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A global flash drought inventory based on soil moisture volatility.

Mahmoud Osman1,2, Benjamin Zaitchik3, Jason Otkin4

  • 1Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, USA. mahosman01@gmail.com.

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|September 4, 2024
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This study introduces a global flash drought inventory using the Soil Moisture Volatility Index (SMVI). This tool helps monitor and predict rapid drought onset, crucial for agriculture and climate strategies.

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Area of Science:

  • * Climate Science
  • * Hydrology
  • * Agricultural Meteorology

Background:

  • * Rapidly developing flash droughts pose significant risks to agriculture and climate mitigation efforts.
  • * Existing drought monitoring systems struggle with timely detection of flash droughts.
  • * There is a critical need for specialized indices to identify and track flash drought onset speed.

Purpose of the Study:

  • * To develop a global flash drought inventory using the Soil Moisture Volatility Index (SMVI).
  • * To expand the SMVI methodology for global application, aiding in understanding and prediction.
  • * To provide a comprehensive dataset for analyzing flash drought drivers and impacts.

Main Methods:

  • * Utilized the Soil Moisture Volatility Index (SMVI) methodology.
  • * Applied the SMVI to a global scale dataset spanning from 1990 to 2021.
  • * Integrated atmospheric variables influencing soil moisture to characterize drought events.

Main Results:

  • * Generated a global flash drought inventory covering 1990–2021.
  • * The dataset includes detailed characteristics like onset, duration, and severity across various climate zones.
  • * The inventory provides a consistent, large-scale dataset for flash drought research.

Conclusions:

  • * The global SMVI-derived flash drought inventory is a valuable tool for research and operational applications.
  • * This dataset facilitates the analysis of flash drought dynamics and their climatic drivers.
  • * The inventory serves as a crucial resource for developing and validating flash drought prediction models.