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| Titel |
Rainstorms able to induce flash floods in a Mediterranean-climate region (Calabria, southern Italy) |
| VerfasserIn |
O. G. Terranova, S. L. Gariano |
| 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 (2014-09-10), S.2423-2434 |
| Datensatznummer |
250118661
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| Publikation (Nr.) |
 copernicus.org/nhess-14-2423-2014.pdf |
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| Zusammenfassung |
| Heavy rainstorms often induce flash flooding, one of the natural disasters
most responsible for damage to man-made infrastructures and loss of lives,
also adversely affecting the opportunities for socio-economic development of
Mediterranean countries. The frequently dramatic damage of flash floods are
often detected, with sufficient accuracy, by post-event surveys, but rainfall
causing them are still only roughly characterized. With the aim of improving
the understanding of the temporal structure and spatial distribution of
heavy rainstorms in the Mediterranean context, a statistical analysis was
carried out in Calabria (southern Italy) concerning rainstorms that mainly
induced flash floods, but also shallow landslides and debris flows. Thus, a
method is proposed – based on the overcoming of heuristically predetermined
threshold values of cumulated rainfall, maximum intensity, and kinetic
energy of the rainfall event – to select and characterize the rainstorms
able to induce flash floods in the Mediterranean-climate countries.
Therefore, the obtained (heavy) rainstorms were automatically classified and
studied according to their structure in time, localization, and extension.
Rainfall-runoff watershed models can consequently benefit from the enhanced
identification of design storms, with a realistic time structure integrated
with the results of the spatial analysis. A survey of flash flood events
recorded in the last decades provides a preliminary validation of the method
proposed to identify the heavy rainstorms and synthetically describe their
characteristics. The notable size of the employed sample, including data
with a very detailed resolution in time that relate to several rain gauges
well-distributed throughout the region, gives robustness to the obtained
results. |
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