DROUGHT MONITORING AND FORECASTING FOR THE U.S. USING CLIMATE MODEL SEASONAL FORECAST

Luo, L, Li, H, Sheffield, J, Wood, E F

Princeton University, 
Environmental Engineering and Water Resources, 
Department of Civil and Environmental Engineering, 
Princeton University, 
Princeton, 
NJ 08544,

Drought is the most costly natural hazard to the U.S. economy. Drought preparation and 
mitigation require skillful predictions of drought on-set, development, and recovery. 
A model-based Drought Monitor and Prediction System (DMAPS) is presented, and it 
provides a real-time quantitative drought assessment and prediction capability for the 
U.S. Using the North America Land Data Assimilation System (NLDAS) realtime 
meteorological forcing and the Variable Infiltration Capacity (VIC) land surface 
model, DMAPS is capable of capturing the development of the recent severe droughts in 
the West and Southeast of the U.S. since the beginning of 2007. Using seasonal climate 
forecasts from NCEP's Climate Forecast System (CFS) as one input, DMAPS also 
successfully predicted the evolution of the droughts several months in advance. The 
realtime monitoring and prediction of drought using DMAPS provides invaluable 
information for drought preparation and drought impact assessment at national and 
local scales. The prediction element of the DMAPS is also tested and evaluated in a 
hindcast mode for selected historical U.S. droughts. In these hindcasts, the system 
uses information from multiple climate model forecasts. In the presentation, an 
evaluation of the predictive skill of DMAPS is presented that includes quantitative 
metrics that measure the severity, area, duration of the drought forecasts. 
http://hydrology.princeton.edu/forecast