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87 results for “Hydropower”
Dataset for Hydropower Expansion in Eco-Sensitive River Basins under Global Energy-Economic Change
<p>The data presented in this repository can be fed into the codes provided in <a href="https://github.com/kamal0013/chowdhury-etal_2023_hydropower">this GitHub repository</a> to reproduce the results of the following paper:</p> <p> </p> <p>Chowdhury, A.F.M.K., Wild, T., Zhang, Y. <em>et al.</em> Hydropower expansion in eco-sensitive river basins under global energy-economic change. <em>Nat Sustain</em> <strong>7</strong>, 213–222 (2024). <a href="https://doi.org/10.1038/s41893-023-01260-z">https://doi.org/10.1038/s41893-023-01260-z</a></p> <p> </p> <p><strong>Summary</strong></p> <p>In this study, we investigate how rapid economic growth and transition to low-carbon energy may impact hydropower development, with potential countervailing effects of increasingly cost-competitive variable renewable energy (VRE). We explore the effects of these forces on hydropower expansion in the world's 20 most eco-sensitive river basins, that have substantial untapped hydropower potential and ecological richness. Our investigation is based on the Global Change Analysis Model (GCAM), an integrated model of global energy-water-economy dynamics. The GCAM outputs and other data provided in this repository, in combination with the Jupyter Notebooks provided in <a href="https://github.com/kamal0013/chowdhury-etal_2023_hydropower">this GitHub repository</a>, can be used to conduct our key analysis, and reproduce the relevant results.</p>
CONUS-wide Balancing Authority Scale Hydropower Projections derived from 9505 Third Assessment
<p>This dataset provides historical and climate projection monthly hydropower generation timeseries for balancing authorities within the contiguous U.S. (CONUS). These data were developed as an extension to the Department of Energy Water Power Technologies Office's SECURE Water Act Section 9505 Third Assessment (9505) and include both federal and non-federal hydropower facilities. Additional modeling detail can be found in <a href="https://iopscience.iop.org/article/10.1088/1748-9326/ad6ceb" target="_blank" rel="noopener">Broman et al., 2024</a> and in the article's <a href="https://github.com/9505-PNNL/broman-etal_2024_erl">metarepository</a>. </p> <p>The dataset is provided in three separate formats to facilitate ease of use:</p> <p>1) Machine-readable csv in 'tidy' data format:</p> <table> <tbody> <tr> <td><strong>Short Name</strong></td> <td><strong>Unit</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>class</td> <td>N/A</td> <td>simulation type; control: historical, cc: climate scenario</td> </tr> <tr> <td>forcing</td> <td>N/A</td> <td>meteorological forcing used to drive hydrology model</td> </tr> <tr> <td>model</td> <td>N/A</td> <td>hydrology model</td> </tr> <tr> <td>hp</td> <td>N/A</td> <td>hydropower model</td> </tr> <tr> <td>gcm*</td> <td>N/A</td> <td>global climate model name</td> </tr> <tr> <td>ds*</td> <td>N/A</td> <td>downscaling method; DBCCA (statistical), RegCM (dynamical)</td> </tr> <tr> <td>balancing_authority</td> <td>N/A</td> <td>balancing authority code</td> </tr> <tr> <td>year</td> <td>N/A</td> <td>year</td> </tr> <tr> <td>month</td> <td>N/A</td> <td>month</td> </tr> <tr> <td>modeled_generation_MWh</td> <td>MWh per month</td> <td>simulated generation</td> </tr> </tbody> </table> <p>* only present in the climate projection (cc) files</p> <p>2) xlsx with balancing authority data by tab</p> <p>3) csv by balancing authority:</p> <p>for historical data: year, <em>month</em>, and <em>HUC4_group</em> columns are the same as above. Data column headers are <em>class</em>_<em>forcing</em>_<em>model</em>_<em>hp</em> and with the units <em>MWh per month</em>.</p> <p>for climate projection (cc) data: year, <em>month</em>, and <em>HUC4_group</em> columns are the same as above. Data column headers are <em>class</em>_<em>forcing</em>_<em>model</em>_<em>hp_gcm_ds</em> and with the units <em>MWh per month</em>.</p>
Water risks to hydropower projects in the face of climate change
<p>This repository hosts the main outputs from an analysis using the <a href="https://waterriskfilter.org/">WWF Water Risk Filter</a> to demonstrate how one such tool can be used to screen for a variety of risks at a global scale, including risks to riverine ecosystems from both climate change and hydropower as well as risks to hydropower projects — and operators, owners, and investors — from climate change and potential regulatory or reputational risk arising from negative impacts to ecosystems. The study <a href="https://www.mdpi.com/2073-4441/14/5/721">Using the WWF Water Risk Filter to Screen Existing and Projected Hydropower Projects for Climate and Biodiversity Risks (DOI 10.3390/w14050721) </a>was published in the special issue of the MDPI journal Water: <a href="https://www.mdpi.com/journal/water/special_issues/hydrometeorological_hazards">"Hydro-Meteorological Hazards under Climate Change"</a>.</p> <p>This product incorporates data from the GRanD v1.3 database which is © Global Water System Project (2011), and from the FHReD database beta version, both datasets available at <a href="http://globaldamwatch.org/">globaldamwatch.org</a> . The source code used in this study is available at <a href="https://github.com/rafaexx/hydropowerClimateChange">https://github.com/rafaexx/hydropowerClimateChange</a></p> <p>See the interactive maps using this data at <a href="https://rcamargo.shinyapps.io/HydropowerClimateChange">https://rcamargo.shinyapps.io/HydropowerClimateChange</a></p>
RICCH: An Interactive Analysis Tool for Risk and Impacts of Climate Change on Hydropower Database
<p>The "RICCH: An Interactive Analysis Tool for Risk and Impacts of Climate Change on Hydropower" Database, referred to as the RICCH Database, includes a "Hydropower Plants Database" and the incorporation of future hydropower usable capacity for 542 hydropower plants in the Global South. The "Hydropower Plants Database" includes the main design and location characteristics of 542 hydropower plants across 52 countries. Additionally, we incorporate the results of future usable capacity simulations using a multi-model ensemble of 21 Global Climate Models (GCMs) and two representative concentration pathways (RCPs). We use a multi-model ensemble of 21 GCMs from NASA's NEX GDDP dataset RCP 4.5 and RCP 8.5. We aggregate the mean monthly usable capacity (MW) for each model and scenario, for the early century (2010-2039), mid-century (2040-2069), and the end-of-the-century (2070-2099). We include these results for every power plant in the RICCH database. </p> <p><em>Example: Itaipu (power plant in Brazil) has a record for its simulated mean monthly usable capacity for January (1) in the early century (2010-2039), under GCM ACCESS1_0 and RCP 4.5.</em></p>
Dispersal of alien species in relation to the historic development of hydropower generation and navigation
<p>Dataset on dispersal of alien species in relation to the historic development of hydropower generation and Navigation along the River Danube.</p>
Hydropower dataset of hourly inflow values for European bidding zones for ACDC-ESM
<p><strong>Energy Climate dataset consistent with ENTSO-E Pan-European Climatic Database (PECD 2021.3) in CSV and netCDF format</strong></p> <p> </p> <p><strong>TL;DR</strong>: this is a nationally aggregated hourly dataset for the capacity factors per unit installed capacity for storage hydropower plants and run-of-river hydropower plants in the European region. All the data is provided for 30 climatic years (1981-2010).</p> <p> </p> <p><strong>Method Description </strong><br> The hydro inflow data is based on historical river runoff reanalysis data simulated by the E-HYPE model. E-HYPE is a pan-European model developed by The Swedish Meteorological and Hydrological Institute (SMHI), which describes hydrological processes including flow paths at the subbasin level. E-hype only provides the time series of daily river runoff entering the inlet of each European subbasin over 1981-2010. To match the operational resolution of the dispatch model, we linearly downscale these time series to hourly. By summing up runoff associated with the inlet subbasins of each country, we also obtain the country-level river runoff.</p> <p>The hydro inflow time series per country is defined as the normalized energy inflows (per unit installed capacity of hydropower) embodied in the country-level river runoff. A dispatch model can be used to decides whether the energy inflows are actually used for electricity generation, stored, or spilled (in case the storage reservoir is already full).