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ARS Home » Plains Area » El Reno, Oklahoma » Oklahoma and Central Plains Agricultural Research Center » Agroclimate and Hydraulics Research Unit » Research » Publications at this Location » Publication #435739
Research Project: Impacts of Variable Land Management and Climate on Water and Soil Resources

Location: Agroclimate and Hydraulics Research Unit

Title: Soil moisture prediction: A review of models, principles, applications and future needs

Author
item KIKOYO, DUNCAN - Texas A&M University
item Fortuna, Ann-Marie
item SMITH, PATRICIA - Texas A&M University
item Hunt, Sherry
item Zhang, Xunchang
item Flanagan, Paul
item Busteed, Phillip
item JEONG, JAEHAK - Texas A&M University

Submitted to: Environments
Publication Type: Review Article
Publication Acceptance Date: 8/2/2026
Publication Date: 8/5/2026
Citation: Kikoyo, D., Fortuna, A., Smith, P., Hunt, S., Zhang, X.J., Flanagan, P.X., Busteed, P.R., Jeong, J. 2026. Soil moisture prediction: A review of models, principles, applications and future needs. Environments. 13(8). Article 441. https://doi.org/10.3390/environments13080441.
DOI: https://doi.org/10.3390/environments13080441

Interpretive Summary:

Technical Abstract: Soil moisture is a fundamental variable controlling hydrologic partitioning, land–atmosphere exchange, and biogeochemical cycling. Although widely characterized through observations, remote sensing, and data-driven approaches, the representation of soil water fluxes and subsurface processes in predictive models remains less systematically synthesized. Here, we present a structured review of widely used predictive models, focusing on their process formulations, spatial discretization, and treatment of soil water fluxes across scales. The synthesis reveals consistent tradeoffs among physical realism, scalability, data intensity, and computational efficiency. Conceptual models based on tip-ping-bucket approaches simplify soil water movement as threshold-driven storage processes and underrepresent transient redistribution and deep soil moisture dynamics. Storage-routing models introduce flux-based redistribution but rely on empirical parameterization that dampen transient flux dynamics. Physically based models resolve hydraulic gradients and provide the highest process fidelity but require extensive data and computational resources. Across scales, the absence or simplification of preferential flow and lateral subsurface flow constitutes the dominant structural limitation, producing systematic smoothing of soil moisture variability. The review provides a comparative basis for model selection and highlights critical directions for improving the representation of sub-surface hydrologic dynamics in predictive frameworks.