Human activities are often subject to damages resulting from exceptional rainy events. The character of exceptionality of a rainy event may be due only to one or to many factors. In particular the total rainfall amounts the maximum intensity, the average intensity and the total duration affect the natural catastrophic phenomena. Based on this concept, a methodology to identify severe rainy events is proposed, with the aim to select and characterize those events potentially more dangerous to human activities. The single normal rainy event is simply defined by being preceded and followed by at least one not rainy day; the events are considered severe according to the overcoming of one or more threshold values of the aforesaid factors, The study refers to the method of peaks over threshold (P.O.T.), based on the theory of the rare events and on the extreme values theory. The first theory was introduced by Poisson and aims exclusively to define the relations between the number of events and their low probability of occurrence; the second theory is usually adopted for the annual maximum analysis and deals just with the size of the events, with no account for their number. Actually the reduction law of the number of events when the threshold values are increasing is illustrated by simple correlative relationships. An application to rainfall data of the North area of Stretta di Catanzaro (Calabria, Southern Italy) is proposed, comparing the results with those of an historical investigation regarding landslides and flooding. The analysis allows to assess the temporal and spatial distribution of the most severe events and to evaluate their hazard for forecasting and/or real-time alert system identification. Particular sites and year periods that are more frequently subject to severe events are identified, in this way we can identify homogeneous regions keeping constant values of parameters.

Probabilistic definition and analysis of severe rainy events

Terranova O
2002

Abstract

Human activities are often subject to damages resulting from exceptional rainy events. The character of exceptionality of a rainy event may be due only to one or to many factors. In particular the total rainfall amounts the maximum intensity, the average intensity and the total duration affect the natural catastrophic phenomena. Based on this concept, a methodology to identify severe rainy events is proposed, with the aim to select and characterize those events potentially more dangerous to human activities. The single normal rainy event is simply defined by being preceded and followed by at least one not rainy day; the events are considered severe according to the overcoming of one or more threshold values of the aforesaid factors, The study refers to the method of peaks over threshold (P.O.T.), based on the theory of the rare events and on the extreme values theory. The first theory was introduced by Poisson and aims exclusively to define the relations between the number of events and their low probability of occurrence; the second theory is usually adopted for the annual maximum analysis and deals just with the size of the events, with no account for their number. Actually the reduction law of the number of events when the threshold values are increasing is illustrated by simple correlative relationships. An application to rainfall data of the North area of Stretta di Catanzaro (Calabria, Southern Italy) is proposed, comparing the results with those of an historical investigation regarding landslides and flooding. The analysis allows to assess the temporal and spatial distribution of the most severe events and to evaluate their hazard for forecasting and/or real-time alert system identification. Particular sites and year periods that are more frequently subject to severe events are identified, in this way we can identify homogeneous regions keeping constant values of parameters.
2002
Istituto di Ricerca per la Protezione Idrogeologica - IRPI
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/8103
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