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| Titel |
Stochastic daily precipitation model with a heavy-tailed component |
| VerfasserIn |
N. M. Neykov, P. N. Neytchev, W. Zucchini |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Sciences ; 14, no. 9 ; Nr. 14, no. 9 (2014-09-03), S.2321-2335 |
| Datensatznummer |
250118652
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| Publikation (Nr.) |
 copernicus.org/nhess-14-2321-2014.pdf |
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| Zusammenfassung |
| Stochastic daily precipitation
models are commonly used to generate scenarios of climate variability or
change on a daily timescale. The standard models consist of two components
describing the occurrence and intensity series, respectively. Binary logistic
regression is used to fit the occurrence data, and the intensity series is
modeled using a continuous-valued right-skewed distribution, such as gamma,
Weibull or lognormal. The precipitation series is then modeled using the
joint density, and standard software for generalized linear models can be
used to perform the computations. A drawback of these precipitation models is
that they do not produce a sufficiently heavy upper tail for the distribution
of daily precipitation amounts; they tend to underestimate the frequency of
large storms. In this study, we adapted the approach of
Furrer and Katz (2008) based on hybrid distributions in order to correct for
this shortcoming. In particular, we applied hybrid gamma–generalized Pareto
(GP) and hybrid Weibull–GP distributions to develop a stochastic
precipitation model for daily rainfall at Ihtiman in western Bulgaria. We
report the results of simulations designed to compare the models based on the
hybrid distributions and those based on the standard distributions. Some
potential difficulties are outlined. |
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