Assimilating multi-source uncertainties of a parsimonious conceptual hydrological model using hierarchical Bayesian modeling
Hierarchical 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.