Identifying Driving Factors in Flood‐Damaging Processes Using Graphical Models

Vogel, Kristin; Weise, L.; Schröter, Kai; Thieken, Annegret H.

doi:10.1029/2018wr022858

Cited by 47 publications

(81 citation statements)

References 49 publications

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“….1), a variety of different hazard, exposure, and vulnerability characteristics are relevant information also for multivariate flood damage modeling in the private households sector. The variable lead time again appears to play a significant role also in multivariate flood damage modeling Vogel et al (2018). came to a similar conclusion when identifying lead time as an important predictor for flood damage estimations using a Bayesian Network.…”

mentioning

confidence: 56%

“…Complementing the HOWAS21 database, a manual outlining the theoretical framework for flood damage assessment and a suggestion for damage documentation was developed (Thieken et al 2009 These requirements are set based on the rationale of ensuring the possibility to link flood damage to hydraulic impact, whereby water depth was found the most important explanatory variable for flood damage in a variety of studies (e.g. Merz et al, 2013;Vogel et al, 2018).…”

Section: Concept and Structurementioning

confidence: 99%

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The object-specific flood damage database HOWAS21

Kellermann¹,

Schröter²,

Thieken³

et al. 2020

Preprint

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Abstract. The Flood Damage Database HOWAS21 contains object-speciﬁc ﬂood damage data resulting from fluvial, pluvial and groundwater flooding in Germany. The datasets incorporate various pieces of information about ﬂood impacts, exposure, vulnerability, and direct tangible damage at properties from several economic sectors. The main purpose of development and design of HOWAS21 is to support forensic flood analysis and the derivation of flood damage estimation models. This paper highlights exemplary analyses to demonstrate the use of HOWAS21 flood damage data in these two application areas. The data applications indicate a large potential of the database for fostering a better understanding and estimation of the consequences of flooding. HOWAS21 recently enlarged its scope and is now also open for international flood damage data.

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mentioning

confidence: 56%

Section: Concept and Structurementioning

confidence: 99%

The object-specific flood damage database HOWAS21

Kellermann¹,

Schröter²,

Thieken³

et al. 2020

Preprint

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“…It is sometimes suggested (Hair et al, 2019) that more rather than fewer variables should be retained in a regression when explanation is the aim rather than prediction. Therefore, we selected not only the most prominent variables in our selection process from Table 4 but also those identified to be more relevant in previous studies that used nonlinear and nonparametric methods over the same or similar data set (Merz et al, 2013;Schröter et al, 2014;Vogel et al, 2018). From these studies, we singled out only those predictors that were selected more than once across their variable selection methods.…”

Section: Variable Selectionmentioning

confidence: 99%

“…The most relevant study to compare ours to is the Markov-Blankets (MBs) approach from (Vogel et al, 2018), in which the authors compared different flood types. In general, our procedure signaled more variables as being relevant for loss-ratio modeling than in the respective MBs (see Table 6).…”

Section: 1029/2019wr025943mentioning

confidence: 99%

A Comparison of Factors Driving Flood Losses in Households Affected by Different Flood Types

Mohor

Hudson

Thieken

2020

Water Resources Research

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Flood loss data collection and modeling are not standardized, and previous work has indicated that losses from different flood types (e.g., riverine and groundwater) may follow different driving forces. However, different flood types may occur within a single flood event, which is known as a compound flood event. Therefore, we aimed to identify statistical similarities between loss-driving factors across flood types and test whether the corresponding losses should be modeled separately. In this study, we used empirical data from 4,418 respondents from four survey campaigns studying households in Germany that experienced flooding. These surveys sought to investigate several features of the impact process (hazard, socioeconomic, preparedness, and building characteristics, as well as flood type). While the level of most of these features differed across flood type subsamples (e.g., degree of preparedness), they did so in a nonregular pattern. A variable selection process indicates that besides hazard and building characteristics, information on property-level preparedness was also selected as a relevant predictor of the loss ratio. These variables represent information, which is rarely adopted in loss modeling. Models shall be refined with further data collection and other statistical methods. To save costs, data collection efforts should be steered toward the most relevant predictors to enhance data availability and increase the statistical power of results. Understanding that losses from different flood types are driven by different factors is a crucial step toward targeted data collection and model development and will finally clarify conditions that allow us to transfer loss models in space and time.

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“…Wagenaar et al () described the derivation of the Bayesian network structure using a combined data and expert‐driven approach. The resulting DAG with a given direction of arcs does not necessarily present a causal relationship (Vogel et al, ). For the calculation of relative building loss (rbloss), it is irrelevant in which direction the arcs are pointing.…”

Section: Consistent Approach For Flood Loss Modeling In Europementioning

confidence: 99%

A Consistent Approach for Probabilistic Residential Flood Loss Modeling in Europe

Lüdtke

Schröter

Steinhausen

et al. 2019

Water Resources Research

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In view of globally increasing flood losses, a significantly improved and more efficient flood risk management and adaptation policy are needed. One prerequisite is reliable risk assessments on the continental scale. Flood loss modeling and risk assessments for Europe are until now based on regional approaches using deterministic depth-damage functions. Uncertainties associated with the risk estimation are hardly known. To reduce these shortcomings, we present a novel, consistent approach for probabilistic flood loss modeling for Europe, based on the upscaling of the Bayesian Network Flood Loss Estimation MOdel for the private sector, BN-FLEMOps. The model is applied on the mesoscale in the whole of Europe and can be adapted to regional situations. BN-FLEMOps is validated in three case studies in Italy, Austria, and Germany. The officially reported loss figures of the past flood events are within the 95% quantile range of the probabilistic loss estimation, for all three case studies. In the Italian, Austrian, and German case studies, the median loss estimate shows an overestimation by 28% (2.1 million euro) and 305% (5.8 million euro) and an underestimation by 43% (104 million euro), respectively. In two of the three case studies, the performance of the model improved, when updated with empirical damage data from the area of interest. This approach represents a step forward in European wide flood risk modeling, since it delivers consistent flood loss estimates and inherently provides uncertainty information. Further validation and tests with respect to adapting the model to different European regions are recommended. Plain Language SummaryWe present a novel, probabilistic approach to calculate flood losses for residential buildings in Europe. We show how the approach can be applied to the European scale and which data sets are required for the calculation. The validity of the approach is proven on the basis of three historic flood events in Italy, Austria, and Germany. This approach which delivers consistent flood loss estimates including uncertainty information supports more efficient flood risk management and adaptation policy in Europe.

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Identifying Driving Factors in Flood‐Damaging Processes Using Graphical Models

Cited by 47 publications

References 49 publications

The object-specific flood damage database HOWAS21

The object-specific flood damage database HOWAS21

A Comparison of Factors Driving Flood Losses in Households Affected by Different Flood Types

A Consistent Approach for Probabilistic Residential Flood Loss Modeling in Europe

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