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Rainfall studies are of utmost utility for understanding nature & hence the behaviour of climate changes. Time series is a set of observations taken at specified times usually at equal interval. The inherent variability displayed by many hydrological time series usually mask trends and periodic patterns. This situation has often led to “something” the original time series so that the effects of random variations are reduced and trends or cyclical patterns enhanced. Thus a set of data depending on time is called a Time series. Here, Rainfall series represent the time series. The time series analysis is helpful to compare the actual performance and analyse the cause of variations. By comparing different time series we can draw important conclusion. Graphical method implies in increasing trend for pre-monsoon, south-west monsoon, north-east monsoon and annually.Geo- informatics module consists of GIS mapping for Location map, Geomorphology map and Season wise Rainfall maps are generated. Autocorrelation indicates the periodicity observed as 37,16 & 6 years (PM), 12, 37 & 16 years (SWM), 8, 18 & 6 years (NEM) and 16, 22 & 8 years (Annual) respectively. Power spectral depicts the cyclicity of 37, 4 & 3 years (PM), 2, 4& 2 years (SWM), 3, 7 & 2 years (NEM) and 2, 4 & 2 years (Annual) respectively. Moving average displays prominent positive correlation coefficients at lags of 18 to 42 years in PM & SWM and 12 to 24 years in NEM & Annual. The southwest and southeast parts of the study area experience the heavy rainfall whereas the least rainfall areas are the northern parts of the study area.The short term and long term cyclicity observed in Autocorrelation, power spectrum and Moving Average. Spatial variation of rainfall for the three seasons and annual has been studied.
[Ramesh L Dikpal and T J Renuka Prasad (2014); Time Series Analysis of Rainfall in North Bangalore Metropolitan Region using Remote Sensing & Geographic Information System Techniques Int. J. of Adv. Res. 2 (4). 0] (ISSN 2320-5407). www.journalijar.com
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