Author/Authors :
Rasel، H. M. نويسنده Department of Civil and Construction Engineering, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, VIC 3122, Australia , , Imteaz، M. A. نويسنده Department of Civil and Construction Engineering, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, VIC 3122, Australia , , Mekanik، F. نويسنده Department of Civil and Construction Engineering, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, VIC 3122, Australia ,
Abstract :
Australian rainfall is related with numerous key climate predictors namely El-Nino
Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Southern Annular Mode (SAM). Some
studies have tried to discover the effects of these climate predictors on rainfall variability of different
parts of Australia, particularly Western Australia, Queensland and Victoria. Nonetheless, clear association
between separate or combined large-scale climate predictors and South Australian spring
rainfall is yet to be established. Past studies showed that maximum rainfall predictability was only
20% considering isolated/individual effects of ENSO and SAM predictors in this region. The present
study further explored these hypotheses by investigating two additional important aspects: investigating
the relationship between lagged individual climate predictors with spring rainfall and linked
(multiple combinations of ENSO and SAM) influences of significant lagged-climate indicators on
spring rainfall forecasting using multiple regression (MR) modeling. Three stations were chosen as
case studies for this region. MR models with combined-lagged climate predictors (SOI-SAM based
models) showed better forecast ability in both model calibration and validation periods for all the
stations. Results demonstrated that rainfall predictability significantly increased using combined
climate predictorʹs influence compared to their individual effect. It was discovered that rainfall predictability
increased up-to 63% using combined climate predictors compared to their single influences.
The maximum attained rainfall predictability for the SOI-SAM based models was 47% for
calibration period that significantly enhanced with combined predictors influence to 97% during
validation period. Therefore, MR analyses delineated the capabilities and influences of remote climate
drivers in forecasting South Australian spring rainfall.
