Advertisement

Establishing a daily rainfall occurrence simulation model for the Langat River catchment, Malaysia

  • Chau Yuan Lian
  • Yuk Feng HuangEmail author
  • Lloyd Ling
Article
  • 29 Downloads

Abstract

For the study of water resources of a catchment, an immediate task would be to establish a good model for predicting the probable daily rainfall occurrence and rainfall amount. This study presents the simulation of daily rainfall occurrence using the generalized linear model (GLM), the non-homogeneous hidden Markov model (NHMM) and the bootstrap aggregated classification tree (BACT) model. The major challenge of NHMM is the determination of optimum number of hidden states, which can be achieved using the Bayesian information criterion score. While the determination of number of grown tree is another challenge for BACT model, this critical task can be achieved with the help of out-of-bag classification error. Both the NHMM and BACT model outperformed the GLM to capture the rainfall persistence and spell lengths distribution. Through the validation phase, the BACT model exhibited better performance with the higher indices of probability of detection, critical success index, Heidke skill score and Peirce skill score, than other models. The prediction ability of the NHMM is equivalent to an unskilled random forecast with the skill scores nearly equal to zero. At the end, the BACT model was recommended as the appropriate daily rainfall occurrence model for this study.

Keywords

Statistical downscaling generalized linear model non-homogeneous hidden Markov model bootstrap aggregated classification tree daily rainfall occurrence model Langat River catchment 

Notes

Acknowledgements

The authors would like to express their sincere appreciations to the Universiti Tunku Abdul Rahman, Bandar Sungai Long, Cheras, 43000 Kajang, Selangor, Malaysia for funding this study.

