A limited-memory acceleration strategy for mcmc sampling in hierarchical bayesian calibration of hydrological models

G. Kuczera (1) , D. Kavetski (1) , Benjamin Renard (2) , M. Thyer (1)

1 School of Engineering
2 UR HHLY - Hydrologie-Hydraulique

Hydrological calibration and prediction using conceptual models is affected by forcing/response data uncertainty and structural model error. The Bayesian Total Error Analysis methodology (BATEA) uses a hierarchical framework to represent individual sources of uncertainty. However, it is shown that standard multi-block Metropolis-within-Gibbs samplers commonly used in traditional Bayesian hierarchical Markov Chain Monte Carlo (MCMC) are exceedingly computationally expensive when applied to hydrologic models based on recursive numerical solution of coupled nonlinear differential equations describing the evolution of catchment states such as soil and groundwater storages. This note develops a limited-memory algorithm for accelerating multi-block 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 fullmemory MCMC methods and is orders of magnitude more efficient than the latter.

Mots clés: STATISTIQUES BAYESIENNE; MODELE HYDROLOGIQUE; INCERTITUDE; METHODE MCMC

Domaines: Sciences de l'environnement

G. Kuczera, D. Kavetski, Benjamin Renard, M. Thyer. A limited-memory acceleration strategy for mcmc sampling in hierarchical bayesian calibration of hydrological models. Water Resources Research, 2010, 46, 15 p. ⟨10.1029/2009WR008985⟩. ⟨hal-00506554⟩