graphical example of 119880(119905) and backward series U1015840(119905) of the MKRS test for annual analysis ((a) (b) and (c)) NE monsoon season((d) and (e)) and SWmonsoon (f) for precipitation and temperature at the stations with significant trends at the 95 confidence level


175

170

165

160

155

Station 9 Annual

1990 2000 2010

Tm

ax(∘

C)250

245

240

235

230

225

220

Tm

in(∘

C)

1970 1980 1990 2000 2010

Station 10 Annual 45

40

35

30

25

20

15

10

05

1970 1980 1990 2000 2010

Tm

in(∘

C)

Station 11 Annual

325

320

315

310

305

300

295

NE monsoon

1990 2000 2010

Tm

ax(∘

C)

Station 9 245

240

235

230

225

220

215

210

205

NE monsoon

Tm

in(∘

C)

Station 10

1970 1980 1990 2000 2010

255

250

245

240

235

230

225

220

NE monsoonStation 11

Tm

in(∘

C)

1970 1980 1990 2000 2010

330

325

320

315

SW monsoon

1990 2000 2010

Year

Tm

ax(∘

C)

Station 9

Annual or seasonal temperatureSignificant trend

245

240

235

230

225

Tm

in(∘

C)

1970 1980 1990 2000 2010

Year

SW monsoonStation 10



255

250

245

240

235

230

225

Tm

in(∘

C)

260

1970 1980 1990 2000 2010

Year


Y = 0035 lowast X minus 532 Y = 00412 lowast X minus 587 Y = 004 lowast X minus 786



Year Year Year

Year Year Year

Figure 6 Results of significant MK trends for annual and seasonal temperature variables in the Langat River Basin at the 95 confidencelevel


006

005

004

003

002

001

000

minus001

Senrsquos

slop

e (∘C)

minus0002

0040043

004

minus001

Significant trendInsignificant trend

Station 11

1E minus 3

Tmax Tmin NE NE SW SW

VariablesTmax Tmin Tmax Tmin

(a)

006

005

004

003

002

001

000

minus001

Senrsquos

slop

e (∘C)


Station 9

0035

001

004

003

minus0007

1E minus 4



(b)

Permanent crops

Idle grasslandHorticultural land

Category

Water body

Urban area

Short-term cropsOthers

Forest landAnimal husbandary

Wetland forest

areas

9

10

11

(c)

006

005

004

003

002

001

000


0008

003

0007

0046

0005

004

Station 10



Senrsquos

slope

(∘C)

(d)

Figure 7The spatial distribution of significant and insignificant trends for annual and seasonal temperatures along with corresponding Senrsquosslope values per year

maximum and minimum temperatures in the Langat RiverBasin These findings reflect other researchers that lookedfor signs of climate change for discrete climate parameters inMalaysia [10 36 37] As far as we are aware the present studyis the only one that simultaneously focused on the slope ofthe trend in 119875 119879Max and 119879Min besides the beginning time ofchange in climate parameters in the Langat River Basin

33 Uncertainty in Trends A bootstrap method was appliedherein on the database to assess the uncertainty in the esti-mated significant slopes associated with the slope estimatormethod in significant trends for the annual and seasonal

precipitation and maximum and minimum temperatures byusing 1000 resampling times The bootstrap histogram boxplot confidence intervals and other useful statistics of thebootstrapped slope values at these stations are shown inTable 8 and Figures 8 and 9 The 95 confidence inter-vals of the median bootstrapped slopes were computedusing the percentile method As shown in Table 7 all ofthe measured Theil-Senrsquos slope values are placed at the95 confidence intervals The box-plot figure also illus-trated more nonnormality in the slopesrsquo distribution fortemperature than with the precipitation in the significantstations


350

300

Annual rainfallStation 6

12 14 16 18 20 22

Slope (mmyr)

250

200

150

100

50

0

Freq

uenc

y

(a)

200

150

100

50

0

Freq

uenc

y

3 4 5 6 7

Slope (mmyr)

Station 6 (rainfall) SW monsoon

(b)

500

400

300

200

100

0

Freq

uenc

y

0036 0038 0040 0042 0044 0046

Slope (∘Cyr)

Station 11 (Tmin) SW monsoon

(c)

400

300

200

100

0

Freq

uenc

y

NW monsoon

0039 0042 0045 0048 0051

Slope (∘Cyr)

Station 10 (Tmin)

(d)

Figure 8 Examples of histogram of bootstrap slopes in significant trend for annual and seasonal temperatures (119879Max 119879Min) and precipitation

4 Conclusions

Changes in the climate variables due to global warmingand regional systems such as monsoon phenomena wereinvestigated at the Langat River Basin to precisely determinethe rate of changes in this strategic river basin The mainobjective of this study was the annual and seasonal trendanalysis for 119875 119879Max and 119879Min at the 11 selected stations in thisriver basin to support the water management policymakersfor planning for the future Three nonparametric methodsthe MK MKRS test and TSS were applied to complete the

detection of trends for the variables The analyses of the MKand TSS were performed after eliminating the influence ofsignificant lag-1 serial correlations from the time series usingthe TFPW technique The results of these trend tests showedsignificant increasing trends at four stations (Stations 3 46 and 8) for annual precipitation one station for the SWmonsoon season (Station 6) one station for the seasonal andannual 119879Max and two stations for the annual and seasonal119879Min at the 95 confidence level The rate of changes inthis river basin showed that north and east of basin havefaced a moderate change in the maximum and minimum


Rang

e of s

lope

(mm

yr)

20

15

10

5

0S3 S4 S6 S8 S6SWM

Station

Significant annual and seasonal rainfall

(a)

Rang

e of s

lope

(∘C

yr)

006

004

002

Significant annual and seasonal temperature

a b c d e f g h i

(b)

Figure 9 Boxplot of slope obtained from bootstrap method for the significant trends of annual and seasonal rainfall and temperatures (a S9(119879Max) b S10 (119879Min) c S11 (119879Min) d S9 (119879Max NEmonsoon) e S10 (119879Min NEmonsoon) f S11 (119879Min NEmonsoon) g S9 (119879Max SWmonsoon)h S10 (119879Min SWmonsoon) i S11 (119879Min SWmonsoon))

Table 8 The bootstrap results for slopes in significant trends for rainfall and maximum and minimum temperatures in the Langat RiverBasin

Station Parameter EstimatedTheil-Senrsquos slope Bootstrap results of slopeThe 95 confidence intervals Mean Standard deviation

S3

Precipitation

126 8369 15738 1201 195S4 153 12732 18293 1534 111S6 171 15244 18748 1689 091S8 134 10172 15516 1325 146S6 (SWmonsoon) 48 3477 6051 477 067S9 (119879Max)

Temperature

0035 0023 0042 0034 0005S10 (119879Min) 003 0029 0037 0032 0002S11 (119879Min) 004 0036 0046 0041 0002S9 (119879Max NE monsoon) 004 0033 0052 004 0005S10 (119879Min NEMonsoon) 0046 0042 005 0047 0002S11 (119879Min NE monsoon) 0043 0040 0046 0044 0001S9 (119879Max SWmonsoon) 003 0025 0038 003 0003S10 (119879Min SWmonsoon) 004 0038 0045 0041 0002S11 (119879Min SWmonsoon) 004 0039 0043 0041 0001

temperatures and rainfall during the past four decades Inother words the weather north and east of basin becamewarmer and wetter during the study period

While the rates of upward significant changes for annualprecipitation ranged from 126 to 171mmper year for the rateof change in SWmonsoon a significant trendwas found to beabout one-third (48mm per year) The TSS test determinedthe slope of an upward trend for the annual and seasonal119879Max in a range of 35 to 4∘C per century It is the samefor the annual and seasonal significant trends for the 119879Minvariable Also all of the significant trends were found atthe stations located in the north east and northeast partsof the basin Their elevations are higher than those stationsin the west region of the river basin The land use of thesestations is forest or urban area The other important resultis that all of the significant long-term trends in precipitationand temperature gradually increased during the study period

