MATLAB data and code used to assess different models describing how atmospheric pressure, soil heating and soil moisture dynamics influence the exchange of trace gases between the atmosphere and the soil

Metadata:
Identification_Information:
Citation:
Citation_Information:
Originator: Massman, William J.
Originator: Frank, John M.
Publication_Date: 2022
Title:
MATLAB data and code used to assess different models describing how atmospheric pressure, soil heating and soil moisture dynamics influence the exchange of trace gases between the atmosphere and the soil
Geospatial_Data_Presentation_Form: MATLAB files
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2022-0065
Description:
Abstract:
This data publication contains both observed and model data. The observed data, which include soil temperature, volumetric water content and pressure measurements, are a (2012) subset of a larger (2008-2014) soil/atmosphere experiment at Manitou Experimental Forest, Colorado. Soil temperature and water content are provided every 5 minutes and pressure provided at 1 hertz. The model data used, provided as MATLAB (*.mat) files, are included in this package as well as the MATLAB code and graphics subroutines for assessing different models of how atmospheric pressure, soil heating and moisture dynamics influence the exchange of trace gases between the atmosphere and the soil.
Purpose:
These data were collected to investigate the influence that variations in atmospheric pressure can have on the movement of trace gases between the soil and the atmosphere. This interaction is termed pressure pumping and these data were used to support investigating the performance of different models that describe pressure pumping in soils and snowpacks.
Supplemental_Information:
These data were published on 08/25/2022. Minor metadata updates, including updates to associated articles, were made on 01/06/2025.

The data included in this package are a subset of the data provided in Frank and Massman (2020). For more information about these data and the model code, see Massman et al. (2022).
Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20120614
Ending_Date: 20121013
Currentness_Reference:
Ground condition
Status:
Progress: Complete
Maintenance_and_Update_Frequency: None planned
Spatial_Domain:
Description_of_Geographic_Extent:
The observed data were obtained within The Manitou Experimental Forest, which is located 28 miles northwest of Colorado Springs, Colorado, and covers about 17,000 acres in the South Platte River drainage. The Forest is representative of the montane ponderosa pine zone in the Front Range which extends from southern Wyoming to northern New Mexico. Elevation ranges from about 7,500 to 9,300 feet. The soil study site was located 1.2 kilometers west (280° from north) of headquarters in a small 0.2 hectare opening of the forest.
Bounding_Coordinates:
West_Bounding_Coordinate: -105.107433
East_Bounding_Coordinate: -105.106833
North_Bounding_Coordinate: 39.102567
South_Bounding_Coordinate: 39.102033
Bounding_Altitudes:
Altitude_Minimum: 2389
Altitude_Maximum: 2392
Altitude_Distance_Units: meters
Keywords:
Theme:
Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: environment
Theme:
Theme_Keyword_Thesaurus: National Research & Development Taxonomy
Theme_Keyword: Ecology, Ecosystems, & Environment
Theme_Keyword: Soil
Theme_Keyword: Natural Resource Management & Use
Theme_Keyword: Water
Theme:
Theme_Keyword_Thesaurus: None
Theme_Keyword: soil pressure pumping
Theme_Keyword: soil pressure
Theme_Keyword: soil temperature
Theme_Keyword: soil volumetric water content
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: Manitou Experimental Forest
Place_Keyword: Colorado
Access_Constraints: None
Use_Constraints:
These data were collected using funding from the U.S. Government and can be used without additional permissions or fees. If you use these data in a publication, presentation, or other research product please use the following citation:

Massman, William J.; Frank, John M. 2022. MATLAB data and code used to assess different models describing how atmospheric pressure, soil heating and soil moisture dynamics influence the exchange of trace gases between the atmosphere and the soil. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2022-0065
Point_of_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: William J. Massman
Contact_Organization: USDA Forest Service, Rocky Mountain Research Station
Contact_Position: Meteorologist
Contact_Address:
Address_Type: mailing and physical
Address: 240 West Prospect Road
City: Fort Collins
State_or_Province: CO
Postal_Code: 80526
Country: USA
Contact_Voice_Telephone: 970-498-1296
Contact_Electronic_Mail_Address: william.massman@usda.gov; wjmassman@gmail.com
Contact Instructions: This contact information was current as of original publication date. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Data_Set_Credit:
This project was funded by the USDA Forest Service, Rocky Mountain Research Station.


