DOI: 10.5061/dryad.zs7h44jkn
title: Predicting soil interpedal macroporosity and hydraulic conductivity dynamics: A model for integrating laser-scanned profile imagery with soil moisture sensor data

description: The size and spatial distribution of soil pores control the infiltration,
 percolation, and retention of water within a pedon. These distributions
 are often represented within hydrologic flux equations as static hydraulic
 properties such as saturated hydraulic conductivity and water retention
 parameters. However, the assumption that these hydraulic properties are
 static does not adequately represent the potentially rapid response of
 highly-structured soil to moisture variability-induced shrink-swell
 processes.  We use a recently-developed, high-resolution (180 um)
 laser imaging technique to capture structural macropore data and derive a
 function that relates interpedal, planar macropore width to matrix water
 content. Subsequently, we develop an expression for transient hydraulic
 conductivity that accounts for dynamic macropore geometries and propose a
 method for partitioning total soil water content obtained from in situ
 sensor data into matrix and macropore water content. The model was applied
 to a soil profile in northeastern Kansas where intact soil monoliths had
 been imaged to quantify soil macorpore properties and continuous soil
 water content data were collected at multiple depths. Model-predicted
 macropore width showed significant sensitivity to matrix water content.
 Rainfall events that followed periods of low soil moisture were predicted
 to allow water to fill macropores - created by the shrinkage of soil
 structural units - which significantly and rapidly increased unsaturated
 hydraulic conductivity. This model offers a means by which to monitor and
 characterize the dynamic hydraulic properties of soils susceptible to
 shrink-swell processes that impact hydrologic partitioning and
 preferential flow.
description: We used soil moisture sensor data, measured soil physical
 properties (particle-size distribution, bulk density, water retention, and
 coefficient of linear extensibility), and macropore data generated from
 multistripe-laser triangulation scanned images of intact soil monoliths
 taken from 3 horizons of an agricultural soil at the Konza Prairie
 Biological Station near Manhattan, KS, USA to test a newly developed
 theory that (1) links properties of soil macropores obtained at one
 moisture state to time series of soil moisture such that macropore
 properties below the surface can be predicted through time at any moisture
 state; (2) partitions soil water content into macropore and matrix water
 contents; and (3) predicts both saturated and unsaturated soil hydraulic
 conductivity in a dynamic dual porosity system. Soil moisture data were
 obtained from installed sensors (ECH2O 5TM, METER Group, Pullman, WA) at a
 depths of 10, 40, and 120 cm and recorded on a data logger (CR1000X,
 Campbell Scientific, Logan, UT). Soil properties were measured by the
 USDA-NRCS Kellogg Laboratory using standard procedures (Soil Survey Staff,
 2022) [Soil Survey Staff. 2022. Kellogg Soil Survey Laboratory methods
 manual. Soil Survey Investigations Report No. 42, Version 6.0. U.S.
 Department of Agriculture, Natural Resources Conservation Service.].
 Coefficient of linear extensibility was measured using a modified
 procedure from Schafer and Singer (1976) [Schafer, W. M., &
 Singer, M. J. 1976. New method of measuring shrink-swell potential using
 soil pastes. Soil Science Society of America Journal, 40 (5), 805-806.
 doi: 10.2136/sssaj1976.03615995004000050050x]. Macropore data were
 generated following Eck et al. (2013) [Eck, D. V., Hirmas, D. R.,
 & Gimenez, D. 2013. Quantifying soil structure from field
 excavation walls using multistripe laser triangulation scanning. Soil
 Science Society of America Journal, 77 (4), 1319-1328.
 doi:10.2136/sssaj2012.0421]. Analyses were conducted in
 R 4.4.2 (R Core Team, 2024) [R Core Team. 2024. R: A language and
 environment for statistical computing [Computer software manual]. Vienna,
 Austria. Retrieved from 
 https://www.R-project.org/].
description: This README.md file was generated on 2025-08-14 by Daniel Hirmas GENERAL
 INFORMATION 1. **Title of Dataset:** Predicting soil interpedal
 macroporosity and hydraulic conductivity dynamics: A model for integrating
 laser-scanned profile imagery with soil moisture sensor data [Dataset] 2.
