Recognizing the Famine Early Warning Systems Network: Over 30 Years of Drought Early Warning Science Advances and Partnerships Promoting Global Food Security

• DOI: 10.1175/bams-d-17-0233.1
• Publication Date: 2019-02-01T19:39:05Z
• Authors: Chris Funk; Shraddhanand Shukla; Wassila Mamadou T...; James Rowland; Andrew Hoell; Amy McNally; Gregory Husak; Nicholas Novella; Michael Budde; Christa Peters-Li...; Aklhalil Adoum; Gideon Galu; Diriba Korecha; Tamuka Magadzire; Mario Rodriguez; Miliaritiana Robjhon; Endalkachew Bekele; Kristi Arsenault; Pete Peterson; Laura Harrison; Steven Fuhrman; Frank Davenport; Marty Landsfeld; Diego Pedreros; Jossy P. Jacob; Curt Reynolds; Inbal Becker-Reshef; James Verdin
• Keywords: Famine; Early warning system

Abstract On a planet with population of more than 7 billion, how do we identify the millions drought-afflicted people who face real threat livelihood disruption or death without humanitarian assistance? Typically, these are poor and heavily dependent on rainfed agriculture livestock. Most live in Africa, Central America, Southwest Asia. When rains fail, incomes diminish while food prices increase, cutting off poorest (most often women children) from access to adequate nutrition. As seen Ethiopia 1984 Somalia 2011, shortages can lead famine. Yet slow-onset disasters also provide opportunities for effective intervention, as 2015 2017. Since 1985, U.S. Agency International Development’s Famine Early Warning Systems Network (FEWS NET) has been providing evidence-based guidance relief efforts. FEWS NET depends Drought System (DEWS) help understand, monitor, model, predict insecurity. Here an overview NET’s DEWS using examples recent climate extremes. While drought monitoring prediction provides just one part system, it draws many disciplines—remote sensing, prediction, agroclimatic monitoring, hydrologic modeling. describe multiagency multidisciplinary Food Security Outlooks. This uses diagnostic analyses guide predictions. Midseason droughts monitored multiple cutting-edge Earth-observing systems. Crop models translate observations into impacts. The resulting information feeds reports, helping save lives by motivating targeting timely assistance.