April 27, 2023
Journal Article

Inverse modeling of hydrologic parameters in CLM4 via generalized polynomial chaos in the Bayesian framework

Abstract

In this study, the applicability of generalized polynomial chaos (gPC) expansion for land surface model parameter estimation is evaluated. We compute the (posterior) distributions of the critical hydrological parameters that are subject to great uncertainty in the community land model (CLM). The unknown parameters include those that have been identified as the most influential factors on the simulations of surface and subsurface runoff, latent and sensible heat fluxes, and soil moisture in CLM4.0. We setup the inversion problem in the Bayesian framework in two steps: (i) build a surrogate model expressing the input-output mapping, and (ii) compute the posterior distributions of the input parameters. Development of the surrogate model is done with a Bayesian procedure, based on the variable selection methods that use gPC expansions. Our approach accounts for bases selection uncertainty and quantifies the importance of the gPC terms, and hence all the input parameters, via the associated posterior probabilities.

Published: April 27, 2023

Citation

Karagiannis G., Z. Hou, M. Huang, and G. Lin. 2022. Inverse modeling of hydrologic parameters in CLM4 via generalized polynomial chaos in the Bayesian framework. Computation 10, no. 5:72. PNNL-SA-175497. doi:10.3390/computation10050072

Research topics