 **Author Information** A. Researcher Information Name: Daniel R. Hirmas
 Institution: Texas Tech University Address: Department of Plant and Soil
 Science, Texas Tech University, Lubbock, TX 79409, USA Email:
 [dhirmas@ttu.edu](mailto:dhirmas@ttu.edu) Name: Hoori Ajami Institution:
 Universidy of California at Riverside Address: Department of Environmental
 Sciences, University of California, Riverside, CA 92521, USA Email:
 [hooria@ucr.edu](mailto:hooria@ucr.edu) Name: Matthew G. Sena Institution:
 University of Delaware Address: Department of Plant and Soil Sciences,
 University of Delaware, Newark, DE 19716, USA Email:
 [senam@udel.edu](mailto:senam@udel.edu) Name: Xi Zhang Institution:
 Louisiana State University–Agricultural Center Address: Red River Research
 Station and School of Plant, Environment, and Soil Sciences, Louisiana
 State University–Agricultural Center, Bossier City, LA, 71112, USA Email:
 [xizhang@agcenter.lsu.edu](mailto:xizhang@agcenter.lsu.edu) Name: Xiaoyang
 Cao Institution: Zaozhuang University Address: College of Tourism,
 Resources and Environment, Zaozhuang University, Zaoshuang, Shandong,
 China Email:
 [xiaoyangcaoxyc@outlook.com](mailto:xiaoyangcaoxyc@outlook.com) Name:
 Bonan Li Institution: Oregon State University Address: College of Earth,
 Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR,
 97331, USA Email: [libon@oregonstate.edu](mailto:libon@oregonstate.edu)
 Name: Karla M. Jarecke Institution: University of Colorado Boulder
 Address: Department of Geography, University of Colorado Boulder, Boulder,
 CO, 80309, USA Email:
 [Karla.Jarecke@colorado.edu](mailto:Karla.Jarecke@colorado.edu) Name:
 Sharon A. Billings Institution: University of Kansas Address: Department
 of Ecology and Evolutionary Biology and Kansas Biological Survey &
 Center for Ecological Research, University of Kansas, Lawrence, KS, 66047,
 USA Email: [sharon.billings@ku.edu](mailto:sharon.billings@ku.edu) Name:
 Julio C. Pachon Institution: University of Sydney Address: Sydney
 Institute of Agriculture, School of Life and Environmental Sciences,
 University of Sydney, New South Wales, Australia Email:
 [julio.pachon@sydney.edu.au](mailto:julio.pachon@sydney.edu.au) Name: Li
 Li Institution: Penn State University Address: Department of Civil and
 Environmental Engineering, Penn State University, University Park, PA,
 16802, USA Email: [lili@engr.psu.edu](mailto:lili@engr.psu.edu) Name:
 Jesse B. Nippert Institution: Kansas State University Address: Division of
 Biology, Kansas State University, Manhattan, KS, 66506, USA Email:
 [nippert@ksu.edu](mailto:nippert@ksu.edu) Name: Lı́gia F.T. Souza
 Institution: Colorado State University Address: Department of Soil and
 Crop Sciences, Colorado State University, Fort Collins, CO, 80523, USA
 Email: [ligia.souza@colostate.edu](mailto:ligia.souza@colostate.edu) Name:
 Alejandro N. Flores Institution: Boise State University Address:
 Department of Geosciences, Boise State University, Boise, ID, 83725, USA
 Email: [lejoflores@boisestate.edu](mailto:lejoflores@boisestate.edu) Name:
 Pamela L. Sullivan Institution: Oregon State University Address: College
 of Earth, Ocean, and Atmospheric Sciences, Oregon State University,
 Corvallis, OR, 97331, USA Email:
 [sullipam@oregonstate.edu](mailto:sullipam@oregonstate.edu) 3. **Date of
 data collection**: 2018-10 - 2021-08 4. **Geographic location of data
 collection**: Konza Prairie Biological Station, Manhattan, KS, USA 5.
