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opencc-by-4.0Nov 2022View details →

F I G U R E 2 in Three-dimensional migratory behaviour of European silver eels (Anguilla anguilla) approaching a hydropower plant

F I G U R E 2 (a) The intake site at the Herting hydropower plant with migration routes available during the study period: a full-depth bypass next to the H1 powerplant and concrete weirs with a nature-like fishway. Location of hydrophones are shown in red. Areas to be analysed are denoted by blue dashed lines. (b) Detailed sketch of the angled rack and bypass at powerhouse H1

opencc-by-4.0Nov 2022View details →

F I G U R E 1 in Three-dimensional migratory behaviour of European silver eels (Anguilla anguilla) approaching a hydropower plant

F I G U R E 1 Map of Sweden and the Ätran catchment, showing dams with or without fish passage solutions

opencc-by-4.0Nov 2022View details →

F I G U R E 6 in Three-dimensional migratory behaviour of European silver eels (Anguilla anguilla) approaching a hydropower plant

F I G U R E 6 Correlation between relative swimming depths and inflow discharge for upstream- and downstream-swimming eels during the day and night in the river reach. Solid lines represent significant relationships, broken lines insignificant ones. Situation:, downstream day;, downstream night;, upstream day;, upstream night

opencc-by-4.0Sep 2024View details →

Monthly Hydropower Generation Dataset for Western Canada

The presented dataset contains the following simulation-based monthly hydropower generation data for 110 facilities in British Columbia and Alberta, to support Western-US interconnect grid system studies: 1) Monthly hydropower generation estimates 2) Monthly hydropower flexibility metrics (minimum and maximum hourly generation and daily fluctuations) The hydropower generation estimates are provided with reference to the facility list that contains the corresponding metadata for each facility. For more details, please refer to Son, Y., Bracken, C., Broman, D. et al. Monthly hydropower generation data for Western Canada to support Western-US interconnect power system studies. Sci Data 12, 874 (2025). <a href="https://doi.org/10.1038/s41597-025-05098-2" target="_blank" rel="noopener">https://doi.org/10.1038/s41597-025-05098-2</a>. Corresponding author(s): Youngjun Son (youngjun.son@pnnl.gov) and Nathalie Voisin (nathalie.voisin@pnnl.gov) For data reproduction, please see the GitHub repository at <a title="tgw-hydro-canada" href="https://github.com/GODEEEP/tgw-hydro-canada" target="_blank" rel="noopener">https://github.com/GODEEEP/tgw-hydro-canada</a>. <h1>Hydropower Facility List</h1> The file, <code>CAN_hydropower_facilities&scaling.csv</code>, provides essential information on 146 hydropower facilities in British Columbia and Alberta, derived from <a title="Renewable Energy Power Plants, 1 MW or more, by Energy Source" href="https://www.eia.gov/trilateral/#!/maps" target="_blank" rel="noopener">Renewable Energy Power Plants, 1 MW or more, by Energy Source</a> by North American Cooperation on Energy Information (NACEI). Additionally, the facility information has been updated with corresponding <a title="National Hydrographic Network (NHN) Work Units" href="https://open.canada.ca/data/en/dataset/a4b190fe-e090-4e6d-881e-b87956c07977">National Hydrographic Network (NHN) Work Units</a>, global reservoir and lake database (<a title="GRanD: Global Reservoirs and Dams Database" href="https://www.globaldamwatch.org/grand" target="_blank" rel="noopener">GRanD: Global Reservoirs and Dams Database</a> and <a title="HydroLAKES" href="https://www.hydrosheds.org/products/hydrolakes" target="_blank" rel="noopener">HydroLAKES</a>), diversion intake flow rates based on water license information (hydropower), and so on. Below are the descriptions for each column in the facility metadata: <ul> <li>fid: Facility id according to NACEI data. New four-digit id starting with '9' are assigned for facilities with no fid in NACEI data</li> <li>Facility: Name of the facility</li> <li>X: Longitude of the facility's powerhouse</li> <li>Y: Latitude of the facility's powerhouse</li> <li>Province: Province where the facility is located</li> <li>Hydro_MW: Nameplate capacity of the facility</li> <li>NHN_Work_U: Associated NHN Work Units</li> <li>GRanD_ID: Associated reservoir id from the GRanD dataset</li> <li>HydroLAKES_ID: Associated lake id from the HydroLAKES dataset</li> <li>GINDEX: Grid id from the mosartwmpy Canada model</li> <li>GINDEX_CONUS: Grid id from the mosartwmpy CONUS model, used for facilities in the Columbia River Basin</li> <li>Basin_Note: Indicator for facilities located in the Columbia River Basin or