Probable Maximum Flood estimation using upper bounded statistical models and its effect on high return period quantiles
This work proposes the estimation of high return period quantiles using upper bounded distribution functions, assuming its upper bound parameter as a statistical estimator of the PMF. It is proposed also to use additional Non-Systematic information in order to reduce the estimation uncertainty of hi...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2012
- Institución:
- Universidad de Medellín
- Repositorio:
- Repositorio UDEM
- Idioma:
- eng
- OAI Identifier:
- oai:repository.udem.edu.co:11407/2328
- Acceso en línea:
- http://hdl.handle.net/11407/2328
- Palabra clave:
- Rights
- restrictedAccess
- License
- http://purl.org/coar/access_right/c_16ec
| Summary: | This work proposes the estimation of high return period quantiles using upper bounded distribution functions, assuming its upper bound parameter as a statistical estimator of the PMF. It is proposed also to use additional Non-Systematic information in order to reduce the estimation uncertainty of high return period quantiles and the Probable Maximum Flood. Three upper bounded cumulative probability distribution functions were applied to some Mediterranean rivers in Spain. Depending on the information scenario, different methods to estimate the upper limit of these distribution functions have been merged with the Maximum Likelihood method. Results show that it is possible to obtain a statistical estimate of the Probable Maximum Flood value and to establish its associated uncertainty. With enough information, the associated estimation uncertainty for very high return period quantiles is considered acceptable, even for the PMF estimate. © 2012 Taylor & Francis Group. |
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