Assimilation of Vegetation States Improves the Representation of Drought in Agricultural Areas

This study presents an evaluation of the land-surface model (LSM) simulation of drought through assimilation of vegetation states. The Noah-MP LSM is used over the continental U.S., driven using surface meteorology and precipitation from the North American Land Data Assimilation System (NLDAS). Remotely-sensed Leaf Area Index (LAI) vegetation states are assimilated into the dynamic vegetation module of Noah-MP using NASA’s Land Information System (LIS) software framework. Drought percentiles and categories are produced from multiple variable outputs of the Noah-MP LSM. U.S. Drought Monitor (USDM) weekly maps are used for the evaluation of drought. Several historical severe drought case studies are examined and will be presented, including depictions of simulated soil moisture, groundwater, and terrestrial water storage. The results show that LAI assimilation into Noah-MP’s dynamic vegetation scheme improves the model's ability to represent drought, particularly in areas with human-managed water management such as irrigation. The assimilation of LAI in these locations not only corrects model errors in the simulation of vegetation, but also can help to represent unmodeled physical processes towards improved simulation of agricultural drought.