Estimation of Low Flow Statistics for Sustainable Water Resources Management in South Australia

Abstract

The Magnitude and occurrence of extreme low flow events are needed in setting minimum flows to protect the instream users. As the true distribution is not normally known, the identification of the most appropriate distribution function that describes the extreme low flow data of a catchment is essential in estimating reliable low flow quantiles at various average recurrence intervals (ARI). The aim of this study is to conduct a comparative assessment of the performance of three plausible distribution functions for estimating low flow quantiles. The investigation was carried out by using 27-gauge stations within South Australia (SA), the driest state in Australia. The best distribution function out of the three selected distributions; Log Normal (LN), Log Pearson Type 3 (LP3), and Generalized Extreme Value (GEV for each of the three selected annual minima series (7-day, 15-day and 30-day) at each gauged catchments was identified. The estimated low flow quantiles from using these three distribution functions were compared using RMSE values estimated through Monte Carlo simulation studies. For the majority of the selected study catchments, GEV fitted using L moments was found to be the best method for estimating low flow quantiles at ARIs over 10 years (≥14%), while at low ARI, LP3 fitted using the Method of Moments (MOM) was shown to outperform (≥17%) the other methods.