Extreme value models and techniques are widely applied in environmental studies to define protection systems against the effects of extreme levels of environmental processes. Regarding the matter related to the climate science, a certain importance is covered by the implication of changes in the hydrological cycle. Among all hydrologic processes, rainfall is a very important variable as it is strongly related to flood risk assessment and mitigation, as well as to water resources availability and drought identification. We implement here a geoadditive model for extremes assuming that the observations follow a generalized extreme value distribution with spatially dependent location. The analyzed territory is the catchment area of the Arno River in Tuscany in Central Italy.

Geoadditive modeling for extreme rainfall data / Chiara Bocci, Enrica Caporali, Alessandra Petrucci. - In: ASTA ADVANCES IN STATISTICAL ANALYSIS. - ISSN 1863-8171. - STAMPA. - 97:(2013), pp. 181-193. [10.1007/s10182-012-0192-7]

Geoadditive modeling for extreme rainfall data

Chiara Bocci;Enrica Caporali;Alessandra Petrucci
2013

Abstract

Extreme value models and techniques are widely applied in environmental studies to define protection systems against the effects of extreme levels of environmental processes. Regarding the matter related to the climate science, a certain importance is covered by the implication of changes in the hydrological cycle. Among all hydrologic processes, rainfall is a very important variable as it is strongly related to flood risk assessment and mitigation, as well as to water resources availability and drought identification. We implement here a geoadditive model for extremes assuming that the observations follow a generalized extreme value distribution with spatially dependent location. The analyzed territory is the catchment area of the Arno River in Tuscany in Central Italy.
2013
97
181
193
Chiara Bocci, Enrica Caporali, Alessandra Petrucci
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/627406
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