[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