View Full Text-PDF

Int.J.Curr.Microbiol.App.Sci (2018) 7(12): 251-259 International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 7 Number 12 (2018) Journal homepage: http://www.ijcmas.com Original Research Article https://doi.org/10.20546/ijcmas.2018.712.031 Study of Weather Parameters at Malaprabha Command Area (MCA): A Trend Approach Anand*, A.R.S. Bhat, Gurulingappa and K.V. Ashalatha Department of Agricultural Statistics, College of Agriculture, UAS, Dharwad – 580005, Karnataka, India *Corresponding author ABSTRACT Climate change and agriculture are interrelated processes, both of which take place on a global scale. The study was undertaken to identify the trend in weather parameters in K e yw or ds Malaprabha Command Area (MCA). The research was based on secondary data of Trend, Sen’s slope, nineteen years (1999 to 2017) on different weather parameters such as precipitation, Malaprabha command temperature, relative humidity and potential evapotranspiration obtained from the area Irrigation Water Management Research Centre, Belavatgi under UAS, Dharwad. The Article Info statistical tools namely trend analysis, Mann Kendall trend test and Sen’s slope estimator were employed. The trend analysis shown cubic and quadratic models are suitable for most Accepted: of the parameters. Mann Kendall trend test revealed that most of the months showed no 04 November 2018 trend for precipitation, temperature and potential evapotranspiration at Belavatagi Farm, Available Online: 10 December 2018 except for few months, temperature showed the decreasing trend respectively in more months. At MCA, temperature and relative humidity showed decreasing and increasing trend in most of the months. Precipitation showed no trend in almost all months. Introduction an average, for example, greater or fewer extreme weather events. Karnataka state has a Climate change and agriculture are pleasant weather. The state is known to have a interrelated processes, both of which take moderate summers and pleasant winters (Raje place on a global scale. Climate change affects Gowda et al., 2012). Global average air agriculture in a number of ways, including temperature near the earth's surface raised changes in average temperatures, rainfall, and 0.74 ± 0.18 °C (1.33 ± 0.32 °F) during the climate extremes changes in pests and twentieth century. Climate change may be diseases. Climate change is a long-term limited to a specific region, or may occur change in the statistical distribution of weather across the whole earth (Sureshbabu, 2013). patterns over periods of time that range from decades to millions of years (Abdulharias et The entire coastal belt of Karnataka has the al., 2010). It may be a change in the average tropical monsoon climate. The climate in the weather conditions or a change in the coastal is hot with excessive rainfall during distribution of weather events with respect to the monsoon. The southern part of Karnataka 251 Int.J.Curr.Microbiol.App.Sci (2018) 7(12): 251-259 experiences hot, seasionally dry tropical 1999-2017. Further, the trend line presented savanna climate. The Northern Karnataka using graphs of Free hand curve fitting to experiences hot, semi-arid, tropical; steppe know the trend of weather parameter over type of climate. The winter begins from time. January and ends in February. It is followed by summer from March to May. Post monsoon Mann-Kendall test season begins in October and lasts till December. The months April and May are The trend values for each weather parameter really very hot and dry. Weather tends to be were determined using non-parametric Mann- harsh during June due to high humidity Kendall test, which were tested for the (Sureshbabu, 2013). significance at 95% level. Since there is fluctuation presents in the weather parameters, In order to study the trend of weather non-parametric Mann-Kendall test is useful parameters in Malaprabha Command Area because its statistic is based on the sign of (MCA), we have collected the data from the differences, not directly on the values of Irrigation Water Management Research random variable and therefore trends Centre, Belavatgi under UAS, Dharwad. determined is less affected by the fluctuations. Malaprabha Command Area covers Belgavi, Mann-Kendall test is applicable to the Bagalkot, Gadag, and Dharwad districts. detection of monotonic trend in time series Malaprabha project comprises the area of a (Sureshbabu, 2013). dam across the river Malaprabha, near Navilutheertha in Belgaum district. The The test statistic S is calculated using the districts benefited under the Malaprabha formula project are Belagavi, Gadag and Bagalkot. The taluks benefited are Bailahongal, Saudatti, Badami, Navalagund, Hubli, Naragund, Ron and Gadag. The statistical investigation on