ORI GIN AL PA PER

Trends and extreme value analysis of rainfall patternover homogeneous monsoon regions of India

T. V. Lakshmi Kumar • K. Koteswara Rao •

Humberto Barbosa • R. Uma

Received: 12 June 2013 / Accepted: 28 February 2014� Springer Science+Business Media Dordrecht 2014

Abstract Trends of pre-monsoon, monsoon and post-monsoon rainfall pattern were

studied on decadal basis over different homogeneous monsoon regions in India for the

period 1871–2008. It is attempted to understand the relation of monsoon rainfall with the

global teleconnections of El Nino and La Nina, for which the correlation analysis has been

carried out with Darwin pressure and Nino 3.4 sea surface temperature (Nino 3.4 SST).

The correlation analysis inferred that the significant correlations were observed when

monsoon rainfall is related to ENSO indices on decadal scale than on annual ones. The

study also found that the north-west region is more affected by the moderate El Nino years

compared to strong El Nino years. The regions Central North-East and North-East could

not make any difference among weak, moderate and strong La Nina events. The authors

also have carried out the extreme value analysis over different homogeneous monsoon

regions of India as well as for whole India. The results show that the return values of

rainfall are increasing with the return periods for the forthcoming 10, 20, 50 and 100 years.

The heterogeneity in number of threshold years that were recorded for the extreme rainfall

over north-east (humid climatic type) and north-west (arid climatic type) described the

climate variability. The results of the present study may be useful for the policy makers in

understanding the rainfall exceedance in different return periods for planning the risk

management strategies.

Keywords Decadal trends of rainfall � ENSO indices � Extreme value

analysis and homogeneous monsoon regions of India

T. V. Lakshmi Kumar (&) � R. UmaAtmospheric Science Research Laboratory, SRM University, Kattankulathur, Indiae-mail: lkumarap@hotmail.com

K. Koteswara RaoClimatology and Hydrometeorology Division, Indian Institute of Tropical Meteorology, Pune, India

H. BarbosaUniversidade Federal de Alagoas – UFAL, Maceio, AL, Brazil

123

Nat HazardsDOI 10.1007/s11069-014-1127-2

1 Introduction

The increasing importance of climate change is due to the high frequency of extreme

events in terms of heavy rainfall events and the prolonged dry spells over many regions of

the globe. In India, the south-west (SW) monsoon attained the pivotal role not only for

understanding the climate variations but also for deciding the country economy. The past

100 years of Indian summer monsoon rainfall (ISMR) did not yield any significant trend

(Naidu et al. 2009), thus giving a challenge to understand its behaviour. Several studies

have been carried out on seasonal and annual Indian rainfall trends and their variability is

well documented (Pant and Rupa Kumar 1997; Webster et al. 1998; Rajeevan et al. 2004;

Takemura et al. 2005; Rajeevan et al. 2008). The variability in rainfall is reported to be due

to the anomalies in global teleconnections such as SOI, Nino 3.4. SST and other geo-

oceanic indices such as Quasi-Biennial Oscillations, Indian Ocean Dipole and Equatorial

Indian Ocean Oscillation (EQUINOO). Besides the inter-annual variability, the relation-

ship between seasonal rainfall and intra-seasonal rainfall fluctuations were discussed by

Krishnamurthy and Shukla 2000; Sperber et al. 2000; Goswami and Mohan 2001 to

understand the origin of this relation. The dominant periodicities in intra-seasonal oscil-

lations of ISMR are 10–20 day and 30–50 day oscillations. The first one referred as the

quasi-biweekly oscillation and the later is referred to Madden–Julian Oscillation. The

strength of these signals varies from bad monsoon to good monsoon, thus bringing deficit

and excess amounts of rain (Uma et al. 2013). After all these, still it is tantalizing about the

certain trend of Indian rainfall as studied by Rupa Kumar et al. (2002) and Lakshmi Kumar

et al. (2012). Attention has been drawn to understand the trends of epochal rainfall which is

also extensively studied by several scientists (Mooley et al. 1981; Parthasarathy et al.

