Predicting soil interpedal macroporosity and hydraulic conductivity dynamics: A model for integrating laser-scanned profile imagery with soil moisture sensor data

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..., 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 Scienti..., , 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: [[e-mail]](mailto:[e-mail])

Name: Hoori Ajami
Institution: Universidy of California at Riverside
Address: Department of Environmental Sciences, University of California, Riverside, CA 92521, USA
Email: [[e-mail]](mailto:[e-mail])

Name: Matthew G. Sena
Institution: University of Delaware
Address: Department of Plant and Soil Sciences, University of Delaware, Newark, DE 19716, USA
Email: [[e-mail]](mailto:[e-mail])

Name:...,