![Hier klicken, um den Treffer aus der Auswahl zu entfernen](images/unchecked.gif) |
Titel |
Including historical data on the Regional Analysis of extreme storm surges |
VerfasserIn |
Roberto Frau, Marc Andreewsky, Pietro Bernardara |
Konferenz |
EGU General Assembly 2017
|
Medientyp |
Artikel
|
Sprache |
en
|
Digitales Dokument |
PDF |
Erschienen |
In: GRA - Volume 19 (2017) |
Datensatznummer |
250141571
|
Publikation (Nr.) |
EGU/EGU2017-5101.pdf |
|
|
|
Zusammenfassung |
The occurrence of rare (up to 10−4 annual probability of occurrence) and extreme
oceano-meteorological conditions is required to design effective coastal protections and to
protect coastal areas from flooding. Statistical methods based on the extreme value theory are
widely used for the characterisation of such events. Unfortunately, this was often performed
from very short observations series (usually 30 years to 50 years long) and the
uncertainties associated to the extrapolation are too wide to be used in a design
approach. Nowadays, larger dataset are available, in particular when looking at the
regional scale. Moreover, in several disciplines the historical evidence of extreme
events observed before the systematic observation periods, has been provided by
specific studies. In the framework, the combination between these two sources of
information is shown to dramatically increase the reliability of the statistical extrapolation.
Merge the two approaches is however challenging because of the need to define
homogenous regions and to consider the fact that historical data are not continuous and
exhaustive. Here, an overall methodology is presented which allows a robust estimation,
including a selection an optimal sampling threshold and a definition a “regional
credible duration”, which describe the amount of information available, taking into
account the unknown period of observation associated with historical events. The
Regional Historical Analysis is applied on a database of extreme skew storm surges
and on a several extreme historical storm surges collected from different sources. |
|
|
|
|
|