HR: 1340h
AN: H13B-0414    [Abstracts]
TI: Uncertainty in Computational Simulations of Geophysical Mass Flows
AU: Pitman, E
EM: pitman@buffalo.edu
AF: Geophysical Mass Flow Group, University at Buffalo, Buffalo, NY 14260 United States
AU: Patra, A
EM: abani@eng.buffalo.edu
AF: Geophysical Mass Flow Group, University at Buffalo, Buffalo, NY 14260 United States
AU: Dalbey, K
EM: kdalbey@buffalo.edu
AF: Geophysical Mass Flow Group, University at Buffalo, Buffalo, NY 14260 United States
AU: Namikawa, L
EM: namikawa@buffalo.edu
AF: Geophysical Mass Flow Group, University at Buffalo, Buffalo, NY 14260 United States
AU: Rupp, B
EM: brrupp@buffalo.edu
AF: Geophysical Mass Flow Group, University at Buffalo, Buffalo, NY 14260 United States
AU: * Bursik, M
EM: mib@geology.buffalo.edu
AF: Geophysical Mass Flow Group, University at Buffalo, Buffalo, NY 14260 United States
AU: Sheridan, M
EM: mfs@geology.buffalo.edu
AF: Geophysical Mass Flow Group, University at Buffalo, Buffalo, NY 14260 United States
AB: We address the uncertainty inherent in modeling and computational simulations of geophysical mass flows. This uncertainty arises from unresolved physics, modeling simplifications, errors in terrain data or constitutive parameters, and the inaccuracies of any numerical method. The errors must be incorporated directly into the modeling and computations, and ensembles of solutions to the deterministic model equations, coupled to statistical analysis, are necessary to provide meaningful results for hazard assessment and risk mitigation associated with geophysical mass flows such as avalanches and landslides. In recent years a set of depth averaged equations (the Savage-Hutter model) with simple constitutive modeling assumptions has come into wide usage. In earlier work we developed the TITAN toolset that uses state of the art numerical methodology (high performance computing, adaptive gridding, etc.) to construct approximate solutions to these systems of equations for modeling flow over natural terrain. We describe in this contribution our efforts at incorporating uncertainty representations into this toolset. We use the recently developed methodology of polynomial chaos to represent uncertainty in the outputs based on input data uncertainty. In applying the polynomial chaos methodology to such systems we have had to overcome a series of technical difficulties. We will describe our solutions to each of these obstacles. Real-world results showing the comparison between model outputs and data collected in the field will be used in illustration.
UR: http://www.gmfg.buffalo.edu
DE: 1894 Instruments and techniques
DE: 1854 Precipitation (3354)
DE: 1860 Runoff and streamflow
DE: 1866 Soil moisture
DE: 1869 Stochastic processes
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting