Assimilation of Remotely Sensed Leaf Area Index Estimates Improves Drought Estimation

Accurate representation of vegetation states is required for the modeling of terrestrial water-energy-carbon exchanges and the characterization of the impacts of natural and anthropogenic vegetation changes on the land surface. This study presents a comprehensive evaluation of the impact of assimilating remote sensing based Leaf Area Index (LAI) retrievals over the Continental U.S. in the Noah-MP land surface model, during a time period of 1979 to 2018. The results demonstrate that the assimilation has a beneficial impact on the simulation of key water budget terms such as soil moisture, evapotranspiration, snow depth, terrestrial water storage and streamflow, when compared with a large suite of reference datasets. In addition, the assimilation of LAI is also found to improve the carbon fluxes of Gross Primary Production (GPP) and Net Ecosystem Exchange (NEE). Most prominent improvements in the water and carbon variables are observed over the agricultural areas of the U.S. The systematic, added improvements from assimilation in a configuration that employs high quality boundary conditions highlight the utility of LAI data assimilation in capturing the impacts of human management and natural vegetation changes. These improvements on the model states and fluxes are also found to improve the estimation of droughts. The assimilation of LAI provides a better representation of root uptake of soil moisture, transpiration and ultimately the root zone soil moisture variability. Quantitative comparisons against the U.S. Drought Monitor confirm the beneficial impact of LAI assimilation on drought estimation.