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Short-term droughts forecast using Markov chain model in Victoria, Australia

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Abstract

A comprehensive risk management strategy for dealing with drought should include both short-term and long-term planning. The objective of this paper is to present an early warning method to forecast drought using the Standardised Precipitation Index (SPI) and a non-homogeneous Markov chain model. A model such as this is useful for short-term planning. The developed method has been used to forecast droughts at a number of meteorological monitoring stations that have been regionalised into six (6) homogenous clusters with similar drought characteristics based on SPI. The non-homogeneous Markov chain model was used to estimate drought probabilities and drought predictions up to 3 months ahead. The drought severity classes defined using the SPI were computed at a 12-month time scale. The drought probabilities and the predictions were computed for six clusters that depict similar drought characteristics in Victoria, Australia. Overall, the drought severity class predicted was quite similar for all the clusters, with the non-drought class probabilities ranging from 49 to 57 %. For all clusters, the near normal class had a probability of occurrence varying from 27 to 38 %. For the more moderate and severe classes, the probabilities ranged from 2 to 13 % and 3 to 1 %, respectively. The developed model predicted drought situations 1 month ahead reasonably well. However, 2 and 3 months ahead predictions should be used with caution until the models are developed further.

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References

  • Araghinejad S (2011) An approach for probabilistic hydrological drought forecasting. Water Resour Manag 25(1):191–200

    Article  Google Scholar 

  • Bacanli U, Firat M, Dikbas F (2009) Adaptive neuro-fuzzy inference system for drought forecasting. Stoch Env Res Risk A 23(8):1143–1154

    Article  Google Scholar 

  • Barua S, Perera BJC, Ng AWM, Tran D (2010) Drought forecasting using an aggregated drought index and artificial neural network. J Water Clim Chang 1(3):193–206

    Article  Google Scholar 

  • Barua S, Ng AWM, Perera BJC (2012) Artificial neural network based drought forecasting using a nonlinear aggregated drought index. J Hydrol Eng 17(12):1408–1413

    Article  Google Scholar 

  • Bonaccorso B, Cancelliere A, Rossi G (2015) Probabilistic forecasting of drought class transitions in Sicily (Italy) using Standardised precipitation index and North Atlantic Oscillation index. J Hydrol. doi:10.1016/jhydrol.2015.01.070

    Google Scholar 

  • Bureau of Meteorology (BoM) (2003). Drought statement. Available at: http://www.bom.gov.au/announcements/ (accessed May 2013)

  • Cancelliere A, Mauro G, Bonaccorso B, Rossi G (2007a) Drought forecasting using the Standardized Precipitation Index. Water Resour Manag 21(5):801–819

    Article  Google Scholar 

  • Cancelliere A, Mauro GD, Bonaccorso B, Rossi G (2007b) Stochastic forecasting of drought indices. G. Rossi, T. Vega and B. Bonaccorso. Methods and Tools for Drought Analysis and Management. Springer Netherlands 62:83–100

    Google Scholar 

  • Durdu OF (2010) Application of linear stochastic models for drought forecasting in the Büyük Menderes river basin, Western Turkey. Stoch Env Res Risk A 24(8):1145–1162

    Article  Google Scholar 

  • Isaacson DL, Madsen R (1976) Markov Chains: Theory and Applications. John Wiley, New York

    Google Scholar 

  • Khattree R, Naik DN (2002) Andrews plots for multivariate data: some new suggestions and applications. J Stat Plann Inference 100(2):411–425

    Article  Google Scholar 

  • Lazri M, Ameur S, Brucker JM, Lahdir M, Sehad M (2015) Analysis of drought areas in Northern Algeria using Markov chains. J Earth Syst Sci 124(1):61–70

    Article  Google Scholar 

  • Lohani VK, Loganathan GV (1997) An early warning system for drought management using the Palmer Drought Index. J Am Water Resour Assoc 33(6):1375–1386

    Article  Google Scholar 

  • Lohani VK, Loganathan GV, Mostaghim S (1998) Long-term analysis and short-term forecasting of dry spells by Palmer Drought Severity Index. Nord Hydrol 29(1):21–40

    Google Scholar 

  • Lyra GB, Oliveira-Júnior JF, Zeri M (2014) Cluster analysis applied to the spatial and temporal variability of monthly rainfall in Alagoas state, Northeast of Brazil. Int J Climatol 34(13):3546–3558

    Article  Google Scholar 

  • McKee, T. B., Doesken, N. J. and Kleist, J. (1993). The relationship of drought frequency and duration to time scales. In: Proc. 8th Conf. on Applied Climatol, 17–22 January, Anaheim, California, Americ Meteorol Soc, Mass. 179–184.

  • Mishra AK, Desai VR (2005) Drought forecasting using stochastic models. Stoch Env Res Risk A 19(5):326–339

    Article  Google Scholar 

  • Mishra AK, Desai VR (2006) Drought forecasting using feed-forward recursive neural network. Ecol Model 198(1–2):127–138

    Article  Google Scholar 

  • Mishra AK, Singh VP (2011) Drought modeling—a review. J Hydrol 403(1–2):157–175

    Article  Google Scholar 

  • Nazahiyah, R. (2015). Methodology for development of Drought Severity-Duration-Frequency (SDF) curves. PhD Thesis. RMIT University, Melbourne, Australia

  • Nazahiyah R, Jayasuriya N, Bhuiyan MA (2014) Assessing droughts using meteorological drought indices in Victoria, Australia. J Hydrol Res. doi:10.2166/nh.2014.105

    Google Scholar 

  • Ochola WO, Kerkides P (2003) A Markov chain simulation model for predicting critical wet and dry spells in Kenya: analysing rainfall events in the Kano Plains. Irrig Drain 52(4):327–342

    Article  Google Scholar 

  • Panu US, Sharma TC (2002) Challenges in drought research: some perspectives and future directions. Hydrol Sci J 47(sup1):S19–S30

    Article  Google Scholar 

  • Paulo A, Pereira L (2007) Prediction of SPI drought class transitions using Markov chains. Water Resour Manag 21(10):1813–1827

    Article  Google Scholar 

  • Paulo A, Pereira L (2008) Stochastic prediction of drought class transitions. Water Resour Manag 22(9):1277–1296

    Article  Google Scholar 

  • Paulo AA, Ferreira E, Coelho C, Pereira LS (2005) Drought class transition analysis through Markov and Loglinear models, an approach to early warning. Agric Water Manag 77(1–3):59–81

    Article  Google Scholar 

  • Ragno G, Luca MD, Ioele G (2007) An application of cluster analysis and multivariate classification methods to spring water monitoring data. Microchem J 87(2):119–127

    Article  Google Scholar 

  • Steinemann A (2003) Drought indicators and triggers: a stochastic approach to evaluation. JAWRA J Am Water Resour Assoc 39(5):1217–1233

    Article  Google Scholar 

  • Wilhite, D. (2005). Drought policy and preparedness: the Australian experience in an International Context. L. Botterill and D. Wilhite. From Disaster Response to Risk Management. Springer Netherlands, 22: 157–176

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Acknowledgments

We would like to acknowledge our funding from Universiti Tun Hussein Onn Malaysia (Vote U418) in support of this research. We thank the reviewer and editor for their helpful comments.

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Correspondence to Siti Nazahiyah Rahmat.

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Rahmat, S.N., Jayasuriya, N. & Bhuiyan, M.A. Short-term droughts forecast using Markov chain model in Victoria, Australia. Theor Appl Climatol 129, 445–457 (2017). https://doi.org/10.1007/s00704-016-1785-y

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