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

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  • 1 Earth Sciences Division, Code 610.9, NASA Goddard Space Flight Center, Greenbelt, MD
  • 2 Science Applications International Corporation, Hydrological Sciences Laboratory, Code 617, NASA Goddard Space Flight Center, Greenbelt MD
  • 3 Department of Geography, University of California, Los Angeles, Los Angeles, CA
  • 4 Department of Earth and Environmental Engineering, Earth Institute and Data Science Institute, Columbia University, New York, NY
  • 5 Environmental Modeling Center, NOAA National Centers for Environmental Prediction, College Park, MD
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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 snow pack 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.

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

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 snow pack 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.

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