NCC RESEARCH REPORT

GICO AL LO R DO EE PT AE RTM M

A EI ND TN I

N

EA RT TIO NN ECA L E TC ALIM

NATIONAL CLIMATE CENTREOFFICE OF THE

ADDITIONAL DIRECTOR GENERAL OF METEOROLOGY (RESEARCH)INDIA METEOROLOGICAL DEPARTMENT

PUNE - 411 005

RR No.

1/2012

MAY

2012

DESIGNED & PRINTED ATCENTRAL PRINTING UNIT, OFFICE OF THEADDITIONAL DIRECTOR GENERAL OF METEOROLOGY (RESEARCH),PUNE

NCC

Trends and variability of

monthly, seasonal and annual rainfall

for the districts of Maharashtra

and spatial analysis of seasonality

index in identifying

the changes in rainfall regime

P. Guhathakurta, and Elijabeth Saji

P. Guhathakurta, and Elijabeth Saji

National Climate Centre

Research Report No: 1/2012

Trends and variability of monthly, seasonal and annual rainfall for the districts of Maharashtra and

spatial analysis of seasonality index in identifying the changes in rainfall regime

Pulak Guhathakurta and Elijabeth Saji

pguha@imdpune.gov.in, pguhathakurta@rediffmail.com

National Climate Centre

India Meteorological Department

PUNE. INDIA

411005

Executive Summary

1 Document title Trends and variability of monthly, seasonal and annual rainfall for the districts of Maharashtra and spatial analysis of seasonality index in identifying the changes in rainfall regime

2 Document type Research Report

3 Issue No. NCC Research Report No. 1/2012

4 Issue date 15th May, 2012

5 Security Classification Unclassified

6 Control Status Unclassified

7 No. of Pages 21

8 No. of Figures 9

9 No. of reference 16

10 Distribution Unrestricted

11 Language English 12 Authors/ Editors Pulak Guhathakurta and Elijabeth Saji

13 Originating Division/Group

Hydrometeorology Division, Office of ADGM (R), India Meteorological Department, Pune

14 Reviewing and Approving Authority

Additional Director General of Meteorology (Research),India Meteorological Department, Pune

15 End users Climatologist, , State and District agriculture and water sectors, Disaster Managers, Hydrological planners and Researchers, Government Officials etc.

16 Abstract Knowledge of mean rainfall and its variability of smaller spatial scale are important for the planners in various sectors including water and agriculture. In the present work long rainfall data series (1901-2006) of districts in monthly and seasonal scales are constructed and then mean rainfall and coefficient of variability are analyzed to get the spatial pattern and variability. For the first time long term changes in monthly rainfall in the district scale is identified by trend analysis of rainfall time series and significant changes are obtained. The seasonality index which is the measure of distribution of precipitation throughout the seasonal cycle is used to classify the different rainfall regime within Maharashtra. Also long term changes of the seasonality index are identified by the trend analysis.

17 Key Words District rainfall, trends, seasonality index.

  1

1. Introduction.

Geographical location of Maharashtra is widely spread to get different types of

climatic features. Due to the climate variability and varied topographical features, the

state is divided in four meteorological subdivisions. The meteorological sub-division

Konkan & Goa is the extreme western part elongated north south along the west coast

of India. Due to these topographical features the region receives very high rainfall

during monsoon season. The Vidarbha region is the extreme eastern parts of the state.

The mean monsoon or annual rainfall is the below to that of Konkan and Goa but more

than the other two sub-divisions. The other two sub-divisions viz. Madhya Maharashtra

and Marathwada are almost having similar mean rainfall with Madhya Maharashtra is

having slightly higher mean monsoon or annual rainfall. But the rainfall patterns are

having high intra seasonal variability. There is high spatial variability of rainfall over

districts of Maharashtra. It has been reported by many researchers (Guhathakurta et al.