outside of the meteorological forcing domain of the perturbed thermodynamics simulations</li> <li>WECC_ADS_2032: Indicator for facilities without the WECC ADS 2032 reference hydropower generation data</li> <li>Intake_Flow_Rate: Diversion intake flow rates based on hydropower water license information</li> <li>Type: Type of facility</li> <li>Water_License: Link to the source of water license information</li> <li>Scaling: Annual total scaling factor (total hydropower generation / total streamflow volume for 2008)</li> <li>Scaling_IntakeCap: Annual total scaling factor, constrained by intake flow rates from hydropower water license (total hydropower generation / total streamflow volume not exceeding intake flow rate constraint for 2008)</li> </ul> Among the 146 hydropower facilities listed, only 110 facilities, which are within the applied meteorological forcings domain and have reference hydropower generation data, are considered for monthly hydropower generation estimates. <h1>Monthly Hydropower Generation Estimates and Flexibility Metrics</h1> Each file contains a monthly timeseries dataset (rows: monthly timestamps) from 1981 to 2019 for 110 facilities (columns: Facility listed in <code>CAN_hydropower_facilities&scaling.csv</code>). <ol> <li><code>CAN_hydropower_monthly_generation_MWh.csv</code>: monthly total hydropower generation in MWh</li> <li><code>CAN_hydropower_monthly_p_min_MW.csv</code>: monthly flexibility metric of minimum generation capacity in MW</li> <li><code>CAN_hydropower_monthly_p_max_MW.csv</code>: monthly flexibility metric of maximum generation capacity in MW</li> <li><code>CAN_hydropower_monthly_p_ador_MW.csv</code>: monthly flexibility metric of the daily operation range in MW</li> </ol> <h1>Update Log</h1> - Version 1.1.0: "Scaling" and "Scaling_IntakeCap" colums have been added to Hydropower Facility List, and the file for hydropower facilities has been renamed from <code>CAN_hydropower_facilities.csv</code> to <code>CAN_hydropower_facilities&scaling.csv</code>. <h1>Funding Acknowledgements</h1> This work was supported under the Laboratory Directed Research and Development (LDRD) Program (Project # 79583) at the Pacific Northwest National Laboratory (PNNL). The PNNL is a multi-program national laboratory operated by Battelle Memorial Institute for the U.S. Department of Energy (DOE) under Contract No. DE-AC05-76RL01830. <h1>Disclaimer</h1> The presented dataset aims to support robust, long-term power system planning under diverse water conditions. However, it should not be used to assess hydropower generation during extreme flood events when facilities may need to be disconnected from power grids due to dam safety and potential loss of control that could propagate into grid instability. Similarly, the dataset should not be utilized for unprecedented drought conditions where reservoir levels may fall below critical power pool levels. Furthermore, evolving water policies, including the Columbia River Treaty, can alter seasonal and monthly hydrological patterns. It is important to note that our hydropower generation dataset, which is derived based on Year 2008, does not account for any historical and future changes in environmental regulations, water management, or water policies. The dataset was prepared as an account of work sponsored by an agency of the U.S. Government. Neither the U.S. Government nor the U.S. Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the U.S. Government or any agency thereof, or Battelle Memorial Institute.

opencc-by-4.0Mar 2022View details →

Supplementary data for article "Small hydropower – small ecological footprint? A multi-annual environmental impact analysis using aquatic macroinvertebrates as bioindicators. Part 2: effects on functional diversity" by Scotti A., et al.

Supplementary data for article "Small hydropower – small ecological footprint? A multi-annual environmental impact analysis using aquatic macroinvertebrates as bioindicators. Part 2: effects on functional diversity" by Scotti A., et al.: - Trait-based distances calculated for each pair of taxa; - CWM, CWM(LN) values, and their difference (CWMDIFF) Refer to the published articles for further details.

opencc-by-4.0Dec 2020View details →

Fig. 1 in Impact Of Designed Quairokkum Hydropower Plant Reconstruction On The Syr Darya River Ichthyofauna

Fig. 1. Syr Darya River basin with main tributaries, reservoirs and towns. Sampling places: 1. 40.2934, 69.6821; 2. 40.2947, 69.7351; 3. 40.2843, 69.8047; 4. 40.2757, 69.8230; 5. 40.3412, 70.2755

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Data for: Water system simulation modeling with hydropower optimization and environmental flows: An example with Pywr

