Can meteorological model forecasts initialize hydrological simulations rather than observed data in ungauged basins?

Floods are among natural disasters which cause the largest damages worldwide each year, inducing fatalities of human lives, destruction of infrastructure and economical losses. Consequently, forecasting this type of events through hydro-meteorological models is still of great importance from a civil protection point of view since it allows to reduce hydrological risk by means of early warning systems. Nevertheless, hydrological model initialization in ungauged basins, where there is lack of direct measurements of meteorological information, is a known issue affecting the entire prediction chain. The present study evaluates the possibility of using forecasts provided by the meteorological model MOLOCH developed by CNR-ISAC forcing the FEST-WB hydrological model developed by Politecnico di Milano to perform discharge simulations assuming that the forecasting errors are negligible when using the first 24 h of time horizon. The study is carried out in the urban catchments of Milan city, the Seveso-Olona-Lambro (SOL) river basins, located in northern Italy. The main hydro-meteorological variables are analysed by comparing the spatialized and observed meteorological data, provided by an official regional network of weather stations plus a citizen scientists' contribution with the meteorological model forecasts. Moreover, a sensitivity analysis following the well-known one-factor-at-a-time methodology is accomplished with the aim of defining which atmospheric forcing, beyond rainfall, mostly affects flowrate forecasts. Results generally show satisfactory correspondences between forecasts and observed data for the discharge variable at daily scale, although an underestimation of precipitation, particularly for severe events in summer, is present. Therefore, using meteorological forecasts to create daily initial conditions for hydrological model, instead of ground observations, might be a reliable and valuable approach, even if some considerations should be borne in mind when coupling the two models.