trend of weather parameters was conducted based on secondary data. The statistical techniques such as, trend analysis and Mann Kendal tests were carried out. Trend analysis was done by fitting polynomial and well known non-linear models separately for each parameter over the years. The suitable prediction models were selected Where n is the number of observed data series, 2 based on R values and the least meansquare xj and xk are the values in periods j and k error. (Swetha, 2009) respectively, j>k. The sampling distribution of S is as follows, Materials and Methods Trend analysis Trend analysis was done by fitting the regression equation separately for each parameter over the years for the period of 252 Int.J.Curr.Microbiol.App.Sci (2018) 7(12): 251-259 Where VAR(S) is determined as Sen’s estimator is computed as Qmed = T(N+1)/2 if N appears odd, and it is considered as Qmed = [TN/2+T(N+2)/2]/2 if N appears even. At the end, Qmed is computed by a two sided test at 100 (1-α) confidence interval and then a true slope can be obtained by the non-parametric test. Positive value of Qi indicates an upward Where q is the number of tied groups, and tp is or increasing trend and a negative value of Qi th the number of data values in the p group. The gives a downward or decreasing trend in the probability associated with this normalized time series. test statistic. The probability density function for a normal distribution with a mean of 0 and Results and Discussion a deviation of 1 is given by the following Prediction models equation: Eleven different non-linear and polynomial models were used for predicting the trend of Microsoft Excel function, NORMSDIST (), weather parameters at Belavatagi farm is was used to calculate this probability. The presented in Table 1 and 2 for temperature, trend is said to be decreasing if Z is negative precipitation and potential evapotranspiration. and the computed probability is greater than For investigating the suitable model for trend th the level of significance. of temperature, 7 degree polynomial model was found to be significant and proved to be 2 The trend is said to be increasing if the Z is the best with (R =0.746) i.e. 74.6 per cent positive and the computed probability is variation in trend is accounted by temperature. greater than the level of significance. If the In case Potential evapotranspiration two computed probability is less than the level of models were found to be significant. significance, there is no trend. Among these models, quadratic and cubic Sen’s slope estimator test models were found to be significant. Among these models, cubic model was proved to be 2 The magnitude of trend is predicted by the best with highest (R = 0.589) followed by 2 Sen’s estimator. Here, the slope (Ti) of all data quadratic model (R = 0.492). With the cubic pairs is computed as (Sen, 1968) model 58.9 per cent of the variation in PET was explained by trend. For precipitation none of the non-linear and polynomial model were for i = 1,2,……..,N significant, indicating variation in precipitation was not accounted because of Where xj and xk are considered as data values presence of trend. Similarly, for the weather at time j and k (j>k) correspondingly. The data of Malaprabha command area, the median of these N values of Ti is represented different models used, is presented in Table 3 as Sen’s estimator of slope which is given as: and 4 for temperature, precipitation and relative humidity. For investigating the suitable model for trend of temperature at MCA, all models were found to be significant except inverse and S model. 253 Int.J.Curr.Microbiol.App.Sci (2018) 7(12): 251-259 Table.1 Comparison of models for investigating the suitable model for trend of weather parameters at Belavatagi Farm of MCA Temperature PET Rainfall Equation Coefficient MSE R Square Coefficient MSE R Square Coefficient MSE R Square Linear -0.041 2.395 0.023 9.222 24442.19 0.104 4.892 36663.88 0.021 Logarithmic -0.451 2.311 0.057 33.583 26515.31 0.029 45.439 36040.84 0.038 Inverse 1.514 2.325 0.051 -57.311 27113.99 0.007 -158 36098.8 0.036 Quadratic -0.476 2.144 0.177 -63.557 14742.06 0.492** 41.965 36042.06 0.095 0.022 3.639 -1.854 Cubic -0.513 2.286 0.177 30.706 12724.84 0.589** -13.735 37397.38 0.119 0.026 -7.847 4.933 0 0.383 -0.226 Compound 0.999 0.004 0.019 1.006 0.012 0.085 1.011 0.153 0.024 Power -0.017 0.004 0.053 0.019 0.013 0.019 0.086 0.152 0.033 S 0.058 0.004 0.05 -0.028 0.013 0.003 -0.265 0.153 0.024 Growth -0.001 0.004 0.019 0.006 0.012 0.085 0.011 0.153 0.024 Exponential -0.001 0.004 0.019 0.006 0.012 0.085 0.011 0.153 0.024 Logistic 1.001 0.004 0.019 0.994 0.012 0.085 0.989 0.153 0.024 7th degree -21.91421 0.964 0.746* - Not fitted - (No Significant fit up to 120 Polynomial Polynomial 12.32798

Load more