Author information:

William J. Massman (retired)
USDA Forest Service, Rocky Mountain Research Station
https://orcid.org/0000-0002-5628-6437

John M. Frank
USDA Forest Service, Rocky Mountain Research Station
https://orcid.org/0000-0001-6543-0333
Cross_Reference:
Citation_Information:
Originator: Frank, John M.
Originator: Massman, William J.
Publication_Date: 2020
Title:
2008-2014 Soil temperature, thermal conductivity, water content, CO2, and pressure at the Manitou Experimental Forest, Colorado during the Bio-hydro-atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics & Nitrogen (BEACHON) study
Geospatial_Data_Presentation_Form: tabular digital data
Publication_Information:
Publication_Place: Ft. Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2020-0061
Cross_Reference:
Citation_Information:
Originator: Massman, William J.
Originator: Frank, John M.
Publication_Date: 2022
Title:
Modeling gas flow velocities in soils induced by variations in surface pressure, heat and moisture dynamics
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Journal of Advancements in Earth Systems Modeling
Issue_Identification: 14(10): e2022MS003086
Online_Linkage: https://doi.org/10.1029/2022ms003086
Online_Linkage: https://research.fs.usda.gov/treesearch/66714
Analytical_Tool:
Analytical_Tool_Description:
MATLAB® combines a desktop environment tuned for iterative analysis and design processes with a programming language that expresses matrix and array mathematics directly.
Tool_Access_Information:
Online_Linkage: https://www.mathworks.com/products/matlab.html
Tool_Access_Instructions:
See website for access instructions
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Data_Quality_Information:
Attribute_Accuracy:
Attribute_Accuracy_Report:
The accuracy for each sensor type is described in the following:

For soil temperature, data were removed based on obvious outliers in their 5-minute averages or standard deviations, or obvious errors in the data logging system. Next, thermocouple errors associated with spurious panel voltage errors were corrected in multiple steps. First, data from 50 centimeter (cm) and 100 cm depths were LOESS (locally estimated scatterplot smoothing) filtered with a 4-day window; the residual noise between the raw signal and the LOESS filtered temperature were assumed to be panel voltage noise. Second, the noise estimated from these eight deep-sensors were weighted and averaged to estimate the noise of the other sensors based on their physical location on the multiplexer wiring; this estimated noise was removed from each sensor except those in replicate 1 between 0-7.5 cm depths, which could not be corrected. Finally, the panel-noise corrected data were LOESS filtered to remove the remaining noise by using individual window sizes that maximized noise removal while minimizing distorting natural transients in the data. The window lengths were 0.5 hours for the 0-0.5 cm depths, 1 hour for the 1.5-2.5 cm depths, and 2 hours for the 3.5-25 cm depths (exceptions were 0.5 hours at replicate 2 at 1.5 cm depth; 0.75 hours at replicate 1 at 1.5 cm depth, replicate 2 at 2.5 cm depth, replicate 4 at 0.5-1.5 cm depth; 1.25 hours at replicate 1 at 2.5 cm depth, replicates 2-3 at 3.5 cm depth; and 1.5 hours at replicate 4 at 3.5 cm depth). Based on these steps, all thermocouple measurements are probably within 0.2 °Celsius (°C) accuracy with less noise for deeper sensors.

For soil water content, a custom equation was used to convert the TDR (time domain reflectometry) measured soil apparent dielectric constant (Ka) to water content (WC) such that WC = -0.15119 + 0.057006*Ka - 0.0030539*Ka² + 0.00006601*Ka³. This calibration was derived in a previous study (Frank and Massman 2007) using TDR probes with soil from the Manitou Experimental Forest. Data were removed based on obvious outliers in their 5-minute values or obvious errors in the data logging system. Next, the data were LOESS filtered to remove noise by using individual window sizes that maximized noise removal while minimizing distorting natural transients in the data. The window lengths were 1.5 hours for the 2 cm depth, 2 hours for the 5 cm depth, 6 hours for the 10 cm depth, 12 hours for the 15 cm depths, 9 hours for the 20 cm depths, and 4 days for the 50-100 cm depths (exceptions were 1.25 hours at replicate 3 at 2 cm depth, 1.75 hours at replicate 3 at 5 cm depth, 2 hours at replicate 2 at 2 cm depth, 3 hours at replicate 2 at 5 cm depth, 4 hours at replicate 3 at 10-15 cm depths, and 9 hours at replicate 4 at 10 cm depth). Based on these steps as well as the results from the calibration of TDR probes using Manitou Experimental Forest soil, the accuracy of TDR water-content measurements was within 0.02 cubic meters per cubic meter (m³/m³) with less noise for deeper sensors.

