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Fig. 2 in Distribution of Siluriformes in a river under the influence of a small hydroelectric power plant of the Paraná River Basin, Brazil
Fig. 2. Mean total abundance of Siluriformes from the Jaguariaiva River, Upper Parana River basin by site [capture-per-unit-effort (CPUE); unit: number of individuals/1,000 m² of nets/16 h]. Vertical bars = standard error.
Fig. 4 in Distribution of Siluriformes in a river under the influence of a small hydroelectric power plant of the Paraná River Basin, Brazil
Fig. 4. Variations in composition of Siluriformes in the three distinct zones in the Nova Jaguaraiva River under the effects of damming. Species richness (beta diversity) was assessed as species dispersion within the three zones using permutational analysis of multivariate dispersions (PERMDISP; e.g., a greater distance to the spatial median indicates a larger dispersion and, therefore, broader beta diversity). The upper and lower hinges correspond to the 25th and 75th quartiles, respectively.
Fig. 1 in Distribution of Siluriformes in a river under the influence of a small hydroelectric power plant of the Paraná River Basin, Brazil
Fig. 1. Location of sampling sites in the Jaguariaiva River, Upper Parana River basin, Brazil: PCH Nova Jaguariaíva (bar); upstream (red circle); reservoir (black circle); downstream (yellow circle).
Fig. 3 in Distribution of Siluriformes in a river under the influence of a small hydroelectric power plant of the Paraná River Basin, Brazil
Fig. 3. Mean abundance of the Siluriformes species from the Jaguariaiva River, Upper Parana River basin by site [capture-per-unit-effort (CPUE); unit: number of individuals/1,000 m² of nets/16 h]. Vertical bars = standard error (Cher, Corydoras ehrhardti; Halb, Hypostomus albopunctatus; Hanc, Hypostomus ancistroides; Hher, Hypostomus hermanni; Hpau, Hypostomus paulinus; Hstr, Hypostomus strigaticeps; Nsel, Neoplecostomus selenae; Rque, Rhamdia quelen; Tcan, Trichomycterus candidus; Cdia, Cambeva diabola).
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
<p>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. </p> <p>The following Sentinel-2 remote sensing data granules were used:<br>S2A_MSIL2A_20230608T084601_N0509_R107_T36TUS_20230608T132103.SAFE S2A_MSIL2A_20230608T084601_N0509_R107_T36TVS_20230608T132103.SAFE<br>S2A_MSIL2A_20230608T084601_N0509_R107_T36TWS_20230608T132103.SAFE<br>S2A_MSIL2A_20230608T084601_N0509_R107_T36TVT_20230608T132103.SAFE<br>S2B_MSIL2A_20230613T084609_N0509_R107_T36TUS_20230613T102806.SAFE<br>S2B_MSIL2A_20230613T084609_N0509_R107_T36TVS_20230613T102806.SAFE<br>S2B_MSIL2A_20230613T084609_N0509_R107_T36TWS_20230613T102806.SAFE<br>S2A_MSIL2A_20230618T084601_N0509_R107_T36TUS_20230618T151602.SAFE<br>S2A_MSIL2A_20230618T084601_N0509_R107_T36TVS_20230618T151602.SAFE<br>S2A_MSIL2A_20230618T084601_N0509_R107_T36TWS_20230618T151602.SAFE</p> <p>The following remote sensing data scenes from Landsat-9 were used:<br>LC09_L1TP_179028_20230609_20230610_02_T1<br>LC09_L1TP_179027_20230609_20230610_02_T1</p> <p>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.</p> <p>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>.</p> <p>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.</p> <p>Repository files:<br>floodMax_UTM36N.zip — contains the shapefile in UTM36N projection (EPSG:32636);<br>floodMax_WGS84.zip — contains the shapefile in geographic coordinates in WGS84 (EPSG:4326);<br>floodMax_WGS84.geojson.zip — contains a GeoJSON file in WGS84 coordinates (EPSG:4326).</p> <div> <h1>Web version of the map</h1> </div> <p>The web version of the maximum water spread map is available at:<br><a href="https://yumoskalenko.github.io/floodmap_Kakhovka2023/" rel="nofollow">https://yumoskalenko.github.io/floodmap_Kakhovka2023/</a></p> <p> </p> <p>Embed code for the map on a webpage:</p> <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> <p><em><strong>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).</strong></em></p>
Data and code for: Impacts of changing snowfall on seasonal complementarity of hydroelectric and solar power
