Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
doi:10.5194/hess-2016-696
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
04 Jan 2017
Review status
This discussion paper is under review for the journal Hydrology and Earth System Sciences (HESS).
Incorporating remote sensing ET into 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, China
2Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou, 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 of 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 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 using the corrected ET. Using the observations of ET, runoff, and soil moisture content as benchmarks, we demonstrate that CLMET greatly reduces the biases existing in CLM. The improvement differs with region, being more significant in eastern CONUS than western CONUS, with the most striking improvement over the southeast CONUS. This regional dependence reflects primarily the regional dependence in the degree to which the relationship between observational and model ET remains time-invariant (a fundamental hypothesis of the Parr et al. method). The bias correction method provides an alternative way to improve the performance of land surface models, which could lead to more realistic drought evaluations with improved ET and soil moisture estimates.

Citation: Wang, D., Wang, G., Parr, D. T., Liao, W., Xia, Y., and Fu, C.: Incorporating remote sensing ET into Community Land Model version 4.5, Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-696, in review, 2017.
Dagang Wang et al.
Dagang Wang et al.
Dagang Wang et al.

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Short summary
Land surface models bear substantial biases in simulating surface water and energy budgets. To reduce model biases, we apply a bias correction method to Community Land Model version 4.5 (CLM4.5) by using remote sensing ET and test its performance over the conterminous US. The results show that the method greatly reduces the biases existing in CLM4.5. The bias correction method provides an alternative way to improve the model performance, which could lead to more realistic drought evaluations.
Land surface models bear substantial biases in simulating surface water and energy budgets. To...
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