Abstract
As vegetation regulates water, carbon, and energy cycles from the local to the global scale, its accurate representation in land surface models is crucial. The assimilation of satellite-based vegetation observations in a land surface model has the potential to improve the estimation of global carbon and energy cycles, which in turn can enhance our ability to monitor and forecast extreme hydroclimatic events, ecosystem dynamics, and crop production. This work proposes the assimilation of a remotely sensed vegetation product (Leaf Area Index, LAI) within the Noah Multi-Parameterization land surface model using an Ensemble Kalman Filter technique. The impact of updating leaf mass along with LAI is also investigated. Results show that assimilating LAI data improves the estimation of transpiration and net ecosystem exchange, which is further enhanced by also updating the leaf mass. Specifically, transpiration anomaly correlation coefficients improve in about 77 and 66% of the global land area thanks to the assimilation of leaf area index with and without updating leaf mass, respectively. Random errors in transpiration are also reduced, with an improvement of the unbiased root mean square error in 70% (74%) of the total area without the update of leaf mass (with the update of leaf mass). Similarly, net ecosystem exchange anomaly correlation coefficients improve from 52 to 75% and random errors improve from 49 to 62% of the total pixels after the update of leaf mass. Better performances for both transpiration and net ecosystem exchange are observed across croplands, but the largest improvement is shown over forests and woodland. The global scope of this work makes it particularly important in data poor regions (e.g., Africa, South Asia), where ground observations are sparse or not available altogether but where an accurate estimation of carbon and energy variables can be critical to improve ecosystem and crop management.
Highlights
Vegetation regulates water, carbon, and energy cycles by supporting critical functions in the biosphere
This work showed that assimilating Global LAnd Surface Satellite (GLASS) Leaf Area Index (LAI) within a land surface model using an Ensemble Kalman Filter (EnKF) technique had an impact on the estimation of several variables related to vegetation (e.g., LAI, leaf mass, transpiration, Net Ecosystem Exchange (NEE)) at the global scale
Updating the leaf mass along with LAI was shown to modify the estimation of carbon variables when compared to only updates LAI
Summary
Vegetation regulates water, carbon, and energy cycles by supporting critical functions in the biosphere. In another study by Albergel et al (2017), a global land data assimilation system (LDAS-Monde) was applied over Europe and the Mediterranean basin to improve land surface variable estimation when SSM and LAI satellite-derived observations were assimilated using a Simplified Extended Kalman Filter. Another work by Ling et al (2019) assimilated GLASS LAI into the Community Land Model with carbon and nitrogen components (CLM4CN) using an Ensemble Adjustment Kalman Filter This experiment showed improvements in ET and gross primary production. Albergel et al (2020) has jointly assimilated SSM and LAI using the Simplified Extended Kalman Filter data assimilation technique to predict the impact of extreme events like heatwaves and droughts on land surface conditions over the globe They have used LDAS-Monde as the land surface model and assimilated ASCAT soil water index (SWI) and LAIGEOV1 LAI observation data within that model. This work investigates the impact of updating an additional prognostic variable (leaf mass) along with LAI at every time step when an observation becomes available on a set of water, energy, and carbon variables
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