50 Years of Water Body Monitoring: The Case of Qaraaoun Reservoir, Lebanon

Abstract: The sustainable management of the Qaraaoun Reservoir, the largest surface water body in Lebanon located in the Bekaa Plain, depends on reliable monitoring of its storage volume despite frequent sensor malfunctions and limited maintenance capacity. This study introduces a sensor-free approach that integrates open-source satellite imagery, advanced water-extent segmentation, and machine learning to estimate the reservoir surface area and volume in near real time. Sentinel-2 and Landsat images are processed, where surface water is delineated using a newly proposed water segmentation index. A machine learning model based on Support Vector Regression (SVR) is trained on a curated dataset that includes water surface area, water level, and water volume calculations using a reservoir bathymetry survey. The model is then able to estimate reservoir volume relying solely on surface area extracted from satellite imagery, without the need for ground measurements. Water segmentation using the proposed index aligns with ground truth for more than 95 percent of the shoreline. Hyperparameter tuning with GridSearchCV yields an optimized SVR performance with error under 1.5 percent of full reservoir capacity and coefficients of determination exceeding 0.98. These results demonstrate the robustness and cost-effectiveness of the method, offering a practical solution for continuous, sensor-independent monitoring of reservoir storage. The proposed methodology can be replicated for other water bodies, and the resulting 50 years of time-series data is valuable for research on climate change and environmental patterns.