Drought is ranked second in type of natural phenomena associated with billion dollars weather disaster during the past years. It is estimated that in EU countries the number of people affected by drought was increased by 20% over the last decades. It is widely recognized that the Standardized Precipitation Index (SPI) can effectively provide drought characteristics in time and space. The paper questions the standard approach to estimate the SPI based on the Gamma probability distribution function, assessing the fitting performance of different biparametric distribution laws to monthly precipitation data. We estimate SPI time series, for different scale of temporal aggregation, on an unprecedented dataset consisting of 332 rain gauge stations deployed across Italy with observations recorded between 1951 and 2000. Results show that the Lognormal distribution performs better than the Gamma in fitting the monthly precipitation data at all time scales, affecting drought characteristics estimated from SPI signals. However, drought events detected using the original and the best fitting approaches does not diverge consistently in terms of return period. This suggests that the SPI in its original formulation can be applied for a reliable detection of drought events and for promoting mitigation strategies over the Italian peninsula.

Raindrop-impact-induced erosion starts when detachment of soil particles from the surface results from an expenditure of raindrop energy. Hence, rain kinetic energy is a widely used indicator of the potential ability of rain to detach soil. Although it is widely recognized that knowledge of rain kinetic energy plays a fundamental role in soil erosion studies, its direct evaluation is not straightforward. Commonly, this issue is overcome through indirect estimation using another widely measured hydrological variable, namely, rainfall intensity. However, it has been challenging to establish the best expression to relate kinetic energy to rainfall intensity. In this study, first, kinetic energy values were determined from measurements of an optical disdrometer. Measured kinetic energy values were then used to assess the applicability of the rainfall intensity relationship proposed for central Italy and those used in the major equations employed to estimate the mean annual soil loss, that is, the Universal Soil Loss Equation (USLE) and its two revised versions (RUSLE and RUSLE2). Then, a new theoretical relationship was developed and its performance was compared with equations found in the literature.

Short duration and high intensity rainfalls are of great importance in the study and modeling of hydrological phenomena in the urban area in general, with particular reference to design and evaluation of hydraulic works. Indeed, in urban watersheds, whose times are almost always less than 60 minutes, the estimate of extreme values of rainfall depth at high time resolutions (short duration) is crucial. In this paper an investigation on short-duration rainfalls is carried out by analyzing the rainfall data of six rain gauges in the Lazio region in central Italy. According to two approaches known in literature, Depth-Duration-Frequency curves are evaluated for the investigated rain gauges and, limited to the statistical representativeness of the sample data, not negligible differences with the respective estimated rainfall values have been found.

A proper study of high intensity precipitation allows a reduction of the uncertainty in the estimation of the return period of the extreme rainfall, useful for hydraulic works design and for the management of hydrological risk. Because of the intrinsic uncertainty of the rainfall phenomena, it is necessary to adopt a probabilistic approach based on the definition of a theoretical probability distribution that can adequately describes observed data. Nevertheless, a recurring problem, concerns the presence of missing values and the discontinuity of the rainfall recorded data. Therefore, it is important to evaluate when an empirical sample can be considered reliable for an objective estimation of the return period associated to an extreme event. In this paper we used observation of annual maxima rainfall to investigate the changes in the intensity- duration-frequency (IDF) curves when varying the considered registration period length. Short duration and high intensity rainfall data recorded by eight rain gauges in Lazio region (central Italy) for the period between 1928 to 2017 are available.