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

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