Abstract
Abstract. Despite the high historical losses attributed to flood events, Canadian flood mitigation efforts have been hindered by a dearth of current, accessible flood extent/risk models and maps. Such resources often entail large datasets and high computational requirements. This study presents a novel, computationally efficient flood inundation modeling framework (“InundatEd”) using the height above nearest drainage (HAND)-based solution for Manning's equation, implemented in a big-data discrete global grid system (DGGS)-based architecture with a web-GIS (Geographic Information Systems) platform. Specifically, this study aimed to develop, present, and validate InundatEd through binary classification comparisons to recently observed flood events. The framework is divided into multiple swappable modules including GIS pre-processing; regional regression; inundation models; and web-GIS visualization. Extent testing and processing speed results indicate the value of a DGGS-based architecture alongside a simple conceptual inundation model and a dynamic user interface.
Highlights
The practice of flood modeling, which aims to understand, quantify, and represent the characteristics and impacts of flood events across a range of spatial and temporal scales, has long informed the sustainable management of watersheds and water resources including flood risk management (Handmer, 1980; Stevens and Hanschka, 2014; Teng et al, 2017, 2019; Towe et al, 2020)
Flood inundation modeling approaches can be broadly divided into three model classes: empirical (Schumann et al, 2009; Smith, 1997); hydrodynamic (Brunner, 2016; DHI, 2012); and simple conceptual (Lhomme et al, 2008; Néelz and Pender, 2010)
The results indicated that the current iteration of the InundatEd flood model was reasonably successful on the basis of moderate–high Matthews correlation coefficient (MCC) values indirect comparisons against the observed flooding extents
Summary
The practice of flood modeling, which aims to understand, quantify, and represent the characteristics and impacts of flood events across a range of spatial and temporal scales, has long informed the sustainable management of watersheds and water resources including flood risk management (Handmer, 1980; Stevens and Hanschka, 2014; Teng et al, 2017, 2019; Towe et al, 2020). The third model class, simple conceptual, has become increasingly well known in the contexts of large study areas, data scarcity, and/or stochastic modeling and encompasses the majority of recent developments in inundation modeling practices (Teng et al, 2017). Relative to the typically complex hydrodynamic model class, simple conceptual models simplify the physical processes and are characterized by much shorter processing times (Teng et al, 2017, 2019). While each class has contributed substantially to the advancement of flood risk mapping and forecasting practices, a consistent barrier has been the trade-off between computer processing time and model complexity (Neal et al, 2018), especially with respect to two-dimensional and three-dimensional hydrodynamic models, which require spe-
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