1991). It is observed that there is coherent multi-decadal variability and the frequency of

large-scale floods has been identified during the epochs 1871–1900 and 1930–1960. The

same has been studied (Parthasarathy et al. 1994; Kripalani et al. 1997; Kripalani and

Kulkarni 1997; Mehta and Lau 1997; Krishnamurthy and Shukla 2000; Torrence and

Webster 1999) and found that the epochal behaviour of ISMR has the multi-decadal quasi

periodicity. The dynamical mechanism of ENSO is reported as the anomalous high

pressure in western Pacific, eastern Indian Ocean and low pressure in the eastern and

central Pacific show impact on the monsoon circulation. This El Nino/La Nina changes the

position of the tropical walker circulation and gives low and high amounts of rain by

altering the convection over Indian region (Krishna Kumar et al. 2011; Maity and Nagesh

Kumar 2006). Though there are studies about the weakening impact of ENSO on ISMR

(Krishna Kumar et al. 2011; Sarkar et al. 2004), still the investigations relating the ENSO

parameters with the ISMR bringing out the new ideas to understand the ISMR more

effectively. Sulochana et al. 2004 studied the ISMR, ENSO and EQUINOO and mentioned

that the response of monsoon to ENSO is yet to understand since the large negative

anomalies of rainfall were observed during the El Nino years. Kawamura et al. 2005

observed that the relationship between ENSO and ISMR was strong in late summer over

central and north-west India before the late 1970s but, after 1970s, the relationship has

been shifted to early summer.

A common problem in meteorology is to estimate the amount of rainfall occurrence for the

regions where the rainfall variability is high and complex to understand. Statistical methods

such as regional frequency analysis (Greenwood et al. 1979), Gumbel distribution (Gumbel

1958) and Weibul distribution offer a great provision to obtain the rainfall values for different

return periods. Also, estimating the frequency of hydrological events such as floods and dry

spells is a major concern for decision makers. This type of analysis requires (i) knowledge of

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data characteristics, (ii) size of the sample, (iii) parameter estimation and (iv) calculation of

threshold years. Doesken and McKee, 1998, studied the extreme value analysis of meteo-

rological aspects such as flood occurrence and extreme storms. Buishand et al. (2008), de

Laurens and Chen (2008) studied the spatial statistics of rainfall using extreme value analysis

and estimated the total rainfall amount for different periods. Smith (1989) and Rao et al.

(1992) used the extreme value analysis to assess the environmental data and estimated their

concentrations to understand the regional dynamics. Palutikof et al. (1999) and Katz et al.

(2002) estimated the statistics of extremes of weather variables like precipitation, wind speed,

river discharge and surge from huge observational data. Mirza et al. (1998), Sarma et al.

(2009, 2010) carried out investigations on extreme value theory on rainfall patterns in

Ganges, Brahmaputra and Meghna river basins, and the later studied on Andhra Pradesh,

respectively, and concluded that the rainfall increases with the return periods.

In the present study, the authors, first of all carried out the investigation on rainfall

variability in different homogeneous monsoon regions of India, namely North-West,

Central North-East, North-East, Peninsular and West Central regions for the period

1871–2008. Not only the variability of rainfall during pre-monsoon (January–May), south-

west monsoon (June–September) and post-monsoon (October–December), but also the

influence of global teleconnections such as Nino 3.4 SST and Darwin pressure (DTEND)

are thoroughly investigated to understand the south-west monsoon variability and trends.

The time scales for the aforementioned seasons were adopted from the Bavadekar (1982);

Kakade and Kulkarni (2013). In the other part of the paper, we drew our attention towards

the extreme value analysis of south-west monsoon rainfall over the above said homoge-

neous regions and has been intensively studied by taking into consideration different

statistical techniques, and finally, we have examined the threshold years for the occurrence

of extreme rainfall events.

