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95 results for “Wind energy”
Wind Value: First Conference 2022, Research Opportunities for Wind Energy, Dave Linehan, Video
<p>VIdeo of 9 mins and 35 seconds, on the Research Opportunities for the Wind Energy Sector, by Dave Linehan of Wind Energy Ireland.</p>
The Diurnal Cycle of Integrated Kinetic Energy and Wind Radii in a Simulated Tropical Cyclone
<p>Model source code and output of a 340-day-long Cloud Model 1 simulation of a tropical cyclone and associated post-processing scripts.</p>
The Economic Integration of Wind Energy: An Analysis of the ECOWAS Subregion
<p>This study evaluates the economic integration of wind energy in the <br>Economic Community of West African States (ECOWAS) between 2010 <br>and 2020. Wind energy is the energy source that can cost-effectively <br>meet the energy needs of the sub-regions due to the theoretical and <br>economic potential of the sub-regions. For this reason, the study uses data <br>from the World Bank Development Indicators using Panel Vector Auto <br>Regression to analyze the determinants underpinning the economic <br>integration of wind energy. The Panel VAR estimate shows a significant <br>direct link between fossil fuel consumption and private sector investment <br>in renewable energy. This implies that the sub-region consumes a <br>significant amount of fossil fuels, hence the need to increase clean energy <br>investments to move the sub-region towards a low-carbon future. <br>Another significant lag variable is the power consumption per capita in <br>the subregion. Per capita electricity consumption in the sub-region is <br>woefully insufficient. Therefore, wind energy can ensure access via the <br>development of small community wind farms where the national power <br>grid cannot be extended to. When assessing the economic justification of <br>wind integration, the LCOE for wind power is 2.98 cents per kilowatt for <br>the lowest cost scenario compared to nuclear power’s 2.26 per kilowatt <br>hour. The FEVD shows that 13.4% of renewable energy investments are <br>self-explanatory within the first and last periods. The FEVD for wind <br>energy illustrates the short-term variance of 16.4 percent and increases to <br>51.1 percent in the following years after system shocks. This implies that <br>the expansion of wind capacity in the sub-region is expected to increase <br>in the long-term. This serves as a blueprint for integrating wind energy <br>into the sub-region.</p>
Solar and Wind Energy Drought Data for 15 BAs in the CONUS
<p><strong>Solar and wind energy drought data for 15 BAs in the CONUS</strong></p> <p>This dataset has 2 components, (1) physically consistent wind, solar and load data for 15 Balancing Authorities (BAs) in the CONUS and (2) pre-computed BA-level energy droughts for a variety of time scales from 1 hour to 5 days. The generation and load data is aggregated from plant level data based on EIA-860 2020 infrastructure. </p> <p>For more information please refer to Bracken et al. 2023, Standardized Benchmark of Historical Compound Wind and Solar Energy Droughts Across the Continental United States, in prep, or refer to the Github repository https://github.com/GODEEEP/energy-droughts</p> <p><strong>Wind, solar and load data</strong></p> <p>The data is broken up with one csv file per time scale, the available time scales are 1-hour, 4-hour, 12-hour, 1-day, 2-day, 3-day, and 5-day. Each file has the following columns</p> <ul> <li>ba - Abbreviated name for the BA </li> <li>year - The current year as an integer</li> <li>period - A unique integer for the current time step</li> <li>solar_gen_mwh - Aggregated solar generation in units of MWh</li> <li>solar_capacity_mwh - Aggregated solar plant capacity expresed as MWh </li> <li>wind_gen_mwh - Aggregated wind generation in units of MWh</li> <li>wind_capacity_mwh - Aggregated wind plant capacity expresed as MWh </li> <li>load_mwh - BA load in MWh</li> <li>load_max_mwh - The maximum BA load over the entire historical period</li> <li>datetime_utc - Time stamp for the current time step, in UTC, All time stamps are beginning of period. </li> <li>timezone - The predominant time zone for the BA</li> <li>wind_cf - Wind capacity factor, wind_gen_mwh/wind_capacity_mwh</li> <li>solar_cf - Solar capacity factor, wind_gen_mwh/wind_capacity_mwh </li> <li>load_cf - Load "capacity factor", expresed as a fraction of the maximum BA load, load_mwh/load_max_mwh </li> </ul> <p><strong>Energy drought data</strong></p> <p>Several kinds of energy droughts are