RR No. NCC RESEARCH REPORT

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1 E N MAY 2012 RR No. 1/2012 NCC RESEARCH REPORT 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 NATIONAL CLIMATE CENTRE OFFICE OF THE ADDITIONAL DIRECTOR GENERAL OF METEOROLOGY (RESEARCH) INDIA METEOROLOGICAL DEPARTMENT PUNE E T E M A I D IN N A T IO N O ROLOGICAL AL C LIM A TE D E P A RT M E R T N CE T

2 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 National Climate Centre India Meteorological Department PUNE. INDIA

3 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/ Issue date 15 th May, Security Classification Unclassified 6 Control Status Unclassified 7 No. of Pages 21 8 No. of Figures 9 9 No. of reference 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 ( ) 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.

4 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 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 1

5 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 and 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 2

6 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 σ i The probability that the data occurred is the product of the probabilities at each point: N Pα i= 1 1 y i ( a+ bxi) exp 2 σ i 2 Δy Maximizing this is equivalent to minimizing: 2 y ( ) i a + bx i σ i If the standard deviation σ i at each point is the same, then this is equivalent to minimizing: ( y ( a bx )) 2 i + i 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 3

7 (that the variables are not correlated). Finally the statistic used for testing the significance is the Student s t-distribution: t = r N r 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 R SI = X n R n= 1 12 where x n 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 and 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 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 4

8 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. 5

9 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 6

10 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 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 and the later 50 year period 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 and 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 and and is between indicating most of the rain occurred in 7

11 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 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 in the period 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 with only changes in Kolhapur district where SI value increased to be in the range 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 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 and , 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 SI k (Walsh and Lawer 1981; Pryor and Schoof, 2008) time series for each of the districts for each year k using the formula: SI k = 1 R k 12 X n= Rk nk

12 where x nk is the rainfall of month n of the year k and R k 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. 9

13 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. 10

14 References : Adejuwon J. O. 2012: Rainfall seasonality in the Niger Delta Belt, Nigeria Journal of Geography and Regional Planning Vol. 5(2), 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, Finkelstein, P L and Truppi, L. E. 1991: Spatial distribution of precipitation seasonality in the United States, Journal of Climate, vol. 4, 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, Guhathakurta P, Rajeevan M : Trends in rainfall pattern over India, Int. J. of Climatol, 28: 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, Kanellopoulou E. A : Spatial distribution of rainfall seasonality in Greece Weather Vol. 57 June, Livada, I. Asimakopoulos D. N. 2005: Individual seasonality index of rainfall regimes in Greece Climate Research, Vol. 28: , Markham C.G : Seasonality of precipitation in the United States. Am Assoc Am Geogr 60: Pryor S. C. and Schoof J. T : Changes in the seasonality of precipitation over the contiguous USA, J. of Geophysical Research, Vol. 113, D21108, doi: /2008jd 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),

15 Sinha Ray K C and Srivastava A. K : Is there any change in extreme events like drought and heavy rainfall?; Curr. Sci. 79(2) 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: /2005gl Walsh, R. P. D. and Lawer, D. M. 1981: Rainfall seasonality: Description, spatial patterns and change through time. Weather, 36, pp Walter M. W : Length of the rainy season in Nigeria. Nigeria Geog. J., 10: Xiao, J. and Moody, A : Photosynthetic activity of US biomes: responses to the spatial variability and seasonality of precipitation and temperature. Global Change Biology 10:

16 S.NO DISTRICT AVAILABILITY MISSING YEARS 1 AHMEDNAGAR AKOLA AMRAOTI AURANGABAD BHANDARA , BEED MUMBAI CITY BULDHANA CHANDRAPUR DHULIA JALGAON RAIGARH KOLHAPUR NAGPUR NANDED NASIK OSMANABAD PARBHANI PUNE RATNAGIRI SANGLI SATARA SOLAPUR THANE YEOTMAL WARDHA LATUR , GADCHIROLI JALNA SINDHUDURG NANDURBAR GONDIA HINGOLI WASHIM MUMBAI SUBURBAN , 1952, 1953, 1967, 1976 Table 1 Availability of rainfall data for the construction and analysis of district rainfall 13

17 Rainfall regime Very equable 0.19 Equable but with a definite wetter season Rather seasonal with a short drier season Seasonal Markedly seasonal with a long drier season Seasonality Index (SI) Most rain in 3 months or less Extreme, almost all rain in 1± 2 months 1.20 Table 2 Seasonality Index (SI) classes and the associated different rainfall regime 14

18 S.no Districts June July August September June Sept 1 Ahmednagar Akola Amraoti Aurangabad Beed Bhandara Bombay city Bombay Sube Buldhana Chandrapur Dhule Gadchiroli Significant at 90 % level 13 Gondia Significant at 95 % level 14 Hingoli Significant at 99 % level 15 Jalgaon Significant at 90 % level 16 Jalna Significant at 95 % level 17 Kolhapur Significant at 99 % level 18 Latur Nagpur Nanded Nandurbar Nasik Osmanabad Parbhani Pune Raigarh Ratnagiri Sangli Satara Solapur Sindhudurg Thane Wardha Washim Yeotmal Table 3. Increase/decrease in rainfall in mm/year for the districts of Maharashtra. 15

19 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 16

20 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 17

21 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. 18

22 Fig. 6 Trends in the seasonal and annual rainfall over the districts of Maharashtra. 19

23 a b Fig. 7 Values of the Seasonality Index (SI) of the districts of Maharashtra during the period (a) and (b)

24 Fig. 8. Changes in the Seasonility Index in the period from the period Fig. 9. Trends in the Seasonility Index over districts of Maharashtra 21

25 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 ) Trends in the rainfall pattern over India, P. Guhathakurta and M. Rajeevan, May ) Trends in Precipitation Extremes over India, U. R. Joshi and M. Rajeevan, October ) On El Nino-Indian Monsoon Predictive Relationships, M. Rajeevan and D. S. Pai, November ) An Analysis of the Operational Long Range Forecasts of 2007 Southwest Monsoon Rainfall, R.C. Bhatia, M. Rajeevan, D.S. Pai, December ) Active and Break Spells of the Indian Summer Monsoon, M. Rajeevan, Sulochana Gadgil and Jyoti Bhate, March ) Indian Summer Monsoon Onset: Variability and Prediction, D.S.Pai and M. Rajeevan, October ) Development of a High Resolution Daily Gridded Temperature Data Set ( ) for the Indian Region, A K Srivastava, M Rajeevan and S R Kshirsagar, June ) A High Resolution Daily Gridded Rainfall Data Set ( ) for Mesoscale Meteorological Studies, M. Rajeevan and Jyoti Bhate, August ) 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 ) Development of a high resolution daily gridded pressure data set ( ) for the Indian region, A. K. Srivastava, S. R. Kshirsagar and H. R. Hatwar, April ) Mapping of Drought Areas over India, P.G. Gore, Thakur Prasad and H.R. Hatwar, January ) 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 ) 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 ) Impact of Climate Change on Land Degradation over India, P.G. Gore, B.A. Roy and H.R. Hatwar. May ) 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

26 DESIGNED & PRINTED AT CENTRAL PRINTING UNIT, OFFICE OF THE ADDITIONAL DIRECTOR GENERAL OF METEOROLOGY (RESEARCH),PUNE NCC