This case study concerned a set of river catchments located in south-eastern France. They are mainly located in mountainous areas and are governed by a variety of hydrological regimes, from snow-influenced to pure pluvial runoff regimes.
The French energy company EDF operates hydropower reservoirs in these catchments. It runs an expert-based hydrological ensemble prediction system to monitor daily the risk of extreme events and to predict inflows to reservoirs located at upstream valley areas.
Forecasts and products are distributed to more than 300 users, including the EDF unit responsible for the optimisation/trading of resources and the guarantee of energy delivery to clients (energy purchase, production and sales), as well as the local hydraulic centres, responsible for dam security and reservoir management.
Decision-making on energy production or any necessary protection action is thus closely related to the river flow forecasts. For better decisions, it is crucial to have forecasts of good quality and to efficiently communicate them to the users.
The needs of hydropower users for accurate and reliable weather forecasts cover a wide range of space and time scales. Within this case study, we want to quantify how better forecasts translate into higher economic values in the hydropower sector, with a focus on the short-to-medium scale. This is expected to improve the forecast decision chain and the capacity of hydropower systems to cope with extreme hydrological events.
We better understand and quantify the impacts of improved predictability of hydro-meteorological events on hydropower planning and decision-making.
The links between quality and value of forecasts will be investigated, with a focus on hourly or daily time steps, according to the catchments’ dynamics and response times, in the short-to-medium range prediction horizon (lead time up to 7 to 10 days).
Tools and models
Our case study was based on a forecast decision modelling chain. Deterministic and ensemble weather forecasts were integrated in a conceptual hydrological model, calibrated for operational purposes. The generated streamflow forecasts were subsequently used as input to a heuristic water reservoir model for the optimisation of energy production.
Data available from stakeholders is complemented with weather forecasts from IMPREX Work Package 3 (Improved meteorological predictability and climate scenarios) and hydrological forecasts from IMPREX Work Package 4 (Improved predictability of hydrological extremes).
Our tools quantify both the quality and the value of hydro-meteorological predictions for the hydropower systems studied.
There are several purposes that guide the development of hydro-meteorological forecasting systems at the French energy company EDF. These include:
- Monitoring of hydrological risks related to extreme events (floods and droughts)
- Short-term (1 to 14 days) flow forecasting in targeted river basins
- Forecasting of low flows at lead times of weeks to months on a few large French river basins
- Prediction of long-term inflows to reservoirs located at upstream valley areas
- Monitoring of storms, strong winds and sticky snow throughout France
- Forecasting of water temperature and sediment transport to ensure water quality and environmental sustainability.
Today, there is a strong need to demonstrate the gain in using and improving probabilistic forecasts in terms of value for energy production. The actual gain of the forecast used in the decision-making process is difficult to quantify and several challenges remain.
We know that hydro-meteorological forecasts have an appraised value, but we still have to quantify the economic value and how it is impacted when the quality of the forecasts is improved.
Photos courtesy of Rémy Garçon, EDF DTG Grenoble, France
Our challenge is to provide insights on how hydro-meteorological forecasts of improved quality can better service hydropower with reliable and valuable information.