Trend Analysis and Change Point Detection of Monthly and Seasonal Precipitation and Temperature Series of Aizawl City

Climate change is the most vulnerable change taking place worldwide. The study on temperature and rainfall has received a great concern worldwide as it directly or indirectly influence our ecosystem, cropping system, lives, and livelihood of the society. The study of change in maximum and minimum temperatures as well as rainfall plays a key role particularly in countries where rainfed agriculture is predominant. The present study examines rainfall and temperature of Aizawl City for 12 years from 2010-2021. For this, temperature and precipitation dataset are statistically tested with respect to homogeneity. Three methods namely standard normal homogeneity test (SNHT), Buishand range test and Pettitt’s test, were chosen to detect the inhomogeneity. The magnitudes of the trend were calculated by employing Mann-Kendall and Sen’s slope method. To evaluate the performance of the methods used, three different variables were used, which are Maximum temperature, Minimum temperature and rainfall. In terms of change detection and trend assessment, all of the climate variables were investigated at a monthly, seasonal and annual scale. Seasonal trend of both the parameters has been investigated on monthly basis and seasonal basis i.e., Pre monsoon (March-May), (June–September) and Post monsoon (October-November) and Winter (December-February). The results of the tests are condensed into three classes. A qualitative interpretation of this classification is given, as well as recommendations for the use of these labeled series in trend analysis and variability analysis of weather extremes. Significant change points were found in temperature. These points fell in monsoon and winter for the seasonal series, and May for the monthly series in maximum temperature and September in minimum temperature. A change point in rainfall was also detected in July. The study concludes that homogeneity tests are of great importance in such analyses to avoid false trend detections.