INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCES Volume 6, No 6, 216 Copyright by the authors - Licensee IPA- Under Creative Commons license 3. Research article ISSN 976 442 A case study of temperature and rainfall trends using Mann-Kendall test in Saurashtra Region (Junagadh) of Chinchorkar S.S 1, Bhavin Ram D.M 2, Paradava 3 and Trivedi M.M 4 1- Assistant Professor, Anand Agricultural University, Dahod, 2- Assistant Professor, Anand Agricultural University, Anand. 3- Research Associate, Anand Agricultural University,Dahod. 4- Associate Professor, Anand Agricultural University, Dahod. sachin_chinchorkar@yahoo.com doi: 1.688/ijes.69 ABSTRACT The long term change in temperature and rainfall has been assessed by linear trend analysis. It is evident from above Figures that monthly mean of maximum (MMAX) temperatures have increased significantly for all the months except the month of October for which a very weak decrease in MMAX temperature is observed. This implies that in Junagadh, the highest increase in MMAX temperature occurs in November by (.21C) during last 32 years. The annual mean of monthly mean of maximum temperatures observed a decreasing trend having an annual increase of.41c per year. The monthly mean of TMRF have increased significantly for the months February, March, April May, July, August, September, whereas it shows decreasing trend in January, June, October, November, and December. The highest increase in TMRF occurs in August by 1.463 mm during the last 32 years. The highest decrease in TMRF occurs in November and decreased by.1.6 mm. the annual mean of monthly mean of total mean rainfall observed an increasing trend having an increase of.482 mm per year. In winter total mean rainfall observed a deceasing trend of 1.291 mm per day where as in summer increasing trend by.64 mm and in monsoon slightly increase trend by 1.12 mm per year in Junagadh station. Annual MMAX temperature shows increasing trend which is statistically significant at 5% level of significance whereas annual TMRF shows increasing trend which is statistically insignificant at 5% level of significance. Keywords: Rainfall, maximum temperature, trend analysis, Mann-Kendall test, rainfall pattern, global warming 1. Introduction Climate change has brought in unexpected changes not only in India but all over the regions across the world. Emergence of global warming due to climate change is the new and most talked subject of today s world as it being the most threatening issue for very existence of life on the earth. One of the consequences of climate change is the alteration of rainfall patterns and increase in temperature. According to Intergovernmental Panel on Climate Change (IPCC,27) reports, the surface temperature of the earth has risen by.6+.2c over the 2th century. Also in the last 5 years, the rise in temperature has been.13 +.7C per decade. As the warming depends on emissions of GHGs in the atmosphere, the IPCC has projected a warming of about.2c per decade. Further, surface air temperature could rise by between 1.1C to 6.4C over 21st century. In case of India, the climate change expected to adversely affect its natural resources, forestry, Received on January 216 Published on May 216 954
agriculture, and change in precipitation, temperature, monsoon timing and extreme events (M. H. Fulekar, R.K. Kale, 21). Due to global warming, precipitation amount, type and timing are changing or are expected to change because of increased evaporation, especially in the tropics (Ritter, 26). The pattern and amount of rainfall are among the most important factors that affects agricultural production. Agriculture is vital to India s economy and the livelihood of its people. Agriculture is contributing 21% to the country s GDP, accounting for 115 of total export, employing 56.4% of the total workforce, and supporting 6 million people directly and indirectly (Beena Shah, 21). The analysis of rainfall records for long periods provides information about rainfall patterns and variability (Lazaro et. al., 21). The main objective of this paper is to analyze the 198 to 211 rainfall and temperature records obtained from Junagadh Agricultural University Junagadh, Gujarat as a basis on sustainability of crop production. 