Bed load sediment transport inferred from seismic signals near a river. Issue 4 (25th April 2016)

• Record Type: Journal Article
• Authors: Roth, Danica L.; Brodsky, Emily E.; Finnegan, Noah J.; Rickenmann, Dieter.; Turowski, Jens M.; Badoux, Alexandre
• Is Part Of: Journal of geophysical research. Volume 121:Issue 4(2016)
• Page Start: 725
• Page End: 747
• Publication Date: 2016-04-25
• Subjects: sediment transport -- seismology -- fluvial -- precipitation -- seismic -- water turbulence; Geomorphology -- Periodicals; 551.3
• DOI: 10.1002/2015JF003782
• Languages: English
• ISSNs: 2169-9003

Abstract

Abstract: We examine broadband (5–480 Hz) seismic data from the Erlenbach stream in the Swiss Prealps, where discharge, precipitation, and bed load transport are independently constrained. A linear inversion of seismic spectra, exploiting isolated discharge or rain events, identifies the signals generated by water turbulence and rainfall. This allows us to remove the contributions of turbulence and rainfall from the seismic spectra, isolating the signal of bed load transport. We calibrate the regression for bed load transport during one storm and then use this regression with precipitation and discharge data to calculate bed load transport rates from 2 months of seismic spectra. Our predicted bed load transport rates correlate reasonably well with transport rates from calibrated geophones embedded in the channel ( r 2 ~ 0.6, p < 10 −10 ). We find that the seismic response to rainfall is broadband (~16–480 Hz), while water turbulence and sediment transport exhibit seismic power primarily in lower frequencies (<100 Hz), likely due to longer attenuation path lengths. We use the varying attenuation at each seismometer to infer that a downstream waterfall is the primary source of the water turbulence signal. Our results indicate that deconstruction of seismic spectra from rivers can provide insight into the component signals generated by water turbulence, rainfall, and sediment transport. Further, the regression of seismic spectra with precipitation, discharge, and bed load transport data for a single calibration period enables the estimation of transport for subsequent periods with only precipitation, discharge, and seismic data. Hence, in combination with precipitation and discharge data, seismic data can be used to monitor bed load sediment transport. Key Points: Water turbulence, rainfall, and sediment transport generate seismic signals near rivers Component signals are isolated through linear inversion of calibration seismic spectra Sediment transport rates predicted using discharge, precipitation, and seismic data