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| Titel |
Modelling the statistical dependence of rainfall event variables through copula functions |
| VerfasserIn |
M. Balistrocchi, B. Bacchi |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 15, no. 6 ; Nr. 15, no. 6 (2011-06-24), S.1959-1977 |
| Datensatznummer |
250012863
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| Publikation (Nr.) |
 copernicus.org/hess-15-1959-2011.pdf |
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| Zusammenfassung |
| In many hydrological models, such as those derived by analytical
probabilistic methods, the precipitation stochastic process is represented
by means of individual storm random variables which are supposed to be
independent of each other. However, several proposals were advanced to
develop joint probability distributions able to account for the observed
statistical dependence. The traditional technique of the multivariate
statistics is nevertheless affected by several drawbacks, whose most evident
issue is the unavoidable subordination of the dependence structure
assessment to the marginal distribution fitting. Conversely, the copula
approach can overcome this limitation, by dividing the problem in two
distinct parts. Furthermore, goodness-of-fit tests were recently made
available and a significant improvement in the function selection
reliability has been achieved. Herein the dependence structure of the
rainfall event volume, the wet weather duration and the interevent time is
assessed and verified by test statistics with respect to three long time
series recorded in different Italian climates. Paired analyses revealed a
non negligible dependence between volume and duration, while the interevent
period proved to be substantially independent of the other variables. A
unique copula model seems to be suitable for representing this dependence
structure, despite the sensitivity demonstrated by its parameter towards the
threshold utilized in the procedure for extracting the independent events.
The joint probability function was finally developed by adopting a Weibull
model for the marginal distributions. |
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