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opencc-by-4.0Nov 2024View details →

Shapefiles with the outline of maximum water spread resulting from the catastrophic release of the Kakhovka Reservoir after the destruction of the Kakhovka Hydroelectric Power Plant by Russian occupying forces

The map is based on remote sensing data from Sentinel-2A (Processing Level L2A), dated June 8, June 13, and June 18, 2023, and Landsat-9 (Collection 2 Level-1), dated June 9, 2023.  The following Sentinel-2 remote sensing data granules were used: S2A_MSIL2A_20230608T084601_N0509_R107_T36TUS_20230608T132103.SAFE S2A_MSIL2A_20230608T084601_N0509_R107_T36TVS_20230608T132103.SAFE S2A_MSIL2A_20230608T084601_N0509_R107_T36TWS_20230608T132103.SAFE S2A_MSIL2A_20230608T084601_N0509_R107_T36TVT_20230608T132103.SAFE S2B_MSIL2A_20230613T084609_N0509_R107_T36TUS_20230613T102806.SAFE S2B_MSIL2A_20230613T084609_N0509_R107_T36TVS_20230613T102806.SAFE S2B_MSIL2A_20230613T084609_N0509_R107_T36TWS_20230613T102806.SAFE S2A_MSIL2A_20230618T084601_N0509_R107_T36TUS_20230618T151602.SAFE S2A_MSIL2A_20230618T084601_N0509_R107_T36TVS_20230618T151602.SAFE S2A_MSIL2A_20230618T084601_N0509_R107_T36TWS_20230618T151602.SAFE The following remote sensing data scenes from Landsat-9 were used: LC09_L1TP_179028_20230609_20230610_02_T1 LC09_L1TP_179027_20230609_20230610_02_T1 The contour of the maximum water spread was constructed using a method of manual visual interpretation of remote sensing data, relying on knowledge of the local terrain. We consciously chose not to use automated methods with water indices such as the Normalized Difference Water Index (NDWI) or the Modified Normalized Difference Water Index (MNDWI), as these do not effectively distinguish water surfaces in areas covered with forest or dense reed thickets. Similarly, we did not use the SRTM digital elevation model due to significant artifacts in the study area, where the model shows the height of the forest canopy instead of the ground surface in forested areas. For visual interpretation of Sentinel-2A remote sensing data, we used combinations of spectral bands NIR-Red-Green (8-4-3) and SWIR2-NIR-Green (12-8-3). For the visual interpretation of Landsat-9 remote sensing data, we used combinations of bands SWIR1-NIR-Red (6-5-4) and NIR-Red-Green (5-4-3). To better align the resolution of Sentinel-2A remote sensing data (10 m/pixel) with that of Landsat-9 (30 m/pixel), the latter's data was enhanced using the panchromatic channel (Band 8) through IHS-based pansharpening to 15 m/pixel. The pansharpening was performed using a custom bash script, utilizing command-line tools and utilities such as ImageMagick (<a href="https://imagemagick.org" rel="nofollow">https://imagemagick.org</a>), listgeo, and geotifcp (<a href="https://github.com/OSGeo/libgeotiff">https://github.com/OSGeo/libgeotiff</a>). To expedite the pansharpening process, both Landsat scenes were cropped to the study region and merged by bands using the gdal_translate and gdal_merge.py utilities from the GDAL library (<a href="https://gdal.org/" rel="nofollow">https://gdal.org/</a>). For convenience, the Sentinel-2A data tiles T36TUS, T36TVS, and T36TWS were also cropped and merged by bands using custom scripts available at <a href="https://doi.org/10.5281/zenodo.13205058" rel="nofollow">https://doi.org/10.5281/zenodo.13205058</a>. During visual interpretation, the above-mentioned remote sensing data were compared with satellite images acquired before the destruction of the Kakhovka Hydroelectric Power Plant. In particular, Landsat-9 remote sensing data were compared with Landsat-8 data from June 1, 2023, and Sentinel-2A data were compared with Sentinel-2B data from June 3, 2023. Repository files: floodMax_UTM36N.zip — contains the shapefile in UTM36N projection (EPSG:32636); floodMax_WGS84.zip — contains the shapefile in geographic coordinates in WGS84 (EPSG:4326); floodMax_WGS84.geojson.zip — contains a GeoJSON file in WGS84 coordinates (EPSG:4326). <div> <h1>Web version of the map</h1> </div> The web version of the maximum water spread map is available at: <a href="https://yumoskalenko.github.io/floodmap_Kakhovka2023/" rel="nofollow">https://yumoskalenko.github.io/floodmap_Kakhovka2023/</a>   Embed code for the map on a webpage: <div> <pre><code><iframe style="border: 1px solid black" src="https://yumoskalenko.github.io/floodmap_Kakhovka2023/index.html" marginwidth="0" marginheight="0" scrolling="no" width="100%" height="360" frameborder="0"></iframe> </code></pre> <div> </div> </div> This scientific and technical product was created by the scientists of the Black Sea Biosphere Reserve of the National Academy of Sciences of Ukraine during the implementation of research on the topic "Monitoring the condition of natural complexes of the Black Sea Biosphere Reserve (‘Chronicle of Nature’)" (state registration number 0121U109174).

