Browsing by Author "Gallardo Donoso. Lidia Jhoanna"

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    Determinación de zonas urbanas de riesgo a inundaciones empleando modelos hidrológicos e hidráulicos en la quebrada Miraflores, cantón Guano, provincia de Chimborazo”.
    (Universidad Técnica de Ambato. Facultad de Ingeniería Civil y Mecánica. Maestría en Hidráulica con mención en obras Hidráulicas, 2025) Gallardo Donoso. Lidia Jhoanna; Castro Solorzano Fidel Alberto
    The present research addresses the identification of urban areas at risk of flooding in the Miraflores stream, Guano canton, Chimborazo province, using hydrological and hydraulic models. This region has experienced extreme rainfall events, such as the disaster on December 11, 2021, highlighting the need for preventive measures to mitigate future risks. The study follows a mixed approach, with a predominance of quantitative methods, and employs tools such as HEC-HMS and HEC-RAS to model flood scenarios. xviii Additionally, Geographic Information Systems (GIS) and specialized software are integrated for geomorphological analysis and basin delineation. Meteorological data were collected from nearby stations (San Juan, Urbina, ESPOCH, and Guaslán), supplemented by data from the Guano station and satellite sources like NASA POWER. These data were processed and statistically validated using tests such as Kolmogórov-Smirnov. Subsequently, design flows for return periods of up to 50 years were estimated, and two-dimensional hydraulic simulations were performed. The results reveal that the basin's topography and deforestation in high areas exacerbate runoff, increasing flood risks in lower areas. The risk maps produced identify critical areas susceptible to flooding, and mitigation strategies are proposed, including reforestation and infrastructure improvement. This research significantly contributes to the analysis of flood risks in Guano, providing technical tools for water resource planning and management. Moreover, it establishes a robust methodological basis for future studies in similar basins, emphasizing the importance of integrating modern technologies and advanced modeling to reduce the impact of natural disasters.