[1] The widespread flood event that affected northeastern and central Italy in November 1966, causing severe damages to vast populated areas including the historical towns of Florence and Venice, is revisited with a modeling approach, made possible by the availability of the ECMWF global reanalysis (ERA-40). A simulated forecasting chain consisting of the ECMWF global model, forcing a cascade of two mesoscale, limited area meteorological models apt to reach a convective resolving scale (about 2 km), is used to predict quantitative precipitation. A hydrological model, nested in the finer-scale meteorological model, is used to reproduce forecasted flood hydrographs for different river basins of the investigated areas. Predicted precipitation is in general very sensitive to initial conditions, especially when associated with convective activity, such as over central Italy, in the Arno river basin. Orographically enhanced precipitation, e.g., the one predicted in the eastern Alps, is quite stable and in good agreement with observations. Hydrological forecasts, made separately in different river basins, reflect the accuracy of the simulated precipitation.

[1] A method for quantifying the uncertainty of hydrological forecasts is proposed. This approach requires the identification and calibration of a statistical model for the forecast error. Accordingly, the probability distribution of the error itself is inferred through a multiple regression, depending on selected explanatory variables. These may include the current forecast issued by the hydrological model, the past forecast error, and the past rainfall. The final goal is to indirectly relate the forecast error to the sources of uncertainty in the forecasting procedure, through a probabilistic link with the explaining variables identified above. Statistical testing for the proposed approach is discussed in detail. An extensive application to a synthetic database is presented, along with a first real-world implementation that refers to a real-time flood forecasting system that is currently under development. The results indicate that the uncertainty estimates represent well the statistics of the actual forecast errors for the examined events.Citation: Montanari, A., and G. Grossi (2008), Estimating the uncertainty of hydrological forecasts: A statistical approach, Water Resour. Res., 44, W00B08,

Mesoscale Alpine Programme Demonstration of Probabilistic Hydrological and Atmospheric Simulation of Flood Events (MAP D-PHASE) is a forecast demonstration project aiming at demonstrating recent improvements in the operational use of end-to-end forecasting system consisting of atmospheric models, hydrological prediction systems, nowcasting tools and warnings for end-users. Both deterministic and ensemble prediction systems (EPSs) have been implemented for the European Alps (atmospheric models) and a selection of mesoscale river basins (hydrological models) in Central Europe. A first insight into MAP D-PHASE with focus on operational ensemble hydrological simulations is presented here.

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Storage facilities are key devices in mitigating the urban drainage impact on receiving water bodies, but their design is\ud still affected by high uncertainty. The analytical-probabilistic approach has recently raised interest, because the\ud facility performances are directly related to probability. Starting from statistically independent storm events,\ud distributions of the meteorological variables must be fitted. Rainfall series, recorded in three Italian raingauges,\ud were examined for appraising two main concerns: the choice of proper probability distributions for rainfall volume\ud and the sample sensitivity with respect to the analysis criterion. The analytical derivation of the model is then finally\ud discussed

Abstract:Precipitation measurements by rain gauges are usually affected by a systematic underestimation, which can be larger in case of snowfall. The wind, disturbing the trajectory of the falling water droplets or snowflakes above the rain gauge, is the major source of error, but when tipping-bucket recording gauges are used, the induced evaporation due to the heating device must also be taken into account. Manual measurements of fresh snow water equivalent (SWE) were taken in Alpine areas of Valtellina and Vallecamonica, in Northern Italy, and compared with daily precipitation and melted snow measured by manual precipitation gauges and by mechanical and electronic heated tipping-bucket recording gauges without any wind-shield: all of these gauges underestimated the SWE in a range between 15% and 66%. In some experimental monitoring sites, instead, electronic weighing storage gauges with Alter-type wind-shields are coupled with snow pillows data: daily SWE measurements from these instruments are in good agreement. In order to correct the historical data series of precipitation affected by systematic errors in snowfall measurements, a simple 'at-site' and instrument-dependent model was first developed that applies a correction factor as a function of daily air temperature, which is an index of the solid/liquid precipitation type. The threshold air temperatures were estimated through a statistical analysis of snow field observations. The correction model applied to daily observations led to 5-37% total annual precipitation increments, growing with altitude (1740 ÷ 2190 m above sea level, a.s.l.) and wind exposure. A second 'climatological' correction model based on daily air temperature and wind speed was proposed, leading to errors only slightly higher than those obtained for the at-site corrections.

[1] The assessment of the efficiency of a storm water storage facility devoted to the sewer overflow control in urban areas strictly depends on the ability to model the main features of the rainfall-runoff routing process and the related wet weather pollution delivery. In this paper the possibility of applying the analytical probabilistic approach for developing a tank design method, whose potentials are similar to the continuous simulations, is proved. In the model derivation the quality issues of such devices were implemented. The formulation is based on a Weibull probabilistic model of the main characteristics of the rainfall process and on a power law describing the relationship between the dimensionless storm water cumulative runoff volume and the dimensionless cumulative pollutograph. Following this approach, efficiency indexes were established. The proposed model was verified by comparing its results to those obtained by continuous simulations; satisfactory agreement is shown for the proposed efficiency indexes.Citation: Balistrocchi, M., G. Grossi, and B. Bacchi (2009), An analytical probabilistic model of the quality efficiency of a sewer tank, Water Resour. Res.,

SUMMARYStream ow data and water levels in reservoirs have been collected at 30 recording sites in the Toce river basin and its surroundings, upstream of Lago Maggiore, one of the target areas of the Mesoscale Alpine Programme (MAP) experiment. These data have been used for two purposes: rstly, the veri cation of a hydrological model, forced by rain-gauge data and the output of a mesoscale meteorological model, for ood simulation and forecasting; secondly, to solve an inverse problem-to estimate rainfall volumes from the runoff data in mountain areas where the in uence of orography and the limits of actual monitoring systems prevent accurate measurement of precipitation. The methods are illustrated for 19-20 September 1999, MAP Intensive Observing Period 2b, an event with a 4-year return period for the Toce river basin. Uncertainties in the estimates of the areal rainfall volumes based on rain-gauge data and via the inverse solution are assessed.