Forecasting Monthly Rainfall in the Western Australian Wheat-Belt up to 18-Months in Advance Using Artificial Neural Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9992)

Abstract

Accurate medium-term rainfall forecasts are a significant constraint to dry land cropping. In Australia, official monthly forecasts for the Western Australian wheat-belt are currently based on output from the Bureau of Meteorology’s general circulation model, the Predictive Ocean Atmosphere Model for Australia (POAMA). These forecasts are provided in a two-category format (above or below median rainfall) up to three months in advance for large grid areas, and are not considered reliable. An alternative approach is presented here for the three locations of Narrogin, Merredin and Southern Cross using artificial neural networks (ANNs) to forecast monthly rainfall up to 18 months in advance. Skilful monthly rainfall forecasts can be achieved at all lead times measured in terms of root mean square error (RMSE) and mean absolute error (MAE). This approach is of practical benefit to wheat growers in this region, with potential application to other locations with long historical temperature and rainfall records.

Keywords

Artificial neural network Machine learning Monthly rainfall forecast Dry land cropping Climate indices 

References

  1. 1.
    ABARE: Australian Crop Report No. 154. Technical report. Australian Bureau of Agricultural and Resource Economics, Canberra, ACT (2010)Google Scholar
  2. 2.
    Anderson, W.K., Hamza, M.A., Sharma, D.L., et al.: The role of management in yield improvement of the wheat crop—a review with special emphasis on Western Australia. Aust. J. Agric. Res. 56, 1137–1141 (2005)CrossRefGoogle Scholar
  3. 3.
    Anderson, W.K.: Closing the gap between actual and potential yield of rainfed wheat. The impacts of environment, management and cultivar. Field Crops Res. 116, 14–22 (2010)CrossRefGoogle Scholar
  4. 4.
    Del Cima, R., D’Antuono, M.F., Anderson, W.K.: The effects of soil type and seasonal rainfall on the optimum seed rate for wheat in Western Australia. Aust. J. Exp. Agr. 44(6), 585–594 (2004)CrossRefGoogle Scholar
  5. 5.
    Hunt, J.R., Kirkegaard, J.A.: Re-evaluating the contribution of summer fallow rain to wheat yield in southern Australia. Crop Pasture Sci. 62, 915–929 (2011)CrossRefGoogle Scholar
  6. 6.
    Pook, M., Lisson, S., Risbey, J., et al.: The autumn break for cropping in southeast Australia: trends, synoptic influences and impacts on wheat yield. Int. J. Climatol. 29, 2012–2026 (2009)CrossRefGoogle Scholar
  7. 7.
    Sharma, D.L., D’Antuono, M.F., Anderson, W.K., et al.: Variability of optimum sowing time for wheat yield in Western Australia. Aust. J. Agr. Res. 59(10), 958–970 (2008)CrossRefGoogle Scholar
  8. 8.
    Sprigg, H., Belford, R., Milroy, S., et al.: Adaptations for growing wheat in the drying climate of Western Australia. Crop Pasture Sci. 65, 627–644 (2014)Google Scholar
  9. 9.
    Zhang, H., Turner, N.C., Simpson, N., et al.: Growing-season rainfall, ear number and the water-limited potential yield of wheat in south-western Australia. Crop Pasture Sci. 61, 296–303 (2010)CrossRefGoogle Scholar
  10. 10.
    French, R.J., Schultz, J.E.: Water use efficiency of wheat in a Mediterranean-type environment: I. The relation between yield, water use and climate. Aust. J. Agric. Res. 35, 743–764 (1984)CrossRefGoogle Scholar
