Abstract
Canada's RADARSAT missions improve the potential to study past flood events; however, existing tools to derive flood depths from this remote-sensing data do not correct for errors, leading to poor estimates. To provide more accurate gridded depth estimates of historical flooding, a new tool is proposed that integrates Height Above Nearest Drainage and Cost Allocation algorithms. This tool is tested against two trusted, hydraulically derived, gridded depths of recent floods in Canada. This validation shows the proposed tool outperforms existing tools and can provide more accurate estimates from minimal data without the need for complex physics-based models or expert judgement. With improvements in remote-sensing data, the tool proposed here can provide flood researchers and emergency managers accurate depths in near-real time.
Highlights
1 Introduction Flooding has become the costliest natural disaster in Canada, with economic losses estimated around $2.5 billion per year (Office of the Parliamentary Budget Officer, 2016). To mitigate this flood risk, large investments in infrastructure and planning have been made by the federal government in the past decade (National Disaster Mitigation Program, 2017; Government of 20 Canada, 2021); accuracy and the absence of data on historical flooding remains challenging for the models underpinning these investments (McGrath et al, 2015; Bryant et al, 2021)
This study develops the novel Rolling HAND Inundation Corrected Depth Estimator (RICorDE) Tool for generating gridded 105 depth estimates of past flood events from approximate inundation polygons and a DEM
To demonstrate the accuracy of RICorDE, depth estimates are generated for two historical flooding events in Canada using publicly available datasets
Summary
Flooding has become the costliest natural disaster in Canada, with economic losses estimated around $2.5 billion per year (Office of the Parliamentary Budget Officer, 2016). While new satellite missions have improved capabilities for mapping inundation extents, data on maximum flood depth, which is commonly found to be the most significant indicator of building damage following European floods (Mohor et al, 2020; Laudan et al, 2017; Merz et al, 2010), remains scarce. The absence of such depth data in Canada limits the utility of flooding research, leading to less informed 25 flood management decisions.
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