Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/930946

OpenURL Link
Title
A limited-memory acceleration strategy for MCMC sampling in hierarchical Bayesian calibration of hydrological models
Author/Creator
Kuczera, George;  Kavetski, Dmitri;  Renard, Benjamin;  Thyer, Mark
Institution
The University of Newcastle. Faculty of Engineering & Built Environment, School of Engineering
Description
Hydrological calibration and prediction using conceptual models is affected by forcing/response data uncertainty and structural model error. The Bayesian Total Error Analysis methodology uses a hierarchical representation of individual sources of uncertainty. However, it is shown that standard multiblock “Metropolis-within-Gibbs” Markov chain Monte Carlo (MCMC) samplers commonly used in Bayesian hierarchical inference are exceedingly computationally expensive when applied to hydrologic models, which use recursive numerical solutions of coupled nonlinear differential equations to describe the evolution of catchment states such as soil and groundwater storages. This note develops a “limited-memory” algorithm for accelerating multiblock MCMC sampling from the posterior distributions of such models using low-dimensional jump distributions. The new algorithm exploits the decaying memory of hydrological systems to provide accurate tolerance-based approximations of traditional “full-memory” MCMC methods and is orders of magnitude more efficient than the latter.
Relation
Water Resources Research Vol. 46
Publisher Link
http://dx.doi.org/10.1029/2009WR008985
Date
2010
Publisher
American Geophysical Union
Keyword(s)
nonlinear equations;  approximation algorithms;  Bayesian networks;  calibration;  catchments;  groundwater
Resource Type
journal article
Identifier
http://hdl.handle.net/1959.13/930946
Identifier
ISSN:0043-1397
Reviewed
Reviewed
7 Visitors 13 Hits 0 Downloads
Save/E-mail Citation
Citation Format
E-mail Address
Subject
"Water Resources Research"
 
OR