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Abstract This study integrates novel data on 100-year flood hazard extents, exposure of residential properties, and place-based social vulnerability to comprehensively assess and compare flood risk between Indigenous communities living on 985 reserve lands and other Canadian communities across 3701 census subdivisions. National-scale exposure of residential properties to fluvial, pluvial, and coastal flooding was estimated at the 100-year return period. A social vulnerability index (SVI) was developed and included 49 variables from the national census that represent demographic, social, economic, cultural, and infrastructure/community indicators of vulnerability. Geographic information system-based bivariate choropleth mapping of the composite SVI scores and of flood exposure of residential properties and population was completed to assess the spatial variation of flood risk. We found that about 81% of the 985 Indigenous land reserves had some flood exposure that impacted either population or residential properties. Our analysis indicates that residential property-level flood exposure is similar between non-Indigenous and Indigenous communities, but socioeconomic vulnerability is higher on reserve lands, which confirms that the overall risk of Indigenous communities is higher. Findings suggest the need for more local verification of flood risk in Indigenous communities to address uncertainty in national scale analysis.

This paper finds that social differentiation in flood impacts is relatively small soon after a flood, with some surprising results such as professionals and homeowners badly affected in the short‐term – but widens over time, with socially disadvantaged groups displaying less recovery. The paper concludes that vulnerability and resilience to flooding are sensitive to financial resources, institutional support (chiefly from a landlord), and capacity to deal with disruption (chiefly time availability, which is low among professionals and high among retired people). An implication of these findings is that existing indices of flood vulnerability that use multiple measures of social deprivation should be used with caution, as not all conventional aspects of social deprivation are necessarily associated with greater vulnerability to flood impacts. , This paper reports household questionnaire survey results on vulnerability and resilience to flooding from one of the largest and most representative samples ( n  = 593) of households up to 12 years after they were flooded, and is one of the first to provide detailed analysis of social differentiation in long‐term flood impacts. A novel finding is that social differentiation in flood impacts is relatively small soon after a flood, but widens over time, with socially disadvantaged groups displaying less recovery. The patterns of social differentiation in vulnerability and resilience to flooding differ markedly according to the type and timescale of the impact, with some normally socially advantaged groups (e.g., professionals and homeowners) being most vulnerable to short‐term impacts. Consistent with some existing studies, we found that older residents (age 70+) have greater resilience to flood impacts, although our sample may not capture the frailest individuals. As in previous research, low income is linked to lower resilience, particularly in the long term. We find that prior experience of flooding, despite enhancing preparedness, overall erodes rather than enhances resilience to flooding. Flood warnings are effective at reducing vulnerability to short‐term impacts. Underlying influences on resilience to natural disasters are complex and may only be revealed by multivariate analysis and not always be evident in simple observed patterns. The paper concludes that vulnerability and resilience to flooding are sensitive to financial resources, institutional support (chiefly from a landlord), and capacity to deal with disruption (chiefly time availability, which is low among professionals and high among retired people). An implication of these findings is that existing indices of flood vulnerability that use multiple measures of social deprivation should be used with caution, as not all conventional aspects of social deprivation are necessarily associated with greater vulnerability to flood impacts.

Abstract While flood risk management planning in the U nited S tates has focused on flood control structures designed to protect the economic value of property, it has consistently undervalued other social impacts associated with flooding. The US A rmy C orps of E ngineers ( USACE ) recently initiated research aimed at understanding how to incorporate social characteristics into the measures currently utilised in flood control project evaluation and consideration. This paper proposes a methodology for incorporating a known measure of social vulnerability, the S ocial V ulnerability I ndex ( SoVI ), into USACE civil works planning. Using the USACE S outh A tlantic D ivision as the study area, this paper evaluates eight different variations of the social vulnerability metric and their potential deployment in USACE projects. Each formulation is compared with the original‐computed SoVI as a means to test its spatial and statistical sensitivity, including an assessment of each variant's robustness, reducibility, scalability, and transferability. Results indicate that while it is possible to create simplified, yet robust, versions of SoVI for individual places, such ‘lite’ metrics tend to fall short in areas of scalability and transferability in relation to the original SoVI formulation.