2 Data and Methodology

The source for monthly rainfall data from 1871 to 2008 for this study is from Indian

Institute of Tropical Meteorology. The data had been averaged from 305 rain gauges set up

by India Meteorological Department that are widespread in India and was developed for

the five homogeneous regions, Peninsular region (50 rainguages), West Central (85 rain-

guages), Central North-East (74 rainguages), North-West (66 rainguages) and North-East

(30 rainguages) by Mooley et al. (1981) (Parthasarathy et al. 1987, 1993, 1995a, b, and

Pant and Rupa Kumar 1997). For brevity sake, these regions are further denoted as PE,

WC, CNE, NW and NE, respectively, in the present study and the figure with these regions

is given as Fig. 1.

The climatology of rainfall on annual as well as decadal basis has been calculated for

the pre-monsoon, monsoon (south-west) and post-monsoon seasons, and their trends and

inter-decadal variability have been analysed. The respective decades that are made for the

present study are denoted by numbers and are used in Fig. 2 as 1—1871 to 1880; 2—1881

to 1890; 3—1891 to 1900; 4—1901 to 1910; 5—1911 to 1920; 6—1921 to 1930; 7—1931

to 1940; 8—1941 to 1950; 9—1951 to 1960; 10—1961 to 1970; 11—1971 to 1980; 12—

1981 to 1990; 13—1991 to 2000; 14—2001 to 2008. The Pearson’s correlation technique

is used to understand the mode of relationship of rainfall with the Darwin pressures

(DTEND) and Nino 3.4 SST and the correlations were obtained for the SW monsoon

months separately as well as for total monsoon rainfall. The formula for the Pearson’s

correlation is given below.

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r ¼ Eððx�EðxÞÞðy�EðyÞÞÞrxry

where numerator denotes the covariance of expected value of the variable and r is the

standard deviation.

To understand the relation of these global teleconnections with ISMR, we used some

gradation in the present paper for the correlations such as poor (\0.2), moderate

(0.2 \ r \ 0.4) and remarkable ([0.4).

The anomalies of the ISMR for the five homogeneous regions are presented for the

composites of weak, moderate and strong El Nino and La Nina years during the study

period of 1951–2008. These composites have been picked up for the period from 1951 to

2008 from the Climate Prediction Centre (Table 1).

The extreme value analysis proposed in Gumbel statistical theory for extremes is applied

in obtaining the threshold number of years (T-years) for the possible rainfall exceedance.

Since the ENSO cycle is reported to be maximum of 7 years (Cane 2004) where the extreme

rainfall situation usually occurs during this period only, the T-years have been calculated by

taking the year of extreme rainfall during each 7 years of study period.

The standard Gumbel distribution is as follows

GðxÞ ¼ expð� expð�ðx� aÞ=bÞÞ

Fig. 1 Rainfall monsoon homogeneous regions over India (Source: www.tropmet.res.in)

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where a and b are location and scale parameters which can be obtained from the given data

and x is the variable (here the variate is rainfall).

The parameters a and b are estimated from the maximum likelihood method, and the

values can be obtained using the numerical solver.

b ¼ �b n�1X

exp�xi

b

� �� �

b� n�1X

xi þP

xi exp �xi

b

� �� �� �P

exp �xi

b

� �� �� � ¼ 0

Fig. 2 Trends (significant) of inter-decadal rainfall in a NW—pre-monsoon, b WC—SW monsoon,c CNE—pre-monsoon, d NE—SW monsoon, e NE—post-monsoon and f PE—post-monsoon

Table 1 Weak, moderate and strong El Nino and La Nina years during 1951–2008

1951–2008 El Nino La Nina

Weak 1951, 1963, 1969, 1976, 1977,2004 and 2006

1950, 1956, 1962, 1967, 1971, 1974,1984, 1995 and 2000

Moderate 1986, 1987, 1994 and 2002 1954, 1964, 1970, 1998, 1999 and 2007

Strong 1957, 1965, 1972, 1982, 1991 and 1997 1955, 1973, 1975 and 1988

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where i = 1, n and n is the number of observations for the variate under consideration.