2. Climate Change Several studies relating to changing pattern of rainfall over India observed that there is no clear trend of increase or decrease in average rainfall over the country (Mooley and Parthasarathy, 1984; Thapliyal and Kulshrestha, 1991; Lal, 21; Kumar et al., 21). Although long-term trends in monsoon rainfall have not been observed on an all-india scale, several studies have found significant trends in rainfall on a regional scale (Koteswaram and Alvi, 1969; Jagannathan and Parthasarathy, 1973; Raghavendra, 1974; Chaudhary and Abhyankar, 1979; Kumar et al., 25; Dash et al., 27; Kumar and Jain, 21). In India, the climate change is expected to adversely cause changes in precipitation, temperature, monsoon timing and extreme events (Fulekar and Kale 21). Due to global warming, precipitation amount, type and timing are changing or are expected to change because of increased evaporation, especially in the tropics (Ritter, 26). The pattern and amount of the rainfall are among the most important factors that affect agriculture production. Agriculture is vital to India s economy and livelihood of its people. Agriculture is contributing 21% to the country s GDP, employing 56.4% of the total workforce and supporting 6 million people directly and indirectly (Beena Shah 21). In India despite recent progress in industrlization, the soundness of economy is significantly dependent upon the gross production of agricultural commodities and agriculture is the mainstay of millions of teeming population with crops pre-dominantly dependent upon natural rainfall. Excepting the south-eastern part of the peninsula and Jammu and Kashmir, the south west monsoon (June Sept.) is the principle source of rain in the entire country. During monsoonal period more than 75% of annual rainfall is received over a major portion of the country. India s economy has traditionally been agricultural in nature and excess climate anomalies, deficient and flooded rainfall years have a dramatic impact on the economy as well as on the living conditions of the inhabitants of the affected regions (Parthasarthy et al. 1988). The green revolution on technology has increased the rice and other food grain production and productivity substantially. 3. Study Area The Junagadh city is located between latitudes 21º 31 N and 7º 49 E in Figure 1. The city is a gate way to famous Gir Forest which is the natural habitat for the last existing population of Asiatic Lion in the wild. Apart from Gir, there is Girnar Ranges, Barda Hills and extensive grasslands known as Vidis, which also support a variety of wildlife especially avifauna. Junagadh has a tropical wet and dry climate, with three distinct seasons observed, a mild International Journal of Environmental Sciences Volume 6 No.6 216 955
winter from November to February, a hot summer from March to June, and a monsoon from July to October. Junagadh faces adverse climatic conditions in the summer months with the temperature ranging from 28 Celsius to 38 Celsius. In the winter months, the temperature ranges from 1 Celsius to 25 Celsius. Various factors such as its close proximity to the sea influence the weather of Junagadh. The latent winds from the sea affect the climatic conditions in the region. 4. Data and Methodology Figure 1: Location map of study area The data used in this paper are the monthly averages of total mean rainfall, minimum and maximum atmospheric temperatures during 198-211 (32 years). The time series is made up of four components known as seasonal, trend, cyclical and irregular (Patterson, [11]). Trend is defined as the general movement of a series over an extended period of time or it is the long term change in the dependent variable over a long period of time (Webber and Hawkins, [18]). Trend is determined by the relationship between the two variables as temperature and time, rainfall and time. The statistical methods such as regression analysis and coefficient of determination R 2 (Murray R. Spiegel, Larry J. Stephens, [1]) are used. The magnitudes of the trends of increasing or decreasing maximum temperatures and total mean rainfall were derived and tested by the Mann-Kendall (M-K),[9] trend test and slope of the regression line using the least squares method. The coefficient of variation for MMAX temperature is highest in the month of January and it is observed as 6.294% whereas it is lowest in the month April and it is 2.957 % for the Junagadh station. This means that maximum temperature is most stable in the month of April and least stable in the month of January for the Junagadh station given in Table 1. Table 1: Statistical summary of monthly mean of MMAX temperatures. Month Mean SD CV (%) January 29.7 1.869 6.294 February 31.85 1.93 6.6 March 36.1 1.431 3.964 April 39.13 1.157 2.957 May 38.81 1.181 3.43 June 35.62 1.147 3.222 July 31.71 1.213 3.826 August 3.48 1.99 3.64 International Journal of Environmental Sciences Volume 6 No.6 216 956
