ReaLSAT, a global dataset of reservoir and lake surface area variations

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: This is version 1 of this Preprint.


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Ankush Khandelwal, Anuj Karpatne, Praveen Ravirathinam, Rahul Ghosh, Zhihao Wei, Hilary Dugan, Paul Hanson, Vipin Kumar


Lakes and reservoirs, as most humans experience and use them, are dynamic bodies of water, with surface extents that increase and decrease with seasonal precipitation patterns, long-term changes in climate, and human management decisions. This paper presents a new global dataset that contains the location and surface area variations of 683,734 medium-sized (0.1 - 100 sq. km.) lakes and reservoirs (south of 50°N) from 1984 to 2015, to enable the study of the impact of human actions and climate change on freshwater availability. Within its scope for size and region covered, this dataset is far more comprehensive than existing datasets such as HydroLakes. While HydroLAKES only provides a static shape, the proposed dataset also has a timeseries of surface area and a shapefile containing monthly shapes for each lake. The paper presents the development and evaluation of this dataset and highlights the utility of novel machine learning techniques in addressing the inherent challenges in transforming satellite imagery to dynamic global surface water maps.



Hydrology, Other Computer Sciences, Water Resource Management



Published: 2022-01-11 04:04

Last Updated: 2022-01-11 12:04


CC BY Attribution 4.0 International

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