Mapping small reservoirs across Brazil from 1984 to 2025

arXiv:2606.00675v2 Announce Type: replace Abstract: Water research in Brazil largely overlooks the widespread damming of small streams for agricultural uses including watering cattle, farm-scale hydropower, irrigation, and aquaculture. These ubiquitous dams and their reservoirs affect water temperature, stream connectivity, aquatic habitats, greenhouse gas emissions, and evaporative water losses. Mapping small reservoirs is challenging because it requires reliably detecting small water bodies and distinguishing artificial reservoirs from natural lakes. As a result, most regional and global datasets exclude them. To address this gap, we trained a deep learning computer vision model to accurately segment small (0.1 to 100 ha), stream-fed, surface water reservoirs in Brazil leveraging data from Landsat 5-9. Applying our model from 1984 to 2025, we created annual reservoir maps for the entire country to evaluate how their count, size, and distribution have changed over time. The number of detected reservoirs grew nearly fourfold from 224,059 to 817,458, while their total surface area increased from 3490 $km^2$ to 8455 $km^2$. To our knowledge, this is the first country-wide annual dataset representing the evolution of small reservoirs over four decades. The publicly available annual maps highlight the extent and cumulative impacts of small stream impoundments across Brazil, providing actionable insights for managing freshwater ecosystems and water resources.

Abstract:Water research in Brazil largely overlooks the widespread damming of small streams for agricultural uses including watering cattle, farm-scale hydropower, irrigation, and aquaculture. These ubiquitous dams and their reservoirs affect water temperature, stream connectivity, aquatic habitats, greenhouse gas emissions, and evaporative water losses. Mapping small reservoirs is challenging because it requires reliably detecting small water bodies and distinguishing artificial reservoirs from natural lakes. As a result, most regional and global datasets exclude them. To address this gap, we trained a deep learning computer vision model to accurately segment small (0.1 to 100 ha), stream-fed, surface water reservoirs in Brazil leveraging data from Landsat 5-9. Applying our model from 1984 to 2025, we created annual reservoir maps for the entire country to evaluate how their count, size, and distribution have changed over time. The number of detected reservoirs grew nearly fourfold from 224,059 to 817,458, while their total surface area increased from 3490 $km^2$ to 8455 $km^2$. To our knowledge, this is the first country-wide annual dataset representing the evolution of small reservoirs over four decades. The publicly available annual maps highlight the extent and cumulative impacts of small stream impoundments across Brazil, providing actionable insights for managing freshwater ecosystems and water resources.

Machine Learning (cs.LG)

arXiv:2606.00675 [cs.LG]

(or arXiv:2606.00675v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.00675 Focus to learn more arXiv-issued DOI via DataCite

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Mapping small reservoirs across Brazil from 1984 to 2025

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