available</p> <ul> <li><strong>lws</strong> - Load, wind, and solar compound droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>rl</strong> - Residual load (load minus wind and solar gen) droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>solar</strong> - Solar only droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>solar_fixed</strong> - Solar only droughts defined using a single 10th percentile threshold </li> <li><strong>wind</strong> - Wind only droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>wind_fixed</strong> - Wind only droughts defined using a single 10th percentile threshold </li> <li><strong>ws</strong> - Wind only droughts defined using a moving 10th percentile threshold based on the week of the year</li> <li><strong>ws_fixed</strong> - Wind and solar droughts defined using a single 10th percentile threshold </li> </ul> <p>Each drought type and time scale is in a csv file with the following columns (not all columns are available for every drought type)</p> <ul> <li>ba - Abbreviated name for the BA </li> <li>run_id - unique id for each drought event</li> <li>datetime_utc - Date stamp for the start of the drought, in UTC</li> <li>timezone - The predominant time zone for the BA</li> <li>run_length - The length of a drought in time steps</li> <li>run_length_days - The length of the drought in days</li> <li>severity_ws - Drought severity for wind and solar droughts, computed using the compound drought magnitude metric</li> <li>severity_lws - Drought severity load, wind, and solar droughts, computed using the compound drought magnitude metric</li> <li>severity_mwh - Drought severity expressed as MWh</li> <li>zero_prob - For solar, this value indicates if the timestep has zero probability of solar production, i.e. night time</li> <li>year - The year of the timestep</li> <li>month - The month of the timestep </li> <li>hour - The hour of the timestep </li> <li>wind_cf - Wind capacity factor for the drought</li> <li>solar_cf - Solar capacity factor for the drought </li> <li>srepi_solar - Standardized renewable energy production index for solar</li> <li>srepi_wind - Standardized renewable energy production index for wind</li> </ul> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p> <p> </p>
Variable Geometry Turbines Industrialization for a Circular Economy of Distributed Wind Energy
<p>In this work we present a novel VAWT with passive variable geometry (PVG), which combines variable radius and variable pitch of the free blades. The turbine was optimized through extensive numerical simulations, with a final design featuring short start-up time, high steady-state efficiency, low construction complexity, and low material costs. A crucial reduction of nearly 50% in the blades' mass was obtained through the use of compression moulded composites. A full-scale physical prototype was tested in a dedicated wind tunnel facility, where the typical intermittent air flow of the UBL was replicated. The turbine was connected to standard power electronics found in the solar photovoltaic market. The measured electrical power output closely matches the numerical simulations, suggesting that our PVG design can achieve a 64% increase in the capacity factor compared to a fixed-geometry turbine. Thanks to this engineering breakthrough we believe that, for the first time, wind installations in the UBL can be competitive on the energy market. We conclude our contribution by discussing industrialization and sustainability aspects of our PVG-VAWT technology, and by projecting a deployment scenario towards the uptake of a distributed urban wind market.</p>
Lesser prairie-chicken habitat selection and survival relative to a wind energy facility located in a fragmented landscape
<p>The overlap of renewable wind energy with the range of lesser prairie-chickens (<em>Tympanuchus</em> <em>pallidicinctus</em>) raises concern of population declines and habitat loss. Lesser prairie-chickens are adversely affected by landscape change, however, it is unclear how this species may respond to wind energy development. Therefore, managers and wind energy developers are currently tasked with making management or siting recommendations of future wind energy facilities based on lesser prairie-chicken behavioral responses to other forms of anthropogenic development or responses of other grouse species to wind energy development. The current strategy of siting wind turbines in cultivated cropland within lesser prairie-chicken range has not been evaluated for its effectiveness at minimizing potential adverse impacts. We