Measuring gravel transport and dispersion in a mountain river using passive radio tracers
Earth Surface Processes and Landforms
By: D. N. Bradley and G. E. Tucker
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Abstract
Random walk models of fluvial sediment transport recognize that grains move intermittently, with short duration steps separated by rests that are comparatively long. These models are built upon the probability distributions of the step length and the resting time. Motivated by these models, tracer experiments have attempted to measure directly the steps and rests of sediment grains in natural streams. This paper describes results from a large tracer experiment designed to test stochastic transport models. We used passive integrated transponder (PIT) tags to label 893 coarse gravel clasts and placed them in Halfmoon Creek, a small alpine stream near Leadville, Colorado, USA. The PIT tags allow us to locate and identify tracers without picking them up or digging them out of the streambed. They also enable us to find a very high percentage of our rocks, 98% after three years and 96% after the fourth year. We use the annual tracer displacement to test two stochastic transport models, the Einstein–Hubbell–Sayre (EHS) model and the Yang–Sayre gamma-exponential model (GEM). We find that the GEM is a better fit to the observations, particularly for slower moving tracers and suggest that the strength of the GEM is that the gamma distribution of step lengths approximates a compound Poisson distribution. Published in 2012. This article is a US Government work and is in the public domain in the USA.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Measuring gravel transport and dispersion in a mountain river using passive radio tracers |
| Series title | Earth Surface Processes and Landforms |
| DOI | 10.1002/esp.3223 |
| Volume | 37 |
| Issue | 10 |
| Year Published | 2012 |
| Language | English |
| Publisher | John Wiley and Sons, Ltd. |
| Contributing office(s) | National Research Program - Central Branch |
| Description | 12 p. |
| First page | 1034 |
| Last page | 1045 |
| Google Analytic Metrics | Metrics page |