14 research outputs found
Rising waves of indecision:How financial incentives can support flood risk management
Full text linkFloods have a devastating impact on society, costing thousands of lives and billions of dollars annually. Scientific projections indicate that flood risk is expected to increase in the future, driven by socio-economic growth and climate change. However, managing flood risk is a complex and costly process that requires decision-making with uncertain future conditions under the fear of making irreversible, inefficient choices. To support decision-makers, flood risk assessments provide estimates of the monetary impacts of floods or the economic efficiency of adaptation investments, although they often lack spatial or temporal dynamics. In addition, homeowners also make decisions at an individual level, such as implementing building-level adaptation measures or purchasing flood insurance. Homeowners’ decisions often deviate from rationality, as it is difficult for individuals to estimate the probability and associated damage of a potential flood. This PhD dissertation explores the extent to which we can incorporate the decision-making dynamics of governments, households, and flood insurance into a flood risk assessment at different spatial scales, and how this may improve flood risk management, applied to cases in the US
Validating Flood Damage Estimations: A framework using plausibility and empirical data
No full textFlood risk models are frequently used to analyse the climate- and socio-economic-driven impact of flooding hazards. However, model validation is rarely done adequately due to the rare occurrence of floods and even less frequent reporting of corresponding damages. In this research, validation is defined as the process of ensuring that a model performs within a range of accuracy and precision, satisfactory for its intended use. To guide experts in their validation efforts, a four-phased framework is developed to validate flood-event damage estimations, created with hazard x exposure x vulnerability models. The framework was applied to two damage estimates created by the Global Flood Risk Tool (GFRT). 1) For damage caused by the Limburg 2021 river flood (The Netherlands - Europe) and 2), for damage caused by a 2019 hurricane-induced coastal flood in Beira (Mozambique - Africa). For the Limburg case, total direct damage was determined at 349,4 million euro. An initial model overestimation of 34% was caused primarily by a large exaggeration of exposed agricultural surface area, and significant modelling errors of linear infrastructure. Furthermore, an uncertainty range was quantified between 271,8 (-23%) and 388,2 million euro (+11%) due to uncertainty in residential assets (across all three model parameters) and an uncertain exposure parameter of agricultural assets. To create additional damage estimates for verification, a Structured Expert Judgement (SEJ) experiment was executed with ten flood-damage experts. Due to the high experiment cost and low expert-informativeness, the method is currently not advised as a validation approach. In situations with limited data, experts may still be a relevant information source.For Beira, damage was determined at 8,1 million US dollar. The model underestimated damage by 82% due to errors in infrastructure, industrial, and commercial assets. Besides, overestimations were found for informal residential- and agricultural assets. The estimate ranges between 5,2 (-36%) and 13,2 million US dollar (+62%). This range excludes uncertainties at port and industrial assets, as insufficient information was available. Contrary to the Limburg case study, insights from the plausibility assessment were too uncertain for quantification, thus the validated estimate is based on damage- and construction cost data. Novel techniques were used to disaggregate the compound damage data, such as comparing wind and flood vulnerability curves and applying employee-based estimations.The significantly altered damage estimate for both case studies demonstrates the usefulness of the framework. However two main limitations remain: first, lacking information on direct damage to critical infrastructure hinders validation. Second, additional detail in data is required to allow parameter calibration that increases accuracy across multiple flooding scenarios. Therefore, the main recommendation for future research is to increase the detail in damage data reporting so that parameter calibration is supported. This may be done by increasing the spatial resolution of reported damages or adding additional variables such as inundation depth in reports.double degree in Civil Engineering Hydraulic Engineering and Construction Management and EngineeringCivil Engineering | Hydraulic Engineerin
Shifting from asset damage to well-being loss within flood risk management
Full text linkFloods can be a cause of poverty. Poverty itself magnifies the impact of floods as poor people are more vulnerable and less resilient. Traditional flood risk assessments (FRAs) focus mostly on asset damages. Yet, poor people own little assets and are often highly exposed to floods. Consequently, traditional FRAs often conclude that it is inefficient to protect the poor and are thus biased against flood risk reduction measures protecting them. The aim of this study is to evaluate FRRIs in a CBA based on the social welfare flood risk reduction benefits. A framework to assess the social welfare flood risk through a Monte Carlo approach is presented herein. A case study illustrates that it is not yet reliable to economically evaluate FRRIs based on the monetized social welfare benefits in CBAs. In addition, a targeted social protection scenario emphasizes the potentially high social welfare benefits that can be realized through risk transfer. Therefore, poverty reduction through risk transfer mechanisms should be considered as a holistic approach to foster socioeconomic growth as such risk transfer mechanisms build socioeconomic resilience in the face of natural disasters. It could have a considerable impact on the lives of people living in vulnerable areas
