Title: Multiple regression modelling approach for rainfall prediction using large-scale climate indices as potential predictors
Authors: H.M. Rasel; Monzur Alam Imteaz; Fatemeh Mekanik
Addresses: Department of Civil and Construction Engineering, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, VIC 3122, Australia ' Department of Civil and Construction Engineering, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, VIC 3122, Australia ' School of Engineering and Mathematical Sciences, La Trobe University, Victoria 3086, Australia
Abstract: Some studies established the associations with different climate indices (Southern Oscillation Index, Indian Ocean Dipole and Southern Annular Mode) and seasonal rainfalls of different parts of Australia. Nevertheless, maximum predictability of South Australian rainfall was only 20% with individual effects of potential predictor. To establish a better relationship for South Australian spring rainfall prediction, this paper presents two further investigations: 1) relationship of lagged climate indices with rainfall; 2) combined influence of these lagged climate indicators on rainfall. Multiple linear regression (MLR) modelling was used to evaluate the influence of combined predictors. Three rainfall stations were selected from South Australia as a case study. It was revealed that significantly increased rainfall predictability has been achieved through MR models using the influences of combine-lagged climate predictors. The rainfall predictability ranging from 41% to 45% has been achieved using combined lagged-indices, whereas maximum 33% predictability can be achieved using individual climate index.
Keywords: rainfall; El Nino Southern Oscillation; ENSO; Southern Annular Mode; SAM; MR model; correlation; multicollinearity; forecasting.
International Journal of Water, 2017 Vol.11 No.3, pp.209 - 225
Received: 23 Mar 2015
Accepted: 16 Feb 2016
Published online: 03 Aug 2017 *