Abstract

This study uses continuous-recording load cell pressure sensors in four, high-elevation (1500-1800 m), Sierra Nevada, headwater streams, to collect high temporal resolution, bedload-movement data for investigating the channel bed movement patterns within these streams for water years 2012-2014. Data show an annual pattern where channel bed material in the thalweg starts to build up in early fall, peaks around peak snow melt, and scours back to baseline levels during hydrograph drawdown and baseflow. This pattern is punctuated by disturbance and recovery of channel bed material associated with short-term, storm events. We propose conceptual model, linking sediment sources at the channel margins to patterns of channel bed fill and scour in the thalweg, based on this and earlier work showing in-stream sources for bedload material. The material in the thalweg represents a balance between sediment supply from the channel margins and sporadic, conveyor-belt-like, downstream transport in the thalweg. The conceptual model highlights not only the importance of production and transport rates but also that seasonal connectedness between the margins and thalweg is a key sediment control, determining both the accumulation rate of sediment stores at the margins, and the redistribution of sediment from margins to thalweg that "feeds" the conveyor-belt. Disturbance and recovery cycles are observed at multiple temporal scales, but long term, the channel beds are stable, suggesting the beds act as short-term storage for sediment, but are in equilibrium interannually. The feasibility of use for these sensors in forested mountain stream environments is tested. Despite a high failure rate (50%), load cell pressure sensors show potential for high-temporal-resolution bedload measurements, allowing for the collection of channel bed movement data to move beyond time-integrated change measurements - where many of the subtleties of bedload movement patterns may be missed - to continuous and/or real-time measurements. This type of high-temporal-resolution data provides insight into short term cycles of bedload movement in high gradient, forested, mountain streams.

Citation

Conklin, M. H.; Martin, S. (2017): A Gap-Filling Procedure for Hydrologic Data Based on Kalman Filtering and Expectation Maximization: Application to Data from the Wireless Sensor Networks of the Sierra Nevada. Fall Meeting, American Geophysical Union, December 2017. Abstract EP53E-07..