Assessment of Short Term Rainfall and Stream Flows in South Australia

Water

Mohammad Kamruzzaman¹  Md Sumon Shahriar²  Simon Beecham¹ 

[1] Centre for Water Management and Reuse (CWMR), School of Natural and Built Environments, University of South Australia, Mawson Lakes, SA 5095, Australia; E-Mail:;CSIRO Computational Informatics (CCI), Hobart, TAS 7001, Australia; E-Mail:

关键词: Artificial Neural Network;    lag response model;    rainfall and stream flow;    wavelet coefficient;    Akaike Information Criterion;   

DOI  :  10.3390/w6113528

来源: mdpi

【 摘 要 】

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 ( 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).

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.