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Abstract. Data and knowledge of surface water bodies (SWB), including large lakes andreservoirs (surface water areas > 1 km2), are critical forthe management and sustainability of water resources. However, the existingglobal or national dam datasets have large georeferenced coordinate offsetsfor many reservoirs, and some datasets have not reported reservoirs andlakes separately. In this study, we generated China's surface water bodies,Large Dams, Reservoirs, and Lakes (China-LDRL) dataset by analyzing allavailable Landsat imagery in 2019 (19 338 images) in Google Earth Engine andvery-high spatial resolution imagery in Google Earth Pro. There were∼ 3.52 × 106 yearlong SWB polygons in China for2019, only 0.01 × 106 of them (0.43 %) were of large size(> 1 km2). The areas of these large SWB polygons accountedfor 83.54 % of the total 214.92 × 103 km2 yearlongsurface water area (SWA) in China. We identified 2418 large dams, including624 off-stream dams and 1794 on-stream dams, 2194 large reservoirs (16.35 × 103 km2), and 3051 large lakes (73.38 × 103 km2). In general, most of the dams and reservoirs in Chinawere distributed in South China, East China, and Northeast China, whereasmost of lakes were located in West China, the lower Yangtze River basin, andNortheast China. The provision of the reliable, accurate China-LDRL dataseton large reservoirs/dams and lakes will enhance our understanding of waterresources management and water security in China. The China-LDRL dataset ispublicly available at https://doi.org/10.6084/m9.figshare.16964656.v3 (Wang et al., 2021b).
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This content will become publicly available on June 2, 2026
Mapping and Analyzing the Distribution, Drivers, and Impacts of Small Agricultural Reservoirs in Brazil from 1984 to 2023
Small stream dams and their reservoirs are ubiquitous in Brazil but mostly unmapped in existing datasets. Often used for cattle watering, farm-scale hydropower, irrigation, and/or fish aqua-culture, these reservoirs can have immense cumulative impacts on aquatic habitats, down-stream water quality, greenhouse gas emissions, and water balances. By applying deep learning to data from Landsat and Sentinel satellites, we created the first comprehensive datasets on small surface-water reservoirs in Brazil, including annual maps from 1984 to 2023. Then, we synthesized these maps with land-use data, property information, and semi-structured inter-views conducted in Mato Grosso state to elucidate the drivers and impacts of reservoir creation and persistence. First, we used high-resolution Sentinel-1 and -2 satellite data from 2021 to map over 1 million in-stream reservoirs smaller than 1 km2, a massive increase from existing datasets. Using weather data and climate models, we estimated that these reservoirs lose approximately 11.7 km3yr-1 to surface evaporation, a number projected to increase by 3-13% as climate change drives warmer and drier weather. Next, we showed that these reservoirs can be consistently and accurately mapped over time using Landsat 5, 7, and 8 data with only a slight de-crease in performance compared to Sentinel. From 1984 to 2023, the number of reservoirs in Brazil increased dramatically from 265,617 to 1,040,754 and the total surface area increased from 3526 km2 to 8629 km2. Then, we drew on interviews and MapBiomas land-use data to demonstrate that creation typically accompanied clearing of forest and savanna for cattle pasture in the first-waves settlement in the so-called hollow frontier of Mato Grosso. In many cases, these reservoirs were obsolete legacies of the hollow frontier, left over after intensified crop agriculture replaced dispersed cattle ranching. However, others are still critical and flexible sources of water, particularly for small-scale dairy, beef, fruit, and vegetable producers. Finally, we analyzed how the overarching discourse of resource abundance in eastern Mato Grosso obscures daily experiences of water scarcity, showing that the abundances of land, capital, and infrastructure contribute to and intersect with water inequality to favor large, export-oriented crop farms over small-scale producers. Applying an interdisciplinary approach to synthesize deep learning and remote sensing with political ecologies of water and agriculture, we highlight the important role of these reservoirs in agricultural expansion and intensification in Brazil and especially Mato Grosso state. Although they have widespread negative environmental impacts, reservoirs are also a critical form of water infrastructure for many. As climate change and the adoption of irrigation alter agricultural water use across Brazil, our results can inform water management decisions that balance the positive and negative social, environmental, and economic effects of stream damming.
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- Award ID(s):
- 1950832
- PAR ID:
- 10628502
- Publisher / Repository:
- University of Colorado, Boulder
- Date Published:
- Format(s):
- Medium: X
- Institution:
- University of Colorado, Boulder
- Sponsoring Org:
- National Science Foundation
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