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:...,土壤孔隙的大小与空间分布决定了单个土体（pedon）内水分的入渗、渗流与持留过程。这类分布常以静态水力特性的形式出现在水文通量方程中，例如饱和导水率（saturated hydraulic conductivity）与持水参数。然而，假设这些水力特性为静态的前提，无法充分表征高度结构化土壤对水分变异诱导的胀缩过程的潜在快速响应。 本研究采用新近开发的高分辨率（180微米）激光成像技术采集结构性大孔隙（macropore）数据，并推导得到将土块间平面大孔隙宽度与基质含水量（matrix water content）相关联的函数。随后，本研究构建了考虑动态大孔隙几何形态的瞬态导水率表达式，并提出一种将原位传感器获取的总土壤含水量划分为基质含水量与大孔隙含水量的方法。本模型…… 本研究利用土壤水分传感器数据、实测土壤物理属性（粒径分布、容重、持水特性与线性膨胀系数），以及从美国堪萨斯州曼哈顿附近孔扎草原生物站的农田土壤3个土壤发生层（horizon）采集的原状土柱（soil monolith）多条纹激光三角扫描图像生成的大孔隙数据，对新开发的理论进行验证。该理论包含三点：(1) 将某一水分状态下获取的土壤大孔隙属性与土壤水分时间序列相关联，从而可在任意水分状态下随时间预测地下大孔隙属性；(2) 将土壤总含水量划分为大孔隙含水量与基质含水量；(3) 在动态双孔隙系统中预测饱和与非饱和土壤导水率。 土壤水分数据通过安装在10 cm、40 cm与120 cm深度的传感器（ECH2O 5TM, METER Group, Pullman, WA）获取，并由数据采集器（CR1000X, 坎贝尔科学公司）记录。 本README.md文件由Daniel Hirmas于2025年8月14日生成。 # 基本信息 1. **数据集标题**：土壤粒间大孔隙度与导水率动态预测：一种整合激光扫描剖面影像与土壤水分传感器数据的模型[数据集] 2. **作者信息** A. 研究者信息 姓名：丹尼尔·R·赫马斯（Daniel R. Hirmas） 所属机构：德克萨斯理工大学（Texas Tech University） 通讯地址：美国德克萨斯州拉伯克市德克萨斯理工大学植物与土壤科学系，邮编79409 电子邮箱：[[e-mail]](mailto:[e-mail]) 姓名：胡里·阿贾米（Hoori Ajami） 所属机构：加利福尼亚大学河滨分校（University of California, Riverside） 通讯地址：美国加利福尼亚州河滨市加利福尼亚大学河滨分校环境科学系，邮编92521 电子邮箱：[[e-mail]](mailto:[e-mail]) 姓名：马修·G·塞纳（Matthew G. Sena） 所属机构：特拉华大学（University of Delaware） 通讯地址：美国特拉华州纽瓦克市特拉华大学植物与土壤科学系，邮编19716 电子邮箱：[[e-mail]](mailto:[e-mail]) 姓名：……