HR: 16:30h
AN: A14A-05    [Abstracts]
TI: Multi-model Ensemble Forecast of Spring Seasonal Flows in the Gunnison River Basin
AU: * Regonda, S K
EM: regonda@colorado.edu
AF: Department of Civil, Environmental,and Architectural Engineering, Campus Box 428, University of Colorado, Boulder, CO 80309 United States
AU: * Regonda, S K
EM: regonda@colorado.edu
AF: Cooperative Institute for Research in Environmental Science, 216 UCB, University of Colorado, Boulder, CO 80309 United States
AU: Rajagopalan, B
EM: Rajagopalan.Balaji@colorado.edu
AF: Department of Civil, Environmental,and Architectural Engineering, Campus Box 428, University of Colorado, Boulder, CO 80309 United States
AU: Rajagopalan, B
EM: Rajagopalan.Balaji@colorado.edu
AF: Cooperative Institute for Research in Environmental Science, 216 UCB, University of Colorado, Boulder, CO 80309 United States
AU: Clark, M
EM: clark@vorticity.colorado.edu
AF: Cooperative Institute for Research in Environmental Science, 216 UCB, University of Colorado, Boulder, CO 80309 United States
AU: Zagona, E
EM: zagona@arroyo.Colorado.EDU
AF: Center for Advanced Decision Support for Water and Environmental Systems, 421 UCB, University of Colorado, Boulder, CO 80309 United States
AB: Water managers need ensemble streamflow forecasts so as to obtain ensemble of decision variables for optimal management and planning of water resources. This is more so the case in the western US. Large scale ocean-atmospheric features in the Pacific, in particular, have a significant impact on the variability of hydroclimatology in the western US. As a result, any ensemble forecast method should include this large-scale forcing information. Furthermore, the streamflows are required at several locations in a basin and the ensemble forecast method should preserve the spatial variability as well. To this end, we propose a multi-model ensemble forecast approach that has the following four steps: (i) Principal Component Analysis is performed on the spatial streamflows to identify the dominant modes of variability (ii) Large scale ocean-atmospheric predictors are identified for the dominant modes, (iii) Objective criteria, Generalized Cross Validation (GCV) is used to select a suite of candidate models, and (iv) Ensembles of forecast of the dominant modes and consequently the spatial flows are issued from the candidate models. The forecast model is local polynomial based nonparametric regression approach. The main advantage of this approach is that by forecasting ensembles for a suite of candidate models, model uncertainty is better captured. In addition, forecasting the dominant modes and then obtaining the flows helps in preserving the spatial variability. The utility of the framework is demonstrated in forecasting Spring streamflows at six locations in the Gunnison river basin. The approach exhibited substantial cross-validated skill.
DE: 1833 Hydroclimatology
DE: 1860 Runoff and streamflow
DE: 3309 Climatology (1620)
DE: 3322 Land/atmosphere interactions
SC: Atmospheric Sciences [A]
MN: 2005 Joint Assembly