Tran, Dung H and Ng, A. W. M (2009) Statistical Trend Analysis of River Streamflows in Victoria. In: Proceedings of H₂O09: 32nd Hydrology and Water Resources Symposium, Newcastle: adapting to change 30 November - 3 December 2009, Newcastle, Australia. Engineers Australia, Barton, ACT, pp. 1019-1027.

Abstract

Victoria's river systems are managed by ten Catchment Management Authorities (CMAs). These river systems play an important role in supplying water for various demands ranging from urban water supply, agricultural irrigation to environmental flows. This paper presents a study of statistical trend analysis of streamflows in major river systems of CMAs in Victoria (Australia) using a network of 14 long term and unregulated gauging stations. At each gauging station, time series data of daily, monthly and yearly streamflow volume are provided. A range of 9 important streamflow statistics including annual maximum, minimum, mean and median, were computed using daily streamflow data at each station. The popular Mann-Kendall test was applied to detect trends in the annual time series data of nine streamflow statistics. The correlation tests were performed for lag-1 serial and cross correlation. The results showed a consistent and significant decreasing trend among 9 streamflow statistics with regards to temporal and spatial aspects. The annual minimum and median flow display the highest number of stations (4 out of 14) with lag-1 correlation. The correlation test was also performed on 4 selected streamflow statistics (i.e. annual median and mean, and number of high and low flow days) that can be useful for streamflow forecasting. There is a consistent and strong positive correlation between annual median and mean flow, while annual mean and number of high flow days has a relatively weak and inconsistent (i.e. positive and negative) correlation at 4 out of 14 stations. The annual median shows almost no correlation with both number of high and low flow days. The outcomes of this study provided an overview on how streamflow patterns have altered with regards to global climate change and also provided background information for streamflow forecasting.

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