Elucidating Dominant Factors Affecting Land Surface Hydrological Simulations of the Community Land Model over China

In order to compare the impacts of the choice of land surface model (LSM) parameterization schemes, meteorological forcing, and land surface parameters on land surface hydrological simulations, and explore to what extent the quality can be improved, a series of experiments with different LSMs, forcing datasets, and parameter datasets concerning soil texture and land cover were conducted. Six simulations are run for the Chinese mainland on 0.1 degrees x 0.1 degrees grids from 1979 to 2008, and the simulated monthly soil moisture (SM), evapotranspiration (ET), and snow depth (SD) are then compared and assessed against observations. The results show that the meteorological forcing is the most important factor governing output. Beyond that, SM seems to be also very sensitive to soil texture information; SD is also very sensitive to snow parameterization scheme in the LSM. The Community Land Model version 4.5 (CLM4.5), driven by newly developed observation-based regional meteorological forcing and land surface parameters (referred to as CMFD_CLM4.5_NEW), significantly improved the simulations in most cases over the Chinese mainland and its eight basins. It increased the correlation coefficient values from 0.46 to 0.54 for the SM modeling and from 0.54 to 0.67 for the SD simulations, and it decreased the root-mean-square error (RMSE) from 0.093 to 0.085 for the SM simulation and reduced the normalized RMSE from 1.277 to 0.201 for the SD simulations. This study indicates that the offline LSM simulation using a refined LSM driven by newly developed observation-based regional meteorological forcing and land surface parameters can better model reginal land surface hydrological processes.

Automated summary

A series of experiments were conducted to compare the impacts of different land surface model parameterization schemes, meteorological forcing, and land surface parameters on land surface hydrological simulations. The results showed that meteorological forcing had the most significant influence on the output. Additionally, soil texture information greatly affected soil moisture simulations, while snow parameterization schemes in the land surface model had a significant impact on snow depth simulations. The study revealed that using a refined land surface model driven by observation-based regional meteorological forcing and land surface parameters can improve the modeling of regional land surface hydrological processes.