Wednesday, 13 January 2016
Season-ahead reservoir inflow forecasts can assist water managers by anticipating and preparing for extreme conditions, such as droughts. Previous studies have developed statistical streamflow models, which rely on hydrologic persistence or large-scale climate variables. This study incorporates both, including local and global predictor information, in a novel hybrid autoregressive-principal component regression framework. The Lower Colorado River Basin in Texas is selected for assessment. Prediction model outputs are subsequently coupled to a reservoir operations model to compare the impacts of incorporating a streamflow forecast into management policies versus simply applying long-term historical averages. Specifically, implications to interruptible water contracts and allocations are assessed, conditioned on meeting end of season storage targets. Results demonstrate increased benefits with the forecast in both short and long-term operations, especially in response to extreme events.
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner