Among the natural hazards, flash flooding is the leading cause of weather-related deaths. Flood risk management (FRM) in this context requires a comprehensive assessment of the social risk component. In this regard, integrated social vulnerability (ISV) can incorporate spatial distribution and contribution and the combined effect of exposure, sensitivity and resilience to total vulnerability, although these components are often disregarded. ISV is defined by the demographic and socio-economic characteristics that condition a population's capacity to cope with, resist and recover from risk and can be expressed as the integrated social vulnerability index (ISVI). This study describes a methodological approach towards constructing the ISVI in urban areas prone to flash flooding in Castilla y León (Castile and León, northern central Spain, 94 223 km², 2 478 376 inhabitants). A hierarchical segmentation analysis (HSA) was performed prior to the principal components analysis (PCA), which helped to overcome the sample size limitation inherent in PCA. ISVI was obtained from weighting vulnerability factors based on the tolerance statistic. In addition, latent class cluster analysis (LCCA) was carried out to identify spatial patterns of vulnerability within the study area. Our results show that the ISVI has high spatial variability. Moreover, the source of vulnerability in each urban area cluster can be identified from LCCA. These findings make it possible to design tailor-made strategies for FRM, thereby increasing the efficiency of plans and policies and helping to reduce the cost of mitigation measures.

The use of high resolution ground-based light detection and ranging (LiDAR) datasets provides spatial density and vertical precision for obtaining highly accurate Digital Surface Models (DSMs). As a result, the reliability of flood damage analysis has improved significantly, owing to the increased accuracy of hydrodynamic models. In addition, considerable error reduction has been achieved in the estimation of first floor elevation, which is a critical parameter for determining structural and content damages in buildings. However, as with any discrete measurement technique, LiDAR data contain object space ambiguities, especially in urban areas where the presence of buildings and the floodplain gives rise to a highly complex landscape that is largely corrected by using ancillary information based on the addition of breaklines to a triangulated irregular network (TIN). The present study provides a methodological approach for assessing uncertainty regarding first floor elevation. This is based on: (i) generation an urban TIN from LiDAR data with a density of 0.5 points·m⁻², complemented with the river bathymetry obtained from a field survey with a density of 0.3 points·m⁻². The TIN was subsequently improved by adding breaklines and was finally transformed to a raster with a spatial resolution of 2 m; (ii) implementation of a two-dimensional (2D) hydrodynamic model based on the 500-year flood return period. The high resolution DSM obtained in the previous step, facilitated addressing the modelling, since it represented suitable urban features influencing hydraulics (e.g., streets and buildings); and (iii) determination of first floor elevation uncertainty within the 500-year flood zone by performing Monte Carlo simulations based on geostatistics and 1997 control elevation points in order to assess error. Deviations in first floor elevation (average: 0.56 m and standard deviation: 0.33 m) show that this parameter has to be neatly characterized in order to obtain reliable assessments of flood damage assessments and implement realistic risk management.

Flash floods are considered to be one of the natural hazards with the greatest capacity to generate risk. Therefore, a change in traditional flood risk management (FRM) is necessary towards an integrated approach, which requires a comprehensive assessment of the social risk component. In this regard, integrated social vulnerability (ISV) gives us the spatial distribution, contribution and combined effect of exposure, sensitivity and resilience to the total vulnerability, although these components are often disregarded. ISV is characterized by the demographic and socioeconomic characteristics that condition a population's capacity to cope with, resist and recover from risk, and it can be derived from assessing the Integrated Social Vulnerability Index (ISVI). As far as we know, it has not yet provided a methodological approach to construct the ISVI in urban areas of Castilla y León (northern central Spain, 94,223 km², 2,478,376 inhabitants) prone to flash flooding. A hierarchical segmentation analysis (HSA) was performed prior to the principal components analysis (PCA), which helped to overcome sample size limitation inherent to PCA. ISVI was obtained from weighting vulnerability factors based on the tolerance statistic. Additionally, latent class cluster analysis (LCCA) was accomplished aiming to identify vulnerability spatial patterns within the study area. Our results show that the ISVI has high spatial variability. Moreover, the LCCA allowed us to identify the source of vulnerability in each urban area cluster. These findings enable a tailored design of FRM strategies, which intends to increase the efficiency of plans and policies, helping reduce implementation costs for mitigation measures.