Using repeat lidar to estimate sediment transport in a steep stream

Abstract

Sediment fluxes in steep mountain streams remain difficult to quantify, despite their importance in geomorphology, ecology, and hazard analysis. In this work, aerial lidar surveys, acquired in 2002, 2008, and 2012, are used to quantify such fluxes in Tahoma Creek, a proglacial stream on Mount Rainier, Washington. As these surveys encompass all coarse sediment sources in the basin, we are able to translate geomorphic change into total bed material transport volumes for the time steps between surveys. By assuming that the relationship between daily sediment transport and daily mean discharge is of the form Qs=a(Q−Qc)b, our two observed total loads and estimates of daily mean discharge allow us to numerically solve for values of a and b to create a bed material sediment rating curve. Comparisons of our transport estimates with sediment deposition in a downstream reservoir indicate that our transport estimates and derived rating curve are reasonable. The method we present thus represents a plausible means of estimating transport rates in energetic settings or during extreme events, applicable whenever at least two cumulative sediment loads and the driving hydrology are known. We use these results to assess the performance of several bed load transport equations. The equations generally overpredict transport at low to moderate flows but significantly underpredict transport rates during an extreme event. Using a critical shear stress value appropriate for steep streams improves agreement at lower flows, whereas a shear‐partitioning technique accounting for form drag losses significantly underpredicts transport at all flows.

Geomorphic response detection and quantification in a steep forested torrent

“…We could argue that the August 2014 extreme event transported the amount of sediments roughly equivalent or even exceeding the 100-year return period, which is in agreement with the estimated rainfall return period. Similarly, also Anderson and Pitlick (2014) found that one extreme event can account for about 80% of all bed transport in a 10-year period or for more than 50% of all transport estimated in more than a 50-year period. Magilligan (1992) proposed a minimum unit stream power threshold of 300 W/m 2 that generally differentiates between the reaches where large-scale geomorphic change occurred from those where geomorphic changes are more limited (Buraas et al, 2014).…”

“…For example, manual bottle sampling is very difficult during extreme events. Measurements of bed material transport can often be complicated (e.g., Anderson and Pitlick, 2014), while sediment measurements using direct conventional methods can underestimate the long-term average rates (Carrivick et al, 2013;Kirchner et al, 2001). However, permanent measuring stations such as the Rio Cordon station (e.g., Lenzi and Marchi, 2000;Mao and Lenzi, 2007;Picco et al, 2012) yield valuable information about the long-term sediment budgets and can yield adequate measuring results under normal and extreme conditions.…”

Sedimentary bed evolution as a mean‐reverting random walk: Implications for tracer statistics

“…The parameter a for pausing times is determined by the frequency of changes in bed elevation, z . In the field, a could be determined directly from repeat bed surveys, which are becoming increasingly accessible [e.g., Anderson and Pitlick , ]. Because particle erosion and deposition generate z fluctuations, we expect the frequency of bed elevation change to be linearly related to the rate of particle movement, i.e., where k is a proportionality constant effectively describing the fraction of passing particles that deposit or erode at a single point on the bed.…”

Camera system considerations for geomorphic applications of SfM photogrammetry

“…Walker and Petrie, 1996), airborne laser scanning (ALS) and terrestrial laser scanning (TLS) (light detection and ranging [LiDAR] collectively; e.g. Heritage and Hetherington, 2007;Anderson and Pitlick, 2014), and more recently digital imagery that feeds structure-from-motion (SfM) photogrammetry (e.g. James and Robson, 2012).…”