HR: 1400h
AN: H53B-11    [Abstracts]
TI: Bayesian Approach in Ensemble Forcing Generation for Improved Ensemble Streamflow Prediction
AU: Henry, H
EM: hiramhenry@hotmail.com
AF: Portland State University, Department of Civil and Environmental Engineering 1930 SW 4th Ave, Suite 200, Portland, OR 97201, United States
AU: * Moradkhani, H
EM: hamidm@cecs.pdx.edu
AF: Portland State University, Department of Civil and Environmental Engineering 1930 SW 4th Ave, Suite 200, Portland, OR 97201, United States
AB: Many studies have demonstrated that precipitation is the primary source of uncertainty affecting streamflow prediction. As part of the Advance Hydrologic Prediction Service (AHPS) of the National Weather Service, improvements have been made to ensemble streamflow prediction (ESP) production. In the current ESP procedure, precipitation ensemble members from historical time series are treated equally. However, there is no strong reason that such a precipitation ensemble properly represents the precipitation uncertainty. Using a Bayesian approach, a weighting method has been developed to give preference to historical precipitation time series which resemble the hydrologic conditions preceding the current watershed state. In an ensemble forecast, this is done with the intent of decreasing the influence of precipitation records occurring during years that are dissimilar to the current state being simulated. In producing probabilistic river discharge forecasts, this would improve both precision and accuracy of probabilistic forecasts. The method uses, but is not restricted to, the NWSRFS soil moisture accounting model and can be applied to both lumped and distributed watershed simulations.
DE: 1816 Estimation and forecasting
DE: 1833 Hydroclimatology
DE: 1854 Precipitation (3354)
DE: 1866 Soil moisture
DE: 1873 Uncertainty assessment (3275)
SC: Hydrology [H]
MN: 2007 Joint Assembly