For soil pressure, data were removed based on obvious outliers in their 5-minute averages or standard deviations. The data were corrected for the arbitrary pressure in the reference cell (see methodological description below for more details) by matching the 5-minute averages to the ambient pressure at the NCAR Manitou Experimental Forest Observatory until 28 September 2012 (https://data.eol.ucar.edu/cgi-bin/codiac/fgr_form/id=496.003) or afterward to an adjusted version of the ambient pressure at the Niwot Ridge AmeriFlux site 110 km north-northwest (https://ameriflux.lbl.gov/sites/siteinfo/US-NR1); the Niwot Ridge data was LOESS filtered with a 2-day window and used as an independent variable to predict the NCAR pressure data from the equation P_NCAR = a*P_Niwot + b + c*sin(2*pi*t/365.25 + d) where a, b, c, and d are empirical fitting parameters and t is an independent variable for time in days. For both the NCAR and adjusted Niwot Data, the mean value of 76.283 kilopascals (kPa) at Manitou was removed from the datasets. The soil pressure data were also corrected for the temperature effect of the pressure within the reference cell; direct measurements of cell temperature were used, and if missing, the soil temperature at 50 cm depth as described above was substituted. A Bayesian statistical analysis predicted the corrected soil pressure as Ps = Ps_raw - P_initial + Density*Tcell*287.058*densityAdj -a*t-b*t²-c*t³, where the P_initial, densityAdj, a, b, and c are empirical fitting parameters with posterior probability distributions. Density is calculated from the NCAR/adjusted Niwot Ridge air pressure and cell temperature, and time is in minutes. The Bayesian analysis estimates the most likely combination of initial reference cell pressure, temperature drift, and time drift between every purging of the reference cell in order for each replicate soil pressure sensor to match the NCAR/adjusted Niwot Ridge air pressure. The Bayesian parameters were adjusted such that the analysis tried to match the four soil pressure sensors with the same precision to each other as to the NCAR air pressure, but it allowed the matching to the adjusted Niwot Ridge air pressure to be less rigorous considering this dataset was a surrogate for the actual pressures at Manitou. Finally, the combined offset of -P_initial + Density*Tcell*287.058*densityAdj -a*t-b*t^2-c*t^3 was calculated for each 5-minute period, and then interpolated to 1 hertz (Hz) and added to the raw pressure data. Based upon this methodology, the average accuracy of the soil pressure sensors is within 17 pascals (Pa) of the NCAR air pressure sensor while the difference in precision between the four soil pressure sensors is 1 Pa.


References

Frank, John M.; Massman, William J. 2007. Effects of fuels reduction treatments on the soil temperature, heat-flux, water content, and CO2 at Manitou Experimental Forest. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. Updated 14 October 2020. https://doi.org/10.2737/RDS-2007-0002

UCAR/NCAR - Earth Observing Laboratory. 2012. BEACHON 5 minute ISFS data, not tilt corrected. Version 1.0. UCAR/NCAR - Earth Observing Laboratory. https://doi.org/10.5065/D6F769XG
Logical_Consistency_Report:
Quality assurance/quality control (QA/QC) checks and corrections generally involved removing data based on obvious outliers in their averages, standard deviations, or other ancillary diagnostics or obvious errors in the data logging system. For more specific details refer to the attribute accuracy report above.
Completeness_Report:
Gaps in data for individual measurements are due to a sensor or instrument being offline, a malfunction, or unrealistic values (e.g., snow on the surface pressure sensor). Gaps in all measurements indicate periods where the entire measurement system was not operating correctly (e.g., datalogger failure due to power outage). Depending on the time resolution of the data files, they contain records every 1 second, 5 minutes, or 30 minutes for the entire period of measurement. Some short gaps in the pressure data were filled by linear interpolation.
Lineage:
Methodology:
Methodology_Type: Lab
Methodolgy_Identifier:
Methodolgy_Keyword_Thesaurus:
None
Methodology_Keyword: thermocouple
Methodology_Keyword: time domain reflectometry (TDR)
Methodology_Keyword: differential capacitance manometer
Methodology_Description:
STUDY LOCATION