Also it showed that the monsoon system is a powerfulphenomenon among all other active systems in the regionThis rate of change in the annual and seasonal rainfall119879Max and 119879Min temperature may drive some changes in landcoverland use of the basin therefore increasing the floodfrequency in this region especially during the NE monsoonseason Furthermore changes in the water demand patternfor all type of usage in the region is another consequencedue to the alterations in climate parameters Future studies intrend analysis for extreme rainfall events and other indices ofprecipitation in the Langat River Basin would be very usefulfor future water resource planning

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper


Acknowledgments

The authors would like to thank the Department of IrrigationandDrainageMalaysia (DID) and theMalaysianMeteorolog-ical Department (MMD) both under theMinistry of NaturalResources and Environment Malaysia (NRE) for providingdata and technical support The authors would also like tosincerely thank the Ministry of Science Technology andInnovation (MOSTI) for the financial support

References

[1] T F Stocker D Qin G-K Plattner et al ldquoClimate change2013 the physical science basisrdquo in Intergovernmental Panel onClimate ChangeWorking Group I Contribution to the IPCC FifthAssessment Report (AR5) Cambridge University Press NewYork NY USA 2013

[2] IPCC2007 Climate Change 2007 The Physical Science BasisIPCC Geneva Switzerland 2007

[3] S Solomon Climate Change 2007mdashThe Physical Science BasisWorking Group I Contribution to the Fourth Assessment Reportof the IPCC vol 4 Cambridge University Press 2007

[4] P Nyeko-Ogiramoi P Willems and G Ngirane-KatashayaldquoTrend and variability in observed hydrometeorologicalextremes in the Lake Victoria basinrdquo Journal of Hydrology vol489 pp 56ndash73 2013

[5] D H Burn andM A H Elnur ldquoDetection of hydrologic trendsand variabilityrdquo Journal of Hydrology vol 255 no 1ndash4 pp 107ndash122 2002

[6] H Tabari B S Somee andM R Zadeh ldquoTesting for long-termtrends in climatic variables in Iranrdquo Atmospheric Research vol100 no 1 pp 132ndash140 2011

[7] M Gocic and S Trajkovic ldquoAnalysis of changes in meteoro-logical variables using Mann-Kendall and Senrsquos slope estimatorstatistical tests in SerbiardquoGlobal and Planetary Change vol 100pp 172ndash182 2013

[8] D Liu S Guo X Chen and Q Shao ldquoAnalysis of trendsof annual and seasonal precipitation from 1956 to 2000 inGuangdongProvince ChinardquoHydrological Sciences Journal vol57 no 2 pp 358ndash369 2012

[9] S Yue P Pilon B Phinney and G Cavadias ldquoThe influenceof autocorrelation on the ability to detect trend in hydrologicalseriesrdquoHydrological Processes vol 16 no 9 pp 1807ndash1829 2002

[10] W NMeng C Alejandro AWahab and A Khairi ldquoA study ofglobal warming in malaysiardquo Jurnal Teknologi F pp 1ndash10 2005

[11] F T Tangang L Juneng E Salimun K M Sei L J Le and HMuhamad ldquoClimate change and variability over Malaysia gapsin science and research informationrdquo Sains Malaysiana vol 41no 11 pp 1355ndash1366 2012

[12] J Suhaila S M Deni W A N Zawiah Zin and A AJemain ldquoTrends in peninsular Malaysia rainfall data duringthe southwest monsoon and northeast monsoon seasons 1975ndash2004rdquo Sains Malaysiana vol 39 no 4 pp 533ndash542 2010

[13] R Nurmohamed and S Naipal ldquoTrends and variation inmonthly rainfall and temperature in surinamerdquo in ProceedingsBALWOIS Conference on Water Observation and Information-System for Decision Support Ohrid FY Republic of Macedonia25ndash29 May 2000 p 3 2004

[14] J Taubenheim ldquoAn easy procedure for detecting a discontinuityin a digital time seriesrdquo Zeitschrift fur Meteorologie vol 39 pp344ndash347 1989

[15] J Honaker G King and M Blackwell ldquoAmelia II a programfor missing datardquo Journal of Statistical Software vol 45 no 7pp 1ndash47 2011

[16] C Data Guidelines on Analysis of Extremes in a ChangingClimate in Support of Informed Decisions for Adaptation 2009

[17] R Bremer ldquoOutliers in statistical datardquo Technometrics vol 37pp 117ndash118 1995

[18] J F Gonzalez-Rouco J L Jimenez V Quesada and F ValeroldquoQuality control and homogeneity of precipitation data in thesouthwest of Europerdquo Journal of Climate vol 14 no 5 pp 964ndash978 2001

[19] A C Costa and A Soares ldquoHomogenization of climate datareview and new perspectives using geostatisticsrdquoMathematicalGeosciences vol 41 no 3 pp 291ndash305 2009

[20] M A Kohler ldquoDouble-mass analysis for testing the consistencyof records and formaking adjustmentsrdquoBulletin of theAmericanMeteorological Society vol 30 pp 188ndash189 1949

[21] SMDeni J SuhailaW ZW Zin andA A Jemain ldquoTrends ofwet spells over Peninsular Malaysia during monsoon seasonsrdquoSains Malaysiana vol 38 no 2 pp 133ndash142 2009

[22] K Chaouche L Neppel C Dieulin et al ldquoAnalyses of precipita-tion temperature and evapotranspiration in a French Mediter-ranean region in the context of climate changerdquo ComptesRendus Geoscience vol 342 no 3 pp 234ndash243 2010

[23] D Machiwal and M K Jha ldquoTime series analysis of hydrologicdata for water resources planning and management a reviewrdquoJournal of Hydrology and Hydromechanics vol 54 pp 237ndash2572009

[24] H Verworn S Kramer M Becker and A Pfister ldquoThe impactof climate change on rainfall runoff statistics in the Emscher-Lippe regionrdquo in Proceedings of the 11th International Conferenceon Urban Drainage pp 1ndash10 Edinburgh UK 2008

[25] X Yang L Xu K Liu C Li J Hu and X Xia ldquoTrends intemperature and precipitation in the zhangweinan river basinduring the last 53 yearsrdquo Procedia Environmental Sciences vol13 pp 1966ndash1974 2012

[26] O E Scarpati L B Spescha J A F Lay andADCapriolo ldquoSoilwater surplus in salado river basin and its variability during thelast forty years (buenos aires province argentina)rdquo Water vol3 pp 132ndash145 2011

[27] R Sneyers On the Statistical Analysis of Series of ObservationsWMO 1991

[28] T Partal and E Kahya ldquoTrend analysis in Turkish precipitationdatardquoHydrological Processes vol 20 no 9 pp 2011ndash2026 2006

[29] H Theil ldquoA rank-invariant method of linear and polynomialregression analysisrdquo inHenri Theilrsquos Contributions to Economicsand Econometrics pp 345ndash381 Springer 1992

[30] Y Dinpashoh D Jhajharia A Fakheri-Fard V P Singh and EKahya ldquoTrends in reference crop evapotranspiration over IranrdquoJournal of Hydrology vol 399 no 3-4 pp 422ndash433 2011

[31] D Karpouzos S Kavalieratou and C Babajimopoulos ldquoTrendanalysis of precipitation data in Pieria region (Greece)rdquo Euro-pean Water vol 30 pp 31ndash40 2010

[32] C J Martinez J J Maleski and M F Miller ldquoTrends inprecipitation and temperature in Florida USArdquo Journal ofHydrology vol 452-453 pp 259ndash281 2012

[33] M Santos and M Fragoso ldquoPrecipitation variability in North-ern Portugal data homogeneity assessment and trends inextreme precipitation indicesrdquo Atmospheric Research vol 131pp 34ndash45 2013


[34] H von Storch and A Navarra Analysis of Climate VariabilityApplications of Statistical Techniques Springer 1999

