Editorial Type:
Article
Article Type:
Research Article

Assimilation of Vegetation Conditions Improves the Representation of Drought over Agricultural Areas

David M. Mocko Science Applications International Corporation, Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland

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https://orcid.org/0000-0001-8688-5708
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Sujay V. Kumar Hydrological Sciences Laboratory, NASA GSFC, Greenbelt, Maryland

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Christa D. Peters-Lidard Earth Sciences Division, NASA GSFC, Greenbelt, Maryland

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Shugong Wang Science Applications International Corporation, Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Online Publication:
13 Apr 2021
Print Publication:
01 May 2021
Collections:
Progress in Advancing Drought Monitoring and Prediction
DOI:
https://doi.org/10.1175/JHM-D-20-0065.1
Page(s):
1085–1098
Restricted access

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 United States 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 toward improved simulation of agricultural drought.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: David M. Mocko, david.mocko@nasa.gov

This article is included in the Progress in Advancing Drought Monitoring and Prediction Special Collection.

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 United States 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 toward improved simulation of agricultural drought.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: David M. Mocko, david.mocko@nasa.gov

This article is included in the Progress in Advancing Drought Monitoring and Prediction Special Collection.

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