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

   https://doi.org/10.1038/s41597-022-01449-5  

   Khandelwal, Ankush; Karpatne, Anuj; Ravirathinam, Praveen; Ghosh, Rahul; Wei, Zhihao; Dugan, Hilary A.; Hanson, Paul C.; Kumar, Vipin (June 2022, Scientific Data)

   Abstract 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 681,137 lakes and reservoirs larger than 0.1 square kilometers (and south of 50 degree 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. 

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2. Mapping and Analyzing the Distribution, Drivers, and Impacts of Small Agricultural Reservoirs in Brazil from 1984 to 2023

   Solvik, Kylen (June 2025, University of Colorado, Boulder)

   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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3. Unexpected Expansion of Rare-Earth Element Mining Activities in the Myanmar–China Border Region

   https://doi.org/10.3390/rs15184597  

   Chinkaka, Emmanuel; Klinger, Julie Michelle; Davis, Kyle Frankel; Bianco, Federica (September 2023, Remote Sensing)

   Mining for rare earth elements is rapidly increasing, driven by current and projected demands for information and energy technologies. Following China’s Central Government’s 2012 strategy to shift away from mining in favor of value-added processing, primary extraction has increased outside of China. Accordingly, changes in mineral exploitation in China and Myanmar have garnered considerable attention in the past decade. The prevailing assumption is that mining in China has decreased while mining in Myanmar has increased, but the dynamic in border regions is more complex. Our empirical study used Google Earth Engine (GEE) to characterize changes in mining surface footprints between 2005 and 2020 in two rare earth mines located on either side of the Myanmar–China border, within Kachin State in northern Myanmar and Nujiang Prefecture in Yunnan Province in China. Our results show that the extent of the mining activities increased by 130% on China’s side and 327% on Myanmar’s side during the study period. We extracted surface reflectance images from 2005 and 2010 from Landsat 5 TM and 2015 and 2020 images from Landsat 8 OLI. The Normalized Vegetation Index (NDVI) was applied to dense time-series imagery to enhance landcover categories. Random Forest was used to categorize landcover into mine and non-mine classes with an overall accuracy of 98% and a Kappa Coefficient of 0.98, revealing an increase in mining extent of 2.56 km2, covering the spatial mining footprint from 1.22 km2 to 3.78 km2 in 2005 and 2020, respectively, within the study area. We found a continuous decrease in non-mine cover, including vegetation. Both mines are located in areas important to ethnic minority groups, agrarian livelihoods, biodiversity conservation, and regional watersheds. The finding that mining surface areas increased on both sides of the border is significant because it shows that national-level generalizations do not align with local realities, particularly in socially and environmentally sensitive border regions. The quantification of such changes over time can help researchers and policymakers to better understand the shifting geographies and geopolitics of rare earth mining, the environmental dynamics in mining areas, and the particularities of mineral extraction in border regions. 

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4. The Boreal–Arctic Wetland and Lake Dataset (BAWLD)

   https://doi.org/10.5194/essd-13-5127-2021  

   Olefeldt, David; Hovemyr, Mikael; Kuhn, McKenzie A.; Bastviken, David; Bohn, Theodore J.; Connolly, John; Crill, Patrick; Euskirchen, Eugénie S.; Finkelstein, Sarah A.; Genet, Hélène; et al (January 2021, Earth System Science Data)

   Abstract. Methane emissions from boreal and arctic wetlands, lakes, and rivers areexpected to increase in response to warming and associated permafrost thaw.However, the lack of appropriate land cover datasets for scalingfield-measured methane emissions to circumpolar scales has contributed to alarge uncertainty for our understanding of present-day and future methaneemissions. Here we present the Boreal–Arctic Wetland and Lake Dataset(BAWLD), a land cover dataset based on an expert assessment, extrapolatedusing random forest modelling from available spatial datasets of climate,topography, soils, permafrost conditions, vegetation, wetlands, and surfacewater extents and dynamics. In BAWLD, we estimate the fractional coverage offive wetland, seven lake, and three river classes within 0.5 × 0.5∘ grid cells that cover the northern boreal and tundra biomes(17 % of the global land surface). Land cover classes were defined usingcriteria that ensured distinct methane emissions among classes, as indicatedby a co-developed comprehensive dataset of methane flux observations. InBAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain)with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetlandclasses, covering ∼ 28 % each of the total wetland area,while the highest-methane-emitting marsh and tundra wetland classes occupied5 % and 12 %, respectively. Lakes, defined to include all lentic open-waterecosystems regardless of size, covered 1.4 × 106 km2(6 % of domain). Low-methane-emitting large lakes (>10 km2) and glacial lakes jointly represented 78 % of the total lakearea, while high-emitting peatland and yedoma lakes covered 18 % and 4 %,respectively. Small (<0.1 km2) glacial, peatland, and yedomalakes combined covered 17 % of the total lake area but contributeddisproportionally to the overall spatial uncertainty in lake area with a95 % confidence interval between 0.15 and 0.38 × 106 km2. Rivers and streams were estimated to cover 0.12  × 106 km2 (0.5 % of domain), of which 8 % was associated withhigh-methane-emitting headwaters that drain organic-rich landscapes.Distinct combinations of spatially co-occurring wetland and lake classeswere identified across the BAWLD domain, allowing for the mapping of“wetscapes” that have characteristic methane emission magnitudes andsensitivities to climate change at regional scales. With BAWLD, we provide adataset which avoids double-accounting of wetland, lake, and river extentsand which includes confidence intervals for each land cover class. As such,BAWLD will be suitable for many hydrological and biogeochemical modellingand upscaling efforts for the northern boreal and arctic region, inparticular those aimed at improving assessments of current and futuremethane emissions. Data are freely available athttps://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021). 

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5. Modeling of Rock Scour using Coupled 3-D Discrete Element and Lattice Boltzmann Methods

   Gardner, Michael H.; Sitar, Nicholas (June 2018, 52nd US Rock Mechanics/Geomechancs Symposium)

   Scour of rock in unlined rock spillway channels is a critical issue facing many of the world’s dams. From a modeling point of view this poses a challenging and interesting problem that combines rock mechanics and hydraulics of turbulent flow. We analyze this interaction between the blocky rock mass and water by directly modeling the solid and fluid phases—the individual polyhedral blocks are modeled using the Discrete Element Method (DEM) while the water is modeled using the Lattice Boltzmann Method (LBM). The LBM mesh is entirely independent of the DEM discretization, making it possible to refine the LBM mesh such that transient and varied fluid pressures acting of the rock surface are directly modeled. This provides the capability to investigate the effect of water pressure inside the fractured rock mass, along potential sliding planes, and can be extended to rock falls and slides into standing bodies of water such as lakes and reservoirs. Herein we present preliminary results to demonstrate the capabilities of the methodology. 

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