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Article

Assessment and Adaptation to Climate Change-Related Flood Risks  

Brenden Jongman, Hessel C. Winsemius, Stuart A. Fraser, Sanne Muis, and Philip J. Ward

The flooding of rivers and coastlines is the most frequent and damaging of all natural hazards. Between 1980 and 2016, total direct damages exceeded $1.6 trillion, and at least 225,000 people lost their lives. Recent events causing major economic losses include the 2011 river flooding in Thailand ($40 billion) and the 2013 coastal floods in the United States caused by Hurricane Sandy (over $50 billion). Flooding also triggers great humanitarian challenges. The 2015 Malawi floods were the worst in the country’s history and were followed by food shortage across large parts of the country. Flood losses are increasing rapidly in some world regions, driven by economic development in floodplains and increases in the frequency of extreme precipitation events and global sea level due to climate change. The largest increase in flood losses is seen in low-income countries, where population growth is rapid and many cities are expanding quickly. At the same time, evidence shows that adaptation to flood risk is already happening, and a large proportion of losses can be contained successfully by effective risk management strategies. Such risk management strategies may include floodplain zoning, construction and maintenance of flood defenses, reforestation of land draining into rivers, and use of early warning systems. To reduce risk effectively, it is important to know the location and impact of potential floods under current and future social and environmental conditions. In a risk assessment, models can be used to map the flow of water over land after an intense rainfall event or storm surge (the hazard). Modeled for many different potential events, this provides estimates of potential inundation depth in flood-prone areas. Such maps can be constructed for various scenarios of climate change based on specific changes in rainfall, temperature, and sea level. To assess the impact of the modeled hazard (e.g., cost of damage or lives lost), the potential exposure (including buildings, population, and infrastructure) must be mapped using land-use and population density data and construction information. Population growth and urban expansion can be simulated by increasing the density or extent of the urban area in the model. The effects of floods on people and different types of buildings and infrastructure are determined using a vulnerability function. This indicates the damage expected to occur to a structure or group of people as a function of flood intensity (e.g., inundation depth and flow velocity). Potential adaptation measures such as land-use change or new flood defenses can be included in the model in order to understand how effective they may be in reducing flood risk. This way, risk assessments can demonstrate the possible approaches available to policymakers to build a less risky future.

Article

Physical Vulnerability in Earthquake Risk Assessment  

Abdelghani Meslem and Dominik H. Lang

In the fields of earthquake engineering and seismic risk reduction the term “physical vulnerability” defines the component that translates the relationship between seismic shaking intensity, dynamic structural uake damage and loss assessment discipline in the early 1980s, which aimed at predicting the consequences of earthquake shaking for an individual building or a portfolio of buildings. In general, physical vulnerability has become one of the main key components used as model input data by agencies when developinresponse (physical damage), and cost of repair for a particular class of buildings or infrastructure facilities. The concept of physical vulnerability started with the development of the earthqg prevention and mitigation actions, code provisions, and guidelines. The same may apply to insurance and reinsurance industry in developing catastrophe models (also known as CAT models). Since the late 1990s, a blossoming of methodologies and procedures can be observed, which range from empirical to basic and more advanced analytical, implemented for modelling and measuring physical vulnerability. These methods use approaches that differ in terms of level of complexity, calculation efforts (in evaluating the seismic demand-to-structural response and damage analysis) and modelling assumptions adopted in the development process. At this stage, one of the challenges that is often encountered is that some of these assumptions may highly affect the reliability and accuracy of the resulted physical vulnerability models in a negative way, hence introducing important uncertainties in estimating and predicting the inherent risk (i.e., estimated damage and losses). Other challenges that are commonly encountered when developing physical vulnerability models are the paucity of exposure information and the lack of knowledge due to either technical or nontechnical problems, such as inventory data that would allow for accurate building stock modeling, or economic data that would allow for a better conversion from damage to monetary losses. Hence, these physical vulnerability models will carry different types of intrinsic uncertainties of both aleatory and epistemic character. To come up with appropriate predictions on expected damage and losses of an individual asset (e.g., a building) or a class of assets (e.g., a building typology class, a group of buildings), reliable physical vulnerability models have to be generated considering all these peculiarities and the associated intrinsic uncertainties at each stage of the development process.

Article

Hazards, Social Resilience, and Safer Futures  

Lena Dominelli

The concepts of hazards and risks began in engineering when scientists were measuring the points at which materials would become sufficiently stressed by the pressures upon them that they would break. These concepts migrated into the environmental sciences to assess risk in the natural terrain, including the risks that human activities posed to the survival of animals (including fish in streams) and plants in the biosphere. From there, they moved to the social sciences, primarily in formal disaster discourses. With the realization that modern societies constantly faced risks cushioned in uncertainties within everyday life, the media popularized the concept of risk and its accoutrements, including mitigation, adaptation, and preventative measures, among the general populace. A crucial manifestation of this is the media’s accounts of the risks affecting different groups of people or places contracting Covid-19, which burst upon a somnambulant world in December 2019 in Wuhan, China. The World Health Organization (WHO) declared Covid-19 a pandemic on March 11, 2020. Politicians of diverse hues sought to reassure nervous inhabitants that they had followed robust, scientific advice on risks to facilitate “flattening the curve” by spreading the rate of infection in different communities over a longer period to reduce demand for public health services. Definitions of hazard, risk, vulnerability, and resilience evolved as they moved from the physical sciences into everyday life to reassure edgy populations that their social systems, especially the medical ones, could cope with the demands of disasters. While most countries have managed the risk Covid-19 posed to health services, this has been at a price that people found difficult to accept. Instead, as they reflected upon their experiences of being confronted with the deaths of many loved ones, especially among elders in care homes; adversities foisted upon the disease’s outcomes by existing social inequalities; and loss of associative freedoms, many questioned whether official mitigation strategies were commensurate with apparent risks. The public demanded an end to such inequities and questioned the bases on which politicians made their decisions. They also began to search for certainties in the social responses to risk in the hopes of building better futures as other institutions, schools, and businesses went into lockdown, and social relationships and people’s usual interactions with others ceased. For some, it seemed as if society were crumbling around them, and they wanted a better version of their world to replace the one devastated by Covid-19 (or other disasters). Key to this better version was a safer, fairer, more equitable and reliable future. Responses to the risks within Covid-19 scenarios are similar to responses to other disasters, including earthquakes, volcanic eruptions, wildfires, tsunamis, storms, extreme weather events, and climate change. The claims of “building back better” are examined through a resilience lens to determine whether such demands are realizable, and if not, what hinders their realization. Understanding such issues will facilitate identification of an agenda for future research into mitigation, adaptation, and preventative measures necessary to protect people and the planet Earth from the harm of subsequent disasters.