Physically Based Lagrangian Stochastic Modeling of Particle Transport in Data-Limited Turbulent Flows

Accurate representation of transport processes is essential for understanding water quality dynamics in surface flow systems, particularly under turbulent conditions where observations are limited in space and time. In such environments, sediment and sediment-associated constituent transport is strongly influenced by multiscale turbulence, intermittency, and correlated particle dynamics, processes that are not adequately captured by conventional deterministic modeling approaches.

This study presents a Lagrangian stochastic framework for modeling particle transport in turbulent flows, with particular emphasis on addressing unresolved variability and the limited availability of Eulerian observations. Particle motion, entrainment, and dispersion are formulated using multivariate and multi-layer stochastic differential equations that explicitly incorporate turbulence-induced intermittency, particle memory, and scale-dependent correlations. Near-threshold sediment entrainment is represented through physically based probabilistic criteria, enabling the modeling of intermittent transport events that dominate sediment flux in regimes close to the threshold of sediment motion.

To capture relative dispersion and correlated motion driven by multiscale turbulent structures, the framework extends beyond single-particle formulations to include two-particle stochastic dynamics. Model development and validation are informed by Direct Numerical Simulation (DNS) data, which provide flow statistics for quantifying particle position, velocity, and correlation structures. This integration allows critical transport characteristics to be inferred even when field-scale monitoring data are limited in space or time.

The proposed stochastic framework provides a physical framework for modeling the transport of particle-associated constituents in surface flows. By emphasizing process-based stochastic representations rather than data-intensive deterministic closures, the approach offers a robust pathway for advancing transport modeling in turbulent flows under data-limited conditions.