NCC RESEARCH REPORT G I C O A L L O R D O E E P T A E R T M M A E I N D T N I N E A R T T I O N N E C A L E T C A L I M NATIONAL CLIMATE CENTRE OFFICE OF THE ADDITIONAL DIRECTOR GENERAL OF METEOROLOGY (RESEARCH) INDIA METEOROLOGICAL DEPARTMENT PUNE - 411 005 RR No. 1/2012 MAY 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 P. Guhathakurta, and Elijabeth Saji P. Guhathakurta, and Elijabeth Saji
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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
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
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.
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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
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
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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
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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