Funding informationFonds Québécois de la recherche sur la nature et les technologies; Québec Ministry of Public Security Coastlines along the St. Lawrence Estuary and Gulf, Eastern Canada, are under increasing risk of flooding due to sea level rise and sea ice shrinking. Efficient and validated regional-scale coastal flood mapping approaches that include storm surges and waves are hence required to better prepare for the increased hazard. This paper compares and validates two different flood mapping methods: numerical flood simulations using XBeach and bathtub mapping based on total water levels, forced with multihazard scenarios of compound wave and water level events. XBeach is validated with hydrodynamic measurements. Simulations of a historical storm event are performed and validated against observed flood data over ã 25 km long coastline using multiple fit metrics. XBeach and the bathtub method correctly predict flooded areas (66 and 78%, respectively), but the latter overpredicts the flood extent by 36%. XBeach is a slightly more robust flood mapping approach with a fit of 51% against 48% for the bathtub maps. Deeper floodwater by~0.5 m is expected with the bathtub approach, and more buildings are vulnerable to a 100-year flood level. For coastal management at regional-scale, despite similar flood extents with both flood mapping approaches, results suggest that numerical simulati on with XBeach outperforms bathtub flood mapping.
Increasingly used shore-based video stations enable a high spatiotemporal frequency analysis of shoreline migration. Shoreline detection techniques combined with hydrodynamic conditions enable the creation of digital elevation models (DEMs). However, shoreline elevations are often estimated based on nearshore process empirical equations leading to uncertainties in video-based topography. To achieve high DEM correspondence between both techniques, we assessed video-derived DEMs against LiDAR surveys during low energy conditions. A newly installed video system on a tidal flat in the St. Lawrence Estuary, Atlantic Canada, served as a test case. Shorelines were automatically detected from time-averaged (TIMEX) images using color ratios in low energy conditions synchronously with mobile terrestrial LiDAR during two different surveys. Hydrodynamic (waves and tides) data were recorded in-situ, and established two different cases of water elevation models as a basis for shoreline elevations. DEMs were created and tested against LiDAR. Statistical analysis of shoreline elevations and migrations were made, and morphological variability was assessed between both surveys. Results indicate that the best shoreline elevation model includes both the significant wave height and the mean water level. Low energy conditions and in-situ hydrodynamic measurements made it possible to produce video-derived DEMs virtually as accurate as a LiDAR product, and therefore make an effective tool for coastal managers.
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