HR: 0800h
AN: H51B-0356    [Abstracts]
TI: A multi-model hydrologic ensemble for seasonal streamflow forecasting in the western U.S.
AU: * Bohn, T J
EM: tbohn@hydro.washington.edu
AF: Dept. of Civil and Environmental Engineering, Box 352700 University of Washington, Seattle, WA 98195 United States
AU: Wood, A W
EM: aww@hydro.washington.edu
AF: Dept. of Civil and Environmental Engineering, Box 352700 University of Washington, Seattle, WA 98195 United States
AU: Akanda, A
EM: asa@hydro.washington.edu
AF: Dept. of Civil and Environmental Engineering, Box 352700 University of Washington, Seattle, WA 98195 United States
AU: Lettenmaier, D P
EM: dennisl@u.washington.edu
AF: Dept. of Civil and Environmental Engineering, Box 352700 University of Washington, Seattle, WA 98195 United States
AB: Since 2003, the Variable Infiltration Capacity (VIC) macroscale hydrology model has been applied in real time over the western U.S. for experimental ensemble hydrologic prediction at lead times of six months to a year. VIC hydrologic initial conditions are produced from gridded station observations during a two-year runup period prior to the forecast date; and hydrologic forecast ensembles are driven by climate forecasts from several sources, including NCEP and NASA climate model outputs, CPC official seasonal outlooks and, as a baseline forecast, Extended Streamflow Prediction (ESP). We are now in the process of expanding this approach to include forecasts made from a Bayesian combination of the results from a suite of land surface models. Our initial set of LSMs includes VIC, the NWS grid-based Sacramento model (HL-RMS) and the NCEP NOAH model. All three LSMs are implemented on the 1/8 degree grid used by the North American Land Data Assimilation System (N-LDAS). Here we present preliminary results from several river basins in the Western US, focusing on both retrospective deterministic simulations and retrospective ESP-based ensemble forecasts and forecast error properties. We compare linear regression and Bayesian methods of combining model results, and investigate seasonal and geographic variations in forecast skill. Our data set includes 20+ years of 1-year, ESP-based, 25-member ensemble forecasts for each model, using both April 1 and October 1 as starting dates, from several basins including the Salmon River, ID, the Feather River, CA, and the San Juan River, UT.
DE: 1805 Computational hydrology
DE: 1816 Estimation and forecasting
DE: 1847 Modeling
DE: 1860 Streamflow
SC: Hydrology [H]
MN: Fall Meeting 2005