2011; Sinharay & Srivastava, 2000) about increasing trends of heavy rainfall events and

also in total rainfall over Madhya Maharashtra and Konkan & Goa. Due to the increase

number of disaster and its high impact on economical and human life, it is necessary for

the district administration to have district rainfall climatology and information about

temporal variability of rainfall in the district levels for better disaster and water

managements and planning. However, so far there is no in-depth study for districts

rainfall climatology, its variability and the changing pattern of rainfall using a long period

of data. The reason is the absence of long period district rainfall series. In the present

paper we have presented monthly rainfall series of all the 35 districts of Maharashtra

for the period 1901- 2006. The mean rainfall pattern and the variability of four seasons

and annual rainfall for each of 35 districts of Maharashtra are presented and are very

useful information to the agriculture and water sectors of this state. The study aims to

find the changing pattern of rainfall over Maharashtra in the district scale which may

have impact on increasing extreme rainfall events and floods over Maharashtra.

The distribution of precipitation throughout the seasonal cycle is as important as

the total annual amount of monthly or annual precipitation when evaluating its impact on

  2

hydrology, ecology, agriculture or in water use. The seasonal distribution of precipitation

is the results of revolution of earth resulting the unequal heating of the earth’s surface

over the year and resulted the atmospheric general circulation. The time and duration of

the seasons of high precipitation at a place or watershed is most important for the

planning and design of agriculture or water managements. It is very much important to

identify the historical changes in the mean annual precipitation. But even in the absence

of changes in annual total precipitation, changes in the seasonal receipt of precipitation

greatly affect partitioning of the water into runoff, evapotranspiration and infiltration and

thus flood forecasting, stream discharge and ecosystem responses [Epstein et al.,

2002; Groisman et al., 2001; Rosenberg et al., 2003; Small et al., 2006; Xiao and

Moody, 2004]. The changing pattern of rainfall is also investigated by computing

Seasonality Index of rainfall. The relative seasonality of rainfall represents the degree of

variability in monthly rainfall throughout the year (Walter, 1967; Walsh and Lawer 1981;

Livada and Asimakopoulos, 2005; Adejuwon , 2012 ). Spatial distribution of precipitation

seasonality in the United States was studied by Finkelstein and Truppi (1991). Markham

(1970) has proposed a quantities technique for measuring precipitation seasonality

based on vector analysis. The understanding of seasonality pattern of precipitation and

also identifying changes in seasonality index is very useful for agricultural planning.

In the present paper we have constructed monthly rainfall series for all the 35

districts of Maharashtra. Statistical features of monthly rainfall series of each district are

studied and then rainfall variability and trends of the monthly total rainfall for each of the

districts are analyzed. The seasonality index was carried out for all the 35 districts for

the period 1901- 1950 and 1951-2000. The changes in the seasonality index are

noticed almost all the district of Maharashtra.

2. Data and Methodology

Monthly rainfall data of around 335 raingauge stations of the state Maharashtra

for the period 1901 to 2006 are collected from National Data Centre of India

Meteorological Department, Pune. Monthly rainfall series of all the 35 districts of

  3

Maharashtra are then computed by arithmetic mean of the monthly rainfall of the

stations under each district. Table 1 gives the status of availability of monthly rainfall

data of each district so computed. Basic statistical properties are computed for the

monthly rainfall time series of thirty-five districts and all the twelve months. The so

called least square linear fit is used to examine the existence of trend in the time series

data.

Least squares linear regression is a maximum likelihood estimate i.e. given a

linear model, the likelihood that this data set could have occurred is estimated. The

method attempts to find the linear model that maximizes this likelihood. If each data

point y i has a measurement error that is independently random and normally distributed

around the linear model with a standard deviation σi

The probability that the data occurred is the product of the probabilities at each point:

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

Δ⎥⎥⎦

⎤

⎢⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛ +−−∏

=ybxay

i

iiN

iP

σα

2

1

)(21exp

Maximizing this is equivalent to minimizing: 2

)(∑ ⎟⎟⎠

⎞⎜⎜⎝

⎛ +−

i

ii bxayσ

If the standard deviation σi at each point is the same, then this is equivalent to

minimizing:

( )2)(∑ +− ii bxay Solving this by finding a and b for which partial derivatives with respect to a and b are

zero, gives the best fit parameters for the regression constant and coefficient The

Pearson product-moment coefficient of linear correlation is used widely to assess

relationships between variables and has a close relationship to least square regression.

It can be shown that the coefficient of determination is the same as the square of the

correlation coefficient. The correlation coefficient can therefore be used to assess how

well the linear model fits the data. Assessing the significance of a sample correlation is

difficult, however, as there is no way to calculate its distribution for the null hypothesis

  4

(that the variables are not correlated). Finally the statistic used for testing the

significance is the Student’s t-distribution:

212

rNrt−−

=

In order to define the seasonal contrasts, the Seasonality Index ( SI ) (Walsh

and Lawer 1981: Kanellopoulou, 2002), which is a function of mean monthly and annual

rainfall, is computed using the following formula:

∑=

−=12

1 121

nn

RR

SI X

where xn is the mean rainfall of month n and R is the mean annual rainfall.

Theoretically, the SI can vary from zero (if all the months have equal rainfall) to 1.83 (if

all the rainfall occurs in one month). Table 2 shows the different class limits of SI and

representative rainfall regimes (Kanellopoulou, 2002).

To investigate the changes in rainfall pattern we have computed the seasonality

index of all the 35 districts for the period 1901-50 and 1951- 2006 and compared the

changes between these periods.

3. Results and Discussions:

3.1 Statistical analysis and spatial variation of rainfall of the districts of Maharashtra

Fig. 1 shows mean rainfall (in mm) for the districts of Maharashtra for the four

seasons. During the winter season (January- February) all the districts of central and

western parts of the state received very low rainfall (even much less than 10 mm). The

districts in the eastern parts received rainfall around 20 – 30 mm with maximum value

of 35.9 mm at Bhandara district. The maximum rainfall zone is shifted from eastern

parts of the state to south western parts in pre-monsoon season (March-May) season.

The maximum rainfall of amount 87.0mm is being received by Kolhapur district. Mean

  5

rainfall for the most of the districts are between 20 -30mm during this season. Both in

monsoon and post-monsoon seasons maximum rainfall received by Ratnagiri district.

The changing pattern of maximum rainfall zone i.e. initially at the eastern parts, then

southern parts in pre-monsoon and slightly northern movement in monsoon season and

remaining there in post-monsoon is clearly seen by comparing the figures a, b, c and d

of Fig.1. Mean annual rainfall pattern over the districts of Maharashtra is shown in Fig.

2. In addition to mean rainfall pattern the knowledge of variability of rainfall is of great

use for hydrological planning and management. Fig. 3 shows the spatial distribution of

coefficient of variation for the four seasons while Fig. 4 is for annual rainfall. Very less

amount of rainfall is being received during the winter season and the variability is very

high in all the districts of Maharashtra. Maximum variability (420.8 %) is over

Ahmednagar and the lowest variability (106.1) is over Nagpur. Variability decreased as

the monsoon reached and again it increased in the post monsoon season. During

monsoon season in spite of very high rainfall, coefficient of Variability of monsoon

rainfall is very high in Mumbai (>35%).

3.2 Trends in the monthly, seasonal and annual rainfall pattern

The study by Guhathakurta and Rajeevan (2008) have revealed the changes in

the rainfall pattern over meteorological sub-divisions of India for the monsoon months

and seasons. However the changes in the further smaller scales are required to be

identified for the better disaster management and also climate change studies. Fig. 5

shows the trends in the monthly rainfall over the districts of Maharashtra. There was no

significant trend in the rainfall in any districts of Maharashtra for the month of November

and December. No district has also reported increasing trend in rainfall for the months

February, March, April and May. Thus the significant decreasing trends in rainfall

activities starting from January and extending up to May indicate a major shift in the

rainfall pattern. The time period or duration of rainfall activities are slowly confining in

the monsoon months only, over Maharashtra region which may have impact in the

agricultural activities over the region.

  6

In January seven districts viz. Ahmednagar (99%) Jalgaon and Buldhana (95%),

Nandurbar, Aurangabad, Dhule and Kohlapur(90%) have shown significant decrease in

rainfall. In February 15 districts viz. Nandurbar(99%), Mumbai, Parbhani, Yeotmal,

Bhandara, Chandrapur,Gadchiroli and Buldhana (95%),Thane, Raigad, Beed, Nanded,

Akola, Washim and Gondia(90%), in March three districts viz. Mumbai, Thane(95%),

Raigad(90%), in April six districts viz. Osmanabad(99%), Aurangabad, Ratnagiri and

Buldhana (95%), Ahmednagar, Yeotmal(90%) and in May three districts viz. Amraoti,

Jalgaon and Washim(90%) have shown significant decrease in rainfall. Along with

progress of the months and from June monthly rainfall has shown increasing trends in

some districts and decreasing trends in district rainfall have reduced. In June one

districts viz. Latur (99%), in July six districts viz. Wardha and Bhandara (95%),

Chandrapur, Osmanabad, Buldhana and Latur (90%) and in September five districts viz.

