Generation of multi-site stochastic daily rainfall with four weather generators: a case study of Gloucester catchment in Australia

Theoretical and Applied Climatology volume 134, pages1027–1046(2018)Cite this article

Abstract

Four weather generators, namely, R-package version of the Generalised Linear Model for daily Climate time series (RGLIMCLIM), Stochastic Climate Library (SCL), R-package multi-site precipitation generator (RGENERATRPREC) and R-package Multi-site Auto-regressive Weather GENerator (RMAWGEN), were used to generate multi-sites stochastic daily rainfall for a small catchment in Australia. The results show the following: (1) All four models produced reasonable results in terms of annual, monthly and daily rainfall occurrence and amount, as well as daily extreme, multi-day extremes and dry/wet spell length. However, they also simulated a large range of variability, which not only demonstrates the advantages of multiple weather generators rather than a single model but also is more suitable for climate change and variability impact studies. (2) Every model has its own advantages and disadvantages due to their different theories and principles. This enhances the benefits of using multiple models. (3) The models can be further calibrated/improved to have a “better” performance in comparison with observations. However, it was chosen not to do so in this case study for two reasons: to obtain a full range of climate variability and to acknowledge the uncertainties associated with observation data. The latter are interpolated from limited stations and therefore have high pairwise correlations—ranging from 0.69 to 0.99 with a median and mean value of 0.87 and 0.88, respectively, for daily rainfall. These conclusions were drawn from a case study in Australia, but could be extended to general guidelines of using weather generators for climate change and variability studies.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

References

  1. Ailliot P, Allard D, Monbet V, Naveau P (2015) Stochastic weather generators: an overview of weather type models. Le Journal de la Société Française de Statistique 156(1):101–113 English version at: http://perso.univ-rennes1.fr/valerie.monbet/doc/papiers_pdf/SWGEN_review.pdf. Accessed 31 Oct 2017.

  2. Ambrosino C, Chandler RE, Todd MC (2014) Rainfall-derived growing season characteristics for agricultural impact assessments in South Africa. Theor Appl Climatol 115:411–426. https://doi.org/10.1007/s00704-013-0896-y

    Article  Google Scholar 

  3. Athreya KB, Roy V (2016) General Glivenko-Cantelli theorems. STAT 5:306–311

    Article  Google Scholar 

  4. Barrett DJ, Couch CA, Metcalfe DJ, Lytton L, Adhikary DP, Schmidt RK (2013) Methodology for bioregional assessments of the impacts of coal seam gas and coal mining development on water resources. A report prepared for the Independent Expert Scientific Committee on Coal Seam Gas and Large Coal Mining Development through the Department of the Environment. http://www.bioregionalassessments.gov.au/methods/bioregional-assessment-methodology. Accessed 11 July 2007

  5. Beesley C, Frost A, Zajaczkowski J (2009) A comparison of the BAWAP and SILO spatially interpolated daily rainfall datasets. In: 18th World IMACS/MODSIM Congress, Cairns, Australia, 13–17 July 2009. http://www.mssanz.org.au/modsim09/I13/beesley.pdf

  6. Chandler RE (2002) GLIMCLIM: Generalised Linear Modelling for Daily Climate Time Series (software and user guide). Research Report No.227, Department of Statistical Science, University College London. http://discovery.ucl.ac.uk/id/eprint/28042. Accessed 31 Oct 2017

  7. Chandler RE (2015) RGLIMCLIM: a multisite, multivariate weather generator based on generalised linear models (software and user guide). http://www.ucl.ac.uk/~ucakarc/work/glimclim.html.Accessed 31 Oct 2017

  8. Chandler RE, Wheater HS (2002) Analysis of rainfall variability using generalized linear models: a case study from the west of Ireland. Water Resour Res 38(10):1192. https://doi.org/10.1029/2001WR000906

    Article  Google Scholar 

  9. Cordano E (2015) RGENERATEPREC: tools to generate daily-precipitation time series [Software] R package version 1.0. http://CRAN.R-project.org/package=RGENERATEPREC. Accessed 31 Oct 2017

