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Underwater : loss, flood insurance, and the moral economy of climate change in the United States
\"Communities around the United States face the threat of being underwater. This is not only a matter of rising waters reaching the doorstep. It is also the threat of being financially underwater, owning assets worth less than the money borrowed to obtain them. Many areas around the country may become economically uninhabitable before they become physically unlivable. In Underwater, Rebecca Elliott explores how families, communities, and governments confront problems of loss as the climate changes. She offers the first in-depth account of the politics and social effects of the U.S. National Flood Insurance Program (NFIP), which provides flood insurance protection for virtually all homes and small businesses that require it. In doing so, the NFIP turns the risk of flooding into an immediate economic reality, shaping who lives on the waterfront, on what terms, and at what cost. Drawing on archival, interview, ethnographic, and other documentary data, Elliott follows controversies over the NFIP from its establishment in the 1960s to the present, from local backlash over flood maps to Congressional debates over insurance reform. Though flood insurance is often portrayed as a rational solution for managing risk, it has ignited recurring fights over what is fair and valuable, what needs protecting and what should be let go, who deserves assistance and on what terms, and whose expectations of future losses are used to govern the present. An incisive and comprehensive consideration of the fundamental dilemmas of moral economy underlying insurance, Underwater sheds new light on how Americans cope with loss as the water rises\"-- Provided by publisher.
Book
Flood damage and risk assessment for urban area in Malaysia
In recent years, flood risk map has been widely accepted as a tool for flood mitigation. The risk of flooding is normally illustrated in terms of its hazard (flood inundation maps), while vulnerability emphasizes the consequences of flooding. In developing countries, published studies on flood vulnerability assessment are limited, especially on flood damage. This paper attempts to establish a flood damage and risk assessment framework for Segamat town in Johor, Malaysia. A combination of flood hazard (flood characteristics), exposure (value of exposed elements), and vulnerability (flood damage function curve) were used for estimating the flood damage. The flood depth and areal extent were obtained from flood modeling and mapping using HEC-HMS/RAS and Arc GIS, respectively. Expected annual damage (EAD) for residential areas (50,112 units) and commercial areas (9,318 premises) were RM12.59 million and RM2.96 million, respectively. The flood hazard map shows that Bandar Seberang area (46,184 properties) was the most affected by the 2011 flood. The flood damage map illustrates similar patterns, with Bandar Seberang suffering the highest damage. The damage distribution maps are useful for reducing future flood damage by identifying properties with high flood risk.
Journal Article
Testing empirical and synthetic flood damage models: the case of Italy
Flood risk management generally relies on economic assessments performed by using flood loss models of different complexity, ranging from simple univariable models to more complex multivariable models. The latter account for a large number of hazard, exposure and vulnerability factors, being potentially more robust when extensive input information is available. We collected a comprehensive data set related to three recent major flood events in northern Italy (Adda 2002, Bacchiglione 2010 and Secchia 2014), including flood hazard features (depth, velocity and duration), building characteristics (size, type, quality, economic value) and reported losses. The objective of this study is to compare the performances of expert-based and empirical (both uni- and multivariable) damage models for estimating the potential economic costs of flood events to residential buildings. The performances of four literature flood damage models of different natures and complexities are compared with those of univariable, bivariable and multivariable models trained and tested by using empirical records from Italy. The uni- and bivariable models are developed by using linear, logarithmic and square root regression, whereas multivariable models are based on two machine-learning techniques: random forest and artificial neural networks. Results provide important insights about the choice of the damage modelling approach for operational disaster risk management. Our findings suggest that multivariable models have better potential for producing reliable damage estimates when extensive ancillary data for flood event characterisation are available, while univariable models can be adequate if data are scarce. The analysis also highlights that expert-based synthetic models are likely better suited for transferability to other areas compared to empirically based flood damage models.
