Articles | Volume 21, issue 12
https://doi.org/10.5194/hess-21-6541-2017
https://doi.org/10.5194/hess-21-6541-2017
Research article
 | 
22 Dec 2017
Research article |  | 22 Dec 2017

Development and evaluation of a stochastic daily rainfall model with long-term variability

A. F. M. Kamal Chowdhury, Natalie Lockart, Garry Willgoose, George Kuczera, Anthony S. Kiem, and Nadeeka Parana Manage

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Cited articles

Bardossy, A. and Plate, E. J.: Space-Time Model for Daily Rainfall Using Atmospheric Circulation Patterns, Water Resour. Res., 28, 1247–1259, https://doi.org/10.1029/91wr02589, 1992.
Bellone, E., Hughes, J. P., and Guttorp, P.: A Hidden Markov Model for Downscaling Synoptic Atmospheric Patterns to Precipitation Amounts, Clim. Res., 15, 1–12, https://doi.org/10.3354/cr015001, 2000.
BoM: Daily Rainfall Data, available at: http://www.bom.gov.au/climate/data/index.shtml (last access: 20 December 2013), Bureau of Meteorology (BoM), Australia, 2013.
Chen, J. and Brissette, F. P.: Comparison of Five Stochastic Weather Generators in Simulating Daily Precipitation and Temperature for the Loess Plateau of China, Int. J. Climatol., 34, 3089–3105, https://doi.org/10.1002/joc.3896, 2014.
Chen, J., Brissette, F. P., and Leconte, R.: A Daily Stochastic Weather Generator for Preserving Low-Frequency of Climate Variability, J. Hydrol., 388, 480–490, https://doi.org/10.1016/j.jhydrol.2010.05.032, 2010.
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Short summary
Stochastic rainfall models are required to be be able to assess the reliability of dams used for urban water supply. Traditional Markov chain stochastic models do well at reproducing the mean and variance of rainfall at daily to weekly resolution but fail to simultaneously reproduce the variability of monthly to decadal rainfall. This paper presents four new extensions to Markov chain models that address this decadal deficiency and compares their performance for two field sites.
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