Evaluating the Effect of Single and Combined Climate Modes on Rainfall Predictability

Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 247)


This study attempts to find the effect of past values of El Nino southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) on future rainfall. Victoria located at southeast Australia has been chosen as the case study. Many studies have tried to establish the relationships of these large-scale climate indices among the rainfalls of different parts of Australia; unlike the other regions no clear relationship can be found between each individual large-scale climate mode and Victorian rainfall. Past studies considering southeast Australian rainfall predictability could achieve a maximum of 30 % predictability. This study looks into the lagged-time relationships of single and combined climate modes with Victorian spring rainfall using the nonlinear technique Artificial Neural Networks (ANN). Using these climate indices in an ANN model increased the model correlation up to 0.99, 0.98 and 0.30 in the testing set for the three case study stations of Horsham, Melbourne and Orbost in Victoria, Australia respectively. It seems that past values of IOD and ENSO both have a great effect on rainfall forecasting however the effect of IOD is higher in centre and west of Victoria compared to ENSO, while both ENSO and IOD seem to have a strong effect on the east side. This method can be used for other parts of the world where a relationship exists between rainfall and large scale climate modes which could not be established by linear methods.


ANN ENSO Forecast IOD Rainfall Victoria 


  1. 1.
    Lau K, Weng H (2001) Coherent modes of global SST and summer rainfall over China: an assessment of the regional impacts of the 1997–98 El Nino. J Clim 14:1294–1308CrossRefGoogle Scholar
  2. 2.
    Yufu G, Yan Z, Jia W (2002) Numerical simulation of the relationships between the 1998 Yangtze River valley floods and SST anomalies. Adv Atmos Sci 19:391–404CrossRefGoogle Scholar
  3. 3.
    Barsugli JJ, Sardeshmukh PD (2002) Global atmospheric sensitivity to tropical SST anomalies throughout the Indo-Pacific basin. J Clim 15:3427–3442CrossRefGoogle Scholar
  4. 4.
    Hartmann H, Becker S, King L (2008) Predicting summer rainfall in the Yangtze River basin with neural networks. Int J Climatol 28:925–936CrossRefGoogle Scholar
  5. 5.
    Chattopadhyay G, Chattopadhyay S, Jain R (2010) Multivariate forecast of winter monsoon rainfall in India using SST anomaly as a predictor: neurocomputing and statistical approaches. CR Geosci 342:755–765CrossRefGoogle Scholar
  6. 6.
    Shukla RP, Tripathi KC, Pandey AC, Das IML (2011) Prediction of Indian summer monsoon rainfall using Niño indices: a neural network approach. Atmos Res 102:99–109CrossRefGoogle Scholar
  7. 7.
    Kirono DGC, Chiew FHS, Kent DM (2010) Identification of best predictors for forecasting seasonal rainfall and runoff in Australia. Hydrol Process 24:1237–1247Google Scholar
  8. 8.
    Risbey JS, Pook MJ, McIntosh PC, Wheeler MC, Hendon HH (2009) On the remote drivers of rainfall variability in Australia. Mon Weather Rev 137:3233–3253CrossRefGoogle Scholar
  9. 9.
    Saji NH, Goswami BN, Vinayachandran PN, Yamagata T (1999) A dipole mode in the tropical Indian ocean. Nature 401:360–363Google Scholar
  10. 10.
    Meneghini B, Simmonds I, Smith IN (2007) Association between Australian rainfall and the Southern annular mode. Int J Climatol 27:109–121CrossRefGoogle Scholar
  11. 11.
    Hendon HH, Thompson DWJ, Wheeler MC (2007) Australian rainfall and surface temperature variations associated with the Southern hemisphere annular mode. J Clim 20(11):2452–2467CrossRefGoogle Scholar
  12. 12.
    Ummenhofer CC, Sen Gupta A, Pook MJ, England MH (2008) Anomalous rainfall over southwest Western Australia forced by Indian Ocean sea surface temperatures. J Clim 21:5113–5134CrossRefGoogle Scholar
  13. 13.
    England MH, Ummenhofer CC, Santoso A (2006) Interannual rainfall extremes over southwest Western Australia linked to Indian Ocean climate variability. J Clim 19:1948–1969CrossRefGoogle Scholar
  14. 14.
    Evans AD, Bennett JM, Ewenz CM (2009) South Australian rainfall variability and climate extremes. Clim Dyn 33:477–493CrossRefGoogle Scholar
  15. 15.
    Nicholls N (2010) Local and remote causes of the southern Australian autumn-winter rainfall decline, 1958–2007. Clim Dyn 34:835–845CrossRefGoogle Scholar
  16. 16.
    Verdon DC, Wyatt AM, Kiem AS, Franks SW (2004) Multidecadal variability of rainfall and streamflow: Eastern Australia. Water Resour Res 40:W10201CrossRefGoogle Scholar
  17. 17.
    Murphy BF, Timbal B (2008) A review of recent climate variability and climate change in Southeastern Australia. Int J Climatol 28:859–879CrossRefGoogle Scholar
  18. 18.
    Verdon-Kidd DC, Kiem AS (2009) On the relationship between large-scale climate modes and regional synoptic patterns that drive Victorian rainfall. Hydrol Earth Syst Sci 13:467–479CrossRefGoogle Scholar
  19. 19.
    Kiem AS, Verdon-Kidd DC (2009) Climatic drivers of Victorian streamflow: is ENSO the dominant influence. Aust J Water Resour 13:17–29Google Scholar
  20. 20.
    Schepen A, Wang QJ, Robertson D (2012) Evidence for using lagged climate indices to forecast Australian seasonal rainfall. J Clim 25(4):1230–1246CrossRefGoogle Scholar
  21. 21.
    Abbot J, Marohasy J (2012) Application of artificial neural networks to rainfall forecasting in Queensland. Aust Adv Atmos Sci 29(4):717–730CrossRefGoogle Scholar
  22. 22.
    Mekanik F, Imteaz MA (2012) A multivariate artificial neural network approach for rainfall forecasting: case study of Victoria, Australia. In: Proceedings of the world congress on engineering and computer Science 2012, Lecture notes in engineering and computer science, 24–26 October, 2012, San Francisco, USA, pp 557–561Google Scholar
  23. 23.
    De Vos N, Rientjes T (2005) Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation. Hydrol Earth Syst Sci Discuss 2(1):365–415CrossRefGoogle Scholar
  24. 24.
    Yilmaz AG, Imteaz MA, Jenkins G (2011) Catchment flow estimation using artificial neural networks in the mountainous Euphrates basin. J Hydrol 410(1–2):134–140CrossRefGoogle Scholar
  25. 25.
    Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw 15:101–124CrossRefGoogle Scholar
  26. 26.
    Cai W, Van Rensch P, Cowan T, Hendon H (2011) Teleconnection pathways of ENSO and the IOD and the mechanisms for impacts on Australian rainfall. J Clim 24(15):3910–3923CrossRefGoogle Scholar
  27. 27.
    Willmott CJ (1982) Some comments on the evaluation of model performance. Bull Am Meteorol Soc 63:1309–1369CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Center for Sustainable Infrastructure (CSI)Swinburne University of TechnologyHawthornAustralia

Personalised recommendations