MAR-MMAX FEB-MMAX JAN-MMAX September 32.72 1.249 3.819 October 35.77 1.415 3.955 November 34.1 1.144 3.355 December 31.15 1.214 3.897 4.1 Trend Analysis of Monthly Mean of Maximum Temperature (MMAX) The trends of monthly mean of maximum temperatures over different years were obtained using linear regression best fit lines. The linear regression trends with their linear regression equations and coefficient of determinations for all the months from January to December are represented in Figure1 and summarized in Table2 below. It is evident from above Figures that monthly mean of maximum (MMAX) temperatures have increased significantly for all the months except the month of October for which a very weak decrease in MMAX temperature is observed. This implies that in Junagadh, the highest increase in MMAX temperature occurs in November by (.21C) during last 31 years. 4 3 y =.88x + 12.21 R 2 =.19 2 1 5 4 3 2 1 y =.43x + 23.23 R 2 =.4 5 4 y =.35x - 33.795 R 2 =.527 3 2 1 International Journal of Environmental Sciences Volume 6 No.6 216 957
JUL-MMAX JUN-MMAX MAY-MMAX APR-MMAX 5 4 y =.375x - 35.665 R 2 =.923 3 2 1 5 4 3 2 1 y = -.34x + 16.75 R 2 =.731 5 4 3 2 1 y =.1x + 15.61 R 2 =.67 5 4 3 2 1 y = -.239x + 79.386 R 2 =.341 International Journal of Environmental Sciences Volume 6 No.6 216 958
DEC-MMAX NOV-MMAX OCT-MMAX 5 4 3 2 1 y = -.259x + 87.441 R 2 =.295 y =.212x - 8.2167 5 R 2 =.32 4 3 2 1 4 3 2 1 y = -.216x + 74.218 R 2 =.278 Figure 1: Linear regression trends of monthly mean of maximum temperatures. International Journal of Environmental Sciences Volume 6 No.6 216 959
MMAX Table 2: Linear regression equations of MMAX temperatures for all the months. Month Regression Line R 2 January Y=.88x+12.21.19 February Y=.43x+23.23.4 March Y=.35x-33.795.527 April Y=.375x-35.66.923 May Y=-.239x+79.38.341 June Y=.1x+15.61.67 July Y=-.239x+79.386.341 August Y= -.152x+6.721.167 September Y= -.455x+123.42.1165 October Y=-.259x+87.441.295 November Y=.212x-8.2167.32 December Y=-.216x+74.218.278 4.2 Trend analysis of annual mean of monthly maximum temperature (MMAX) The annual mean of monthly mean of maximum temperatures observed a decreasing trend having an annual increase of.41c per year, as represented in Figure 2 during the last 31 years. 45 y = -.36x + 41.283 R 2 =.34 3 15 Figure 2: Trend of annual mean of monthly maximum temperature The coefficient of variation for TMRF observed highest in the month of April and it is 52.79% whereas coefficient of variation is minimum for the month of July and it is 62.97% for the Junagadh Station. This shows that rainfall is more stable in the month of July and is more variable in the month of April for the Junagadh district and summarized in Table 3. Table 3: Statistical summary of monthly mean of total mean rainfall (TMRF) Month Mean SD CV (%) January.11.53 459.11 February.34 1.54 451.1 March.7.26 387.45 April.21 1.9 52.79 May 1.95 3.66 187.84 June 5.41 73.6 146. International Journal of Environmental Sciences Volume 6 No.6 216 96
FEB-TMRF JAN-TMRF July 85.48 53.82 62.97 August 47.78 36.13 75.6 September 33.55 33.36 99.44 October 7.82 11.91 152.25 November 3.3 9.22 34.2 December.12.47 41.77 Annual 975.5 477.32 48.93 Winter 34.38 65.29 189.91 Summer 66.6 78.79 118.31 Monsoon 874.52 473.97 54.2 The trends of monthly mean of total mean rainfall over different years were obtained using linear regression best fit lines. The linear regression trends with their linear regression equations and coefficient of determinations for all the months from January to December are represented in Figure 3 and summarized in Table 4 below. It is evident from above Figures that monthly mean of TMRF have increased significantly for the months February, March, April May, July, August, September, whereas it shows decreasing trend in January, June, October, November, and December for the Junagadh district. This implies that in Junagadh district the highest increase in TMRF occurs in August by 1.463 mm during the last 31 years. The highest decrease in TMRF occurs in November and decreased by.1.6 mm. 4 3 y =.3x -.4246 R 2 = 2E-5 2 1 1 8 6 4 2 y =.25x - 4.623 R 2 =.152 International Journal of Environmental Sciences Volume 6 No.6 216 961
