The role of morphology in the spatial distribution of short-duration rainfall extremes in Italy

• Published in: Hydrology and earth system sciences Vol. 26; no. 6; pp. 1659 - 1672
• Main Authors: Mazzoglio, Paola, Butera, Ilaria, Alvioli, Massimiliano, Claps, Pierluigi
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 30-03-2022; Copernicus Publications
• Subjects: Analysis; Annual rainfall; Bias; Daily rainfall; Datasets; Distribution; Elevation; Extreme weather; Geomorphology; Heterogeneity; Landforms; Maxima; Maximum rainfall; Mean annual precipitation; Morphology; Multivariate analysis; Obstructions; Precipitation; Precipitation variability; Rain; Rain and rainfall; Regression analysis; Regression models; Spatial analysis; Spatial distribution; Spatial variability; Spatial variations; Subdivisions; Variability; Alps; Italy
• ISSN: 1607-7938; 1027-5606; 1607-7938
• DOI: 10.5194/hess-26-1659-2022

The dependence of rainfall on elevation has frequently been documented in the scientific literature and may be relevant in Italy, due to the high
degree of geographical and morphological heterogeneity of the country. However, a detailed analysis of the spatial variability of short-duration
annual maximum rainfall depths and their connection to the landforms does not exist. Using a new, comprehensive and position-corrected rainfall
extreme dataset (I2-RED, the Improved
Italian-Rainfall Extreme Dataset), we present a systematic study of the relationship between geomorphological forms and the average annual maxima
(index rainfall) across the whole of Italy. We first investigated the dependence of sub-daily rainfall depths on elevation and other landscape
indices through univariate and multivariate linear regressions. The results of the national-scale regression analysis did not confirm the assumption
of elevation being the sole driver of the variability of the index rainfall. The inclusion of longitude, latitude, distance from the coastline,
morphological obstructions and mean annual rainfall contributes to the explanation of a larger percentage of the variance, even though this was in different ways for
different durations (1 to 24 h). After analyzing the spatial variability of the regression residuals, we repeated the analysis on
geomorphological subdivisions of Italy. Comparing the results of the best multivariate regression models with univariate regressions applied to
small areas, deriving from morphological subdivisions, we found that “local” rainfall–topography relationships outperformed the country-wide
multiple regressions, offered a uniform error spatial distribution and allowed the effect of morphology on rainfall extremes to be better reproduced.