Uncertainty Analysis for Computationally Expensive Models with Multiple Outputs

J Agric Biol Environ Stat. 2012 Dec;17(4):623-640. doi: 10.1007/s13253-012-0091-0. Epub 2012 May 12.


Bayesian MCMC calibration and uncertainty analysis for computationally expensive models is implemented using the SOARS (Statistical and Optimization Analysis using Response Surfaces) methodology. SOARS uses a radial basis function interpolator as a surrogate, also known as an emulator or meta-model, for the logarithm of the posterior density. To prevent wasteful evaluations of the expensive model, the emulator is built only on a high posterior density region (HPDR), which is located by a global optimization algorithm. The set of points in the HPDR where the expensive model is evaluated is determined sequentially by the GRIMA algorithm described in detail in another paper but outlined here. Enhancements of the GRIMA algorithm were introduced to improve efficiency. A case study uses an eight-parameter SWAT2005 (Soil and Water Assessment Tool) model where daily stream flows and phosphorus concentrations are modeled for the Town Brook watershed which is part of the New York City water supply. A Supplemental Material file available online contains additional technical details and additional analysis of the Town Brook application.

Keywords: Bayesian calibration; Computer experiments; Groundwater modeling; Inverse problems; Markov chain Monte Carlo; Radial basis functions; SOARS; SWAT model; Surrogate model; Town Brook watershed; Uncertainty analysis.