Assessment of Short Term Rainfall and Stream Flows in South Australia

作者：Kamruzzaman Mohammad^*; Shahriar Md Sumon; Beecham Simon

来源：Water (Switzerland), 2014, 6(11): 3528-3544.

DOI：10.3390/w6113528

• Artificial Neural Network
• lag response model
• rainfall and stream flow
• wavelet coefficient
• Akaike Information Criterion

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 R-2 values (R-adj(2)), minimum sigma square (sigma(2)), 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 R-adj(2) 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).

• 出版日期2014-11
• 单位CSIRO