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

TL;DR

This paper evaluates the use of generalized polynomial chaos within a Bayesian framework to estimate uncertain hydrological parameters in the CLM4 land surface model, providing a probabilistic understanding of key parameters affecting hydrological fluxes.

Contribution

It introduces a novel Bayesian gPC-based surrogate modeling approach for parameter estimation in land surface models, including uncertainty quantification and variable selection.

Findings

Successfully estimated posterior distributions of key hydrological parameters.

Quantified the importance of different parameters using posterior probabilities.

Enhanced understanding of parameter uncertainties in CLM4 simulations.

Abstract

In this study, the applicability of generalized polynomial chaos (gPC) expansion for land surface model parameter estimation is evaluated. We compute the (posterior) distribution 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 this 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.