  Inflow forecasts play an essential role in the management of hydropower
reservoirs. Forecasts help operators schedule power generation in advance to
maximise economic value, mitigate downstream flood risk, and meet environmental
requirements. The horizon of operational inflow forecasts is often limited in
range to ~2 weeks ahead, marking the predictability barrier of deterministic
weather forecasts. Reliable inflow forecasts in the sub-seasonal to seasonal
(S2S) range would allow operators to take proactive action to mitigate risks of
adverse weather conditions, thereby improving water management and increasing
revenue. This study outlines a method of deriving skilful S2S inflow forecasts
using a case study reservoir in the Scottish Highlands. We generate ensemble
inflow forecasts by training a linear regression model for the observed inflow
onto S2S ensemble precipitation predictions from the European Centre for
Medium-range Weather Forecasting (ECMWF). Subsequently, post-processing
techniques from Ensemble Model Output Statistics are applied to derive
calibrated S2S probabilistic inflow forecasts, without the application of a
separate hydrological model. We find the S2S probabilistic inflow forecasts
hold skill relative to climatological forecasts up to 6 weeks ahead. The inflow
forecasts hold greater skill during winter compared with summer. The forecasts,
however, struggle to predict high summer inflows, even at short lead-times. The
potential for the S2S probabilistic inflow forecasts to improve water
management and deliver increased economic value is confirmed using a stylised
cost model. While applied to hydropower forecasting, the results and methods
presented here are relevant to broader fields of water management and S2S
forecasting applications.