<p>Data and code to reproduce analyses in manuscript entitled: Influence of changing snowfall on seasonal complementarity of hydroelectric and solar power. Submitted to Environmental Research: Infrastructure and Sustainability.</p> <p>The contents include the following scripts and files, listed below. Scripts are listed in the order needed to reproduce the analysis, though intermediate data products have been saved so it is not necessary to reproduce the initial analytical steps.</p> <ul> <li>R/ <ul> <li>eia923_860.R: extracts solar and hydropower production data; requires local download of EIA data.</li> <li>gridMET_swep.R: downloads and summarises gridmet data; does not require prior local download.</li> <li>fdr.R: function to calculate the p-value associated with a given false discovery rate as described in the associated manuscript.</li> <li>combine_data.R combines solar, hydropower, and SWE/P data</li> <li>analysis.Rmd: primary script in which analyses are conducted</li> </ul> </li> <li>data/ <ul> <li>annual_swep.csv: output from gridMET_swep.R with annual SWE/P for each watershed in the study</li> <li>monthly_hydro.csv: output from eia923_860.R</li> <li>monthly_solar.csv: output from eia923_860.R</li> <li>combined_variables.csv: combines variables above in one CSV</li> <li>watersheds_wbd_ss: shapefiles for watersheds that drain to each dam used in the study, derived as described in the manuscript.</li> </ul> </li> </ul>
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)
<p>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). </p> <p> </p> <p>References:</p> <p>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.</p>
Fig 4 in Diversity and taxonomic structure of aquatic macroinvertebrates in a fluvio-lacustrine system in south-west Côte d'Ivoire: The case of the Soubré hydroelectric dam lake
Fig 4: Hierarchical classification of sampling stations based on the similarity of assemblages of aquatic macroinvertebrate families.
Рис. 1. Карта-схема р. Амазар. Цифрами обозначены: I — места Αобычи россыпного зоΛота; II — участки иссΛеΑования в 2018–2019 гг.: 1 — реки Амазар и БоΛьшая Чичатка в районе пгт. Амазар, 2 — воΑохраниΛище, 3 — р. Крестовая, 4 — р. Амазар в нижнем течении Fig. 1. Schematic map of the Amazar River. Legend: I — placer gold mining areas; II — survey areas in 2018–2019: 1 — the Amazar and the Bolshaya Chichatka Rivers in the area of Amazar urban-type settlement, 2 — water storage reservoir, 3 — the Krestovaya River, 4 — the lower reaches of the Amazar River in Dynamics and current status of the Amazar River ichthyofauna after the construction of the PPM «Polyarnaya» hydroelectric complex
Рис. 1. Карта-схема р. Амазар. Цифрами обозначены: I — места Αобычи россыпного зоΛота; II — участки иссΛеΑования в 2018–2019 гг.: 1 — реки Амазар и БоΛьшая Чичатка в районе пгт. Амазар, 2 — воΑохраниΛище, 3 — р. Крестовая, 4 — р. Амазар в нижнем течении Fig. 1. Schematic map of the Amazar River. Legend: I — placer gold mining areas; II — survey areas in 2018–2019: 1 — the Amazar and the Bolshaya Chichatka Rivers in the area of Amazar urban-type settlement, 2 — water storage reservoir, 3 — the Krestovaya River, 4 — the lower reaches of the Amazar River
Рис. 4. РаспреΑеΛение рыб в верхнем и нижнем бьефе в периоΑ осенней миграции 2018 г. (а) и верхнем бьефе в весеннюю миграцию 2019 г. (б). Точками показаны места фиксации рыб эхоΛотом Fig. 4. Distribution of fish upstream and downstream of the reservoir during the autumn migration of 2018 (а) and upstream of the reservoir during the spring migration of 2019 (b). Points indicate the locations where fish were registered by echosounder in Dynamics and current status of the Amazar River ichthyofauna after the construction of the PPM «Polyarnaya» hydroelectric complex
Рис. 4. РаспреΑеΛение рыб в верхнем и нижнем бьефе в периоΑ осенней миграции 2018 г. (а) и верхнем бьефе в весеннюю миграцию 2019 г. (б). Точками показаны места фиксации рыб эхоΛотом Fig. 4. Distribution of fish upstream and downstream of the reservoir during the autumn migration of 2018 (а) and upstream of the reservoir during the spring migration of 2019 (b). Points indicate the locations where fish were registered by echosounder
