Abstract

A timely and cost-effective method of creating inundation maps could assist first responders in allocating resources and personnel in the event of a flood or in preparation of a future disaster. The Height Above Nearest Drainage (HAND) model could be implemented into an on-the-fly flood mapping application for a Canada-wide service. The HAND model requires water level (m) data inputs while many sources of hydrological data in Canada only provide discharge (m3/sec) data. Synthetic rating curves (SRCs), created using river geometry/characteristics and the Manning’s formula, could be utilized to provide an approximate water level given a discharge input. A challenge with creating SRCs includes representing how multiple different land covers will slow impact flow due to texture and bulky features (i.e., smooth asphalt versus rocky river channel); this relates to the roughness coefficient (n). In our study, two methods of representing multiple n values were experimented with (a weighted method and a minimum-median method) and were compared to using a fixed n method. A custom ArcGIS tool, Canadian Estimator of Ratings Curves using HAND and Discharge (CERC-HAND-D), was developed to create SRCs using all three methods. Control data were sourced from gauge stations across Canada in the form of rating curves. Results indicate that in areas with medium to medium–high river gradients (S > 0.002 m/m) or with river reaches under 5 km, the CERC-HAND-D tool creates more accurate SRCs (NRMSE = 3.7–8.8%, Percent Bias = −7.8%—9.4%), with the minimum-median method being the preferred n method.

Highlights

  • In the last decade, major flood events across Canada have caused heavy damage to infrastructure and homes and have cost the lives of Canadians

  • We developed a custom ArcGIS tool, Canadian Estimator of Ratings Curves using Height Above Nearest Drainage (HAND) and Discharge (CERC-HAND-D), that can assist in the creation of synthetic rating curves (SRCs) through the method described by Zheng et al (2018a)

  • Except for the Riviere Richelieu study site, every study site had at least one Synthetic rating curves (SRCs) table where the estimated data fell within the 15% acceptance range, with the minimum and weighted n SRCs always producing an AR score above 0% (Table 3)

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Summary

Introduction

Major flood events across Canada have caused heavy damage to infrastructure and homes and have cost the lives of Canadians. If an accurate and computationally rapid flood model was combined with continuous hydrological/meteorological measurements and/or prediction data, it would be possible to create flood maps in near-real time that represent near-future forecasted flood conditions, or even simulate experimental flood conditions based on user inputs. These onthe-fly flood maps could support emergency personnel and decision makers in being better informed about ongoing flood events in their respective jurisdictions.

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