HR: 17:45h
AN: H14A-08    [Abstracts]
TI: Hierarchical Data Assimilation for Multiple Source Uncertainty Estimation
AU: * Bastidas, L A
EM: Luis.Bastidas@usu.edu
AF: Utah State University, Utah Water Research Laboratory 8200 Old Main Hill, Logan, UT 84322-8200, United States
AU: Tcherednichenko, I A
EM: irinat@u.arizona.edu
AF: University of Arizona, Civil Engineering and Engineering Mechanics, Tucson, AZ 85721, United States
AU: Hooten, M
EM: mevin.hooten@usu.edu
AF: Utah State University, Department of Mathematics and Statistics Old Main Hill, Logan, UT 84322, United States
AB: Even the most intensive of today data-acquisition platforms cannot provide sufficient access to the spatial heterogeneity of geophysical system or the biological states of the environment and often are a source of conflicting information. Hydrologic models are laden, therefore, with a profusion of unobserved state variables. Reconciling model with observed behavior (inverse modeling) in order to improve understanding is quintessentially an issue of demonstrating, beyond reasonable doubt, that matching of the two approximations of the truth has not been achieved at the expense of imposing absurd values to the model parameters. We use a hierarchical data assimilation approach to provide a convenient mechanism for explicitly accounting for uncertainty by specifying manageable joint distributions that can be formulated by three separate components: Data Model, Process Model, and Parameter Model. In this way a quite complex joint statistical model can be specified in terms of a sequence of conditional models. For example, distinctly different data models can be specified so that they are conditioned on the same underlying process, and this underlying process can then be, in turn, specified so that it is conditioned on a set of model parameters. The hierarchical models also have the ability for explicit accounting of uncertainty in multiple components of the model. The specification of mechanistic models in the process component of a hierarchical framework explicitly assumes the model is wrong, but can account for this source of uncertainty by allowing for a quantifiable process model error term; at the same time, the framework allows the parameters pertaining to the distributional forms of the data models and process models to be random and directly accounts for their inherent uncertainty. The framework also allows for the incorporation of multiple data types within the same model (useful for simultaneous use of observations of natural processes in several distinct ways simultaneously). In the present work we present an application of this hierarchical framework to the SAC-SMA model as a step towards application in distributed modeling. Some of the significant computational challenges involved are also discussed.
DE: 0550 Model verification and validation
DE: 1805 Computational hydrology
DE: 1846 Model calibration (3333)
DE: 1873 Uncertainty assessment (3275)
DE: 3260 Inverse theory
SC: Hydrology [H]
MN: 2007 Fall Meeting