Рис. 5. РаспреΑеΛение рыб в верхнем бьефе воΑохраниΛища по размерным группам в периоΑ осенней миграции. Размеры рыб: а — меΛкие (Αо 10 см); б — среΑние (10–20 см); в — крупные (боΛее 20 см) Fig. 5. Distribution of fish upstream of the reservoir by size during the autumn migration. Fish sizes: а — small (under 10 cm); b — medium (10–20 cm); c — large (exceeding 20 cm) in Dynamics and current status of the Amazar River ichthyofauna after the construction of the PPM «Polyarnaya» hydroelectric complex
Рис. 5. РаспреΑеΛение рыб в верхнем бьефе воΑохраниΛища по размерным группам в периоΑ осенней миграции. Размеры рыб: а — меΛкие (Αо 10 см); б — среΑние (10–20 см); в — крупные (боΛее 20 см) Fig. 5. Distribution of fish upstream of the reservoir by size during the autumn migration. Fish sizes: а — small (under 10 cm); b — medium (10–20 cm); c — large (exceeding 20 cm)
Рис. 2. UPGMA-ΑенΑрограмма схоΑства виΑового состава (А) и фаунистическая структура (B) сообществ земΛероек в пяти ΛокаΛитетах Амурской обΛасти: ЗЗ — Зейский заповеΑник; НЗ — Норский заповеΑник; ХЗ — Хинганский заповеΑник; ЧФЗ — ХинганоАрхаринский заказник; НБС — территория зоны вΛияния Нижнебурейской ГЭС. ΔТФ — Αревнетаежная фауна; БФ — бореаΛьная фауна; НФ — немораΛьная фауна; Αр. — преΑставитеΛи Αругих фауногенетических группировок (пояснения в тексте) Fig. 2. UPGMA dendrogram of species composition similarity (A) and fauna structure (B) of shrew communities in five Amur region localities: ZZ — Zeya nature reserve; NZ — Norsky nature reserve; KhZ — Khingansky nature reserve; ChFZ — KhinganoArkharinsky nature reserve; NBS — the area influenced by the Nizhnebureyskaya hydroelectric power station. DTP — ancient taiga fauna; BF — boreal fauna; NF — nemoral fauna; others — representatives of other faunagenetic groups (explained in the text) in Shrew species composition and fauna structure in the Norsky reserve
Рис. 2. UPGMA-ΑенΑрограмма схоΑства виΑового состава (А) и фаунистическая структура (B) сообществ земΛероек в пяти ΛокаΛитетах Амурской обΛасти: ЗЗ — Зейский заповеΑник; НЗ — Норский заповеΑник; ХЗ — Хинганский заповеΑник; ЧФЗ — ХинганоАрхаринский заказник; НБС — территория зоны вΛияния Нижнебурейской ГЭС. ΔТФ — Αревнетаежная фауна; БФ — бореаΛьная фауна; НФ — немораΛьная фауна; Αр. — преΑставитеΛи Αругих фауногенетических группировок (пояснения в тексте) Fig. 2. UPGMA dendrogram of species composition similarity (A) and fauna structure (B) of shrew communities in five Amur region localities: ZZ — Zeya nature reserve; NZ — Norsky nature reserve; KhZ — Khingansky nature reserve; ChFZ — KhinganoArkharinsky nature reserve; NBS — the area influenced by the Nizhnebureyskaya hydroelectric power station. DTP — ancient taiga fauna; BF — boreal fauna; NF — nemoral fauna; others — representatives of other faunagenetic groups (explained in the text)
Figure 5 in Diversity and spatio-temporal variation of Anopheles (Diptera: Culicidae) before and after the construction of the Jirau hydroelectric plant, state of Rondônia, Brazil
Figure 5 Canonical correlation analysis (CCA) ordering diagram between environmental factors and Anopheles species in the pre (a) and post-construction (b) phases of the Jirau hydroelectric plant: Relative Humidity of the air (R. H%); Temp (Temperature ° C); Subtitle: Anopheles albit – An. albitarsis; Anopheles argyrit – An. argyritarsis; Anopheles benar – An. benarrochi; Anopheles braz – An. braziliensis; Anopheles darl – An. darlingi; Anopheles evan – An.evansae; Anopheles mattog – An. mattogrossensis; Anopheles mediop – An. mediopunctatus; Anopheles osw – An. oswaldoi; Anopheles per – An. peryassui; Anopheles rang – An. rangeli; Anopheles trian – An. triannulatus.
Figure 3 in Diversity and spatio-temporal variation of Anopheles (Diptera: Culicidae) before and after the construction of the Jirau hydroelectric plant, state of Rondônia, Brazil
Figure 3 Density of Anopheles species (x) in the sampled months (January to August) before (a) and after (March to October) the construction (b) of the Jirau hydroelectric w plant, in Rondônia, Brazil.
Figure 1 in Diversity and spatio-temporal variation of Anopheles (Diptera: Culicidae) before and after the construction of the Jirau hydroelectric plant, state of Rondônia, Brazil
Figure 1 Sampling points of anophelines in the area covered by the Jirau hydroelectric plant, in the stretch between the locations of Jaci Paraná and Abunã (squares), in the pre (black) and post-construction (gray) phases.