Parbhani (99%), Osmanabad, Yeotmal, Nanded and Gadchiroli(95%) have shown

significant decrease in rainfall

In June rainfall five districts (Satara, and Sangli (99%), Pune (95%), and Nasik

and Kolhapur(90%) ), in July rainfall three districts (Satara and Kolhapur(99%),

Mumbai(90%)), in August rainfall twenty two districts viz. Sindhudurg, Satara,

Nandurbar, Kolhapur, Dhule, Jalgaon, Buldhana, Akola, Amraoti, (99%), Mumbai,

Aurangabad, Yeotmal, Nanded, Parbhani (95%), Nasik, Thane, Ahmednagar, Pune,

Ratnagiri, Solapur, Washim and Gadchiroli(90%)), in September rainfall three districts

(Kolhapur(99%), Pune (95%), Satara(90%)) and in October rainfall thirteen districts

(Nanded and Hingoli (99%), Sangli, Solapur, Osmanabad, Beed, Parbhani, Akola(95%),

Satara, Jalna, Washim and Yeotmal and Latur (90%) ) showed significant increasing

trends. There were no significant decreasing trends in any districts rainfall for the

month of August and October. One district in June, six districts in July and six districts

in September all from the eastern parts of the state have reported decreasing trends in

rainfall.

These results are reflected in the seasonal and annual rainfall trends (Fig. 6).

Both in winter and pre-monsoon seasons no district except Latur has shown increasing

  7

trend in rainfall. Thirteen districts in winter and three districts in premonsoon seasons

have shown significant decreasing trends. During monsoon season nine districts all

along the north-south direction along the western coast viz. Nandurbar, Dhule, Nasik,

Pune, Mumbai, Raigad, Satara, Kolhapur, Sindhudurg have showed significant

increase in rainfall while only four districts viz. Latur, Bhandara, Osmanabad and

Wardha have shown significant decreasing trends. In the post monsoon season when

the rainfall are being received mostly due to northeast monsoon, rainfall for the districts

Osmanabad, Jalna, Hingoli, Nanded and Latur all from southeastern parts of the state

have shown significant increasing trend. The Latur district has shown significant

increasing trend in rainfall during winter, pre monsoon and post monsoon seasons, but

decreasing trend in monsoon season and as a result annual rainfall of Latur has

decreased significantly.

Thus after analysis of district rainfall it is found monsoon rainfall is increasing for

the most of the districts of Madhya Maharashtra and for only three districts of Konkan

region While for two districts of both Marathwada and Vidarva rainfall have decreased

significantly. However the using of subdivision rainfall it has been reported that the

significant increasing trends for the subdivisions (Guhathakurta and Rajeevan 2008).

Also the period of data considered by them was 1901-2003. Thus impact of climate

changes are better revealed by analysis of data series of smaller spatial scales.

3.3 Changes in the seasonality index

The seasonality index has been computed for all the districts of Maharashtra for

two different period viz. the first 50 year 1901-1950 and the later 50 year period 1951-

2000. This will help to find the changes (if any) in the seasonality index in the last 100

years. Fig. 7 shows the seasonality index during 1901-50 and 1951-2000 periods. From

the Table 1. We can see that the lower seasonality index value indicates better

distribution of monthly rainfall among the months of the year. The maximum seasonality

index value is over Thane, Mumbai, Raigad and Ratnagiri districts in both the period

1901-50 and 1951-2000 and is between 1.2-1.3 indicating most of the rain occurred in

  8

the northern parts of Konkan areas in one to three months. The spatial distribution of

seasonality index remained almost same in both the period. The lower value of

seasonality index was over Sangli and Solapur and was in the range 0.8-0.9. This

indicates that only in these two districts rainfall was purely seasonal and was evenly

distributed in four months though the mean monsoon rainfall was less compare to other

districts of Maharashtra. Rainfall of the six districts viz. Ahmednagar, Aurangabad,

Beed, Osmanabad, Amraoti and Latur was marked seasonal with rainfall evenly

distributed in three to four months as SI was in the range 0.9-1.0 in the period 1901-50.