 **Information about funding sources that supported the collection of the
 data**: National Science Foundation, Award: 2034214, 2034232, 2121760,
 2121639, 2121621, 2121595, 2121694, 1656006 and US Department of
 Agriculture-National Institute of Food and Agriculture, Award:
 2021-67019-34341, 2021-67019-34338, 2021-67019-34340 **SHARING/ACCESS
 INFORMATION** 1. Licenses/restrictions placed on the data: CC0 1.0
 Universal (CC0 1.0) Public Domain Dedication license. 2. Recommended
 citation for this dataset: Hirmas, Daniel R.; Ajami, Hoori; Sena, Matthew
 G. et al. 2025. Predicting soil interpedal macroporosity and hydraulic
 conductivity dynamics: A model for integrating laser-scanned profile
 imagery with soil moisture sensor data [Dataset]. Dryad.
 [https://doi.org/10.5061/dryad.zs7h44jkn](https://doi.org/10.5061/dryad.zs7h44jkn) **METHODOLOGICAL INFORMATION** 1. Description of methods used for collection/generation of data: Water Resources Research paper 2. Methods for processing the data: Water Resources Research paper 3. Instrument- or software-specific information needed to interpret the data: Latest versions of R available at [https://cran.r-project.org/](https://cran.r-project.org/) and R Studio available at [https://posit.co/download/rstudio-desktop/](https://posit.co/download/rstudio-desktop/) **DATA-SPECIFIC INFORMATION** 1.     **Data Analysis.zip**  A Zipped folder. Contains six folders: Data, R Project, R Scripts, Functions, Output Files, and Plots. 1.1 **Data** A folder that contains the following files: 1.1.1 **wchold.csv** *Comma Delimited Text File* that holds the 10, 40, and 120 cm deep soil volumetric water content (v/v) for the Konza APT soil. Columns labeled "_5TM_1_VWC", "_5TM_2_VWC", and "_5TM_3_VWC" correspond measured water contents at the 10, 40, and 120 cm depths, respectively. 1.1.2 **KONZA_APT_Ap1_0.18mmResolution_Analyze.csv** *Comma Delimited Text File* that holds measured 2-D geometric information of each macropore in the MLT scanned data of the Ap1 horizon of the Konza APT soil processed through ImageJ. Meanings of column names can be found in the ImageJ Documentation ([https://imagej.net/ij/docs/menus/analyze.html](https://imagej.net/ij/docs/menus/analyze.html)). All length units are in mm. 1.1.3 **KONZA_APT_Bt_0.18mmResolution_Analyze.csv** *Comma Delimited Text File* that holds measured 2-D geometric information of each macropore in the MLT scanned data of the Bt horizon of the Konza APT soil processed through ImageJ. Meanings of column names can be found in the ImageJ Documentation ([https://imagej.net/ij/docs/menus/analyze.html](https://imagej.net/ij/docs/menus/analyze.html)). All length units are in mm. 1.1.4 **KONZA_APT_Btkss2_0.18mmResolution_Analyze.csv** *Comma Delimited Text File* that holds measured 2-D geometric information of each macropore in the MLT scanned data of the Btkss2 horizon of the Konza APT soil processed through ImageJ. Meanings of column names can be found in the ImageJ Documentation ([https://imagej.net/ij/docs/menus/analyze.html](https://imagej.net/ij/docs/menus/analyze.html)). All length units are in mm. 1.1.5 **20220312_KansasEPSCoR_KonzaAPT_SelectedData_aDRH.csv** *Comma Delimited Text File* that holds the upper ("top", cm) and lower ("bot", cm) depths, measured field-capacity bulk denisty (reported on a dry basis; "rhofc", g/cm^3^), oven-dried bulk density ("rhood", g/cm^3^), total porosity ("phi"), coefficient of linear extensibility ("COLE"), field-capacity volumetric water content ("thetafc", v/v), wilting-point volumetric water content ("thetawp", v/v), and air-dried volumetric water content ("thetaad", v/v) data from the USDA-NRCS Kellogg Laboratory for the Ap1, Bt, and Btkss2 horizons of the Konza APT soil (Pedon ID: S2018KS161104; Lab Pedon No.: 19N0205; available from the USDA NCSS Lab Data Mart - [https://ncsslabdatamart.sc.egov.usda.gov/](https://ncsslabdatamart.sc.egov.usda.gov/)). Volumetric