The threshold level of the year (T-year) for an extreme event can be estimated using the

formula.

T � year ¼ 1

ð1� GðxÞÞ

3 Results and discussions

The trends of inter-annual variability of rainfall for the pre-monsoon, monsoon and

post-monsoon seasons have shown insignificant trends (also reported by several people

as mentioned in introduction part) and so were excluded and not discussed in the

present context for brevity sake. Figure 2a–f shows the inter-decadal variability of

rainfall for the homogeneous regions NW, WC, CNE, NE and PE where in rainfall

trend is significant at 0.05–0.10 levels. This statement implies that in other seasons of

above regions, the rainfall trend is insignificant despite their inter-annual variability.

The variability in pre-monsoon season showed increasing trend in NW (Fig. 2a) and

CNE (Fig. 2c) regions with a low and high of -28, -26 % and ?36, ?24 % to the

normal, respectively. The tri-decades during 1951–1980 and 1981–2008 recorded the

below and above normal rainfall values during pre-monsoon, thus leading to increased

trend in the above said regions. Though the inter-decadal variability of SW monsoon

for these two regions are more, they could not yield to any significant trend, and in

particular, the CNE region recorded maximum number of negative anomalies of rainfall

during 1951–2008. During the post-monsoon season, the anomalies are evenly dis-

tributed. The decade 1951–1960 recorded ?40 and ?30 % to the normal rainfall in

NW and CNE regions, respectively (Figures are not shown here). Interestingly, the

trends in inter-decadal anomalies of summer monsoon rainfall in WC (Fig. 2b) and NE

(Fig. 2d) regions showed the decreasing trends. There is no coherence among these

regions in recording the inter-decadal anomalies, rather considerable decrement had

been observed in WC region by recording the negative anomalies compared to NE

region during the tetra-decades of 1971–2008 despite the more number of normal

monsoons during this period except 2002, 1987, 1979, 1974 and 1972. NE region

showed a high rate of decrement, and a reduction of 2 % rainfall is observed in the

monsoon season. The post-monsoon trends in NE (Fig. 2e) and PE (Fig. 2f) regions

show the increasing trend and the inter-decadal variability of post-monsoon rainfall is

more in NE region. There is no consistency in these two regions in recording the

anomalies but is constrained to *15 % above and below the normal rainfall. The

increasing trend in NE region during post-monsoon season might be due to an incre-

ment of rainfall amount (approximately 20 mm) in October from 1940s, whereas for

the PE region, it is 40 mm from 1990s.

The correlation analysis of summer monsoon rainfall (SMR) over the test homoge-

neous regions with the global teleconnections, Nino 3.4 SST and Darwin pressure

(DTEND) shows that the correlation (r) values are better coming on decadal scales than

on annual scales. In the case of annual scale, none of the regions maintained the sig-

nificant correlation with Darwin pressure (DTEND) throughout the monsoon except in

September where in, all the five regions showed the moderate correlations, 0.24, 0.31,

0.22, 0.21 and 0.30, respectively. Unlike the DTEND, Nino 3.4 SST showed moderate

correlations in the month of June (WC = -0.29; CNE = -0.33 and NE = -0.23) and

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July (NW = -0.26; CNE = -0.31; NE = -0.20 and PE = -0.26), respectively. The

correlation values on the decadal scale yielded considerably better than the earlier case

and are of high in July over CNE (r = -0.71) and NE (r = -0.43) regions and in

August, they are 0.89 and -0.68 over NW and WC regions, respectively. The above

analysis may recommend that the ENSO–ISMR relationship could be better explicable

and reasonable, if the attention is being shifted to decadal scale rather than on annual

scales. The weak correlations of rainfall with DTEND when compared to SSTs might be

due to the randomly varying nature of DTEND than SSTs (Lakshmi Kumar et al. 2013).

It is also evidenced from the studies of Krishna Kumar et al. (2011), Krishnamurthy and

Shukla (2000) that the monsoon variance is explained by ENSO with the help of inter-

decadal shift of the relationship between them. Also, it is reported that the frequency and

amplitude of ENSO (Wang and Wang 1996), ENSO–monsoon relationship (Shukla 1995)

have the inter-decadal changes.

