Bayesian Total Error Analysis For Hydrologic Models: Markov Chain Monte Carlo Methods To Evaluate The Posterior Distribution
نویسندگان
چکیده
Calibration and prediction in conceptual rainfallrunoff (CRR) modelling is affected by the sampling and measurement uncertainty in the forcing/response data and by the structural error of the model conceptualisation. The Bayesian Total Error Analysis methodology (BATEA) offers a robust approach to deal with these multiple sources of uncertainty. The core idea is to pose the model calibration as a Bayesian hierarchical model with latent variables describing uncertainties in the data and the CRR model. This provides the opportunity to directly and comprehensively address all sources of uncertainty.
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