water contents were converted from measured gravimetric water contents using the field-capacity bulk density for field-capacity water content and oven-dried bulk density for wilting-point and air-dried water contents. Total porosity was calculated using the field-capacity bulk density. The file also holds the area of the cross-section analyzed ("Axs", mm^2^), total area of the surface scan gaps ("Assg", mm^2^), total perimeter of the surface scan gaps ("Pssg", mm), and average macropore width measured at a dry state ("dds", mm) as well as ROSETTA (Schaap et al., 2001) [Schaap, M.G., F.J. Leij, and M.Th. van Genuchten. 2001. ROSETTA: A computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. Journal of Hydrology 251:163-176. doi: 10.1016/S0022-1694(01)00466-8] pedotransfer function predicted residual ("thetar") and saturated ("thetas) volumetric water contents, the van Genuchten (1980) [van Genuchten, M.Th. 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal 44:892-898. doi: 10.2136/sssaj1980.03615995004400050002x] water retention function fitting parameters ("alpha" given in units of 1/cm and "n"), and saturated hydraulic conductivity ("Ksm", cm/d). 1.1.6 **20220319_EPSCoR_KonzaAPT_COLERod_RawData_aMS_eDRH.csv** *Comma Delimited Text File* that holds coefficient of linear extensibility (COLE) data from a modified form of the COLE rod procedure described in the Water Resources Research paper. Data are from selected depths of four pedons in a larger Kansas EPSCoR project of which the Konza APT soil is a part. The other three pedons in this file are the Hays APT soil (Pedon ID: S2018KS051003; Lab Pedon No.: 19N0202; available from the USDA NCSS Lab Data Mart - [https://ncsslabdatamart.sc.egov.usda.gov/](https://ncsslabdatamart.sc.egov.usda.gov/)), the Konza PPT soil (Pedon ID: S2018KS161103; Lab Pedon No.: 19N0204), and the Konza NPT soil (Pedon ID: S2018KS161102; Lab Pedon No.: 19N0203). The file holds the monolith ID ("tray"), upper ("top", cm) and lower ("bot", cm) depths of the sampled monoliths, the replicate number ("rep") sampled from the monolith, gravimetric water content ("gwc", g/g) for each determination time, and the rod length ("rodlength", mm), COLE rod value ("colerod", unitless), and standard COLE value ("cole", unitless) calculated following Schafer and Singer (1976) [Schafer, W.M., and M.J. Singer. 1976. New method of measuring shrink-swell potential using soil pastes. Soil Science Society of America Journal 40:805-806. doi: 10.2136/sssaj1976.03615995004000050050x] for each determination time. The numbers 1-5 in the column names refer to the initial, 2-hour, 4-hour, 8-hour, and 24-hour determinations, respectively. The "finalrodlength" and "finalgwc" correspond to the 48-hour rod length and gravimetric water content measurements, respectively. NA values refer to gravimetric water contents that were not measured in the analysis. 1.1.7 **AWE012.csv** *Comma Delimited Text File* that holds daily meteorological data between 1982 and 2020 from the Konza Prairie Biological Station headquarters weather station. The year, month, day is given in the columns "RECYEAR", "RECDAY", and "RECDAY" and the Julian day is given as "DAYOFYEAR". Maximum, minimum, and average temperature (C) is provided in columns labeled "TMAX", "TMIN", and "TAVE", respectively. Daily average relative humidity (%), total solar radiation (MJ/m^2^), precipitation (mm), maximum, minimum, and average soil temperature (C) at 25 cm, and average wind speed (m/s) are in the columns labeled "DHUMID", "DSRAD", "DPPT", "SMAX", "SMIN", "S_AVE", and "WAVE", repsectively; "." values refer to data that were missing or were not measured. Data were obtained from Nippert (2024) [Nippert, J. 2024. AWE01 Meteorological data from the Konza Prairie headquarters weather station. Environmental Data Initiative. [http://dx.doi.org/10.6073/pasta/910469efbf1f7e8d54c2b1ca864edec9](http://dx.doi.org/10.6073/pasta/910469efbf1f7e8d54c2b1ca864edec9)]. 