The space-time representation of extraordinary rainfall events
- Salvatore Manfreda
Salvatore Manfreda
Universita degli Studi di Napoli Federico II Dipartimento di Ingegneria Civile Edile e Ambientale
Corresponding Author:salvatore.manfreda@unina.it
Author ProfileAbstract
Extraordinary events are rarely observable in a single rainfall gauge,
and this make extremely challenging the correct prediction of their
arrivals. However, it may be possible to develop a more robust approach
by employing a space-time modelling scheme that is able to capture the
spatial dynamics of such phenomena. Therefore, a space-time Poisson
model of rainfall cells with circular shape and random depth has been
exploited for the first time to interpret the behaviour of this family
of extraordinary events. This category of events that may be connected
to larger meteorological phenomena not necessarily connected with local
heterogeneity of the landscape. Following the identification of the
observed extraordinary event across southern Italy, six zones with
significantly different dynamics in terms of the frequency of such
extremes were identified. Subsequently, a simple mathematical
representation was adopted to calibrate the model parameters, leading to
an estimate of regional probability distributions defined on the
space-time occurrences of extraordinary events over homogeneous zones.
The approach allows to overcome the limitations posed by point
observations allowed the definition of a probability distribution that
pertains to an entire area rather than just a point. The obtained
quantiles of rainfall estimated seems to align well with the upper bound
of the probability distribution of the annual maxima observed over the
areas of interests.Submitted to Ecohydrology 13 Mar 2024Reviewer(s) Assigned
08 Apr 2024Review(s) Completed, Editorial Evaluation Pending
08 Jul 20241st Revision Received
17 Jul 2024Submission Checks Completed
17 Jul 2024Assigned to Editor
17 Jul 2024Review(s) Completed, Editorial Evaluation Pending
07 Nov 2024Editorial Decision: Accept