Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Some best-fit probability distributions for at-site flood frequency analysis of the Ume River
Stockholm University, Faculty of Social Sciences, Department of Statistics.ORCID iD: 0000-0003-2889-0263
Show others and affiliations
2020 (English)In: Journal of Flood Risk Management, E-ISSN 1753-318X, Vol. 13, no 3, article id e12640Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2020. Vol. 13, no 3, article id e12640
Keywords [en]
generalised extreme value, generalised logistic, Gumble, L-moments, log-normal, maximumlikelihood
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:su:diva-183744DOI: 10.1111/jfr3.12640ISI: 000552665500001OAI: oai:DiVA.org:su-183744DiVA, id: diva2:1455714
Available from: 2020-07-28 Created: 2020-07-28 Last updated: 2023-11-08Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records

Ul Hassan, MahmoodBhatti, Sajjad Haider

Search in DiVA

By author/editor
Ul Hassan, MahmoodBhatti, Sajjad Haider
By organisation
Department of Statistics
In the same journal
Journal of Flood Risk Management
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 94 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf