An asset-based approach to social risk management : a conceptual framework

There is increasing concern about the vulnerability of poor and near-poor rural households, who have limited capabilities to manage risk and often resort to strategies that can lead to a vicious cycle of poverty. Household-related risk is ususally considered individual or private, but measures to manage risk are actually social or public in nature. Furthermore, various externality issues are associated with household-related risk, such as its links to economic development, poverty reduction, social cohesion, and environmental quality. Hence the need for a holistic approach to risk management, or"social risk management,"which encompasses a broad spectrum of private and public actions. An asset-based approach to social risk management is presented, which provides an integrated approach to considering household, community, and extra-community assets and risk-management strategies. The conceptual framework for social risk management focuses on rural Sub-Saharan Africa. The report concludes with several suggestions on moving from concepts to actions.Health Economics&Finance,Insurance&Risk Mitigation,Banking Law,Environmental Economics&Policies,Banks&Banking Reform

Soil-related geohazard assessment for climate-resilient UK infrastructure

UK (United Kingdom) infrastructure networks are fundamental for maintaining societal and economic wellbeing. With infrastructure assets predominantly founded in the soil layer (< 1.5m below ground level) they are subject to a range of soil-related geohazards. A literature review identified that geohazards including, clay-related subsidence, sand erosion and soil corrosivity have exerted significant impacts on UK infrastructure to date; often resulting in both long-term degradation and ultimately structural failure of particular assets. Climate change projections suggest that these geohazards, which are themselves driven by antecedent weather conditions, are likely to increase in magnitude and frequency for certain areas of the UK through the 21st century. Despite this, the incorporation of climate data into geohazard models has seldom been undertaken and never on a national scale for the UK. Furthermore, geohazard risk assessment in UK infrastructure planning policy is fragmented and knowledge is often lacking due to the complexity of modelling chronic hazards in comparison to acute phenomenon such as flooding. With HM Government's recent announcement of £50 million planned infrastructure investment and capital projects, the place of climate resilient infrastructure is increasingly pertinent. The aim of this thesis is therefore to establish whether soil-related geohazard assessments have a role in ensuring climate-resilient UK infrastructure. Soil moisture projections were calculated using probabilistic weather variables derived from a high-resolution version of the UKCP09 (UK Climate Projections2009) weather generator. These were then incorporated into a geohazard model to predict Great Britain's (GB) subsidence hazard for the future scenarios of 2030 (2020-2049) and 2050 (2040-2069) as well as the existing climatic baseline (1961-1990). Results suggest that GB is likely to be subject to increased clay-related subsidence in future, particularly in the south east of England. This thesis has added to scientific understanding through the creation of a novel, national-scale assessment of clay subsidence risk, with future assessments undertaken to 2050. This has been used to help create a soil- informed maintenance strategy for improving the climate resilience of UK local roads, based on an extended case study utilising road condition data for the county of Lincolnshire, UK. Finally, a methodological framework has been created, providing a range of infrastructure climate adaptation stakeholders with a method for incorporating geohazard assessments, informed by climate change projections, into asset management planning and design of new infrastructure. This research also highlights how infrastructure networks are becoming increasingly interconnected, particularly geographically, and therefore even minor environmental shocks arising from soil-related geohazards can cause significant cascading failures of multiple infrastructure networks. A local infrastructure hotspot analysis methodology and case-study is provided

ADDRESSING CASCADING CONSEQUENCES FOR CRITICAL INFRASTRUCTURE AND VITAL SOCIETAL FUNCTIONS IN FLOODING EVENTS

Although there have been significant advances in the research field of critical infrastructures and vital societal functions during the last decade, there still exist many challenges in implementing and carrying out studies in practice. One of these challenges is a feasible method for mapping, analysing and visualising the cascading consequences that arise for critical infrastructures and societal functions affected by large spatial hazards. The presented study is the result from commissioned work for the Swedish Civil Contingencies Agency (MSB), aiming at contributing to improved risk, vulnerability and continuity management for regions in Sweden at risk of being affected by severe spatial hazards. The study takes it basis from, and connects to, ongoing work in Sweden relating to the risk of severe flooding events in accordance to the EU Floods Directive and work related to critical infrastructure protection in accordance to the EU Directive on European Critical Infrastructures. The results from the study where mainly derived through a literature review and workshops, utilising a flood prone region in Sweden as a case. The literature review focused on methods and approaches, both scientific and in grey literature, for estimation, visualisation and weighing of consequence arising for critical infrastructures and vital societal functions for large spatial hazards. Here a specific focus was on literature addressing the issue of interdependencies and the use of GIS. The workshops involved participants from critical infrastructure operators, municipalities, regional county boards, MSB, Statistics Sweden, among others, aiming at the practical needs and challenges for a method and for testing the developed method. From the literature review it was clear that most studies focus on analysing the direct consequences of large spatial hazards. Only few studies address the indirect consequences that arise due to interdependencies, revealing that indirect consequences can be as high or higher than the direct consequences. This necessitates the need for addressing indirect consequences systematically. The review also highlighted that the required underlying data is not easily attainable and comes with several challenges with respect to collection, analysis and visualization of the results for decision making. The developed method is concluded to both fulfil a need, as expressed by the participants in the workshops, and was considered as a feasible approach to start addressing the issue of cascading consequences during large spatial events. However, we also conclude that, based on the literature review and the practical challenges present in this area, ample research opportunities exist

Economic Evaluation of Coastal Land Loss in Louisiana

Louisiana has lost approximately 1,880 square miles of land over the past eighty years. Projections suggest that in a future without action, the next fifty years could result in the loss of 1,750 additional square miles of land area. As land loss continues, a large portion of the natural and man-made capital stocks of coastal Louisiana will be at greater risk of damage, either from land loss or from the associated increase in storm damage. We estimate the replacement cost of capital stock directly at risk from land loss ranges from approximately $2.1 billion to$3.5 billion with economic activity at risk ranging from $2.4 billion to$3.1 billion in output. Increases in storm damage to capital stock range from $8.7 billion to as much as$133 billion with associated disruptions to economic activity ranging from an additional $1.9 billion to$23 billion in total lost output

Prediction and mitigation of scour and scour damage to Vermont bridges

Over 300 Vermont bridges were damaged in the 2011 Tropical Storm Irene and many experienced significant scour. Successfully mitigating bridge scour in future flooding events depends on our ability to reliably estimate scour potential, design safe and economical foundation elements accounting for scour potential, design effective scour prevention and countermeasures, and design reliable and economically feasible monitoring systems, which served as the motivation for this study. This project sought to leverage data on existing Vermont bridges and case studies of bridge scour damage, and integrate available information from stream geomorphology to aid in prediction of bridge scour vulnerability. Tropical Storm Irene’s impact on Vermont bridges was used as a case study, providing damage information on a wide range of bridges throughout the State. Multiple data sources were combined in an effort to include data, which represents the complex, interconnected processes of stream stability and bridge scour, then identify and incorporate feature that would be useful in a probabilistic model to predict bridge susceptibility to scour damage. The research also sought to identify features that could be included in inspections and into a scour rating system that are capable of assessing network-level scour vulnerability of bridges more holistically. This research also sought to review existing scour countermeasures and scour monitoring technologies available in the literature and examine efficacy of new, indirect scour countermeasures and passive scour monitoring techniques. The specific objectives of this research were to: (1) review the literature and identify methods/technologies that are adaptable to Vermont; (2) analyze Tropical Storm Irene bridge damage information and observations by collecting and geo-referencing all available bridge records and stream geomorphic assessment data into a comprehensive database for identifying features that best represent bridge scour damage; (3) conduct watershed analysis on all bridges, including creation of stream power data to assess if watershed stream power improves the prediction of bridge scour damage; and (4) investigate new scour countermeasures and monitoring technologies, and provide recommendations on implementations

Stochastic Dynamics of Cascading Failures in Electric-Cyber Infrastructures

Emerging smart grids consist of tightly-coupled systems, namely a power grid and a communication system. While today\u27s power grids are highly reliable and modern control and communication systems have been deployed to further enhance their reliability, historical data suggest that they are yet vulnerable to large failures. A small set of initial disturbances in power grids in conjunction with lack of effective, corrective actions in a timely manner can trigger a sequence of dependent component failures, called cascading failures. The main thrust of this dissertation is to build a probabilistic framework for modeling cascading failures in power grids while capturing their interactions with the coupled communication systems so that the risk of cascading failures in the composite complex electric-cyber infrastructures can be examined, analyzed and predicted. A scalable and analytically tractable continuous-time Markov chain model for stochastic dynamics of cascading failures in power grids is constructed while retaining key physical attributes and operating characteristics of the power grid. The key idea of the proposed framework is to simplify the state space of the complex power system while capturing the effects of the omitted variables through the transition probabilities and their parametric dependence on physical attributes and operating characteristics of the system. In particular, the effects of the interdependencies between the power grid and the communication system have been captured by a parametric formulation of the transition probabilities using Monte-Carlo simulations of cascading failures. The cascading failures are simulated with a coupled power-system simulation framework, which is also developed in this dissertation. Specifically, the probabilistic model enables the prediction of the evolution of the blackout probability in time. Furthermore, the asymptotic analysis of the blackout probability as time tends to infinity enables the calculation of the probability mass function of the blackout size, which has been shown to have a heavy tail, e.g., power-law distribution, specifically when the grid is operating under stress scenarios. A key benefit of the model is that it enables the characterization of the severity of cascading failures in terms of a set of operating characteristics of the power grid. As a generalization to the Markov chain model, a regeneration-based model for cascading failures is also developed. The regeneration-based framework is capable of modeling cascading failures in a more general setting where the probability distribution of events in the system follows an arbitrarily specified distribution with non-Markovian characteristics. Further, a novel interdependent Markov chain model is developed, which provides a general probabilistic framework for capturing the effects of interactions among interdependent infrastructures on cascading failures. A key insight obtained from this model is that interdependencies between two systems can make two individually reliable systems behave unreliably. In particular, we show that due to the interdependencies two chains with non-heavy tail asymptotic failure distribution can result in a heavy tail distribution when coupled. Lastly, another aspect of future smart grids is studied by characterizing the fundamental bounds on the information rate in the sensor network that monitors the power grid. Specifically, a distributed source coding framework is presented that enables an improved estimate of the lower bound for the minimum required communication capacity to accurately describe the state of components in the information-centric power grid. The models developed in this dissertation provide critical understanding of cascading failures in electric-cyber infrastructures and facilitate reliable and quick detection of the risk of blackouts and precursors to cascading failures. These capabilities can guide the design of efficient communication systems and cascade aware control policies for future smart grids

Improving Detection And Prediction Of Bridge Scour Damage And Vulnerability Under Extreme Flood Events Using Geomorphic And Watershed Data

Bridge scour is the leading cause of bridge damage nationwide. Successfully mitigating bridge scour problems depends on our ability to reliably estimate scour potential, design safe and economical foundation elements that account for scour potential, identify vulnerabilities related to extreme events, and recognize changes to the environmental setting that increase risk at existing bridges. This study leverages available information, gathered from several statewide resources, and adds watershed metrics to create a comprehensive, georeferenced dataset to identify parameters that correlate to bridges damaged in an extreme flood event. Understanding the underlying relationships between existing bridge condition, fluvial stresses, and geomorphological changes is key to identifying vulnerabilities in both existing and future bridge infrastructure. In creating this comprehensive database of bridge inspection records and associated damage characterization, features were identified that correlate to and discriminate between levels of bridge damage. Stream geomorphic assessment features were spatially joined to every bridge, marking the first time that geomorphic assessments have been broadly used for estimating bridge vulnerability. Stream power assessments and watershed delineations for every bridge and stream reach were generated to supplement the comprehensive database. Individual features were tested for their significance to discriminate bridge damage, and then used to create empirical fragility curves and probabilistic predictions maps to aid in future bridge vulnerability detection. Damage to over 300 Vermont bridges from a single extreme flood event, the August 28, 2011 Tropical Storm Irene, was used as the basis for this study. Damage to historic bridges was also summarized and tabulated. In some areas of Vermont, the storm rainfall recurrence interval exceeded 500 years, causing widespread flooding and damaging over 300 bridges. With a dataset of over 330 features for more than 2,000 observations to bridges that were damaged as well as not damaged in the storm, an advanced evolutionary algorithm performed multivariate feature selection to overcome the shortfalls of traditional logistic regression analysis. The analysis identified distinct combinations of variables that correlate to the observed bridge damage under extreme food events