The hydrographic basin of Ribeira Grande (S. Miguel Island, Azores) has a set of characteristics that enhance the occurrence of shallow slides that have been triggered by rainfall and earthquakes. Two landslide inventories were built according to the landslide triggers: Landslide Inventory 2 (LI 2), which includes 174 earthquake-triggered shallow slides occurred in 2005; and Landslide Inventory 1 (LI 1), which includes 442 shallow slides triggered by rainfall in several periods from 2005 to 2016. Both landslide inventories were characterized and compared from the morphometric point of view and were used individually to produce susceptibility models to failure using a simple bivariate state-of-the-art statistical method (the Information Value). The landslide susceptibility Models were validated using success rates, prediction rates, and Kappa statistics. The results show that shallow slides triggered by rainfall and earthquakes in the study area have different morphometric characteristics. It was verified that models produced with LI 1 are very effective in predicting the spatial location of LI 2, but the same does not happen in the inverse situation. Finally, landslide susceptibility models developed with LI 1 and LI 2 for the upper sector of the hydrographic basin (where most landslides occurred), and latter applied to the complete watershed, present more modest predictive results but are more reliable to characterize the landslide susceptibility in the study area.

São Miguel Island comprises five active volcanic systems, including three central volcanoes with calderas and two basaltic fissure systems. Volcanic eruptions in São Miguel are of basaltic and trachytic nature ( s.l. ), including Hawaiian, Strombolian, sub-Plinian, Plinian and Vulcanian events, the more explosive ones frequently including hydromagmatic phases. Large Plinian eruptions are related to caldera-forming events that occurred in the past. With reference to the Fogo A stratigraphic marker, a total of 73 individual volcanic eruptions have been identified in the last 5 ka, giving a recurrence interval of 68.5 years. Taking into account that only six events have occurred in historical times, the recurrence interval increases to 95 years and, clearly, a future event is overdue because the most recent eruption occurred in 1652. It should be noted, however, that some volcanic eruptions in the past have occurred in clusters. The eruptive frequencies of the last 5 ka of activity have been determined for all types of eruptions and related hazards, including lava flows, pyroclastic falls, pyroclastic density currents (PDCs) and lahars. The areas susceptible to volcanic products have been mapped and modelled under different eruptive conditions.

Le volcan Fogo, le plus grand des trois volcans actifs sur l’île São Miguel dans les Açores, montre une variété d’aléas qui sont liés à des processus non éruptifs. Souvent nommés aléas volcaniques indirects, ils sont, sur le Fogo, le produit d’interactions entre l’édifice volcanique instable et les processus contrôlés par des mécanismes sismiques, hydrothermaux, gravitaires et hydrologiques. Il s’avère que de nombreuses maisons, routes et ponts sont en danger si une activité séismique importante se manifestait. Or, depuis que l’île a été colonisée, les tremblements de terre supérieurs à IX sur l’échelle européenne macrosismique (EMS 98) ont frappé en 1522, 1713, 1811 et 1935. Environ 45 000 personnes habitent dans le district de Fogo et, si aucune action pour réduire la vulnérabilité n'est mise en place, des dommages seront inévitables dans le futur. Des gaz sont émis à plusieurs endroits sur le Fogo et, parmi eux, le CO₂ est un gaz dangereux quand il se concentre dans des dépressions. Des concentrations supérieures à 15 % conduisent souvent à l’asphyxie et à la mort, aussi les risques induits par les émissions de gaz représentent-ils un danger important pour les habitants du Fogo. Au cours des cinq derniers siècles, l’île São Miguel a été affectée par plusieurs glissements de terrain destructeurs et des crues soudaines qui ont été provoqués par des tremblements de terre, des éruptions volcaniques et des périodes de fortes pluies. Parmi ceux-ci, un événement de grande magnitude (X, EMS 98) en octobre 1522 a produit une coulée boueuse qui a complètement détruit la ville de Vila Franca do Campo. Environ 5000 habitants furent tués et une surface de 4,5 km² fut recouverte par des millions de mètres cubes de matériel. Les aléas volcaniques indirects sont une menace constante pour les habitants du volcan Fogo, mais ce n’est que récemment que des recherches approfondies leur sont consacrées.

The main objective of this study is to better understand and quantify the consequences for landslide susceptibility assessment caused by (i) the discrimination (or not) of landslide typology and (ii) the use of different predisposing factor combinations. The study area for this research was Lajedo parish (Flores Island, Azores—Portugal). For the landslide susceptibility modeling, 12 predisposing factors and a historical landslide inventory with a total of 474 individual landslide rupture areas were used as inputs, and the Information Value method was then applied. It was concluded that susceptibility models developed specifically for each landslide typology achieve better results when compared to the model developed for the total inventory, which suffers from a bias caused by the strong spatial abundance of one landslide typology. A total of 4095 susceptibility models were tested for each typology, and the best models were selected according to their goodness of fit. The best model for both falls and slides has seven predisposing factors, some of which do not correspond to the factors that have the best individual discriminatory capabilities. The number of expected and observed unique terrain conditions for each model allowed us to conclude that with the successive addition of predisposing factors, there is an inability of the territory to generate new observed unique terrain conditions. This consequence was directly related to the inability to increase the goodness of fit of the computed models. For each landslide typology, the predictive capacity of the best susceptibility model was assessed by computing the Prediction Rate Curves and the Area Under the Curve.