Análise de áreas suscetíveis a escorregamentos translacionais rasos na cidade do Recife-Pe utilizando o modelo Shalstab

Abstract

Mass movements are natural disasters of significant socioeconomic impact which have increased in frequency and intensity over the years. Brazil, which is a country with rugged terrain and with the occurrence of high-intensity rain events, is consequently prone to these mass movement events. Among the prevention measures, slope stability models, which are applied at low cost and time, stand out. The most used model in tropical areas is the SHALSTAB due to the history of coherent results, and it is hence used in this work. This model is a combination of the infinite slope model and the hydrological model, which uses topographic, pedological, and hydrological data to assess susceptible areas to translational landslides. Therefore, this work aims to identify areas with a predisposition to shallow translational landslides in the city of Recife, Pernambuco, Brazil, which recurrently suffers from natural disasters of this type. Topographic data results from calculations using the Digital Terrain Model (MDT) in QGis, which is a geographic information system (GIS) tool. The MDT also served to measure hydrological data because to map the spatial pattern of saturated soil equilibrium, it uses the upstream contribution area, soil transmissivity, and local slope. In order to obtain the pedological data, a bibliographic review of works that measured the parameters in the region was carried out, together with the Exploratory Survey for Recognition of Soils in the State of Pernambuco, developed by EMBRAPA. Seven different modelings were performed. In the first six, there was a variation of cohesion and friction angle of the soil, and all parameters were uniformly distributed in the area. Modeling 7 was a discretized modeling based on pedology. The models were compared with the occurrence data of Civil Defense for the neighborhoods of Brejo da Guarabira and Cohab, as they are the neighborhoods that suffer the most from landslides in the region. The results were very consistent with reality, with three models being chosen (Modeling 4, Modeling 1, and Modeling 6) to represent different scenarios (high, medium, and low stability), in order to create guidelines for managers and public sectors. Moreover, it can be seen that, in addition to the aforementioned neighborhoods, those located in their surroundings are also vulnerable to landslides.