JUN-TMRF MAY-TMRF APR-TMRF MAR-TMRF 4 3 2 y =.72x - 14.35 R 2 =.639 1 8 6 4 y =.294x - 58.52 R 2 =.625 2 14 y =.147x - 26.88 12 R 2 =.695 1 8 6 4 2 4 3 y = -2.1528x + 4346.4 R 2 =.729 2 1 International Journal of Environmental Sciences Volume 6 No.6 216 962
OCT-TMRF SEP-TMRF AUG-TMRF JUL-TMRF 25 2 15 1 5 y = -.465x + 113.4 R 2 =.64 16 14 12 1 8 6 4 2 y = 1.463x - 2871.5 R 2 =.1398 15 y = 1.665x - 294.7 R 2 =.871 1 5 6 y = -.1516x + 31.33 R 2 =.138 4 2 International Journal of Environmental Sciences Volume 6 No.6 216 963
DEC-TMRF NOV-TMRF 6 y = -.169x + 324.15 R 2 =.26 4 2 6 4 2 y = -.12x + 2.433 R 2 =.392 Figure 3: Linear regression trends of monthly mean of total mean rainfall Table 4: Linear regression equations of TMRF for all the months Month Regression equations R 2 January y =.3x -.4246 2E-5 February y =.25x - 4.623.152 March y =.72x - 14.35.639 April y =.294x - 58.52.625 May y =.147x - 26.88.695 June y = -2.1528x + 4346.4.729 July y = -.465x + 113.4.64 August y = 1.463x - 2871.5.1398 September y = 1.665x - 294.7.871 October y = -.1516x + 31.33.138 November y = -.169x + 324.15.26 December y = -.12x + 2.433.392 Annual y =.4826x + 12.553 9E-5 Winter y = -1.2916x + 2611.7.334 Summer y =.6446x - 1219.6.57 Monsoon y = 1.1295x - 1379.5.5 International Journal of Environmental Sciences Volume 6 No.6 216 964
SUMMER-TMRF WINTER-TMRF ANNUAL-TMRF 4.4 Trend Analysis of annual, winter, summer, monsoon mean of total mean rainfall (TMRF) 3 25 2 15 1 5 y =.4826x + 12.553 R 2 = 9E-5 35 3 y = -1.2916x + 2611.7 R 2 =.334 25 2 15 1 5 35 3 y =.6446x - 1219.6 R 2 =.57 25 2 15 1 5 International Journal of Environmental Sciences Volume 6 No.6 216 965
MONSOON-TMRF 3 25 2 15 1 5 y = 1.1295x - 1379.5 R 2 =.5 Figure 4: Trend of annual, winter, summer, monsoon mean of monthly total mean rainfall. From the figure 4, the annual mean of monthly mean of total mean rainfall observed an increasing trend having an increase of.482 mm per year. In winter total mean rainfall observed a deceasing trend of 1.291 mm per day where as in summer increasing trend by.64 mm and in monsoon slightly increase trend by 1.12 mm per year. The MANN-Kendall Test for trend The Mann-Kendall test is a nonparametric test for identifying trends in time series data. The test was suggested by Mann (1945) and has been extensively used with environmental time series (Hipel and McLeod, 25). The test compares the relative magnitudes of sample data rather than the data values them. One benefit of this test is that the data need not confirm to any particular distribution. Let X1, X2. Xn represents n data points where Xj represents the data point at time j. Then the Mann-Kendall statistic (S) is given by A very high positive value of S is an indicator of an increasing trend, and a very low negative value indicates a decreasing trend. However, it is necessary to compute the probability associated with S and the sample size, n, to statistically quantify the significance of the trend. For a sample size >1, a normal approximation to the Mann-Kendall test may be used. For this, variance of S is obtained as, International Journal of Environmental Sciences Volume 6 No.6 216 966
The presence of a statistically significant trend is evaluated using Z value. 5. Conclusions It is observed that the long term change in temperature and rainfall has been assessed by linear trend analysis. It is evident from above figures that monthly mean of maximum (MMAX) temperatures have increased significantly for all the months except the month of October for which a very weak decrease in MMAX temperature is observed. This implies that in Junagadh, the highest increase in MMAX temperature occurs in November by (.21C) during last 32 years. The annual mean of monthly mean of maximum temperatures observed an decreasing trend having an annual increase of.41c per year. The monthly mean of TMRF have increased significantly for the months February, March, April May, July, August, September, whereas it shows decreasing trend in January, June, October, November, and December.The highest increase in TMRF occurs in August by 1.463 mm during the last 32 years. The highest decrease in TMRF occurs in November and decreased by.1.6 mm. the annual mean of monthly mean of total mean rainfall observed an increasing trend having an increase of.482 mm per year. In winter total mean rainfall observed an deceasing trend of 1.291 mm per day where as in summer increasing trend by.64 mm and in monsoon slightly increase trend by 1.12 mm per year in Junagadh station. Annual MMAX temperature shows increasing trend which is statistically significant at 5% level of significance whereas annual TMRF shows increasing trend which is statistically insignificant at 5% level of significance. 