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| Titel |
Nonstationarities in the occurrence rates of flood events in Portuguese watersheds |
| VerfasserIn |
A. T. Silva, M. M. Portela, M. Naghettini |
| 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 ; 16, no. 1 ; Nr. 16, no. 1 (2012-01-25), S.241-254 |
| Datensatznummer |
250013126
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| Publikation (Nr.) |
 copernicus.org/hess-16-241-2012.pdf |
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| Zusammenfassung |
| An exploratory analysis on the variability of flood occurrence rates in 10
Portuguese watersheds is made, to ascertain if that variability is concurrent
with the principle of stationarity. A peaks-over-threshold (POT) sampling
technique is applied to 10 long series of mean daily streamflows and to 4
long series of daily rainfall in order to sample the times of occurrence (POT
time data) of the peak values of those series. The kernel occurrence rate
estimator, coupled with a bootstrap approach, was applied to the POT time
data to obtain the time dependent estimated occurrence rate curves,
λˆ(t), of floods and extreme rainfall events. The results of the
analysis show that the occurrence of those events constitutes an
inhomogeneous Poisson process, hence the occurrence rates are nonstationary.
An attempt was made to assess whether the North Atlantic Oscillation (NAO)
casted any influence on the occurrence rate of floods in the study area.
Although further research is warranted, it was found that years with a
less-than-average occurrence of floods tend to occur when the winter NAO is
in the positive phase, and years with a higher occurrence of floods (more
than twice the average) tend to occur when the winter NAO is in the negative
phase. Although the number of analyzed watersheds and their uneven spatial
distribution hinders the generalization of the findings to the country scale,
the authors conclude that the mathematical formulation of the flood frequency
models relying on stationarity commonly employed in Portugal should be
revised in order to account for possible nonstationarities in the occurrence
rates of such events. |
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