captured 60 female and 66 male lesser prairie-chicken from leks located along a gradient from wind turbines in southern Kansas, USA, from 2017–2021. Over the study period, we collected lesser prairie-chicken location data and demographic information to evaluate resource selection, movements, and demography relative to environmental predictors and metrics associated with the wind energy facility. Lesser prairie-chickens used habitats in close proximity to wind turbines, provided that turbine density was low; however, avoidance associated with cultivated cropland appeared to be more predictive than the presence of wind turbines. We observed movement between turbines suggesting that wind turbines did not act as a barrier to local movements. We did not detect an influence of wind turbines on nest success or individual survival during breeding or non-breeding periods, a relationship that is consistent among multiple grouse species using habitats near wind energy infrastructure. Additional research is necessary to evaluate impacts associated with wind energy development in intact lesser prairie-chicken habitats, but placing wind turbines in cultivated croplands or other fragmented landscapes appears to be an important siting measure when considering wind energy facility siting across the lesser prairie-chicken range.</p>
European offshore wind farms and marine energy deployements
<p>Three distinct dataset used to forecast the development of marine energy in Europe in the upcoming three decades:</p> <p>- European offshore wind farms</p> <p>- tidal energy converter deployements in Europe</p> <p>- wave energy converter deployements in Europe</p>
Dataset for "Disentangling mechanisms responsible for wind energy effects on European bats"
<p>Data used in the study "Disentangling mechanisms responsible for wind energy effects on European bats".</p> <p><strong>site </strong>= ID attributed to the studied site ; <strong>ID_Parc</strong> = ID attributed to the studied wind farm ; <strong>night </strong>= date of the sampling ; <strong>SM4 </strong>= ID attributed to the recorder ; <strong>NbWT_1500m</strong> = number of wind turbines in a 1500m buffer ; <strong>Dist_WT</strong> = distance to the nearest wind turbine ; <strong>Dist_water</strong> = distance to the nearest water body or water course ; <strong>Dist_forest</strong> = distance to the nearest forest ; <strong>avg_temp</strong> = average temperature of the night ; <strong>Prev_wind_mod1</strong> = prevailing wind direction of the night ; <strong>count_prev_wind22.5</strong> = number of hours during which the wind direction was close (± 22.5°) to the prevailing wind direction (mode) of the night ; <strong>angle </strong>= angle between the axis wind-turbine - detector and the north direction ; <strong>MES_year</strong> = year of commissioning of the nearest wind turbines ; <strong>Model </strong>= model of the nearest wind turbine ; <strong>Rotor_diameter</strong> = diameter of the rotor of the nearest wind turbine ; <strong>Hub_height</strong> = height of the hub of the nearest wind turbine ; <strong>ID_WT</strong> = ID attributed to the nearest wind turbine</p>
Lesser prairie-chicken habitat selection and survival relative to a wind energy facility located in a fragmented landscape
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Data from: Geographic source of bats killed at wind-energy facilities in the eastern United States
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A predictive flight-altitude model for avoiding future conflicts between an emblematic raptor and wind energy development in the Swiss Alps
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Data from: Patterns in lek persistence and attendance by lesser prairie chicken <em>Tympanuchus pallidicinctus</em> near a wind energy facility in southern Kansas
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Resource selection and survival of plains sharp-tailed grouse at a wind energy facility
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Data from: Towards a better understanding of avian collisions in wind energy facilities using automatic detection systems
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Wind energy development can lead to guild-specific habitat loss in boreal forest bats
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Data from: Mortality limits used in wind energy impact assessment underestimate impacts of wind farms on bird populations
