The application of sub-seasonal to seasonal (S2S) predictions for\n hydropower forecasting
Le résumé fourni par la source
Inflow forecasts play an essential role in the management of hydropower\nreservoirs. Forecasts help operators schedule power generation in advance to\nmaximise economic value, mitigate downstream flood risk, and meet environmental\nrequirements. The horizon of operational inflow forecasts is often limited in\nrange to ~2 weeks ahead, marking the predictability barrier of deterministic\nweather forecasts. Reliable inflow forecasts in the sub-seasonal to seasonal\n(S2S) range would allow operators to take proactive action to mitigate risks of\nadverse weather conditions, thereby improving water management and increasing\nrevenue. This study outlines a method of deriving skilful S2S inflow forecasts\nusing a case study reservoir in the Scottish Highlands. We generate ensemble\ninflow forecasts by training a linear regression model for the observed inflow\nonto S2S ensemble precipitation predictions from the European Centre for\nMedium-range Weather Forecasting (ECMWF). Subsequently, post-processing\ntechniques from Ensemble Model Output Statistics are applied to derive\ncalibrated S2S probabilistic inflow forecasts, without the application of a\nseparate hydrological model. We find the S2S probabilistic inflow forecasts\nhold skill relative to climatological forecasts up to 6 weeks ahead. The inflow\nforecasts hold greater skill during winter compared with summer. The forecasts,\nhowever, struggle to predict high summer inflows, even at short lead-times. The\npotential for the S2S probabilistic inflow forecasts to improve water\nmanagement and deliver increased economic value is confirmed using a stylised\ncost model. While applied to hydropower forecasting, the results and methods\npresented here are relevant to broader fields of water management and S2S\nforecasting applications.\n
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