Skip to Main Content
Assimilating multi-source uncertainties of a parsimonious conceptual hydrological model using hierarchical Bayesian modelingAuthor(s): Wei Wu; James Clark; James Vose
Source: Journal of Hydrology 394(3-4):436-446
Publication Series: Scientific Journal (JRNL)
Download Publication (1.47 MB)
DescriptionHierarchical Bayesian (HB) modeling allows for multiple sources of uncertainty by factoring complex relationships into conditional distributions that can be used to draw inference and make predictions. We applied an HB model to estimate the parameters and state variables of a parsimonious hydrological model – GR4J – by coherently assimilating the uncertainties from the model, observations, and parameters at Coweeta Basin in western North Carolina. A state-space model was within the Bayesian hierarchical framework to estimate the daily soil moisture levels and their uncertainties. Results show that the posteriors of the parameters were updated from and relatively insensitive to priors, an indication that they were dominated by the data. The uncertainties of the simulated streamflow increased with streamflow increase. By assimilating soil moisture data, the model could estimate the maximum capacity of soil moisture accounting storage and predict storm events with higher precision compared to not assimilating soil moisture data. This study has shown that hierarchical Bayesian model is a useful tool in water resource planning and management by acknowledging stochasticity.
- You may send email to email@example.com to request a hard copy of this publication.
- (Please specify exactly which publication you are requesting and your mailing address.)
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationWu, Wei; Clark, James S.; Vose, James M. 2010. Assimilating multi-source uncertainties of a parsimonious conceptual hydrological model using hierarchical Bayesian modeling. Journal of Hydrology 394(3-4):436-446.
KeywordsHierarchical Bayesian modeling, Hydrological modeling, Soil moisture, Streamflow
- Response of hydrology to climate change in the southern Appalachian mountains using Bayesian inference
- A state-space modeling approach to estimating canopy conductance and associated uncertainties from sap flux density data
- Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments
XML: View XML