Reply on RC1

Paola Mazzoglio

doi:10.5194/hess-2021-503-ac2

Abstract

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 rainfall extremes and their connection to the landforms does not exist. Using a new, comprehensive and position-corrected rainfall extreme dataset (I2-RED), we present a systematic study of the relationship between geomorphological forms and the average of rainfall extremes (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. After analyzing the results, we repeated the analysis on geomorphological subdivisions of Italy. The results of the national-scale regression analysis did not confirm the assumption of elevation being the sole driver of the variability of rainfall extremes. The longitude, latitude, distance from the coastline, morphological obstructions and mean annual rainfall resulted to be significantly related to the index rainfall, and to play different roles for different durations (1- to 24-hours). However, when comparing the results of the best multivariate regression models with univariate regressions for morphological subdivisions, we found that “local” rainfall-topography relationships within the geomorphological subdivisions outperformed the country-wide multiple regressions and offered a reasonable representation of the effect of morphology on rainfall extremes.

Highlights

Introduction and backgroundThe spatial patterns of rainfall depth statistics are known to be affected by the geomorphological setting (Smith, 1979; Prudhomme and Reed, 1998; Prudhomme and Reed, 1999)
The better results achieved in terms of root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe model efficiency (NSE) at the national scale are due to the improvements obtained for the two main islands (Sicily and Sardinia)
In order to clarify the drawbacks that large-scale regression models can produce, we compared the residuals obtained from the four-region multiple regression model (Figure 5a) in the areas identified by GC4 with the residuals of the GC4 regression model by selecting: 1) the GC4 areas that were statistically significant (Figure 5b) and 2) the entire nation (Figure 5c)

Summary

Introduction

Introduction and backgroundThe spatial patterns of rainfall depth statistics are known to be affected by the geomorphological setting (Smith, 1979; Prudhomme and Reed, 1998; Prudhomme and Reed, 1999). The impact of the orography on extreme rainfall depths and the complicated atmosphere-orography interactions for large areas 30 are still not sufficiently understood for sub-daily rainfall events. The extent of the Italian territory ( 300.000 km2), its high degree of morphological heterogeneity (Figure 1) and significant exposure to Mediterranean storms (Claps et al, 2000) call for a detailed study on the variability of rainfall extremes as a function of the morphology over the entire country. Most of the existing studies have focused on limited areas (Allamano et al, 2009; Caracciolo et al, 2012; Furcolo and Pelosi, 2018; Libertino et al, 2018) and the only attempt to deal with countrywide sub-daily data was made by Avanzi et al (2015)

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Conclusion