</p> <p><strong>Data coverage</strong><br> This dataset considers two types of hydropower plants, namely storage hydropower plant (STO) and run-of-river hydropower plant (ROR). Not all countries have both types of hydropower plants installed (see table). </p> <p>The countries and their acronyms for both technologies included in this dataset are:</p> <table> <thead> <tr> <th scope="col">Country</th> <th scope="col">Run-of-River </th> <th scope="col">Storage</th> </tr> </thead> <tbody> <tr> <td>Austria</td> <td>AT_ROR</td> <td>AT_STO</td> </tr> <tr> <td>Belgium</td> <td>BE_ROR</td> <td>BE_STO</td> </tr> <tr> <td>Bulgaria</td> <td>BG_ROR</td> <td>BG_STO</td> </tr> <tr> <td>Switzerland</td> <td>CH_ROR</td> <td>CH_STO</td> </tr> <tr> <td>Cyprus</td> <td>CZ_ROR</td> <td>CZ_STO</td> </tr> <tr> <td>Germany</td> <td>DE_ROR</td> <td>DE_STO</td> </tr> <tr> <td>Denmark</td> <td>DK_ROR</td> <td> </td> </tr> <tr> <td>Estonia</td> <td>EE_ROR</td> <td> </td> </tr> <tr> <td>Greece</td> <td>EL_ROR</td> <td>EL_STO</td> </tr> <tr> <td>Spain</td> <td>ES_ROR</td> <td>ES_STO</td> </tr> <tr> <td>Finland</td> <td>FI_ROR</td> <td>FI_STO</td> </tr> <tr> <td>France</td> <td>FR_ROR</td> <td>FR_STO</td> </tr> <tr> <td>Great Britain</td> <td>GB_ROR</td> <td>GB_STO</td> </tr> <tr> <td>Croatia</td> <td>HR_ROR</td> <td>HR_STO</td> </tr> <tr> <td>Hungary</td> <td>HU_ROR</td> <td>HU_STO</td> </tr> <tr> <td>Ireland</td> <td>IE_ROR</td> <td>IE_STO</td> </tr> <tr> <td>Italy</td> <td>IT_ROR</td> <td>IT_STO</td> </tr> <tr> <td>Luxembourg</td> <td>LU_ROR</td> <td> </td> </tr> <tr> <td>Latvia</td> <td>LV_ROR</td> <td> </td> </tr> <tr> <td>the Netherlands</td> <td>NL_ROR</td> <td> </td> </tr> <tr> <td>Norway</td> <td>NO_ROR</td> <td>NO_STO</td> </tr> <tr> <td>Poland</td> <td>PL_ROR</td> <td>PL_STO</td> </tr> <tr> <td>Portugal</td> <td>PT_ROR</td> <td>PT_STO</td> </tr> <tr> <td>Romania</td> <td>RO_ROR</td> <td>RO_STO</td> </tr> <tr> <td>Sweden</td> <td>SE_ROR</td> <td>SE_STO</td> </tr> <tr> <td>Slovenia</td> <td>SI_ROR</td> <td>SI_STO</td> </tr> <tr> <td>Slovakia</td> <td>SK_ROR</td> <td>SK_STO</td> </tr> </tbody> </table> <p> </p> <p><strong>Data structure description</strong><br> The files is provided in CSV (.csv) format with a comma (,) as separator and double-quote mark (") as text indicator. The first row stores the column labels. The columns contain the following:</p> <ul> <li>first column (or A) contains the row number <ul> <li>Label: unlabeled</li> <li>Contents: interger range [1,262968]</li> </ul> </li> <li>second column (or B) contains the valid-time <ul> <li>Label: T1h</li> <li>Contents represent time with text as [DD/MM/YYYY HH:MM])</li> </ul> </li> <li>column 3-52 (or C-AY) each contain the capacity factor for each valid combination of a country and hydropower plant type <ul> <li>Label: XX_YYY the two letter country code (XX) and the hydropower plant type (YYY) acronym for storage hydropower plant (STO) and run-of-river hydropower plant (ROR)</li> <li>Contents represent the capacity factor as a floating value in the range [0,1], the decimal separator is a point (.).</li> </ul> </li> </ul> <p><strong>DISCLAIMER</strong>: <em>the content of this dataset has been created with the greatest possible care. However, we invite to use the original data for critical applications and studies. </em></p>
Filling Africa's largest hydropower dam should consider engineering realities
<p>This repository contains engineering data for the Grand Ethiopian Renaissance Dam, including reservoir geometry, evaporation rates, tailwater curve, and outlet capacities.</p>
JRC-EFAS-Hydropower
<p>The JRC-EFAS-Hydropower dataset contains the weekly hydropower inflow of pure storage plants and the daily run-of-river generation for 27 European countries. The dataset is based on the river discharge provided by the European Flood Awareness System (EFAS) and on the JRC Hydropower Database.</p> <p>The data is provided as a Tabular Data Package. We also provide two Python scripts to download the EFAS data and to extract the time-series needed to create the dataset</p> <p>Pre-print paper: https://eartharxiv.org/repository/view/1735/</p>
Climate Forced Hydropower Simulations Using NASA NEX-GDDP