This dataset was used in the CenSierraPywr model created for the project "Optimizing Hydropower Operations While Sustaining Ecosystem Functions in a Changing Climate", for the California Energy Commission. Specifically, this data is to support reproducibility of the article describing the basic methods (Rheinheimer et al., in review). The model was built in Pywr, an open-source, linear programming-based Python package for modeling basin-scale water systems in the Central Sierra Nevada, California. Here, we focus on the Stanislaus and Upper San Joaquin River basins as they have high elevation hydropower typically operated to maximize revenue. CenSierraPywr consists of daily water allocations that include both hydroeconomic drivers for hydropower and more advanced environmental flows. Piecewise linear electricity prices from simulated hourly price data are used to drive discretionary hydropower, while environmental flows include the addition of ramping rates. Hydrological inputs include runoff data at the sub-basin level, based on the historical (1950 to 2011) daily gridded (1/16 degree) runoff data generated by the Variable Infiltration Capacity (VIC) hydrologic model developed by Livneh et al. (2013), forced with observed meteorological data and bias-corrected using local gauge data. All data inputs for reproducibility of CenSierraPywr for the Stanislaus and Upper San Joaquin Rivers are included, including original and preprocessed electricity and hydrological data and management-related data specific to certain hydropower projects or facilities.

opencc-zeroJul 2022View details →

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 →

Data: Biodiversity Risks and Safeguards of China's Hydropower Financing in BRI Countries

Data used in: Narain D, Teo HC, Lechner AM, Watson JEM, Maron M. 2021. Biodiversity risks and safeguards of China’s hydropower financing in BRI countries. (In press with One Earth)

opencc-by-4.0Aug 2022View details →

Multi-model Hydropower Projections for the United States Federal Power Marketing Areas under CMIP5 Climate Change Conditions

This dataset contains an ensemble of monthly hydropower generation projections for the United States Federal Hydropower plants for the periods of 1966-2005 (historical period) and 2011-2050 (future period). The dataset includes the monthly hydropower projections developed in (Kao et al. 2016) based on the Watershed Runoff-Energy Storage (WRES) model and is complemented with another ensemble based on the process-based Water Management Power (WMP) model. The hydrologic projections are estimated through a cascading modeling toolchain that include ten global climate change model projections (ACCESS1-0, BCC-CSM1-1, CCSM4, CMCC-CM, GFDL-ESM2M, MIROC5, MPI-ESM-MR, MRI-CGCM3, NorESM1-M and IPSL-CM5A-LR) under RCP8.5 scenario, which are dynamically downscaled with a regional climate model (RegCM4) ( Pal et al. 2007, Giorgi et al. 2012)), which then inform the Variable Infiltration Capacity (VIC) hydrology model (Liang et al. 1994). The ensemble of hydrologic projections is then informing two processes to translate runoff into hydropower projections. First, WRES models monthly river routing and employs a non-linear statistical approach relating monthly natural flow to hydropower generation, including processes such as spilling. Second, MOSART-WM (Voisin et al. 2013), a large-scale river routing and water management model, provides daily reservoir storage and regulated release at dam locations as well as regulated flow at run-of-the-river power plants. The WMP model then translates reservoir and regulated river dynamics into hydropower projections (Zhou et al. 2018). Those projections are further calibrated to monthly generation provided by the federal utilities. The US federal hydropower plants analyzed in this study include 132 facilities that were built and/or are operated by the US Army Corps of Engineers (USACE), the Bureau of Reclamation (Reclamation), and the International Boundary and Water Commission (IBWC). The electricity generation projected for these hydropower plants were aggregated by four Power Marketing Administrations (PMAs), including Bonneville Power Administration (BPA), Southeastern Power Administration (SEPA), Southwestern Power Administration (SWPA), and Western Area Power Administration (WAPA), and their associate subregions. The two files, SWA9505V2_Gsim_PMA_WRES.mat and SWA9505V2_Gsim_PMA_WMP.mat, represent model outputs from the two hydropower models, WRES and WMP respectively. Each file contains 6 variables: 1) “Models”: the 10 global climate models (GCMs). 2) “PMA_areas”: the 18 subregions of PMAs as defined in (Kao et al. 2015). 3) “PMA_G_mn_6605”:  1966-2005 projected monthly hydropower generation for each PMA sub-regions. Dimension: (12 [months], 40 [years], 18 [subregions], 10 [GCMs]). Unit: MWH. 4) “PMA_G_mn_1150”:  Same as “PMA_G_mn_6605”, but for 2011-2050 projected hydropower generation. 5) “PMA_G_yr_6605”:  1966-2005 projected annual hydropower generation. Dimension: (40 [years], 18 [subregions], 10 [GCMs]) . Unit: MWH. 6) “PMA_G_yr_1150”:  Same as “PMA_G_yr_6605”, but for 2011-2050 projected hydropower generation. The following journal paper details the method in creating the dataset: Impacts of Climate Change on Subannual Hydropower Generation: A Multi-model Assessment of the United States Federal Hydropower Plants Zhou et al. (2022) Preparing for submission to Environmental Research Letters.

