Abstract
Stochastic models are often fitted to historical data in order to produce streamflow scenarios. These scenarios are used as input data for simulation/optimization models that support operational decisions for water resource systems. The streamflow scenarios are sampled from probability distributions conditioned on the available information, such as recent streamflow data. In this paper we introduce a procedure for further conditioning the probability distributions by considering the recent measurements of climatic variables, such as sea temperatures, that are used to describe the occurrence of El Nino. We adopt an auto-regressive model and use the “El Nino information” to refine the parameter estimation process for each time step. The corresponding methodology is tested for the monthly energy time series, “inflowing” to the power plants of Colombia. This is a linear combination of streamflow values for the 18 most important rivers of the country.
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