Index-based approaches are widely employed for measuring flood vulnerability. Nevertheless, the uncertainties in the index construction are rarely considered. Here, we conducted a sensitivity analysis of a flood vulnerability index in the Maquiné Basin, Southern Brazil, considering distinct normalization, aggregation, classification methods, and their effects on the model outputs. The robustness of the results was investigated by considering Spearman’s correlations, the shift in the vulnerability rank, and spatial analysis of different normalization techniques (min-max, z-scores, distance to target, and raking) and aggregation methods (linear and geometric). The final outputs were classified into vulnerability classes using natural breaks, equal interval, quantiles, and standard deviation methods. The performance of each classification method was evaluated by spatial analysis and the Akaike’s information criterion (AIC). The results presented low sensitivity regarding the normalization step. Conversely, the geometric aggregation method produced substantial differences on the spatial vulnerability and tended to underestimate the vulnerability where indicators with low values compensated for high values. Additionally, the classification of the vulnerability into different classes led to overly sensitive outputs. Thus, given the AIC performance, the natural breaks method was most suitable. The obtained results can support decision-makers in reducing uncertainty and increasing the quality of flood vulnerability assessments.

We present an overview of the unprecedent hydrological event that occurred in Rio Grande do Sul state in late April and early May 2024, causing floods and landslides in a scale that was not seen before in Brazil. The rainfall that caused the disaster is incomparable to any recent event in Brazil at large spatial and temporal scale. The intense and long-lasting precipitation led to record floods from small rivers with drainage areas below 1000 km² to the Patos Lagoon, which drains more than 180,000 km². The rainfall also led to landslides that rank first in Brazil in terms of spatial scale and number of occurrences. Mountain rivers experienced flash floods with extreme water levels, while in the lowlands the large rivers and the Patos Lagoon experienced long duration flooding, with stage remaining high for about 30 days. The flood event is probably ranked first in terms of displaced people and damage to property and infrastructure in Brazil. Almost 200 fatalities occurred due to the disaster and protection structure failures. The April-May hydrological event show a glimpse of what can be expected along the coming decades due to climate change in a region that covers South Brazil, and parts of the neighboring countries.

Despite scientific progress and international efforts, records of natural disasters and their losses are increasing. Natural disasters are constituted by the mutual interactions between social and environmental factors over time and space. Hence, the challenges of this dramatic scenario demand that we understand the interwoven social and environmental processes. Against this background, a perspective that understands humans and nature as a coupled system, like the socio-hydrological approach, can be a key to disaster risk reduction. We conducted exploratory research to demonstrate the potential of socio-hydrology to contribute to natural disaster risk reduction according to two central arguments: (i) developing an understanding of bidirectional interactions between social and environmental factors; and (ii) supporting integrated water resources and disaster risk management. As a result, we propose an integrative framework based on internal interactions between social and natural sciences, and also external interactions between the scientific community and society.

Given the increasing occurrence of landslides worldwide, the improvement of predictive models for landslide mapping is needed. Despite the influence of geotechnical parameters on SHALSTAB model outputs, there is a lack of research on models’ performance when considering different variables. In particular, the role of geotechnical units (i.e., areas with common soil and lithology) is understudied. Indeed, the original SHALSTAB model considers that the whole basin has homogeneous soil. This can lead to the under-or-overestimation of landslide hazards. Therefore, in this study, we aimed to investigate the advantages of incorporating geotechnical units as a variable in contrast to the original model. By using locally sampled geotechnical data, 13 slope-instability scenarios were simulated for the Jaguar creek basin, Brazil. This allowed us to verify the sensitivity of the model to different input variables and assumptions. To evaluate the model performance, we used the Success Index, Error Index, ROC curve, and a new performance index: the Detective Performance Index of Unstable Areas. The best model performance was obtained in the scenario with discretized geotechnical units’ values and the largest sample size. Results indicate the importance of properly characterizing the geotechnical units when using SHALSTAB. Hence, future applications should consider this to improve models’ predictivity.

By presenting some legal aspects related to disaster management in Brazil, the present work showed that the Brazilian classification of natural disasters is not well established. In the focus on an analysis of the historical views of debris flow disaster management in Brazil, technical-scientific studies about large disaster with debris flows which occurred only in Brazil during the period 1900-2014 were searched mainly through a Web-based survey. The searching result shows that most of bibliographies are proceedings papers and that the papers in scientific journals are only 13% of all the publications. The historical analysis suggests five urgent actions to Brazilian organizations : (i) establishment of correct and uniform terminology about debris flow ; (ii) systematization of hydrometeorological monitoring, its forecasting and topographic survey ; (iii) occurrence registration and database construction ; (iv) debris flow hazard mapping ; and (v) woody-debris flow researches. Based on the Brazilian current situation, the present work proposes comprehensive measures to reduce debris flow disasters, by constructing and using school catchments which are defined as experimental catchments that are used for scientific researches and environmental education for local people.