Regonda, Satish K听1听;听Rajagopalan, Balaji听2听;听Clark, Martyn听3听;听Zagona, Edith听4
1听Dept of Civil, Environmental & Architectural Engineering, Cooperative Institute for Research in Environmental Sciences, University of Colorado at 麻豆影院
2听Dept of Civil, Environmental & Architectural Engineering, Cooperative Institute for Research in Environmental Sciences, University of Colorado at 麻豆影院
3听Cooperative Institute for Research in Environmental Sciences, University of Colorado at 麻豆影院
4听Center for Advanced Decision Support for Water and Environmental Systems, University of Colorado at 麻豆影院
We propose a multi-model ensemble forecast framework for streamflow forecasts at multiple locations that incorporates large-scale climate information. It has four broad steps - (i) Principal Component Analysis is performed on the spatial streamflows to identify the dominant modes of variability; (ii) Potential predictors of the dominant streamflow modes are identified from among large-scale climate features and snow water equivalent information; (iii) Objective criterion is used to select a suite of candidate nonlinear regression models each with different predictors and; (iv) Ensemble forecasts of the dominant streamflow modes are generated from the candidate models and are combined objectively to produce a multi-model ensemble 鈥� which are then back transformed to produce spatially coherent streamflow forecasts at all the locations. The utility of the framework is demonstrated in the skillful forecast of spring seasonal streamflows at six locations in the Gunnison River Basin at several lead times. The generated ensemble streamflow forecast provide valuable and useful information for optimal management and planning of water resources in the basin.
Regonda, S., B. Rajagopalan, M. Clark, and E. Zagona, 2005,A Multi-model Ensemble Forecast Framework: Application to Spring Seasonal Flows in the Gunnison River Basin , Water Resources Research, (accepted, pending revisions).