A velocity-dissipation stochastic trajectory model for dispersal of heavy particles inside canopies

Thursday, 18 December 2014
Tomer Duman1, Ana Trakhtenbrot1, Davide Poggi2, Massimo Cassiani3 and Gabriel George Katul1, (1)Duke University, Durham, NC, United States, (2)Politecnico di Torino, Dipartimento di Idraulica, Trasporti ed Infrastrutture Civili, Torino, Italy, (3)NILU, Kjeller, Norway
While the importance of dispersal of windborne heavy particles such as seeds or pollen inside canopies is rarely disputed, the details needed to describe turbulent fluctuations in such applications continue to draw significant research attention. Turbulence and heavy-particle dispersal within canopies are sensitive to interactions between meteorological conditions and canopy structure as well as on particle shape and mass. In many applications, dispersal of heavy particles is required over a broad range of time scales ranging from hours to several decades thereby frustrating any attempt to resolve all aspects of turbulence. In recent years, Lagrangian stochastic trajectory models have been favored for predicting seed dispersal and are viewed as an acceptable compromise between empirical models with their ad-hoc parameterizations and computationally intensive Large Eddy Simulations. Here, an important feature of turbulence, namely the intermittency in dissipation rate, is incorporated into such trajectory models. Adding this effect has been recently shown to alter scalar dispersion patterns, especially in the far field. This method is applied here to heavy particles, where the long distance dispersal is deemed significant for many applications. This modeling approach was first evaluated using controlled laboratory experiments, where uniform-sized spheres were released within a canopy comprised of uniform cylinders inside a flume (see figure). The extended model that includes intermittency effects, as well as inertial drag forces on the particles, was shown to provide superior fit with the measured dispersal kernel than simpler models that add a constant settling velocity for each particle and/or do not include intermittency. The extended model results captured short distance dispersal and the heavy tails. Next the extended model was evaluated against a field experiment, where plant seeds were manually released inside a hardwood forest canopy (see figure). This experiment explored the extended approach in light of uncertainty in the flow and in canopy density inherent to all field conditions. Whether increased complexity of dispersal models leads to improved predictive skills under uncertainty inherently present across all field conditions is discussed.