Probable Maximum Flood estimation using upper bounded statistical models and its effect on high return period quantiles

F. Francés, B. A. Botero

Resultado de la investigación: Contribución a una conferenciaArtículoInvestigación

Resumen

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.
Idioma originalInglés estadounidense
Páginas323-328
Número de páginas6
EstadoPublicada - 13 feb 2012
EventoProceedings of the 3rd International Forum on Risk Analysis, Dam Safety Dam Security and Critical Infrastructure Management, 3IWRDD-FORUM -
Duración: 13 feb 2012 → …

Conferencia

ConferenciaProceedings of the 3rd International Forum on Risk Analysis, Dam Safety Dam Security and Critical Infrastructure Management, 3IWRDD-FORUM
Período13/02/12 → …

Huella dactilar

return period
river
effect
distribution
method

Citar esto

Francés, F., & Botero, B. A. (2012). Probable Maximum Flood estimation using upper bounded statistical models and its effect on high return period quantiles. 323-328. Papel presentado en Proceedings of the 3rd International Forum on Risk Analysis, Dam Safety Dam Security and Critical Infrastructure Management, 3IWRDD-FORUM, .
Francés, F. ; Botero, B. A. / Probable Maximum Flood estimation using upper bounded statistical models and its effect on high return period quantiles. Papel presentado en Proceedings of the 3rd International Forum on Risk Analysis, Dam Safety Dam Security and Critical Infrastructure Management, 3IWRDD-FORUM, .6 p.
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Probable Maximum Flood estimation using upper bounded statistical models and its effect on high return period quantiles. / Francés, F.; Botero, B. A.

2012. 323-328 Papel presentado en Proceedings of the 3rd International Forum on Risk Analysis, Dam Safety Dam Security and Critical Infrastructure Management, 3IWRDD-FORUM, .

Resultado de la investigación: Contribución a una conferenciaArtículoInvestigación

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AB - 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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Francés F, Botero BA. Probable Maximum Flood estimation using upper bounded statistical models and its effect on high return period quantiles. 2012. Papel presentado en Proceedings of the 3rd International Forum on Risk Analysis, Dam Safety Dam Security and Critical Infrastructure Management, 3IWRDD-FORUM, .