The study was conducted on a gentle east facing slope with all sensors confined to a 15 m x 15 m plot that was mostly open from the forested canopy, 1.2 km west of the Manitou Experimental Forest headquarters. At the center of the plot was an elevated structure with control boxes, dataloggers, solar panels, and batteries. Replicate soil pits were located in four directions (rep 1 = northwest, rep 2 = northeast, rep 3 = southeast, rep 4 = southwest) about 4 m from the center. Each replicate had three pits, one for temperature and thermal conductivity probes that was ~1.3 m deep, one for soil moisture probes that was ~1 m deep, and one for soil CO₂ probes that was ~1 m deep. A final pit was dug for soil pressure sensors ~9 m east of the plot center that was ~1 m deep and large enough to hold a 0.5 m x 0.5 m sensor/control box.


DATA COLLECTION

Temperature and moisture sensors (replicates 1 and 2 only) were installed from 20-26 November 2008 (day 325-331). Data logging began on 18 December 2008 (day 353). CO₂ pits and sensors were installed on 14 May 2009 (day 134) with replicate 3 connected on 8 July 2009 (day 189) and replicate 4 connected on 6 August 2009 (day 216). Soil moisture pits for replicates 3 and 4 were dug and sensors installed on 3 September 2009 (day 246). The pressure sensors were installed on 9 June 2011 (day 160) with data being logged on 20 July 2011 (day 201) and the pit being backfilled on 1 September 2011 (day 244). There were prominent gopher holes near the replicate 2 pits that were noted on 18 November 2009 (day 322) and again on 5 May 2010 (day 125), including possible disruption of the soil temperature sensor at 1.5 cm depth with disruption first noted on 1 October 2009 (day 275). All sensors were removed on 14 August 2014.

Soil temperature was measured in each replicate at 0, 0.5, 1.5, 2.5, 3.5, 7.5, 12.5, 17.5, 25, 75, and 125 cm depth. Temperatures were measured with type T thermocouples (PVC insulated, Omega Engineering, Sanford, CT) that were welded and covered with epoxy (Omegabond 101, Omega Engineering). Sensors were measured and recorded with a 23X datalogger (Campbell Scientific, Logan, UT) via an AM25T multiplexer. Measurements were made every 1 minute and averages and standard deviations were recorded every 5 minutes.

Soil water content was measured in each replicate at 2, 5, 10, 15, 20, 50, and 100 cm depth. Soil water content was measured using time-domain reflectometry, TDR, with a TDR100 and SDMX50SP multiplexer (Campbell Scientific) and measurements were initialized and recorded with a 23X datalogger every five minutes. CS610 TDR probes (Campbell Scientific) were used for all measurements. Sensors at 50 cm depth were noted as difficult to install due to a very hard soil layer.

Soil pressure was measured in a separate location in one pit with inlets at 0, 10, 20, and 50 cm. Each inlet used a stainless steel fitting (Swagelok, Solon, OH) with one end covered with porous stainless steel mesh but otherwise open to the soil and the other end connected to Dekoron tubing (.95 cm O.D., 0.64 cm I.D). The tubing was buried ~0.5 m deep and each tube was connected to one side of a differential pressure transducer (226A Baratron differential capacitance manometer, mKS Instruments, Andover, MA). The other side of the differential pressure transducer was connected to a ~1L stainless steel cylinder to be used as a reference. The other end of the reference cylinder was connected to a solenoid (Skinner valve, model 71215SN2MN00N0, Parker, New Britain, CT) that was periodically opened and closed to refresh the pressure within the reference cylinder whenever the pressure transducer drifted out of range; the cylinder attached to the 50 cm inlet did not have a solenoid valve and instead was plugged. The transducers and reference volumes were placed in a control box that was buried ~0.5 m under the soil. A CR3000 datalogger (Campbell Scientific), located in a separate enclosure located on the soil surface, measured and recorded each differential pressure transducer at 1 Hz, and whenever the data for an inlet was out of range (>500 Pa or < -500 Pa) the datalogger opened the corresponding solenoid for 2 seconds before closing it. The datalogger also recorded the temperature inside the buried control box with a thermistor (Temp 107 probe, Campbell Scientific) and the temperature within the 50 cm reference cylinder; instead of a solenoid on this cylinder there was a plug that encased and sealed a platinum resistance thermometer within it (Omega 100-ohm platinum-RTD, model RTD-810, with a Omega signal conditioning module, model OM5IP4-N100-C, Omega Engineering). This methodology provided differential pressure measurements between the soil pressure and a somewhat arbitrary reference pressure (i.e., the pressure captured within the reference cylinder at the time of the last opening/closing of the solenoid valve plus any temperature changes within he cylinder, as well as possible leaks and molecular exchange across the pressure sensor's diaphragm). A detailed description of the reconstruction of the absolute soil pressure is provided in the attribute accuracy report above.  

