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Seasonal Drought Forecasting on the Example of the USA

  • Eric F. WoodEmail author
  • Xing Yuan
  • Joshua K. Roundy
  • Ming Pan
  • Lifeng Luo
Reference work entry

Abstract

Drought is a slowly developing process and usually begins to impact a region without much warning once the water deficit reaches a certain threshold. Predicting the drought a few months in advance will benefit a variety of sectors for drought planning and preparedness. In response to the National Integrated Drought Information System (NIDIS), the Princeton land surface hydrology group has been working on drought monitoring and forecasting for over 10 years and has developed a seasonal drought forecasting system based on global climate forecast models and a large-scale land surface hydrology model. This chapter will showcase the performances of the system in predicting soil moisture drought area, frequency, and severity over the Conterminous United States (CONUS) at seasonal scales; discuss about the challenges in forecasting streamflow for hydrologic drought; and provide an outlook for future developments and applications.

Keywords

Drought Seasonal forecast Hydrology Soil moisture Streamflow Severity Climate model Land surface model CFSv2 VIC NMME Ensemble prediction Postprocessing Downscaling Bayes 

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Eric F. Wood
    • 1
  • Xing Yuan
    • 2
  • Joshua K. Roundy
    • 3
  • Ming Pan
    • 1
  • Lifeng Luo
    • 4
  1. 1.Department of Civil and Environmental EngineeringPrinceton UniversityPrincetonUSA
  2. 2.RCE-TEA, Inst. of Atmosph. Phys.Chinese Academy of SciencesBeijingChina
  3. 3.Department of Civil, Environmental and Architectural EngineeringUniversity of KansasLawrenceUSA
  4. 4.Department of GeographyMichigan State UniversityEast LansingUSA

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