Landslide susceptibility maps are widely used in landslide hazard management. Although many models have been proposed, mapping unit definition is a matter that still needs to be fully examined. In the literature, the most reported mapping units are pixels and slope units, while in this work, developed in the Rio de Janeiro region (Brazil), the use of drainage basins as a mapping unit is examined; even if their use leads to the definition of maps with a coarser spatial resolution than pixels-based maps, they convey information that can be easily and rapidly handled by civil defense organizations. However, for the morphometrical characterization of entire basins, a standardized procedure does not exist, and the susceptibility results may be sensitive to the approach used. To investigate this issue, a random forest model was used to assess landslide susceptibility, using 12 independent variables: four categorical (land use, soil type, lithology and slope orientation) and eight numerical variables (slope gradient, elevation, slope curvature, profile curvature, planar curvature, flow accumulation, topographic wetness index, stream power index). For each basin, the numerical variables were aggregated according to different approaches, which, in turn, were used to set up four different model configurations: i) maximum values, ii) mean values, iii) standard deviation values, iv) joint use of all the above. The resulting maps showed noticeable differences and a quantitative validation procedure showed that the best configurations were the ones based on mean values of independent variables, and the one based on the combination of all the values of the numerical variables. The main outcomes of this work consist of a landslide susceptibility map of the study area, to be used in operational procedures of risk management and in some insights on the best approaches to aggregate raster cell data into wider spatial units.

In 2011 Brazil experienced the worst disaster in the country’s history. There were 918 deaths and thousands made homeless in the mountainous region of Rio de Janeiro State due to several landslides triggered by heavy rainfalls. This area constantly suffers high volumes of rain and episodes of landslides. Due to these experiences, we used the MaCumBa (Massive CUMulative Brisk Analyser) software to identify rainfall intensity–duration thresholds capable of triggering landslides in the most affected municipalities of this region. More than 3000 landslides and rain data from a 10-year long dataset were used to define the thresholds and one year was used to validate the results. In this work, a set of three thresholds capable of defining increasing alert levels (moderate, high and very high) has been defined for each municipality. Results show that such thresholds may be used for early alerts. In the future, the same methodology can be replicated to other Brazilian municipalities with different datasets, leading to more accurate warning systems.

Even though the occurrences of social and economic losses and fatal victims caused by landslides in Pernambuco are recurrent, one must consider that there is a gap in the systematization of information pertaining to such processes in a georeferenced spatial database at a state level, especially with regard to the location and types of triggering conditioning factors. This work is intended to evaluate the occurrence of landslides in the state of Pernambuco, the conditioning factors thereof, and the most affected areas. For such, a Geographic Database (Banco de Dados Geográfico - BDG) and an inventory map were created, based on the capture of landslides in the state between 1988 and 2019, both through secondary and primary sources. Lastly, all of the occurrences were catalogued in information plans, vectorized in a GIS (Geographic Information System) environment and correlated with thematic variables of the main conditioning factors. 3,138 landslides in Pernambuco were inventoried, which enabled the verification of behavior patterns regarding the occurrence of said processes in the state. The formation of this georeferenced spatial database may contribute to better understand the incidence of these processes, and thus subsidize the establishment of investigative methodologies and strategies related to the subject. Keywords: Inventory; Database; Mass Movements; Northeastern Brazil.