Use of Bathymetric and LiDAR Data in Generating Digital Elevation Model over the Lower Athabasca River Watershed in Alberta, Canada

Abstract

Abstract:The lower Athabasca River watershed is one of the most important regions for Alberta and elsewhere due to fact that it counts for the third largest oil reserve in the world. In order to support the oil and gas extraction, Athabasca River provides most of the required water supply. Thus, it is critical to understand the characteristics of the river and its watershed in order to develop sustainable water management strategies. Here, our main objective was to develop a digital elevation model (DEM) over the lower Athabasca River watershed including the main river channel of Athabasca River (i.e., approximately 128 km from Fort McMurray to Firebag River confluence). In this study, the primary data were obtained from the Alberta Environmental Monitoring, Evaluation and Reporting Agency. Those were: (i) Geoswath bathymetry at 5-10 m spatial resolution; (ii) point cloud LiDAR data; and (iii) river cross-section survey data. Here, we applied spatial interpolation methods like inverse distance weighting (IDW) and ordinary kriging (OK) to generate the bathymetric surface at 5 m × 5 m spatial resolution using the Geoswath bathymetry data points. We artificially created data gaps in 24 sections each in the range of 100 to 400 m along the river and further investigated the performance of the methods based on statistical analysis. We observed that the DEM generated using the both IDW and OK methods were quite similar, i.e., r 2 , relative error, and root mean square error were approximately 0.99, 0.002, and 0.104 m, respectively. We also evaluated the performance of both methods over individual sections of interest; and overall deviation was found to be within ±2.0 m while approximately 96.5% of the data fell within ±0.25 m. Finally, we combined the Geoswath-derived DEM and LiDAR-derived DEM in generating the final DEM over the lower Athabasca River watershed at 5 m × 5 m resolution.

Comparison of different spatial interpolation methods for historical hydrographic data of the lowermost Mississippi River

“…Our ultimate result shows both OK with anisotropic consideration (UK and OKA) and TPS (from RBF) interpolators have the best performance on the crosssectional data in LMR. This finding is similar to the conclusions from several above mentioned studies (Merwade, Maidment, and Goff 2006;Šiljeg, Lozić, and Šiljeg 2014;Chowdhury et al 2017;Chen et al 2018a). All these researches addressed that both geostatistical and deterministic interpolation methods have similar performance on generating bathymetric surface.…”

“…For example, Curtarelli et al (2015) found that lowest RMSE value was 0.92 m by using OK method for mapping the bathymetry of an Amazonian reservoir. Moreover, Chowdhury et al (2017) reported that RMSE value ranged between 0.48 and 0.67 m for both IDW and OKA methods in the lower Athabasca River, Canada. However, although the RMSE values in our study are greater than 1.7 m, which is higher than other study; the maximum depths and range are generally greater; therefore, this is in part a scale factor.…”

“…Conversely, other studies proposed that geostatistical methods, especially OK, are appropriate interpolators to generate bathymetric topography not only in river channels (e.g., Carter and Shankar 1997;Legleiter and Kyriakidis 2008;Merwade 2009;Curtarelli et al 2015), but also in other waterbodies, such as lakes or coastal regions (e.g., Bello-Pineda and Hernández-Stefanoni, 2007;Moskalik et al 2013). Further, some other studies indicated that both geostatistical and deterministic methods have similar performance on creating bathymetric surface in their study area (e.g., Merwade, Maidment, and Goff 2006;Šiljeg, Lozić, and Šiljeg 2014;Chowdhury et al 2017). In the entire Mississippi River and its tributaries, several studies have attempted to construct interpolated bathymetric maps with different interpolators (e.g., Pinter and Heine 2005;Flanagin et al 2007;Little and Biedenharn 2014;Wang and Xu 2018).…”

“…A traditional approach for mapping river channels involves conducting a sequence of cross-sectional surveys along the river that gauge water depth information, typically a series of x, y, z coordinates along multiple transects. Many reach-scale studies rely on the cross-sectional surveys (e.g., Harmar et al 2005;Yang, Townsend, and Daneshfar 2006;Legleiter and Kyriakidis 2008;Merwade 2009;Vetter et al 2011;Conner and Tonina, 2014;Chowdhury et al 2017), especially for the historical hydrographic surveys (e.g., Dury 1970;Sherwood et al 1990;Hudson 2002). When historical hydrographic transects are collected in the same location repeatedly, these provide knowledge of how the river bathymetry varies through time.…”

Geospatial Modeling of River Systems

“…The system, MoRE (Modeling of Regionalized Emissions), also provides modelling capabilities to track pathway-specific emissions and river loads on a catchment scale and scenarios of emission reductions to be simulated. Accurate digital elevation models are also vital to the setup of geospatial models, an example of which is provided by Chowdhury et al [82] for the lower portion of the Athabasca River basin in Canada.…”