Enhancing Reservoir Management for Sustainable Hydropower Generation: A Machine Learning-Driven Approach in Response to Increasing Extreme Events in Ecuador

In recent years, frequent climate extreme events have significantly impacted various sectors, especially critical ones like hydropower generation. In Latin America and the Caribbean, hydropower constitutes a pivotal element, contributing 45% to the electricity supply. Among the countries in the region, Ecuador heavily relies on hydropower generation (80%). However, since October 2023, Ecuador has faced unprecedented challenges marked by significant deficits in energy production, not witnessed in the last few decades. This crisis, attributed to severe drought events in the Amazon region, directly impacts one of Ecuador's most crucial hydropower systemsthe Paute system. In addition to the crisis, suboptimal reservoir management practices exacerbate these impacts due to the lack of provision for extreme events. The resultant energy deficits are currently causing extensive power outages throughout the country, highlighting the urgency of addressing the issues in reservoir management. In this research, we introduce an innovative approach to enhance reservoir management efficiency. This approach involves integrating hydrometeorological in-situ and satellite-based data to develop forecasting models for reservoir water levels. We use Ecuadors largest reservoir, the Mazar reservoir belonging to the Paute system, as a case study. The modeling will employ advanced machine learning (ML) techniques, such as the proven-effective Long-Short Term Memory (LSTM), with the aim of identifying key influencers that significantly impact reservoir level forecasting. Furthermore, we will complement the modeling with the Shapley Additive Explanation method to enhance interpretability, providing insights into hydrological processes. This is intended not only to deepen our understanding of the relationship between hydrometeorological variables and reservoir water levels but also to enrich the input space for our reservoir level forecasting models, contributing to a more accurate and comprehensive predictive framework. The results of the innovative approach will be then used to develop a methodological framework named ML-Driven Reservoir Management with Integrated Extreme Events Forecasting for the Mazar reservoir, aimed at enhancing reservoir management efficiency during extreme events. The expected results include the identification of crucial hydrometeorological variables for Mazar level forecasting, with models capable of predicting reservoir levels at 15-day to monthly intervals based on dominant variables. This will provide a tangible demonstration of its application to improve management in future extreme event scenarios. Beyond optimizing reservoir management for enhanced hydropower generation efficiency, this approach aims to mitigate adverse impacts on Ecuador's developing sectors, fostering sustainability. By addressing inefficiencies in reservoir management, our study contributes to a more resilient and sustainable hydropower sector in Ecuador.