Over the last decade, flood disasters have affected millions of people and caused massive economic losses. Social vulnerability assessment uses a combination of several factors to represent a population's differential access to resources and its ability to cope with and respond to hazards. In this paper, social vulnerability assessment to flood risk was applied to the third most populous Portuguese municipality. The study was developed at the neighbourhood level, allowing for social vulnerability analysis at inter civil parish, intra civil parish, and municipality scales. A geographic information system-based multicriteria decision analysis (GIS-MCDA) was applied to social vulnerability and allows for an increased understanding and improved monitoring of social vulnerability over space, identifying ‘hot spots’ that require adaptation policies. Mafamude, Oliveira do Douro, Vila Nova de Gaia, and Avintes civil parishes display the greatest vulnerability to flooding. According to the most pessimistic scenario 57%–68% of the area of these civil parishes is classed at a high or very high level of social vulnerability. The GIS-MCDA helps to assess what and who is at risk, and where targeted impact-reduction strategies should be implemented. The results demonstrate the importance of an urban-scale approach instead of a river basin scale to urban flood risk management plans.

A flood vulnerability index was constructed by coupling Geographic Information System (GIS) mapping capabilities with an Analytic Hierarchy Process (AHP) Group Decision-Making (GDM) resulting from a paired comparison matrix of expert groups to assign weights to each of the standardised criteria. A survey was sent to 25 flood experts from government organisations, universities, research institutes, NGOs, and the private sector (56% academics and 44% non-academics). Respondents made pairwise comparisons for several criteria (population, socio-economic, buildings, and exposed elements) and sub-criteria. The group priorities were obtained by combining the Consistency Ratio (CR) and Euclidean Distance (ED) measures to assess the weight of each expert and obtain a final weight for each criterion and sub-criteria. In Portugal, 23 flood-prone areas were considered, and this work contributes with a tool to assess the flood vulnerability and consequently the flood risk. The flood vulnerability index was calculated, and the relevance of the proposed framework is demonstrated for flood-prone areas, in mainland Portugal. The results showed that in all five hydrographic regions, flood-prone areas with very high vulnerability were found, corresponding to areas with a high probability of flooding. The most vulnerable areas are Ponte de Lima in the North, Coimbra, and Pombal in the Centre; Loures in the Tagus and West Region; Setúbal and Alcácer do Sal in the Alentejo Region and Monchique in the Algarve Region. This methodology has the potential to be successfully applied to other flood-prone areas, combining the opinions of stakeholders validated by a mathematical model, which allows the vulnerability of the site to be assessed.

According to the EU flood risks directive, flood hazard map must be used to assess the flood risk. These maps can be developed with hydraulic modelling tools using a Digital Surface Runoff Model (DSRM). During the last decade, important evolutions of the spatial data processing has been developed which will certainly improve the hydraulic models results. Currently, images acquired with Red/Green/Blue (RGB) camera transported by Unmanned Aerial Vehicles (UAV) are seen as a good alternative data sources to represent the terrain surface with a high level of resolution and precision. The question is if the digital surface model obtain with this data is adequate enough for a good representation of the hydraulics flood characteristics. For this purpose, the hydraulic model HEC-RAS was run with 4 different DSRM for an 8.5 km reach of the Lis River in Portugal. The computational performance of the 4 modelling implementations is evaluated. Two hydrometric stations water level records were used as boundary conditions of the hydraulic model. The records from a third hydrometric station were used to validate the optimal DSRM. The HEC-RAS results had the best performance during the validation step were the ones where the DSRM with integration of the two altimetry data sources.

Over the last few decades, we witnessed a revolution in acquiring very high resolution and accurate geo-information. One of the reasons was the advances in photonics and LiDAR, which had a remarkable impact in applications requiring information with high accuracy and/or elevated completeness, such as flood modelling, forestry, construction, and mining. Also, miniaturization within electronics played an important role as it allowed smaller and lighter aerial cameras and LiDAR systems to be carried by unmanned aerial vehicles (UAV). While the use of aerial imagery acquired with UAV is becoming a standard procedure in geo-information extraction for several applications, the use of LiDAR for this purpose is still in its infancy. In several countries, companies have started to commercialize products derived from LiDAR data acquired using a UAV but not always with the necessary expertise and experience. The LIDAR-derived products’ price has become very attractive, but their quality must meet the contracted specifications. Few studies have reported on the quality of outsourced LiDAR data acquired with UAV and the problems that need to be handled during production. There can be significant differences between the planning and execution of a commercial project and a research field campaign, particularly concerning the size of the surveyed area, the volume of the acquired data, and the strip processing. This work addresses the quality control of LiDAR UAV data through outsourcing to develop a modelling-based flood forecast and alert system. The contracted company used the Phoenix Scout-16 from Phoenix LiDAR Systems, carrying a Velodyne VLP-16 and mounted on a DJI Matrice 600 PRO Hexacopter for an area of 560 ha along a flood-prone area of the Águeda River in Central Portugal.