Fig. 4 in Growing, losing or introducing? Cage aquaculture as a vector for the introduction of non-native fish in Furnas Reservoir, Minas Gerais, Brazil

Fig. 4. Main events along the production system (i.e. juvenile stocking, length classification and fish capture) and the moments in which escapes occur (solid arrows: AC = accidental; IN = intentional). S = small-sized fish; M = mediumsized; L = large-sized.

Fig. 2 in Growing, losing or introducing? Cage aquaculture as a vector for the introduction of non-native fish in Furnas Reservoir, Minas Gerais, Brazil

Fig. 2. Frequency of fish farmers (%) operating different number of cages in Furnas Reservoir (n = 19).

Fig. 3 in Growing, losing or introducing? Cage aquaculture as a vector for the introduction of non-native fish in Furnas Reservoir, Minas Gerais, Brazil

Fig. 3. Frequency of fish farmers (%) reporting the occurrence of fish escapes during different events of the production chain (n = 19). Accidental: length classification (LC); fish removal (FR); juvenile stocking (JS); cage damage (CD). Deliberate: intentional releases (IR).

Fig. 1 in Growing, losing or introducing? Cage aquaculture as a vector for the introduction of non-native fish in Furnas Reservoir, Minas Gerais, Brazil

Fig. 1. Furnas Reservoir, Minas Gerais, Brazil. The circle indicates the study area (Carmo do Rio Claro town).

opencc-by-4.0Mar 2022View details →

Surface Water Area Variations of Global Lakes and Reservoirs

Monthly surface area timeseries of large lakes and reservoirs generated from Sentinel-1 SAR backscatter data from January 2017 through December 2019.

opencc-by-4.0Sep 2020View details →

Hydrometeorological dataset and SWAT simulated outputs of Jaguari basin in the Cantareira reservoirs system

Dataset used in the accepted paper "Hydrologic Impact of Climate Change in the Jaguari River in the Cantareira Reservoir System" (Domingues et al., 2022), from Water. A README.txt file is available within each folder with details of the dataset.

opencc-by-4.0Nov 2014View details →

Fig. 2 in Absorbing Hybridization Of Cobitis Taenia And Sabanejewia Aurata (Cypriniformes, Cobitidae) In Water Reservoirs Of Northern Ukraine Connected With Diploid-Polyploid Complex Formation

Fig. 2. Electrophoretic spectra of enzymes coding by allozymic loci: aspartate amynotransferase (1 — Aat- 1100/100, 2 — Aat-1100/110-110, 3 — Aat-195/110-110, 4 — Aat-1100-100/110, 5 — Aat-195-95/110, 6 — Aat-195-100/110), lactate dehydrogenase (1 — Ldh-B90/90, 2 — Ldh-B100/100, 3 — Ldh-B90/100-100, 4 — Ldh-B90/100/110), malate dehydrogenase (1 — Mdh-1A100/100, 2 — Mdh-1A100/110-110, 3 — Mdh-1A100-100/110).

opencc-by-4.0Apr 2022View details →

Implementation and sensitivity analysis of a Dam-Reservoir OPeration model (DROP v1.0) over Spain - Supplement

Supplement of the following scientific paper submitted in GMD : Sadki M., Munier S., Boone A., Ricci S. : Implementation and sensitivity analysis of a Dam-Reservoir OPeration model (DROP v1.0) over Spain, Geoscientific Model Development, 2022.

opencc-by-4.0Apr 2021View details →

Estimating drivers and pathways for hydroelectric reservoir methane emissions using a new mechanistic model (estimated methane emissions for hydropower reservoir surfaces and potential dam emissions)

Methane emissions data from hydropower reservoir surfaces and dams, as estimated with the ResME model.  Emissions estimates available for hydropower reservoirs in the GRanD database (Lehner et al., 2011).    References: Lehner, B., Liermann, C. Reidy, Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J.C., Rodel, R., Sindorf, N., and Wisser, D. (2011). High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management. Frontiers in Ecology and the Environment, 9 (9): 494-502. https://doi.org/10.1890/100125.

opencc-by-4.0Aug 2022View details →

Dataset of observations of waterbird community wintering in Crimea Reservoirs in 2009-2021

The result of waterbird counting carried out in winter 2009-2021 in Crimean Reservoirs is presented. The dataset contains the number of individual bird species, as well as data on volume and water surface area. In the script file, there is a code applied to analyse the data.