An agent-based model for evaluating reforms of the National Flood Insurance Program: A benchmarked model applied to Jamaica Bay, NYC
Full text linkCoastal flood risk is expected to increase as a result of climate change effects, such as sea level rise, and socioeconomic growth. To support policymakers in making adaptation decisions, accurate flood risk assessments that account for the influence of complex adaptation processes on the developments of risks are essential. In this study, we integrate the dynamic adaptive behavior of homeowners within a flood risk modeling framework. Focusing on building-level adaptation and flood insurance, the agent-based model (DYNAMO) is benchmarked with empirical data for New York City, USA. The model simulates the National Flood Insurance Program (NFIP) and frequently proposed reforms to evaluate their effectiveness. The model is applied to a case study of Jamaica Bay, NY. Our results indicate that risk-based premiums can improve insurance penetration rates and the affordability of insurance compared to the baseline NFIP market structure. While a premium discount for disaster risk reduction incentivizes more homeowners to invest in dry-floodproofing measures, it does not significantly improve affordability. A low interest rate loan for financing risk-mitigation investments improves the uptake and affordability of dry-floodproofing measures. The benchmark and sensitivity analyses demonstrate how the behavioral component of our model matches empirical data and provides insights into the underlying theories and choices that autonomous agents make.</p
Climate-proofing the National Flood Insurance Program
Full text linkReforms are required to maintain a healthy and robust flood insurance market under future climate conditions for the United States. Therefore, policymakers should implement premiums that reflect flood risk and incentivize household-level risk reduction, complemented with regional flood adaptation investments
A micro-scale cost-benefit analysis of building-level flood risk adaptation measures in Los Angeles
Full text linkCost-benefit analysis (CBA) of flood risk adaptation strategies offers policymakers insight into economically optimal strategies for adapting to sea level rise. However, building-level adaptation measures such as floodproofing or building elevation are often evaluated at aggregated spatial scales, which may result in sub-optimal investment decisions. In this paper, we develop a flood risk model and combine it with a micro-scale CBA at the building level to obtain an optimal mix of adaptation measures per area. We apply this approach to Venice Beach in Los Angeles and Naples in Long Beach. We subsequently compare our results with the conventional, spatially aggregated area-based CBA approach. Our findings show that a mix of 35%–45% dry-floodproofing measures and 55%–65% building elevation measures is optimal. Elevation works best in areas with high inundation depths, while dry-floodproofing is preferable in areas with shallow inundation depths. The optimal mix of measures derived from our micro-scale approach results in an economic efficiency up to 85% higher than that yielded by the commonly applied spatially aggregated approach. We therefore recommend that economic evaluations of building-level adaptation measures are conducted at the smallest possible scale, or that CBAs are performed on disaggregated areas based on inundation depth
How the USA can benefit from risk-based premiums combined with flood protection
Full text linkFlood risk management in the USA is largely embedded in the National Flood Insurance Program (NFIP). Climate change and increasing exposure in flood plains pose a challenge to flood risk managers and make it vital to reduce risk in the future. The proposed reforms are steering the NFIP to risk-based premiums, but it is uncertain if the reforms will result in unaffordability and incentivize risk-reduction investments or how the NFIP is affected by large-scale adaptation efforts. Using an agent-based model approach for current and future scenarios, we demonstrate that risk-based premiums will yield a positive societal benefit (US26 billion). We suggest that transitioning the NFIP to risk-based premiums can only be secured by additional investments in large-scale flood protection infrastructure.</p
An assessment of best practices of extreme weather insurance and directions for a more resilient society
No full textExtreme weather resilience has been defined as being based on three pillars: resistance (the ability to lower impacts), recovery (the ability to bounce back), and adaptive capacity (the ability to learn and improve). These resilience pillars are important both before and after the occurrence of extreme weather events. Extreme weather insurance can influence these pillars of resilience depending on how particular insurance mechanisms are structured. We explore how the lessons learnt from the current best insurance practices can improve resilience to extreme weather events. We employ an extensive inventory of private property and agricultural crop insurance mechanisms to conduct a multi-criteria analysis of insurance market outcomes. We draw conclusions regarding the patterns in the best practice from six European countries to increase resilience. We suggest that requirements to buy a bundle extreme weather event insurance with general insurance packages are strengthened and supported with structures to financing losses through public-private partnerships. Moreover, support for low income households through income vouchers could be provided. Similarly, for the agricultural sector we propose moving towards comprehensive crop yield insurance linked to general agricultural subsidies. In both cases a nationally representative body can coordinate the various stakeholders into acting in concert
