HR: 1340h
AN: H13B-0409    [Abstracts]
TI: Combined States and Parameters Estimation in Transient Groundwater Modeling by the Simulated Based Particle Filtering
AU: * shu, q
EM: qiangshu@cc.usu.edu
AF: Utah Water Research Laboratory,Utah State University, UTAH STATE UNIV UMC8200 UWRL , LOGAN, UT 84322
AU: Mariush, K
EM: mkem@cc.usu.edu
AF: Utah Water Research Laboratory,Utah State University, UTAH STATE UNIV UMC8200 UWRL , LOGAN, UT 84322
AB: Transient modeling of groundwater flow and transport is done in a sequential pattern, and modelers make the predictions of the system states over a series of discrete time points. The nonlinearity of the system dynamic and sparse knowledge of the model parameters make such a task difficult, and the uncertainty about the spatial distributed parameters, such as hydraulic conductivity, dispersitivity, make it even more challenging. Generally the prior knowledge about the model parameters is initially used in modeling, but the sequential observations of system states can also be utilized to improve the predictions and decrease the uncertainty of the model parameters. Such observations are usually available in the real situations. The objective of this research is to introduce the simulated-based particle filtering into groundwater modeling and inverse modeling. By sequentially sampling the states predictions and model parameters conditioning in observation, the statistic of the dynamic states and the model parameters can be evaluated simultaneously, without assumption of normal distribution and linearization of differential equation of model dynamics. Its applications in the lumped and multi-dimensional modeling of groundwater flow and transport are explored.
DE: 1829 Groundwater hydrology
DE: 1832 Groundwater transport
DE: 1869 Stochastic processes
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting