Trend analysis of climatic variables in an arid and semi-arid region of the Ajmer District, Rajasthan, India

Abstract

In the present study, trends and variations in climatic variables (i.e. rainfall, wet day frequency, surface temperature, diurnal temperature, cloud cover, and reference and potential evapotranspiration) were analyzed on seasonal (monsoon and non-monsoon) and annual time scales for the Ajmer District of Rajasthan, India. This was done using non-parametric statistical techniques, i.e. the Mann–Kendall (MK) and Modified Mann–Kendall (MMK) tests, over a period of 100 years. The MK test with prewhitening (MK–PW) of climatic series was also applied to climatic variables and the results were compared to those obtained through the MK and MMK tests in order to assess the performance of trend detection methods. The Pettitt–Mann–Whitney (PMW) test was applied to detect the temporal shift in climatic series. The trend analysis revealed that annual and seasonal rainfall did not show any statistically significant trend at a 10% significant level. A noticeable trend increase was found in wet day frequency, surface temperature and reference evapotranspiration (ET) during the non-monsoon season from the three non-parametric statistical tests at a 10% significance level. A statistically significant decrease in maximum temperature was found during the non-monsoon season by the MK–PW test alone. This analysis of several climatic variables at the district scale is helpful for the planning and management of water resources and the development of adaptation strategies in adverse climatic conditions.

Evaluation of Small-Scale Fishers’ Perceptions on Climate Change and Their Coping Strategies: Insights from Lake Malawi

“…Another inferential statistic used was the Pearson's correlation coefficient, which determined whether any relationship existed between the maximum annual temperature and total annual precipitation. Furthermore, the Mann-Kendall (MK) test [89,90] was used to establish the presence of trends in precipitation and temperature of the meteorological time series data [91]. In order to verify if the required conditions for the MK test were met, one of the assumptions was to check if there was any random correlation between the variables being tested.…”

Spatio-temporal variation of rainfall over Bihar State, India

“…Missing values and consistency of the long-term data set were checked and the presence of serial dependency over the time series was determined by applying lag-1 auto-correlation [CUNDERLIK, BURN 2004]. The non-parametric Mann-Kendall trend and Sen's slope estimator test were applied to monthly, annual and seasonal data sets for all the districts in Bihar [KENDALL 1975;PINGALE et al 2016;SEN 1968;THEIL 1950]. The Mann-Kendall test was applied at a 95% confidence level and overall results are represented by positive significant, negative significant, positive and negative trends.…”

Stochastic Analysis of Typhoon-Induced Storm Surge in the Coastal Area of the Korean Peninsula: Inference from a Nonstationary, Bayesian, Poisson, Generalized Pareto Distribution

“…The M-K test is applied under the null hypothesis that there is no tendency using the calculated test statistics , and the null hypothesis can be rejected for the region of | | > 1− /2 . Here, follows the standard normal distribution, and the M-K test is performed at a 10% significance level (Pingale et al, 2016).…”