TurbSeed—A model for wind dispersal of seeds in turbulent currents based on publicly available climate data

A numerical model (TurbSeed) is described which calculates explicitly the dispersal of plant seeds by wind, especially the fraction of seeds, which are transported more than 100 m starting at the inflorescence. Such rare events are called here long-distance dispersal. The framework of TurbSeed includes three main modules:(1)A turbulence model (TM) for a specified location and time period using meteorological parameters from publicly available mesoscale weather models or simulated climate data.(2)A Lagrangian model (LM) which calculates the seed transport in the turbulent boundary layer and(3)A module which considers the effect of weather conditions on release of seeds (RS).In this approach the turbulent field is prescribed by a coarse wind field derived from the mesoscale climate simulations superimposed by a random velocity component. Similar approaches are known as Eulerian–Lagrangian approaches. The main difference in this new approach (TM) lies in the construction of the turbulent structure of the wind field. The introduction of such a random velocity component uses the Monin–Obukhov similarity theory. An inverse random phase Fast-Fourier-Transformation reconstructs the time structure of turbulence using a parameterized power spectrum of turbulence which is accepted as standard in micro meteorology.In the LM module Monte-Carlo-Simulations for numerous trajectories are used to incorporate the effects of turbulence. The RS module takes into account various conditions of release of plant seeds, such as the date of maturity of the seeds and the effect of wind speed on the release of seeds.This model can be driven not only with data from observed weather conditions, but also with output of mesoscale climate scenarios. Thus, TurbSeed can study the effect of climate change on wind dispersal of seeds.

► A model for turbulence fluctuations of vertical velocity was developed.
► Coupled to lagrangian particle model to simulate seed dispersal.
► Combining public available meteorological data with anemochory.
► Database different parameters like, fall velocities, flowering height and period.
► Several simulations using regional climate modeling data (REMO).