Bias in Flood Hazard Grid Aggregation

Abstract

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 modellers. 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.This article is protected by copyright. All rights reserved.

Technical Note: Resolution Enhancement of Flood Inundation Grids

“…To better evaluate W SE downscaling algorithms, we adapt the Resample Case framework from Bryant et al (2023) to classify each cell in the s2 domain into one of four cases with similar disaggregation behaviour. Each case is defined by comparing the local coarse water depth value (W SH s2,j ) to the corresponding fine values (W SH s1,i ) where cell j is composed of a block of i cells as shown graphically in Fig.…”