Impacts of data length on optimal parameter and uncertainty estimation of a land surface model

[1] The optimal parameters and uncertainty estimation of land surface models require that appropriate length of forcing and calibration data be selected for computing error functions. Most of the previous studies used less than two years of data to optimize land surface models. In this study, 18-year hydrometeorological data at Valdai, Russia, were used to run the Chameleon Surface Model (CHASM). The optimal parameters were obtained by employing a global optimization technique called very fast simulated annealing. The uncertainties of model parameters were estimated by the Bayesian stochastic inversion technique. Forty-four experiments were conducted by using different lengths of data from the 18-year record, and a total of about 3 million parameter sets were produced. This study found that different calibration variables require different lengths of data to obtain optimal parameters and uncertainty estimates which are insensitive to the period selected. In the case of optimal parameters, monthly root-zone soil moisture, runoff, and evapotranspiration require 8, 3, and 1 years of data, respectively. In the case of uncertainty estimates, monthly root-zone soil moisture, runoff, and evapotranspiration require 8, 8, and 3 years of data, respectively. Spin-up has little impact on the selection of optimal parameters and uncertainty estimates when evapotranspiration and runoff were calibrated. However, spin-up affects the selection of optimal parameters when soil moisture was calibrated.