  11. 11.
    Carberry, P.S., Hammer, G.L., Meinke, H., et al.: The potential value of seasonal climate forecasting in managing cropping systems. In: Hammer, G., Nicholls, N., Mitchell, C. (eds.) Application of Seasonal Climate Forecasting in Agricultural and Natural Ecosystems—The Australian experience, pp. 167–181. Kluwer Academic Publishers, Dordrecht (2000)CrossRefGoogle Scholar
  12. 12.
    Dolling, P.J., Fillery, I.R.P., Ward, P.R., et al.: Consequences of rainfall during summer–autumn fallow on available soil water and subsequent drainage in annual-based cropping systems. Aust. J. Agric. Res. 57, 281–296 (2006)CrossRefGoogle Scholar
  13. 13.
    Stephens, D.J., Lyons, T.J.: Variability and trends in sowing dates across the Australian wheatbelt. Aust. J. Agric. Res. 49, 1111–1118 (1998)CrossRefGoogle Scholar
  14. 14.
    Stephens, D.J., Lyons, T.J.: Rainfall-yield relationships across the Australian wheatbelt. Aust. J. Agr. Res. 49, 211–223 (1998)CrossRefGoogle Scholar
  15. 15.
    Pook, M.J., Risbey, J.S., McIntosh, P.C.: The synoptic climatology of cool-season rainfall in the central wheatbelt of Western Australia. Mon. Weather Rev. 140, 28–43 (2012)CrossRefGoogle Scholar
  16. 16.
    Fox, G., Turner, J., Gillespie, T.: The value of precipitation forecast information in winter wheat production. Agr. Forest Meteorol. 95, 99–111 (1999)CrossRefGoogle Scholar
  17. 17.
    Anwar, M.R., Rodriguez, D., Liu, D.L., et al.: Quality and potential utility of ENSO-based forecasts of spring rainfall and wheat yield in south-eastern Australia. Aust. J. Agr. Res. 59(2), 112–126 (2008)CrossRefGoogle Scholar
  18. 18.
    Hammer, G.L., Hansen, J.W., Phillips, J.G., et al.: Advances in application of climate prediction in agriculture. Agr. Syst. 70, 515–553 (2001)CrossRefGoogle Scholar
  19. 19.
    Hammer, G.L., Holzworth, D.P., Stone, R.: The value of skill in seasonal climate forecasting to wheat crop management in a region with high climatic variability. Aust. J. Agr. Res. 47, 717–737 (1996)CrossRefGoogle Scholar
  20. 20.
    Marshall, G.R.: Risk attitude, planting conditions and the value of seasonal forecasts to a dryland wheat grower. Aust. J. Agr. Econ. 40(3), 211 (1996)Google Scholar
  21. 21.
    Potgieter, A.B., Hammer, G.L., Meinke, H., et al.: Three putative types of El Nino revealed by spatial variability in impact on Australian wheat yield. J. Climate 18(10), 1566–1574 (2005)CrossRefGoogle Scholar
  22. 22.
    Podesta, G.P., Messina, C.D., Grondona, M.O., et al.: Associations between grain crop yields in central-eastern Argentina and El Nino-Southern Oscillation. J. Appl. Meteorol. 38(10), 1488–1498 (1999)CrossRefGoogle Scholar
  23. 23.
    Alberto, C.M., Streck, N.A., Heldwein, A.B., et al.: Soil water and wheat, soybean, and maize yields associated to El Nino Southern Oscilation. Pesquisa Agropecuaria Bras. 41(7), 1067–1075 (2006)CrossRefGoogle Scholar
  24. 24.
    Hansen, J.W., Jones, J.W., Irmak, A., et al.: El Nino-Southern Oscillation impacts on crop production in the southeast United States. In: Hatfield, J.L., Volenec, J.J. Dick, W.A. (eds.) Proceedings of Impacts of El Nino and Climate Variability on Agriculture, vol. 63, pp. 55–76. ASA Special Publication (2001)Google Scholar
  25. 25.
    Legler, D.M., Bryant, K.J., O’Brien, J.J.: Impact of ENSO-related climate anomalies on crop yields in the US. Clim. Change 42(2), 351–375 (1999)CrossRefGoogle Scholar
