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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 17, issue 11
Hydrol. Earth Syst. Sci., 17, 4541–4553, 2013
https://doi.org/10.5194/hess-17-4541-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 17, 4541–4553, 2013
https://doi.org/10.5194/hess-17-4541-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 15 Nov 2013

Research article | 15 Nov 2013

Probability distributions for explaining hydrological losses in South Australian catchments

S. H. P. W. Gamage1, G. A. Hewa1,2, and S. Beecham1,2 S. H. P. W. Gamage et al.
  • 1School of Natural and Built Environments, University of South Australia, Adelaide, Australia
  • 2Centre for Water Management and Reuse (CWMR), University of South Australia, Adelaide, Australia

Abstract. Accurate estimation of hydrological losses is required for making vital decisions in design applications that are based on design rainfall models and rainfall–runoff models. The use of representative single values of hydrological losses, despite their wide variability, is common practice, especially in Australian studies. This practice leads to issues such as over or under estimation of design floods. The probability distribution method is potentially a better technique to describe losses. However, a lack of understanding of how losses are distributed can limit the use of this technique. This paper aims to identify a probability distribution function that can successfully describe hydrological losses of a catchment of interest. The paper explains the systematic process of identifying probability distribution functions, the problems faced during the distribution fitting process and a new generalised method to test the adequacy of fitted distributions. The goodness-of-fit of the fitted distributions are examined using the Anderson–Darling test and the Q–Q plot method and the errors associated with quantile estimation are quantified by estimating the bias and mean square error (MSE). A two-parameter gamma distribution was identified as one that successfully describes initial loss (IL) data for the selected catchments. Further, non-parametric standardised distributions that describe both IL and continuing loss data are also identified. This paper will provide a significant contribution to the Australian Rainfall and Runoff (ARR) guidelines that are currently being updated, by improving understanding of hydrological losses in South Australian catchments. More importantly, this study provides new knowledge on how IL in a catchment is characterised.

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