A sequential Bayesian approach for hydrologic model selection and prediction

Authors

Kuo-Lin Hsu, Portland State UniversityFollow
Hamid Moradkhani, Portland State UniversityFollow
Soroosh SorooshianFollow

Published In

Water Resources Research

Document Type

Article

Publication Date

1-1-2009

Subjects

Hydrologic models, Soil moisture, Bayesian statistical decision theory -- Applications to hydrology, Streamflow -- Forecasting

Abstract

When a single model is used for hydrologic prediction, it must be capable of estimating system behavior accurately at all times. Multiple-model approaches integrate several model behaviors and, when effective, they can provide better estimates than that of any single model alone. This paper discusses a sequential model fusion strategy that uses the Bayes rule. This approach calculates each model's transient posterior distribution at each time when a new observation is available and merges all model estimates on the basis of each model's posterior probability. This paper demonstrates the feasibility of this approach through case studies that fuse three hydrologic models, auto regressive with exogenous inputs, Sacramento soil moisture accounting, and artificial neural network models, to predict daily watershed streamflow.

Description

Copyright 2009 American Geophysical Union

DOI

10.1029/2008WR006824

Persistent Identifier

http://archives.pdx.edu/ds/psu/8281

Citation Details

Hsu, K., H. Moradkhani, and S. Sorooshian (2009), A sequential Bayesian approach for hydrologic model selection and prediction, Water Resour. Res., 45, W00B12.