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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