Assimilation of vegetation conditions improves the representation of drought over agricultural areas

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  • 1 Science Applications International Corporation, Hydrological Sciences Laboratory, NASA Goddard Space Flight Center (GSFC), Greenbelt, MD
  • 2 Hydrological Sciences Laboratory, NASA GSFC, Greenbelt, MD
  • 3 Deputy Director for Hydrosphere, Biosphere, and Geophysics, Earth Sciences Division at NASA GSFC, Greenbelt, MD
  • 4 Science Applications International Corporation, Hydrological Sciences Laboratory, NASA GSFC, Greenbelt, MD
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Abstract

This study presents an evaluation of the impact of vegetation conditions on a land-surface model (LSM) simulation of agricultural drought. The Noah-MP LSM is used to simulate water and energy fluxes and states, which are transformed into drought categories using percentiles over the continental U.S. from 1979 to 2017. Leaf Area Index (LAI) observations are assimilated into the dynamic vegetation scheme of Noah-MP. A weekly operational drought monitor (the U.S. Drought Monitor) is used for the evaluation. The results show that LAI assimilation into Noah-MP’s dynamic vegetation scheme improves the model's ability to represent drought, particularly over cropland areas. LAI assimilation improves the simulation of the drought category, detection of drought conditions, and reduces the instances of drought false alarms. 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 such as irrigation towards improved simulation of agricultural drought.

Corresponding author: David M. Mocko, Science Applications International Corporation, Hydrological Sciences Laboratory, NASA GSFC, Code 617, Greenbelt, MD 20771.

Abstract

This study presents an evaluation of the impact of vegetation conditions on a land-surface model (LSM) simulation of agricultural drought. The Noah-MP LSM is used to simulate water and energy fluxes and states, which are transformed into drought categories using percentiles over the continental U.S. from 1979 to 2017. Leaf Area Index (LAI) observations are assimilated into the dynamic vegetation scheme of Noah-MP. A weekly operational drought monitor (the U.S. Drought Monitor) is used for the evaluation. The results show that LAI assimilation into Noah-MP’s dynamic vegetation scheme improves the model's ability to represent drought, particularly over cropland areas. LAI assimilation improves the simulation of the drought category, detection of drought conditions, and reduces the instances of drought false alarms. 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 such as irrigation towards improved simulation of agricultural drought.

Corresponding author: David M. Mocko, Science Applications International Corporation, Hydrological Sciences Laboratory, NASA GSFC, Code 617, Greenbelt, MD 20771.
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