opencc-by-4.0Oct 2022View details →

Apulian Aqueduct demo site: daily time series of reservoirs inflows 2010-2019

This dataset contains the time series of net inflows to the main reservoirs of Apulian aqueduct (demo site 1), computed with a mass-balance equation at a daily time step. <ul> <li>Temporal coverage: 2010-2019</li> <li>Spatial coverage: Reservoirs: Conza, Locone, Monte Cotugno, Occhito, Pertusillo</li> <li>Unit of measure: m3/sec</li> </ul> More information and details on the content of this dataset can be found in Project Ô Deliverable D4.1.

opencc-by-4.0Dec 2020View details →

Рис. 2. А – Тусингайское водохранилиЩе; В – оросительный канал Дустлик у г. Гулистан. Фото Н. РуЗикуловой, 2020 г. Fig. 2. A – Tusingay water reservoir; B – irrigation channel Dustlik near the Gulistan Town. Photo by N. Ruzikulova, 2020. in Patterns of ecology and life cycles of aquatic molluscs from Central Asia

Рис. 2. А – Тусингайское водохранилиЩе; В – оросительный канал Дустлик у г. Гулистан. Фото Н. РуЗикуловой, 2020 г. Fig. 2. A – Tusingay water reservoir; B – irrigation channel Dustlik near the Gulistan Town. Photo by N. Ruzikulova, 2020.

FIGURE 5 in Impacts on fisheries assessed by local ecological knowledge in a reservoir cascade in the lower São Francisco River, northeastern Brazil

FIGURE 5 | Maximum Similitude Tree showing the expression and relationship of impacts over fisheries in the four areas sampled on the Sub-Middle and on Lower São Francisco, based on fishers' testimonies (n = 107).

FIGURE 4 in Impacts on fisheries assessed by local ecological knowledge in a reservoir cascade in the lower São Francisco River, northeastern Brazil

FIGURE 4 | Non-Metric Multidimensional Scaling (NMDS) analysis concerning impacts over the ichthyofauna in the four sampled areas on the Sub-Middle and Lower São Francisco.

FIGURE 3 in Impacts on fisheries assessed by local ecological knowledge in a reservoir cascade in the lower São Francisco River, northeastern Brazil

FIGURE 3 | List of main impacts mentioned in A. Area 1, B. Area 2, C. Area 3, and D. Area 4, according to fishers from the final portion of the Sub-Middle and Lower São Francisco. Dam: dams; Mac: macrophytes; Aqu: aquaculture; Pol: pollution; Ovf: overfishing; Abr: absence of rain; Def: deforestation; Sil: silting; Salt: salinity.

FIGURE 2 in Impacts on fisheries assessed by local ecological knowledge in a reservoir cascade in the lower São Francisco River, northeastern Brazil

FIGURE 2 | Frequency (A) and Word Cloud (B) analyses regarding the impacts over the ichthyofauna, according to fishers from the four sampled areas on the Sub-Middle and on Lower São Francisco. a. Dam; b. Macrophytes; c. Pollution; d. Overfishing; e. Absence of rain; f. Aquaculture; g. Deforestation; h. Silting; i. Water transfer; j. Non-native species; k. Irrigation; l. Pesticide; m. Salinization.

FIGURE 1 in Impacts on fisheries assessed by local ecological knowledge in a reservoir cascade in the lower São Francisco River, northeastern Brazil

FIGURE 1 | Fishers' communities on the four areas visited on the Sub-Middle and on Lower São Francisco, Northeastern Brazil. PE = Pernambuco State; BA = Bahia State; AL = Alagoas State; SE = Sergipe State.

opencc-by-4.0May 2023View details →

Data from: Rates and drivers of carbon emissions from hydropower reservoirs in the southeastern United States

Reservoirs are a significant source of carbon (C) to the atmosphere, but their emission rates vary in space and time. We compared C emissions via diffusive and ebullitive pathways at several stations in six large hydropower reservoirs in the southeastern US that were previously sampled in summer 2012. We found that carbon dioxide (CO2) diffusion was the dominant flux pathway during 2012 and 2022, with only three exceptions where methane (CH4) diffusion or CH4 ebullition dominated. CH4 diffusion rates were positively associated with water temperature. However, we found no clear predictors of CH4 ebullition, which had extremely high variability, with rates ranging from 0 to 739 mg C m-2 day-1. For CO2 diffusion, the direction of the flux shifted between 2012 and 2022, where all but three stations across all reservoirs emitted CO2 in summer 2012, but every station sequestered CO2 in summer 2022. Here, indicators of greater algal production were associated with CO2 sequestration, including surface chlorophyll-a concentration, surface dissolved oxygen saturation, and pH. Additional sampling campaigns outside the summer season highlighted the importance of seasonal phenology in primary production on the direction of CO2 diffusive fluxes, which shifted to positive CO2 fluxes by the end of August as productivity decreased. Our results demonstrate the importance of capturing CO2 sequestration in field and modelling measurements and understanding the seasonal drivers of these estimates. Measuring C emissions from multiple pathways in reservoirs and understanding their spatiotemporal responses and variability is vital to reducing uncertainties in global upscaling efforts.