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. Vegetation plays a fundamental role not only in theenergy and carbon cycles but also in the global water balance bycontrolling surface evapotranspiration (ET). Thus, accurately estimatingvegetation-related variables has the potential to improve our understandingand estimation of the dynamic interactions between the water, energy, andcarbon cycles. This study aims to assess the extent to which a land surface model(LSM) can be optimized through the assimilation of leaf area index (LAI)observations at the global scale. Two observing system simulationexperiments (OSSEs) are performed to evaluate the efficiency of assimilatingLAI into an LSM through an ensemble Kalman filter (EnKF) to estimate LAI,ET, canopy-interception evaporation (CIE), canopy water storage (CWS),surface soil moisture (SSM), and terrestrial water storage (TWS). Resultsshow that the LAI data assimilation framework not only effectively reduceserrors in LAI model simulations but also improves all the modeled waterflux and storage variables considered in this study (ET, CIE, CWS, SSM, andTWS), even when the forcing precipitation is strongly positively biased(extremely wet conditions). However, it tends to worsen some of the modeledwater-related variables (SSM and TWS) when the forcing precipitation isaffected by a dry bias. This is attributed to the fact that the amount ofwater in the LSM is conservative, and the LAI assimilation introduces morevegetation, which requires more water than what is available within the soil.

The influence of assimilating leaf area index in a land surface model on global water fluxes and storages