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Research Article

Segmentation-based approach for trend analysis and structural breaks in rainfall time series (1851–2006) over India

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Pages 1583-1595 | Received 11 Sep 2019, Accepted 09 Mar 2020, Published online: 18 May 2020

References

  • Adarsh, S. and Janga Reddy, M., 2015. Trend analysis of rainfall in four meteorological subdivisions of southern India using nonparametric methods and discrete wavelet transforms. International Journal of Climatology, 35 (6), 1107–1124. doi:10.1002/joc.2015.35.issue-6
  • Agbazo, M.N., et al., 2019. Fractal analysis of the long-term memory in precipitation over Bénin (West Africa). Advances in Meteorology, 2019, 12. Article ID 1353195. doi:10.1155/2019/1353195
  • Alijani, B., 1997. Some statistical characteristics of temperature variations in Iran. Journal Research Geography, 31, 24–33.
  • Andrews, D.W.K., 1993. Tests for parameter instability and structural change with unknown change point. Econometrica, 61, 821–856. doi:10.2307/2951764
  • Beaulieu, C., Ouarda, T., and Seidou, O., 2010. A Bayesian normal homogeneity test for the detection of artificial discontinuities in climate series. International Journal of Climatology, 30, 2342–2357. doi:10.1002/joc.2056
  • Benbachir, S. and Alaoui, M.H., 2011. A multifractal detrended fluctuation analysis of the Moroccan Dirham with respect to the US dollar. International Economics & Finance Journal, 6 (2, July–December), 287–300.
  • Biswas, A., Zeleke, T.B., and Si, B.C., 2012. Multifractal detrended fluctuation analysis in examining scaling properties of the spatial patterns of soil water storage, Nonlin. Nonlinear Processes in Geophysics, 19, 227–238. doi:10.5194/npg-19-227-2012
  • Chowdhury, M.F., Selouani, S., and O’Shaughnessy, D., 2012. Bayesian on-line spectral change point detection: a soft computing approach for online asr. International Journal of Speech Technology, 15 (1), 5–23. doi:10.1007/s10772-011-9116-2
  • Fankhauser, S. and Tol, R.S., 1997. The social costs of climate change: the IPCC second assessment report and beyond. Mitigation and Adaptation Strategies for Global Change, 1 (4), 385–403. doi:10.1007/BF00464889
  • Gallagher, C. and Lund, R., 2013. Changepoint detection in climate time series with long-term trends. Journal of Climate, 26, 4994–5006. American Meteorological Society, 2013. doi:10.1175/JCLI-D-12-00704.1
  • Geng, L., et al., 2019. Detecting spatiotemporal changes in vegetation with the BFAST model in the Qilian Mountain Region during 2000–2017. Remote Sensing, 11, 103. doi:10.3390/rs11010103
  • Goswami, B.N., et al., 2007. Increasing trend of extreme rain events over India in a warming environment. Science, 314 (5804), 1442–1445. doi:10.1126/science.1132027
  • Hou, W., et al., 2018. Multifractal analysis of the drought area in seven large regions of China from 1961 to 2012. Meteorology and Atmospheric Physics, 130, 459–471. doi:10.1007/s00703-017-0530-0
  • Hurst, H.E., 1951. Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineers, 116, 770–808.
  • Ihlen, E.A.F., 2012. Introduction to multifractal detrended fluctuation analysis in Matlab. Frontiers in Physiology, 3, Article 141. Methods article published: 04June2012, June 2012. doi:10.3389/fphys.2012.00141
  • Jiang, L., Zhao, L., and Zhao, Z., 2017. On the difference of scaling properties for temperature and precipitation over China. Advances in Meteorology, 2017, 10. Article ID 5761275. doi:10.1155/2017/5761275
  • Kantelhardt, J.W., et al., 2002. Multifractal detrended fluctuation analysis of non-stationary time series. Physica A: Statistical Mechanics and Its Applications, 316, 87–114. doi:10.1016/S0378-4371(02)01383-3
  • Kumar, V., Jain, S.K., and Singh, Y., 2010. Analysis of long-term rainfall trend in India. Hydrological Science Journal, 55 (4), 484–496. doi:10.1080/02626667.2010.481373
  • Meenu, R., Rehana, S., and Mujumdar, P., 2013. Assessment of hydrologic impacts of climate change in Tunga–Bhadra river basin, India with HEC-HMS and SDSM. Hydrological Processes, 27 (11), 1572–1589. doi:10.1002/hyp.v27.11
