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Treatment of uncertainty using ensemble methods: Comparison of sequential data assimilation and Bayesian model averaging
Author(s) -
Vrugt Jasper A.,
Robinson Bruce A.
Publication year - 2007
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2005wr004838
Subject(s) - data assimilation , ensemble kalman filter , probabilistic logic , bayesian probability , kalman filter , uncertainty analysis , computer science , ensemble forecasting , uncertainty quantification , bayesian inference , sensitivity analysis , a priori and a posteriori , hydrological modelling , machine learning , artificial intelligence , extended kalman filter , meteorology , climatology , simulation , philosophy , epistemology , physics , geology
Predictive uncertainty analysis in hydrologic modeling has become an active area of research, the goal being to generate meaningful error bounds on model predictions. State‐space filtering methods, such as the ensemble Kalman filter (EnKF), have shown the most flexibility to integrate all sources of uncertainty. However, predictive uncertainty analyses are typically carried out using a single conceptual mathematical model of the hydrologic system, rejecting a priori valid alternative plausible models and possibly underestimating uncertainty in the model itself. Methods based on Bayesian model averaging (BMA) have also been proposed in the statistical and meteorological literature as a means to account explicitly for conceptual model uncertainty. The present study compares the performance and applicability of the EnKF and BMA for probabilistic ensemble streamflow forecasting, an application for which a robust comparison of the predictive skills of these approaches can be conducted. The results suggest that for the watershed under consideration, BMA cannot achieve a performance matching that of the EnKF method.