Reducing flood risk through disaster planning and risk management requires accurate estimates of exposure, damage, casualties, and environmental impacts. Models can provide such information; however, computational or data constraints often lead to the construction of such models by aggregating high‐resolution flood hazard grids to a coarser resolution, the effect of which is poorly understood. Through the application of a novel spatial classification framework, we derive closed‐form solutions for the location (e.g., flood margins) and direction of bias from flood grid aggregation independent of any study region. These solutions show bias of some key metric will always be present in regions with marginal inundation; for example, inundation area will be positively biased when water depth grids are aggregated and volume will be negatively biased when water surface elevation grids are aggregated through averaging. In a separate computational analysis, we employ the same framework to a 2018 flood and successfully reproduce the findings of our study‐region‐independent derivation. Extending the investigation to the exposure of buildings, we find regions with marginal inundation are an order of magnitude more sensitive to aggregation errors, highlighting the importance of understanding such artifacts for flood risk modelers. Of the two aggregation routines considered, averaging water surface elevation grids better preserved flood depths at buildings than averaging of water depth grids. This work provides insight into, and recommendations for, aggregating grids used by flood risk models.
N2 - Key Points:Through a novel framework, we show analytically that hazard grid aggregation leads to bias of key metrics independent of any study region
This aggregation is shown to always positively bias inundation area when water depth grids are aggregated
For example, aggregating from 1 to 512 m resolution resulted in a doubling of the inundated area for a 2018 flood in Canada