HR: 1340h
AN: H33E-1432    [Abstracts]
TI: Scale Dependence and Subgrid-Scale Closures in Numerical Simulations of Landscape Evolution
AU: * Passalacqua, P
EM: passa018@umn.edu
AF: St. Anthony Falls Laboratory, University of Minnesota, 2 Third Ave. SE, Minneapolis, MN 55414 United States
AU: Porte'-Agel, F
EM: fporte@umn.edu
AF: St. Anthony Falls Laboratory, University of Minnesota, 2 Third Ave. SE, Minneapolis, MN 55414 United States
AU: Foufoula-Georgiou, E
EM: efi@umn.edu
AF: St. Anthony Falls Laboratory, University of Minnesota, 2 Third Ave. SE, Minneapolis, MN 55414 United States
AU: Paola, C
EM: cpaola@umn.edu
AF: St. Anthony Falls Laboratory, University of Minnesota, 2 Third Ave. SE, Minneapolis, MN 55414 United States
AB: Landscapes share many similarities with turbulence: both systems are chaotic, multifractal, and their dynamics can be described using very similar prognostic equations. In particular, modified versions of the Kardar-Parisi-Zhang (KPZ) equation (a low-dimensional analog to the Navier-Stokes equations) are becoming popular to describe and simulate landscape evolution. Therefore, modeling techniques developed for turbulence can potentially be useful in landscape simulations. Using a landscape evolution model based on a modified 2-D Kardar-Parisi-Zhang equation, in this work we study the resolution dependence of the simulated results in terms of fluxes, variances and spectral density. The simulated landscapes obtained with that model show a clear dependence on grid resolution. In particular, mean longitudinal profiles of elevation at steady state have an undesirable dependence on grid resolution due to the fact that the erosion rate increases with resolution. A new subgrid-scale parameterization is proposed to account for the scale dependence of the sediment fluxes. The erosion coefficient, assumed exactly known at one resolution, is multiplied by a new scale-dependence coefficient, which is computed dynamically at different time steps and positions in the landscape based on the dynamics of the resolved scales. This is achieved by taking advantage of the scale similarity that characterizes landscapes over a wide range of scales. The simulated landscapes obtained with the new model show a realistic evolution and a relatively small dependence on resolution.
DE: 1824 Geomorphology: general (1625)
DE: 1869 Stochastic hydrology
SC: Hydrology [H]
MN: Fall Meeting 2005