| Remote sensing
Addis-Ziway which includes Addis Ababa and its surroundings, Bishoftu lakes, Ziway and Koka reservoirs.
Hayq-Hashenge. Tana site which covers mainly Lake Tana. Abaya-Chamo site includes Arba-Minch and the lakes of Abaya and Chamo.
The results confirmed that machine learning using Landsat data produces reliable results for surface water monitoring and provides spatio-temporal information to support surface water management and water policy in Ethiopia. Mathias Tesfaye and Lutz Breuer say the results of their study may have important implications for the country’s policy makers and water resource planners, particularly in the context of sustainable water management and achieving regional sustainable development goals.
Modelling dam failure
A casualty of the Ukrainian and Russian war, the Kakhovka hydropower dam on the Dnipro River in Ukraine collapsed in June 2023, causing widespread flooding in Europe’s greatest industrial and ecological disaster for decades. Once a major water resource supplier for more than 700,000 people, the reservoir had covered an area of 2000km2
. As reliable data is not readily
available in this war-torn country, scant details exist about how the reservoir rapidly emptied. However, understanding how it drained is crucial for both scientific research and societal concerns. Quantification of the discharge process is a key factor in flood modelling and will contribute to policy formulation for post-disaster migration and reconstruction.
Recent studies have used three types of remote sensing satellite data (altimetry, SAR/optimal imagery, and gravimetry) to track changes in the reservoir level, area, and mass. These observations were then used in a discharge model to understand the drainage dynamics of the reservoir. This approach meant it was possible to determine details of the reservoir drainage process by estimating the size of the breach, initial volumetric flow rates, and total water loss. Authors of the research published in Water
Resources Research believe their study, which provides a paradigm of incorporating a variety of state-of-the-art satellite remote sensing observations into a discharge model, will not only shed light on this flood disaster but also help future relevant studies. By leveraging remote sensing data, they say their methodology serves as a reliable tool for understanding and addressing similar challenges in the aftermath of catastrophic events
Above: After the destruction of the Kakhovka dam in the Ukraine
References
Remote sensing assessment of dam impact on arid basins in Southern Saudi Arabia: A machine learning and space- for-time approach. Raid Almalki, Mehdi Khaki, Patricia M. Saco, Jose F. Rodriguez. Journal of Hydrology: Regional Studies 58 (2025) 102221.
https://doi.org/10.1016/j. ejrh.2025.102221
Remote sensing with machine learning for multi-decadal surface water monitoring in Ethiopia Mathias Tesfaye & Lutz Breuer. Scientific Reports (2025) 15:12444. https://doi. org/10.1038/s41598-025- 96955-y
A Remote Sensing Approach of Land and Water Content Change Between 2014 and 2024 to the Porsuk Dam And Its Near Surroundings Kübra Günbey, Harun Böcük. Eskişehir Technical University Journal of Science and Technology C -Life Sciences and Biotechnology Estuscience – Life, 2025, 14 [1] pp. 1-13, DOI: 0.18036/estubtdc.1509648
Yi,S.,Li,H.-s.,Han,S.-C.,Sneeuw,N., Yuan,C.,Song,C.,et al.(2025). Quantification of the flood discharge following the 2023 Kakhovka Dam breach using satellite remote sensing. Water Resources Research,61,
e2024WR038314.https://doi. org/10.1029/ 2024WR038314
Left: Consequences of the failure of the Kakhovka hydropower dam in Ukraine. Understanding how the reservoir drained is a key factor in flood modelling and contributes to policy formulation for post-disaster migration and reconstruction
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