  • Mehrotra, R., et al., 2013. Assessing future rainfall projections using multiple GCMs and a multi-site stochastic downscaling model. Journal of Hydrology, 488, 84–100. doi:10.1016/j.jhydrol.2013.02.046
  • Mirza, M.Q., et al., 1998. Trends and persistence in precipitation in the Ganges, Brahmaputra and Meghna river basins. Hydrological Sciences Journal, 43 (6), 845–858. doi:10.1080/02626669809492182
  • Nalley, D., Adamowski, J., and Khalil, B., 2012. Using discrete wavelet transforms to analyse trends in streamflow and precipitation in Quebec and Ontario (1954–2008). Journal of Hydrology, 475, 204–228. doi:10.1016/j.jhydrol.2012.09.049
  • Nema, M.K., et al., 2018. Spatio-temporal analysis of rainfall trends in Chhattisgarh State, Central India over the last 115 years. Journal of Water and Land Development, 36, 117–128. doi:10.2478/jwld-2018-0012
  • Niculita, O., Skaf, Z., and Jennions, I.K., 2014. The application of bayesian change point detection in UAV fuel systems. ScienceDirect, Procedia CIRP, 22, 115–121. 3rd International Conference on Through-life Engineering Services. doi:10.1016/j.procir.2014.07.119
  • Nnaji, C.C., 2011. Time series analysis of monthly rainfall in Nigeria with emphasis on self-organized criticality. Journal of Science and Technology, 31 (1), 139.
  • Pandey, B.K., Tiwari, H., and Khare, D., 2017. Trend analysis using discrete wavelet transform (DWT) for long-term precipitation (1851–2006) over India. Hydrological Sciences Journal, 62 (13), 2187–2208. doi:10.1080/02626667.2017.1371849
  • Parth Sarthi, P., Ghosh, S., and Kumar, P., 2015. Possible future projection of Indian Summer Monsoon Rainfall (ISMR) with the evaluation of model performance in Coupled Model Inter-comparison Project Phase 5 (CMIP5). Global and Planetary Change, 129, 92–106. doi:10.1016/j.gloplacha.2015.03.005
  • Rahmani, V., et al., 2015. Analysis of temporal and spatial distribution and change-points for annual precipitation in Kansas, USA. International Journal of Climatology, 35 (13), 3879–3887. doi:10.1002/joc.4252
  • Ruggieri, E., 2013. A Bayesian approach to detecting change points in climatic records. International Journal of Climatology, 33, 520–528. doi:10.1002/joc.3447
  • Ruggieri, E. and Charles, E.L., 2012. The Bayesian change point and variable selection algorithm: application to the δ18o proxy record of the plio-pleistocene. Journal of Computational and Graphical Statistics, 23 (1), 87–110. doi:10.1080/10618600.2012.707852.
  • Sontakke, N.A., Singh, N., and Singh, H.N., 2008. Instrumental period rainfall series of the Indian region (AD 1813–2005): revised reconstruction, update and analysis. The Holocene, 18 (7), 1055–1066. doi:10.1177/0959683608095576
  • Tabari, H., and Hosseinzadeh Talaee, P., 2011. Temporal variability of precipitation over Iran: 1966–2005. Journal of Hydrology, 396 (3), 313–320.
  • Tan, X. and Gan, T.Y., 2017. Multifractality of Canadian precipitation and streamflow. International Journal of Climatology, 37, 1221–1236. doi:10.1002/joc.5078
  • Thompson, J.R. and Wilson, J.R., 2016. Multifractal detrended fluctuation analysis: practical applications to financial time series. Mathematics and Computers in Simulation, 126, 63–88. doi:10.1016/j.matcom.2016.03.0003
  • Wan, S., et al., 2016. Nonlinearity and fractal properties of climate change during the past 500 years in northwestern China. Discrete Dynamics in Nature and Society, 2016, 7. Article ID 4269431. doi:10.1155/2016/4269431
  • Wilson, R.C., Nassar, M.R., and Gold, J.I., 2010. Bayesian online learning of the hazard rate in change-point problems. Neural Computation, 22 (9), 2452–2476. doi:10.1162/NECO_a_00007
  • Zarenistanak, M., Dhorde, A.G., and Kripalani, R.H., 2014. Trend analysis and change point detection of annual and seasonal precipitation and temperature series over southwest Iran. Journal of Earth System Science, 123 (2, March), 281–295. doi:10.1007/s12040-013-0395-7
  • Zaveri, E., et al., 2016. Invisible water, visible impact: groundwater use and Indian agriculture under climate change. Environmental Research Letters, 11 (8), 084005. doi:10.1088/1748-9326/11/8/084005

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