The less availability of data for the districts Jalna and Latur caused missing SI value in

Fig 7 a. This pattern was almost same in the next period 1951-2000 with only changes

in Kolhapur district where SI value increased to be in the range 1.0-1.1 indicating rainfall

distribution in three month or less. In the six districts viz. Nandurbar, Sindhudurg,

Chandrapur, Gadchiroli, Bhandara and Gondia seasonality index value was between

1.1-1.2 indicating that the rainfall distribution in less than three months.

As mentioned above low value of seasonality index indicates that the type of

rainfall regime with shorter dry season and high value indicates most of the rain occurs

within few months (2-3 months). An increasing trend in seasonality index is thus an

indicator of alarming situation for the agriculture. Though Fig. 7 highlights that the

rainfall regime was mostly similar in the period 1901-50 and 1951-2000, but to identify

the changes in the seasonality index, we have computed the differences in the

seasonality index (Fig.8). The seasonality index has increased in all the districts except

Satara, Osmanabad, Nanded, Wardha and Bhandara. In order to find out whether

these changes are significant or not, we have carried out seasonality index SIk (Walsh

and Lawer 1981; Pryor and Schoof, 2008) time series for each of the districts for each

year k using the formula:

∑=

−=12

1 121

n

knk

kk

RR

SI X

  9

where xnk is the rainfall of month n of the year k and Rk is the total annual rainfall for the year k.

Trend analysis has been done on the SI for each of the districts. Fig. 9 shows

the trends in the Seasonility Index over the districts of Maharashtra. Significant

increasing trends (95%) in Mumbai, Pune, Buldhana and Amraoti districts SI has also

showed increasing trend for the districts of Thane, Nanurbar, Dhule, Jalgaon, Nasik,

Ahmednagar, Solapur, Satara, Sangli, Kohlapur, Ratnagiri, Sindhudurg, Akola, Wahim,

Yeotmal, Nanded and Gadchiroli. Significant decreasing trend (95%) which is good as it

indicates increase in evenly distribution in the monthly scale are being noticed in Jalna

and Nasik while the distrits Raigad, Aurangabad, Beed, Osmanabad, Parbhani, Hingoli,

Wardha, Nagpur, Bhandara, Gondia and Chandrapur have shown decreasing trend.

4. Conclusions

We have analyzed the rainfall data of more than 100 years over Maharashtra, a

large state in western parts of India which plays a significant industrial and agricultural

contribution in the overall growth of India. The analysis includes variability of rainfall,

trends in rainfall pattern and changes in spatial and temporal pattern of seasonality

index. The impact of climate changes on temporal and spatial pattern over smaller

spatial scales is clearly noticed in this analysis. Significant decreasing trends in monthly

rainfall are being observed in many areas (districts) from the month of January (seven

districts) to May(three districts) with maximum decrease in February (15 districts). Not a

single district of Maharashtra reported increasing trends in rainfall from the month

January to May. These changing patterns are very crucial in agriculture or hydrological

point of view. In spite of increasing trends in monsoon rainfall in many areas, the

decreasing trends in the first five months of the year have resulted increase heating,

and may have effect in shortage of soil moisture, ground water and lowering the ground

water level. Out of twelve months, August has shown very good for the state

Maharashtra as most of the districts have shown increasing trends in August rainfall.

  10

Second good month is October. Analysis of seasonality index helps to have idea about

the distribution of the rainfall among the months and separate the states in different

rainfall regimes. In coastal areas SI is greater than 1.2 indicating the extreme rainfall

regime where most of the rain occurs in one to two months. Eastern parts and western

parts (just east of Konkan region) have SI in between 1 to 1.2 indicating a rainfall

regime where most rain occurs in three months or less. The central parts of the state

has seasonal rainfall regime having four months or rainy season indicating good for

agriculture. The most warning situation for the agriculture and water sectors is the

increasing trends in the seasonality index in most of the districts. Spatial analysis of

both the trends in monthly total rainfall and trends in seasonality index will help the

planners in all the sectors dependable in rainfall in identifying the zones in Maharashtra

for better management and planning.

  11

References :

Adejuwon J. O. 2012: Rainfall seasonality in the Niger Delta Belt, Nigeria Journal of Geography and Regional Planning Vol. 5(2), 51-60.