  26. 26.
    Shuai, J., Zhang, Z., Sun, D.Z., et al.: ENSO, climate variability and crop yields in China. Clim. Res. 58(2), 133–148 (2013)CrossRefGoogle Scholar
  27. 27.
    Selvaraju, R.: Impact of El Nino-southern oscillation on Indian food grain production. Int. J. Climatol. 23(2), 187–206 (2003)CrossRefGoogle Scholar
  28. 28.
    Fawcett, R.J.B., Stone, R.C.: A comparison of two seasonal rainfall forecasting systems for Australia. Aust. Meteorol. Oceanogr. J. 60, 15–24 (2010)Google Scholar
  29. 29.
    National Climate Centre, BOM, pers. comm., July 2014Google Scholar
  30. 30.
    Grains Research and Development Corporation: Changing weather in the western wheatbelt, Australian government (2014). http://www.grdc.com.au/Media-Centre/Ground-Cover/Ground-Cover-Issue-101/Changing-weather-in-the-western-wheatbelt
  31. 31.
    Petersen, E.H., Fraser, R.W.: An assessment of the value of seasonal forecasting technology for Western Australian farmers. Agr. Syst. 70, 259–274 (2001)CrossRefGoogle Scholar
  32. 32.
    Hawthorne, S., Wang, Q.J., Schepen, A., et al.: Effective use of general circulation model outputs for forecasting monthly rainfalls to long lead times. Water Resour. Res. 49, 5427–5436 (2013)CrossRefGoogle Scholar
  33. 33.
    Langford, S., Hendon, H.H.: Improving reliability of coupled model forecasts of Australian seasonal rainfall. Mon. Weather Rev. 141, 728–741 (2013)CrossRefGoogle Scholar
  34. 34.
    Shao, Q., Li, M.: An improved statistical analogue downscaling procedure for seasonal precipitation forecast. Stoch. Env. Res. Risk A. 27, 819–830 (2013)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Stone, R.C., Hammer, G.L., Marcussen, T.: Prediction of global rainfall probabilities using phases of the Southern Oscillation Index. Nature 384, 252–255 (1996)CrossRefGoogle Scholar
  36. 36.
    Vizard, A.L., Anderson, G.A., Buckley, D.J.: Verification and value of the Australian Bureau of Meteorology township seasonal rainfall forecasts in Australia, 1997–2005. Meteorol. Appl. 12, 343–355 (2005)CrossRefGoogle Scholar
  37. 37.
    Silverman, D., Dracup, J.A.: Artificial neural networks and long-range precipitation prediction in California. J. Appl. Meteorol. 39(1), 57–66 (2000)CrossRefGoogle Scholar
  38. 38.
    Goyal, M.K.: Monthly rainfall prediction using wavelet regression and neural network: an analysis of 1901–2002 data, Assam, India. Theor. Appl. Climatol. 118(1–2), 25–34 (2014)CrossRefGoogle Scholar
  39. 39.
    Abbot, J., Marohasy, J.: Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks. Atmos. Res. 138, 166–178 (2014)CrossRefGoogle Scholar
  40. 40.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)MATHGoogle Scholar
  41. 41.
    Aytek, A., Asce, M., Alp, M.: An application of artificial intelligence for rainfall-runoff modelling. J. Earth Syst. Sci. 117(2), 145–155 (2008)CrossRefGoogle Scholar
  42. 42.
    Trambaiolli, L.R., Lorena, A.C., Fraga, F.J., et al.: Improving Alzheimer’s disease diagnosis with machine learning techniques. Clin. EEG Neurosci. 42(3), 160–165 (2011)CrossRefGoogle Scholar
  43. 43.
    Schepen, A., Wang, Q.J., Robertson, D.: Improving rainfall forecasts for seasonal streamflow forecasts. In: Proceedings of the 34th Hydrology and Water Resources Symposium, pp. 1117–1124 (2012)Google Scholar
  44. 44.
    Risbey, J.S., Pook, M.J., Mcintosh, P.C., et al.: On the remote drivers of rainfall variability in Australia. Mon. Weather Rev. 137, 3233–3253 (2009)CrossRefGoogle Scholar