[35] D H Burn J M Cunderlik and A Pietroniro ldquoHydrologicaltrends and variability in the Liard River basinrdquo HydrologicalSciences Journal vol 49 no 1 pp 53ndash68 2004

[36] T Stocker D Qin G Plattner et al Summary for PolicymakersCambridge University Press New York NY USA 2013

[37] N Palizdan Y Falamarzi Y F Huang T S Lee and A HGhazali ldquoRegional precipitation trend analysis at the LangatRiver Basin Selangor Malaysiardquo vol 117 no 3-4 pp 589ndash6062014

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Hindawi Publishing Corporationhttpwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2014

Mining


Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal of

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OceanographyInternational Journal of


Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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GeochemistryHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

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OceanographyHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in


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MeteorologyAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Paleontology JournalHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Geological ResearchJournal of


Geology Advances in


N

W E

S

101∘20

998400E 101∘30

998400E 101∘40

998400E 101∘50

998400E 102∘E

3∘10

998400N

3∘N

2∘50

998400N

2∘40

998400N

2∘30

998400N

2∘20

998400N

Langat River Basin

Temperature stationRainfall stationRiver network

1

2

3

4

5

6

7

8

9

10

11

Peninsular Malaysia

0 5 10 20

(km)

Thailand

Straitof Malacca

SouthChina

sea

Singapore

Figure 1 Geographic location and spatial distribution of stations in the Langat River Basin

While there were interests in the examination of theexistence of trends for many climatic variables in Malaysiaa limited number of studies have been done in this countryMeng et al [10] investigated the trend of regional warmingin Malaysia using a mean annual temperature time seriesof approximately 50 years Their findings showed completeagreement with the temperature increments reported by theIPCC for Malaysia which are 099 to 344∘C per 100 yearsTangang et al [11] applied the Mann-Kendall test for 32stations over whole peninsula of Malaysia for the period of1974ndash2004TheMK test for mean variability and persistenceof wet spells revealed positive and negative trends in thestations east of the peninsula during the northeast monsoonand southwest monsoon seasons respectively They alsoconcluded that there is a wide gap in the knowledge of climatechange throughout Malaysia

Suhaila et al [12] investigated the existing trends in dailyrainfall in peninsular Malaysia based on seasonal rainfallduring 1975ndash2004 The results of the Mann-Kendall testindicated a decreasing trend in the total amount of rainfalland frequency of wet days as well as the increasing trendin rainfall intensity during the southwest monsoon On thecontrary the results of the test showed reverse trends in theparameters for northeast monsoon flow

The comparison associated with preceding studies inclimate change in Malaysia indicated that there is a gap inmany climate parameters such as maximum temperatureminimum temperature and annual precipitationmdashone of themost important factor Furthermore the beginning of trendis actually another concern that was not addressed in the pastresearches

In this study we analysed and examined the signs ofchange in the annual precipitation as well as the maximumand minimum temperature regime specific to Langat RiverBasin (a strategic basin) located in Malaysia (Figure 1) TheLangat River Basin is the most urbanised river basin inMalaysia located in the southern part of KlangValley Duringthe last decade this watershed has experienced rapid devel-opment in urbanisation agriculture and industrialisationThis watershed supplies water to two-thirds of the state ofSelangor for its domestic hydroelectric plant industry andagricultural consumption As Selangor State is currently fac-ing a water shortage problem especially during the southwestmonsoon season analysing the trends of rainfall and themaximum and minimum temperatures is a vital activityin management of the water resources for the future Theobjectives of this research were achieved using the Mann-Kendall and Mann-Kendal rank statistic methods The ratesof changes in these variables were then determined using theTheil-Senrsquos slope method

2 Materials and Methods

21 Study Area The total area of the Langat River Basin isapproximately 2352 km2 It lies between latitudes 2∘4010158401510158401015840to 3∘1610158401510158401015840N and longitudes 101∘1710158402010158401015840 to 101∘5510158401010158401015840 E inthe southern part of the Klang Valley region The northernpart of the basin is a mountainous area while its southernpart consists of a flat area The mean areal annual rainfall ofthe Langat River Basin is 19941mm The highest recordedmonthly rainfall is about 3271mm occurring in Novemberwhile the lowest is 976mm in JuneThese twomaximum and




















Precipitation




11 119879Max 328 042 004 324 066 049 329 04 013119879Min 240 057 031 237 061 027 242 058 023







119878 =

119899minus1

sum

119894=1

119899

sum

119895=119894+1

Sgn (119909119895minus 119909119894) (1)

34

33

32

31

25

24

23

22

21

1985 1990 1995 2000 2005 2010 2015

Year

Station 9

Station 10

Station 11


Tm

in(∘

C)T

max

(∘C)


where

Sgn (119909) =

1 119909 gt 0

0 119909 = 0

minus1 119909 lt 0

(2)


series


101∘30

9984000998400998400E 101

∘50

9984000998400998400E

3∘10

9984000998400998400N

3∘09984000998400998400N

2∘50

9984000998400998400N

2∘40

9984000998400998400N

2∘30

9984000998400998400N

2∘20

9984000998400998400N


Short-term crops


N

W E

S

(a)


Rainfall (mm)1813

1885

1955

2023

2087

2149

2214

0 5 10 20

(km)

(b)

Maximum temperature



N

0 5 10 20 30 40

(km)

(c)

lt218



0 5 10 20 30 40

(km)


(d)




119881 =119899 (119899 minus 1) (2119899 + 5)

18minus

119892

sum

119894=1

119905119894(119905119894minus 1) (2119905

119894+ 5)

18 (3)



119885 =

119904 minus 1

11988105119878 gt 0

0 119878 = 0

119904 + 1

11988105119878 lt 0

(4)

When |119885| gt 1198851minus1205722


1minus1205722and 119875




119905and 1198801015840

119905


1 1199092 119909








119905119894=

119894

sum

119894=1

119899119894 (5)



119864 (119905) =119899 (119899 minus 1)

4

Var (119905119894) =

1

72[119894 (119894 minus 1) (2119895 + 5)]

(6)


by

119880119905=119905119894minus 119864 (119905)

radicVar (119905119894)

(7)



119905and 1198801015840

119905


119905

and1198801015840119905


119905and 1198801015840

119905



120573 = Median(119909119895minus 119909119894

119895 minus 1) 119894 lt 119895 (8)

where 119909119895and 119909





sets using

119884119905= 119883119905minus 120573119905 (9)


119905is the





1198841015840

119905

= 119884119905minus 1199031119884119905minus1 (10)

where 1198841015840119905



3200

3000

2800

2600

2400

2200

2000

1800

1600

1400

1200

1000

Ann

ual r

ainf

all (

mm

)Station 3

1980 1990 2000 2010

Year

Y = 121X minus 2194292600

2400

2200

2000

1800

1600

1400

1200

1000

Ann

ual r

ainf

all (

mm

)

800

600

Station 4

Y = 153X minus 290641

1980 1990 2000 2010

Year1970

2400

2200

2000

1800

1600

1400

1200

1000

Ann

ual r

ainf

all (

mm

)

800

600

400

1980 1990 2000 2010

Year1970

Station 6

2200

2000

1800

1600

1400

1200

1000Ann

ual r

ainf

all (

mm

)

800

600

900

800

700

600

500

400

300

200

Seas

onal

rain

fall

(mm

)


1980 1990 2000 2010

Year1980 1990 2000 2010

Year1970


Y = 171X minus 325595

Y = 134X minus 251079 Y = 48X minus 90252







11988410158401015840

119905

= 1198841015840

119905

+ 120573119905 (11)

where 11988410158401015840119905


119905











1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840



1015840)



1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840




















1015840












1980 1990 2000 2010

Year

Station 3

2

0

minus2

UtU998400t

(a)

2

0

minus2

minus4

19801970 1990 2000 2010

Year

Station 4

UtU998400t

(b)

minus4

2

0

minus2

4

1980 1990 2000 2010

Year

Station 9

UtU998400t

(c)

