Current and future flood risk of new build homes across different socio-economic neighbourhoods in England and Wales

Despite improvements in the management of flood risk and the introduction of new regulations, losses from flooding remain high. An important driver is the continuation of new assets being built in flood prone locations. Over the last decade over 120,000 new homes in England and Wales have been built in flood prone areas. While the yearly rates of new homes in flood risk areas have increased only moderately on the national level, significant differences between and within regions as well as between different flood types exist. Using property level data on new homes built over the last decade and information on the socio-economic development of neighbourhoods, we analyse spatial clusters of disproportional increase in flood exposure from recently built homes and investigate how these patterns evolve under different future climate scenarios. We find that a disproportionately higher number of homes built in struggling or declining neighbourhoods between 2008 and 2018 is expected to end up in areas at a high risk of flooding over their lifetime as a result of climate change. Based on these findings, we discuss issues regarding future spending on flood defences, affordability of private level flood protection and insurance as well as the role of spatial planning for adaptation in the face of climate change

From managing risk to increasing resilience: a review on the development of urban flood resilience, its assessment and the implications for decision making

Driven by urban growth in hazard prone areas such as along coasts or rivers as well as by climate change induced sea-level rise and increase in extreme rainfall, flood risk in urban areas is increasing. Better understanding of risks, risk drivers and its consequences in urban areas have revealed shortcomings in the existing flood risk management approaches. This has led to a paradigm shift in dealing with floods from managing the risk to reduce damages, to making urban communities resilient to flooding. Often described as a complex and at times confusing concept, this systematic review identifies and summarises the different dimensions and approaches of urban flood resilience and how they are applied in practice. Our analysis shows that urban flood resilience as a concept has evolved over the last two decades. From an engineering concept with a strong focus on ensuring that the built environment can withstand a flood to a more recent definition as a transformative process with the aim to enable all parts of the urban system to live with floods and learn from previous shocks. This evolved understanding is also reflected in the increasing number of dimensions considered in urban flood resilience assessments and decision support tools. A thematic analysis of the challenges in conceptualising and applying urban flood resilience reported in the literature has revealed a number of issues including around fairness and equity of the applied approaches, a lack of data and widely accepted methods as well as uncertainty around changing risks as a result of climate change. Based on these findings we propose a new research agenda, focusing on meta studies to identify the key dimensions and criteria for urban flood resilience, supporting a transparent and evidence-led operationalisation

Multiple resilience dividends at the community level: a comparative study on disaster risk reduction interventions in different countries

The costs of disasters have been increasing in many parts of the world as a result of an increase in exposed and vulnerable assets as well as the effects of climate change. However, investments in disaster risk reduction (DRR)remain insufficient to manage these growing risks. To make investments in DRR more attractive and to shift investments from post-event response and recovery to pre-event resilience, there has been a push to account for the full range of benefits of those investments including economic, ecological and social ‘resilience dividends’. While the concept of ‘multiple resilience dividends’ is now frequently used to strengthen the DRR narrative, it has not yet been widely applied in practice when appraising DRR interventions. The paper analyses the knowledge gaps and challenges that arise from applying the ‘multiple resilience dividends’ in planning, implementation and evaluation of disaster risk reduction interventions on the community level. A newly developed framework is used to analyse empirical survey data on community level DRR interventions as well as five in-depth community case studies in Vietnam, Nepal, Indonesia, Afghanistan and the UK. The analysis reveals a disconnect between the available planning tools and the evidence on materialized multiple resilience dividends, which pose a key obstacle in successfully applying the concept on the community level. The paper concludes that a structured consideration of multiple dividends of resilience from the planning to the monitoring stage is important to secure local buy-in and to ensure that the full range of benefits can materialize

Hierarchical Bayesian approach for modeling spatiotemporal variability in flood damage processes

Flood damage processes are complex and vary between events and regions. State-of-the-art flood loss models are often developed on the basis of empirical damage data from specific case studies and do not perform well when spatially and temporally transferred. This is due to the fact that such localized models often cover only a small set of possible damage processes from one event and a region. On the other hand, a single generalized model covering multiple events and different regions ignores the variability in damage processes across regions and events due to variables that are not explicitly accounted for individual households. We implement a hierarchical Bayesian approach to parameterize widely used depth-damage functions resulting in a hierarchical (multilevel) Bayesian model (HBM) for flood loss estimation that accounts for spatiotemporal heterogeneity in damage processes. We test and prove the hypothesis that, in transfer scenarios, HBMs are superior compared to generalized and localized regression models. In order to improve loss predictions for regions and events for which no empirical damage data are available, we use variables pertaining to specific region- and event-characteristics representing commonly available expert knowledge as group-level predictors within the HBM

