A general Lagrangian tracking methodology for riverine flow and transport

Abstract This article presents a new public domain tool for generalized Lagrangian particle tracking in rivers. The approach can be applied with a variety of two‐ and three‐dimensional flow solvers. Particle advection by the flow is incorporated using flow fields from the chosen solver assuming particles follow the Reynolds‐averaged flow, although some other simple passive and active particle behaviors are also treated. Turbulence effects are treated using a random walk algorithm with spatial step lengths randomly chosen from Gaussian distributions characterized by the diffusivity from the flow solver. Our work extends this concept to a general framework that is solver and coordinate system independent to allow easy comparisons between differing flow treatments. To better treat problems where detailed information is required in specific regions, the approach includes novel cloning and colligation algorithms which enhance local resolution at modest computational expense. We also provide tools for computing local concentrations and total exposure over a user‐specified time interval. Several examples of predictions are provided to illustrate applications of the technique, including examination of the role of curvature‐driven secondary flows, storage in lateral separation eddies, treatment of larval drift, treatment of fuel spill dispersion, river‐floodplain connections, and sedimentation in floodplain ponds by tie channel connections. We also demonstrate that the model can reproduce analytically derived concentration profiles for simple diffusivities. These examples show that the Lagrangian particle tracking approach and the extensions proposed here are broadly applicable and viable for treating difficult river problems with multiple temporal and spatial scales. The examples also illustrate the utility of the cloning/colligation extensions and show how these can decrease the computational effort required on problems where high local resolution is required. Enhancement of the tools and even broader applicability can be achieved through the inclusion of multiple particle populations and particle–particle interactions.