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This study demonstrates the possibility of inverting hydrologic parametersusing surface flux and runoff observations in version 4 of the CommunityLand Model (CLM4). Previous studies showed that surface flux and runoffcalculations are sensitive to major hydrologic parameters in CLM4 overdifferent watersheds, and illustrated the necessity and possibility ofparameter calibration. Both deterministic least-square fitting andstochastic Markov-chain Monte Carlo (MCMC)-Bayesian inversion approaches areevaluated by applying them to CLM4 at selected sites with different climateand soil conditions. The unknowns to be estimated include surface andsubsurface runoff generation parameters and vadose zone soil waterparameters. We find that using model parameters calibrated by thesampling-based stochastic inversion approaches provides significantimprovements in the model simulations compared to using default CLM4parameter values, and that as more information comes in, the predictiveintervals (ranges of posterior distributions) of the calibrated parametersbecome narrower. In general, parameters that are identified to besignificant through sensitivity analyses and statistical tests are bettercalibrated than those with weak or nonlinear impacts on flux or runoffobservations. Temporal resolution of observations has larger impacts on theresults of inverse modeling using heat flux data than runoff data. Soil andvegetation cover have important impacts on parameter sensitivities, leadingto different patterns of posterior distributions of parameters at differentsites. Overall, the MCMC-Bayesian inversion approach effectively andreliably improves the simulation of CLM under different climates andenvironmental conditions. Bayesian model averaging of the posteriorestimates with different reference acceptance probabilities can smooth theposterior distribution and provide more reliable parameter estimates, but atthe expense of wider uncertainty bounds

Inverse modeling of hydrologic parameters using surface flux and runoff observations in the Community Land Model