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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 16, issue 9
Hydrol. Earth Syst. Sci., 16, 3451–3460, 2012
https://doi.org/10.5194/hess-16-3451-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 16, 3451–3460, 2012
https://doi.org/10.5194/hess-16-3451-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 24 Sep 2012

Research article | 24 Sep 2012

On the utility of land surface models for agricultural drought monitoring

W. T. Crow1, S. V. Kumar2,3, and J. D. Bolten2 W. T. Crow et al.
  • 1USDA Hydrology and Remote Sensing Laboratory, Beltsville, MD, USA
  • 2NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 3Science Applications International Corporation, Beltsville, MD, USA

Abstract. The lagged rank cross-correlation between model-derived root-zone soil moisture estimates and remotely sensed vegetation indices (VI) is examined between January 2000 and December 2010 to quantify the skill of various soil moisture models for agricultural drought monitoring. Examined modeling strategies range from a simple antecedent precipitation index to the application of modern land surface models (LSMs) based on complex water and energy balance formulations. A quasi-global evaluation of lagged VI/soil moisture cross-correlation suggests, when globally averaged across the entire annual cycle, soil moisture estimates obtained from complex LSMs provide little added skill (< 5% in relative terms) in anticipating variations in vegetation condition relative to a simplified water accounting procedure based solely on observed precipitation. However, larger amounts of added skill (5–15% in relative terms) can be identified when focusing exclusively on the extra-tropical growing season and/or utilizing soil moisture values acquired by averaging across a multi-model ensemble.

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