Flood damage model bias caused by aggregation

Abstract

Flood risk models provide important information for disaster planning through estimating flood damage to exposed assets, such as houses. At large scales, computational constraints or data coarseness leads to the common practice of aggregating asset data using a single statistic (e.g., the mean) prior to applying non-linear damage functions. While this simplification has been shown to bias model results in other fields, the influence of aggregation on flood risk models has received little attention. This study provides a first order approximation of such errors in 344 damage functions using synthetically generated depths. We show that errors can be as high as 40 % of the total asset value under the most extreme example considered, but this is highly sensitive to the level of aggregation and the variance of the depth values. These findings identify a potentially significant source of error in large-scale flood risk assessments introduced, not by data quality or model transfers, but by modelling approach.