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Optimizing Reservoir Operations to Mitigate Drought Impacts in Arid Regions: Integrating Ground-Based and Satellite Data Analysis
Abstract
This study explores the relationship between dam operational plans and downstream drought occurrences, aiming to optimize dam operations for drought mitigation. Ground-based data from well measurements and dam volumes, as well as satellite-based information on precipitation, temperature, soil moisture, and humidity, are used for two locations: Qanona and Baysh dams in Saudi Arabia.
The methodology involves catchment delineation, upstream-downstream zone identification, correlation analysis, and data visualization. A matrix is created to compare monthly averages with annual averages, highlighting potential drought and groundwater recharge months.
Results indicate August as the wettest month, with June and July having the highest temperatures. Relative humidity is lowest in July, and soil moisture hits its lowest in May. Groundwater levels drop significantly in June for Qanona and July for Baysh.
The study identifies June and July as optimal months for dam water release to mitigate downstream drought for Qanona and Baysh dams. It showcases the potential of satellite data in detecting drought conditions.
In conclusion, this research emphasizes the importance of optimizing dam operations to address downstream drought impacts and offers insights into the interaction between dam management and hydroclimatic factors. Future work should test this methodology in diverse regions using long-term data.
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