HR: 11:50h
AN: H32E-06    [Abstracts]
TI: DROUGHT MONITORING AND FORECASTING FOR THE U.S. USING CLIMATE MODEL SEASONAL FORECAST
AU: * Luo, L
EM: lluo@princeton.edu
AF: Princeton University, Environmental Engineering and Water Resources, Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544,
AU: * Luo, L
EM: lluo@princeton.edu
AF: Princeton University, Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ 08543,
AU: Li, H
EM: haibinli@princeton.edu
AF: Princeton University, Environmental Engineering and Water Resources, Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544,
AU: Sheffield, J
EM: justin@princeton.edu
AF: Princeton University, Environmental Engineering and Water Resources, Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544,
AU: Wood, E F
EM: efwood@princeton.edu
AF: Princeton University, Environmental Engineering and Water Resources, Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544,
AB: 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.
UR: http://hydrology.princeton.edu/forecast
DE: 1812 Drought
DE: 1847 Modeling
DE: 1854 Precipitation (3354)
DE: 1860 Streamflow
DE: 3245 Probabilistic forecasting (3238)
SC: Hydrology [H]
MN: 2007 Fall Meeting