Trend Analysis of Rainfall in Telangana State (India) Using Advanced Statistical Approaches

The primary objective of this study is to analyse the annual and seasonal rainfall trends in the Bapatla district of Andhra Pradesh over the long term. The seasonal rainfall data for Bapatla district was collected from the NASA Power website and covering the period from January 1982 to December 2021. To gain insights into the patterns present in the rainfall data for Bapatla district, a combination of parametric methods, including linear regression, and non-parametric tests such as the Mann-Kendall test, Sen’s slope test, Modified-Mann Kendall test, Innovative trend analysis were employed in this approach. The randomness of the rainfall data under investigation was assessed using the Wallis and Moore test. To detect the single change point of the rainfall pattern Pettitt test was employed. The results of linear regression trend method exhibited both increasing and declining trends in rainfall pattern. Notably, the months of April, May and June months exhibited a statistically significant increasing trend other months exhibited no significant trend in Mann-Kendall test, modified Mann-Kendall test, Sen’s slope tests. The monsoon season exhibited a statistically significant trend and pre-monsoon, post-monsoon periods and anuual rainfall exhibited non-significant trends in Mann-Kendall and Sen’s slope tests. Moreover, the pre monsoon and monsoon seasons exhibited significant trend in rainfall pattern of Bapatla district by modified Mann-Kendal test. This study serves to raise awareness among agricultural stakeholders, particularly farmers, regarding the fluctuations in monthly and seasonal rainfall patterns and enables them to more effectively allocate resources and prepare for anticipated water shortages during non-monsoon months.

“…Within the various seasons, the monsoon season exhibited a lower CV of 28.73%. These findings align with those reported by Jyostna et al, [16].…”

Section: Descriptive Statisticssupporting

confidence: 94%

Long Term and Seasonal Rainfall Trend in Bapatla District of Andhra Pradesh Using Advanced Statistical Methods

Swarnalatha,

Srinivas,

Yamini Leela A.

et al. 2023

“…Randomness of the time series for monthly, seasonal and annual rainfall data was identified using Wallis and Moore Phase-Frequency test [23]. Table 4 shows that the monthly, seasonal, and annual rainfall data were non-random in nature as exact level of significances for all series were found to be greater than 5%.…”

Section: Wallis and Moore Phase-frequency Testmentioning

confidence: 99%

Trend Analysis of Rainfall and Detection of Change Point in Terai Zone of West Bengal

Vinay H. T.,

Ghosh,

Ojha

et al. 2024

Analyzing rainfall trends is crucial for understanding climate patterns to managing water resources and planning agriculture. Trends in rainfall can reveal shifts in climate conditions, whether it is an increase, decrease, or fluctuate over time. The present study intends to examine the long-term trend in monthly, seasonal and annual rainfall in Cooch Behar district of West Bengal spanning from 1969 to 2022. Wallis and Moore test applied to determine the randomness of rainfall, whereas, linear regression trend line, Mann-Kendall (MK) test, Modified-Mann Kendall (MMK) test, and Innovative Trend Analysis (ITA) are used to understand the trend pattern in rainfall. MMK test reveals a significant decreasing trend in November month. In contrast, the remaining months, seasonal and annual rainfall revealed non-significant trend. According to the ITA approach, there was a significant increase in rainfall of January and a significant decrease in rainfall of November and December. The Pettitt’s test identified November 2006 as a trend changing year and there was no significant change point for the remaining months, seasonal and annual rainfall.

“…However, there is a significant difference in the variance of temperature across the entire year (Annual) with a p-value of 0.02 according to the Wallis and Moore test. The modified Mann-Kendall test was employed for analysis due to its enhanced efficiency in handling autocorrelation within the data [16].…”

Section: Trend Of Annual Temperaturementioning

confidence: 99%

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

Laasya,

Kallakuri,

Rathod

et al. 2024

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.