Air Temperature Time Series Trend Analysis of Upper Ganga Canal Command by Mann Kendall Test

Worldwide  climatologists  are  investigating  to  find  a  possible  relation  of  climate  change  with anthropogenic behavior by studying trends in different climatic parameters. However, the changes in temperature are not equal for all regions especially in India and have localized intensity and must be quantified locally to manage the natural resources. Aim of the study is to determine trend in annual mean and monthly Temperature time series using nonparametric methods (i.e. the Mann–Kendall and Sen’s T tests). The magnitudes of trend in a Temperature time series have been estimated by Sen’s estimator method. Auto correlation effect is reduced from the Temperature series before applying the Mann–Kendall test. In the  present study, an investigation has been made to study the  spatial and temporal variability in the maximum, the minimum of Upper Ganga Canal Command located in Uttar Pradesh and Uttarakhand on monthly, annual and seasonal series from 1901 to 2018. The annual maximum and minimum temperatures have increased by 0.63°C and 0.64°C, respectively, over the past  118  years.  On  a  seasonal  basis,  the  winters  are  warmer  than  summers.  The  temperature decreased during the less urbanized period of 1901 to 1951 and increased during the more urbanized period  of  1961  to  2018.  It  is  also  found  that  the  minimum  temperature  increased  at  higher  rate (0.43°C) and the maximum (0.33°C) air temperatures, during the more urbanized period. The study analyzed the temperature data of 118 years from 1901 to 2018 to determine the trend of temperature in the  Upper Ganga  Canal Command region.  As  this region is rapidly  growing, any change  in the temperature trend pattern may have considerable impact on the people of this region. The Z values of the MK Test revealed an increasing trend in temperature. It can, therefore, be concluded that there may be an impact on climate change, contributing to the prolonged and higher temperatures which are rising with time. Similarly, Sen’s Slope Estimator has also estimated an increasing magnitude of slope for the temperature data.

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