Epstein, H. E., R. A. Gill, J. M. Paruelo, W. K. Lauenroth, G. J. Jia, and I. C. Burke 2002 : The relative abundance of three plant functional types in temperate grasslands and shrublands of North and South America: Effects of projected climate change, J. Biogeogr., 29, 875–888.

Finkelstein, P L and Truppi, L. E. 1991: Spatial distribution of precipitation seasonality in the United States, Journal of Climate, vol. 4, 373-385.

Groisman, P., R. Knight, and T. Karl 2001: Heavy precipitation and high stream flow in the contiguous United States: Trends in the twentieth century, Bull. Am. Meteorol. Soc., 82, 219–246.

Guhathakurta P, Rajeevan M. 2008 : Trends in rainfall pattern over India, Int. J. of Climatol, 28: 1453–1469.

Guhathakurta P, Sreejith O P and Menon P A 2011 : Impact of climate change on extreme rainfall events and flood risk in India, J. Earth Syst. Sci. 120, No. 3, 359–373. Kanellopoulou E. A. 2002 : Spatial distribution of rainfall seasonality in Greece Weather Vol. 57 June, 215- 219 Livada, I. Asimakopoulos D. N. 2005: Individual seasonality index of rainfall regimes in Greece Climate Research, Vol. 28: 155–161, Markham C.G. 1970 : Seasonality of precipitation in the United States. Am Assoc Am Geogr 60:593–597

Pryor S. C. and Schoof J. T. 2008 : Changes in the seasonality of precipitation over the contiguous USA, J. of Geophysical Research, Vol. 113, D21108, doi:10.1029/2008JD01025.

Rosenberg, N. J., R. A. Brown, R. C. Izaurralde, and A. M. Thomson 2003 : Integrated assessment of Hadley Centre (HadCM2) climate change projections on agricultural productivity and irrigation water supply in the conterminous United States. I. Climate change scenarios and impacts on irrigation water supply simulated with the HUMUS model, Agric. For. Meteorol., 117(1–2), 73– 96.

  12

Sinha Ray K C and Srivastava A. K. 2000 : Is there any change in extreme events like drought and heavy rainfall?; Curr. Sci. 79(2) 155–158. Small, D., S. Islam, and R. M. Vogel 2006 : Trends in precipitation and streamflow in the eastern US: Paradox or perception?, Geophys. Res. Lett., 33, L03403, doi:10.1029/2005GL024995. Walsh, R. P. D. and Lawer, D. M. 1981: Rainfall seasonality: Description, spatial patterns and change through time. Weather, 36, pp. 201 - 208 Walter M. W. 1967 : Length of the rainy season in Nigeria. Nigeria Geog. J., 10: 127-128. Xiao, J. and Moody, A. 2004 : Photosynthetic activity of US biomes: responses to the

spatial variability and seasonality of precipitation and temperature. Global Change

Biology 10: 437-451.

  13

S.NO DISTRICT AVAILABILITY MISSING YEARS

1 AHMEDNAGAR 1901-2004 2 AKOLA 1901-2006 3 AMRAOTI 1901-2006 4 AURANGABAD 1901-2006 5 BHANDARA 1901-2005 1964, 1965 6 BEED 1901-2004 1941 7 MUMBAI CITY 1901-2006 1922 8 BULDHANA 1901-2006 9 CHANDRAPUR 1901-2006 10 DHULIA 1901-2005 11 JALGAON 1901-2006 12 RAIGARH 1901-2006 13 KOLHAPUR 1901-2006 14 NAGPUR 1901-2006 15 NANDED 1901-2006 16 NASIK 1901-2006 17 OSMANABAD 1901-2006 2005 18 PARBHANI 1913-2006 19 PUNE 1901-2006 20 RATNAGIRI 1901-2006 21 SANGLI 1901-2006 22 SATARA 1901-2006 23 SOLAPUR 1901-2006 24 THANE 1901-2006 25 YEOTMAL 1901-2006 26 WARDHA 1901-2006 2005 27 LATUR 1951-2006 1952, 1967 28 GADCHIROLI 1901-2004 29 JALNA 1951-2004 30 SINDHUDURG 1901-2006 31 NANDURBAR 1901-2004 32 GONDIA 1901-2006 33 HINGOLI 1931-2004 1989 34 WASHIM 1901-2006 2005

