Application Of Google Earth Engine For Reservoir Sedimentation Assessment: The Case Of Two Reservoirs In Upper Awash River Basin, Ethiopia

Abstract

Globally reservoirs are experiencing severe problems of sedimentation, which have resulted in reduced storage capacity, increased flood risks, and reduce the integrity of dams. Accordingly, the consequences of sedimentation cause several economic, social, and environmental problems. Therefore, it became essential to evaluate sediment accumulation in all reservoirs. Various methods and techniques have been used to analyze sediment deposits in these reservoirs. Among these methods, the satellite remote sensing technique has proven to be an effective. Nevertheless, accurate and automated methods for calculating the reservoir storage capacity using satellite imagery are not yet available. Therefore, the present study developed an automated methodology using Google Earth Engine to assess reservoir sedimentation and applied it on Koka and Gefersa I/II reservoirs located in the upper Awash River Basin. The capability of the Google Earth Engine (GEE) for the assess reservoir sedimentation was then examined by comparing its results with those of recent bathymetric surveys. Landsat imagery, reservoir water level data, pre impoundment reservoir capacity and bathymetry survey data, were utilized. The storage capacity of the Koka reservoir between the MDDL 1583.54 m.a.s.l and FSL 1590.6 m.a.s.l was estimated to be 1044 Mm3 for the year 2021-2022. It was then compared to the original live storage capacity, and there is a loss of 606 Mm3 live storage capacity within a period of 60 years (from 1960 to 2021). Thus, the average annual loss was 10.1 Mm3 year-1 (0.97 %). Similarly, the storage capacity of Gefersa I/II between MDDL 2580.01 m.a.s.l and FSL 2587.11 m.a.s.l was estimated to be 8.102 Mm3 for the year 2021/2022. It was then compared to the original live storage capacity, and there is a loss of 0.398 Mm3 live storage capacity. Thus, the average annual loss was 0.007 Mm3 year-1 (0.08%). The findings revealed that the result found from GEE is in agreement with the results of the bathymetry. In addition, P-test and T-test results showed there is there is a close correspondence between the bathymetry survey result and GEE result for both reservoirs. Therefore, results demonstrated the effectiveness of utilizing Google Earth Engine as a powerful tool in monitoring and analyzing reservoir sedimentation.