1.2 **R Project** A folder that contains the following file: 1.2.1 **PredictingMacroporosityandConductivityDynamics.RProj** *R Studio File* that holds the R Studio project file used to process the data. 1.3 **R Scripts** A folder that contains the following files: 1.3.1 **20250614_PredictingMacroporosityandConductivityDynamics_RScript.R** *R Text File* that holds the master script for conducting the analyses and generating the figures for the Water Resources Research paper. After the R Studio project file is opened to launch R Studio, this R text file should be navigated to and opened through the Files tab within R Studio. The script in this file should be executed to change the appropriate working directories to match the 6-folder stucture within the Data Analysis folder, and to call the following R scripts in the appropriate order. 1.3.2 **20250614_PredictingMacroporosityandConductivityDynamics_PartitionScript.R** *R Text File* that holds the script called by the master script file to partition the sensor water content into macropore and matrix water contents. 1.3.3 **20250614_PredictingMacroporosityandConductivityDynamics_WRCParameterScript.R** *R Text File* that holds the script called by the master script file to develop a model to predict the water retention parameters of the macropore (structural) domain for any matrix water content. 1.3.4 **20250614_PredictingMacroporosityandConductivityDynamics_PoreResponseScript.R** *R Text File* that holds the script called by the master script file to use the calculated matrix water content values to examine the effect on pore metrics. 1.3.5 **20250614_PredictingMacroporosityandConductivityDynamics_KSurfacePlotScript.R** *R Text File* that holds the script called by the master script file to plot the unsaturated hydraulic conductivity surface for any matrix and macropore water contents. 1.3.6 **20250614_PredictingMacroporosityandConductivityDynamics_ValidationScript.R** *R Text File* that holds the script called by the master script file to validate the Ks predictions from this theory against the transient Ks calculated using the theory from Stewart et al. (2016) [Stewart, R.D., M.R. Abou Najm, D.E. Rupp, and J.S. Selker. 2016. Modeling multidomain hydraulic properties of shrink-swell soils. Water Resources Research 52:7911-7930. doi: 10.1002/2016WR019336]. 1.3.7 **20250614_PredictingMacroporosityandConductivityDynamics_SensitivityScript.R** *R Text File* that holds the script called by the master script file to evaluate the sensitivity of the model to variation in the input parameters: Ap, P, and dds. 1.4 **Functions** A folder that contains the following file: 1.4.1 **20250614_PredictingMacroporosityandConductivityDynamics_RFunctions.R** *R Text File* that holds a script with R functions developed for the Water Resources Research paper and called by the master script. 1.5 **Output Files** A folder that is used by the master script to place output tables from the analysis with the current date. This folder contains the following files: 1.5.1 **20250614_PredictingMacroporosityandConductivityDynamics_KonzaAPT_PredictionCoefficientsforalpha2andn2.csv** *Comma Delimited Text File* that holds the model output shown in Table 1 of the Water Resources Research paper. 1.5.2 **20250614_PredictingMacroporosityandConductivityDynamics_KonzaAPT_MatrixAbsorptionCoeficients.csv** *Comma Delimited Text File* that holds the model output shown in Table 2 of the Water Resources Research paper. 1.5.3 **20250614_PredictingMacroporosityandConductivityDynamics_KonzaAPT_SoilShrinkageCurveParameters.csv** *Comma Delimited Text File* that holds the soil shrinkage curve model output shown in Table 5 of the Water Resources Research paper. 1.6 **Plots** A folder that contains no files but is used by the master script to place the plots generated from the analysis with the current date.