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This paper investigates how atmospheric circulation and orography affect the spatial variability of extreme precipitation in terms of depth-duration-frequency (DDF) curve parameters. To this aim, the Italian territory was considered because it is characterized by a complex orography and different precipitation dynamics and regimes. A database of 1494 time series with more than 20 years of maximum annual precipitation data was collected for the durations of 1, 3, 6, 12, and 24 h. For each data series, the parameters of DDF curves were estimated using a statistical simple scale invariance model. Hence, the combined effect of orography and atmospheric fields on parameter variability was investigated considering the spatial distribution of the parameters and their relation with elevation. The vertically integrated atmospheric moisture flux J was used as a measurement of the principal direction of the vapor transport at a given location. The analysis highlights the variability of DDF parameters and quantiles according to orography and precipitation climatology. This is confirmed by the evaluation of J modal direction over the study area. The variability of DDF parameters with mere elevation shows that maxima at high elevations seem to be upper bounded and more variable than those at lower elevations. Moreover, the mean of maximum annual precipitation of unit duration decreases with elevation. This last phenomenon is defined as 'reverse orographic effect' on extreme precipitation of short durations. [ABSTRACT FROM AUTHOR]

Focuses on potential climate changes that produces substantial effects on the hydrological cycle. How major changes in runoff and rainfall-driven processes could occur; Analysis of such climate changes focusing on the Arno River basin of central Italy; Effects of transient scenarios that explicitly describes the processes involved in the rainfall-runoff transformation; Discussion of the changes in the most significant components of the water balance with respect to seasonality, distributional properties and extremes; Potential implications on water resources.

Discusses long-term simulations of temporal rainfall and temperature under transient global climate conditions of atmospheric inputs at the basin scale in the Arno River in central Italy. Discussion of a global circulation model (GCM), simulating climate changes due to an increase in the greenhouse effect; How one can infer future changes in storm duration and depth from GCM trend variables; Data suggesting that this approach provides local precipitation that, together with temperature scenarios, can be used for hydrological simulation of basin water fluxes in the Arno River.

Abstract: The annual maximum 3-day snow fall depth H 72 is often adopted as an input for avalanche hazard mapping procedure. The quantile of H 72 with a reference return period T, in years, is evaluated using standard statistical distributions. The choice of the proper distribution is a key step. This is particularly critical in those sites where short observed series of H 72 are available, because the required return periods are in the order of 300 years. Here, the distribution of H 72 is investigated for a network of 40 gauging stations in the Alpine part of Lombardia region, in the central Italian Alps, using a data set dating back to 1985. A regional approach based on the index value is developed to estimate the T-years return period quantiles of H 72 at each single site i, H 72i (T). The index value is the single site sample average, μ H72i . For each site, the values of H 72i scaled, i.e. divided, by μ H72i , H 72i * are calculated. These are dimensionless quantiles with given return period. Some standard statistical tests are carried out, showing that the distribution of H 72i * is homogeneous in space, i.e. it is similar for each site in the considered area, which can then be viewed as a homogeneous region. Therefore, it is possible to use a regional approach for the frequency estimation of H 72*. The values of H 72i * are then grouped together in only one sample and their frequency of occurrence is accommodated by a general extreme value, GEV, probability distribution. The proposed distribution, valid in each site of the study area, can be used to assess the T-years return period quantiles of H 72i *. Then, by multiplying H 72i * by the single site sample index value μ H72i , an estimate of the T-years quantile of H 72i at the single site is obtained. A sampling strategy is also suggested to estimate the single site index value, i.e. the average μ H72i , critical for the evaluation of the single site distribution of H 72i . The final result is an estimate of the 3-day snow fall depth with a given return period, H 72i (T) for each site. Also, the standard error of estimation of H 72i (T) is given. It is shown that the value of H 72i (T) estimated by the regional approach is far more reliable than that calculated by the distribution fitting carried out using the data at each single site, particularly for high return periods. The values of H 72i (T) can be used as inputs to dynamic models, for avalanche hazard mapping exercise. The sensitivity analysis of the avalanche hazard maps to the uncertainty in the estimation of H 72i (T) can be also carried out, using the reference methods available in the present literature. [Copyright &y& Elsevier]