  10. Cordano E, Eccel E (2012) RMAWGEN: a software project for a daily multi-site weather generator with R, European Geosciences Union: General Assembly 2012, Vienna, Austria, pp 22–27

  11. Cordano E, Eccel E (2014) RMAWGEN: Multi-site Auto-regressive Weather GENerator. R package version 1.3.0. http://CRAN.R-project.org/package=RMAWGEN. Accessed 31 Oct 2017

  12. Cordano E, Eccel E (2016) Tools for stochastic weather series generation in R environment, Ital. J Agrometeorol 21(3):31–42. 10.19199/2016.3.2038-5625.031

    Article  Google Scholar 

  13. CSIRO (2012) Climate and water availability in south-eastern Australia: a synthesis of findings from phase 2 of the South Eastern Australian Climate Initiative (SEACI), CSIRO, Australia, September 2012, 41 pp

  14. Department of the Environment (2013) Significant impact guidelines 1.3: coal seam gas and large coal mining developments—impacts on water resources. https://www.environment.gov.au/system/files/resources/d078caf3-3923-4416-a743-0988ac3f1ee1/files/sig-water-resources.pdf. Accessed 11 July 2007

  15. Frost AJ, Charles SP, Timbal B, Chiew FHS, Mehrotra R, Nguyen KC, Chandler RE, McGregor JL, Fu G, Kirono DGC, Fernandez E, Kent DM (2011) A comparison of multi-site daily rainfall downscaling techniques under Australian conditions. J Hydrol 408:1–18. https://doi.org/10.1016/j.jhydrol.2011.06.021

    Article  Google Scholar 

  16. Fu G, Viney NR, Charles SP (2010a) Evaluation of various root transformations of daily precipitation amounts fitted with a normal distribution for Australia. Theor Appl Climatol 99:229–238. https://doi.org/10.1007/s00704-009-0137-6

    Article  Google Scholar 

  17. Fu G, Viney NR, Charles SP, Liu J (2010b) Long-term temporal variation of extreme rainfall events in Australia: 1910–2006. J Hydrometeorol 11:950–965

    Article  Google Scholar 

  18. Fu G, Charles SP, Kirshner S (2013a) Daily rainfall projections from general circulation models with a downscaling nonhomogeneous hidden Markov model (NHMM) for south-eastern Australia. Hydrol Process 27:3663–3673. https://doi.org/10.1002/hyp.9483

    Article  Google Scholar 

  19. Fu G, Liu Z, Charles SP, Xu Z, Yao Z (2013b) A score-based method for assessing the performance of GCMs: a case study of southeastern Australia. J Geophys Res Atmos 118:4154–4167. https://doi.org/10.1002/jgrd.50269

    Article  Google Scholar 

  20. Liu W, Fu G, Liu C, Charles SP (2013) A comparison of three multi-site statistical downscaling models for daily rainfall in the North China Plain. Theor Appl Climatol 111:585–600. https://doi.org/10.1007/s00704-012-0692-0

    Article  Google Scholar 

  21. Luetkepohl H (2007) New introduction to multiple time series analysis, 2nd edn. Springer-Verlag, Berlin

    Google Scholar 

  22. Maraun D, Wetterhall F, Ireson A, Chandler R, Kendon E, Widmann M, Brienen S, Rust H, Sauter T, Themel M, Venema VKC, Chun KP, Goodess CM, Jones RG, Onof C, Vrac M, Thiele-Eich I (2010) Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48(3). https://doi.org/10.1029/2009RG000314

  23. McVicar TR, Langhi L, Barron OV, Rachakonda PK, Zhang YQ, Dawes WR, Macfarlane C, Holland KL, Wilkes PG, Raisbeck-Brown N, Marvanek SP, Li LT, Van Niel TG (2014) Context statement for the Gloucester subregion. Product 1.1 from the Northern Sydney Basin Bioregional Assessment. Department of the Environment, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia http://www.bioregionalassessments.gov.au/assessments/11-context-statement-gloucester-subregion. Accessed 11 July 2007