Probabilistic Models Significantly Reduce Uncertainty in Hurricane Harvey Pluvial Flood Loss Estimates

Pluvial flood risk is mostly excluded in urban flood risk assessment. However, the risk of pluvial flooding is a growing challenge with a projected increase of extreme rainstorms compounding with an ongoing global urbanization. Considered as a flood type with minimal impacts when rainfall rates exceed the capacity of urban drainage systems, the aftermath of rainfall-triggered flooding during Hurricane Harvey and other events show the urgent need to assess the risk of pluvial flooding. Due to the local extent and small-scale variations, the quantification of pluvial flood risk requires risk assessments on high spatial resolutions. While flood hazard and exposure information is becoming increasingly accurate, the estimation of losses is still a poorly understood component of pluvial flood risk quantification. We use a new probabilistic multivariable modeling approach to estimate pluvial flood losses of individual buildings, explicitly accounting for the associated uncertainties. Except for the water depth as the common most important predictor, we identified the drivers for having loss or not and for the degree of loss to be different. Applying this approach to estimate and validate building structure losses during Hurricane Harvey using a property level data set, we find that the reliability and dispersion of predictive loss distributions vary widely depending on the model and aggregation level of property level loss estimates. Our results show that the use of multivariable zero-inflated beta models reduce the 90% prediction intervalsfor Hurricane Harvey building structure loss estimates on average by 78% (totalling U.S.$3.8 billion) compared to commonly used models.Bundesministerium für Bildung und Forschung (BMBF) http://dx.doi.org/10.13039/501100002347NSF GRFPFulbright Doctoral ProgramPeer Reviewe

Toward an adequate level of detail in flood risk assessments

Flood risk assessments require different disciplines to understand and model the underlying components hazard, exposure, and vulnerability. Many methods and data sets have been refined considerably to cover more details of spatial, temporal, or process information. We compile case studies indicating that refined methods and data have a considerable effect on the overall assessment of flood risk. But are these improvements worth the effort? The adequate level of detail is typically unknown and prioritization of improvements in a specific component is hampered by the lack of an overarching view on flood risk. Consequently, creating the dilemma of potentially being too greedy or too wasteful with the resources available for a risk assessment. A “sweet spot” between those two would use methods and data sets that cover all relevant known processes without using resources inefficiently. We provide three key questions as a qualitative guidance toward this “sweet spot.” For quantitative decision support, more overarching case studies in various contexts are needed to reveal the sensitivity of the overall flood risk to individual components. This could also support the anticipation of unforeseen events like the flood event in Germany and Belgium in 2021 and increase the reliability of flood risk assessments

Multiple resilience dividends at the community level: A comparative study of disaster risk reduction interventions in different countries

Climate-related disasters are increasing in many parts of the world, yet investment in disaster risk reduction (DRR) remains insufficient to manage these risks. This is despite growing recognition that DRR interventions can reduce potential impacts from disasters as well as deliver broader economic, ecological, and social co-benefits. Focusing on the net benefits of DRR, beyond avoiding losses and damages, is considered as an important strategy to strengthen the case for DRR as part of a sustainable development by academics and international organizations alike. However, there is very limited evidence of on-the-ground accounting of these “multiple resilience dividends” by those who act to reduce disaster risk at the local level. Using an innovative analytical approach, we investigate the knowledge gaps and challenges associated with considering multiple resilience dividends in the planning, implementation, and evaluation of DRR interventions at the community level for the example of flood risk. We use a newly developed framework to analyze empirical survey data on community-level DRR interventions as well as five in-depth case studies from Vietnam, Nepal, Indonesia, Afghanistan, and the United Kingdom. The analysis reveals a disconnect between available planning tools and the evidence of materialized multiple resilience dividends, which is a key obstacle to successfully apply the concept at the community level. Structured consideration of multiple resilience dividends from the planning to the monitoring and evaluation stages is required to secure local buy-in and to ensure that these dividends materialize as intended

Probabilistic Models Significantly Reduce Uncertainty in Hurricane Harvey Pluvial Flood Loss Estimates

