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Study region Italy. Study focus Knowing magnitude and frequency of extreme precipitation is necessary to reduce their impact on vulnerable areas. Here we investigate the performance of the Generalized Extreme Value ( G E V ) distribution, using a fine-resolution satellite-based gridded product, to analyze 13,247 daily rainfall annual maxima samples. A non-extreme value distribution with a power-type behavior, that is, the Burr Type XII ( B r X I I ), is also evaluated and used to test the reliability of the G E V in describing extreme rainfall. New hydrological insights for the region (1) in 44.9 % of the analyzed samples the G E V predicts an upper rainfall limit; we deem this is an artifact due to sample variations; (2) we suggest the G E V + distribution, that is, the G E V with shape parameters restricted only to positive values as a more consistent model complying with the nature of extreme precipitation; (3) G E V , G E V + , and B r X I I performed equally well in describing the observed annual precipitation, yet all distributions underestimate the observed sample maximum; (4) the B r X I I , for large return periods, predicts larger rainfall amounts compared to G E V indicating that G E V estimates could underestimate the risk of extremes; and (5) the correlation between the predicted rainfall and the elevation is investigated. Based on the results of this study, we suggest instead of using the classical G E V to use the G E V + and non-extreme value distributions such as the B r X I I to describe precipitation extremes.

Study region We investigate samples from two Italian regions, i.e. Lazio and Sicily, located in central and south Italy, respectively, and characterized by two diverse climates. Study focus In engineering practice, the study of maxima daily rainfall values is commonly dealt with light-tailed probability distribution functions, such as the Gumbel. The choice of a distribution rather than another may cause estimation errors of rainfall values associated to specific return periods. Recently, several studies demonstrate that heavy-tailed distributions are preferable for extreme events modelling. Here, we opt for six theoretical probability distribution functions and evaluate their performance in fitting extreme precipitation samples. We select the samples with two common methods, i.e. the Peak-Over-Threshold and the Annual Maxima. We assess the best fitting distribution to the empirical samples of extreme values through the Ratio Mean Square Error Method and the Kolmogorov-Smirnov test. New hydrological insights for the region The assessment of the best fitting distribution to daily rainfall of the two different areas investigated here leads to interesting remarks. Despite the diversity of their climate, results suggest that heavy-tailed distributions describe more accurately empirical data rather than light-tailed ones. Therefore, extreme events may have been largely underestimated in the past in both areas. The proposed investigation can prompt the choice of the best fitting probability distribution to evaluate the design hydrological quantities supporting common engineering practice.

Erosion is an exogenous phenomenon, antagonistic to the constructive forces both of an endogenous nature (i.e. orogeny) and of a biological nature (i.e. coral reef), leading to the dismantling of the earth's surface and filling, with the clastic material produced, sedimentary basins and depressions in order to level differences in altitude [1]. It is estimated that, in Europe, 25 million hectares of soil are subject to "accelerated erosion processes" [2] and that, in several areas, the magnitude of soil loss is around a few tens of tons per year. Approximately 77% of Italy is at risk of accelerated erosion [3], also due to the lack of conservative measures for soil (hydraulic-agrarian measures, drainage, grassing, etc.). The phenomenon takes on particular importance in cultivated areas, where drastic changes have been imposed in soil use and sometimes, instead of natural and stable land cover, bare soil is subject to erosive agents. In this paper, the erosivity density approach introduced in Revised Universal Soil Loss Equation 2 (RUSLE2 [4]), will be used to develop monthly erosivity maps for the Lazio region (in central Italy). This was achieved through a full investigation carried out on the monthly (Pmd), daily (P24h) and sub-daily (15-minute time-scale, P15) precipitation amounts available from rain gauges deployed across the study area.

Monthly precipitation amount often influences governments and stakeholders in making decisions about water management. The ability of anticipating harmful events is therefore crucial to mitigate economic and social damages. In this study we propose simple linear regression models correlating the total monthly precipitation (i.e., the predictand) to temperature (i.e., the predictor) which can be potentially used to predict future precipitation amounts through forecasts of temperature. We define three different temperature predictors, considering the average of one, two or three months prior the analyzed month. We use raster maps of precipitation and temperature at ~30 km resolution from 1895 to 2017 of the U.S. Midwest to calibrate and validate our models. We find, overall, low prediction skill. For the summer months (June-July-August), the skill of the models in reproducing the observed precipitation slightly increases when considering a shorter period among which the average temperature is referred. The best skill is obtained for the months of June and July when using the previous month's average temperature as predictor.

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.

Droughts are one of the most challenging issues in water resource management in urban areas due to their major socio-economic impacts. The identification and evaluation of droughts are commonly based on the Standardized Precipitation Index (SPI), which is estimated through easily accessible information (i.e., monthly rainfall). In this work, we show a preliminary analysis on the role played by the nature of the probability distribution in the calculation of the one-month SPI. Long-term rainfall time series from two Italian regions are investigated.