Urban flood modelling allows for developing effective strategies for mitigating flood risks and improving urban resilience. Different urban models can be selected according to the desired objective, ranging from simplified frameworks to more sophisticated numerical models. While several studies explored the advantages and limitations of existing model types, very few publications address the urban flood modelling issue based on data availability and data collection. Urban data regarding digital elevation models, sewer networks, and small‐scale features can vary significantly. Thus, the availability and resolution of available data strongly influence urban flood modelling. This study reviews existing approaches to achieve satisfactory urban flood modelling according to the available data and desired objectives. We explored the advantages and limitations of data‐rich environments and the benefits of the simplifications. We identified two primary objectives most concerned with ongoing urban flood simulation research: flood modelling for preliminary flood susceptibility assessment and flood hazard mapping. For each category, we investigated how previous authors approached data collection tasks and which resolution they considered sufficiently high to retain their objectives achieved. This study provides valuable guidance for scientists, engineers, and decision‐makers, promoting a more informed and mindful approach to urban flood modelling.

This study suggests a rapid methodology to delineate areas prone to flood using machine learning techniques. Based on available historically flooded areas, the model employs and combines globally collectible and reproducible conditioning factors to analyze flood susceptibility. The flood inventory map includes historically flooded areas from 1920 that occurred over the study area—Southern Italy. The impact of each factor is examined using correlation attribute evaluation and information gain ratio, while the performances of the model are evaluated by using area under receiving operating characteristics. Findings demonstrate that machine learning models can help in quick flood-prone areas analysis, especially in areas where flood hazard maps are not available.

Flood control reservoir design requires estimating the total storable water volume or the maximum allowable discharge. This study proposes a novel Generalized Semi‐Analytical Approach (GS‐AA) to identify the maximum outlet discharge a flood control reservoir can handle for a specific return period. The approach exploits the analytical expression of Flow Duration Reduction (FDR) curves and combines them with an optimization algorithm to find the critical hydrograph and, thus, the hydrograph providing the maximum outlet discharge from the reservoir. The approach models the runoff process of the basin upstream of the reservoir, allowing users to choose any runoff model (RM). The proposed approach shows faster computational times than a fully numerical procedure, enabling potential users to explore and compare multiple reservoir configurations easily. Moreover, this approach addresses flow data limitation issues that prevent FDR curve derivation by correlating them to Intensity Duration Frequency curve parameters, expanding the potential applicability of the procedure in ungauged basins. Finally, results demonstrated the functionality of the procedure regardless of the chosen RM, offering widespread flexibility for users. The proposed GS‐AA is a robust and adaptable tool for design and verification purposes to improve flood management strategies.