We compare two flood models applied to Riohacha. MODCEL, a conceptual model, had already been calibrated; IBER, a physically based 2D hydraulic model, has been calibrated and validated now with the same dataset used in MODCEL. MODCEL performs better according to several indicators. One cause of this difference is the low resolution of topography, a key input for IBER. Additionally, IBER is weak in representing hydraulic works, particularly concerning their real maintenance status, an information that MODCEL instead includes as the cells schematization is based on the actual flow directions reported by the people interviewed. This procedure however requires a deep insight into the actual behaviour of the physical system and a vast modelling experience. Furthermore, MODCEL software is less user-friendly than IBER. Both models, anyway, in the case at hand, capture sufficiently well the behaviour of urban flooding and the impact of interventions. Which is why they constitute key planning tools in the face of the flood problem in Riohacha and similar cases.
En el presente trabajo se propone un enfoque metodológico para el modelado de inundaciones urbanas, que integra herramientas numéricas basadas en variables físicas y socio-hidrológicas (SH-V). Esta metodología se aplica al estudio de caso de la ciudad de Riohacha (Colombia). Se encontraron correlaciones entre SH-V y el comportamiento de las inundaciones mediante pruebas univariadas y multivariadas. Se utilizó TELEMAC-2D como modelo para simular un evento de inundación extrema y se llevó a cabo un proceso iterativo utilizando Maquinas de Soporte Vectorial - SVM para optimizar la calibración del modelo. Se evaluaron métricas de desempeño hasta encontrar los valores óptimos del número de curva - CN y el coeficiente de fricción de Manning. Los resultados muestran que SH-V brinda información valiosa sobre el comportamiento de la inundación y puede representar mejor los parámetros hidráulicos como CN y coeficientes de fricción para fines de modelado. La metodología propuesta se establece como una herramienta para optimizar los procesos de modelado.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.