<p>Due to the limited length of locally available sequences of precipitation extremes, point rainfall depth associated with given duration and return period is usually estimated through regional frequency analysis. Several statistical regionalization methods proposed in the literature enable one to exploit sequences of precipitation extremes observed at homogeneous pooling groups of sites, that supposedly share the same frequency regime of rainfall extremes with the site of interest. Homogeneous sites can be identified by looking at specific climatic descriptors; for instance, some reliable authors successfully utilize Mean Annual Precipitation (MAP) as the sole proxy for locally characterizing the frequency regime of sub-daily rainfall extremes, and for grouping sequences of rainfall extremes records. We aim at advancing this traditional approach (1) by relaxing the hypothesis of the existence of a homogeneous pooling group of sites characterized by a unique regional parent distribution and (2) by incorporating additional morphological and climatic information in the regional model. We rely on more than 2350 Annual Maximum Series of rainfall depth for different time-aggregation intervals between 1 and 24 hours, observed since 1928 to 2011 in a vast study area in Northern Italy.  We refer to MAP as well as to additional morphologic descriptors (e.g.  minimum distance to Tyrrhenian (Adriatic) Sea, mean elevation and slope around the station, etc.).</p><p>We train a probabilistic neural network that models the frequency regime of observed annual maxima of rainfall depth resorting to a Generalized Extreme Value (GEV) distribution, whose parameters are data-driven functions of the local values of the selected descriptors and duration. Then, several cross-validation experiments are performed to assess the accuracy of the developed regional model relative to a simpler regional GEV model, whose parameters are functions of MAP and time-aggregation intervals.</p><p>Our analyses address several research problems: (a) identifying the most descriptive morphological proxies to combine with MAP for representing the frequency regime of sub-daily rainfall extremes in the study area, (b) highlighting limitations and potential of data-driven multivariate regional models of the frequency regime of rainfall extremes, (c) the advantages of a multivariate approach relative to a regionalization scheme based on MAP alone.</p>

<p>Regional envelope curves represent the current level of information about the most extreme flood events observed in a region. In this study, we derive and compare regional envelope curves across European regions, with a multi-scale approach. A large flood database, containing more than 7000 annual maximum discharge series from gauges located all over Europe, is used for the analysis. Multiple spatial scales are adopted to take into account the uneven gauge density in the study domain. In each region, we derive the slope of the regional envelope curve and the envelope flood for a representative catchment of size 1000km2. Based on the framework of probabilistic envelope curves, we also make a probabilistic statement about the regional envelope curves in terms of its return period. Results show that the slope of the regional envelope curves varies substantially across European regions and the correlation between envelope flood and the estimated return period is investigated.</p>

<p>Due to the limited length of locally available sequences of precipitation extremes, estimates of design rainstorms at a given location (i.e. point rainfall depth associated with given durations and non-exceedance probabilities) are traditionally obtained from regional frequency analysis. Several statistical regionalization methods proposed in the literature enable one to exploit sequences of precipitation extremes observed at a number of sites that supposedly share the same frequency regime of rainfall extremes with the site of interest (herein also referred to as a homogeneous pooling group of sites). Homogeneous pooling groups of sites can be identified by looking at specific climatic descriptors; for instance, some reliable authors successfully utilize Mean Annual Precipitation (MAP) as the sole proxy for locally characterizing the frequency regime of sub-daily rainfall extremes and for grouping sequences of rainfall extremes records. We aim at advancing this traditional approach (1) by relaxing the hypothesis of the existence of a homogeneous pooling group of sites characterized by a unique regional parent distribution and (2) by incorporating additional morphological and climatic information in the regional model. Our research focuses on a large study area in Northern Italy, counting more than 2350 Annual Maximum Series of rainfall depth for different time-aggregation intervals between 1 and 24 hours, that have been collected between 1928 and 2011 in the Italian Rainfall Extreme Dataset (I2-RED).  We refer to local MAP value as well as to several other morphologic descriptors (e.g. minimum distance to the coast, elevation of orographic barriers, aspect, terrain slope, etc.) for characterizing the frequency regime of sub-daily rainfall extremes. We train a probabilistic neural network that uses the descriptors cited above as input layers for modeling the local frequency regime of observed rainfall annual maxima. We resort to a Generalized Extreme Value (GEV) distribution whose parameters are data-driven functions of the local morphoclimatic descriptors as well as the time-aggregation interval. We then perform a series of cross-validation experiments targeted at assessing the accuracy of the developed data-driven regional frequency model relative to a simpler regional model in which GEV parameters are functions of MAP and time aggregation intervals.</p><p>Our results address the following research problems: (a) identification of the most descriptive morphological proxies for representing the frequency regime of sub-daily rainfall extremes, (b) assessment of potential, limitations, and robustness of data-driven multivariate regional frequency models of sub-daily rainfall extremes relative to simpler and more traditional regionalization schemes.</p>