Journal Article
A Review of Flood Loss Models as Basis for Harmonization and Benchmarking
Risk-based approaches have been increasingly accepted and operationalized in flood risk management during recent decades. For instance, commercial flood risk models are used by the insurance industry to assess potential losses, establish the pricing of policies and determine reinsurance needs. Despite considerable progress in the development of loss estimation tools since the 1980s, loss estimates still reflect high uncertainties and disparities that often lead to questioning their quality. This requires an assessment of the validity and robustness of loss models as it affects prioritization and investment decision in flood risk management as well as regulatory requirements and business decisions in the insurance industry. Hence, more effort is needed to quantify uncertainties and undertake validations. Due to a lack of detailed and reliable flood loss data, first order validations are difficult to accomplish, so that model comparisons in terms of benchmarking are essential. It is checked if the models are informed by existing data and knowledge and if the assumptions made in the models are aligned with the existing knowledge. When this alignment is confirmed through validation or benchmarking exercises, the user gains confidence in the models. Before these benchmarking exercises are feasible, however, a cohesive survey of existing knowledge needs to be undertaken. With that aim, this work presents a review of flood loss-or flood vulnerability-relationships collected from the public domain and some professional sources. Our survey analyses 61 sources consisting of publications or software packages, of which 47 are reviewed in detail. This exercise results in probably the most complete review of flood loss models to date containing nearly a thousand vulnerability functions. These functions are highly heterogeneous and only about half of the loss models are found to be accompanied by explicit validation at the time of their proposal. This paper exemplarily presents an approach for a quantitative comparison of disparate models via the reduction to the joint input variables of all models. Harmonization of models for benchmarking and comparison requires profound insight into the model structures, mechanisms and underlying assumptions. Possibilities and challenges are discussed that exist in model harmonization and the application of the inventory in a benchmarking framework.
Journal Article
Predicting flood damage using the flood peak ratio and Giovanni Flooded Fraction
A spatially-resolved understanding of the intensity of a flood hazard is required for accurate predictions of infrastructure reliability and losses in the aftermath. Currently, researchers who wish to predict flood losses or infrastructure reliability following a flood usually rely on computationally intensive hydrodynamic modeling or on flood hazard maps (e.g., the 100-year floodplain) to build a spatially-resolved understanding of the flood’s intensity. However, both have specific limitations. The former requires both subject matter expertise to create the models and significant computation time, while the latter is a static metric that provides no variation among specific events. The objective of this work is to develop an integrated data-driven approach to rapidly predict flood damages using two emerging flood intensity heuristics, namely the Flood Peak Ratio (FPR) and NASA’s Giovanni Flooded Fraction (GFF). This study uses data on flood claims from the National Flood Insurance Program (NFIP) to proxy flood damage, along with other well-established flood exposure variables, such as regional slope and population. The approach uses statistical learning methods to generate predictive models at two spatial levels: nationwide and statewide for the entire contiguous United States. A variable importance analysis demonstrates the significance of FPR and GFF data in predicting flood damage. In addition, the model performance at the state-level was higher than the nationwide level analysis, indicating the effectiveness of both FPR and GFF models at the regional level. A data-driven approach to predict flood damage using the FPR and GFF data offer promise considering their relative simplicity, their reliance on publicly accessible data, and their comparatively fast computational speed.
Journal Article
A review of flood damage analysis for a building structure and contents
As a natural hazard, flood can cause a significant damage to buildings. Buildings are one of the important components of an economy which are providing the necessary space for human activities. In this regard, any considerable changes to their serviceability affect living condition of people locally, regionally, and even globally. Thus, building damage analysis forms a crucial part of a flood risk analysis. This review paper provides an insight into flood damage analysis for a building structure and contents: past works, current state, and required improvements. The discussed buildings include residential, commercial, and industrial types. The methods are divided into two main categories: (1) using real data and empirical models, and (2) using what-if analysis and analytical models. Differences in damage analysis of a building structure and its contents are explained in a separate section. Flood factors influencing the damage to a building structure and its contents are presented. How a method considers some of those flood factors is described. Limitations and shortcomings of each method alongside their advantages and strength are discussed. Lack of reliable data for both model construction and validation is one of the main issues with the methods in both categories. Inability to convey the uncertainty is the other main issue identified in the literature.
Journal Article
Effect of uncertainty in land use, damage models and inundation depth on flood damage estimates
With the recent transition to a more risk-based approach in flood management, flood risk models—being a key component in flood risk management—are becoming increasingly important. Such models combine information from four components: (1) the flood hazard (mostly inundation depth), (2) the exposure (e.g. land use), (3) the value of elements at risk and (4) the susceptibility of the elements at risk to hydrologic conditions (e.g. depth–damage curves). All these components contain, however, a certain degree of uncertainty which propagates through the calculation and accumulates in the final damage estimate. In this study, an effort has been made to assess the influence of uncertainty in these four components on the final damage estimate. Different land-use data sets and damage models have been used to represent the uncertainties in the exposure, value and susceptibility components. For the flood hazard component, inundation depth has been varied systematically to estimate the sensitivity of flood damage estimations to this component. The results indicate that, assuming the uncertainty in inundation depth is about 25 cm (about 15% of the mean inundation depth), the total uncertainty surrounding the final damage estimate in the case study area can amount to a factor 5–6. The value of elements at risk and depth–damage curves are the most important sources of uncertainty in flood damage estimates and can both introduce about a factor 2 of uncertainty in the final damage estimates. Very large uncertainties in inundation depth would be necessary to have a similar effect on the uncertainty of the final damage estimate, which seem highly unrealistic. Hence, in order to reduce the uncertainties surrounding potential flood damage estimates, these components deserve prioritisation in future flood damage research. While absolute estimates of flood damage exhibit considerable uncertainty (the above-mentioned factor 5–6), estimates for proportional changes in flood damages (defined as the change in flood damages as a percentage of a base situation) are much more robust.
