Improving flood impact estimations Sieg, Tobias; Thieken, Annegret H
Environmental research letters,
06/2022, Volume:
17, Issue:
6
Journal Article
Peer reviewed
Open access
Abstract
A reliable estimation of flood impacts enables meaningful flood risk management and rapid assessments of flood impacts shortly after a flood. The flood in 2021 in Central Europe and the ...analysis of its impacts revealed that these estimations are still inadequate. Therefore, we investigate the influence of different data sets and methods aiming to improve flood impact estimates. We estimated economic flood impacts to private households and companies for a flood event in 2013 in Germany using (a) two different flood maps, (b) two approaches to map exposed objects based on OpenStreetMap and the Basic European Asset Map, (c) two different approaches to estimate asset values, and (d) tree-based models and Stage-Damage-Functions to describe the vulnerability. At the macro scale, water masks lead to reasonable impact estimations. At the micro and meso-scale, the identification of affected objects by means of water masks is insufficient leading to unreliable estimations. The choice of exposure data sets is most influential on the estimations. We find that reliable impact estimations are feasible with reported numbers of flood-affected objects from the municipalities. We conclude that more effort should be put in the investigation of different exposure data sets and the estimation of asset values. Furthermore, we recommend the establishment of a reporting system in the municipalities for a fast identification of flood-affected objects shortly after an event.
Understanding and quantifying total economic impacts of flood events is essential for flood risk management and adaptation planning. Yet, detailed estimations of joint direct and indirect ...flood-induced economic impacts are rare. In this study an innovative modeling procedure for the joint assessment of short-term direct and indirect economic flood impacts is introduced. The procedure is applied to 19 economic sectors in eight federal states of Germany after the flood events in 2013. The assessment of the direct economic impacts is object-based and considers uncertainties associated with the hazard, the exposed objects and their vulnerability. The direct economic impacts are then coupled to a supply-side Input-Output-Model to estimate the indirect economic impacts. The procedure provides distributions of direct and indirect economic impacts which capture the associated uncertainties. The distributions of the direct economic impacts in the federal states are plausible when compared to reported values. The ratio between indirect and direct economic impacts shows that the sectors Manufacturing, Financial and Insurance activities suffered the most from indirect economic impacts. These ratios also indicate that indirect economic impacts can be almost as high as direct economic impacts. They differ strongly between the economic sectors indicating that the application of a single factor as a proxy for the indirect impacts of all economic sectors is not appropriate.
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Hydrometeorological hazards caused losses of approximately 110 billion U.S. Dollars in 2016 worldwide. Current damage estimations do not consider the uncertainties in a comprehensive way, and they ...are not consistent between spatial scales. Aggregated land use data are used at larger spatial scales, although detailed exposure data at the object level, such as openstreetmap.org, is becoming increasingly available across the globe. We present a probabilistic approach for object‐based damage estimation which represents uncertainties and is fully scalable in space. The approach is applied and validated to company damage from the flood of 2013 in Germany. Damage estimates are more accurate compared to damage models using land use data, and the estimation works reliably at all spatial scales. Therefore, it can as well be used for pre‐event analysis and risk assessments. This method takes hydrometeorological damage estimation and risk assessments to the next level, making damage estimates and their uncertainties fully scalable in space, from object to country level, and enabling the exploitation of new exposure data.
Key Points
The proposed method resolves differences between risk assessments at different spatial scales
Resulting probability distributions capture uncertainties associated with hazard, exposure, and vulnerability at all scales
The object‐based method performs as well as or better than state‐of‐the‐art land use‐based models
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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 losses have steadily increased in the past and are expected to grow even further owing to climate and socioeconomic change. The reduction of flood vulnerability, for example, through ...adaptation, plays a key role in the mitigation of future flood risk. However, lacking knowledge about vulnerability dynamics, which arise from the interaction between floods and the ensuing response by society, limits the scope of current risk projections. We present a socio‐hydrological method for flood risk assessment that simulates the interaction between society and flooding continuously, including changes in vulnerability through collective (structural) and private (non structural) measures. Our probabilistic approach quantifies uncertainties and exploits empirical data to chart risk dynamics including how society copes with flooding. In a case study for the commercial sector in Dresden, Germany, we show that increased adaptation is necessary to counteract the expected four‐fold growth in flood risk due to transient hydroclimatic and socioeconomic boundary conditions. We further use our holistic approach to identify solutions for effective long‐term adaptation, demonstrating that integrated adaptation strategies (i.e., combined structural and non structural measures) can reduce the average risk by up to 60% at the study site. Ultimately, our case study highlights the benefit of the model for robust flood risk assessment as it can capture unintended, adverse feedbacks of adaptation measures such as the levee effect. Consequently, our socio‐hydrological method contributes to a more systemic and reliable flood risk assessment that can inform adaptation planning by exploring the possible system evolutions comprehensively including unlikely futures.
Plain Language Summary
The rise in flood losses due to climate and societal changes calls for effective strategies to reduce risks. Understanding how floods interact with society and affect vulnerability is crucial in addressing this challenge. However, current flood risk assessments lack this comprehensive insight. We have developed a novel method that integrates floods and society into a single model, enabling us to comprehend how society's vulnerability to floods changes over time. Our approach examines how communities respond to floods, considering both collective (like constructing levees) and private actions (such as individual property precautions). By factoring in uncertainties and utilizing real‐world data, we improve our understanding of societal flood adaptation. Using the commercial sector in Dresden, Germany, as a case study, we reveal a potential four‐fold increase in future flood risk due to climate and socioeconomic shifts. We propose a combination of collective and private measures, potentially reducing flood risk by up to 60% at the study site. In summary, our method is capable of simulating a wide range of potential futures and uncovering unforeseen challenges that may arise when societies attempt to shield themselves from floods. This aids in robust flood risk management and facilitates better planning for adaptation.
