  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.
