Streamflow forecasts on seasonal and interannual time scales for reservoir management

Monday, 15 December 2014
Andrew William Robertson, Columbia University of New York, International Research Institute for Climate and Society, Palisades, NY, United States, Mengqian Lu, Columbia University of New York, Palisades, NY, United States and Upmanu Lall, Columbia Univ, New York, NY, United States
Seasonal climate forecasts are beginning to be complemented by improved forecasting capabilities at both sub-seasonal and interannual annual timescales, with the future prospect of seamless climate forecasts for water system operations. While seasonal predictability is often very limited by physical and modeling constraints, harnessing additional predictable components of the climate system may in some cases substantially increase their usable information content, and provide more flexible forecasts in terms of the kinds of management decisions that can be informed.

Here we present an example of combining season and year-ahead streamflow forecasts as input to a multi-use reservoir optimization model, applied to the Bhakra Dam in NW India. Bi-timescale forecasts are made with a seasonal periodic autoregressive (PAR) model with exogenous climate-forecast inputs, together with an annual PAR model fit to observed flows used as a baseline for year-ahead forecasts. Annual net revenue from irrigation and hydropower supplies are calculated with contracts optimized using the reservoir optimization model. With Bhakra Dam inflows deriving from both winter storms/snow melt and the summer monsoon, it is found that net annual revenue is maximized when new contracts are initiated in March and June. We explore various choices of PARX model seasonal predictors based on climate model output and data and show that, with the choice of a good start date, even forecasts with relatively low skill can have value.