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Improving the operational effectiveness of hydropower systems is becoming more relevant considering the shift to renewable energy sources and the rising social, financial, and environmental costs associated with the construction of new hydropower facilities. This challenge is interdisciplinary as it involves technical, managerial, and institutional aspects. This paper focuses on a technical aspect, more specifically on the relationship between the quality of short-term ensemble streamflow forecasts and the energy produced by a hydropower system. A secondary objective is to measure the contribution of both the meteorological and structural uncertainties on the energy output. To achieve this, a numerical experiment comprising multiple sets of hydrologic ensemble forecasts of different quality and a suite of reservoir optimization models is developed for a case study in Canada (the hydropower system of the Gatineau River basin). These ensemble forecasts are processed by the short-term reservoir operation model in rolling-horizon mode over a planning period of 6 years. Each day, the short-term optimization model seeks to maximize the energy output over the 14-day forecast lead time considering the expected future value of the system derived from a midterm optimization model. The relationship between hydropower generation and common statistical scores characterizing the ensemble forecasts indicates that although there is a link between the quality of the forecasts and the energy production, it is not a one-to-one causal relationship. Our results also show that the diversity of hydrological models is beneficial to the production of energy, indicating that the diversity of model structures compensates the deficiencies of individuals models and adds value to the forecast. [ABSTRACT FROM AUTHOR]

The performance of different models and procedures for forecasting aggregated May–July streamflow for the Churchill Falls basin on the Québec-Labrador peninsula is compared. The models compared have different lead times and include an autoregressive model using only past streamflow data, an autoregressive with exogenous input model utilizing both past streamflow and precipitation, and a linear regression model using the principal components of exogenous measures of atmospheric circulation inferred from the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis project. The forecast skills of the different approaches are compared using a variety of measures of performance. The results indicate that relatively accurate forecasts using only measures of atmospheric circulation can be issued as early as in December of the prior year. A multimodel combination approach is found to be more effective than the use of a single forecast model. In addition, it is concluded that forecasting models utilizing atmospheric circulation data are useful, especially for basins where hydroclimatic observations are scarce and for basins where flows and other hydroclimatic variables are not strongly autocorrelated (do not depend on their past). [ABSTRACT FROM AUTHOR]

This study compares sequential and variational streamflow assimilation techniques for short-term hydrological forecasting based on a lumped conceptual rainfall-runoff model and two dissimilar watersheds (Canada and Germany). The assessment targets the Ensemble Kalman filter (EnKF) and variational data assimilation (VDA). Deterministic streamflow forecasts are computed on a daily time step over a 10-day forecast horizon, using meteorological observations as inputs to the model. Results show that the EnKF leads to the highest performance for all forecast horizons while the optimal set-up for the VDA, which often competes with the EnKF, varies from one watershed to the other. EnKF surpasses forecasts without assimilation for all horizons and for both watersheds where the NSE varies between 0.88 and 0.79 on the au Saumon watershed in Canada and between 0.92 and 0.87 on the Schlehdorf watershed in Germany on a 10-day horizon, which is not always true for the VDA. The naïve output assimilation is also assessed and is only helpful for the first two days of the forecasts. [ABSTRACT FROM AUTHOR]

To model water-pipe breaks a generalization is proposed of an approach where a Weibull exponential model was used for simulation. In this study, a generalization of this approach was performed by developing a general model using three or more probability distributions to describe the time of occurrence of a pipe's failure. The previous two-distribution model constitutes a special case of this new general model. In addition, explanatory variables were incorporated into the proposed model. Finally, from the proposed general model, the Weibull-exponential-exponential (WEE) model was derived to model water-distribution pipe breaks in a small city in Canada. [ABSTRACT FROM AUTHOR]