EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Stochastic flow forecast tool in Mediterranean watersheds for hydropower plants management at operational time scales

Raquel Gómez-Beas1,2, María José Polo1,3, María Fátima Moreno3, Manuel del Jesus4, and Cristina Aguilar1,2
Raquel Gómez-Beas et al.
  • 1Fluvial Dynamics and Hydrology Research Group, Andalusian Institute for Earth System Research, University of Córdoba, Campus Rabanales, Edificio Leonardo da Vinci, Área de Ingeniería Hidráulica, 14017 Córdoba, Spain
  • 2University of Córdoba, Department of Mechanics, Campus Rabanales, Edificio Leonardo da Vinci, Área de Ingeniería Mecánica, 14017 Córdoba, Spain
  • 3University of Córdoba, Department of Agronomy, Unit of Excellence María de Maeztu (DAUCO), Campus Rabanales, Edificio Leonardo da Vinci, Área de Ingeniería Hidráulica, 14017 Córdoba, Spain
  • 4IHCantabria – Instituto de Hidráulica Ambiental de la Universidad de Cantabria, Santander, Spain


The operation of hydropower systems is carried out based on operation rules and environmental flows requirements. The effects of the highly temporal variability of hydrological regime in Mediterranean areas are more pronounced in Run-of-River hydropower systems, located in mountainous areas as they must often cease operation due to flow rates either below the turbine minimum discharge and the environmental flow requirements, or over the turbine maximum discharge. Conversely, regulated basins with storage systems are more resilient to changes in the short-medium term. In any case, having a forecast operational tool with delimited uncertainty and sufficient reliability would mean an improvement in the hydropower production planning, as well as a decrease in opportunity costs.

A stochastic flow forecast tool is applied in two selected Mediterranean hydropower systems (Southern Spain). In particular, the mini hydropower plants in Poqueira river, as representative of Run-of-River systems; and Los Hurones plant, with a reservoir, are presented. The forcing agents of the increase in humidity were first identified, being the snow and rainfall regimes in Poqueira, and the atmospheric pressure and NAO index in Los Hurones respectively. Secondly, the statistical modelling of dependent variables was carried out with parametric and non-parametric approaches to, finally, generate the probability distribution functions of occurrence of the flow regime. This structure of Bayesian dynamics forecast of water inputs to the plants on a week-month and month-season scale allows the forecast based on observable and verifiable antecedent conditions in quasi-real time.

The operationality of the hydropower plants refers to the probability of producing energy, so that its complementary value, probability of failure, is defined as the number of days in which the plant is not operating. Failure frequency and the associated operationality were calculated from the 250 stochastic replications of the 20-year period of the forcing agent in the selected case study. Including the 250 replications of the inputs allows considering the effect of different combinations of wet and dry years on the variables analysed, and provides the uncertainty associated with both, a certain value of operationality, and a fixed value of the hydrological variable at the desired time scale.

Results reveal that the higher operationality in Poqueira is given between April and May when the snowmelt produces greater flows, with a 25% probability of having less than 4 days of failure, lower than in the winter months (December to February), with a 25% probability of having 8-18 days of failure. In Los Hurones, with a 25% probability, the lowest failure will be 8-12 days between April and June, being significantly higher the rest of the year. Operational graphs obtained from the uncertainty analysis allow estimating how to plan the operation of hydroelectric plants to maximize its production based on the data observed in previous weeks and months.


Acknowledgments: This work has been funded by project TED2021-130937A-I00, ENFLOW-MED "Incorporating climate variability and water quality aspects in the implementation of environmental flows in Mediterranean catchments" with the economic collaboration of MCIN/AEI/10.13039/501100011033 and European Union "NextGenerationEU"/Plan de Recuperación, Transformación y Resiliencia.

How to cite: Gómez-Beas, R., Polo, M. J., Moreno, M. F., del Jesus, M., and Aguilar, C.: Stochastic flow forecast tool in Mediterranean watersheds for hydropower plants management at operational time scales, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16465,, 2023.