opencc-by-4.0Apr 2024View details →

Map of China Pumped Storage Hydropower Plant Geospatal Distribution

A map of PSH geospatial distribution in China, updated as of April 2024. All information are collected by using open-source on Internet. If you want to cite this map, please retain the copyright of Jingcai Cai. This map will also be upload in ArcGIS online soon, also the dataset will be released soon. If you want to contact author, could sent e-mal to me to get the dataset and have deeper communication with him.

FIGURE 3 in Protection of spawning habitat for potamodromous fish, an urgent need for the hydropower planning in the Andes

FIGURE 3 | Correlations between geomorphological and physicochemical variables, and ichthyoplankton density. Basin: basin area, Sinuous: channel sinuosity index, Flood: floodplain area, Cond: conductivity, Trans: transparency, Temp: temperature. α = 0.05.

FIGURE 2 in Protection of spawning habitat for potamodromous fish, an urgent need for the hydropower planning in the Andes

FIGURE 2 | Spatial variation in the median density of ichthyoplankton among tributaries. (Kruskal-Wallis H = 208.29, df = 9, p-value <2.2e-16). T01: Samaná River; T02: Nare River; T03: Espíritu Santo River; T04: Carare River; T05: Opón River; T06: Sogamoso River; T07: Boque River; T08: Nechí River; T09: San Jorge River and T10: Cesar River.

FIGURE 4 in Protection of spawning habitat for potamodromous fish, an urgent need for the hydropower planning in the Andes

FIGURE 4 | Potential spawning grounds for 13 sampled potamodromous fish species of the Magdalena basin. A. Baseline (current) scenario, and B. Full hydroelectric projects development scenario.

FIGURE 1 in Protection of spawning habitat for potamodromous fish, an urgent need for the hydropower planning in the Andes

FIGURE 1 | Location of the sampling tributaries (black circles) in the Magdalena River basin. T01: Samaná River; T02: Nare River; T03: Espíritu Santo River; T04: Carare River; T05: Opón River; T06: Sogamoso River; T07: Boque River; T08: Nechí River; T09: San Jorge River and T10: Cesar River. Magdalena River runs north.

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.

FIGURE 4 in Hydropower affects fish trophic structure both downstream of the dam and upstream of the reservoir

FIGURE 4 | A. Non-metrical multidimensional scaling (NMDS) of trophic structure. B. NMDS of species composition. (BU= Before/ upstream, BD= Before/downstream, AU= After/upstream, AD= After/downstream).

FIGURE 3 in Hydropower affects fish trophic structure both downstream of the dam and upstream of the reservoir

FIGURE 3 | A. Trophic structure of the fish assemblage represented by relative biomass of the trophic guilds. Line inside the box = median; box = 25th and 75th percentiles; whiskers = 1.5 x IQR; dots = outliers. B. Non-metrical multidimensional scaling (NMDS) of the relative biomass for each trophic guild. (Detr= Detritivores, Herb= Herbivores, Inse= Insectivores, Omni= Omnivore, Pisc= Piscivore, BU= Before/upstream, BD= Before/downstream, AU= After /upstream, AD=After/downstream).

FIGURE 2 in Hydropower affects fish trophic structure both downstream of the dam and upstream of the reservoir

FIGURE 2 | Daily average flow for three fluviometric stations. S1 and S2 are located upstream of the reservoir, and S3 is located downstream of the dam. The gray dashed line represents the date in which the dam was closed. The Energy Company of Minas Gerais (CEMIG) provided historical flow data (1996 – 2016) of the fluviometric stations nº 1743023 (S1), 1642029 (S2), and 1642044 (S3).

FIGURE 1 in Hydropower affects fish trophic structure both downstream of the dam and upstream of the reservoir

FIGURE 1 | Map of Irapé Hydroelectric Power Plant showing the fish sampling points (1 through 4) and fluviometric stations (S1, S2, S3).