How Important Is the Physical Interpretation and the Role of the Model User in Urban Flooding Simulation?

Sousa, Matheus Martins de; Oliveira, Antonio Krishnamurti Beleño de; Silva, Bianca Maria Gomes da; Miguez, Marcelo Gomes

doi:10.1007/978-3-319-99867-1_90

Cited by 1 publication

(2 citation statements)

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“…The flooding depths obtained in vicinity of Piazza della Pace showed an approximate value of 1 m, with the greatest water depth reaching 1.36 m (cells 6, 7, and 8 shown in Figure 5), and they were closely related to the water depths observed by the available images of the real event. These results were also similar to those obtained by the SWMM simulation and by Occhiuto [57]. The number of each modeled cell in the watershed and the respective water depth ranges can be observed in Figure 7.…”

Section: Modcelsupporting

confidence: 87%

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Innovations in Best Practices: Approaches to Managing Urban Areas and Reducing Flood Risk in Reggio Calabria (Italy)

et al. 2021

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Urbanization increases imperviousness and reduces infiltration, retention, and evapotranspiration, frequently aggravating urban flooding due to greater runoff and higher and faster discharge peaks. Effective strategies to mitigate flood risks require a better understanding of the watershed dynamics and space to reverse the negative impacts. However, often cities do not have proper data sets to feed mathematical models that would be helpful in mapping water dynamics. Attempts to reduce flood risks have been made for decades by means of structural interventions but were frequently designed within the logic of a local scale, using limited available spaces and often merely shifting flooding downstream. Therefore, assessing urban floods requires a modeling approach capable of reflecting the watershed scale, considering interactions between hydraulic structures and urban landscape, where best practices and non-structural measures aim to improve community flood resilience through the reduction of social and financial costs in the long run. This paper proposes an integrated approach to analyze low impact development (LID) practices complemented by non-structural measures in a case study in southern Italy, supported by mathematical modeling in a strategy to overcome a context of almost no available data and limited urban open spaces.

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Section: Modcelsupporting

confidence: 87%

“…Later, this model was adapted to the urban environment, gaining a series of new facilities. MODCEL proved to be a useful tool when compared to different models [57] and can be found in detail in Miguez et al [44]. Some previous applications of this model can be found in the literature [21,[58][59][60][61].…”

Section: Mathematical Modelingmentioning

confidence: 99%