Figure 2 in Diversity and spatio-temporal variation of Anopheles (Diptera: Culicidae) before and after the construction of the Jirau hydroelectric plant, state of Rondônia, Brazil
Figure 2 Housing types (a-d) spatial variation of Anopheles darlingi before (e) and after (f) the construction of the Jirau hydroelectric plant, in Rondônia, Brazil. Subtitle: AB – Abunã; JHP – Jirau Hydroeletric Plant; JP – Jaci Paraná; NMP – Nova Mutum Paraná.
Data set for the manuscript "Development of Hydropower and the Environmental Impacts of Hydroelectric Dam Construction: A Case Study of the Three Gorges Dam"
<p>This document provides the data set supplementary to the manuscript "Development of Hydropower and the Environmental Impacts of Hydroelectric Dam Construction: A Case Study of the Three Gorges Dam"</p> <p>Data content: Tables 1-3</p> <ul> <li>Table 1. The global annual data of GDP, surface temperature anomalies, carbon dioxide emissions and different kinds of renewable energy during 2000-2020. The renewable energy includes hydropower, wind, solar, geothermal, biomass and others (unit: TWh).</li> <li>Table 2. The annual mean temperature, CO2 emissions, and renewable energy data in China from 2000 to 2020. The renewable energy data include hydro, solar, and wind electricity generation (unit: kWh).</li> <li>Table 3. Annual variation of precipitation and biodiversity in the TGD area. Annual mean precipitation data over China and the TGD are provided. Biodiversity data include the number of Yangtze Finless Porpoise, Carp Egg and Larvae, and the number of spawners in spawning ground for Acipenser_sinensis.</li> </ul> <p> </p>
Figure 1 in Avifauna of the region of the Volta Grande Hydroelectric Power Plant in Southeast Brazil
Figure 1. Grande River Basin located at the border between the states of Minas Gerais and São Paulo in Southeast Brazil. The five study sites are shown as: 1 and 2 located in Minas Gerais, and 3, 4 and 5 located in São Paulo.
Figure 2. Bird species accumulation curve and estimated richness curve obtained from the Chao 1 in Avifauna of the region of the Volta Grande Hydroelectric Power Plant in Southeast Brazil
Figure 2. Bird species accumulation curve and estimated richness curve obtained from the Chao 1 index for the study area located throughout the reservoir of the Volta Grande Hydroelectric Power Plant in Southeast Brazil. Vertical bars represent the standard deviation of the estimate.
River dams and the stability of bird communities: A hierarchical Bayesian analysis in a tropical hydroelectric power plant
<ol> <li>The effects of anthropogenic disturbance upon the stability of wildlife communities depend on the heterogeneity and connectivity of habitat remnants on multiple scales. The number of hydroelectric dams in biodiversity hotspots (Africa, South America and Asia) is growing rapidly. To establish their environmental impact, it is essential to understand the dynamics of wildlife communities before and following the establishment of dams.</li> <li>We evaluated the impacts of the filling of the Serra do Facão hydroelectric reservoir in the São Marcos river, central Brazil, upon the bird community. Using data from 1,145 surveys across 20 sampling sites over eight years, two years before and six years after the filling of the reservoir, we assessed the resistance, i.e., maintenance close to an equilibrium state during the disturbance, and resilience, i.e., ability to return to the original state following the disturbance, of the bird community. We used spatiotemporal hierarchical Bayesian models to assess the effects of reservoir filling on five community parameters: abundance, richness, phylogenetic diversity, functional diversity and species composition.</li> <li>In the period subsequent to reservoir filling, there was (i) a marked reduction in bird abundance, richness, phylogenetic diversity and functional diversity, and (ii) a reduction in the proportion of forest species, coupled with an increase in the proportion of savanna species. Except for bird abundance, none of the other community attributes returned to their original levels, even after six years. Our findings indicate that Cerrado bird communities have both low resistance and low resilience to habitat loss associated with the establishment of hydroelectric reservoirs.</li> <li> <i>Synthesis and applications.</i> The environmental costs of hydroelectric dams are still underestimated or neglected in Brazil. A new paradigm in the assessment of their environmental impacts is warranted, incorporating (i) models of spatiotemporal variations based on long-term monitoring with surveys initiated before disturbances and (ii) measures of functional and phylogenetic diversity, such that society can understand the costs and benefits of the establishment of new hydroelectric dams and make informed decisions. Biodiversity loss could be minimized by ensuring the preservation and connectivity of alluvial habitats, capable of maintaining the supply of resources and the functional and phylogenetic attributes of bird communities associated with such habitats.</li> </ol>
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