Articles | Volume 21, issue 7
Research article
14 Jul 2017
Research article |  | 14 Jul 2017

Incorporating remote sensing-based ET estimates into the Community Land Model version 4.5

Dagang Wang, Guiling Wang, Dana T. Parr, Weilin Liao, Youlong Xia, and Congsheng Fu

Abstract. Land surface models bear substantial biases in simulating surface water and energy budgets despite the continuous development and improvement of model parameterizations. To reduce model biases, Parr et al. (2015) proposed a method incorporating satellite-based evapotranspiration (ET) products into land surface models. Here we apply this bias correction method to the Community Land Model version 4.5 (CLM4.5) and test its performance over the conterminous US (CONUS). We first calibrate a relationship between the observational ET from the Global Land Evaporation Amsterdam Model (GLEAM) product and the model ET from CLM4.5, and assume that this relationship holds beyond the calibration period. During the validation or application period, a simulation using the default CLM4.5 (CLM) is conducted first, and its output is combined with the calibrated observational-vs.-model ET relationship to derive a corrected ET; an experiment (CLMET) is then conducted in which the model-generated ET is overwritten with the corrected ET. Using the observations of ET, runoff, and soil moisture content as benchmarks, we demonstrate that CLMET greatly improves the hydrological simulations over most of the CONUS, and the improvement is stronger in the eastern CONUS than the western CONUS and is strongest over the Southeast CONUS. For any specific region, the degree of the improvement depends on whether the relationship between observational and model ET remains time-invariant (a fundamental hypothesis of the Parr et al. (2015) method) and whether water is the limiting factor in places where ET is underestimated. While the bias correction method improves hydrological estimates without improving the physical parameterization of land surface models, results from this study do provide guidance for physically based model development effort.

Short summary
Land surface models bear substantial biases. To reduce model biases, we apply a simple but efficient bias correction method to a land surface model. We first derive a relationship between observations and model simulations, and apply this relationship in the application period. While the bias correction method improves model-based estimates without improving the model physical parameterization, results do provide guidance for physically based model development effort.