886 Developing Reservoir Seasonal Inflow Forecasts Based on Dynamic Climate Forecasts and Large-Scale Climate Information

Wednesday, 10 January 2018
Exhibit Hall 3 (ACC) (Austin, Texas)
Yanan Duan, Auburn Univ., Auburn, AL; and H. M. Gonzalez and D. Tian

Skillful reservoir inflow forecasts are essential for water resources planning and management in a river basin, particularly during extreme years. In this study, seasonal inflow forecasts for reservoir systems in the Alabama-Coosa-Tallapoosa (ACT) River Basin are developed based on the North American Multi-Model Ensemble (NMME) seasonal forecasts and large-scale climate phenomenon information. Reservoir seasonal inflow forecasts are computed from 1 to 10 months ahead of every season based on random forest and principal component regression with NMME seasonal precipitation, temperature, and Nino3.4 forecasts, and observed NAO, PDO, AMO, and NOI indices and precipitation, temperature, and inflow from the month preceding each season being used as predictors. The skill of our forecast streamflow is assessed in deterministic and probabilistic terms for all initialization months and seasons via leave-one-out cross validation. Overall, the system produces relatively skillful inflow forecasts. Working with water managers, the developed reservoir inflow forecasts are expected to be translated into improved reservoir operations for hydropower generation and water supply management.
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