Abstract:
The efficient management of water reservoirs is crucial for ensuring water security, flood control,
and hydroelectric power generation. Accurate reservoir volume measurements are essential for
optimizing these functions. Traditional methods, which rely on in-situ measurements and physical
surveys, are often time-consuming, resource-intensive, and impractical in many countries due to
financial constraints or complex environmental conditions. To address these challenges, this
research presents the development of a novel remote sensing tool that offers an effective and
globally accessible method for evaluating reservoir volumes.
This study explores the integration of satellite imagery and advanced geospatial analysis
techniques to estimate reservoir volumes. Using high-resolution imagery from Sentinel-2, along with
storage capacity data from official repositories and the GLOBAthy dataset, the tool automatically
calculates surface area and water volume at varying water levels over a user-defined time period.
The tool features an intuitive interface that allows users to easily select reservoirs, date ranges, and
cloud cover percentages, generating time-series data on volume. A key aspect lies in the use of
machine learning algorithms, which enhance the accuracy of water surface delineation and volume
estimation, even in the presence of built structures like bridges or challenging environmental
conditions such as cloud cover.
The tool was tested across reservoirs of varying sizes and topographies in Portugal and California,
USA. Comparative analyses showed a high degree of accuracy, with a mean absolute percentage
error of 5.4% and a correlation coefficient (R²) of 0.9 in comparison to official measurements. This
cost-effective, scalable, and timely alternative offers significant improvements for reservoir volume
evaluation, particularly in remote or hard-to-access regions. Ultimately, this research makes a
substantial contribution to global water resource management by enhancing the resilience and
sustainability of dam operations worldwide.
