Urban areas (i.e. cities, towns and suburbs) provide a home to over 70% of the EU‑population, and this number is expected to exceed 80% by 2050 (Tapia et al., ECOL INDIC, 2017). The increase in frequency and intensity of extreme precipitation events caused by the changing climate (e.g. cloudbursts, rainstorms, heavy rainfall, hail, heavy snow) combined with the high population density and concentration of assets in urban areas makes them particularly vulnerable to pluvial flooding, hence, assessing their vulnerability under current and future climate scenarios is of paramount importance. Detailed hydrologic-hydraulic numerical modelling is resource intensive and therefore scarcely suitable for a consistent hazard assessment across large urban settlements. Given the steadily increasing availability of LiDAR (Light Detection And Ranging) high-resolution DEMs (Digital Elevation Models), several studies highlighted the potential for consistent pluvial flood hazard characterization of fast-processing DEM-based methods, such as the Hierarchical Filling and Spilling or Puddle-to-Puddle Dynamic Filling and Spilling (see e.g. Zhang et al., J HYDROL, 2014; Chu et al., WATER RESOUR RES, 2013). As part of the activities of the EIT Climate-KIC Demonstrator project SAFERPLACES (https://saferplaces.co/), we developed a fast-processing algorithm, named Safer_RAIN, that enables one to map pluvial flooding in large urban areas by implementing a filling and spilling procedure that accounts for spatially distributed rainfall input and infiltration processes (Green Ampt method). We present the first applications of the algorithm to model recent urban inundations occurred in Northern Italy. These preliminary applications, compared against ground evidence and detailed output from a two-dimensional hydrologic and hydraulic numerical model, highlight limitations and potential of Safer_RAIN for identifying pluvial-hazard hotspots across large urban environments.

Smart Climate Hydropower Tool is an innovative web-cloud-based service that implements a set of data-driven methods for river discharge. An application for two catchments in South America is discussed (test cases), where management of hydropower plants can benefit from knowledge of incoming discharge forecasts up to 6 months in advance. SCHT has been developed inside H2020 project “CLARA - Climate forecast enabled knowledge service” and exploits several Artificial Intelligence algorithms, evolving by R&D activity to test new available ones. Although tangible results using AI have been published (i.e. Callegari, et al., 2015, De Gregorio et. al 2017) challenges remain for seasonal lead times and rainfall dominated catchments, where forecast of meteorological variables plays a critical role. In this contribution we show results of application of different AI algorithms (from supervised learning regression techniques, to artificial neural networks). Each algorithm is trained over past decades datasets of recorded data, forecast performances are then evaluated using separate test sets with reference to benchmarks (historical average of discharge values and simpler multiparametric regressions). Major operative advantages of AI with respect to mechanistic hydrological models include limited to none a priori knowledge of involved physical phenomena, high level of flexibility when managing heterogeneous sets of variables related to discharge, and quick setup time of the forecast. Major efforts are requested to identity informative input features ranging from earth observation to gauging stations data, to public meteorological forecasts (i.e Copernicus Climate Change Service-C3S). Using AI techniques many combinations of features can be tested together, to predict river discharge to the reservoirs, choosing the best performing one and tailoring the service to the catchment of interest. Once trained, each algorithm just needs to retrieve online data to perform forecasts, with limited maintenance (i.e. annual re-training to consider new available hydrological data). For demonstrational purposes we prototyped a cloud-based service, for immediate visualization, through a common browser, of both past and forecasted data, and get on fly performance metrics calculation of the forecasts.