Please note, that the data used in this data package are a subset of the data described in Frank and Massman (2020).
Methodology_Citation:
Citation_Information:
Originator: Massman, William J.
Originator: Frank, John M.
Publication_Date: 2022
Title:
Modeling gas flow velocities in soils induced by variations in surface pressure, heat and moisture dynamics
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Journal of Advancements in Earth Systems Modeling
Issue_Identification: 14(10): e2022MS003086
Online_Linkage: https://doi.org/10.1029/2022ms003086
Online_Linkage: https://research.fs.usda.gov/treesearch/66714
Source_Information:
Source_Citation:
Citation_Information:
Originator: Frank, John M.
Originator: Massman, William J.
Publication_Date: 2020
Title:
2008-2014 Soil temperature, thermal conductivity, water content, CO2, and pressure at the Manitou Experimental Forest, Colorado during the Bio-hydro-atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics & Nitrogen (BEACHON) study
Geospatial_Data_Presentation_Form: tabular digital data
Publication_Information:
Publication_Place: Ft. Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2020-0061
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20120614
Ending_Date: 20121013
Source_Currentness_Reference:
Publication Date
Source_Citation_Abbreviation:
Frank and Massman (2020)
Source_Contribution:
Soil temperature and water content data were obtained from this data publication.
Process_Step:
Process_Description:
MODELING

This package includes the MATLAB code to compare three different models of atmospheric pressure pumping in soils. One model is described by a linear parabolic partial differential equation (PDE). The other two are variants of a non-linear hyperbolic PDE. Numerical solutions to these two different PDEs are accomplished with different finite different schemes. For the parabolic PDE, the numerical scheme is an implicit Crank-Nicolson scheme combined with a Newton-Raphson iterative solver. The hyperbolic PDEs are solved using an implicit Lax-Friedrich scheme with a Newton-Raphson iterative solver. Both finite difference schemes yield a tridiagonal matrix, which is inverted using the Thomas algorithm. The Newton-Raphson step is optional, and the number of iterations can be controlled, but three iterations (the default) was found to be more than adequate to ensure a very high degree of convergence.

All modeling results were obtained at the Rocky Mountain Research Station, 240 West Prospect Road, Fort Collins, CO 80526.


DATA

The pressure data used as the upper boundary condition for driving the models are taken directly from the data set Frank and Massman (2020). Any gaps (which comprise less than about 0.2% of the data) are linearly interpolated. This 1 Hz pressure data are then input into the model within the subroutine, BoundaryCPres0.m, to define the soil surface pressure Boundary Condition. The model has a T/F option to filter the input data. If “Filter == ‘T’”, the data are filtered using a loess filter to yield low (periods greater than 5 hours), mid (periods between 0.5 and 5 hours) and high (periods between 1 second and 0.5 hours) frequency data. The cutoff periods defining these frequency bands can be adjusted or altered as desired. The FREQ switch then choses among the frequency bands to drive the model. The 5-minute soil temperature and moisture data are also directly from the data set Frank and Massman (2020) and are concurrent with the pressure data, except they are sampled less frequently than the pressure data. The infrequent gaps in soil temperature and moisture are not filled. Rather these 5-minute data are then fit using a relatively complex analytical function of time, which is then used to create 1 Hz time series (data) of both soil temperature and moisture. This curve fitting approach essentially interpolates the soil temperature and moisture data. These data sets can be input as desired to the model to investigate how soil heating and moisture dynamics can influence the soil pressure field and its associated advective velocity field.
Process_Date: 2022
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Entity_and_Attribute_Information:
Overview_Description:
Entity_and_Attribute_Overview:
Below is a list and description of the files included in this data publication.