References

  1. Ahmed K, Shahid S, Haroon S B and Wang X 2015 Multilayer perceptron neural network for downscaling rainfall in arid region: A case study of Baluchistan, Pakistan; J. Earth Syst. Sci. 124(6) 1325–1341.CrossRefGoogle Scholar
  2. Beckmann B R and AdriBuishand T 2002 Statistical downscaling relationships for precipitation in the Netherlands and North Germany; Int. J. Climatol. 22(1) 15–32,  https://doi.org/10.1002/joc.718.CrossRefGoogle Scholar
  3. Breiman L 2001 Random forests; Mach. Learn. 45 5–32,  https://doi.org/10.1023/A:1010933404324.CrossRefGoogle Scholar
  4. Buishand T A, Shabalova M V and Brandsma T 2004 On the choice of the temporal aggregation level for statistical downscaling of precipitation; J. Clim. 17(9) 1816–1827.CrossRefGoogle Scholar
  5. Chau K W and Wu C L 2010 A hybrid model coupled with singular spectrum analysis for daily rainfall prediction; J. Hydroinform. 12(4) 458–473,  https://doi.org/10.2166/hydro.2010.032.CrossRefGoogle Scholar
  6. Fealy R and Sweeney J 2007 Statistical downscaling of precipitation for a selection of sites in Ireland employing a generalised linear modelling approach; Int. J. Climatol. 27(15) 2083–2094,  https://doi.org/10.1002/joc.1506.CrossRefGoogle Scholar
  7. Fu G, Charles S P and Kirshner S 2013 Daily rainfall projections from general circulation models with a downscaling non-homogeneous hidden Markov model (NHMM) for south-eastern Australia; Hydrol. Process. 27(25) 3663–3673,  https://doi.org/10.1002/hyp.9483.CrossRefGoogle Scholar
  8. Gaitan C F, Hsieh W W and Cannon A J 2014 Comparison of statistically downscaled precipitation in terms of future climate indices and daily variability for southern Ontario and Quebec, Canada; Clim. Dyn. 43(12) 3201–3217,  https://doi.org/10.1007/s00382-014-2098-4.CrossRefGoogle Scholar
  9. Greene A M, Robertson A W, Smyth P and Triglia S 2011 Downscaling projections of Indian monsoon rainfall using a non-homogeneous hidden Markov model; Quart. J. Roy. Meteorol. Soc. 137 347–359,  https://doi.org/10.1002/qj.788.CrossRefGoogle Scholar
  10. He X, Chaney N W, Schleiss M and Sheffield J 2016 Spatial downscaling of precipitation using adaptable random forests; Water Resour. Res. 52 8217–8237,  https://doi.org/10.1002/2016WR019034.CrossRefGoogle Scholar
  11. Jing W, Yang Y, Yue X and Zhao X 2016 A comparison of different regression algorithms for downscaling monthly satellite-based precipitation over north China; Remote Sens. 8(10) 835–851,  https://doi.org/10.3390/rs8100835.
  12. Kannan S and Ghosh S 2011 Prediction of daily rainfall state in a river basin using statistical downscaling from GCM output; Stoch. Environ. Res. Risk Assess. 25(4) 457–474,  https://doi.org/10.1007/s00477-010-0415-y.CrossRefGoogle Scholar
  13. Kannan S and Ghosh S 2013 A nonparametric kernel regression model for downscaling multisite daily precipitation in the Mahanadi basin; Water Resour. Res. 49(3) 1360–1385,  https://doi.org/10.1002/wrcr.20118.CrossRefGoogle Scholar
  14. Kenabatho P K, Mclntyre N R, Chandler R E and Wheater H S 2012 Stochastic simulation of rainfall in semi-arid Limpopo basin, Botswana; Int. J. Climatol. 32(7) 1113–1127,  https://doi.org/10.1002/joc.2323.CrossRefGoogle Scholar
  15. Kioutsioukis I, Melas D and Zanis P 2008 Statistical downscaling of daily precipitation over Greece; Int. J. Climatol. 28(5) 679–691,  https://doi.org/10.1002/joc.1557.CrossRefGoogle Scholar
  16. Lee T C, Chan K Y, Chan H S and Kok M H 2011 Projections of extreme rainfall in Hong Kong in the \(21{{\rm st}}\) century; Acta Meteorol. Sin. 25(6) 691–709.CrossRefGoogle Scholar
  17. Liaw A and Wiener M 2002 Classification and regression by random forest; R. News 2(3) 18–22.Google Scholar
  18. Liu W, Fu G, Liu C and Charles S P 2013 A comparison of three multi-site statistical downscaling models for daily rainfall in the North China Plain; Theor. Appl. Climatol. 111(3–4) 585–600,  https://doi.org/10.1007/s00704-012-0692-0.CrossRefGoogle Scholar
  19. Liu Z, Xu Z, Charles SP, Fu G and Liu L 2011 Evaluation of two statistical downscaling models for daily precipitation over an arid basin in China; Int. J. Climatol. 31(13) 2006–2020,  https://doi.org/10.1002/joc.2211.CrossRefGoogle Scholar
  20. Mandal S, Srivastav R K and Simonovic S P 2016 Use of beta regression for statistical downscaling of precipitation in the Campbell River basin, British Columbia, Canada; J. Hydrol. 538 49–62,  https://doi.org/10.1016/j.jhydrol.2016.04.009.CrossRefGoogle Scholar
  21. Mares C, Mares I, Huebener H, Mihailescu M, Cubasch U and Stanciu P 2014 A hidden Markov model applied to the daily spring precipitation over the Danube Basin; Adv. Meteorol. 2014 ID 237247.Google Scholar
  22. Memarian H, Balasundram S K, Abbaspour K C, Talib J B, Christopher Teh B S and Sood A M 2014 SWAT-based hydrological modelling of tropical-use scenarios; Hydrol. Sci. J. 59(10) 1808–1829.Google Scholar
  23. Ng J L, Aziz S A, Huang Y F, Wayayok A and Rowshon M K 2017 Generation of a stochastic precipitation model for the tropical climate; Theor. Appl. Climatol.,  https://doi.org/10.1007/s00704-017-2202-x.CrossRefGoogle Scholar
  24. Pineda A N and Willems P 2016 Multisite downscaling of seasonal predictions to daily rainfall characteristics over Pacific-Andean River Basins in Eucadoe and Peru using a nonhomogeneous hidden Markov model; J. Hydrolmeteorol. 17(2) 481–498.CrossRefGoogle Scholar
  25. Robertson A W, Kirshner S and Smyth P 2004 Downscaling of daily rainfall occurrence over northeast Brazil using a hidden Markov model; J Climate 17(22) 4407–4424,  https://doi.org/10.1175/JCLI-3216.1.CrossRefGoogle Scholar
  26. Robertson A W, Moron V and Swarinoto Y 2009 Seasonal predictability of daily rainfall statistics over Indramayu district, Indonesia; Int. J. Climatol. 29(10) 1449–1462,  https://doi.org/10.1002/joc.1816.CrossRefGoogle Scholar
  27. Salvi K, Kannan S and Ghosh S 2013 High-resolution multi-site daily rainfall projections in India with statistical downscaling for climate change impacts assessment; J. Geophys. Res. Atmos. 118(9) 3557–3578,  https://doi.org/10.1002/jgrd.50280.CrossRefGoogle Scholar
  28. Saudi A S M, Juahir H, Azid A, Toriman M E, Kamarudin M K A, Saudi M M, Mustafa A D and Amran M A 2015 Flood risk pattern recognition by using environmetric technique: A case study in Langat river basin; J. Teknol. 77(1) 145–152.Google Scholar
  29. Schoof J T and Pryor S C 2001 Downscaling temperature and precipitation: A comparison of regression-based methods and artificial neural networks; Int. J. Climatol. 21(7) 773–790,  https://doi.org/10.1002/joc.655.CrossRefGoogle Scholar
  30. Shi Y and Song L 2015 Spatial downscaling of monthly TRMM precipitation based on EVI and other geospatial variables over the Tibetan Plateau from 2001 to 2012; Mt. Res. Dev. 35(2) 180–194,  https://doi.org/10.1659/MRD-JOURNAL-D-14-00119.1.CrossRefGoogle Scholar
  31. Singh D, Jain S K and Gupta R D 2015 Statistical downscaling and projection of future temperature and precipitation change in middle catchment of Sutlej River Basin, India; J. Earth Syst. Sci. 124(4) 843–860.CrossRefGoogle Scholar
  32. Sullivan C A and Huntingford C 2009 Water resources, climate change and human vulnerability; Proceedings of the 18th World IMACS/MODSIM Congress, Cairns, Australia, pp. 3984–3990.Google Scholar
  33. Wilby R L and Dawson C W 2013 The statistical downscaling model: Insights from one decade of application; Int. J. Climatol. 33(7) 1707–1719,  https://doi.org/10.1002/joc.3544.CrossRefGoogle Scholar
  34. Wu C L, Chau K W and Fan C 2010 Prediction of rainfall time series using modular artificial neural network coupled with data-preprocessing techniques; J. Hydrol. 389(1–2) 146–167,  https://doi.org/10.1016/j.hydrol.2010.05.040.CrossRefGoogle Scholar

Copyright information

© Indian Academy of Sciences 2019

Authors and Affiliations

  1. 1.Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and ScienceUniversiti Tunku Abdul RahmanKajangMalaysia

Personalised recommendations