Abstract. Understanding catchment hydrological processes is essential for water resources management, in particular in data scarce regions. The Gilgel Abay catchment (a major tributary into Lake Tana, source of the Blue Nile) is undergoing intensive plans for water management, which is part of larger development plans in the Blue Nile basin in Ethiopia. To obtain a better understanding of the water balance dynamics and runoff generation mechanisms and to evaluate model transferability, catchment modeling has been conducted using the conceptual hydrological model HBV. Accordingly, the catchment of the Gilgel Abay has been divided into two gauged sub-catchments (Upper Gilgel Abay and Koga) and the un-gauged part of the catchment. All available data sets were tested for stationarity, consistency and homogeneity and the data limitations (quality and quantity) are discussed.Manual calibration of the daily models for three different catchment representations, i.e. (i) lumped, (ii) lumped with multiple vegetation zones, and (iii) semi-distributed with multiple vegetation and elevation zones, showed good to satisfactory model performances with Nash-Sutcliffe efficiencies R eff > 0.75 and > 0.6 for the Upper Gilgel Abay and Koga sub-catchments, respectively. Better model results could not be obtained with manual calibration, very likely due to the limited data quality and model insufficiencies. Increasing the computation time step to 15 and 30 days improved the model performance in both sub-catchments to R eff > 0.8. Model parameter transferability tests have been conducted by interchanging parameters sets between the two gauged sub-catchments. Results showed poor performances for the daily models (0.30< R eff < 0.67), but better Correspondence to: S. Uhlenbrook (s.uhlenbrook@unesco-ihe.org) performances for the 15 and 30 days models, R eff > 0.80. The transferability tests together with a sensitivity analysis using Monte Carlo simulations (more than 1 million model runs per catchment representation) explained the different hydrologic responses of the two sub-catchments, which seems to be mainly caused by the presence of dambos in Koga sub-catchment. It is concluded that daily model transferability is not feasible, while it can produce acceptable results for the 15 and 30 days models. This is very useful for water resources planning and management, but not sufficient to capture detailed hydrological processes in an ungauged area.
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 km 2 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.
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. We present here a new approach for issuing hourly reservoir inflow forecasts that aims to improve on existing forecasting models that are in place operationally, without needing to modify the pre-existing approach, but instead formulating an additive or complementary model that is independent and captures the structure the existing model may be missing. 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. The procedure presented comprises an error model added on top of an un-alterable constant parameter conceptual model, the models being demonstrated with reference to 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. Deterministic and probabilistic evaluations revealed an overall significant improvement in forecast accuracy for lead-times up to 17 h. Season based evaluations indicated that the improvement in inflow forecasts varies across seasons and inflow forecasts in autumn and spring are less successful with the 95% prediction interval bracketing less than 95% of the observations for lead-times beyond 17 h.
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