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

Abstract

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° × 0.1° 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.

摘要

本文利用不同模式参数化方案、气象**迫和地表参数设计了六个模拟试验，并与观测进行对比分析，揭示了影响**区域陆面水文要素（土壤湿度、蒸散发和雪深等）模拟的主要因子。结果表明，气象**迫是陆面水文过程模拟的主要影响因子，土壤湿度和雪深的模拟分别与土壤质地信息和陆面模式参数化方案紧密相关。利用新发展的融合观测信息的**区域气象**迫和地表参数信息驱动陆面模式模拟，显著提高了**大部分区域陆面水文过程模拟精度，并减少了模拟的不确定性。土壤湿度模拟与观测的相关系数从0.46提高到0.54，雪深模拟与观测的相关系数从0.54提高到0.67；土壤湿度模拟与观测的均方根误差从0.093降低到0.085，雪深模拟与观测的均方根误差从1.277降低到0.201。