<p>* This update includes the corrected values for all Peruvian Hydropower Plants included in the original dataset. </p> <p>This dataset includes the results of simulations of future hydropower usable capacity for power plants in Brazil, Colombia, and Peru. These simulations have been forced using NASA's Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset, which includes maximum temperature, minimum temperature, and precipitation simulations from 21 Global Climate Models (GCM) and three scenarios. The scenarios include a retrospective run (1950-2005) and two projection runs for Representative Concentration Pathways (RCP) 4.5 and 8.5. There is a folder for each country that includes a power plant characteristics file, with a list of all the power plants and the characteristics used for the analysis (installed capacity, effective height, reservoir specifications, etc.). Additionally, there is a folder including the dates for the usable capacity files. Each file inside the usable capacity folder is labeled "power_", followed by the power plant name (e.g. "tres_irmaos"), and the scenario (e.g. "rcp45_2006_2099"). </p> <p>This work is based on the future publication: Caceres, A.L., Jaramillo, P., Matthews, H.S., Samaras, C. & Nijssen B. "Hydropower under climate uncertainty: characterizing the usable capacity of Brazilian, Colombian and Peruvian power plants under climate scenarios".</p>
hydropower impact_river flow_Sweden
<p>River flow (m<sup>3</sup> yr<sup>-1</sup>) for 12 selected hydropower plants in Swedish rivers as well as the total river discharge to the Swedish coast. The dataset include both Naturalised River Flow and Regulated River Flow, in daily time-series from 1981-2010, calculated with the HYPE model code (HYPE_version_4_3_1) in S-HYPE model set-ups (QR: s-hype2012_version_1_2_1 resp. QN: s-hype2012QN_version_1_2_1). Model code can be downloaded from: http://hypecode.smhi.se/ and model results with high spatial resolution can be downloaded from http://vattenwebb.smhi.se/. The results for the whole country of Sweden have been published in: Arheimer, B. and Lindström, G. 2014. Electricity vs Ecosystems – understanding and predicting hydropower impact on Swedish river flow. Evolving Water Resources Systems: Understanding, Predicting and Managing Water–Society Interactions. Proceedings of ICWRS2014, Bologna, Italy, June 2014; IAHS Publ. 364:313-319.</p>
CONUS-wide HUC4 Watershed Scale Hydropower Projections derived from 9505 Third Assessment
<p>This dataset provides historical and climate projection monthly hydropower generation timeseries for HUC4 watersheds within the contiguous U.S. (CONUS). These data were developed as an extension to the Department of Energy Water Power Technologies Office's SECURE Water Act Section 9505 Third Assessment (9505) and include both federal and non-federal hydropower facilities. Additional modeling detail can be found in <a href="https://iopscience.iop.org/article/10.1088/1748-9326/ad6ceb">Broman et al., 2024</a> and in the article's <a href="https://github.com/9505-PNNL/broman-etal_2024_erl">metarepository</a>. Note that in some instances multiple HUC4 watersheds have been merged together into a HUC4 group, and that HUC4 watersheds without hydropower facilities are excluded. A hydropower facility's physical location, rather than the source of water, was used in assigning facilities to HUC4 watersheds.</p> <p>The dataset is provided in three separate formats to facilitate ease of use:</p> <p>1) Machine-readable csv in 'tidy' data format:</p> <table> <tbody> <tr> <td><strong>Short Name</strong></td> <td><strong>Unit</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>class</td> <td>N/A</td> <td>simulation type; control: historical, cc: climate scenario</td> </tr> <tr> <td>forcing</td> <td>N/A</td> <td>meteorological forcing used to drive hydrology model</td> </tr> <tr> <td>model</td> <td>N/A</td> <td>hydrology model</td> </tr> <tr> <td>hp</td> <td>N/A</td> <td>hydropower model</td> </tr> <tr> <td>gcm*</td> <td>N/A</td> <td>global climate model name</td> </tr> <tr> <td>ds*</td> <td>N/A</td> <td>downscaling method; DBCCA (statistical), RegCM (dynamical)</td> </tr> <tr> <td>HUC4_group</td> <td>N/A</td> <td>HUC4 group; HUC4 numeric ID or multiple IDs</td> </tr> <tr> <td>year</td> <td>N/A</td> <td>year</td> </tr> <tr> <td>month</td> <td>N/A</td> <td>month</td> </tr> <tr> <td>modeled_generation_MWh</td> <td>MWh per month</td> <td>simulated generation</td> </tr> </tbody> </table> <p>* only present in the climate projection (cc) files</p> <p>2) xlsx with HUC4 group data by tab</p> <p>3) csv by HUC4 group:</p> <p>for historical data: year, <em>month</em>, and <em>HUC4_group</em> columns are the same as above. Data column headers are <em>class</em>_<em>forcing</em>_<em>model</em>_<em>hp</em> and with the units <em>MWh per month</em>.</p> <p>for climate projection (cc) data: year, <em>month</em>, and <em>HUC4_group</em> columns are the same as above. Data column headers are <em>class</em>_<em>forcing</em>_<em>model</em>_<em>hp_gcm_ds</em> and with the units <em>MWh per month</em>.</p>
