Land surface evapotranspiration (ET) is important in land-atmosphere interactions of water and energy cycles. However, regional ET simulation has a great uncertainty. In this study, a highly-efficient parameter optimization framework was applied to improve ET simulations of the Community Land Model version 4.0 (CLM4) in China. The CLM4 is a model at land scale, and therefore, the monthly ET observation was used to evaluate the simulation results. The optimization framework consisted of a parameter sensitivity analysis (also called parameter screening) by the multivariate adaptive regression spline (MARS) method and sensitivity parameter optimization by the adaptive surrogate modeling-based optimization (ASMO) method. The results show that seven sensitive parameters were screened from 38 adjustable parameters in CLM4 using the MARS sensitivity analysis method. Then, using only 133 model runs, the optimal values of the seven parameters were found by the ASMO method, demonstrating the high efficiency of the method. For the optimal parameters, the ET simulations of CLM4 were improved by 7.27%. The most significant improvement occurred in the Tibetan Plateau region. Additional ET simulations from the validation years were also improved by 5.34%, demonstrating the robustness of the optimal parameters. Overall, the ASMO method was found to be efficient for conducting parameter optimization for CLM4, and the optimal parameters effectively improved ET simulation of CLM4 in China.
Improved Land Evapotranspiration Simulation of the Community Land Model Using a Surrogate-Based Automatic Parameter Optimization Method