2

0

minus2

19801970 1990 2000 2010

Year


UtU998400t

(d)

2

0

minus2

4

1980 1990 2000 2010

Year


UtU998400t

Ut

U998400t

(e)

2

0

minus2

198019701960 1990 2000 2010

Year


UtU998400t

Ut

U998400t

(f)



175

170

165

160

155

Station 9 Annual

1990 2000 2010

Tm

ax(∘

C)250

245

240

235

230

225

220

Tm

in(∘

C)

1970 1980 1990 2000 2010


40

35

30

25

20

15

10

05

1970 1980 1990 2000 2010

Tm

in(∘

C)

Station 11 Annual

325

320

315

310

305

300

295

NE monsoon

1990 2000 2010

Tm

ax(∘

C)

Station 9 245

240

235

230

225

220

215

210

205

NE monsoon

Tm

in(∘

C)

Station 10

1970 1980 1990 2000 2010

255

250

245

240

235

230

225

220


Tm

in(∘

C)

1970 1980 1990 2000 2010

330

325

320

315

SW monsoon

1990 2000 2010

Year

Tm

ax(∘

C)

Station 9


245

240

235

230

225

Tm

in(∘

C)

1970 1980 1990 2000 2010

Year




255

250

245

240

235

230

225

Tm

in(∘

C)

260

1970 1980 1990 2000 2010

Year





Year Year Year

Year Year Year



006

005

004

003

002

001

000

minus001

Senrsquos

slop

e (∘C)

minus0002

0040043

004

minus001


Station 11

1E minus 3



(a)

006

005

004

003

002

001

000

minus001

Senrsquos

slop

e (∘C)


Station 9

0035

001

004

003

minus0007

1E minus 4



(b)

Permanent crops


Category

Water body

Urban area



Wetland forest

areas

9

10

11

(c)

006

005

004

003

002

001

000


0008

003

0007

0046

0005

004

Station 10



Senrsquos

slope

(∘C)

(d)






350

300


12 14 16 18 20 22

Slope (mmyr)

250

200

150

100

50

0

Freq

uenc

y

(a)

200

150

100

50

0

Freq

uenc

y

3 4 5 6 7

Slope (mmyr)


(b)

500

400

300

200

100

0

Freq

uenc

y

0036 0038 0040 0042 0044 0046

Slope (∘Cyr)


(c)

400

300

200

100

0

Freq

uenc

y

NW monsoon

0039 0042 0045 0048 0051

Slope (∘Cyr)

Station 10 (Tmin)

(d)


4 Conclusions




Rang

e of s

lope

(mm

yr)

20

15

10

5

0S3 S4 S6 S8 S6SWM

Station


(a)

Rang

e of s

lope

(∘C

yr)

006

004

002


a b c d e f g h i

(b)




S3

Precipitation


Temperature








Acknowledgments


References
















































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in




















Precipitation




11 119879Max 328 042 004 324 066 049 329 04 013119879Min 240 057 031 237 061 027 242 058 023







119878 =

119899minus1

sum

119894=1

119899

sum

119895=119894+1

Sgn (119909119895minus 119909119894) (1)

34

33

32

31

25

24

23

22

21

1985 1990 1995 2000 2005 2010 2015

Year

Station 9

Station 10

Station 11


Tm

in(∘

C)T

max

(∘C)


where

Sgn (119909) =

1 119909 gt 0

0 119909 = 0

minus1 119909 lt 0

(2)


series


101∘30

9984000998400998400E 101

∘50

9984000998400998400E

3∘10

9984000998400998400N

3∘09984000998400998400N

2∘50

9984000998400998400N

2∘40

9984000998400998400N

2∘30

9984000998400998400N

2∘20

9984000998400998400N


Short-term crops


N

W E

S

(a)


Rainfall (mm)1813

1885

1955

2023

2087

2149

2214

0 5 10 20

(km)

(b)

Maximum temperature



N

0 5 10 20 30 40

(km)

(c)

lt218



0 5 10 20 30 40

(km)


(d)




119881 =119899 (119899 minus 1) (2119899 + 5)

18minus

119892

sum

119894=1

119905119894(119905119894minus 1) (2119905

119894+ 5)

18 (3)



119885 =

119904 minus 1

11988105119878 gt 0

0 119878 = 0

119904 + 1

11988105119878 lt 0

(4)

When |119885| gt 1198851minus1205722


1minus1205722and 119875




119905and 1198801015840

119905


1 1199092 119909








119905119894=

119894

sum

119894=1

119899119894 (5)



119864 (119905) =119899 (119899 minus 1)

4

Var (119905119894) =

1

72[119894 (119894 minus 1) (2119895 + 5)]

(6)


by

119880119905=119905119894minus 119864 (119905)

radicVar (119905119894)

(7)



119905and 1198801015840

119905


119905

and1198801015840119905


119905and 1198801015840

119905



120573 = Median(119909119895minus 119909119894

119895 minus 1) 119894 lt 119895 (8)

where 119909119895and 119909





sets using

119884119905= 119883119905minus 120573119905 (9)


119905is the





1198841015840

119905

= 119884119905minus 1199031119884119905minus1 (10)

where 1198841015840119905



3200

3000

2800

2600

2400

2200

2000

1800

1600

1400

1200

1000

Ann

ual r

ainf

all (

mm

)Station 3

1980 1990 2000 2010

Year

Y = 121X minus 2194292600

2400

2200

2000

1800

1600

1400

1200

1000

Ann

ual r

ainf

all (

mm

)

800

600

Station 4

Y = 153X minus 290641

1980 1990 2000 2010

Year1970

2400

2200

2000

1800

1600

1400

1200

1000

Ann

ual r

ainf

all (

mm

)

800

600

400

1980 1990 2000 2010

Year1970

Station 6

2200

2000

1800

1600

1400

1200

1000Ann

ual r

ainf

all (

mm

)

800

600

900

800

700

600

500

400

300

200

Seas

onal

rain

fall

(mm

)


1980 1990 2000 2010

Year1980 1990 2000 2010

Year1970


Y = 171X minus 325595

Y = 134X minus 251079 Y = 48X minus 90252







11988410158401015840

119905

= 1198841015840

119905

+ 120573119905 (11)

where 11988410158401015840119905


119905











1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840



1015840)



1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840




















1015840












1980 1990 2000 2010

Year

Station 3

2

0

minus2

UtU998400t

(a)

2

0

minus2

minus4

19801970 1990 2000 2010

Year

Station 4

UtU998400t

(b)

minus4

2

0

minus2

4

1980 1990 2000 2010

Year

Station 9

UtU998400t

(c)

2

0

minus2

19801970 1990 2000 2010

Year


UtU998400t

(d)

2

0

minus2

4

1980 1990 2000 2010

Year


UtU998400t

Ut

U998400t

(e)

2

0

minus2

198019701960 1990 2000 2010

Year


UtU998400t

Ut

U998400t

(f)



175

170

165

160

155

Station 9 Annual

1990 2000 2010

Tm

ax(∘

C)250

245

240

235

230

225

220

Tm

in(∘

C)

1970 1980 1990 2000 2010


40

35

30

25

20

15

10

05

1970 1980 1990 2000 2010

Tm

in(∘

C)

Station 11 Annual

325

320

315

310

305

300

295

NE monsoon

1990 2000 2010

Tm

ax(∘

C)

Station 9 245

240

235

230

225

220

215

210

205

NE monsoon

Tm

in(∘

C)

Station 10

1970 1980 1990 2000 2010

255

250

245

240

235

230

225

220


Tm

in(∘

C)

1970 1980 1990 2000 2010

330

325

320

315

SW monsoon

1990 2000 2010

Year

Tm

ax(∘

C)

Station 9


245

240

235

230

225

Tm

in(∘

C)

1970 1980 1990 2000 2010

Year




255

250

245

240

235

230

225

Tm

in(∘

C)