Pluvial flood risk is mostly excluded in urban flood risk assessment. However, the risk of pluvial flooding is a growing challenge with a projected increase of extreme rainstorms compounding with an ongoing global urbanization. Considered as a flood type with minimal impacts when rainfall rates exceed the capacity of urban drainage systems, the aftermath of rainfall‐triggered flooding during Hurricane Harvey and other events show the urgent need to assess the risk of pluvial flooding. Due to the local extent and small‐scale variations, the quantification of pluvial flood risk requires risk assessments on high spatial resolutions. While flood hazard and exposure information is becoming increasingly accurate, the estimation of losses is still a poorly understood component of pluvial flood risk quantification. We use a new probabilistic multivariable modeling approach to estimate pluvial flood losses of individual buildings, explicitly accounting for the associated uncertainties. Except for the water depth as the common most important predictor, we identified the drivers for having loss or not and for the degree of loss to be different. Applying this approach to estimate and validate building structure losses during Hurricane Harvey using a property level data set, we find that the reliability and dispersion of predictive loss distributions vary widely depending on the model and aggregation level of property level loss estimates. Our results show that the use of multivariable zero‐inflated beta models reduce the 90% prediction intervalsfor Hurricane Harvey building structure loss estimates on average by 78% (totalling U.S.$3.8 billion) compared to commonly used models.Key Points Recent severe pluvial flood events highlight the need to integrate pluvial flooding in urban flood risk assessment Probabilistic models provide reliable estimation of pluvial flood loss across spatial scales Beta distribution model reduces the 90$3.8 billion or 78%Bundesministerium für Bildung und Forschung (BMBF) http://dx.doi.org/10.13039/501100002347NSF GRFPFulbright Doctoral Progra

Probabilistic Models Significantly Reduce Uncertainty in Hurricane Harvey Pluvial Flood Loss Estimates

Pluvial flood risk is mostly excluded in urban flood risk assessment. However, the risk of pluvial flooding is a growing challenge with a projected increase of extreme rainstorms compounding with an ongoing global urbanization. Considered as a flood type with minimal impacts when rainfall rates exceed the capacity of urban drainage systems, the aftermath of rainfall-triggered flooding during Hurricane Harvey and other events show the urgent need to assess the risk of pluvial flooding. Due to the local extent and small-scale variations, the quantification of pluvial flood risk requires risk assessments on high spatial resolutions. While flood hazard and exposure information is becoming increasingly accurate, the estimation of losses is still a poorly understood component of pluvial flood risk quantification. We use a new probabilistic multivariable modeling approach to estimate pluvial flood losses of individual buildings, explicitly accounting for the associated uncertainties. Except for the water depth as the common most important predictor, we identified the drivers for having loss or not and for the degree of loss to be different. Applying this approach to estimate and validate building structure losses during Hurricane Harvey using a property level data set, we find that the reliability and dispersion of predictive loss distributions vary widely depending on the model and aggregation level of property level loss estimates. Our results show that the use of multivariable zero-inflated beta models reduce the 90% prediction intervalsfor Hurricane Harvey building structure loss estimates on average by 78% (totalling U.S.$3.8 billion) compared to commonly used models.Bundesministerium für Bildung und Forschung (BMBF) http://dx.doi.org/10.13039/501100002347NSF GRFPFulbright Doctoral ProgramPeer Reviewe

A comparative survey of the impacts of extreme rainfall in two international case studies

Flooding is assessed as the most important natural hazard in Europe, causing thousands of deaths, affecting millions of people and accounting for large economic losses in the past decade. Little is known about the damage processes associated with extreme rainfall in cities, due to a lack of accurate, comparable and consistent damage data. The objective of this study is to investigate the impacts of extreme rainfall on residential buildings and how affected households coped with these impacts in terms of precautionary and emergency actions. Analyses are based on a unique dataset of damage characteristics and a wide range of potential damage explaining variables at the household level, collected through computer-aided telephone interviews (CATI) and an online survey. Exploratory data analyses based on a total of 859 completed questionnaires in the cities of Münster (Germany) and Amsterdam (the Netherlands) revealed that the uptake of emergency measures is related to characteristics of the hazardous event. In case of high water levels, more efforts are made to reduce damage, while emergency response that aims to prevent damage is less likely to be effective. The difference in magnitude of the events in Münster and Amsterdam, in terms of rainfall intensity and water depth, is probably also the most important cause for the differences between the cities in terms of the suffered financial losses. Factors that significantly contributed to damage in at least one of the case studies are water contamination, the presence of a basement in the building and people's awareness of the upcoming event. Moreover, this study confirms conclusions by previous studies that people's experience with damaging events positively correlates with precautionary behaviour. For improving future damage data acquisition, we recommend the inclusion of cell phones in a CATI survey to avoid biased sampling towards certain age groups.Sanitary EngineeringWater Resource