Bayesian inference of uncertainties in precipitation-streamflow modeling in a snow affected catchment
Article first published online: 10 NOV 2012
©2012. American Geophysical Union. All Rights Reserved.
Water Resources Research
Volume 48, Issue 11, November 2012
How to Cite
2012), Bayesian inference of uncertainties in precipitation-streamflow modeling in a snow affected catchment, Water Resour. Res., 48, W11513, doi:10.1029/2011WR011773., , , , (
- Issue published online: 10 NOV 2012
- Article first published online: 10 NOV 2012
- Manuscript Accepted: 2 OCT 2012
- Manuscript Revised: 14 AUG 2012
- Manuscript Received: 27 DEC 2011
- hydrological modeling;
 Bayesian inference is used to study the effect of precipitation and model structural uncertainty on estimates of model parameters and confidence limits of predictive variables in a conceptual rainfall-runoff model in the snow-fed Rudbäck catchment (142 ha) in southern Finland. The IHACRES model is coupled with a simple degree day model to account for snow accumulation and melt. The posterior probability distribution of the model parameters is sampled by using the Differential Evolution Adaptive Metropolis (DREAM(ZS)) algorithm and the generalized likelihood function. Precipitation uncertainty is taken into account by introducing additional latent variables that were used as multipliers for individual storm events. Results suggest that occasional snow water equivalent (SWE) observations together with daily streamflow observations do not contain enough information to simultaneously identify model parameters, precipitation uncertainty and model structural uncertainty in the Rudbäck catchment. The addition of an autoregressive component to account for model structure error and latent variables having uniform priors to account for input uncertainty lead to dubious posterior distributions of model parameters. Thus our hypothesis that informative priors for latent variables could be replaced by additional SWE data could not be confirmed. The model was found to work adequately in 1-day-ahead simulation mode, but the results were poor in the simulation batch mode. This was caused by the interaction of parameters that were used to describe different sources of uncertainty. The findings may have lessons for other cases where parameterizations are similarly high in relation to available prior information.