Abstract The DRASTIC technique is commonly used to assess groundwater vulnerability. The main disadvantage of the DRASTIC method is the difficulty associated with identifying appropriate ratings and weight assignments for each parameter. To mitigate this issue, ratings and weights can be approximated using different methods appropriate to the conditions of the study area. In this study, different linear (i.e., Wilcoxon test and statistical approaches) and nonlinear (Genetic algorithm [GA]) modifications for calibration of the DRASTIC framework using nitrate (NO 3 ) concentrations were compared through the preparation of groundwater vulnerability maps of the Meshqin‐Shahr plain, Iran. Twenty‐two groundwater samples were collected from wells in the study area, and their respective NO 3 concentrations were used to modify the ratings and weights of the DRASTIC parameters. The areas found to have the highest vulnerability were in the eastern, central, and western regions of the plain. Results showed that the modified DRASTIC frameworks performed well, compared to the unmodified DRASTIC. When measured NO 3 concentrations were correlated with the vulnerability indices produced by each method, the unmodified DRASTIC method performed most poorly, and the Wilcoxon–GA–DRASTIC method proved optimal. Compared to the unmodified DRASTIC method with an R 2 of 0.22, the Wilcoxon–GA–DRASTIC obtained a maximum R 2 value of 0.78. Modification of DRASTIC parameter ratings was found to be more efficient than the modification of the weights in establishing an accurately calibrated DRASTIC framework. However, modification of parameter ratings and weights together increased the R 2 value to the highest degree. , Article impact statement : The results showed that both linear and nonlinear methods are useful in modifying the ratings and weights of the DRASTIC method for assessing the groundwater vulnerability.

Mapping the delineation of areas that are flooded due to water control infrastructure failure is a critical issue. Practical difficulties often present challenges to the accurate and effective analysis of dam-break hazard areas. Such studies are expensive, lengthy, and require large volumes of incoming data and refined technical skills. The creation of cost-efficient geospatial tools provides rapid and inexpensive estimates of instantaneous dam-break (due to structural failure) flooded areas that complement, but do not replace, the results of hydrodynamic simulations. The current study implements a Geographic Information System (GIS) based method that can provide useful information regarding the delineation of dam-break flood-prone areas in both data-scarce environments and transboundary regions, in the absence of detailed studies. Moreover, the proposed tool enables, without advanced technical skills, the analysis of a wide number of case studies that support the prioritization of interventions, or, in emergency situations, the simulation of numerous initial hypotheses (e.g., the modification of initial water level/volume in the case of limited dam functionality), without incurring high computational time. The proposed model is based on the commonly available data for masonry dams, i.e., dam geometry (e.g., reservoir capacity, dam height, and crest length), and a Digital Elevation Model. The model allows for rapid and cost-effective dam-break hazard mapping by evaluating three components: (i) the dam-failure discharge hydrograph, (ii) the propagation of the flood, and (iii) the delineation of flood-prone areas. The tool exhibited high accuracy and reliability in the identification of hypothetical dam-break flood-prone areas when compared to the results of traditional hydrodynamic approaches, as applied to a dam in Basilicata (Southern Italy). In particular, the over- and under-estimation rates of the proposed tool, for the San Giuliano dam, Basilicata, were evaluated by comparing its outputs with flood inundation maps that were obtained by two traditional methods whil using a one-dimensional and a two-dimensional propagation model, resulting in a specificity value of roughly 90%. These results confirm that most parts of the flood map were correctly classified as flooded by the proposed GIS model. A sensitivity value of over 75% confirms that several zones were also correctly identified as non-flooded. Moreover, the overall effectiveness and reliability of the proposed model were evaluated, for the Gleno Dam (located in the Central Italian Alps), by comparing the results of literature studies concerning the application of monodimensional numerical models and the extent of the flooded area reconstructed by the available historical information, obtaining an accuracy of around 94%. Finally, the computational efficiency of the proposed tool was tested on a demonstrative application of 250 Italian arch and gravity dams. The results, when carried out using a PC, Pentium Intel Core i5 Processor CPU 3.2 GHz, 8 GB RAM, required about 73 min, showing the potential of such a tool applied to dam-break flood mapping for a large number of dams.