Articles | Volume 19, issue 8
Hydrol. Earth Syst. Sci., 19, 3695–3714, 2015
Hydrol. Earth Syst. Sci., 19, 3695–3714, 2015

Research article 27 Aug 2015

Research article | 27 Aug 2015

Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework

A. S. Gragne1, A. Sharma2, R. Mehrotra2, and K. Alfredsen1 A. S. Gragne et al.
  • 1Department of Hydraulic and Environmental Engineering, Norwegian University of Science and Technology, Trondheim, Norway
  • 2School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia

Abstract. Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead time is considered within the day-ahead (Elspot) market of the Nordic exchange market. A complementary modelling framework presents an approach for improving real-time forecasting without needing to modify the pre-existing forecasting model, but instead formulating an independent additive or complementary model that captures the structure the existing operational model may be missing. We present here the application of this principle for issuing improved hourly inflow forecasts into hydropower reservoirs over extended lead times, and the parameter estimation procedure reformulated to deal with bias, persistence and heteroscedasticity. The procedure presented comprises an error model added on top of an unalterable constant parameter conceptual model. This procedure is applied in the 207 km2 Krinsvatn catchment in central Norway. The structure of the error model is established based on attributes of the residual time series from the conceptual model. Besides improving forecast skills of operational models, the approach estimates the uncertainty in the complementary model structure and produces probabilistic inflow forecasts that entrain suitable information for reducing uncertainty in the decision-making processes in hydropower systems operation. Deterministic and probabilistic evaluations revealed an overall significant improvement in forecast accuracy for lead times up to 17 h. Evaluation of the percentage of observations bracketed in the forecasted 95 % confidence interval indicated that the degree of success in containing 95 % of the observations varies across seasons and hydrologic years.

Short summary
We present a forecasting system comprising additively set-up conceptual and simple error model. Parameters of the conceptual model were left unaltered, as are in most operational set-ups, and the data-driven model was arranged to forecast the corrective measures the conceptual model needs. We demonstrate that the present procedure could effectively improve forecast accuracy over extended lead times with a reliability degree varying inter-annually and inter-seasonally.