Estimation of Low Flow Statistics for Sustainable Water Resources Management in South Australia
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 essen...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Hydrology |
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| Online Access: | https://www.mdpi.com/2306-5338/9/9/152 |
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| author | Niranjani P. K. Semananda Guna A. Hewa |
| author_facet | Niranjani P. K. Semananda Guna A. Hewa |
| author_sort | Niranjani P. K. Semananda |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T23:50:33Z |
| format | Article |
| id | doaj.art-555a1b16b77143e9b28187cace6d1c4e |
| institution | Directory Open Access Journal |
| issn | 2306-5338 |
| language | English |
| last_indexed | 2024-03-09T23:50:33Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Hydrology |
| spelling | doaj.art-555a1b16b77143e9b28187cace6d1c4e2023-11-23T16:34:52ZengMDPI AGHydrology2306-53382022-08-019915210.3390/hydrology9090152Estimation of Low Flow Statistics for Sustainable Water Resources Management in South AustraliaNiranjani P. K. Semananda0Guna A. Hewa1Division of Civil Engineering Technology, Institute of Technology, University of Moratuwa (ITUM), Diyagama, Homagama 10200, Sri LankaUniSA STEM, University of South Australia, Mawson Lakes, SA 5095, AustraliaThe 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.https://www.mdpi.com/2306-5338/9/9/152low flowclimate changewater resourcessustainableRMSEARI |
| spellingShingle | Niranjani P. K. Semananda Guna A. Hewa Estimation of Low Flow Statistics for Sustainable Water Resources Management in South Australia Hydrology low flow climate change water resources sustainable RMSE ARI |
| title | Estimation of Low Flow Statistics for Sustainable Water Resources Management in South Australia |
| title_full | Estimation of Low Flow Statistics for Sustainable Water Resources Management in South Australia |
| title_fullStr | Estimation of Low Flow Statistics for Sustainable Water Resources Management in South Australia |
| title_full_unstemmed | Estimation of Low Flow Statistics for Sustainable Water Resources Management in South Australia |
| title_short | Estimation of Low Flow Statistics for Sustainable Water Resources Management in South Australia |
| title_sort | estimation of low flow statistics for sustainable water resources management in south australia |
| topic | low flow climate change water resources sustainable RMSE ARI |
| url | https://www.mdpi.com/2306-5338/9/9/152 |
| work_keys_str_mv | AT niranjanipksemananda estimationoflowflowstatisticsforsustainablewaterresourcesmanagementinsouthaustralia AT gunaahewa estimationoflowflowstatisticsforsustainablewaterresourcesmanagementinsouthaustralia |