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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/hess-2019-504
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-504
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 22 Oct 2019

Submitted as: research article | 22 Oct 2019

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A revised version of this preprint is currently under review for the journal HESS.

The Influence of Assimilating Leaf Area Index in a Land Surface Model on Global Water Fluxes and Storages

Xinxuan Zhang1, Viviana Maggioni1, Azbina Rahman1, Paul Houser1, Yuan Xue1, Timothy Sauer1, Sujay Kumar2, and David Mocko2 Xinxuan Zhang et al.
  • 1George Mason University, Fairfax, VA, USA
  • 2Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA

Abstract. Vegetation plays a fundamental role not only in the energy and carbon cycle, but also the global water balance by controlling surface evapotranspiration. Thus, accurately estimating vegetation-related variables has the potential to improve our understanding and estimation of the dynamic interactions between the water and carbon cycles. This study aims to assess to what extent a land surface model can be optimized through the assimilation of leaf area index (LAI) observations at the global scale. Two observing system simulation experiments (OSSEs) are performed to evaluate the efficiency of assimilating LAI through an Ensemble Kalman Filter (EnKF) to estimate LAI, evapotranspiration (ET), interception evaporation (CIE), canopy water storage (CWS), surface soil moisture (SSM), and terrestrial water storage (TWS). Results show that the LAI data assimilation framework effectively reduces errors in LAI simulations. LAI assimilation also improves the model estimates of all the water flux and storage variables considered in this study (ET, CIE, CWS, SSM, and TWS), even when the forcing precipitation is strongly positively biased (extremely wet condition). However, it tends to worsen some of the model estimated water-related variables (SSM and TWS) when the forcing precipitation is affected by a dry bias. This is attributed to the fact that the amount of water in the land surface model is conservative and the LAI assimilation introduces more vegetation, which requires more water than what available within the soil. Future work should investigate a multi-variate data assimilation system that concurrently merges both LAI and soil moisture (or TWS) observations.

Xinxuan Zhang et al.

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Xinxuan Zhang et al.

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Short summary
This study assesses to what extent a land surface model can be optimized through the assimilation of Leaf Area Index (LAI) observations at global scale. The model performance is evaluated by the model estimated LAI and five water flux/storage variables. Results show the LAI assimilation reduces errors of the model estimated LAI. The LAI assimilation also improves the five water variables under wet condition, but some of the model estimated water variables tend to be worse under dry condition.
This study assesses to what extent a land surface model can be optimized through the...
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