260

1970 1980 1990 2000 2010

Year





Year Year Year

Year Year Year



006

005

004

003

002

001

000

minus001

Senrsquos

slop

e (∘C)

minus0002

0040043

004

minus001


Station 11

1E minus 3



(a)

006

005

004

003

002

001

000

minus001

Senrsquos

slop

e (∘C)


Station 9

0035

001

004

003

minus0007

1E minus 4



(b)

Permanent crops


Category

Water body

Urban area



Wetland forest

areas

9

10

11

(c)

006

005

004

003

002

001

000


0008

003

0007

0046

0005

004

Station 10



Senrsquos

slope

(∘C)

(d)






350

300


12 14 16 18 20 22

Slope (mmyr)

250

200

150

100

50

0

Freq

uenc

y

(a)

200

150

100

50

0

Freq

uenc

y

3 4 5 6 7

Slope (mmyr)


(b)

500

400

300

200

100

0

Freq

uenc

y

0036 0038 0040 0042 0044 0046

Slope (∘Cyr)


(c)

400

300

200

100

0

Freq

uenc

y

NW monsoon

0039 0042 0045 0048 0051

Slope (∘Cyr)

Station 10 (Tmin)

(d)


4 Conclusions




Rang

e of s

lope

(mm

yr)

20

15

10

5

0S3 S4 S6 S8 S6SWM

Station


(a)

Rang

e of s

lope

(∘C

yr)

006

004

002


a b c d e f g h i

(b)




S3

Precipitation


Temperature








Acknowledgments


References
















































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


101∘30

9984000998400998400E 101

∘50

9984000998400998400E

3∘10

9984000998400998400N

3∘09984000998400998400N

2∘50

9984000998400998400N

2∘40

9984000998400998400N

2∘30

9984000998400998400N

2∘20

9984000998400998400N


Short-term crops


N

W E

S

(a)


Rainfall (mm)1813

1885

1955

2023

2087

2149

2214

0 5 10 20

(km)

(b)

Maximum temperature



N

0 5 10 20 30 40

(km)

(c)

lt218



0 5 10 20 30 40

(km)


(d)




119881 =119899 (119899 minus 1) (2119899 + 5)

18minus

119892

sum

119894=1

119905119894(119905119894minus 1) (2119905

119894+ 5)

18 (3)



119885 =

119904 minus 1

11988105119878 gt 0

0 119878 = 0

119904 + 1

11988105119878 lt 0

(4)

When |119885| gt 1198851minus1205722


1minus1205722and 119875




119905and 1198801015840

119905


1 1199092 119909








119905119894=

119894

sum

119894=1

119899119894 (5)



119864 (119905) =119899 (119899 minus 1)

4

Var (119905119894) =

1

72[119894 (119894 minus 1) (2119895 + 5)]

(6)


by

119880119905=119905119894minus 119864 (119905)

radicVar (119905119894)

(7)



119905and 1198801015840

119905


119905

and1198801015840119905


119905and 1198801015840

119905



120573 = Median(119909119895minus 119909119894

119895 minus 1) 119894 lt 119895 (8)

where 119909119895and 119909





sets using

119884119905= 119883119905minus 120573119905 (9)


119905is the





1198841015840

119905

= 119884119905minus 1199031119884119905minus1 (10)

where 1198841015840119905



3200

3000

2800

2600

2400

2200

2000

1800

1600

1400

1200

1000

Ann

ual r

ainf

all (

mm

)Station 3

1980 1990 2000 2010

Year

Y = 121X minus 2194292600

2400

2200

2000

1800

1600

1400

1200

1000

Ann

ual r

ainf

all (

mm

)

800

600

Station 4

Y = 153X minus 290641

1980 1990 2000 2010

Year1970

2400

2200

2000

1800

1600

1400

1200

1000

Ann

ual r

ainf

all (

mm

)

800

600

400

1980 1990 2000 2010

Year1970

Station 6

2200

2000

1800

1600

1400

1200

1000Ann

ual r

ainf

all (

mm

)

800

600

900

800

700

600

500

400

300

200

Seas

onal

rain

fall

(mm

)


1980 1990 2000 2010

Year1980 1990 2000 2010

Year1970


Y = 171X minus 325595

Y = 134X minus 251079 Y = 48X minus 90252







11988410158401015840

119905

= 1198841015840

119905

+ 120573119905 (11)

where 11988410158401015840119905


119905











1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840



1015840)



1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840




















1015840












1980 1990 2000 2010

Year

Station 3

2

0

minus2

UtU998400t

(a)

2

0

minus2

minus4

19801970 1990 2000 2010

Year

Station 4

UtU998400t

(b)

minus4

2

0

minus2

4

1980 1990 2000 2010

Year

Station 9

UtU998400t

(c)

2

0

minus2

19801970 1990 2000 2010

Year


UtU998400t

(d)

2

0

minus2

4

1980 1990 2000 2010

Year


UtU998400t

Ut

U998400t

(e)

2

0

minus2

198019701960 1990 2000 2010

Year


UtU998400t

Ut

U998400t

(f)



175

170

165

160

155

Station 9 Annual

1990 2000 2010

Tm

ax(∘

C)250

245

240

235

230

225

220

Tm

in(∘

C)

1970 1980 1990 2000 2010


40

35

30

25

20

15

10

05

1970 1980 1990 2000 2010

Tm

in(∘

C)

Station 11 Annual

325

320

315

310

305

300

295

NE monsoon

1990 2000 2010

Tm

ax(∘

C)

Station 9 245

240

235

230

225

220

215

210

205

NE monsoon

Tm

in(∘

C)

Station 10

1970 1980 1990 2000 2010

255

250

245

240

235

230

225

220


Tm

in(∘

C)

1970 1980 1990 2000 2010

330

325

320

315

SW monsoon

1990 2000 2010

Year

Tm

ax(∘

C)

Station 9


245

240

235

230

225

Tm

in(∘

C)

1970 1980 1990 2000 2010

Year




255

250

245

240

235

230

225

Tm

in(∘

C)

260

1970 1980 1990 2000 2010

Year





Year Year Year

Year Year Year



006

005

004

003

002

001

000

minus001

Senrsquos

slop

e (∘C)

minus0002

0040043

004

minus001


Station 11

1E minus 3



(a)

006

005

004

003

002

001

000

minus001

Senrsquos

slop

e (∘C)


Station 9

0035

001

004

003

minus0007

1E minus 4



(b)

Permanent crops


Category

Water body

Urban area



Wetland forest

areas

9

10

11

(c)

006

005

004

003

002

001

000


0008

003

0007

0046

0005

004

Station 10



Senrsquos

slope

(∘C)

(d)






350

300


12 14 16 18 20 22

Slope (mmyr)

250

200

150

100

50

0

Freq

uenc

y

(a)

200

150

100

50

0

Freq

uenc

y

3 4 5 6 7

Slope (mmyr)


(b)

500

400

300

200

100

0

Freq

uenc

y

0036 0038 0040 0042 0044 0046

Slope (∘Cyr)


(c)

400

300

200

100

0

Freq

uenc

y

NW monsoon

0039 0042 0045 0048 0051

Slope (∘Cyr)

Station 10 (Tmin)

(d)


4 Conclusions




Rang

e of s

lope

(mm

yr)

20

15

10

5

0S3 S4 S6 S8 S6SWM

Station


(a)

Rang

e of s

lope

(∘C

yr)

006

004

002


a b c d e f g h i

(b)




S3

Precipitation


Temperature








Acknowledgments


References
















































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in



119881 =119899 (119899 minus 1) (2119899 + 5)

18minus

119892

sum

119894=1

119905119894(119905119894minus 1) (2119905

119894+ 5)

18 (3)



119885 =

119904 minus 1

11988105119878 gt 0

0 119878 = 0

119904 + 1

11988105119878 lt 0

(4)

When |119885| gt 1198851minus1205722


1minus1205722and 119875




119905and 1198801015840

119905


1 1199092 119909








119905119894=

119894

sum

119894=1

119899119894 (5)



