HR: 1340h
AN: H13A-1314    [Abstracts]
TI: An Efficient and Effective Strategy in Uncertainty Evaluation of Conceptual Hydrologic Models
AU: * Moradkhani, H
EM: moradkha@uci.edu
AF: University of California, Irvine, E/4130 Engineering Gateway Center for Hydrometeorology and remote Sensing Department of Civil And Environmental Engineering, Irvine, CA 91697 United States
AU: Hsu, K
EM: kuolinh@uci.edu
AF: University of California, Irvine, E/4130 Engineering Gateway Center for Hydrometeorology and remote Sensing Department of Civil And Environmental Engineering, Irvine, CA 91697 United States
AU: Sorooshian, S
EM: soroosh@uci.edu
AF: University of California, Irvine, E/4130 Engineering Gateway Center for Hydrometeorology and remote Sensing Department of Civil And Environmental Engineering, Irvine, CA 91697 United States
AB: Two elementary issues in contemporary earth system science are (1) the specification of model parameters which characterize a system, (2) the estimation of dynamic state (prognostic) variables which express the system dynamic. Reliable estimation of these elements is needed to enable the model to generate the forecasts as accurate as possible. Emerging technologies in Bayesian estimation within the Monte Carlo framework provides a platform for improved estimation of hydrologic model components and uncertainty assessment by complete representation of forecast and analysis probability distributions. In this study, the major effort goes into introducing the recursive Bayesian information fusion technique within the context of stochastic filtering as an alternative approach to batch calibration to characterize and reduce the uncertainties associated with hydrologic model parameters and state variables. The issues of sequential sampling/resampling and also the sensitivity of the model performance to the resampling and ensemble size as two key components in this scheme are emphasized. The power and effectiveness of the procedures are demonstrated by streamflow data assimilation into a conceptual hydrologic model where predictive uncertainty as the final product of the methodology is obtained.
DE: 1816 Estimation and forecasting
DE: 1846 Model calibration (3333)
DE: 1860 Streamflow
DE: 1869 Stochastic hydrology
DE: 1873 Uncertainty assessment (3275)
SC: Hydrology [H]
MN: Fall Meeting 2005