From Probabilistic Seasonal Streamflow Forecasts to Optimal Reservoir Operations: A Stochastic Programming Approach

Abstract

Abstract We investigate the potential use of seasonal streamflow forecasts for the realtime operation of Angat reservoir (Philippines). The system is characterized by a strong intra- and inter-annual variability in the inflow process, which is further amplified by the El Nino Southern Oscillation (ENSO). We bank on the relationship between ENSO indices and local hydro-climatological processes to issue probabilistic streamflow forecasts (with a 3-month forecast horizon) and then integrate them within a Multistage Stochastic Programming (MSP) approach. The rolling-horizon, forecast-informed scheme is adopted for the period 1968–2014 and benchmarked against deterministic optimization solutions with perfect forecasts, climatology, and mean forecasts. We also compare its performance with the current operating rules, and the operating rules obtained by solving a Stochastic Dynamic Programming problem. Results show that the MSP approach can help reduce the severity of failures during prolonged droughts caused by ENSO.