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Water 2014, 6(11), 3528-3544; doi:10.3390/w6113528

Assessment of Short Term Rainfall and Stream Flows in South Australia

Centre for Water Management and Reuse (CWMR), School of Natural and Built Environments, University of South Australia, Mawson Lakes, SA 5095, Australia
CSIRO Computational Informatics (CCI), Hobart, TAS 7001, Australia
Author to whom correspondence should be addressed.
Received: 12 June 2014 / Revised: 17 September 2014 / Accepted: 12 November 2014 / Published: 19 November 2014
(This article belongs to the Special Issue Water Resources in a Variable and Changing Climate)
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The aim of this study is to assess the relationship between rainfall and stream flow at Broughton River in Mooroola, Torrance River in Mount Pleasant, and Wakefield River near Rhyine, in South Australia, from 1990 to 2010. Initially, we present a short term relationship between rainfall and stream flow, in terms of correlations, lagged correlations, and estimated variability between wavelet coefficients at each level. A deterministic regression based response model is used to detect linear, quadratic and polynomial trends, while allowing for seasonality effects. Antecedent rainfall data were considered to predict stream flow. The best fitting model was selected based on maximum adjusted R2 values (R2adj ), minimum sigma square (σ2), and a minimum Akaike Information Criterion (AIC). The best performance in the response model is lag rainfall, which indicates at least one day and up to 7 days (past) difference in rainfall, including offset cross products of lag rainfall. With the inclusion of antecedent stream flow as an input with one day time lag, the result shows a significant improvement of the R2adj values from 0.18, 0.26 and 0.14 to 0.35, 0.42 and 0.21 at Broughton River, Torrance River and Wakefield River, respectively. A benchmark comparison was made with an Artificial Neural Network analysis. The optimization strategy involved adopting a minimum mean absolute error (MAE). View Full-Text
Keywords: Artificial Neural Network; lag response model; rainfall and stream flow; wavelet coefficient; Akaike Information Criterion Artificial Neural Network; lag response model; rainfall and stream flow; wavelet coefficient; Akaike Information Criterion

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Kamruzzaman, M.; Shahriar, M.S.; Beecham, S. Assessment of Short Term Rainfall and Stream Flows in South Australia. Water 2014, 6, 3528-3544.

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