Preface: Damage of natural hazards: assessment and mitigation

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Are OpenStreetMap building data useful for flood vulnerability modelling?

Flood risk modelling aims to quantify the probability of flooding and the resulting consequences for exposed elements. The assessment of flood damage is a core task that requires the description of complex flood damage processes including the influences of flooding intensity and vulnerability characteristics. Multi-variable modelling approaches are better suited for this purpose than simple stage–damage functions. However, multi-variable flood vulnerability models require detailed input data and often have problems in predicting damage for regions other than those for which they have been developed. A transfer of vulnerability models usually results in a drop of model predictive performance. Here we investigate the questions as to whether data from the open-data source OpenStreetMap is suitable to model flood vulnerability of residential buildings and whether the underlying standardized data model is helpful for transferring models across regions. We develop a new data set by calculating numerical spatial measures for residential-building footprints and combining these variables with an empirical data set of observed flood damage. From this data set random forest regression models are learned using regional subsets and are tested for predicting flood damage in other regions. This regional split-sample validation approach reveals that the predictive performance of models based on OpenStreetMap building geometry data is comparable to alternative multi-variable models, which use comprehensive and detailed information about preparedness, socio-economic status and other aspects of residential-building vulnerability. The transfer of these models for application in other regions should include a test of model performance using independent local flood data. Including numerical spatial measures based on OpenStreetMap building footprints reduces model prediction errors (MAE – mean absolute error – by 20 % and MSE – mean squared error – by 25 %) and increases the reliability of model predictions by a factor of 1.4 in terms of the hit rate when compared to a model that uses only water depth as a predictor. This applies also when the models are transferred to other regions which have not been used for model learning. Further, our results show that using numerical spatial measures derived from OpenStreetMap building footprints does not resolve all problems of model transfer. Still, we conclude that these variables are useful proxies for flood vulnerability modelling because these data are consistent (i.e. input variables and underlying data model have the same definition, format, units, etc.) and openly accessible and thus make it easier and more cost-effective to transfer vulnerability models to other regions.Peer Reviewe

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

Quantifying Flood Vulnerability Reduction via Private Precaution

Private precaution is an important component in contemporary flood risk management and climate adaptation. However, quantitative knowledge about vulnerability reduction via private precautionary measures is scarce and their effects are hardly considered in loss modeling and risk assessments. However, this is a prerequisite to enable temporally dynamic flood damage and risk modeling, and thus the evaluation of risk management and adaptation strategies. To quantify the average reduction in vulnerability of residential buildings via private precaution empirical vulnerability data (n = 948) is used. Households with and without precautionary measures undertaken before the flood event are classified into treatment and nontreatment groups and matched. Postmatching regression is used to quantify the treatment effect. Additionally, we test state-of-the-art flood loss models regarding their capability to capture this difference in vulnerability. The estimated average treatment effect of implementing private precaution is between 11 and 15 thousand EUR per household, confirming the significant effectiveness of private precautionary measures in reducing flood vulnerability. From all tested flood loss models, the expert Bayesian network-based model BN-FLEMOps and the rule-based loss model FLEMOps perform best in capturing the difference in vulnerability due to private precaution. Thus, the use of such loss models is suggested for flood risk assessments to effectively support evaluations and decision making for adaptable flood risk management.European Union http://dx.doi.org/10.13039/100011102Peer Reviewe

Modelling flood losses of micro-businesses in Ho Chi Minh City, Vietnam

Micro-businesses are important sources of livelihood for low- and middle-income households. In Ho Chi Minh City (HCMC), Vietnam, many micro-businesses are set up on the ground floor of residential houses susceptible to urban floods. Increasing flood risk in HCMC threatens the financial resources of micro-businesses by damaging business contents and causing business interruptions. Since flood loss estimations are rarely conducted at an object-level resolution and are often focused on households or large companies, the commercial losses suffered by micro-businesses are often overlooked. This study aims to derive the drivers of flood losses [%] for micro-businesses by applying a conditional random forest to survey data (relative business content losses: n=317; relative business interruption losses: n=361) collected from micro-businesses in HCMC. The variability in the losses of business contents and losses due to business interruption were adequately explained by the revenue of the businesses from monthly sales, the age of the building where the business is established, and the hydrological characteristics of the flood. Based on the identified drivers, probabilistic loss models (nonparametric Bayesian networks) were developed using a combination of data-driven and expert-based model formulation. The models estimated the flood losses for HCMC's micro-businesses with a mean absolute error of 3.8 % for content losses (observed mean: 4.7 %, Q50: 0.0) and 18.7 % for business interruption losses (observed mean: 18.2 %, Q50: 10). The Bayesian network model for business interruption had similar predictive performance when it was regionally transferred and applied to comparable survey data from another Vietnamese city, Can Tho. The flood loss models introduced in this study make it possible to derive flood risk metrics specific to micro-businesses to support adaptation decision-making and risk transfer mechanisms.</p

Review article: Assessing the costs of natural hazards - state of the art and knowledge gaps

Efficiently reducing natural hazard risks requires a thorough understanding of the costs of natural hazards. Current methods to assess these costs employ a variety of terminologies and approaches for different types of natural hazards and different impacted sectors. This may impede efforts to ascertain comprehensive and comparable cost figures. In order to strengthen the role of cost assessments in the development of integrated natural hazard management, a review of existing cost assessment approaches was undertaken. This review considers droughts, floods, coastal and Alpine hazards, and examines different cost types, namely direct tangible damages, losses due to business interruption, indirect damages, intangible effects, and the costs of risk mitigation. This paper provides an overview of the state-of-the-art cost assessment approaches and discusses key knowledge gaps. It shows that the application of cost assessments in practice is often incomplete and biased, as direct costs receive a relatively large amount of attention, while intangible and indirect effects are rarely considered. Furthermore, all parts of cost assessment entail considerable uncertainties due to insufficient or highly aggregated data sources, along with a lack of knowledge about the processes leading to damage and thus the appropriate models required. Recommendations are provided on how to reduce or handle these uncertainties by improving data sources and cost assessment methods. Further recommendations address how risk dynamics due to climate and socio-economic change can be better considered, how costs are distributed and risks transferred, and in what ways cost assessment can function as part of decision support

Critical research in the water-related multi-hazard field

We believe that the transdisciplinary studies on water-related multi-hazards are innovative and critical research by the water community, thus answering the call of the recent Nature Sustainability Editorial ‘Too much and not enough’1 for water science ideas that are not derivative or stagnant. This domain of water studies focuses on the specific contexts where water-related hazardous events occur simultaneously, in cascade or cumulatively with other events. Characteristic of the field is the intensive collaboration of scientists and practitioners from different disciplines working together to better understand, assess and manage water-related multi-hazards. At the recent Asia Oceania Geosciences Society–European Geosciences Union Joint Conference on New Dimensions for Natural Hazards in Asia, we discussed the statement ‘Too much and not enough’1 and here suggest three reasons why transdisciplinary collaborations have led to many new ideas and notable advancements in the field of water-related multi-hazard research in recent years