Abstract. Forecasting flash floods some hours in advance is still a challenge, especially in environments made up of many small catchments. Hydrometeorological forecasting systems generally allow for predicting the possibility of having very intense rainfall events on quite large areas with good performances, even with 12–24 h of anticipation. However, they are not able to predict the exact rainfall location if we consider portions of a territory of 10 to 1000 km2 as the order of magnitude. The scope of this work is to exploit both observations and modelling sources to improve the discharge prediction in small catchments with a lead time of 2–8 h. The models used to achieve the goal are essentially (i) a probabilistic rainfall nowcasting model able to extrapolate the rainfall evolution from observations, (ii) a non-hydrostatic high-resolution numerical weather prediction (NWP) model and (iii) a distributed hydrological model able to provide a streamflow prediction in each pixel of the studied domain. These tools are used, together with radar observations, in a synergistic way, exploiting the information of each element in order to complement each other. For this purpose observations are used in a frequently updated data assimilation framework to drive the NWP system, whose output is in turn used to improve the information as input to the nowcasting technique in terms of a predicted rainfall volume trend; finally nowcasting and NWP outputs are blended, generating an ensemble of rainfall scenarios used to feed the hydrological model and produce a prediction in terms of streamflow. The flood prediction system is applied to three major events that occurred in the Liguria region (Italy) first to produce a standard analysis on predefined basin control sections and then using a distributed approach that exploits the capabilities of the employed hydrological model. The results obtained for these three analysed events show that the use of the present approach is promising. Even if not in all the cases, the blending technique clearly enhances the prediction capacity of the hydrological nowcasting chain with respect to the use of input coming only from the nowcasting technique; moreover, a worsening of the performance is observed less, and it is nevertheless ascribable to the critical transition between the nowcasting and the NWP model rainfall field.

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Severe and organized thunderstorms are historically responsible for high‐impact hydrometeorological events in the western Mediterranean area (Spain, France and Italy). This study presents a meteorological indices‐based forecasting tool developed by the Meteo‐Hydrological Functional Center of Civil Protection of Liguria Region for the prediction of these phenomena. The tool, in the form of a checklist, takes into account all the physical processes and thermodynamic ingredients driving the formation and the spatio‐temporal evolution of thunderstorms and their level of organization. The study was carried out on a series of events occurring in 2009–2014 to assess the checklist performance and identify the most relevant parameters and variables from a predictive ability standpoint. Some indices were found to be useful for general thunderstorm recognition but not necessarily for thunderstorms impacting most from a socio‐economic standpoint, namely strong and organized ones; other parameters exhibited a higher degree of reliability for the identification of severe and organized phenomena in Liguria. Furthermore, the analysis was useful for the identification of a seasonal dependence for many variables, for the recalibration of some thresholds in a new version of the checklist and for the identification of local patterns of low level winds favourable to the development and maintenance of well organized and stationary convective systems. The assessment of the new checklist for all the events in 2009–2014 showed a significant enhancement of the performance and helped to identify and sort out the role of each parameter better.

Abstract. Forecasting flash floods with anticipation of some hours is still a challenge especially in environments made by a collection of small catchments. Hydrometeorological forecasting systems generally allow to predict the possibility of having very intense rainfall events on quite large areas with good performances even with 12–24 hours of anticipation. However, they are not able to predict exactly rainfall location if we consider portions of territory of 10 to 103 km2 as order of magnitude. The scope of this work is to exploit both observations and modeling sources to improve the discharge prediction in small catchments with time horizon of 2–8 hours. The models used to achieve the goal are essentially three i) a probabilistic rainfall nowcasting model able to extrapolate the rainfall evolution from observations; ii) a non hydrostatic high-resolution numerical weather prediction (NWP) model; iii) a distributed hydrological model able to provide a streamflow prediction in each pixel of the studied domain. These tools are used, together with radar observations, in a synergistic way, exploiting the information of each element in order to complement each other: observations are used in a frequently updated data assimilation framework to drive the NWP system, whose output is in turn used to improve the information in input to a nowcasting technique; finally nowcasting and NWP outputs are blended, generating an ensemble of rainfall scenarios used to feed the hydrological model and produce a prediction in terms of streamflow. The flood prediction system is applied to three major events occurred on Liguria Region (Italy) first to produce a standard analysis on predefined basin control sections, then using a distributed approach that exploit the capabilities of the employed hydrological model. The results obtained for these three analyzed events show that the use of the present approach is promising. Even if not in all the cases, the blending technique clearly enhances the prediction capacity of the hydrological nowcasting chain with respect to the use of input coming only from the nowcasting technique; moreover, a worsening of the performance is rarely observed and it is nevertheless ascribable to the critical transition between the nowcasting and the NWP model rainfall field.