In January 2011 the mountainous region of Rio de Janeiro state was devastated by the greatest sediment disaster recorded in Brazil. In July 2013 the Government launched GIDES Project for Strengthening National Strategy of Integrated Risk Management to provide the transfer of Japanese sediment disasterrelated technology and knowledge regarding recognition, prevention and early warning in order to reduce the risk of social-economic losses. This paper describes hazard and risk assessment development carried out for 4 main sediment related phenomena : slope failures, landslides, debris flows and rockfalls. The work involved bibliographic conceptual alignment, inventory of natural disasters data and statistical data analysis. The studies allowed the definition of potential triggering angles and maximum reach distances. When these data were confronted with the Japanese parameters some adjustments were necessary to frame a more realistic synergic method, due to Brazilian field peculiarities. These procedures permitted to define hazard sediment disaster prone areas and assessment of risk. The method has been applied in five Brazilian municipalities and showed good results.

Risco Geológico: Uma Abordagem Integrada entre Métodos de Mapeamento Geotécnico Analisados em Nova Friburgo, RJ

Sediment disasters in Brazil induced by climate change are intensifying, posing substantial risks. Studies on Brazilian disaster risk reduction are abundant but those on federal risk assessment surveys are scarce. To address this gap, we analysed five surveys: the Municipal Risk Reduction Plan (PMRR), Geological Risk Survey (GRS), Susceptibility Survey (SS), Geotechnical Aptitude for Urbanization Chart (GAUC), and Geological Hazard Survey (GHS). We conducted a meta-analysis of 300 scholarly publications and public datasets to assess these surveys, evaluating input data, methods, outcomes, applicability, effectiveness, and cost–benefit, guided by global recommendations. Spearman's rank correlation and McDonald's omega were employed to evaluate survey associations with initiatives. The results reveal each survey's unique contributions and challenges, such as limited national coverage and underutilization of quantitative methods. GHS stands out for its versatility, including climate change adaptation countermeasures and decision-maker relevance, but it lacks legal support and limited coverage. GRS and SS are well established but need considerable methodological updates, while GAUC is underutilized due to complexity and high costs. Despite the reproducibility and cost–time efficiency challenges, PMRR exhibits substantial correlation with implementing disaster risk reduction activities. Recommendations include standardizing procedures, enhancing data collection and analysis, improving outputs, and a systematic progressive multi-level approach.

Landslide runout distance is governed not only by collapsed magnitude but also by site-specific geoenvironmental conditions. While remote sensing techniques has advanced landslide susceptibility mapping, its application to runout modeling remains limited. This study examined the role of collecting data on various site conditions through remote sensing and field surveys datasets in predicting the landslide travel distance from the 2022 disaster in Petrópolis, Rio de Janeiro. A total of 218 multivariate linear regression models were developed using morphometric, remote sensing, and field survey variables collected across collapse, transport, and deposition zones. Results show that predictive accuracy was limited when based solely on landslide scale (R² = 0.06–0.10) but improved substantially with the inclusion of site condition data across collapse, transport, and deposition zones (R² = 0.49–0.51). Additionally, model performance was strongly influenced by runout path typology, with channelized flows producing the most stable and accurate predictions (R² = 0.73–0.90), while obstructed and open-slope paths performed worse (R² = 0.39–0.61). These findings demonstrate that empirical models integrating multizonal site-condition data and runout path typology offer a scalable, reproducible framework for landslide hazard mapping in data-scarce, complex mountainous urban environments.

Climate change-induced sediment-related disasters in Brazil are intensifying, posing substantial risks. Studies on Brazilian disaster risk reduction are abundant, but those on federal risk assessment surveys are scarce. To address this gap, we analyzed five surveys, including the Municipal Risk Reduction Plan (PMRR), Geological Risk Survey (GRS), Susceptibility Survey (SS), Geotechnical Aptitude for Urbanization (GAUC), and Geological Hazard Survey (GHS). We conducted a meta-analysis of 300 scholarly publications and public datasets to assess these surveys, evaluating input data, methods, outcomes, applicability, effectiveness, and cost–benefit, guided by global recommendations. Spearman’s rank correlation and McDonald’s Omega were employed to evaluate survey associations with initiatives. The results reveal each survey’s unique contributions and challenges, such as limited national coverage and underutilization of quantitative methods. GHS stands out for its versatility, including climate change adaptation countermeasures and decision-maker relevance, but it lacks legal support and limited coverage. GRS and SS are well established but need considerable methodological updates, while GAUC is underutilized due to complexity and high costs. Despite the reproducibility and cost-time efficiency challenges, PMRR exhibits substantial correlation with implementing disaster risk reduction activities. Recommendations include standardizing procedures, enhancing data collection and analysis, improving outputs, and a progressive multilevel approach.