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Climate change has been a significant subject in recent years all around the world. Statistical analysis of climatic parameters such as rainfall can investigate the actual status of the atmosphere. As a result, this study aimed to look at the pattern of mean annual rainfall in India from 1901 to 2016, considering 34 meteorological subdivisions. The Mann–Kendall (MK) test, Modified Mann–Kendall (MMK) test, Bootstrapped MK (BMK) test, and Innovative Trend Analysis (ITA) were used to find trends in yearly rainfall time-series results. Rainfall forecasting was evaluated using detrended fluctuation analysis (DFA). Because the research comprised 34 meteorological subdivisions, it may be challenging to convey the general climatic conditions of India in a nutshell. The MK, MMK, and BMK tests showed a significant (p < 0.01 to p < 0.1) negative trend in 9, 8, and 9 sub-divisions, respectively. According to the ITA, a negative trend was found in 17 sub-divisions, with 9 sub-divisions showing a significance level of 0.01 to 0.1. The ITA outperformed the other three trend test techniques. The results of DFA showed that 20 sub-divisions would decrease in future rainfall, suggesting that there was a link between past and future rainfall trends. Results show that highly negative or decreasing rainfall trends have been found in broad regions of India, which could be related to climate change, according to the results. ITA and DFA techniques to discover patterns in 34 sub-divisions across India have yet to be implemented. In developing management plans for sustainable water resource management in the face of climate change, this research is a valuable resource for climate scientists, water resource scientists, and government officials. [ABSTRACT FROM AUTHOR]

This study analysed long-term rainfall data (1851–2006) over seven climatic zones of India at seasonal and annual scales based on three techniques: (i) linear regression, (ii) multifractal detrended fluctuation analysis (MFDFA) and (iii) Bayesian algorithm. The linear regression technique was used for trend analysis of short-term (30 years) and long-term (156 years) rainfall data. The MFDFA revealed small- and large-scale fluctuations, whereas the Bayesian algorithm helped in quantifying the uncertainty in break-point detection from the rainfall time series. Major break points years identified through Bayesian algorithm were 1888, 1904 and 1976. The MFDFA technique identified that high fluctuation years were between 1871–1890, 1891–1910 and 1951–1970. Linear regression-based analysis revealed 1881–1910 and 1971–2006 as break-point periods in the North Mountainous Indian region. A similar analysis was carried out for India as a whole, as well as its seven climatic zones. [ABSTRACT FROM AUTHOR]

Abstract: In the study reported in this paper we have dealt with the intrinsic complexity of the daily total ozone time series over Mumbai, India (19°23′N, 72 15°00′E) by means of detrended fluctuation analysis and phase portrait analysis. From the detrended fluctuation analysis of first order it is revealed that the said time series is characterized by persistent power-law correlation which is stronger in shorter time scale than in the longer time scale. The phase portrait analysis is based on a non-linear autonomous system generated from regression equations; infinitely many stable nodes are found along the straight line x =273.556 and from the behaviors of the contours, it has been revealed that the daily total ozone concentration is unlikely to go beyond 315DU (approximately) and it is almost stable. [Copyright &y& Elsevier]

Abstract: Persistence analysis of air pollutant concentration and corresponding exceedance time series is carried out to examine for temporal evolution. For this purpose, air pollutant concentrations, namely, CO, NO2 and O3 observed during 2000–2009 at a traffic site in Delhi are analyzed using detrended fluctuation analysis. Two types of extreme values are analyzed; exceeded concentrations to a threshold provided by national pollution controlling agency and time interval between two exceedances. The time series of three pollutants is observed to possess persistence property whereas the extreme value time series of only primary pollutant concentrations is found to be persistent. Two time scaling regions are observed to be significant in extreme time series of CO and NO2, mainly attributed to implementation of CNG in vehicles. The presence of persistence in three pollutant concentration time series is linked to the property of self-organized criticality. The observed persistence in the time interval between two exceeded levels is a matter of concern as persistent high concentrations can trigger health problems. [Copyright &y& Elsevier]

The time series data of the monthly rainfall records (for the time period 1871–2002) in All India and different regions of India are analyzed. It is found that the distributions of the rainfall intensity exhibit perfect power law behavior. The scaling analysis revealed two distinct scaling regions in the rainfall time series. [ABSTRACT FROM AUTHOR]