Statistical Assessment of Temperature Trends and Change Points in Telangana State, India

Abstract

Aim: This study aimed to analyse the trends and changes in long-term monthly and annual temperature patterns in Telangana state, India. For this study monthly temperature data of Telangana state from January 1960 to December 2022 was collected from the IMD website. The linear regression trend line and the non-parametric tests, such as Mann-Kendall test, Modified-Mann Kendall test and Innovative trend analysis tests, were used to understand the trend present in the temperature data of Telangana state. To gain insights into temperature pattern, we applied Pettit's test. This test helps identify the changes in temperature data, contributing to a deeper understanding of the overall trends. Wallis and Moore test was used to test the randomness of the temperature data under consideration. Results: An Increasing trend pattern was seen in the linear regression trend method for given temperature data. The modified Mann-Kendall test results revealed noteworthy significant trends (* P < 0.05) in January, November, and December, while remaining months showed non-significant trends (NS), suggesting no significant change in temperature patterns. The statistically significant trends (* P < 0.05) were observed in Pre-Summer, Post-Summer, and annual temperature patterns. However, no significant trend (NS) was observed in summer temperatures in the modified Mann-Kendall test. The innovative trend results suggested that most of the trend slopes are positive in nature, indicating an overall increasing trend in temperature for the analysed months and seasonal periods. The Pettit’s test results unveiled significant shifts in temperature across months January, November, December, Pre summer, Post Summer and Annual temperature patterns. Conclusion: These findings can be valuable for understanding climate patterns and informing decision-making processes related to adaptation and mitigation strategies, and help to create the appropriate policy measures in advance.