Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 425037
Title Hydrologic data assimilation using particle Markov chain Monte Carlo simulation: Theory, concepts and applications
Author(s) Vrugt, J.A.; Braak, C.J.F. ter; Diks, C.G.H.
Source Advances in Water Resources 51 (2013). - ISSN 0309-1708 - p. 457 - 478.
DOI http://dx.doi.org/10.1016/j.advwatres.2012.04.002
Department(s) Biometris (PPO/PRI)
Biometris (WU MAT)
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2013
Keyword(s) rainfall-runoff models - stochastic parameter-estimation - ensemble kalman filter - global optimization - differential evolution - streamflow simulation - automatic calibration - metropolis algorithm - genetic algorithm - input uncertainty
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.
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