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Theoretical and Applied Climatology

, Volume 131, Issue 1–2, pp 181–200 | Cite as

Annual statistical downscaling of precipitation and evaporation and monthly disaggregation

  • D. A. SachindraEmail author
  • B. J. C. Perera
Original Paper

Abstract

Development of downscaling models for each calendar month using the data of predictors specifically selected for each calendar month may assists in better capturing the time-varying nature of the predictor-predictand relationships. Such approach will not allow the explicit modelling of the persistence of the predictand (e.g. lag-1 correlation). However, downscaling at an annual time step and subsequent disaggregation to monthly values can explicitly consider the modelling of the persistence of the predictand. This study investigated the potential of annual downscaling of a predictand and subsequent disaggregation of annual values to monthly values, in comparison to the potential of downscaling models separately developed for each calendar month. In the case study, annual and monthly downscaling models were developed for precipitation and evaporation at two stations located in Victoria, Australia. The output of the annual downscaling models was then disaggregated into monthly values using four different methods based on the method of fragments. It was found that the annual to monthly disaggregation methods and monthly downscaling models are able to reproduce the average of monthly observations with relatively higher accuracy in comparison to their ability in reproducing standard deviation, skewness and lag-1 serial correlation. Downscaling models separately developed for each calendar month were able to show relatively smaller root mean square errors for their time series indicating better overall agreement with observations in comparison to their counterpart annual to monthly disaggregation methods. Furthermore, it was found that not only the bias in the output of an annual downscaling model but also the presence of annual totals in the records of observations of a predictand that are very similar in magnitude, but having significantly different sets of fragments, can largely contribute to the poor performance of an annual to monthly disaggregation method.

Notes

Acknowledgments

The authors wish to thank the SILO team of the Queensland Climate Change Centre of Excellence, Australia, for their clarifications on observed data used in this study.

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Copyright information

© Springer-Verlag Wien 2016

Authors and Affiliations

  1. 1.Institute for Sustainability and Innovation, College of Engineering and ScienceVictoria UniversityMelbourneAustralia

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