2026-09-20T23:47:45Zhttps://iris.unica.it/oai/request

oai:iris.unica.it:11584/2668632022-10-20T10:31:08Zcom_11584_207615com_11584_111066col_11584_265854

Extreme rainfall regime characterization in Sardinia using daily rainfall data HELLIES, MATTEO BM GEV GP Kriging POT eventi estremi extreme events regional analysis regionalizzazione soglia ottimale Settore ICAR/02 - Costruzioni Idrauliche e Marittime e Idrologia For the design of hydraulic structures for flood conveyance and discharge, or protection of territory against flood is fundamental the knowledge of the ``extreme rainfall regime'' in the area where the hydraulic structures must be set up. Indeed the design flood is commonly evaluated as output of rainfall-runoff models that receive as input the quantitative description of a rainfall extreme event with a given exceedance probability. This dissertation assesses the performance of different statistical approaches in characterizing extreme rainfall in the island of Sardinia (Italy). After a detailed review of the theoretical bases of existing methodologies, we compare the results obtained from the use of: a) a Generalized Extreme value (GEV) distribution model, and a Two component Extreme Value (TCEV) distribution model, both applied to yearly maxima of daily rainfall, and b) a Generalized Pareto (GP) distribution model applied to rainfall excesses above a properly specified threshold. For the latter purpose, we use the Multiple Threshold Method (MTM) developed by Deidda(2010), which demonstrate good performance also in the case of quantized records. In order to describe the spatial variation of TCEV, GEV and GP model parameters a regional approach based on homogeneous regions, and two versions of Kriging (a commonly used geostatistical approach) i.e. ordinary Kriging (OK), and Kriging for uncertain Data (KUD), are compared. The obtained results are very promising, pointing towards the use of: a)a GEV distribution model for yearly rainfall maxima, and a KUD model to describe the spatial variation of model parameters, and b)a GP model for rainfall excesses and either an OK or a KUD model for the spatial variation of model parameters. The reason why the OK and KUD approaches lead to the same results in the GP case, is attributed to the robustness of the MTM method. 2016-02-09 info:eu-repo/semantics/doctoralThesis http://hdl.handle.net/11584/266863 eng numberofpages:249 info:eu-repo/semantics/openAccess Università degli Studi di Cagliari license:Non specificato