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Advances in Land Surface Models and Indicators for Drought Monitoring and Prediction

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  • 1 Earth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, Maryland
  • | 2 Science Applications International Corporation, Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland
  • | 3 Department of Geography, University of California, Los Angeles, Los Angeles, California
  • | 4 Department of Earth and Environmental Engineering, and Earth Institute, Columbia University, New York, New York
  • | 5 Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado
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Abstract

Millions of people across the globe are affected by droughts every year, and recent droughts have highlighted the considerable agricultural impacts and economic costs of these events. Monitoring the state of droughts depends on integrating multiple indicators that each capture particular aspects of hydrologic impact and various types and phases of drought. As the capabilities of land surface models and remote sensing have improved, important physical processes such as dynamic, interactive vegetation phenology, groundwater, and snowpack evolution now support a range of drought indicators that better reflect coupled water, energy, and carbon cycle processes. In this work, we discuss these advances, including newer classes of indicators that can be applied to improve the characterization of drought onset, severity, and duration. We utilize a new model-based drought reconstruction to illustrate the role of dynamic phenology and groundwater in drought assessment. Further, through case studies on flash droughts, snow droughts, and drought recovery, we illustrate the potential advantages of advanced model physics and observational capabilities, especially from remote sensing, in characterizing droughts.

CURRENT AFFILIATION—Environmental Modeling Center, NOAA/National Centers for Environmental Prediction, College Park, Maryland

© 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: Christa D. Peters-Lidard, christa.d.peters-lidard@nasa.gov

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

Abstract

Millions of people across the globe are affected by droughts every year, and recent droughts have highlighted the considerable agricultural impacts and economic costs of these events. Monitoring the state of droughts depends on integrating multiple indicators that each capture particular aspects of hydrologic impact and various types and phases of drought. As the capabilities of land surface models and remote sensing have improved, important physical processes such as dynamic, interactive vegetation phenology, groundwater, and snowpack evolution now support a range of drought indicators that better reflect coupled water, energy, and carbon cycle processes. In this work, we discuss these advances, including newer classes of indicators that can be applied to improve the characterization of drought onset, severity, and duration. We utilize a new model-based drought reconstruction to illustrate the role of dynamic phenology and groundwater in drought assessment. Further, through case studies on flash droughts, snow droughts, and drought recovery, we illustrate the potential advantages of advanced model physics and observational capabilities, especially from remote sensing, in characterizing droughts.

CURRENT AFFILIATION—Environmental Modeling Center, NOAA/National Centers for Environmental Prediction, College Park, Maryland

© 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: Christa D. Peters-Lidard, christa.d.peters-lidard@nasa.gov

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

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