A Review of Flood Loss Models as Basis for Harmonization and Benchmarking

<div><p>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.</p></div

Structure of the flood loss model inventory.

<p>Structure of the flood loss model inventory.</p

Characteristics of flood loss models contained in the inventory.

<p>Characteristics of flood loss models contained in the inventory.</p

Global distribution of flood loss models and functions for different sectors contained in the inventory.

<p>Global distribution of flood loss models and functions for different sectors contained in the inventory.</p

Loss functions for one-cut silage [61]; example for an absolute, deterministic model using multi-variable loss functions.

<p>D = total damage [£/ha], GMJ = energy from grass lost due to flooding [MJ/ha], RF = cost of replacement feed [£/ha], C = additional costs incurred (+) or saved (-) [£/ha].</p

Compilation of harmonized flood loss models for residential buildings based on common variables.

<p>Compilation of harmonized flood loss models for residential buildings based on common variables.</p

Characteristics of the selected example models.

<p>Characteristics of the selected example models.</p

Damage model of Zhai et al. [12] with the damage-influencing factors residing period, income and inundation depth; example for an absolute, deterministic model using multi-variable loss functions.

<p>Damage model of Zhai et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159791#pone.0159791.ref012" target="_blank">12</a>] with the damage-influencing factors residing period, income and inundation depth; example for an absolute, deterministic model using multi-variable loss functions.</p

Loss functions of residential buildings in HAZUS-MH [43]; example of a relative, deterministic model using uni-variable loss functions (negative inundation depth refers to inundation in the basement of a building).

<p>Loss functions of residential buildings in HAZUS-MH [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159791#pone.0159791.ref043" target="_blank">43</a>]; example of a relative, deterministic model using uni-variable loss functions (negative inundation depth refers to inundation in the basement of a building).</p

Flood Damage Modeling on the Basis of Urban Structure Mapping Using High-Resolution Remote Sensing Data

The modeling of flood damage is an important component for risk analyses, which are the basis for risk-oriented flood management, risk mapping, and financial appraisals. An automatic urban structure type mapping approach was applied on a land use/land cover classification generated from multispectral Ikonos data and LiDAR (Light Detection And Ranging) data in order to provide spatially detailed information about the building stock of the case study area of Dresden, Germany. The multi-parameter damage models FLEMOps (Flood Loss Estimation Model for the private sector) and regression-tree models have been adapted to the information derived from remote sensing data and were applied on the basis of the urban structure map. To evaluate this approach, which is suitable for risk analyses, as well as for post-disaster event analyses, an estimation of the flood losses caused by the Elbe flood in 2002 was undertaken. The urban structure mapping approach delivered a map with a good accuracy of 74% and on this basis modeled flood losses for the Elbe flood in 2002 in Dresden were in the same order of magnitude as official damage data. It has been shown that single-family houses suffered significantly higher damages than other urban structure types. Consequently, information on their specific location might significantly improve damage modeling, which indicates a high potential of remote sensing methods to further improve risk assessments