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Some best‐fit probability distributions for at‐site flood frequency analysis of the Ume River
Abstract At‐site flood frequency analysis is a direct method of flood estimation at a given site. The choice of an appropriate probability distribution and parameter estimation method plays a vital role in at‐site frequency analysis. In the current article, flood frequency analysis is carried out at five gauging sites of the Ume River in Sweden. Generalised extreme value, three‐parameter log‐normal, generalised logistic and Gumbel distributions are fitted to the annual maximum peak flow data. The L‐moment and the maximum likelihood methods are used to estimate the parameters of the distributions. Based on different goodness‐of‐fit tests and accuracy measures, the three‐parameter log‐normal distribution has been identified as the best‐fitted distribution by using the L‐moments method of estimation for gauging sites Harrsele Krv, Gardiken and Överuman Nedre. The generalised extreme value distribution with the L‐moments estimation provided the best fit to maximum annual streamflow at gauging sites Solberg and Stornorrfors Krv. Finally, the best‐fitted distribution for each gauging site is used to predict the maximum flow of water for return periods of 5, 10, 25, 50, 100, 200, 500, and 1000 years.
Some best‐fit probability distributions for at‐site flood frequency analysis of the Ume River
Abstract At‐site flood frequency analysis is a direct method of flood estimation at a given site. The choice of an appropriate probability distribution and parameter estimation method plays a vital role in at‐site frequency analysis. In the current article, flood frequency analysis is carried out at five gauging sites of the Ume River in Sweden. Generalised extreme value, three‐parameter log‐normal, generalised logistic and Gumbel distributions are fitted to the annual maximum peak flow data. The L‐moment and the maximum likelihood methods are used to estimate the parameters of the distributions. Based on different goodness‐of‐fit tests and accuracy measures, the three‐parameter log‐normal distribution has been identified as the best‐fitted distribution by using the L‐moments method of estimation for gauging sites Harrsele Krv, Gardiken and Överuman Nedre. The generalised extreme value distribution with the L‐moments estimation provided the best fit to maximum annual streamflow at gauging sites Solberg and Stornorrfors Krv. Finally, the best‐fitted distribution for each gauging site is used to predict the maximum flow of water for return periods of 5, 10, 25, 50, 100, 200, 500, and 1000 years.
Some best‐fit probability distributions for at‐site flood frequency analysis of the Ume River
Samara Kousar (author) / Abrar Raza Khan (author) / Mahmood Ul Hassan (author) / Zahra Noreen (author) / Sajjad Haider Bhatti (author)
2020
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Some best‐fit probability distributions for at‐site flood frequency analysis of the Ume River
| Wiley | 2020
Some best‐fit probability distributions for at‐site flood frequency analysis of the Ume River
| Wiley | 2020
Maximum entropy probability distributions for flood frequency analysis
| Taylor & Francis Verlag | 1987