119864 (119905) =119899 (119899 minus 1)

4

Var (119905119894) =

1

72[119894 (119894 minus 1) (2119895 + 5)]

(6)


by

119880119905=119905119894minus 119864 (119905)

radicVar (119905119894)

(7)



119905and 1198801015840

119905


119905

and1198801015840119905


119905and 1198801015840

119905



120573 = Median(119909119895minus 119909119894

119895 minus 1) 119894 lt 119895 (8)

where 119909119895and 119909





sets using

119884119905= 119883119905minus 120573119905 (9)


119905is the





1198841015840

119905

= 119884119905minus 1199031119884119905minus1 (10)

where 1198841015840119905



3200

3000

2800

2600

2400

2200

2000

1800

1600

1400

1200

1000

Ann

ual r

ainf

all (

mm

)Station 3

1980 1990 2000 2010

Year

Y = 121X minus 2194292600

2400

2200

2000

1800

1600

1400

1200

1000

Ann

ual r

ainf

all (

mm

)

800

600

Station 4

Y = 153X minus 290641

1980 1990 2000 2010

Year1970

2400

2200

2000

1800

1600

1400

1200

1000

Ann

ual r

ainf

all (

mm

)

800

600

400

1980 1990 2000 2010

Year1970

Station 6

2200

2000

1800

1600

1400

1200

1000Ann

ual r

ainf

all (

mm

)

800

600

900

800

700

600

500

400

300

200

Seas

onal

rain

fall

(mm

)


1980 1990 2000 2010

Year1980 1990 2000 2010

Year1970


Y = 171X minus 325595

Y = 134X minus 251079 Y = 48X minus 90252







11988410158401015840

119905

= 1198841015840

119905

+ 120573119905 (11)

where 11988410158401015840119905


119905











1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840



1015840)



1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840




















1015840












1980 1990 2000 2010

Year

Station 3

2

0

minus2

UtU998400t

(a)

2

0

minus2

minus4

19801970 1990 2000 2010

Year

Station 4

UtU998400t

(b)

minus4

2

0

minus2

4

1980 1990 2000 2010

Year

Station 9

UtU998400t

(c)

2

0

minus2

19801970 1990 2000 2010

Year


UtU998400t

(d)

2

0

minus2

4

1980 1990 2000 2010

Year


UtU998400t

Ut

U998400t

(e)

2

0

minus2

198019701960 1990 2000 2010

Year


UtU998400t

Ut

U998400t

(f)



175

170

165

160

155

Station 9 Annual

1990 2000 2010

Tm

ax(∘

C)250

245

240

235

230

225

220

Tm

in(∘

C)

1970 1980 1990 2000 2010


40

35

30

25

20

15

10

05

1970 1980 1990 2000 2010

Tm

in(∘

C)

Station 11 Annual

325

320

315

310

305

300

295

NE monsoon

1990 2000 2010

Tm

ax(∘

C)

Station 9 245

240

235

230

225

220

215

210

205

NE monsoon

Tm

in(∘

C)

Station 10

1970 1980 1990 2000 2010

255

250

245

240

235

230

225

220


Tm

in(∘

C)

1970 1980 1990 2000 2010

330

325

320

315

SW monsoon

1990 2000 2010

Year

Tm

ax(∘

C)

Station 9


245

240

235

230

225

Tm

in(∘

C)

1970 1980 1990 2000 2010

Year




255

250

245

240

235

230

225

Tm

in(∘

C)

260

1970 1980 1990 2000 2010

Year





Year Year Year

Year Year Year



006

005

004

003

002

001

000

minus001

Senrsquos

slop

e (∘C)

minus0002

0040043

004

minus001


Station 11

1E minus 3



(a)

006

005

004

003

002

001

000

minus001

Senrsquos

slop

e (∘C)


Station 9

0035

001

004

003

minus0007

1E minus 4



(b)

Permanent crops


Category

Water body

Urban area



Wetland forest

areas

9

10

11

(c)

006

005

004

003

002

001

000


0008

003

0007

0046

0005

004

Station 10



Senrsquos

slope

(∘C)

(d)






350

300


12 14 16 18 20 22

Slope (mmyr)

250

200

150

100

50

0

Freq

uenc

y

(a)

200

150

100

50

0

Freq

uenc

y

3 4 5 6 7

Slope (mmyr)


(b)

500

400

300

200

100

0

Freq

uenc

y

0036 0038 0040 0042 0044 0046

Slope (∘Cyr)


(c)

400

300

200

100

0

Freq

uenc

y

NW monsoon

0039 0042 0045 0048 0051

Slope (∘Cyr)

Station 10 (Tmin)

(d)


4 Conclusions




Rang

e of s

lope

(mm

yr)

20

15

10

5

0S3 S4 S6 S8 S6SWM

Station


(a)

Rang

e of s

lope

(∘C

yr)

006

004

002


a b c d e f g h i

(b)




S3

Precipitation


Temperature








Acknowledgments


References
















































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


3200

3000

2800

2600

2400

2200

2000

1800

1600

1400

1200

1000

Ann

ual r

ainf

all (

mm

)Station 3

1980 1990 2000 2010

Year

Y = 121X minus 2194292600

2400

2200

2000

1800

1600

1400

1200

1000

Ann

ual r

ainf

all (

mm

)

800

600

Station 4

Y = 153X minus 290641

1980 1990 2000 2010

Year1970

2400

2200

2000

1800

1600

1400

1200

1000

Ann

ual r

ainf

all (

mm

)

800

600

400

1980 1990 2000 2010

Year1970

Station 6

2200

2000

1800

1600

1400

1200

1000Ann

ual r

ainf

all (

mm

)

800

600

900

800

700

600

500

400

300

200

Seas

onal

rain

fall

(mm

)


1980 1990 2000 2010

Year1980 1990 2000 2010

Year1970


Y = 171X minus 325595

Y = 134X minus 251079 Y = 48X minus 90252







11988410158401015840

119905

= 1198841015840

119905

+ 120573119905 (11)

where 11988410158401015840119905


119905











1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840



1015840)



1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840




















1015840












1980 1990 2000 2010

Year

Station 3

2

0

minus2

UtU998400t

(a)

2

0

minus2

minus4

19801970 1990 2000 2010

Year

Station 4

UtU998400t

(b)

minus4

2

0

minus2

4

1980 1990 2000 2010

Year

Station 9

UtU998400t

(c)

2

0

minus2

19801970 1990 2000 2010

Year


UtU998400t

(d)

2

0

minus2

4

1980 1990 2000 2010

Year


UtU998400t

Ut

U998400t

(e)

2

0

minus2

198019701960 1990 2000 2010

Year


UtU998400t

Ut

U998400t

(f)



175

170

165

160

155

Station 9 Annual

1990 2000 2010

Tm

ax(∘

C)250

245

240

235

230

225

220

Tm

in(∘

C)

1970 1980 1990 2000 2010


40

35

30

25

20

15

10

05

1970 1980 1990 2000 2010

Tm

in(∘

C)

Station 11 Annual

325

320

315

310

305

300

295

NE monsoon

1990 2000 2010

Tm

ax(∘

C)

Station 9 245

240

235

230

225

220

215

210

205

NE monsoon

Tm

in(∘

C)

Station 10

1970 1980 1990 2000 2010

255

250

245

240

235

230

225

220


Tm

in(∘

C)

1970 1980 1990 2000 2010

330

325

320

315

SW monsoon

1990 2000 2010

Year

Tm

ax(∘

C)

Station 9


245

240

235

230

225

Tm

in(∘

C)

1970 1980 1990 2000 2010

Year




255

250

245

240

235

230

225

Tm

in(∘

C)

260

1970 1980 1990 2000 2010

Year





Year Year Year

Year Year Year



006

005

004

003

002

001

000

minus001

Senrsquos

slop

e (∘C)

minus0002

0040043

004

minus001


Station 11

1E minus 3



(a)

