부산대학교 도서관

In the field of hydrological prediction for medium-sized watersheds, characterized by complex orography and short response times, forecasts cannot rely only upon observed precipitation: predicted rainfall is in this case an essential input for hydrological models. However, the quality and reliability of deterministic numerical precipitation forecasts driving a hydrological model are often unsatisfactory, because uncertainty in Quantitative Precipitation Forecasts (QPFs) is considerable at the scales of interest for hydrological purposes. The uncertainty inherent in precipitation forecast can be accounted for better estimating the uncertainty associated with the flood forecast, in order to provide a more informative hydrological prediction. The methodology proposed and adopted in this work is based on a hydrological ensemble forecasting approach that uses multiple precipitation scenarios provided by different high-resolution numerical weather prediction models, driving the same hydrological model. In this way, the uncertainty associated with the meteorological forecasts can propagate into the hydrological models and be used in warnings and decision making procedures relying upon a probabilistic approach. In the framework of RISK AWARE, an INTERREG III B EU project, a detailed analysis of two cases of intense precipitation affecting the Reno river basin, a medium-sized catchment in northern Italy, has been performed. One case study has been performed using lateral boundary values derived from analysed fields, the other simulating a real time forecast, i.e., using forecasted boundary conditions. Four different meteorological models (Lokal Modell, RAMS, BOLAM and MOLOCH), operating at different horizontal resolutions, provide QPFs which are used to force the hydrological model. The discharge predictions are obtained by means of the physically based rainfall-runoff model TOPKAPI. The results provide examples of the uncertainties inherent in the QPF and show that the hydrological response of the Reno river basin, as simulated by the TOPKAPI model, is highly sensitive to the correct space-time localization of precipitation, even if the total amount of rainfall is, on average, well forecasted. The system seems able to provide useful information concerning the discharge peaks (amount and timing) for warning purposes.