35 MUMBAI SUBURBAN 1901-1989

1916, 1952, 1953, 1967, 1976

Table 1 Availability of rainfall data for the construction and analysis of district rainfall

  14

Rainfall regime Seasonality Index (SI)

Very equable ≤ 0.19

Equable but with a definite wetter season 0.20 -0.39

Rather seasonal with a short drier season 0.40 – 0.59

Seasonal 0.60 – 0.79

Markedly seasonal with a long drier season 0.80 – 0.99

Most rain in 3 months or less 1.00 -1.19

Extreme, almost all rain in 1± 2 months ≥ 1.20

Table 2 Seasonality Index (SI) classes and the associated different rainfall regime

  15

S.no Districts June July August September June‐Sept1 Ahmednagar 0.051 ‐0.064 0.326 ‐0.078 0.2352 Akola 0.141 ‐0.284 0.837 ‐0.257 0.4543 Amraoti ‐0.118 ‐0.14 1.012 ‐0.302 0.4514 Aurangabad ‐0.047 ‐0.233 0.569 ‐0.148 ‐0.2165 Beed  0.247 ‐0.016 0.394 ‐0.418 0.2076 Bhandara ‐0.452 ‐1.05 ‐0.574 ‐0.531 ‐2.6087 Bombay city 0.741 1.986 2.612 0.791 6.0258 Bombay Sube 2.08 2.391 2.844 0.764 6.7979 Buldhana ‐0.101 ‐0.359 0.905 ‐0.376 0.06810 Chandrapur ‐0.026 ‐0.685 0.532 ‐0.541 ‐0.8111 Dhule 0.26 0.152 0.627 0.039 1.07912 Gadchiroli 0.422 ‐0.286 0.912 ‐0.699 0.35 ↑ Significant at 90 % level13 Gondia ‐0.118 ‐0.308 0.202 ‐0.033 ‐0.321 ↑ Significant at 95 % level14 Hingoli 0.304 ‐0.492 0.56 0.011 0.384 ↑ Significant at 99 % level15 Jalgaon 0.037 ‐0.276 0.962 ‐0.24 0.266 ↓ Significant at 90 % level16 Jalna 0.188 ‐0.938 ‐0.033 ‐0.378 ‐1.16 ↓ Significant at 95 % level17 Kolhapur 2.266 2.241 2.759 0.703 7.971 ↓ Significant at 99 % level18 Latur ‐1.036 ‐1.388 ‐0.409 ‐0.581 ‐4.36319 Nagpur ‐0.324 ‐0.166 0.035 ‐0.334 ‐0.7920 Nanded 0.013 0.252 0.977 ‐0.81 0.51121 Nandurbar 0.473 0.236 1.208 0.076 2.02522 Nasik 0.541 ‐0.02 0.556 0.182 1.25823 Osmanabad ‐0.328 ‐0.455 ‐0.081 ‐0.675 ‐1.52224 Parbhani ‐0.188 0.189 1.091 ‐1.086 0.00625 Pune 0.536 0.136 0.617 1.504 1.50426 Raigarh 0.878 0.473 1.541 0.026 3.327 Ratnagiri 0.476 ‐1.652 1.668 0.118 0.6128 Sangli 0.665 ‐0.161 0.192 0.246 0.72629 Satara 1.634 5.801 2.59 0.576 5.80130 Solapur 0.122 0.154 0.363 0.049 0.59931 Sindhudurg 0.952 0.48 2.074 0.304 4.15632 Thane 0.588 ‐0.367 1.431 0.51 2.16333 Wardha ‐0.366 ‐0.765 0.497 ‐0.731 ‐1.36634 Washim  0.236 0.043 0.6 ‐0.105 0.5735 Yeotmal 0.104 ‐0.256 0.714 ‐0.611 ‐0.048

Table 3. Increase/decrease in rainfall in mm/year for the districts of Maharashtra.

  16

Fig. 1 Mean rainfall (mm) over the districts of Maharashtra for the four seasons.