006

005

004

003

002

001

000

minus001

Senrsquos

slop

e (∘C)


Station 9

0035

001

004

003

minus0007

1E minus 4



(b)

Permanent crops


Category

Water body

Urban area



Wetland forest

areas

9

10

11

(c)

006

005

004

003

002

001

000


0008

003

0007

0046

0005

004

Station 10



Senrsquos

slope

(∘C)

(d)






350

300


12 14 16 18 20 22

Slope (mmyr)

250

200

150

100

50

0

Freq

uenc

y

(a)

200

150

100

50

0

Freq

uenc

y

3 4 5 6 7

Slope (mmyr)


(b)

500

400

300

200

100

0

Freq

uenc

y

0036 0038 0040 0042 0044 0046

Slope (∘Cyr)


(c)

400

300

200

100

0

Freq

uenc

y

NW monsoon

0039 0042 0045 0048 0051

Slope (∘Cyr)

Station 10 (Tmin)

(d)


4 Conclusions




Rang

e of s

lope

(mm

yr)

20

15

10

5

0S3 S4 S6 S8 S6SWM

Station


(a)

Rang

e of s

lope

(∘C

yr)

006

004

002


a b c d e f g h i

(b)




S3

Precipitation


Temperature








Acknowledgments


References
















































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in





1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840



1015840)



1199031

1015840

1199031

1199031

1015840

1199031

1199031

1015840




















1015840












1980 1990 2000 2010

Year

Station 3

2

0

minus2

UtU998400t

(a)

2

0

minus2

minus4

19801970 1990 2000 2010

Year

Station 4

UtU998400t

(b)

minus4

2

0

minus2

4

1980 1990 2000 2010

Year

Station 9

UtU998400t

(c)

2

0

minus2

19801970 1990 2000 2010

Year


UtU998400t

(d)

2

0

minus2

4

1980 1990 2000 2010

Year


UtU998400t

Ut

U998400t

(e)

2

0

minus2

198019701960 1990 2000 2010

Year


UtU998400t

Ut

U998400t

(f)



175

170

165

160

155

Station 9 Annual

1990 2000 2010

Tm

ax(∘

C)250

245

240

235

230

225

220

Tm

in(∘

C)

1970 1980 1990 2000 2010


40

35

30

25

20

15

10

05

1970 1980 1990 2000 2010

Tm

in(∘

C)

Station 11 Annual

325

320

315

310

305

300

295

NE monsoon

1990 2000 2010

Tm

ax(∘

C)

Station 9 245

240

235

230

225

220

215

210

205

NE monsoon

Tm

in(∘

C)

Station 10

1970 1980 1990 2000 2010

255

250

245

240

235

230

225

220


Tm

in(∘

C)

1970 1980 1990 2000 2010

330

325

320

315

SW monsoon

1990 2000 2010

Year

Tm

ax(∘

C)

Station 9


245

240

235

230

225

Tm

in(∘

C)

1970 1980 1990 2000 2010

Year




255

250

245

240

235

230

225

Tm

in(∘

C)

260

1970 1980 1990 2000 2010

Year





Year Year Year

Year Year Year



006

005

004

003

002

001

000

minus001

Senrsquos

slop

e (∘C)

minus0002

0040043

004

minus001


Station 11

1E minus 3



(a)

006

005

004

003

002

001

000

minus001

Senrsquos

slop

e (∘C)


Station 9

0035

001

004

003

minus0007

1E minus 4



(b)

Permanent crops


Category

Water body

Urban area



Wetland forest

areas

9

10

11

(c)

006

005

004

003

002

001

000


0008

003

0007

0046

0005

004

Station 10



Senrsquos

slope

(∘C)

(d)






350

300


12 14 16 18 20 22

Slope (mmyr)

250

200

150

100

50

0

Freq

uenc

y

(a)

200

150

100

50

0

Freq

uenc

y

3 4 5 6 7

Slope (mmyr)


(b)

500

400

300

200

100

0

Freq

uenc

y

0036 0038 0040 0042 0044 0046

Slope (∘Cyr)


(c)

400

300

200

100

0

Freq

uenc

y

NW monsoon

0039 0042 0045 0048 0051

Slope (∘Cyr)

Station 10 (Tmin)

(d)


4 Conclusions




Rang

e of s

lope

(mm

yr)

20

15

10

5

0S3 S4 S6 S8 S6SWM

Station


(a)

Rang

e of s

lope

(∘C

yr)

006

004

002


a b c d e f g h i

(b)




S3

Precipitation


Temperature








Acknowledgments


References
















































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in






1015840












1980 1990 2000 2010

Year

Station 3

2

0

minus2

UtU998400t

(a)

2

0

minus2

minus4

19801970 1990 2000 2010

Year

Station 4

UtU998400t

(b)

minus4

2

0

minus2

4

1980 1990 2000 2010

Year

Station 9

UtU998400t

(c)

2

0

minus2

19801970 1990 2000 2010

Year


UtU998400t

(d)

2

0

minus2

4

1980 1990 2000 2010

Year


UtU998400t

Ut

U998400t

(e)

2

0

minus2

198019701960 1990 2000 2010

Year


UtU998400t

Ut

U998400t

(f)



175

170

165

160

155

Station 9 Annual

1990 2000 2010

Tm

ax(∘

C)250

245

240

235

230

225

220

Tm

in(∘

C)

1970 1980 1990 2000 2010


40

35

30

25

20

15

10

05

1970 1980 1990 2000 2010

Tm

in(∘

C)

Station 11 Annual

325

320

315

310

305

300

295

NE monsoon

1990 2000 2010

Tm

ax(∘

C)

Station 9 245

240

235

230

225

220

215

210

205

NE monsoon

Tm

in(∘

C)

Station 10

1970 1980 1990 2000 2010

255

250

245

240

235

230

225

220


Tm

in(∘

C)

1970 1980 1990 2000 2010

330

325

320

315

SW monsoon

1990 2000 2010

Year

Tm

ax(∘

C)

Station 9


245

240

235

230

225

Tm

in(∘

C)

1970 1980 1990 2000 2010

Year




255

250

245

240

235

230

225

Tm

in(∘

C)

260

1970 1980 1990 2000 2010

Year





Year Year Year

Year Year Year



006

005

004

003

002

001

000

minus001

Senrsquos

slop

e (∘C)

minus0002

0040043

004

minus001


Station 11

1E minus 3



(a)

006

005

004

003

002

001

000

minus001

Senrsquos

slop

e (∘C)


Station 9

0035

001

004

003

minus0007

1E minus 4



(b)

Permanent crops


Category

Water body

Urban area



Wetland forest

areas

9

10

11

(c)

006

005

004

003

002

001

000


0008

003

0007

0046

0005

004

Station 10



Senrsquos

slope

(∘C)

(d)






350

300


12 14 16 18 20 22

Slope (mmyr)

250

200

150

100

50

0

Freq

uenc

y

(a)

200

150

100

50

0

Freq

uenc

y

3 4 5 6 7

Slope (mmyr)


(b)

500

400

300

200

100

0

Freq

uenc

y

0036 0038 0040 0042 0044 0046

Slope (∘Cyr)


(c)

400

300

200

100

0

Freq

uenc

y

NW monsoon

0039 0042 0045 0048 0051

Slope (∘Cyr)

Station 10 (Tmin)

(d)


4 Conclusions




Rang

e of s

lope

(mm

yr)

20

15

10

5

0S3 S4 S6 S8 S6SWM

Station


(a)

Rang

e of s

lope

(∘C

yr)

006

004

002


a b c d e f g h i

(b)




S3

Precipitation


Temperature








Acknowledgments


References
















































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


1980 1990 2000 2010

Year

Station 3

2

0

minus2

UtU998400t

(a)

2

0

minus2

minus4

19801970 1990 2000 2010

Year

Station 4

UtU998400t

(b)

minus4

2

0

minus2

4

1980 1990 2000 2010

Year

Station 9

UtU998400t

(c)

