DOI: 10.5061/dryad.c2fqz61h9
title: Data for: Timescales of Autogenic Noise in River Bedform Evolution and Stratigraphy

description: Bedforms are ubiquitous features on alluvial river channels. Bedform
 deposits—fluvial cross strata— are the fundamental sedimentary structures
 of the rock record on Earth and Mars. Bedform evolution and preserved
 cross strata respond to floods; however, it is unclear which flood
 durations are likely to be represented in bedform evolution and cross
 strata. To address this, we quantified the structure of autogenic noise in
 bedform evolution using high-resolution spatiotemporal data from a
 steady-state, physical experiment of bedform evolution.  The data
 herein accompanies the manuscript “Timescales of Autogenic Noise in
 Bedform Evolution and Fluvial Cross Strata ” by Vamsi Ganti, Madeline M.
 Kelley, Debsmita Das and Robert C. Mahon. In this manuscript, we
 quantified the scales of autogenic noise in sediment efflux, bedform
 evolution, and preserved deposition rates in fluvial cross strata. We
 accomplish this using a steady-state experiment of bedform evolution and
 perform spectral analysis of bed elevation, sediment efflux and preserved
 deposits. We find that bedform-group (quasi-stable collection of bedforms)
 turnover timescale sets the lower limit for detecting flood signals in
 bedform evolution, and floods with duration shorter than bedform turnover
 timescale can be severely degraded in bedform evolution and cross strata.
description: All data associated with this manuscript were collected from a
 steady state experiment of bedform evolution at the Experimental
 Sedimentology Laboratory at UC Santa Barbara. The experiment was conducted
 in a 15 m-long, 2 m-wide and 1 m-deep sediment and water recirculating
 flume. Bedforms evolved under a constant flow discharge of 0.28 m3/s and
 the flow depth varied from 0.35 m to 0.3 m over the 7 m-long test section.
 We used quartz sand with a median grain size of 0.35 mm as sediment in the
 experiment.  We collected two sets of bathymetric data
 for the spectral and the bedform-tracking analysis. The spanwise extent of
 these datasets was centered along the flume centerline. For the spectral
 analysis, we monitored a 32 mm-by-251 mm patch of bed, located at x = 10
 m, at a spatial and temporal resolution of 1 mm and 12 s, respectively,
 for 65 hrs. This was in conjunction with high resolution sediment flux
 data from weigh pans at the downstream end of the flume. For bedform
 tracking, we monitored a 7 m-by-251 mm patch, starting at x = 5 m, at a
 spatial and temporal resolution of 1 mm and 5 mins, respectively, for 14
 hrs. We include these datasets here.  We computed
 bedform geometry and kinematics using the multi-scale bedform tracking
 tool from Lee et al., 2021. We constructed stratigraphy from the
 bathymetric data by stacking time series of bed elevation profiles and
 clipping away eroded portions after Ganti et al., 2013. We generated
 discrete time-power spectral densities for bed evolution (12 s interval
 data), sediment efflux, and preserved deposition rates using a multi-taper
 method from Huybers & Curry, 2006. To identify the key autogenic
 timescales in bedform evolution, we analyzed PSD in log-log space and
 quantified the time period of gradient breaks in PSD using the
 ‘findchangepts’ function in MATLAB (Griffin et al., 2023).  Refer to the
 associated manuscript for the references and further details pertaining to
 the experiments and data analysis techniques.
 <br><br>
description: # Data submission for ‘Timescales of Autogenic Noise in River Bedform
 Evolution and Stratigraphy’
 [https://doi.org/10.5061/dryad.c2fqz61h9](https://doi.org/10.5061/dryad.c2fqz61h9) These materials consist of Code and Data. The data consists of the bathymetric data files in .mat (MATLAB) format for the associated manuscript ‘Timescales of Autogenic Noise in Bedform Evolution and Fluvial Cross Strata’. The code provides the MATLAB script for estimating the sediment flux. ## Description of the data and file structure \--All data and code are within the file Data.zip Data \#steady_state_longscans_detrend.mat This mat file contains three variables: X -vector representing coordinates in the longitudinal or downstream direction. Y -vector representing coordinates in the lateral or cross-stream direction. dt_z1- 3-dimensional matrix containing the time series of bed elevation scans used for computing bedform geometry and kinematics. Within dt_zl, the first dimension indicates the scan number while the second and third dimensions represent bed elevation in longitudinal and lateral directions, respectively. \#steady_state_23Hz_allthousand.mat This mat file contains three variables: X - a vector representing coordinates in the longitudinal or downstream direction. Y -a vector representing coordinates in the lateral or cross-stream direction. dt_znew -3-dimensional matrix containing the time series of bed elevation scans used for the spectral analysis. Within dt_znew, the first dimension indicates the scan number while the second and third dimensions represent bed elevation in the longitudinal and lateral directions, respectively. \#sediment_efflux.mat This mat file contains the table TimeSeries. This is the original data from the flume tipping pan system. Note the four weight columns (1R,2,3,4), which provide the weight of the accumulated sand in each pan for each time step. Also recorded are the ‘Date’, ‘ClockTime’, ‘T’ (day and time), ‘WaterSurfaceElevation’, ‘PrimaryFlowrate’, and ‘PrimaryPumpFreq’. Additionally, once the weigh pans reached 1.8 kg, sediment was automatically dumped into the recirculation system. This is accounted for in our flux calculation script ‘qs_calculation.m’ ## Code/Software Code \#qs_calculation.m The ‘qs_calculation.m’ script produces a new table 'TimeSeries_c' with column 'qs_mean' for PSD function input. For each pan, we calculate the unit mass flux (Ws) from detrended weigh pan series. The data are then filtered and smoothed (12 seconds to match bed elevation data). The mean of all four ‘Ws’ series is calculated and then converted to unit volume flux (qs). A new column is created with the mean unit volume flux from all four pans ‘TimeSeries_c.qs_mean’.