Water quality is essential for sustainable development, especially in Peru, where water scarcity and unequal distribution impact ecosystems and communities. High-altitude Andean reservoirs, such as El Pañe in the Arequipa region, are vital water sources but are increasingly threatened by eutrophication due to nutrient overloading, limited watershed management, and climate variability. Algal blooms caused by eutrophication reduce water quality and pose risks to public health, agriculture, and aquatic ecosystems. However, severe climate conditions and difficult accessibility limit the continued monitoring that is required to control eutrophication events in these environments.

This study presents a predictive model to identify and anticipate eutrophication events in Andean reservoirs of Peru by integrating climatic parameters with satellite spectral data. A dataset was constructed using Normalized Difference Chlorophyll Index (NDCI) for the El Pañe reservoir, climatic data (temperature, precipitation, radiation, wind speed), soil moisture, nitrogen oxide concentrations, and estimated sediment loss. A Random Forest model was trained to predict chlorophyll concentration and contrasted to chlorophyll estimations by an empirical non-linear model based on NDCI values. The model was able to estimate chlorophyll-a concentrations but struggled to adequately predict high chlorophyll-a values. Alternatively, a classification model that groups chlorophyll-a concentrations based on the Carlson’s Trophic State Index (TSI) was also explored. The binary classification model (eutrophic vs. non-eutrophic) achieved 85.9% accuracy and an AUC of 0.92, demonstrating strong performance in detecting algal blooms. Multi-class classification of five trophic states also showed satisfactory results, with AUC values ranging from 0.72 to 0.82. Furthermore, Principal Component Analysis (PCA) was used to assess relative importance of variables used in the Random Forest model. It revealed that radiation, wind speed, and soil moisture were the most influential factors affecting chlorophyll concentrations. Overall, these findings offer valuable insights into the drivers of eutrophication and provide a cost-effective, scalable approach for monitoring water quality in remote Andean reservoirs. The proposed models support data-driven decision-making for water resource management and early intervention in vulnerable ecosystems.