Hydrologic data assimilation using particle Markov chain Monte Carlo simulation: Theory, concepts and applications

J.A. Vrugt, C.J.F. ter Braak, C.G.H. Diks

Research output: Contribution to journalArticleAcademicpeer-review

116 Citations (Scopus)

Abstract

During the past decades much progress has been made in the development of computer based methods for parameter and predictive uncertainty estimation of hydrologic models. The goal of this paper is twofold. As part of this special anniversary issue we first shortly review the most important historical developments in hydrologic model calibration and uncertainty analysis that has led to current perspectives. Then, we introduce theory, concepts and simulation results of a novel data assimilation scheme for joint inference of model parameters and state variables. This Particle-DREAM method combines the strengths of sequential Monte Carlo sampling and Markov chain Monte Carlo simulation and is especially designed for treatment of forcing, parameter, model structural and calibration data error. Two different variants of Particle-DREAM are presented to satisfy assumptions regarding the temporal behavior of the model parameters. Simulation results using a 40-dimensional atmospheric “toy” model, the Lorenz attractor and a rainfall–runoff model show that Particle-DREAM, P-DREAM(VP) and P-DREAM(IP) require far fewer particles than current state-of-the-art filters to closely track the evolving target distribution of interest, and provide important insights into the information content of discharge data and non-stationarity of model parameters. Our development follows formal Bayes, yet Particle-DREAM and its variants readily accommodate hydrologic signatures, informal likelihood functions or other (in)sufficient statistics if those better represent the salient features of the calibration data and simulation model used.
Original languageEnglish
Pages (from-to)457-478
JournalAdvances in Water Resources
Volume51
DOIs
Publication statusPublished - 2013

Keywords

  • rainfall-runoff models
  • stochastic parameter-estimation
  • ensemble kalman filter
  • global optimization
  • differential evolution
  • streamflow simulation
  • automatic calibration
  • metropolis algorithm
  • genetic algorithm
  • input uncertainty

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