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In this work, generalized polynomial chaos (gPC) expansion for land surface model parameter estimation is evaluated. We perform inverse modeling and compute the posterior distribution of the critical hydrological parameters that are subject to great uncertainty in the Community Land Model (CLM) for a given value of the output LH. 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 set up the inversion problem in the Bayesian framework in two steps: (i) building a surrogate model expressing the input–output mapping, and (ii) performing inverse modeling and computing the posterior distributions of the input parameters using observation data for a given value of the output LH. The development of the surrogate model is carried out 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 of the input parameters, via the associated posterior probabilities. 

We calibrate a mathematical model of renal tubulointerstitial fibrosis by Hao et al which is used to explore potential drugs for Lupus Nephritis, against a real data set of 84 patients. For this purpose, we present a general calibration procedure which can be used for the calibration analysis of other biological systems as well. Central to the procedure is the idea of designing a Bayesian Gaussian process (GP) emulator that can be used as a surrogate of the fibrosis mathematical model which is computationally expensive to run massively at every input value. The procedure relies on detecting influential model parameters by a GP‐based sensitivity analysis, and calibrating them by specifying a maximum likelihood criterion, tailored to the application, which is optimized via Bayesian global optimization.