Figure 3a–b shows the composite rainfall anomalies for the five homogeneous

regions during the weak, moderate and strong El Nino as well as La Nina years during

the study period, respectively. Even though, there is a notorious impact on Indian

rainfall because of these short-term climate signals, the influence is distinctly varied

from region to region. And it is to worth mention that the impact of moderate El Nino

is not necessarily associated with severe impacts and likewise a weak El Nino does not

imply an absence of impacts. It is also reported, though there is strong correlation

between ENSO and ISMR, 73 % of ISMR variance could not be explained by ENSO

(Hu et al. 2005). During the moderate El Nino, the NW and PE regions were highly

affected (-23 and -17 % from the normal) than weak and strong composites (Fig. 3a).

It is also found that the difference between the long term (1951–2008) and the

composite El Nino years is 53 and 57 mm over NW and PE regions, respectively,

which evidences the subdued activity of south-west monsoon during El Nino years. It

is observed from the correlation analysis, that the Pearson’s correlation value is

insignificant for the two regions during weak and strong El Nino’s (-0.13, -0.02;

-0.19, -0.06), whereas it is remarkable during moderate (-0.48 and -0.43). In NW

part, remarkable statistical relation was found during June (r = -0.65) and July

(r = -0.43) as compared to other monsoon months in August (r = -0.18) and Sep-

tember (r = –0.21). The regions WC, CNE and NE are affected with -14, -8 and

-5 % from the normal rainfall during the strong composite El Nino. But in the case of

composite La Nina’s, CNE did not make any difference among strong, moderate and

weak La Nina and more or less, same situation prevailed over NE region (variation is

around 0–5 % above the normal) (Fig. 3b); meanwhile, NW part is highly influenced

by the strong La Nina (25 % above normal). The correlation analysis discloses the

relationship between rainfall and DTEND which clearly shows the reasonable corre-

lations (0.31 and 0.33) during weak and moderate La Nina’s in CNE region, whereas

during strong La Nina, the correlation is negative (-0.55) which supports the presence

of La Nina also could give India severe dry spells as a few dry spells were witnessed

in the La Nina years 1988 and 2007 (Rajeevan et al. 2010). Correlation for June

(r = 0.35) and for August (r = 0.28) are found to be good for the CNE region during

weak and strong La Nina’s, respectively. On the other way, NE region could able to

trigger more rainfall during moderate La Nina’s than the other two modes and is

explicable by ?0.42 correlation which seemed to be good than the rest (r = -0.21 and

r = -0.07). The distinct correlations of rainfall with Nino 3.4 SST and DTEND

among different months of monsoon throughout India may be due to the movement of

walker circulation. The ENSO causes the eastward migration of this walker circulation

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123

which inhibits the convection over different areas of the country (Caviedes 2001; Sen

Roy et al. 2003).

Using the Gumbel Distribution, the rainfall values for the return periods 10, 20, 50

and 100 years have been calculated for the five homogeneous regions. The methods used

are method of moments (Christopeit 1994), method of quantiles (Dupuis 1999), method

of probability-weighted moments (Hosking et al. 1985) and maximum likelihood method

(Prescott and Walden 1980). Nevertheless, all the methods saw the increased amount of

rainfall with the return periods. The rainfall amounts for the respective return periods

are given in Table 2. Comparatively, maximum likelihood method drew more values of

rainfall for the respective return periods. In the case of All India, the exceedance rainfall

values for the four corresponding methods ranged from 958 to 999 mm, 1005 to

1061 mm, 1065 to 1142 mm and 1111 to 1202 mm for 10, 20, 50 and 100 years,

respectively. These return values of rainfall were obtained with the probability of 80, 90,

95 and 98 % for the respective return years. Since the rainfall return level is a clima-

tological quantity rather than the quantity associated with the weather observations for a

particular day or year, the exceedance of each rainfall event would be of important in

risk management. This type of analysis has already been done by taking 10-day rainfall

