Refine
Year of publication
Document Type
Keywords
- damage (7)
- adaptation (5)
- vulnerability (5)
- Germany (4)
- floods (4)
- preparedness (4)
- August 2002 flood (3)
- Central Europe (3)
- Europe (3)
- Floods Directive (3)
- June 2013 flood (3)
- companies (3)
- early warning (3)
- governance (3)
- motivation (3)
- pluvial floods (3)
- risk management cycle (3)
- uncertainty (3)
- Bayesian networks (2)
- Costs (2)
- Damage (2)
- Disasters (2)
- Event (2)
- Hazards (2)
- Inoperability (2)
- June 2013 (2)
- Losses (2)
- Model (2)
- Random Forests (2)
- august 2002 (2)
- business interruption (2)
- capacities (2)
- effectiveness (2)
- emergency response (2)
- flood loss (2)
- frequency (2)
- hydro-meterological hazards (2)
- insurance (2)
- mitigation (2)
- object-based damage modeling (2)
- people (2)
- private households (2)
- probabilistic (2)
- probabilistic approaches (2)
- recovery (2)
- resources (2)
- risk (2)
- risk assessment (2)
- risk governance (2)
- social science (2)
- spatial scales (2)
- strategies (2)
- surface water flooding (2)
- variable importance (2)
- Can Tho (1)
- Climate adaptation (1)
- Damage modeling (1)
- Damage reduction (1)
- Flood (1)
- Flood risk management (1)
- Hurricane Harvey (1)
- Land-use planning (1)
- Mekong Delta (1)
- Natural hazard (1)
- Netherlands (1)
- Precaution (1)
- Rhine basin (1)
- Risk zoning (1)
- Vietnam (1)
- affected residents (1)
- average treatment effect (1)
- buildings (1)
- catchment (1)
- circulation patterns (1)
- climate change (1)
- climate change adaptation (1)
- coastal floods (1)
- continuous simulation (1)
- curves (1)
- damage assessment (1)
- damage model (1)
- damage surveys (1)
- data-mining (1)
- derived flood risk analysis (1)
- diagnostics (1)
- emergency (1)
- emergency preparedness (1)
- expected annual damage (1)
- extreme flood (1)
- extremes (1)
- flood (1)
- flood damage (1)
- flood events (1)
- flood genesis (1)
- flood loss estimation (1)
- flood loss model transfer (1)
- flood mechanisms (1)
- flood modelling; (1)
- flood risk (1)
- flood risk management (1)
- flood typology (1)
- flood-affected residents (1)
- flooding (1)
- fluvial floods (1)
- global environmental change (1)
- global sensitivity analysis (1)
- heavy-tailed distributions (1)
- historical floods (1)
- hydroclimatology of floods (1)
- hydrodynamic interactions (1)
- impact (1)
- impact forecasting (1)
- june 2013 Flood (1)
- loss modeling (1)
- loss models (1)
- losses (1)
- matching methods (1)
- mitigation behavior (1)
- mitigation measures (1)
- model (1)
- model validation (1)
- models (1)
- multi-sector risk (1)
- multilevel probabilistic flood loss model (1)
- multiparameter (1)
- multivariable (1)
- natural hazards (1)
- open data (1)
- pluvial flooding (1)
- policy (1)
- probabilistic modeling (1)
- public-participation (1)
- regression tree (1)
- remote (1)
- residential buildings (1)
- response (1)
- risk analysis (1)
- risk model chain (1)
- risk perceptions (1)
- river floods (1)
- sensing (1)
- surprise (1)
- upper tail behaviour (1)
- urban flooding (1)
- validation (1)
Losses due to floods have dramatically increased over the past decades, and losses of companies, comprising direct and indirect losses, have a large share of the total economic losses. Thus, there is an urgent need to gain more quantitative knowledge about flood losses, particularly losses caused by business interruption, in order to mitigate the economic loss of companies. However, business interruption caused by floods is rarely assessed because of a lack of sufficiently detailed data. A survey was undertaken to explore processes influencing business interruption, which collected information on 557 companies affected by the severe flood in June 2013 in Germany. Based on this data set, the study aims to assess the business interruption of directly affected companies by means of a Random Forests model. Variables that influence the duration and costs of business interruption were identified by the variable importance measures of Random Forests. Additionally, Random Forest-based models were developed and tested for their capacity to estimate business interruption duration and associated costs. The water level was found to be the most important variable influencing the duration of business interruption. Other important variables, relating to the estimation of business interruption duration, are the warning time, perceived danger of flood recurrence and inundation duration. In contrast, the amount of business interruption costs is strongly influenced by the size of the company, as assessed by the number of employees, emergency measures undertaken by the company and the fraction of customers within a 50 km radius. These results provide useful information and methods for companies to mitigate their losses from business interruption. However, the heterogeneity of companies is relatively high, and sector-specific analyses were not possible due to the small sample size. Therefore, further sector-specific analyses on the basis of more flood loss data of companies are recommended.
