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Abstract

In this poster, we will present the most recent results of applying multisite parametric uncertainty quantification (UQ) workflow for dimensionality reduction of ACME Land Model followed by targeted, site-specific, low-dimensional accurate surrogate model construction. Surrogate modeling is the key ingredient of the presented work, as it presents a reasonable approximation of input-output maps, as well as provides efficient means for uncertainty propagation and global sensitivity analysis (GSA), otherwise called variance-based decomposition. Specifically, we develop polynomial chaos (PC) surrogates using Bayesian inference. However, the PC surrogate construction still requires a large ensemble of simulations, especially when the number of parameters is large. Here we apply a new procedure, the weighted Iterative Bayesian Compressive Sensing (WIBCS) algorithm, which allows a sparse, high-dimensional PC surrogate with very few model evaluations, also quantifying uncertainties due to lack of enough model simulations.

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