Value of various elements of the hydrological forecasting chain: Is there a successful pathway for improving the overall performance?

• Hydrological forecast value is evaluated for hydropower generation. • Elements of the forecasting chain are altered to determine their relative importance. • No single element has a dominant contribution to forecast value. • To improve forecast value, at least three elements must be improved simultaneously. • Hydrological model, forcing data and objective function are the most important elements. Water resources management relies heavily on hydrological forecasting, and continuous improvements are made to better manage hydropower reservoirs and improve their profitability. The significance of hydrological forecasting has been extensively studied in the literature, but the relative value of the various elements composing a forecasting system has been less investigated to date, making it hard to pinpoint which element to focus research on to improve the overall profitability of hydropower systems. This paper investigates if one or more of the following four elements of the hydrological forecasting chain has more impact on the variance in profit generation in an operational context, namely 1) the hydrological model, 2) the hydrometeorological dataset, 3) the objective function used for calibration, and 4) the bias/dispersion in the ensemble streamflow prediction (ESP) system. The value of these elements is assessed by making a full factorial design experiment. The elements are changed in various combinations to generate various ESPs, feeding a test bench which simulates a single hydropower generating reservoir. A linear programming algorithm is then used to optimize water management decisions. The value of the analyzed elements in the forecasting chain is evaluated by comparing the variance in the average profit generated by each of the ESPs, grouped by combination for each element. The impacts of other constraints such as energy purchase price and minimum load constraints are also evaluated. For the studied system, results show that the elements taken independently have little impact on the average profit variance, while higher-order interactions between the elements lead to a larger impact on profitability. However, bias/dispersion and its interactions with other elements show no significant impact on the profit variance under the operational conditions in this study. Results show that multiple elements need to be simultaneously improved to achieve this goal.