Losses due to floods have dramatically increased over the past decades, and losses of companies, comprising direct and indirect losses, have a large share of the total economic losses. Thus, there is an urgent need to gain more quantitative knowledge about flood losses, particularly losses caused by business interruption, in order to mitigate the economic loss of companies. However, business interruption caused by floods is rarely assessed because of a lack of sufficiently detailed data. A survey was undertaken to explore processes influencing business interruption, which collected information on 557 companies affected by the severe flood in June 2013 in Germany. Based on this data set, the study aims to assess the business interruption of directly affected companies by means of a Random Forests model. Variables that influence the duration and costs of business interruption were identified by the variable importance measures of Random Forests. Additionally, Random Forest-based models were developed and tested for their capacity to estimate business interruption duration and associated costs. The water level was found to be the most important variable influencing the duration of business interruption. Other important variables, relating to the estimation of business interruption duration, are the warning time, perceived danger of flood recurrence and inundation duration. In contrast, the amount of business interruption costs is strongly influenced by the size of the company, as assessed by the number of employees, emergency measures undertaken by the company and the fraction of customers within a 50 km radius. These results provide useful information and methods for companies to mitigate their losses from business interruption. However, the heterogeneity of companies is relatively high, and sector-specific analyses were not possible due to the small sample size. Therefore, further sector-specific analyses on the basis of more flood loss data of companies are recommended.
Flood loss modeling is a central component of flood risk analysis. Conventionally, this involves univariable and deterministic stage-damage functions. Recent advancements in the field promote the use of multivariable and probabilistic loss models, which consider variables beyond inundation depth and account for prediction uncertainty. Although companies contribute significantly to total loss figures, novel modeling approaches for companies are lacking. Scarce data and the heterogeneity among companies impede the development of company flood loss models. We present three multivariable flood loss models for companies from the manufacturing, commercial, financial, and service sector that intrinsically quantify prediction uncertainty. Based on object-level loss data (n = 1,306), we comparatively evaluate the predictive capacity of Bayesian networks, Bayesian regression, and random forest in relation to deterministic and probabilistic stage-damage functions, serving as benchmarks. The company loss data stem from four postevent surveys in Germany between 2002 and 2013 and include information on flood intensity, company characteristics, emergency response, private precaution, and resulting loss to building, equipment, and goods and stock. We find that the multivariable probabilistic models successfully identify and reproduce essential relationships of flood damage processes in the data. The assessment of model skill focuses on the precision of the probabilistic predictions and reveals that the candidate models outperform the stage-damage functions, while differences among the proposed models are negligible. Although the combination of multivariable and probabilistic loss estimation improves predictive accuracy over the entire data set, wide predictive distributions stress the necessity for the quantification of uncertainty.
