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La province du Nouveau-Brunswick, situee dans l’est du Canada, est tres affectee par les inondations. Bien que moins documentee que l’alea, la dimension humaine du risque que represente la vulnerabilite est importante pour l’adaptation des populations. Cet article fait un survol des principaux concepts lies a la vulnerabilite et presente leur application a l’echelle d’un bassin versant de taille moyenne. Les resultats montrent la necessite de considerer simultanement la perception et la preparation au risque d’inondation. En effet, si certains residents dans les zones a risque ont une bonne connaissance des inondations et une perception realiste du risque, cela ne se traduit pas necessairement par une preparation adaptee et adequate face au risque. La reduction du risque passe indeniablement par une meilleure sensibilisation et education de la population.

Climate change has induced considerable changes in the dynamics of key hydro-climatic variables across Canada, including floods. In this study, runoff projections made by 21 General Climate Models (GCMs) under four Representative Concentration Pathways (RCPs) are used to generate 25 km resolution streamflow estimates across Canada for historical (1961–2005) and future (2061–2100) time-periods. These estimates are used to calculate future projected changes in flood magnitudes and timings across Canada. Results obtained indicate that flood frequencies in the northernmost regions of Canada, and south-western Ontario can be expected to increase in the future. As an example, the historical 100-year return period events in these regions are expected to become 10–60 year return period events. On the other hand, northern prairies and north-central Ontario can be expected to experience decreases in flooding frequencies in future. The historical 100-year return period flood events in these regions are expected to become 160–200 year return period events in future. Furthermore, prairies, parts of Quebec, Ontario, Nunavut, and Yukon territories can be expected to experience earlier snowmelt-driven floods in the future. The results from this study will help decision-makers to effectively manage and design municipal and civil infrastructure in Canada under a changing climate.

Abstract The database of the Quebec Ministry of Transport allowed us to analyze the occurrence of ice-block falls and snow avalanches for the past decades along national road 132. The results show that ice structure collapse may be categorized into three distinct phases by using daily temperatures (minimum, maximum, and average) and the cumulative degree day (temperatures above 0°C) since the March 1 st , corresponding to the beginning of the ice wall melting period: 1) a short and intense period of ice-block falls from the mid-April to the beginning of May; 2) a period of constant activity, mainly during the two first weeks of May; and 3) isolated residual activity, with a low frequency of ice-block falls until the month of June. The snow avalanche days were mainly characterized by significant snowfalls or rain-on-snow events with temperature>0°C. The multi-hazard probability was then evaluated based on the timing and relative frequency of ice-block fall and the modeling of sufficient snowpack for avalanching. This simple method to assess the synergistic effect of hillslope processes allows a better understanding of the spring avalanche regime related to the collapse of ice structures. These findings are expected to assist in the management of natural hazards and to improve our knowledge of spatiotemporal dynamics of mass-wasting events on highways.

Abstract A disproportionate share of the global economic and human losses caused by environmental shocks is borne by people in the developing nations. The mountain region of Hindu-Kush Himalaya (HKH) in South Asia is threatened by numerous flooding events annually. An efficient disaster risk reduction often needs to rest upon location-based synoptic view of vulnerability. Resolving this deficit improves the ability to take risk reduction measures in a cost-effective way, and in doing so, strengthens the resilience of societies to flooding disasters. The central aim of this research is to identify the vulnerable locations across HKH boundary from the perspective of reported history of economic and human impacts due to occurrence of flooding disasters. A detailed analysis indicates a very high spatial heterogeneity in flooding disaster occurrence in the past 6 decades. The most recent decade reported highest number of disasters and greater spatial coverage as compared to the earlier decades. The data indicates that, in general, economic impacts of flooding disasters were notably higher in Pakistan, Afghanistan and Nepal. On the other hand, vulnerability scenarios with respect to human impacts were diverse for different countries. In terms of morbidity and mortality, Bangladesh, Pakistan, Bhutan and India were detected to be most susceptible to human impacts. Although Bhutan had seen lesser number of flooding disasters, higher population living within disaster prone region make them vulnerable. In summary, complex interactions between natural and socio-economic conditions play a dominant role to define and characterize the type and magnitude of vulnerability of HKH countries to disaster occurrence and their economic and human impacts.

The global impacts of river floods are substantial and rising. Effective adaptation to the increasing risks requires an in-depth understanding of the physical and socioeconomic drivers of risk. Whereas the modeling of flood hazard and exposure has improved greatly, compelling evidence on spatiotemporal patterns in vulnerability of societies around the world is still lacking. Due to this knowledge gap, the effects of vulnerability on global flood risk are not fully understood, and future projections of fatalities and losses available today are based on simplistic assumptions or do not include vulnerability. We show for the first time (to our knowledge) that trends and fluctuations in vulnerability to river floods around the world can be estimated by dynamic high-resolution modeling of flood hazard and exposure. We find that rising per-capita income coincided with a global decline in vulnerability between 1980 and 2010, which is reflected in decreasing mortality and losses as a share of the people and gross domestic product exposed to inundation. The results also demonstrate that vulnerability levels in low- and high-income countries have been converging, due to a relatively strong trend of vulnerability reduction in developing countries. Finally, we present projections of flood losses and fatalities under 100 individual scenario and model combinations, and three possible global vulnerability scenarios. The projections emphasize that materialized flood risk largely results from human behavior and that future risk increases can be largely contained using effective disaster risk reduction strategies.

Flood risk assessments provide inputs for the evaluation of flood risk management (FRM) strategies. Traditionally, such risk assessments provide estimates of loss of life and economic damage. However, the effect of policy measures aimed at reducing risk also depends on the capacity of households to adapt and respond to floods, which in turn largely depends on their social vulnerability. This study shows how a joint assessment of hazard, exposure and social vulnerability provides valuable information for the evaluation of FRM strategies. The adopted methodology uses data on hazard and exposure combined with a social vulnerability index. The relevance of this state-of-the-art approach taken is exemplified in a case-study of Rotterdam, the Netherlands. The results show that not only a substantial share of the population can be defined as socially vulnerable, but also that the population is very heterogeneous, which is often ignored in traditional flood risk management studies. It is concluded that FRM measures, such as individual mitigation, evacuation or flood insurance coverage should not be applied homogenously across large areas, but instead should be tailored to local characteristics based on the socioeconomic characteristics of individual households and neighborhoods.

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.

County-level socioeconomic and demographic data were used to construct an index of social vulnerability to environmental hazards, called the Social Vulnerability Index (SoVI) for the United States based on 1990 data. Copyright (c) 2003 by the Southwestern Social Science Association.

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.