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

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

doi:https://doi.org/10.5194/hess-21-3557-2017

Articles | Volume 21, issue 7

https://doi.org/10.5194/hess-21-3557-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue:

Observations and modeling of land surface water and energy...

https://doi.org/10.5194/hess-21-3557-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

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

Data sets

GLEAM ET dataset version 3.0a Global Land Evaporation Amsterdam Model (GLEAM) team http://www.GLEAM.eu

MOD16 Global Terrestrial Evapotranspiration dataset Numerical Terradynamic Simulation Group (NTSG) http://www.ntsg.umt.edu/project/mod16

FLUXNET-MTE ET dataset Max Planck Institute for Biogeochemistry https://www.bgc-jena.mpg.de/geodb/projects/Data.php

Global streamflow characteristic dataset, multi-year annual average Amsterdam Critical Zone Hydrology Group http://hydrology-amsterdam.nl/valorisation/GSCD.html

North American Soil Moisture Database (NASMD) soil moisture dataset Department of Geography's Climate Science Lab at Texas A&M University http://soilmoisture.tamu.edu/

Latent flux measurements AmeriFlux network http://ameriflux.lbl.gov/

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.