(Feng et al. 2007) and monthly rainfall (Begueria and Serrano 2006). The authors in this

paper have shown that the possible exceedance of rainfall amounts in different return

periods such as in coming 50 and 100 years. This analysis does not conclude that the

extreme rainfall events are increasing. But the increment of rainfall over different return

periods may be because of the increasing frequency of extreme weather events also as

reported by several people (Goswami et al. 2006; Krishnamurthy 2011). It is also to be

noted that the rainfall values for the different return periods over WC and NE regions

have shown increasing values though their decadal trends for the period 1871–2008 are

decreasing. This may be due to an increment in rainy days ([10 mm) in future climate

change scenario as reported by Rao et al. 2014. At this juncture, we have made an

attempt to compare our results which were derived from the various statistical methods

with the regional climate model (RCM) projections. The simulated projections of a high-

resolution RCM, providing regional climates for impact studies reported by Krishna

Kumar et al. 2011 were used in the present comparison. According to their study, the

projected monsoon (June–September) rainfall for 2020, 2050 and 2080 is 911, 980 and

1024 mm, respectively. Our results have shown higher values than the aforementioned

model projections and are estimated as 958, 1005 and 1065 mm for the next 10-,

20- and 50-year return periods from the study period 2008. These rainfall estimations

are taken from the method of moments (theoretical), and the other methods which were

used in this study have shown higher values than the model projections. But the sim-

ilarity we found is that the model projections as well as the statistical estimations have

shown higher rainfall amounts during the SW monsoon season over India in the

forthcoming decades.

Figure 4(a–e) shows the rainfall growth factor for each return period right from 1 to

50 years for the respective homogeneous regions. The rainfall growth factor is calculated

as the ratio of the difference between previous year and present year’s rainfall to the mean

rainfall. It can be seen from the figures that the growth factor of rainfall is not monoto-

nously increasing as the return period increases. All the five test regions have undergone

highest value of rainfall growth factor during below 5 years, and thereafter till 10 years,

there was no significant change in growth factor. Among all regions, CNE region saw the

second highest growth factor that is about 0.7 with a return period of 35 years (Fig. 4c).

WC region has shown the lowest peak of growth factor compared to other regions

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123

(Fig. 4b). The variability in rainfall growth factor is more in NE region and low in WC

region. The threshold (T) years for the extreme high rainfall amounts during the study

period yielded a steady raise in all five homogeneous regions but the amount of increment

varied depending upon the rainfall range (Fig. 5a–e). The figures infer that the peak in

Fig. 3 a Composite anomalies of rainfall for weak, moderate and strong El Nino years. b Compositeanomalies of rainfall for weak, moderate and strong La Nina years

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Tab

le2

Am

ou

nt

of

rain

fall

(mm

)fo

rd

iffe

ren

tre

turn

per

iods

for

the

resp

ecti

ve

ho

mo

gen

eou

sre

gio

ns

Reg

ion

Rai

nfa

ll(m

m)

