资源环境科技发展态势分析平台

The quality of the forecasts, that is, the accuracy in predicting the observed streamflow, affects the decisions that can be taken thus determining the success or failure of hydropower operations, that is, the so-called forecast value. Although preprocessing techniques can be employed to improve forecast quality, the corresponding improvement in forecast value to hydropower is not straightforward to anticipate because of the complex relationship between quality and value, which depends on the hydrometeorological regime and water system features. The objective of this paper is to demonstrate the value to hydropower reservoir operations of preprocessed (i.e., bias corrected and downscaled) subseasonal forecasts and to compare the forecast quality and value across and within seasons. We use the forecast ensembles provided by the European Center for Medium-Range Weather Forecasts (ECMWF). We assess forecast quality in terms of bias, Continuous Ranked Probability Score (CRPS), and Continuous Ranked Probability Skill Score (CRPSS), and forecast value in terms of mean revenue and avoided unproductive spill. We consider daily subseasonal hydrometeorological forecasts covering lead times up to 1 month and a quantile mapping technique to preprocess the hydrometeorological forecasts. Forecasts are then used in a rolling horizon set up to optimize hydropower operations of the Verzasca hydropower system in the Alps (CH). Results show that preprocessing is essential to improve both forecast quality and value. Although hydropower reservoir operations benefit from considering forecasts all the yearlong, the relationship between forecast quality and value is complex and strongly depends on the metrics used to assess forecast quality and value and on the season.