Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Hydrol. Earth Syst. Sci., 21, 3557-3577, 2017
https://doi.org/10.5194/hess-21-3557-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
14 Jul 2017
Incorporating remote sensing-based ET estimates into the Community Land Model version 4.5
Dagang Wang1,2,3,4, Guiling Wang4, Dana T. Parr4, Weilin Liao1,2, Youlong Xia5, and Congsheng Fu4 1School of Geography and Planning, Sun Yat-sen University, Guangzhou, P.R. China
2Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou, P.R. China
3Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong High Education Institute, Sun Yat-sen University, Guangzhou, P.R. China
4Department of Civil and Environmental Engineering, University of Connecticut, Storrs, USA
5National Centers for Environmental Prediction/Environmental Modeling Center, and I. M. System Group at NCEP/EMC, College Park, Maryland, USA
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.

Citation: Wang, D., Wang, G., Parr, D. T., Liao, W., Xia, Y., and Fu, C.: Incorporating remote sensing-based ET estimates into the Community Land Model version 4.5, Hydrol. Earth Syst. Sci., 21, 3557-3577, https://doi.org/10.5194/hess-21-3557-2017, 2017.
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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.
Land surface models bear substantial biases. To reduce model biases, we apply a simple but...
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