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| Titel |
A multiple threshold method for fitting the generalized Pareto distribution to rainfall time series |
| VerfasserIn |
R. Deidda |
| 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 ; 14, no. 12 ; Nr. 14, no. 12 (2010-12-14), S.2559-2575 |
| Datensatznummer |
250012531
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| Publikation (Nr.) |
 copernicus.org/hess-14-2559-2010.pdf |
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| Zusammenfassung |
Previous studies indicate the generalized Pareto distribution (GPD) as a
suitable distribution function to reliably describe the exceedances of daily
rainfall records above a proper optimum threshold, which should be selected
as small as possible to retain the largest sample while assuring an
acceptable fitting. Such an optimum threshold may differ from site to site,
affecting consequently not only the GPD scale parameter, but also the
probability of threshold exceedance.
Thus a first objective of this paper is to derive some expressions to
parameterize a simple threshold-invariant three-parameter distribution
function which assures a perfect overlapping with the GPD fitted on the
exceedances over any threshold larger than the optimum one. Since the proposed
distribution does not depend on the local thresholds adopted for fitting the
GPD, it is expected to reflect the on-site climatic signature and thus appears
particularly suitable for hydrological applications and regional analyses.
A second objective is to develop and test the Multiple Threshold Method (MTM)
to infer the parameters of interest by using exceedances over a wide range of
thresholds applying again the concept of parameters threshold-invariance. We
show the ability of the MTM in fitting historical daily rainfall time series
recorded with different resolutions and with a significative percentage
of heavily quantized data. Finally, we prove the supremacy of the
MTM fit against the standard single threshold fit, often adopted for partial
duration series, by evaluating and comparing the performances on Monte Carlo
samples drawn by GPDs with different shape and scale parameters and different
discretizations. |
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