Despite considerable advances in our understanding of drought mechanisms (role of sea surface temperature, land atmosphere feedbacks, etc.), there has been little improvement in drought predictions on seasonal timescales. In fact, seasonal forecast information appears to provide little additional skill to hydrologic forecasts, beyond that obtained from the initial land conditions, though some improvement can be achieved by conditioning the forecasts on the El Niño–Southern Oscillation (ENSO).
The goal of this research was to improve hydrologic (precipitation, soil moisture, streamflow) prediction skill on subseasonal to seasonal timescales by developing and evaluating a prototype drought prediction system that takes advantage of a number of recent advances in our modeling and understanding of precipitation variability, as well as improvements in the soil moisture initial conditions.
This project was part of the Modeling Analysis, Predictions, and Projections (MAPP) Program and National Integrated Drought Information System (NIDIS) supported Drought Task Force I.
For more information, please contact Amanda Sheffield (firstname.lastname@example.org).