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.

The collection and management of hydrological data in Italy has been dealt with at national level, initially, by the National Hydrological Service (SIMN), and at regional level in the last 40 years. This change has determined problems in the availability of complete and homogeneous data for the whole country. As of 2020, an updated and quality-controlled dataset of the historical annual maxima rainfall in Italy is still lacking. The Italian Rainfall Extreme Dataset (I-RED) has recently been created to allow studies to be performed with a homogeneous dataset at a national level. In this paper, the methodological approach adopted to build an improved and quality-controlled version of I-RED (in terms of both the rainfall depth values and the position of the rain gauges) is presented. The new database can be used as a more reliable research support for the frequency analysis of the rainfall extremes. This new I²-RED database contains rainfall annual maxima rainfall of 1, 3, 6, 12 and 24 h from 1916 until 2019, counts 5265 rain gauges and has been corroborated by a re-positioning and elevation-checking of 15% of the stations. A descriptive analysis of the maximum values of the stations, which provides an additional quality check and reveals different intriguing spatial features of Super-Extreme rainfall events, is also presented.

The main potential benefits of rainwater harvesting, namely water saving and storm water management, are easily evaluable at a building scale when well-known behavioral models are used. However, the evaluation is often more complex at an urban scale, due to a lack of building characteristics and demographic data. In the present paper, we propose a method, which is based on the representative building concept that can be used to quantify the potential benefits of rainwater harvesting at different scales, that is, from the building scale to the district and city scales. Particular attention has been paid to the sizing of the system so that it can be used for different rainwater collection purposes. The method has been applied to the city of Turin (Italy) considering different scenarios: 1) domestic use (e.g., toilet flushing and the washing machine), where buildings are independent of each other, and 2) two public uses (the irrigation of public green areas and street washing), for which we have hypothesized that the rainwater collection takes place at a district scale. The non-potable water saving for domestic use varies across the city from 29% to 62%, according to the characteristics of the buildings, while the reduction of the flow peak conveyed to the sewerage system, during extreme storms, is quite constant (in the 57–67% range). Irrigation and street washing require a lower amount of water, thus about 80% of water can be saved, but the retention efficiency is low, and a slight reduction in the flow peaks can be expected. The aim of the methodology presented in this work is to provide a suitable decision-making tool for policy makers and urban planners to evaluate the capability and efficiency of rainwater harvesting systems for buildings, districts, and cities.