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Data from Gambill et al. (2025): Exploring the influence of channel intermittency and discharge on transient storage and hyporheic exchange in stream systems: Insights from multiple logjams and channels


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Created: Apr 16, 2021 at 1:40 a.m. (UTC)
Last updated: Jan 29, 2026 at 5:54 p.m. (UTC) (Metadata update)
Published date: Jan 29, 2026 at 5:54 p.m. (UTC)
DOI: 10.4211/hs.5535e4618ce545f5a66087ca784d7150
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Sharing Status: Published
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Abstract

These data are published as part of Gambill, I., Marshall, A., Benson, D.A., McFadden, S., Navarre-Sitchler, A., Wohl, E., and Singha, K. (2025). Exploring the influence of morphologic heterogeneity and discharge on transient storage in stream systems: 1. Insights from the field. Water Resources Research, doi: 10.1029/2023WR036031.

Here, we explore how differences in morphologic heterogeneity due to logjams and secondary channels drive transient storage across discharge in two stream reaches within the Front Range of Colorado, USA. During three tracer tests conducted from baseflow to near-peak snowmelt, we collected instream fluid conductivity measurements and conducted electrical resistivity surveys to characterize tracer movement in the surface and subsurface of the stream system. The reach with two logjams and an intermittent secondary channel exhibited greater heterogeneity in surface transient storage, driving heterogeneity in hyporheic exchange flows, compared to the reach with a single logjam and a perennial secondary channel. As discharge increased, (a) backwater pools created by logjams increased in size in both systems, (b) channel complexity increased as logjams forced flow into secondary channels, and (c) subsurface flowpath distribution increased. Various transient storage indices provide some insight on solute retention but compressing data from this system into simple values was unintuitive given the noise in breakthrough-curve tails and secondary peaks in concentration. While subsurface exchange increases with discharge in both reaches, retention may not. Flushing of subsurface tracers is highest at medium discharge as interpreted from the electrical resistivity inversions in both reaches, perhaps because of a tradeoff between the increasing extent of subsurface flowpaths with discharge and larger pressure gradients for driving flow. This work is one of the first to explore controls on exchange and retention in stream systems with multiple logjams and evolving channel planform using geophysical data to constrain the subsurface movement of solutes.

Subject Keywords

Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:
Little Beaver Creek
North Latitude
40.6205°
East Longitude
-105.5376°
South Latitude
40.6161°
West Longitude
-105.5514°

Temporal

Start Date:
End Date:

Content

readme.md

Overview of Information on this HydroShare Page

Three tracer tests, outlined in Ian Gambill’s 2023 thesis and the associated paper were collected during the summer of 2019. Here, we include the field data from that work and modified deconvolution code, described below.
Inside the Field_Data folder, you will find:

1. Transducer_Data

This folder includes data from transducers.

EC_transducer_calibration: data on Hobo Fresh Water Conductivity Data Logger calibration including the calculation and method for converting electrical conductivity (EC) to total dissolved solids (TDS) [EC_to_TDS] and the effect of temperature on EC (EC_vs_temperature)

Pressure_Data: includes pressure data from stream and air, labelled by date of each tracer test, used to calculate water surface level during each tracer test.

Temperature_Stakes: surface and subsurface temperature data collected using iButton sensors.

Tracer_tests_EC: EC data collected using Hobo Fresh Water Conductivity Data Loggers, labelled by the date of each tracer test. 1A is upstream of multiple-logjam, 1B is downstream of multiple-logjam, 2A is upstream of single-logjam, and 2B is downstream of single-logjam.


2. Electrical_Resistivity

This folder includes folders of the original field electrical resistivity (ER) data from three tracer tests (output directly from two IRIS Syscal Pro units; one IRIS at Reach 1 and another IRIS at Reach 2).

Dated folders: within each folder labelled by date, data are separated by reach, where Reach 1 is our complex reach and Reach 2 is our less complex reach. Data from each reach must be processed to separate each transect (e.g., Reach 1 must be separated into 1A and 1B)

Electrode_location_spacing: This folder includes real electrode spacing for each reach (real_electrode_spacing_Reach1 and real_electrode_spacing_Reach2), spatial information from a survey on relative location and elevation (LBC_Electrode_location_elevation), and which electrodes were submerged during each tracer test (Electrodes_in_water_LBC).


3. Site_information

This folder includes data on stage/discharge levels including discharge measurements collected through stream gauging and hydrographs (LBC_discharge). Additionally, this folder includes information about each tracer test including backround stream EC, injection rates, tracer EC, mass of NaCl injected, and tracer start times (Tracer_info).


Also included is a zipped file deconvolution_main.zip that includes Matlab codes to deconvolve two 1-D signals (i.e., time series).
As written, Matlab reads in a .mat file that has 3 column vectors of equal length: time, input, and output. There are example .mat files included here for the data set posted here. The tracer tests were conducted at low, medium, and high flows, hence the folder names.
The code is based on the algorithm of Cirpka et al. (2007), Groundwater, 45: 318-328. https://doi.org/10.1111/j.1745-6584.2006.00293.x. The original code was written by Olaf Cirpka. We added a routine to discern and use the sample autocovariance function of the transfer function in order to estimate the transfer function itself, on the next iteration. For more information, contact dbenson@mines.edu.
In the folders you'll find the .m files "deconv_dave_2.m" that look for a specific .mat file in the folder and perform the deconvolution. The user must specify the name of the .mat file with input data, the distance between input and output signals (assumes collection in a stream, say), and an initial guess at the covariance function of the filter, and a maximum allowable amount of epistemic noise. Each folder has data from a "Reach 1 (R1)" and "Reach 2 (R2)" There is also a data_prep.m file that you can use to make your .mat files. Also included are several files that perform the deconvolution of apparent "bulk" electric conductivity from geophysical electrical resistivity measurements from "input" fluid (stream) EC. These are called deconv_dave_MIM.m

Related Resources

This resource is described by Gambill, I., Marshall, A., Benson, D.A., McFadden, S., Navarre-Sitchler, A., Wohl, E., and Singha, K. (2025). Exploring the influence of morphologic heterogeneity and discharge on transient storage in stream systems: 1. Insights from the field. Water Resources Research, doi: 10.1029/2023WR036031.

Credits

Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
National Science Foundation Emergent Hydrological Properties Associated with Multiple Channel-Spanning Logjams EAR-1819134
National Science Foundation Collaborative Research: Network Cluster: Quantifying controls and feedbacks of dynamic storage on critical zone processes in western montane watersheds EAR-2012730

Contributors

People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.

Name Organization Address Phone Author Identifiers
Audrey Sawyer The Ohio State University
Ellen Wohl Colorado State University

How to Cite

Gambill, I., D. A. Benson, J. Randell, S. McFadden, K. Singha (2026). Data from Gambill et al. (2025): Exploring the influence of channel intermittency and discharge on transient storage and hyporheic exchange in stream systems: Insights from multiple logjams and channels, HydroShare, https://doi.org/10.4211/hs.5535e4618ce545f5a66087ca784d7150

This resource is shared under the Creative Commons Attribution-NoCommercial CC BY-NC.

http://creativecommons.org/licenses/by-nc/4.0/
CC-BY-NC

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