Unit acronyms used:
1/m = per meter
K = Kelvin
m = meters
m2 = square meters
m3 = cubic meters
m/s = meters per second
mm = millimeters
Pa = Pascal
Pa/m = Pascal per meter
Pa/s = Pascal per second
Pa s = Pascal seconds
s = seconds

CODE DESCRIPTION INFORMATION (1)

1. \code_description.pdf: Portable Document Format (PDF) file containing a detailed description of the functions, variables, and data files as they appear in the execution process. This file can be used as a flowchart to follow what was done. It also includes a description of the variables found in each data file and all of the variables used in the MATLAB scripts.

Columns include:
Model/Function/Data Filename = name of model, function, or data file
Variable or Selection = name of variable or specific selection
Units = units (if applicable)
Precision = precision (if applicable)
Description = description of variable or selection


VARIABLE DESCRIPTION FILE (1)

1. \Data\_variable_descriptions.csv: Comma-separated values (CSV) file containing a list and description of the variables found in the MATLAB data files. (Note: this same information is available in code_description.pdf)

Columns include:
Filename = name of data file
Variable = name of variable
Units = units (if applicable)
Precision = precision (if applicable)
Description = description of variable


MATLAB DATA FILES (41)
All data are provided as MATLAB (version 2017b) *.mat files. The variables in each of these files are fully described in \_variable_descriptions.csv. (Note: this same information is available in code_description.pdf)

1. \Data\June2012166.mat: observed pressure data (and standard deviations) for 14-20 June 2012

2-7. \Data\LinearR1L*.mat: model input of real October data and output from LinearPP_model (FF=full frequency or unfiltered, H=includes heat and moisture forcing/dynamics, HG=high frequency or filtered, LW=low frequency, MD=mid frequency)

8-13. \Data\LinearRL*.mat: model input of real June data and output from LinearPP_model (FF=full frequency or unfiltered, H=includes heat and moisture forcing/dynamics, HG=high frequency or filtered, LW=low frequency, MD=mid frequency)

14-25. \Data\MuskatNonLinR1*.mat: model input of real October data and output from Muskat_model (A=approximate Muskat formulation, M=original Muskat formulation, FF=full frequency or unfiltered, H=includes heat and moisture forcing/dynamics, HG=high frequency or filtered, LW=low frequency, MD=mid frequency)

26-37. \Data\MuskatNonLinR*.mat: model input of real June data and output from Muskat_model (A=approximate Muskat formulation, M=original Muskat formulation, FF=full frequency or unfiltered, H=includes heat and moisture forcing/dynamics, HG=high frequency or filtered, LW=low frequency, MD=mid frequency)

38. \Data\Oct2012276.mat: observed pressure data (and standard deviations) for 2-13 October 2012

39. \Data\TempProf276.mat: temperature dynamics for October 2012 model simulation

40. \Data\TempThProf166.mat: temperature and moisture dynamics for June 2012 model simulation

41. \Data\ThetaProf276.mat: moisture dynamics for October 2012 model simulation


MATLAB DRIVER CODE FILES (3)
There are the 3 main files containing the MATLAB Driver code (*.m) used to execute the models.

1. \Supplements\LinearPP_model.m: linear pressure pumping model

2. \Supplements\Muskat_model.m: nonlinear pressure pumping models (Muskat and Approximate models)

3. \Supplements\PP_ConsistCh.m: Conservation of Mass (COM) approach to estimate advective velocity induced by pressure pumping, requires numerical integration


ADDITIONAL MATLAB SCRIPTS/FUNCTIONS (94)
There are additional MATLAB scripts and functions (*.m) that are used within the MATLAB Driver code files. These files can all be found in the \Supplements folder. A description of the principal files can be found, in the order executed, in code_description.pdf (utility *.m files are not defined in this *.pdf because they are self-explanatory).


MATLAB GRAPHICS SCRIPT FILES (8)
These files are all MATLAB script files (*.m) for graphics. Below we describe the specific graphical output.

