Inverse Modeling of Hydrologic Parameters Using Surface Flux and Streamflow Observations in the Community Land Model

This study aims at demonstrating the possibility of calibrating hydrologic parameters using surface flux and streamflow observations in version 4 of the Community Land Model (CLM4). Previously we showed that surface flux and streamflow calculations are sensitive to several key hydrologic parameters in CLM4, and discussed the necessity and possibility of parameter calibration. In this study, we evaluate performances of several different inversion strategies, including least-square fitting, quasi Monte-Carlo (QMC) sampling based Bayesian updating, and a Markov-Chain Monte-Carlo (MCMC) Bayesian inversion approach. The parameters to be calibrated include the surface and subsurface runoff generation parameters and vadose zone soil water parameters. We discuss the effects of surface flux and streamflow observations on the inversion results and compare their consistency and reliability using both monthly and daily observations at various flux tower and MOPEX sites. We find that the sampling-based stochastic inversion approaches behaved consistently - as more information comes in, the predictive intervals of the calibrated parameters as well as the misfits between the calculated and observed observations decrease. In general, the parameters that are identified to be significant through sensitivity analyses and statistical tests are better calibrated than those with weak or nonlinear impacts on flux or streamflow observations. We also evaluated the possibility of probabilistic model averaging for more consistent parameter estimation.