The occurrence of rainfall Extraordinary Extreme Events (EEEs) in Mediterranean areas causes serious concerns to the engineers involved in the design of flood and landslide risk mitigation plans as well as of strategic hydraulic engineering structures, such as dams. These extraordinary maxima are characterized by very low frequencies and spatial extent scales that are smaller than those of ordinary maxima, and are usually identified as outliers by classical regional frequency analysis. Extreme Value mixture models, such as the Two-Component Extreme Value distribution, have been introduced in regional frequency analysis in order to overcome this problem. Nevertheless, the rainfall maxima series available for carrying out these regional analyses present coarse spatial spacing and small temporal extent, when compared with EEEs spatial structures and frequencies. Thus, regional statistical analyses with mixture models tend to overestimate EEEs return periods as well. This study presents a new operational statistical approach to properly retrieve the EEEs frequency from the available database and thus to avoid dramatic underestimations of the rainfall depth at very high return periods. The proposed approach implies the analysis of the EEEs at a given support scale in order to assess (a) the percentage of EEEs among the annual maxima and (b) the conditional distribution of the annual maxima given the occurrence of an EEE. The descriptive properties of the proposed procedure have been tested in Italy, by comparing the performances of the proposed procedure in predicting rainfall depths at very high return periods (i.e., larger than 100 years) with those provided by the TCEV-based regional analysis currently adopted as national reference approach.

The characterization of extraordinary extreme events (EEEs) for the assessment of design rainfall depths with high return periods

Pelosi A.;Furcolo P.;Rossi F.;Villani P.
2020-01-01

Abstract

The occurrence of rainfall Extraordinary Extreme Events (EEEs) in Mediterranean areas causes serious concerns to the engineers involved in the design of flood and landslide risk mitigation plans as well as of strategic hydraulic engineering structures, such as dams. These extraordinary maxima are characterized by very low frequencies and spatial extent scales that are smaller than those of ordinary maxima, and are usually identified as outliers by classical regional frequency analysis. Extreme Value mixture models, such as the Two-Component Extreme Value distribution, have been introduced in regional frequency analysis in order to overcome this problem. Nevertheless, the rainfall maxima series available for carrying out these regional analyses present coarse spatial spacing and small temporal extent, when compared with EEEs spatial structures and frequencies. Thus, regional statistical analyses with mixture models tend to overestimate EEEs return periods as well. This study presents a new operational statistical approach to properly retrieve the EEEs frequency from the available database and thus to avoid dramatic underestimations of the rainfall depth at very high return periods. The proposed approach implies the analysis of the EEEs at a given support scale in order to assess (a) the percentage of EEEs among the annual maxima and (b) the conditional distribution of the annual maxima given the occurrence of an EEE. The descriptive properties of the proposed procedure have been tested in Italy, by comparing the performances of the proposed procedure in predicting rainfall depths at very high return periods (i.e., larger than 100 years) with those provided by the TCEV-based regional analysis currently adopted as national reference approach.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4745479
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