Data and code in support of "Rethinking energy planning to mitigate environmental and climatic impacts of future African hydropower"
<p>This dataset contains all the data and processing needed to produce results and figures reported in the manuscript "Rethinking energy planning to mitigate environmental and climatic impacts of future African hydropower".</p> <p> </p> <p>The README file guides through the material available to support replication of the results and figures.</p>
F I G U R E 1 0 in Three-dimensional migratory behaviour of European silver eels (Anguilla anguilla) approaching a hydropower plant
F I G U R E 1 0 Heatmap of search time for eels within 3 m of the bar rack for 53 attempts by 34 individual eels. Successful passes are shown on the left, and unsuccessful passes on the right. The hydraulic gate is located on the right side. The colours represent the proportion of search time in each zone, averaged over all attempts
F I G U R E 8 in Three-dimensional migratory behaviour of European silver eels (Anguilla anguilla) approaching a hydropower plant
F I G U R E 8 Upper: eel observations along four transects (a–d, with simulated relative water velocities). Observations are coloured by swimming direction. Lower: the density distribution of relative velocities at eel locations for upstream and downstream swimming, with the cross-section relative water velocity distribution in grey., downstream;, upstream;, cross-section
F I G U R E 7 in Three-dimensional migratory behaviour of European silver eels (Anguilla anguilla) approaching a hydropower plant
F I G U R E 7 Vertical and lateral eel distribution in river cross-sections in three parts of the river: the upstream river reach (river), the ponded forebay area (ponded) and the intake channel (channel) (see Figure 2a) for combinations of swimming directions and time of day. The observations are weighted by eel ground velocity and the colours represent scaled density
F I G U R E 5 in Three-dimensional migratory behaviour of European silver eels (Anguilla anguilla) approaching a hydropower plant
F I G U R E 5 Relative depth (a) and width (b) distribution of eels in the upstream river reach. The observations are grouped by eels moving downstream or upstream and by time of day. The observations are weighted by eel ground velocity, and the depth data are from the middle 90% of the river width. Situation:, downstream day;, downstream night;, upstream day;, upstream night
F I G U R E 4 in Three-dimensional migratory behaviour of European silver eels (Anguilla anguilla) approaching a hydropower plant
F I G U R E 4 Detection of eels entering the study area at the Herting hydropower plant grouped by inflow discharge and day/night, displayed as (a) number of eels and (b) eels per hour of current discharge., day;, night
F I G U R E 3 in Three-dimensional migratory behaviour of European silver eels (Anguilla anguilla) approaching a hydropower plant
F I G U R E 3 Schematics of a river cross-section: (a) natural model and (b) rectangular model. Relative depth is defined as zrel ¼ z=d and relative width as Wrel ¼ x=W, where W is the river width and d is the local depth
F I G U R E 1 1 in Three-dimensional migratory behaviour of European silver eels (Anguilla anguilla) approaching a hydropower plant
F I G U R E 1 1 Typical search patterns of eels along the intake screen at the Herting hydropower plant. Top, vertical view; bottom, plan view
F I G U R E 9 in Three-dimensional migratory behaviour of European silver eels (Anguilla anguilla) approaching a hydropower plant
F I G U R E 9 Distribution of first observations of eels closer than 3 m from bar rack at Herting hydropower plant: (a) horizontal distribution and (b) vertical distribution. Distributions are based on 41 observed interactions with the bar rack
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