são utilizados neste trabalho. Os resultados mostram que o vento a 10 metros de altura sobre a AGES sopra predominantemente de nordeste, norte, e leste durante o ano, com intensidade moderada (entre 4,0 e 7,0 m.s -1 ). O vento norte é mais intenso do que o vento de quadrante sul, que ocorre durante a passagem de sistemas transientes. A velocidade média do vento depende da posição do Anticiclone Subtropical do Atlântico Sul, que inluencia o gradiente de pressão à superfície sobre a área em estudo. O vento é mais fraco durante o outono; atinge uma velocidade média mensal de 5,3 m.s -1 em abril e é mais intenso em setembro e janeiro (7,3 e 7,1 m.s -1 , respectivamente). A pressão atmosférica ao nível médio do mar oscila entre 1012,3 hPa no verão e 1019,5 hPa no inverno; a temperature do ar a 2 metros de altura varia entre 23,2°C em setembro e 27,4°C em março; e a umidade relativa do ar a 2 metros de altura exibe um mínimo de 72,7% em maio e um máximo de 84,2% em dezembro. Com relação à frequência de sistemas frontais, uma média de 30,2 sistemas atingem o sul da AGES a cada ano, com um máximo em setembro (3,9 sistemas) e um mínimo em fevereiro (0,8 sistema).
<p>Environmental hazards represent a major socio-economic challenge where floods events are the most impactful in terms of global population affected (UNDRR, 2020). Coastal areas are exposed to multiple met-ocean extreme events which can occur separately or combined. Storm surges associated with wind waves, heavy rainfall and tides can lead to catastrophic inundation events associated with breakdown of structures, food and water insecurities and loss of lives. Additionally, climate changes are associated with two coastal risk factors: a) an increase of extreme events (Schiermeier, 2011; Vitousek et al., 2017) and b) an increase of sea level rise (IPCC, 2018).</p><p>Different approaches exist to flood modelling (Vousdoukas et al.,2016; Dottori, Martina and Figueiredo, 2018), varying by complexity and accuracy. Simple hydrological models, which operate by integrating the 2D shallow water equation in a flood-plain, offer a good trade-off between computational demand and good skills in simulating real coastal flood events (Smith, Bates and Hayes, 2012). Since accurate inundation modelling is of great importance for risk prevention and management of coastal areas, a system that can be reallocated and calibrated for different regions is a forefront of the research topic.</p><p>As a first case study, the flood event of February 2015 in Emilia-Romagna Region (Italy) was selected. The event was characterized by a combination of heavy rain, waves and tides which leads to one of the highest water levels ever recorded in the area (Perini et al., 2015). The model was run with different Digital Elevation Models and forced with water levels provided by <em>Istituto Superiore per la Protezione e la Ricerca Ambientale</em> (ISPRA) station. The results were compared with observational data of inundation maps.&#160; A broad agreement was found between inundation maps produced by the model and observational data, though with significant local discrepancies. The main differences between model and observations can be ascribed mainly to DEM&#8217;s local uncertainty. Work is in progress to include the different types of forcings and to elaborate machine-learning based protocols of calibration to locally improve the model skill, by a) optimizing the mean elevation of the DEM using the modelled and observed flooded areas and b) best-fitting Manning coefficients over the DEM using land use data.</p>
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