Aug 2026· Environments· Vol 13, pp. 441· 0 citations· 95 references
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 trade-offs among physical realism, scalability, data intensity, and computational efficiency. Conceptual models based on tipping-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 subsurface hydrologic dynamics in predictive frameworks.
Accurate prediction of dissolved phosphorus (P) transport to groundwater, and especially to drain tiles, is a major challenge, particularly in structured soils where preferential flow dominates subsurface hydrology. Existing vadose zone models rely on advection–dispersion formulations that insufficiently represent macropore-driven transport, leading to underestimation of dissolved P losses. This study develops a simplified convective–preferential (CP) transport equation to describe event-scale movement of dissolved P through the vadose zone to groundwater. The equation conceptualizes the soil profile as a near-surface distribution zone that acts as a linear reservoir with a transmission zone below where preferential flow is activated when imposed fluxes exceed the effective matrix conductivity. Under these conditions, P-rich event water bypasses much of the soil matrix, rapidly reaching the groundwater. The CP equation uses a small set of physical parameters, based on partitioning the flux between the matrix and preferential pathways. Model results were validated against laboratory leaching experiments, published column studies of various soil textures, and tile-drained field soils. The CP equation reproduced observed dissolved P breakthrough patterns and flow-dependent concentration in tile drains. Model results confirm that significant P transport occurs during high-intensity rainfall on saturated soils with restrictive layers on structured soils with a low-permeable hardpan.
N. Brindt, B. Richards, T. Steenhuis· Water· 0 citations
The Richards equation (RE) is widely used to model water flow in unsaturated soils, but its performance in persistently wet grassland systems remains uncertain. This is particularly relevant in Irish grasslands, where soils often remain close to saturation for extended periods and seasonal waterlogging is common. Here, we evaluate the RE against three soil moisture datasets from County Wexford, Ireland, spanning different locations, soil types, and observation periods. We show that the standard RE formulation systematically over-predicts soil moisture under prolonged near-saturated conditions. We find that this arises from the commonly used Feddes plant water uptake function, which suppresses water losses under anaerobic conditions, despite continued evaporation from near-saturated soils. To address this limitation, we introduce a simple modification that retains a small non-zero water loss rate in the anaerobic regime. The modified model produces substantially improved agreement with observations across all three datasets. These results provide a systematic evaluation of RE-based soil moisture modelling in Irish grasslands. More broadly, they identify an important limitation of conventional RE implementations in waterlogged environments and demonstrate a practical approach for improving soil moisture predictions in persistently wet soils.
S. Kumar, S. Mathias, E. Ruelle et al.· 0 citations
Moisture source–receptor diagnostics based on Lagrangian moisture tracking models are essential numerical tools for understanding global and regional hydrologic processes, valued for their computational efficiency and spatial accuracy. Among these diagnostic methods, WaterSip is the most widely used one, with its reliability further enhanced by the bias correction capabilities of the Heat And MoiSture Tracking framework (HAMSTER). However, these methods predominantly employ uniform humidity and altitude thresholds for identifying precipitation and evaporation moisture, limiting their adaptability to diverse surface properties and climatic conditions globally. This study proposes a dynamic filtering (DF) method that employs spatiotemporally varying relative humidity (RH) and altitude thresholds to improve the trajectory selection and moisture identification. The DF method enables the specific humidity changes of selected particles to more faithfully represent precipitation loss and evaporation gain during moisture transport, and enhances the recognition of spatial contrasts in moisture contributions over source regions with complex land–sea distributions. Comparison with the Weather Research and Forecasting model with Water Vapor Tracers (WRF‐WVTs) indicates that the DF method brings WaterSip simulations into closer agreement with the WRF‐WVTs results, showing potential to reduce the systematic overestimation of nearby source contributions and underestimation of distant contributions. In a case study over the Tibetan Plateau (TP), the DF method highlights the contribution of westerly moisture sources to precipitation in the region. Nevertheless, the standalone DF method may still retain biases in total tracked moisture, necessitating subsequent bias correction (e.g., HAMSTER) to adjust overall moisture amounts.
