Towards a global Reservoir Assessment Tool for predicting hydrologic impacts and operating patterns of existing and planned reservoirs. (June 2021)

• Record Type: Journal Article
• Authors: Biswas, Nishan Kumar; Hossain, Faisal; Bonnema, Matthew; Lee, Hyongki; Chishtie, Farrukh
• Is Part Of: Environmental modelling & software. Volume 140(2021)
• Publication Date: 2021-06
• Subjects: Reservoir monitoring -- Satellite remote sensing -- Landsat -- Global hydrology; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105043
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Dam construction in developing nations is on the rise. Monitoring these dams is essential to understanding downstream hydrologic impacts and for better planning and management of water resources. Satellite observations and advancements in information technology now present a unique opportunity to overcome the traditional limitations of reservoir monitoring. In this study, a global reservoir monitoring framework was developed as an online tool for near real-time monitoring and impact analysis of existing and planned reservoirs based on publicly available and global satellite observations. The framework used a mass balance approach to monitor 1598 reservoirs in South America, Africa, and Southeast Asia. Simulated streamflow of the developed tool was validated in 25 river basins against a multidecadal record of in-situ discharge. The simulated storage change was validated against in-situ data from 77 reservoirs. The framework was able to capture reservoir state realistically for more than 75% of these reservoirs. At most in-situ gaging locations, the reservoir tool was able to capture streamflow with a correlation of more than 0.9 and a normalized root mean square error of 50% or less. The tool can now be used to study existing or planned reservoirs for short and long-term decision making and policy analysis.