  45. 45.
    Montroy, D.L., Richman, M.B., Lamb, P.J.: Observed nonlinearities of monthly teleconnections between tropical Pacific sea surface temperature anomalies and central and eastern North American precipitation. J. Climate 11, 1812–1835 (1998)CrossRefGoogle Scholar
  46. 46.
    Chen, C.C., McCarl, B., Hill, H.: Agricultural value of ENSO information under alternative phase definition. Clim. Change 54, 305–325 (2002)CrossRefGoogle Scholar
  47. 47.
    Hill, H.S.J., Butler, D., Fuller, S.W., et al.: Effects of seasonal climate variability and the use of climate forecasts on wheat supply in the United States, Australia, and Canada. In: Proceedings of Impacts of El Nino and Climate Variability on Agriculture, vol. 63, pp. 101–123. ASA Special Publication (2001)Google Scholar
  48. 48.
    Power, S., Haylock, M., Colman, R., et al.: The predictability of interdecadal changes in ENSO activity and ENSO teleconnections. J. Climate 19, 4755–4771 (2006)CrossRefGoogle Scholar
  49. 49.
    Abbot, J., Marohasy, J.: Application of artificial neural networks to rainfall forecasting in Queensland, Australia. Adv. Atmos. Sci. 29, 717–730 (2012)CrossRefGoogle Scholar
  50. 50.
    Abbot, J. Marohasy, J.: Improving monthly rainfall forecasts using artificial neural networks and single-month optimisation: a case study of the Brisbane catchment, Queensland, Australia. In: Water Resources Management, vol. VIII, pp. 3–13. WIT Press (2015)Google Scholar
  51. 51.
    Abbot J., Marohasy, J.: Forecasting of monthly rainfall in the Murray Darling Basin, Australia. In: Miles as a case study. River Basin Management, vol. VIII, pp. 149–159. WIT Press (2015)Google Scholar
  52. 52.
    Hansen, J.W.: Realizing the potential benefits of climate prediction to agriculture: issues, approaches, challenges. Agr. Syst. 74, 309–330 (2002)CrossRefGoogle Scholar
  53. 53.
    Hansen, J.W.: Integrating seasonal climate prediction and agricultural models for insights into agricultural practice. Philos. Trans. R. Soc. London B. 360, 2037–2047 (2005)CrossRefGoogle Scholar
  54. 54.
    Mjelde, J.L., Dixon, B.L.: Valuing the lead time of periodic forecasts in dynamic production systems. Agr. Syst. 42(1–2), 41–55 (1993)CrossRefGoogle Scholar
  55. 55.
    Moeller, C.I., Smith, S., Asseng, F., et al.: The potential value of seasonal forecasts of rainfall categories—case studies from the wheatbelt in Western Australia’s Mediterranean region. Agr. Forest Meteorol. 148, 606–618 (2008)CrossRefGoogle Scholar
  56. 56.
    Ash, A., McIntosh, P., Cullen, B., et al.: Constraints and opportunities in applying seasonal climate forecasts in agriculture. Aust. J. Agr. Res. 58, 952–965 (2007)CrossRefGoogle Scholar
  57. 57.
    Zimmerman, B.G., Vimont, D.J., Block, P.J.: Utilizing the state of ENSO as a means for season-ahead predictor selection. Water Res. Res. 52(5), 3761–3774 (2016)CrossRefGoogle Scholar
  58. 58.
    Abbot J., Marohasy, J.: Forecasting extreme monthly rainfall events in regions of Queensland, Australia using Artificial Neural Networks. Int. J. Sust. Dev. Plann (in press)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Climate Modelling LaboratoryNoosavilleAustralia
  2. 2.Department of EngineeringUniversity of TasmaniaHobartAustralia
  3. 3.Institute of Public AffairsMelbourneAustralia

Personalised recommendations