Journal
ADVANCES IN WATER RESOURCES
Volume 51, Issue -, Pages 457-478Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.advwatres.2012.04.002
Keywords
Particle filtering; Bayes; Sequential Monte Carlo; Markov chain Monte Carlo; DREAM; Parameter and state estimation
Categories
Funding
- Department of Energy, Office of Biological and Environmental Research
- J. Robert Oppenheimer Fellowship of the Los Alamos National Laboratory postdoctoral program
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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. Published by Elsevier Ltd.
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