  24. Mehrotra R, Srikanthan R, Sharma A (2006) A comparison of three stochastic multi-site precipitation occurrence generators. J Hydrol 331:280–292

    Article  Google Scholar 

  25. Murphy B, Timbal B (2008) A review of recent climate variability and climate change in south-eastern Australia. Int J Climatol 28(7):859–879

    Article  Google Scholar 

  26. Peel MC, Srikanthan R, McMahon TA, Karoly DJ (2015) Approximating uncertainty of annual runoff and reservoir yield using stochastic replicates of global climate model data. Hydrol Earth Syst Sci 19:1615–1639

    Article  Google Scholar 

  27. Potter NJ, Chiew FHS, Frost AJ (2010) An assessment of the severity of recent reductions in rainfall and runoff in the Murray-Darling Basin. J Hydrol 381:52–64. https://doi.org/10.1016/j.jhydrol.2009.11.025

    Article  Google Scholar 

  28. Srikanthan R, McMahon TA (2005) Automatic evaluation of stochastically generated rainfall data. Aust J Water Resour 8(2):195–201

    Google Scholar 

  29. Srikanthan S, Chiew FHS, Frost AJ (2007) Stochastic Climate Library (SCL), user guide manual. https://toolkit.ewater.org.au/Tools/SCL. Accessed 11 July 2007

  30. Timbal B, Drosdowsky W (2013) The relationship between the decline of south eastern Australia rainfall and the strengthening of the sub-tropical ridge. Int J Climatol 33:1021–1034. https://doi.org/10.1002/joc.3492

    Article  Google Scholar 

  31. Wilks D (1998) Multisite generalization of a daily stochastic precipitation generation model. J Hydro 210:178–191

    Article  Google Scholar 

  32. Wilks D (2010) Use of stochastic weather generators for precipitation downscaling. Wires Clim Chang 1:898–907

    Article  Google Scholar 

  33. Wilks D (2012) Stochastic weather generators for climate-change downscaling, part ii: multivariable and spatially coherent multisite downscaling. Wires Clim Chang 3:267–278

    Article  Google Scholar 

  34. Wilks D, Wilby R (1999) The weather generation game: a review of stochastic weather models. Progress in Phys Geogr 23(3):329–357

    Article  Google Scholar 

  35. Yan Z, Bate B, Chandler RE, Isham V, Wheater H (2006) Changes in extreme wind speeds in NW Europe simulated by generalized linear models. Theor Appl Climatol 83:121–137

    Article  Google Scholar 

  36. Yang C, Chandler RE, Isham VS, Wheater HS (2005) Spatial-temporal rainfall simulation using generalized linear models. Water Resour Res 41:W11415. https://doi.org/10.1029/2004WR003739

    Article  Google Scholar 

Download references

Acknowledgements

This research was partly funded by a CSIRO strategic project. We would also like to thank Dr. Dirk Mallants (Project leaders), Dr. Nick Potter and Dr. Tony Zhao of CSIRO Land and Water (CSIRO internal review), Dr. Tamás Weidinger (Editor of TAAC) and two anonymous reviewers for their invaluable comments and constructive suggestions used to improve the quality of the manuscript. A special thanks is also extended to all developers of models we used, especially Dr. Chandler and Dr. Cordano, who have answered my many questions thorough email correspondences.

Author information

Affiliations

  1. CSIRO Land and Water, Private Bag 5, Wembley, WA, 6913, Australia

    Guobin Fu

  2. CSIRO Land and Water, GPO Box 1700, Canberra, Australian Capital Territory, Australia

    Francis H. S. Chiew

  3. Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK

    Xiaogang Shi

Authors
  1. Guobin Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Francis H. S. Chiew
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaogang Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guobin Fu.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Fu, G., Chiew, F.H.S. & Shi, X. Generation of multi-site stochastic daily rainfall with four weather generators: a case study of Gloucester catchment in Australia. Theor Appl Climatol 134, 1027–1046 (2018). https://doi.org/10.1007/s00704-017-2306-3

Download citation