The number of natural catastrophes that affect people worldwide is increasing; among these, the hydro-meteorological events represent the worst scenario due to the thousands of deaths and huge damages to private and state ownership they can cause. To prevent this, besides various structural measures, many non-structural solutions, such as the implementation of flood warning systems, have been proposed in recent years. In this study, we suggest a low computational cost method to produce a probabilistic flood prediction system using a single forecast precipitation scenario perturbed via a spatial shift. In fact, it is well-known that accurate forecasts of heavy precipitation, especially associated with deep moist convection, are challenging due to uncertainties arising from the numerical weather prediction (NWP), and high sensitivity to misrepresentation of the initial atmospheric state. Inaccuracies in precipitation forecasts are partially due to spatial misplacing. To produce hydro-meteorological simulations and forecasts, we use a flood forecasting system which comprises the physically-based rainfall-runoff hydrological model FEST-WB developed by the Politecnico di Milano, and the MOLOCH meteorological model provided by the Institute of Atmospheric Sciences and Climate (CNR-ISAC). The areas of study are the hydrological basins of the rivers Seveso, Olona, and Lambro located in the northern part of Milan city (northern Italy) where this system works every day in real-time. In this paper, we show the performance of reforecasts carried out between the years 2012 and 2015: in particular, we explore the ‘Shift-Target’ (ST) approach in order to obtain 40 ensemble members, which we assume equally likely, derived from the available deterministic precipitation forecast. Performances are shown through statistical indexes based on exceeding the threshold for different gauge stations over the three hydrological basins. Results highlight how the Shift-Target approach complements the deterministic MOLOCH-based flood forecast for warning purposes.

Nowadays, a major issue is land-use change by urban development that alters the catchment response to meteorological events. Urban basins have less storage capacity and more rapid runoff, so urban rivers rise more quickly during storms and have higher peak discharge rates than rural catchments. An exemplary case of this situation is the city of Milan (northern Italy) and its whole territory that extends towards the north collecting meteoric precipitation through the Seveso, Olona and Lambro (SOL) rivers. To assess the impact of anthropogenic development on urban catchment scale hydrology, a reanalysis of 40 years of simulations was carried out with the Curve Number (CN) map based on current land use and compared to simulations using the CN maps based on past land use. A coupled hydro-meteorological system was built that combined a physically based rainfall-runoff hydrological model FEST-WB, developed by the Politecnico di Milano, with the ERA5-Land hourly dataset for the period 1981 to 2020, that was provided by the ECMWF under the framework of the Copernicus Climate Change Service Programme. The study (named SOL40) analyses 40 year trends in the main meteorological (air temperature, precipitation) and hydrological variables (runoff) over the SOL area and tried to quantify and separate the impact of land use change from that of climate change.

This paper presents a system for supporting hydropower production on mountainous areas. The system couples the outputs of a numerical weather prediction model and a snow melting and accumulation temperature-based model. Several procedures are presented for interpolating meteorological variables and calibrating and validating model parameters that can be generalized to any other mountainous area where the estimation of current and forecasted snow water equivalent and melting amount is required. The system reliability has been assessed through the validation of three components: spatial interpolation of meteorological data, mathematical modeling, and quantitative meteorological forecast. The results show that good accuracy of meteorological data spatial interpolation can be achieved when the data from snow gauges are used for assessing the precipitation lapse rate at higher altitudes, and the temperature lapse rate is computed from data at each time step. The temperature-based hydrological model proved to be effective in simulating lake inflow water volume and energy production. No clear result has been found for snow melt forecast due to the difficulties in providing reliable quantitative weather forecast in complex alpine area.