Simulating historical flood events at the continental scale: observational validation of a large-scale hydrodynamic model

Abstract

Abstract. Continental–global-scale flood hazard models simulate design floods, i.e. theoretical flood events of a given probability. Since they output phenomena unobservable in reality, large-scale models are typically compared to more localised engineering models to evidence their accuracy. However, both types of model may share the same biases and so not validly illustrate their predictive skill. Here, we adapt an existing continental-scale design flood framework of the contiguous US to simulate historical flood events. A total of 35 discrete events are modelled and compared to observations of flood extent, water level, and inundated buildings. Model performance was highly variable, depending on the flood event chosen and validation data used. While all events were accurately replicated in terms of flood extent, some modelled water levels deviated substantially from those measured in the field. Despite this, the model generally replicated the observed flood events in the context of terrain data vertical accuracy, extreme discharge measurement uncertainties, and observational field data errors. This analysis highlights the continually improving fidelity of large-scale flood hazard models, yet also evidences the need for considerable advances in the accuracy of routinely collected field and high-river flow data in order to interrogate flood inundation models more comprehensively.

U.S. flood risk in the Anthropocene

“…For the 5-year ood -more di cult to model owing to its modest size and thus sensitivity to channel parameterisation and microtopography -the similarity between Bates et al and the Iowa Flood Center was 69%. Wing et al 24 furthered the validation of the Bates et al model by simulating historical ood events and comparing them to observations. They found roughly 87% similarity between modelled inundation and observations, and a mean bias of 0.17 m compared to observed ood depths.…”

“…The present and future impact of sea level rise, tropical cyclones, and changing weather patterns are all explicitly represented. Crucially, they benchmark their model against high-quality local ood maps, ood claims information, and, in Wing et al, 24 observations of real ood events. These validation exercises determined the skill of the Bates et al US-wide ood model to be approaching that of local studies and historical observations (80-90% ood extent similarity), while providing a consistent and comprehensive picture of ood hazard spatially.…”

Inequitable patterns of US flood risk in the Anthropocene

“…The present and future impact of sea level rise, tropical cyclones and changing weather patterns are all explicitly represented. Crucially, the model is benchmarked against high-quality local flood maps, flood claims information and observations of real flood events 23,24 . These validation exercises have determined the skill of the US-wide flood model to be approaching that of local studies and historical observations (80-90% flood extent similarity), while providing a consistent and comprehensive picture of flood hazard spatially.…”

InundatEd-v1.0: a height above nearest drainage (HAND)-based flood risk modeling system using a discrete global grid system

“…For instance, with respect to the MCC, an urban flood model produced by Rahmati et al (2020) provided an MCC value of 0.76 when compared to historical flood risk areas. Esfandiari et al (2020) compared two flood simulations: a HAND-based flood model and Bates et al (2021) achieved CSI values of 0.69 and 0.82 for a 100-year return period flood model of the conterminous United States at a 30 m resolution. It must be noted that direct comparisons between the works listed here and this study must be viewed with caution, due to differences in methodologies, assumptions, data sources, data availability, and return periods between the studies.…”

“…Furthermore, the extent comparison scores are not necessarily objective measures of performance of the simulation model. They can vary depending on the severity of the flood, catchment characteristics, and quality of the benchmark data (Mason et al, 2009;Stephens et al, 2014;Wing et al, 2021). Additionally, the median F 1 score (Chicco and Jurman, 2020) for the Grand River watershed was 0.85.…”