for

the

retu

rnp

erio

ds

of

10

,2

0,

50

and

10

0y

ears

Met

ho

do

fm

om

ents

(em

pir

ical

)M

etho

do

fq

uan

tile

sM

eth

od

of

pro

bab

ilit

y-w

eig

hte

dm

om

ents

Max

imu

mli

kel

iho

od

met

ho

d

10

20

50

10

01

02

05

01

00

10

20

50

10

01

02

05

01

00

NW

66

47

39

83

69

08

68

97

65

86

39

36

66

97

46

84

59

20

71

58

09

93

11

,022

WC

1,0

92

1,1

63

1,2

56

1,3

24

1,1

55

1,2

43

1,3

58

1,4

43

1,0

99

1,1

74

1,2

70

1,3

42

1,1

41

1,2

31

1,3

47

1,4

35

CN

E1

,157

1,2

23

1,2

84

1,3

73

1,1

52

1,2

09

1,2

84

1,3

40

1,1

59

1,2

26

1,3

13

1,3

78

1,2

05

1,2

90

1,4

00

1,4

83

NE

1,5

89

1,6

63

1,7

58

1,8

29

1,6

11

1,6

88

1,7

87

1,8

61

1,5

97

1,6

75

1,7

75

1,8

50

1,6

16

1,7

00

1,8

08

1,8

89

PE

79

48

52

92

69

82

78

48

37

90

59

56

79

88

57

93

49

91

81

98

86

97

21

,037

All

Ind

ia9

58

1,0

05

1,0

65

1,1

11

98

11

,030

1,0

92

1,1

39

96

61

,016

1,0

81

1,1

30

99

91

,06

11

,14

21

,202

Nat Hazards

123

rainfall did maintain a sort of skewness as compared to the other values. NE region showed

a very less increment of rainfall (5 %) in a maximum of 34 years from which we can say

that this region will not show any drastic increment of rainfall due to climate change

(Fig. 5d). Among the others, NW region showed a considerable percentage increment of

rainfall (13 %) from the preceding peak in a period of 10 years (Fig. 5a). The heteroge-

neity of threshold years in recording the extreme rainfall describes the regional climate

impact. The gradualness of rainfall increment in NE regions (humid climate type) and

rapidness in arid region like NW region suggest the climate variability statistically. The

linear relation between T-years and rainfall is regressed for the estimation of one from the

other and are tabulated (Table 3).

Fig. 4 Rainfall growth factor against the return period (in years) over a NW, b WC, c CNE, d NE, and e PErainfall homogeneous regions of India

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4 Conclusions

Since the trends of All India annual and summer monsoon rainfall have not shown any

significant trend, the study prompted to examine the trends of pre-monsoon, monsoon and

post-monsoon rainfall over five rainfall homogeneous regions in India which led to

interesting results. The increasing trend of pre-monsoon rainfall in NE, CNE regions

Fig. 5 Linear regression of SMR versus T-years for a NW, b WC, c CNE, d NE, e PE rainfallhomogeneous regions of India

Table 3 Regression expression for T-years using extreme rainfall

S. No Homogeneous region Regression expression

1 NW T = 0.064 (RF)–34.95

2 WC T = 0.059 (RF)–57.01

3 CNE T = 0.098 (RF)–104.8

4 NE T = 0.108 (RF)–160.6

5 PE T = 0.104 (RF)–73.09

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declines in monsoon over WC, NE regions and increasing trend of post-monsoon in NE,

PE regions claims that the climate change impact, and in particular, the October month

rainfall increased in NE and PE regions. The correlation with Darwin pressures was poor

during the monsoon except September where in the r values in this case are 0.24, 0.31,

0.22, 0.21 and 0.30 for NW, WC, CNE, NE and PE regions, respectively. Unlike

the DTEND, Nino 3.4 SST showed the moderate correlations in the month of June

(WC = -0.29; CNE = -0.33; NE = -0.23) and July (NW = -0.26; CNE = -0.31;

NE = -0.20 and PE = -0.26), respectively. The present study with this gist explores the

weak relationship of Darwin pressures with summer monsoon rainfall of five homogeneous

regions, and even the relationship is not found to be strong with sea surface temperatures of

Nino 3.4 region on annual scales. The relations seemed to be consistent if the statistics

were taken on decadal basis. The analysis of rainfall during composite of strong, moderate

and weak El Nino and La Nina have shown that NW and PE regions are left with notorious

foot prints during moderate El Nino than the strong ones. The regions CNE and NE did not

show any difference of recording rainfall during all weak, moderate and strong La Nina.

The Gumbel extreme value analysis revealed that the rainfall increase with the return

period. The T-years which are derived for the extreme rainfall denotes the climate change

impact that is going to be experienced by India. This analysis unravels the steady state of

NE region in attaining the more rainfall during forthcoming decades. The regression

expressions for obtaining T-years from the extreme rainfall are of immense helpful in risk

management.

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