6. References 1. Beena Shah, (21), Global and National Concerns on Climate Change, University News, 48(24), pp 15-23. 2. Chaudhary A, Abhyankar VP., (1979), Does precipitation pattern foretell Gujarat climate becoming arid. Mausam 3, pp 85 9. 3. Dash SK, Jenamani RK, Kalsi SR, Panda SK., (27), Some evidence of climate change in twentieth-century India, Climatic Change 85, pp 299 321. 4. Deshmukh, D. T., Lunge, H. S., (213), A Study of Temperature and Rainfall Trends In. Buldana District of Vidarbha, India. International Journal of Scientific & Technology Research, 2(2). 5. Fulekar, M.H., Kale, R.K., (21), Impact of Climate Change: Indian Scenario, University News, 48(24), pp 15-23. 6. IPCC, Climate Change-A Synthesis Report of the IPCC, Technical Report, Inter governmental Panel on Climate Change (27). 7. Jagannathan P, Parthasarathy B, (1973), Trends and periodicities of rainfall over India, Monthly Weather Review, 11, pp 371 375. International Journal of Environmental Sciences Volume 6 No.6 216 967
8. Jestinos Mzezewa, Titus Misi and Leon D van Rensburg, (29), Characterization of rainfall at a semi-arid ecotope in the Limpopo Province (South Africa) and its implications for sustainable crop production. 9. Julious M. Huho, Josephine K.W. Ngaira, Harun O.Ogindo and Nelly Masayi, (212), The changing rainfall pattern and the associated impacts on subsistence agriculture in Laikipia East District, Kenya, Journal of Geography and Regional Planning, 5(7), pp 198-26. 1. Koteswaram P, Alvi SMA, (1969), Secular trends and periodicities in rainfall at west coast stations in India, Current Science 38, pp 229 231. 11. Kumar V, Singh P, Jain SK, (25), Rainfall trends over Himachal Pradesh, Western Himalaya, India. In: Development of Hydro Power Projects A Prospective Challenge. Conference, Shimla, pp 2 22. 12. Kumar V, Jain SK, Singh Y, (21), Analysis of long-term rainfall trends in India, Hydrological Sciences Journal 55(4), pp 484 496 13. Lal M, (21), Climatic change implications for India s water resources. Journal of Indian Water Resource Society 21, pp 11 119. 14. Lazaro R, Rodrigo FS, Gutierrez L, (21), Domingo Fand Puigdefafregas J Analysis of a 3-year rainfall record (1967-1997) in semi-arid SE Spain for implications on vegetation, J. Arid Environ. 48, pp 373-395. 15. Mooley DA, Parthasarthy B, (1984), Fluctuations of all India summer monsoon rainfall during 1871 1978. Climatic Change 6, pp 287 31. 16. Murray R. Spigel, Larry J. Stephens, Schaum s outlines Statistics, third edition, TATA Mcgraw-Hill Edition, (2). 17. Mann, H.B., (1945), Nonparametric tests against trend, Econometrica, 13, pp 245-259. 18. Parthasarthy, B. A.A. Munot and D.R. Kothawale, (1988), Regression model for estimation of India s food grain production from summer monsoon rainfall. Agricultural and Forest Meteorology 42, pp 167-182. 19. Patterson, P.E. Statistical Methods, Richard D. Irwin INC, Homewood, IL, (1987). 2. Raghavendra VK, (1974), Trends and periodicities of rainfall in subdivisions of Maharashtra state. Indian Journal of Meteorology and Geophysics 25, pp 197 21. 21. Ritter ME The physical environment: an introduction to physical Geography. 22. Del Rio S, R.Fraile, L. Herrero and A.Penas, Analysis of Recent Trends in Mean Maximum and Minimum Temperatures in a Region of the NW of Spain (Castillay Leon), Theorotical and Applied Climatology, 9(1-2), pp 1-12. International Journal of Environmental Sciences Volume 6 No.6 216 968
23. Thapliyal V, Kulshrestha SM, (1991), Decadal changes and trends over India, Mausam, 42, pp 333 338. 24. Webber, J. and Hawkins, C. Statistical Analysis Application to Business and Economics, Harper and Row, New York,(198). International Journal of Environmental Sciences Volume 6 No.6 216 969