Location: Agroclimate and Hydraulics Research Unit
Project Number: 3070-13000-014-023-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Aug 15, 2026
End Date: Aug 14, 2027
Objective:
The primary objective of this multi-phased project is to develop and validate a methodology for directly quantifying bed shear stress and coupled hydraulic–geotechnical processes in partially saturated soils. This objective will be achieved through three integrated efforts: (1) construction of a controlled laboratory erosion flume capable of repeatable partially saturated testing, (2) integration of a distributed fiber optic sensing network to capture high-resolution temperature and strain fields, and (3) development of measurement-driven relationships to refine existing erosion models currently under development at the USDA. The fiber optic sensing network will have to be tested as part of the proof of concept. Data collection and analysis and reporting on research outcomes are to be expected. If the proof-of-concept is successful, additional phases of testing will be expected at a larger scale.
Approach:
For objective 1, a recirculating laboratory erosion flume will be constructed to provide precise control over hydraulic and geotechnical boundary conditions. Flow velocity, discharge, and channel slope will be independently controlled, allowing systematic evaluation of their influence on erosion processes. Soil beds will be prepared using standardized compaction procedures. Plunging erosion and laminar flow erosion will be measured.
For objective 2, a distributed fiber optic sensing network will be embedded within the soil profile to enable continuous, high-resolution monitoring of temperature and strain. The wetting front propagation will be identified during testing. The sensing network will be strategically configured to maximize spatial resolution in regions of active erosion. Longitudinal sensor placement will provide continuous coverage along the flow direction, while vertical profiling will be distributed every one inch.
All measurements will be synchronized through a centralized data acquisition system. Continuous sampling will be employed to resolve transient processes, including the evolution of bed shear stress, infiltration dynamics, and progressive erosion. Data requires rigorous signal processing to ensure reliability. Procedures to remove high-frequency noise and to preserve gradients will be implemented. Wavelet-based denoising will be applied to isolate transient features such as wetting front advancement, and baseline correction procedures will account for thermal drift. These steps will be implemented prior to derivative-based calculations to prevent amplification of noise in computed quantities such as strain gradients and front velocities. Bed shear stress will be quantified through integration of distributed strain measurements, hydraulic parameters, and experimentally derived calibration relationships. Unlike traditional approaches that rely on indirect proxies such as velocity or empirical coefficients, this method leverages physically measured deformation within the soil to infer applied stresses at the boundary.
For objective 3, supplemental instrumentation will be used to validate measurements from objective 2. Soil moisture probes will provide independent verification of infiltration and wetting front progression, while time-lapse photographic analysis will track surface deformation and material loss. These datasets will serve as calibration and validation benchmarks, ensuring that fiber optic–derived interpretations remain physically grounded.
If the proof-of-concept for the fiber optic sensing network is successful at a smaller scale, additional phases may be implemented with collaborating partners with USDA-ARS and Oklahoma State University. These other phases will be conducted at a larger scale at the USDA-ARS Agroclimate and Hydraulic Engineering Unit in Stillwater, Oklahoma. This information in conjunction with data collected from objective 2 and other phased testing will be used to refine USDA partially saturated soil erodibility prediction models.