Fig. 2 Mean annual rainfall (mm) over the districts of Maharashtra

  17

Fig. 3 Distribution of coefficient of variation (%) of rainfall over the districts of Maharashtra during the four seasons

Fig. 4 Distribution of coefficient of variation (%) over the districts of Maharashtra of annual rainfall

  18

Fig. 5 Trends in the monthly rainfall over the districts of Maharashtra. There is no

significant trend in the rainfall in any districts of Maharashtra for the month of November

and December.

  19

Fig. 6 Trends in the seasonal and annual rainfall over the districts of Maharashtra.

  20

a

b

Fig. 7 Values of the Seasonality Index (SI) of the districts of Maharashtra during the

period (a) 1901-50 and (b) 1951-2000.

  21

Fig. 8. Changes in the Seasonility Index in the period 1951-2000 from the period 1901-1950.

Fig. 9. Trends in the Seasonility Index over districts of Maharashtra

  22

PREVIOUS NCC RESEARCH REPORTS

1) New statistical models for long range forecasting of southwest monsoon rainfall over India, M. Rajeevan, D. S. Pai and Anil Kumar Rohilla, September 2005.

2) Trends in the rainfall pattern over India, P. Guhathakurta and M. Rajeevan, May 2006.

3) Trends in Precipitation Extremes over India, U. R. Joshi and M. Rajeevan, October 2006.

4) On El Nino-Indian Monsoon Predictive Relationships, M. Rajeevan and D. S. Pai, November 2006.

5) An Analysis of the Operational Long Range Forecasts of 2007 Southwest Monsoon Rainfall, R.C. Bhatia, M. Rajeevan, D.S. Pai, December 2007.

6) Active and Break Spells of the Indian Summer Monsoon, M. Rajeevan, Sulochana Gadgil and Jyoti Bhate, March 2008.

7) Indian Summer Monsoon Onset: Variability and Prediction, D.S.Pai and M. Rajeevan, October 2007.

8) Development of a High Resolution Daily Gridded Temperature Data Set (1969-2005) for the Indian Region, A K Srivastava, M Rajeevan and S R Kshirsagar, June 2008.

9) A High Resolution Daily Gridded Rainfall Data Set (1971-2005) for Mesoscale Meteorological Studies, M. Rajeevan and Jyoti Bhate, August 2008.

10) Impact of MJO on the Intraseasonal Variation of Summer Monsoon Rainfall over India, D. S. Pai, Jyoti Bhate, O. P. Sreejith and H.R. Hatwar, April 2009.

11) Development of a high resolution daily gridded pressure data set (1969-2007) for the Indian region, A. K. Srivastava, S. R. Kshirsagar and H. R. Hatwar, April 2009.

12) Mapping of Drought Areas over India, P.G. Gore, Thakur Prasad and H.R. Hatwar, January 2010.

13) District-wise Drought Climatology Of The Southwest Monsoon Season over India Based on Standardized Precipitation Index (SPI), D. S. Pai, Latha Sridhar, Pulak Guhathakurta and H. R. Hatwar, Mar. 2010.

14) Changes in extreme rainfall events and flood risk in India during the last century, P. Guhathakurta, Preetha Menon, A. B. Mazumdar and O. P. Sreejith, Dec. 2010.

15) Impact of Climate Change on Land Degradation over India, P.G. Gore, B.A. Roy and H.R. Hatwar. May 2011.

16) New rainfall series for the districts, meteorological sub-divisions and country as whole of India, P. Guhathakurta, A.L Koppar, Usha Krishnan, Preetha Menon, Sept. 2011.

NCC RESEARCH REPORT

GICO AL LO R DO EE PT AE RTM M

A EI ND TN I

N

EA RT TIO NN ECA L E TC ALIM

NATIONAL CLIMATE CENTREOFFICE OF THE

ADDITIONAL DIRECTOR GENERAL OF METEOROLOGY (RESEARCH)INDIA METEOROLOGICAL DEPARTMENT

PUNE - 411 005

RR No.

1/2012

MAY

2012

DESIGNED & PRINTED ATCENTRAL PRINTING UNIT, OFFICE OF THEADDITIONAL DIRECTOR GENERAL OF METEOROLOGY (RESEARCH),PUNE

NCC

Trends and variability of

monthly, seasonal and annual rainfall

for the districts of Maharashtra

and spatial analysis of seasonality

index in identifying

the changes in rainfall regime

P. Guhathakurta, and Elijabeth Saji

P. Guhathakurta, and Elijabeth Saji