Skip navigation
Skip navigation

Bayesian inference of uncertainties in precipitation-streamflow modeling in a snow affected catchment

Koskela, Jarkko J; Croke, Barry; Koivusalo, H.; Jakeman, Anthony; Kokkonen, T.

Description

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 Rudbck 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...[Show more]

dc.contributor.authorKoskela, Jarkko J
dc.contributor.authorCroke, Barry
dc.contributor.authorKoivusalo, H.
dc.contributor.authorJakeman, Anthony
dc.contributor.authorKokkonen, T.
dc.date.accessioned2015-12-10T23:33:25Z
dc.identifier.issn0043-1397
dc.identifier.urihttp://hdl.handle.net/1885/69311
dc.description.abstractBayesian 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 Rudbck 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 Rudbck 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.
dc.publisherAmerican Geophysical Union
dc.rightsAuthor/s retain copyright
dc.sourceWater Resources Research
dc.subjectKeywords: Auto-regressive; Batch modes; Bayesian inference; Confidence limit; Degree-day model; Differential Evolution; Generalized Likelihood function; Informative Priors; Input uncertainty; Latent variable; Model parameters; Posterior distributions; Predictive va
dc.titleBayesian inference of uncertainties in precipitation-streamflow modeling in a snow affected catchment
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume48
dc.date.issued2012
local.identifier.absfor040608 - Surfacewater Hydrology
local.identifier.ariespublicationf5625xPUB1987
local.type.statusPublished Version
local.contributor.affiliationKoskela, Jarkko J, Aalto University
local.contributor.affiliationCroke, Barry, College of Medicine, Biology and Environment, ANU
local.contributor.affiliationKoivusalo, H., Aslto University
local.contributor.affiliationJakeman, Anthony , College of Medicine, Biology and Environment, ANU
local.contributor.affiliationKokkonen, T., Aalto University
local.bibliographicCitation.issue11
local.bibliographicCitation.startpage1
local.bibliographicCitation.lastpage19
local.identifier.doi10.1029/2011WR011773
local.identifier.absseo960909 - Mountain and High Country Land and Water Management
dc.date.updated2016-02-24T08:52:34Z
local.identifier.scopusID2-s2.0-84869387851
local.identifier.thomsonID000310962200002
dcterms.accessRightsOpen Access
CollectionsANU Research Publications

Download

File Description SizeFormat Image
01_Koskela_Bayesian_inference_of_2012.pdf1.43 MBAdobe PDF


Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  19 May 2020/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator