Geological Society of America

21 Remote Sensing Data, Modeling Frameworks and Applications

Thursday, 10 June 2010
8:00 AM - 12:00 PM
Centennial

A LAND DATA ASSIMILATION SYSTEM FOR FAMINE EARLY WARNING

Paper number: 21-2
Program: Special Sessions
Time: 8:10 AM – 8:30 AM

Authors

James P. Verdin Presenting Author
Physical Scientist, USGS/EROS, NIDIS Program Office, Boulder, CO
Christopher C. Funk
Research Geographer, USGS/EROS, Geography Department, Santa Barbara, CA
Christa Peters-Lidard
NASA Goddard Space Flight Center, Greenbelt, MD

Abstract

A custom instance of NASA's Land Information System (LIS) is being created to support operations of the Famine Early Warning Systems Network (FEWS NET), the US Agency for International Development’s (USAID) decision support system for international food aid programs. In the regions of concern, rural livelihood systems are typically based on subsistence agriculture and pastoralism, and are highly climate-sensitive. Drought can deal a heavy blow to communities that barely get by under normal climatic conditions. A land data assimilation system is being designed specifically for the domains, data streams, and monitoring/forecast requirements associated with food security assessment in these data-sparse, developing country settings.

USGS/FEWS NET presently handles a wide range of gridded satellite remote sensing and atmospheric model data products from NASA and NOAA to monitor and forecast crop growing conditions in the most food insecure countries of the world. They are used in a set of modeling applications that has developed in a piecemeal fashion over the years. The new FEWS NET Land Data Assimilation System (FLDAS) will be implemented to achieve more effective use of limited available hydroclimatic observations. It is being developed in partnership with the NASA Goddard Space Flight Center, with assistance from the University of Washington to integrate the latest version of the Variable Infiltration Capacity (VIC) model into LIS and FLDAS. USGS/FEWS NET will gain the capacity to make multi-model ensemble runs for weekly monitoring and seasonal forecasting of land surface variables. It will also be possible to use LIS to apply climate change modeling results to produce 21st century scenarios of land surface states relevant to food security assessment in regions of concern in Africa.