The application of sub-seasonal to seasonal (S2S) predictions for hydropower forecasting

TL;DR

This paper presents a method for generating skillful sub-seasonal to seasonal inflow forecasts for hydropower reservoirs using ensemble weather predictions and post-processing, demonstrating skill up to 6 weeks ahead and potential economic benefits.

Contribution

It introduces a novel approach combining ensemble weather forecasts with post-processing to produce calibrated probabilistic inflow forecasts without hydrological models.

Findings

Forecasts show skill up to 6 weeks ahead, especially in winter.

Forecasts struggle with high summer inflows.

Potential economic benefits confirmed through a stylised cost model.

Abstract

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.