Segmentation-based approach for trend analysis and structural breaks in rainfall time series (1851–2006) over India. Issue 9 (3rd July 2020)

• Record Type: Journal Article
• Authors: Priyadarshi, Niraj; Bandyopadhyay, Soumya; Chowdary, V. M.; Chandrasekar, K.; Chockalingam, Jeganathan; Raj, Uday; Jha, Chandra Shekhar
• Is Part Of: Hydrological sciences journal. Volume 65:Issue 9(2020)
• Page Start: 1583
• Page End: 1595
• Publication Date: 2020-07-03
• Subjects: time series -- rainfall -- linear regression -- detrended fluctuation analysis -- Bayesian algorithm; Hydrology -- Periodicals; 551.4805
• DOI: 10.1080/02626667.2020.1761022
• Languages: English
• ISSNs: 0262-6667

Abstract

ABSTRACT: 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.