Advances in the space-time analysis of rainfall extremes

Statistical estimation of design rainfall is considered a consolidated topic in hydrology. However, extreme rainfalls and their consequences still constitute one of the most critical natural risks worldwide, particularly in urban environments. Additional efforts for improving the spatio-temporal analysis of extreme rainfalls are then required, particularly at the regional scale. In this work, a new set of data and techniques for improving the spatial statistical analysis of extreme rainfall is proposed. Italy is considered a challenging case study, due to its specific geographic and orographic settings, associated with recurring storm-induced disasters. At first, the rain-gauge data patchiness resulting from the evolution of the monitoring agencies and networks, is tackled with the "patched kriging" methodology. The technique, involving a sequential annual interpolation, provides complete annual maxima series consistent with the available data. This allows to extract all the information avaialble from the gauge records, considering also the information "hidden" in the shortest series, increasing the robustness of the results. Interpolation techniques, however, can only reflect the estimation variance determined by the spatial and temporal data resolution. Additional improvements can be obtained integrating the rain gauge information with remote sensing products, able to provide more details on the spatial structure of rainstorms. In this direction, a methodology aimed at maximizing the efficiency of weather radar when dealing with large rainfall intensities is developed. It consists in a quasi-real-time calibration procedure, adopting confined spatial and temporal domains for an adaptive estimation of the relation between radar reflectivity and rainfall rate. This allows one to follow the well-known spatio-temporal variability of the reflectivity-rainfall relation, making the technique suitable for a systematic operational use, regardless of the local conditions. The methodology, applied in a comprehensive case study reduces the bias and increases the accuracy of the radar-based estimations of severe rainfall intensities. The field of the satellite estimation of preciptation is then explored, by analyzing the ability of both the Tropical Rainfall Measurement Mission (TRMM) and the recently launched Global Precipitation Measurement (GPM) mission to help identifying the timing of severe rainfall events on wide spatial domains. For each considered product, the date of occurrence of the most intense annual daily records are identified and compared with the ones extracted from a global rain-gauge database. The timing information can help in tracking the pattern of deep convective systems and support the identification of localized rainfall system in poorly gauged areas. The last part of the work deals with the analysis of rainfall extremes at the country scale, with a particular focus on the most severe rainfall events occurred in Italy in the last century. Many of these events have been studied as individual case studies, due to the large recorded intensities and/or to their severe consequences, but they have been seldom expressly addressed as a definite population. To try to provide new insights in a data-drived approach, a comprehensive set of annual rainfall maxima has been compiled, collecting data from the different regional authorities in charge. The database represents the reference knowledge for extremes from 1 to 24 hours durations in Italy, and includes more than 4500 measuring points nationwide, with observation spanning the period 1916-2014. Exploratory statistical analyses for providing information on the climatology of extreme rainfall at the national scale are carried out and the stationarity in time of the highest quantiles is analysed by pooling up all the data for each duration together. The cumulative empirical distributions are explored looking for clues of the existence of a class of "super-extremes" with a peculiar statistical behavior. The analysis of the spatial the distribution of the records exceeding the 1/1000 overall empirical probability shows an interesting spatial clustering. However, once removed the influence of the uneven density of the rain gauge network in time and space, the spatial susceptibility to extraordinary events seems quite uniformly distributed at the country scale. The analyses carried out provide quantitative basis for improving the rainstorm estimation in gauged and ungauged locations, underlining the need of further research efforts for providing maps for hydrological design with uniform reliability at the various scales of technical interest