Abstract:Interannual variations in seasonal sediment transfer in two High Arctic non-glacial watersheds were evaluated through three summers of field observations (2003)(2004)(2005). Total seasonal discharge, controlled by initial watershed snow water equivalence (SWE) was the most important factor in total seasonal suspended sediment transfer. Secondary factors included melt energy, snow distribution and sediment supply. The largest pre-melt SWE of the three years studied (2004) generated the largest seasonal runoff and disproportionately greater suspended sediment yield than the other years. In contrast, 2003 and 2005 had similar SWE and total runoff, but reduced runoff intensity resulted in lower suspended sediment concentrations and lower total suspended sediment yield in 2005. Lower air temperatures at the beginning of the snowmelt period in 2003 prolonged the melt period and increased meltwater storage within the snowpack. Subsequently, peak discharge and instantaneous suspended sediment concentrations were more intense than in the otherwise warmer 2005 season. The results for this study will aid in model development for sediment yield estimation from cold regions and will contribute to the interpretation of paleoenvironmental records obtained from sedimentary deposits in lakes.

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Fine-resolution Light Detection and Ranging (LiDAR) data often exhibit excessive surface roughness that can hinder the characterization of topographic shape and the modeling of near-surface flow processes. Digital elevation model (DEM) smoothing methods, commonly low-pass filters, are sometimes applied to LiDAR data to subdue the roughness. These techniques can negatively impact the representation of topographic features, most notably drainage features, such as headwater streams. This paper presents the feature-preserving DEM smoothing (FPDEMS) method, which modifies surface normals to smooth the topographic surface in a similar manner to approaches that were originally designed for de-noising three-dimensional (3D) meshes. The FPDEMS method has been optimized for application with raster DEM data. The method was compared with several low-pass filters while using a 0.5-m resolution LiDAR DEM of an agricultural area in southwestern Ontario, Canada. The findings demonstrated that the technique was better at removing roughness, when compared with mean, median, and Gaussian filters, while also preserving sharp breaks-in-slope and retaining the topographic complexity at broader scales. Optimal smoothing occurred with kernel sizes of 11–21 grid cells, threshold angles of 10°–20°, and 3–15 elevation-update iterations. These parameter settings allowed for the effective reduction in roughness and DEM noise and the retention of terrace scarps, channel banks, gullies, and headwater streams.