Open AccessFlood frequency analysis for an urban watershed: comparison between several statistical methodologies simulating synthetic rainfall events
Author(s) -
Notaro V.,
Fontanazza C.M.,
La Loggia G.,
Freni G.
Publication year - 2018
Publication title -
journal of flood risk management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.049
H-Index - 36
ISSN - 1753-318X
DOI - 10.1111/jfr3.12283
Subject(s) - flooding (psychology) , flood myth , watershed , frequency analysis , environmental science , computer science , hydrology (agriculture) , multivariate statistics , statistics , geography , mathematics , geology , machine learning , geotechnical engineering , archaeology , psychology , psychotherapist
To obtain the flooding frequency distribution for an urban watershed, different methods based on simulations of synthetic rainfall events were compared with an empirical analysis of the flooding data and with the results of long‐term simulations. A copula‐based multivariate statistical analysis of the main hydrological variables was proposed to generate synthetic hyetographs. Two different approaches were adopted to assess a temporal pattern to the synthetic rainfall: one analyses all available historical rainfall patterns, and another adopts the cluster analysis in three different variants to reduce the computational effort of the analysis. To test the methodology reliability, the analysis was carried out for a real urban watershed. To carry out the flooding frequency analysis, all generated synthetic hyetographs were used as input of a dual drainage mathematical model of the analysed drainage system. Results showed that the method based on the analysis of all historical rainfall patterns was efficient in the estimation of flooding frequency, especially for higher return periods and approximately halved the computational costs of ordinary long‐term analysis. Regarding clustering approaches, although attractive for their computational efficiency, their adoption must be carefully evaluated because it could neglect relevant information and result in a less accurate flood frequency analysis.