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Ice jams present a significant flood risk in communities located along northern rivers, especially during the breakup of ice cover, resulting in increased backwater levels and flooding beyond riverbanks. The accurate simulation of ice formation relies on precise streamflow data, a vital input for hydraulic models. This study aims to evaluate how different streamflow products influence ice formation, focusing on simulating ice-jam flooding of the Athabasca River at Fort McMurray, Canada, with the broader goal of assessing the suitability of global datasets for predicting such events at a local scale. In our investigation, we integrate MizuRoute, a river network routing tool, and RIVICE, a one-dimensional, hydrodynamic, and river-ice hydraulic model. By employing various large-scale runoff from different models and datasets, such as MESH, ERA5, and VIC among others, our goal is to comprehensively understand how each product impacts the formation of ice jams and the subsequent flooding events. The incorporation of these runoff products is particularly relevant to investigate utilizing global datasets for predicting ice-jam flooding at a local scale.
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Ghoreishi et al. (2024) studied this question.
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