Journal Article
A probabilistic approach to estimating residential losses from different flood types
Residential assets, comprising buildings and household contents, are a major source of direct flood losses. Existing damage models are mostly deterministic and limited to particular countries or flood types. Here, we compile building-level losses from Germany, Italy and the Netherlands covering a wide range of fluvial and pluvial flood events. Utilizing a Bayesian network (BN) for continuous variables, we find that relative losses (i.e. loss relative to exposure) to building structure and its contents could be estimated with five variables: water depth, flow velocity, event return period, building usable floor space area and regional disposable income per capita. The model’s ability to predict flood losses is validated for the 11 flood events contained in the sample. Predictions for the German and Italian fluvial floods were better than for pluvial floods or the 1993 Meuse river flood. Further, a case study of a 2010 coastal flood in France is used to test the BN model’s performance for a type of flood not included in the survey dataset. Overall, the BN model achieved better results than any of 10 alternative damage models for reproducing average losses for the 2010 flood. An additional case study of a 2013 fluvial flood has also shown good performance of the model. The study shows that data from many flood events can be combined to derive most important factors driving flood losses across regions and time, and that resulting damage models could be applied in an open data framework.
Journal Article
Damage Analysis of the Eifel Route Railroad Infrastructure After the Flash Flood Event in July 2021 in Western Germany
Extreme rainfall events characterized by small catchments with high-velocity flows pose critical challenges to infrastructure resilience, particularly the rail infrastructure, due to its partial location near rivers and in mountainous regions, and the limited availability of alternative routes. This can lead to severe damages, often resulting in long-term route closures. To mitigate flash flood damage, detailed information about affected structures and damage processes is necessary. Therefore, this study presents a newly developed multi-criteria flash flood damage assessment framework for the rail infrastructure and a QGIS-based analysis of the most frequent damages. Applying the framework to Eifel route damages in Western Germany after the July 2021 flood disaster shows that nearly 45% of the damages affected the track superstructure, especially tracks and bedding. Additionally, power supply systems, sealing and drainage systems, as well as railway overpasses or bridges, were impacted. Approximately 30% of the railway section showed washout of ballast, gravel and soil. In addition, deposit of wood or stones occurred. Most damages were classified as minor (47%) or moderate (34%). Furthermore, damaged track sections were predominantly located within a 50 m distance to the Urft river, whereas undamaged track sections are often located at a greater distance to the Urft river. These findings indicate that the proposed framework is highly applicable to assess and classify damages. Critical elements and relations could be identified and can help to adapt standards and regulations, as well as to develop preventive measures in the next step.
Journal Article
A forensic engineering framework for flood management of cascade reservoir systems
Extreme floods are increasing in frequency and severity, exposing critical gaps in reservoir operation policies and post-event accountability. This study proposes a forensic engineering framework to evaluate and optimize flood management in cascade reservoir systems. The framework is applied to Iran’s Karkheh Basin, Iran, and combines scenario-based analysis, a dynamic Standard Operating Policy (SOP) extended to gated spillways, and real-time optimization using inflow forecasts. Five scenarios were analyzed for the April 2019 flood: (1) actual historical operation, (2–3) SOP-based rule curves driven by observed and forecasted inflows, (4) ideal hindsight operation, and (5) rolling-horizon real-time optimization with SVR (monthly) and LSTM (daily) inflow forecasts embedded in a Genetic Algorithm. Results show that, without the proposed Mashoureh Reservoir, relative downstream flood damage was reduced by 18%, 8%, 59%, and 39% under Scenarios 2–5, respectively, compared to Scenario 1. Inclusion of the Mashoureh reservoir reduces damage by 34%, 37%, 100%, and 55% according to scenarios 2–5. However, the impact of the Mashoureh reservoir was limited because 71% of the flood volume originated downstream this reservoir. These findings underscore the importance of combining structural and non-structural measures in the analysis of reservoir operation for flood-control purpose, and demonstrate the value of forensic scenario evaluation for flood preparedness, real-time operations, and planning of future infrastructure.
Journal Article