Key Points
We present a socio‐hydrological model for continuous flood risk projection that captures vulnerability dynamics and quantifies uncertainty
Our case study highlights the need for effective adaptation to intensifying flood risk and the potential of integrated flood risk management
The robust flood risk assessment method explores the possibility space comprehensively including adverse feedbacks such as the levee effect
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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.
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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 €0.529 bn and €8.865 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.
Plain Language Summary
We provide spatially consistent flood risk estimates for the residential, commercial and agricultural sectors of Germany. The Regional Flood Model (RFM) simulates a 5000‐year flood event catalogue from which the flood risk curves are derived based on the losses per economic sector. The RFM is a process‐based model chain, that couples the weather generator providing spatially consistent precipitation fields with the hydrological and hydrodynamic models considering processes such as dike overtopping and hinterland storage. The coherent heterogeneous return period flows result in flood maps consisting of inundation depth and duration. These are intersected with sector specific assets at high spatial resolution. Detailed flood loss models are used to estimate losses. From the risk curves, we estimate the Expected Annual Damage and losses corresponding to a 200‐year return period for Germany to be €0.529 bn and €8.865 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. Owing to the process‐based, spatially consistent approach implemented, our risk estimates for extreme events are more realistic compared to other large‐scale assessments.
Key Points
Regional Flood Model provides spatially consistent flood risk estimates for residential, commercial and agriculture sectors for Germany
Flood risk is derived using a 5000‐year event catalog, yielding a realistic representation of risk along with uncertainty quantification
The median Expected Annual Damage and Value At Risk at 99.5% confidence for Germany is estimated to be €0.53 bn and €8.87 bn, respectively
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Reliable flood risk analyses, including the estimation of damage, are an important prerequisite for efficient risk management. However, not much is known about flood damage processes affecting ...companies. Thus, we conduct a flood damage assessment of companies in Germany with regard to two aspects. First, we identify relevant damage‐influencing variables. Second, we assess the prediction performance of the developed damage models with respect to the gain by using an increasing amount of training data and a sector‐specific evaluation of the data. Random forests are trained with data from two postevent surveys after flood events occurring in the years 2002 and 2013. For a sector‐specific consideration, the data set is split into four subsets corresponding to the manufacturing, commercial, financial, and service sectors. Further, separate models are derived for three different company assets: buildings, equipment, and goods and stock. Calculated variable importance values reveal different variable sets relevant for the damage estimation, indicating significant differences in the damage process for various company sectors and assets. With an increasing number of data used to build the models, prediction errors decrease. Yet the effect is rather small and seems to saturate for a data set size of several hundred observations. In contrast, the prediction improvement achieved by a sector‐specific consideration is more distinct, especially for damage to equipment and goods and stock. Consequently, sector‐specific data acquisition and a consideration of sector‐specific company characteristics in future flood damage assessments is expected to improve the model performance more than a mere increase in data.
Key Points
Different damage‐influencing variables are identified for the various company sectors and assets
Prediction accuracies for random forests improve slightly with an increasing amount of training data
A sector‐specific consideration of flood damage is more effective than an increase in training data
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Socio-hydrological flood risk models describe the temporal co-evolution of coupled human-flood systems. However, most models oversimplify the flood loss processes and do not consider companies' ...substantial contribution to total losses. This work presents a socio-hydrological flood risk model for companies that focuses on changes in vulnerability. In addition, we augment the socio-hydrological model with a process-oriented, sector-specific loss model in order to capture damage processes more realistically. In a case study, we simulate the historical flood risk dynamics of companies in the floodplain of Dresden, Germany, over the course of 120 years. Our analysis suggests that the companies in Dresden increase their exposure more cautiously than private households and decrease their vulnerability more actively through private precaution. The augmentation, consisting of informative predictors, a refined probabilistic model, and the incorporation of additional data, improves the accuracy and reliability of the flood loss estimates and reduces their uncertainty.
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10.
Probabilistic Flood Loss Models for Companies Schoppa, Lukas; Sieg, Tobias; Vogel, Kristin ...
Water resources research,
September 2020, 2020-09-00, 20200901, Volume:
56, Issue:
9
Journal Article
Peer reviewed
Open access
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.
Plain Language Summary
River floods are among the costliest natural disasters. The appraisal of financial flood loss is integral to flood risk analysis. Scientists and practitioners use stage‐damage functions to assess flood loss from the water depth at an inundated building. However, flood loss is also controlled by other factors (e.g., building characteristics and private flood precaution), and stage‐damage functions only infrequently provide information on their reliability. Therefore, researchers developed more complex flood loss models, which consider multiple variables and provide information on associated uncertainties. While these novel flood loss models exist for private households, they are lacking for companies. In this study, we present three complex flood loss models for companies. We employ company loss data from flood events in Germany to compare the skill of the proposed flood loss models to each other and to two stage‐damage functions. Our results show that the complex models estimate company flood loss more accurately than the stage‐damage functions. The three complex models work equally well. Yet their loss estimates remain fairly uncertain, underlining the importance of reliability information. Planning authorities, engineering consultancies, and the insurance industry benefit from the improved loss estimates and the transparent treatment of uncertainty of the presented flood loss models.
Key Points
Multivariable flood loss models describe damage processes better and outperform conventional stage‐damage functions
Bayesian networks, Bayesian regression, and random forest perform equally well with respect to accuracy and reliability
Predictive uncertainty in flood loss estimates for companies remains high and requires quantification through probabilistic models
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