One common approach to cope with floods is the implementation of structural flood protection measures, such as levees or flood-control reservoirs, which substantially reduce the probability of flooding at the time of implementation. Numerous scholars have problematized this approach. They have shown that increasing the levels of flood protection can attract more settlements and high-value assets in the areas protected by the new measures. Other studies have explored how structural measures can generate a sense of complacency, which can act to reduce preparedness. These paradoxical risk changes have been described as "levee effect", "safe development paradox" or "safety dilemma". In this commentary, we briefly review this phenomenon by critically analysing the intended benefits and unintended effects of structural flood protection, and then we propose an interdisciplinary research agenda to uncover these paradoxical dynamics of risk.
Damage due to floods has increased during the last few decades, and further increases are expected in several regions due to climate change and growing vulnerability. To address the projected increase in flood risk, a combination of structural and non-structural flood risk mitigation measures is considered as a promising adaptation strategy. Such a combination takes into account that flood defence systems may fail, and prepares for unexpected crisis situations via land-use planning and private damage reduction, e.g. via building precautionary measures, and disaster response. However, knowledge about damage-reducing measures is scarce and often fragmented since based on case studies. For instance, it is believed that private precautionary measures, like shielding with water shutters or building fortification, are especially effective in areas with frequent flood events and low flood water levels. However, some of these measures showed a significant damage-reducing effect also during the extreme flood event in 2002 in Germany. This review analyses potentials of land-use planning and private flood precautionary measures as components of adaptation strategies for global change. Focus is on their implementation, their damage-reducing effects and their potential contribution to address projected changes in flood risk, particularly in developed countries.
Preface
(2016)
Floods frequently cause substantial economic and human losses, particularly in developing countries. For the development of sound flood risk management schemes that reduce flood consequences, detailed insights into the different components of the flood risk management cycle, such as preparedness, response, flood impact analyses and recovery, are needed. However, such detailed insights are often lacking: commonly, only (aggregated) data on direct flood damage are available. Other damage categories such as losses owing to the disruption of production processes are usually not considered, resulting in incomplete risk assessments and possibly inappropriate recommendations for risk management. In this paper, data from 858 face-to-face interviews among flood-prone households and small businesses in Can Tho city in the Vietnamese Mekong Delta are presented to gain better insights into the damage caused by the 2011 flood event and its management by households and businesses.
Flood risk management in Europe and worldwide is not static but constantly in a state of flux. There has been a trend towards more integrated flood risk management in many countries. However, the initial situation and the pace and direction of change is very different in the various countries. In this paper, we will present a conceptual framework that seeks to explain why countries opt for different flood risk management portfolios. The developed framework utilises insights from a range of policy science concepts in an integrated way and considers, among others, factors such as geographical characteristics, the experience with flood disasters, as well as human behavioural aspects.
Large-scale flood risk assessments are crucial for decision making, especially with respect to new flood defense schemes, adaptation planning and estimating insurance premiums. We apply the process-based Regional Flood Model (RFM) to simulate a 5000-year flood event catalog for all major catchments in Germany and derive risk curves based on the losses per economic sector. The RFM uses a continuous process simulation including a multisite, multivariate weather generator, a hydrological model considering heterogeneous catchment processes, a coupled 1D-2D hydrodynamic model considering dike overtopping and hinterland storage, spatially explicit sector-wise exposure data and empirical multi-variable loss models calibrated for Germany. For all components, uncertainties in the data and models are estimated. We estimate the median Expected Annual Damage (EAD) and Value at Risk at 99.5% confidence for Germany to be euro0.529 bn and euro8.865 bn, respectively. The commercial sector dominates by making about 60% of the total risk, followed by the residential sector. The agriculture sector gets affected by small return period floods and only contributes to less than 3% to the total risk. The overall EAD is comparable to other large-scale estimates. However, the estimation of losses for specific return periods is substantially improved. The spatial consistency of the risk estimates avoids the large overestimation of losses for rare events that is common in other large-scale assessments with homogeneous return periods. Thus, the process-based, spatially consistent flood risk assessment by RFM is an important step forward and will serve as a benchmark for future German-wide flood risk assessments.