Coupling hydrological modeling and support vector regression to model hydropeaking in alpine catchments.

Area of Science:

• Environmental science
• Hydrology
• Machine learning

Background:

• Water management in alpine regions significantly impacts streamflow.
• Hydropower production causes hydropeaking, characterized by sudden river stage fluctuations due to reservoir operations.
• Accurate modeling of hydropeaking often requires sensitive reservoir management data, which is typically unavailable.

Purpose of the Study:

• To develop a method for reproducing hydropeaking without needing explicit reservoir management rules.
• To couple a calibrated hydrological model with a machine learning approach for enhanced hydropeaking prediction.
• To assess the influence of energy prices on river discharge dynamics in relation to hydropeaking.

Main Methods:

• Utilized the Soil Water Assessment Tool (SWAT) for hydrological modeling.
• Employed a Support Vector Machine (SVM) machine learning model.
• Trained the SVM using SWAT outputs, day of the week, and energy price data.
• Applied wavelet analysis and wavelet coherence analysis to evaluate model performance and identify influences.

Main Results:

• Energy price was found to have a significant influence on river discharge.
• The coupled SVM model demonstrated improved performance compared to the SWAT model alone.
• The SVM model successfully captured streamflow fluctuations associated with hydropeaking, even with complex temporal dynamics in energy price and river discharge.

Conclusions:

• Machine learning, specifically SVM, can effectively reproduce hydropeaking dynamics when integrated with hydrological models.
• This approach overcomes the limitation of unavailable reservoir management data.
• The findings highlight the significant impact of energy prices on hydropower-induced streamflow variations.