2

0

minus2

19801970 1990 2000 2010

Year


UtU998400t

(d)

2

0

minus2

4

1980 1990 2000 2010

Year


UtU998400t

Ut

U998400t

(e)

2

0

minus2

198019701960 1990 2000 2010

Year


UtU998400t

Ut

U998400t

(f)



175

170

165

160

155

Station 9 Annual

1990 2000 2010

Tm

ax(∘

C)250

245

240

235

230

225

220

Tm

in(∘

C)

1970 1980 1990 2000 2010


40

35

30

25

20

15

10

05

1970 1980 1990 2000 2010

Tm

in(∘

C)

Station 11 Annual

325

320

315

310

305

300

295

NE monsoon

1990 2000 2010

Tm

ax(∘

C)

Station 9 245

240

235

230

225

220

215

210

205

NE monsoon

Tm

in(∘

C)

Station 10

1970 1980 1990 2000 2010

255

250

245

240

235

230

225

220


Tm

in(∘

C)

1970 1980 1990 2000 2010

330

325

320

315

SW monsoon

1990 2000 2010

Year

Tm

ax(∘

C)

Station 9


245

240

235

230

225

Tm

in(∘

C)

1970 1980 1990 2000 2010

Year




255

250

245

240

235

230

225

Tm

in(∘

C)

260

1970 1980 1990 2000 2010

Year





Year Year Year

Year Year Year



006

005

004

003

002

001

000

minus001

Senrsquos

slop

e (∘C)

minus0002

0040043

004

minus001


Station 11

1E minus 3



(a)

006

005

004

003

002

001

000

minus001

Senrsquos

slop

e (∘C)


Station 9

0035

001

004

003

minus0007

1E minus 4



(b)

Permanent crops


Category

Water body

Urban area



Wetland forest

areas

9

10

11

(c)

006

005

004

003

002

001

000


0008

003

0007

0046

0005

004

Station 10



Senrsquos

slope

(∘C)

(d)






350

300


12 14 16 18 20 22

Slope (mmyr)

250

200

150

100

50

0

Freq

uenc

y

(a)

200

150

100

50

0

Freq

uenc

y

3 4 5 6 7

Slope (mmyr)


(b)

500

400

300

200

100

0

Freq

uenc

y

0036 0038 0040 0042 0044 0046

Slope (∘Cyr)


(c)

400

300

200

100

0

Freq

uenc

y

NW monsoon

0039 0042 0045 0048 0051

Slope (∘Cyr)

Station 10 (Tmin)

(d)


4 Conclusions




Rang

e of s

lope

(mm

yr)

20

15

10

5

0S3 S4 S6 S8 S6SWM

Station


(a)

Rang

e of s

lope

(∘C

yr)

006

004

002


a b c d e f g h i

(b)




S3

Precipitation


Temperature








Acknowledgments


References
















































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


175

170

165

160

155

Station 9 Annual

1990 2000 2010

Tm

ax(∘

C)250

245

240

235

230

225

220

Tm

in(∘

C)

1970 1980 1990 2000 2010


40

35

30

25

20

15

10

05

1970 1980 1990 2000 2010

Tm

in(∘

C)

Station 11 Annual

325

320

315

310

305

300

295

NE monsoon

1990 2000 2010

Tm

ax(∘

C)

Station 9 245

240

235

230

225

220

215

210

205

NE monsoon

Tm

in(∘

C)

Station 10

1970 1980 1990 2000 2010

255

250

245

240

235

230

225

220


Tm

in(∘

C)

1970 1980 1990 2000 2010

330

325

320

315

SW monsoon

1990 2000 2010

Year

Tm

ax(∘

C)

Station 9


245

240

235

230

225

Tm

in(∘

C)

1970 1980 1990 2000 2010

Year




255

250

245

240

235

230

225

Tm

in(∘

C)

260

1970 1980 1990 2000 2010

Year





Year Year Year

Year Year Year



006

005

004

003

002

001

000

minus001

Senrsquos

slop

e (∘C)

minus0002

0040043

004

minus001


Station 11

1E minus 3



(a)

006

005

004

003

002

001

000

minus001

Senrsquos

slop

e (∘C)


Station 9

0035

001

004

003

minus0007

1E minus 4



(b)

Permanent crops


Category

Water body

Urban area



Wetland forest

areas

9

10

11

(c)

006

005

004

003

002

001

000


0008

003

0007

0046

0005

004

Station 10



Senrsquos

slope

(∘C)

(d)






350

300


12 14 16 18 20 22

Slope (mmyr)

250

200

150

100

50

0

Freq

uenc

y

(a)

200

150

100

50

0

Freq

uenc

y

3 4 5 6 7

Slope (mmyr)


(b)

500

400

300

200

100

0

Freq

uenc

y

0036 0038 0040 0042 0044 0046

Slope (∘Cyr)


(c)

400

300

200

100

0

Freq

uenc

y

NW monsoon

0039 0042 0045 0048 0051

Slope (∘Cyr)

Station 10 (Tmin)

(d)


4 Conclusions




Rang

e of s

lope

(mm

yr)

20

15

10

5

0S3 S4 S6 S8 S6SWM

Station


(a)

Rang

e of s

lope

(∘C

yr)

006

004

002


a b c d e f g h i

(b)




S3

Precipitation


Temperature








Acknowledgments


References
















































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


006

005

004

003

002

001

000

minus001

Senrsquos

slop

e (∘C)

minus0002

0040043

004

minus001


Station 11

1E minus 3



(a)

006

005

004

003

002

001

000

minus001

Senrsquos

slop

e (∘C)


Station 9

0035

001

004

003

minus0007

1E minus 4



(b)

Permanent crops


Category

Water body

Urban area



Wetland forest

areas

9

10

11

(c)

006

005

004

003

002

001

000


0008

003

0007

0046

0005

004

Station 10



Senrsquos

slope

(∘C)

(d)






350

300


12 14 16 18 20 22

Slope (mmyr)

250

200

150

100

50

0

Freq

uenc

y

(a)

200

150

100

50

0

Freq

uenc

y

3 4 5 6 7

Slope (mmyr)


(b)

500

400

300

200

100

0

Freq

uenc

y

0036 0038 0040 0042 0044 0046

Slope (∘Cyr)


(c)

400

300

200

100

0

Freq

uenc

y

NW monsoon

0039 0042 0045 0048 0051

Slope (∘Cyr)

Station 10 (Tmin)

(d)


4 Conclusions




Rang

e of s

lope

(mm

yr)

20

15

10

5

0S3 S4 S6 S8 S6SWM

Station


(a)

Rang

e of s

lope

(∘C

yr)

006

004

002


a b c d e f g h i

(b)




S3

Precipitation


Temperature








Acknowledgments


References
















































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


350

300


12 14 16 18 20 22

Slope (mmyr)

250

200

150

100

50

0

Freq

uenc

y

(a)

200

150

100

50

0

Freq

uenc

y

3 4 5 6 7

Slope (mmyr)


(b)

500

400

300

200

100

0

Freq

uenc

y

0036 0038 0040 0042 0044 0046

Slope (∘Cyr)


(c)

400

300

200

100

0

Freq

uenc

y

NW monsoon

0039 0042 0045 0048 0051

Slope (∘Cyr)

Station 10 (Tmin)

(d)


4 Conclusions




Rang

e of s

lope

(mm

yr)

20

15

10

5

0S3 S4 S6 S8 S6SWM

Station


(a)

Rang

e of s

lope

(∘C

yr)

006

004

002


a b c d e f g h i

(b)




S3

Precipitation


Temperature








Acknowledgments


References
















































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


Rang

e of s

lope

(mm

yr)

20

15

10

5

0S3 S4 S6 S8 S6SWM

Station


(a)

Rang

e of s

lope

(∘C

yr)

006

004

002


a b c d e f g h i

(b)




S3

Precipitation


Temperature








Acknowledgments


References
















































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


Acknowledgments


References
















































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in















Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in

research article recent trends in temperature and...

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