Abstract
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.
Highlights
Hydrologic models can deliver information useful for management of natural resources and natural hazards (Beven, 2009)
As this study aims to improve hydrologic forecasts into the hydropower reservoirs by complementing the conceptual model by an error model, we assume that the predictions from the HBV model are made using the best possible input data
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 to be made to outputs of the conceptual models to provide more accurate inflow forecasts into hydropower reservoirs several hours ahead
Summary
Hydrologic models can deliver information useful for management of natural resources and natural hazards (Beven, 2009). Different hydrologists have incorporated their perceptions of the functioning of hydrologic systems into their models and come up with several rival models; some of them process based and others data based (for thorough reviews of the historic development of hydrologic modelling refer to Todini, 2007 and Beven, 2012) These models can be grouped into two main classes, conceptual and data-driven models. Lumped conceptual hydrologic models are the most commonly used models in operational forecasting Models of this class use sets of mathematical expressions to provide a simplified generalization of the complex natural processes of the hydrologic systems in the headwater areas of reservoirs. Application of such models conventionally requires estimating the model parameters by conditioning them to observed hy-
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