1. \Supplements\Graphics_Script\GraphAdvectForce.m: Data vs time plots for the input soil surface pressure, the soil temperature and soil moisture data and the output data files generated by Muskat_model.m (MuskatNonLinR*.mat) and LinearPP_model.m (LinearRL*.mat) and plots advective velocity calculations from PP_ConsistCh.m.

2. \Supplements\Graphics_Script\GraphAdvectiveSol.m: (Superseded by other Graphics routines) – Data vs time plots for the input soil surface pressure and the advective velocities generated by Muskat_model.m (MuskatNonLinR*.mat) and LinearPP_model.m (LinearRL*.mat) and plots vertical profiles of the standard deviation and average vertical velocities from the models.

3. \Supplements\Graphics_Script\GraphAdvectiveVel.m: Similar to GraphAdvectForce.m and GraphAdvectiveSol.m, excep0t for low frequencies forcing and model solutions.

4. \Supplements\Graphics_Script\GraphAdvectiveVelA.m: MatLab script file for graphics – Updated version of GraphAdvectiveVel.m above and so is similar to GraphAdvectForce.m and GraphAdvectiveSol.m, except for low frequencies forcing and model solutions.

5. \Supplements\Graphics_Script\GraphAdvectiveVelB.m: Similar to GraphAdvectiveVelA.m above, except for Mid-range frequencies.

6. \Supplements\Graphics_Script\GraphAdvectiveVelC.m: Plots vertical profiles of the standard deviations of model solutions for pressure and advective velocities from Muskat_model.m (MuskatNonLinR*.mat), LinearPP_model.m (LinearRL*.mat) and PP_ConsistCh.m.

7. \Supplements\Graphics_Script\GraphAdvectiveVelD.m: Similar to GraphAdvectiveVelA.m and to GraphAdvectiveVelB.m above, except for high frequencies.

8. \Supplements\Graphics_Script\GraphPresAdvectiveVel.m: Data vs time plots for mid and high frequency soil surface pressure data.
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Distribution_Information:
Distributor:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service, Research and Development
Contact_Position: Research Data Archivist
Contact_Address:
Address_Type: mailing and physical
Address: 240 West Prospect Road
City: Fort Collins
State_or_Province: CO
Postal_Code: 80526
Country: USA
Contact_Voice_Telephone: see Contact Instructions
Contact Instructions: This contact information was current as of January 2025. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Resource_Description: RDS-2022-0065
Distribution_Liability:
Metadata documents have been reviewed for accuracy and completeness. Unless otherwise stated, all data and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. However, neither the author, the Archive, nor any part of the federal government can assure the reliability or suitability of these data for a particular purpose. The act of distribution shall not constitute any such warranty, and no responsibility is assumed for a user's application of these data or related materials.

The metadata, data, or related materials may be updated without notification. If a user believes errors are present in the metadata, data or related materials, please use the information in (1) Identification Information: Point of Contact, (2) Metadata Reference: Metadata Contact, or (3) Distribution Information: Distributor to notify the author or the Archive of the issues.
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Format_Version_Number: see Format Specification
Format_Specification:
ASCII text file (*.txt or *.m)
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: https://doi.org/10.2737/RDS-2022-0065
Digital_Form:
Digital_Transfer_Information:
Format_Name: MAT
Format_Version_Number: see Format Specification
Format_Specification:
MATLAB file (*.mat)
Digital_Transfer_Option:
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Network_Address:
Network_Resource_Name: https://doi.org/10.2737/RDS-2022-0065
Digital_Form:
Digital_Transfer_Information:
Format_Name: CSV
Format_Version_Number: see Format Specification
Format_Specification:
Comma-separated values file (*.csv)
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: https://doi.org/10.2737/RDS-2022-0065
Fees: None
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Metadata_Reference_Information:
Metadata_Date: 20250106
Metadata_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: William J. Massman
Contact_Organization: USDA Forest Service, Rocky Mountain Research Station
Contact_Position: Meteorologist
Contact_Address:
Address_Type: mailing and physical
Address: 240 West Prospect Road
City: Fort Collins
State_or_Province: CO
Postal_Code: 80526
Country: USA
Contact_Voice_Telephone: 970-498-1296
Contact_Electronic_Mail_Address: william.massman@usda.gov; wjmassman@gmail.com
Contact Instructions: This contact information was current as of original publication date. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Metadata_Standard_Name: FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001.1-1999
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