<p>In this archive we share the data and R code used for the construction of population models for seven bird species (Common Starling, Black-tailed Godwit<strong>,</strong> Marsh Harrier, Eurasian Spoonbill, White Stork, Common Tern and White-tailed Eagle) for our assessment of the effects of wind farms (Schippers et al. 2020). In most cases we parameterized our population models based on species-specific survival and reproduction rates from scientific articles and reports, but in the case of the Western Marsh Harrier we analyzed previously unpublished nest success and capture-mark-resighting data. Below we first describe per species which data we used for model parameterization, and then describe per data file what each variable represents.</p> <p>We selected populations of seven species based on the availability of data, considerable likelihood to collide with wind turbines and contrasting ages of first reproduction. For species for which long time series of demographic data were available with population trends clearly changing over time, we separately assessed periods with contrasting population trends, as detailed in the species descriptions below. Mean survival and reproduction rates, standard deviations and additional information like the age of first reproduction can be found in the accompanying paper by Schippers et al. (2020). </p> <p> </p> <p><strong>Common Starling</strong></p> <p>On the fast-slow continuum of reproductive capacity, the common starling is the fastest of the seven species we selected: it starts reproducing at an age of one year. We used the mean survival and reproductive rates for the whole Dutch breeding population (Versluijs et al. 2016), distinguishing three separate periods: 1960-1978, 1978-1990 and 1990-2012. In the first period (1960-1978) the population grew at 10% per year. This was followed by a period where the population was relatively stable (1978-1990). During the last period (1990-2012) the population declined strongly.</p> <p> </p> <p><strong>Black tailed Godwit</strong></p> <p>Kentie et al. (2017) studied two Dutch populations of the Black-tailed Godwit in southwestern Fryslân (Skriezekrite and Kuststrook) over four to five annual transitions (Kentie et al. 2017). Godwits started reproducing at age two, but only had 0.5-0.6 fledglings per breeding pair per year. The adults are rather long-lived with an 86% annual survival rate. We construct separate matrix models for the two populations.</p> <p> </p> <p><strong>Marsh Harrier</strong></p> <p>Mean vital rates of the Dutch breeding population of Marsh Harriers were estimated for 1997-2015 using respectively ring recoveries available at the Dutch Centre for Avian Migration and Demography NIOO-KNAW and reproduction data from the Dutch Raptor Working Group. Annual survival of Marsh Harriers was analyzed using live re-sightings and dead recoveries of 12,059 birds ringed as nestling between 1991 and 2016 and 74 birds ringed as ‘adult’ in the same period (due to low sample sizes, birds ringed in their first and second calendar year were lumped with older birds in the ‘adult’ category; see ‘marshHarrierSurvival.csv’ below). Nest success was estimated using data of 1914 nests, which were followed from the beginning to the end of the nest cycle, in the Netherlands between 1997 and 2015 (see ‘marshHarrierReproduction.csv’ below; we thank Rob G. Bijlsma for making the data available). </p> <p> </p> <p><strong>Spoonbill</strong></p> <p>For each year in the 1994-2008 period, age-specific (first-year, second-year, third-year, older) annual survival rates were derived for the Dutch Spoonbill population from van der Jeugd et al. (2014). Participation in the breeding population was 0% in the first three years and went up from 63% at age four to 95% at age 6 and older.</p> <p> </p> <p><strong>White Stork</strong></p> <p>Schaub et al. (2004) analyzed demographic data on White Storks in Switzerland from 1977 till 2000. Here we extracted annual survival and reproduction rates from the COMADRE Animal Matrix Database (version 2.0.1; Salguero-Gómez et al., 2016). Storks start reproducing at age 3, with breeding participation increasing with age from 48% to 100%. </p> <p> </p> <p><strong>Common Tern</strong></p> <p>For the Common Tern we used mean vital rate estimates published by van der Jeugd et al. (2014) for the Dutch Waddenzee population, including the Northern part of the IJsselmeer, between 2000 and 2010 (van der Jeugd et al. 2014). The total Waddenzee and IJsselmeer population is estimated at 7,630 pairs (average population 2010-2014), constituting approximately 40% of the Dutch breeding population of about 20,000 pairs (Sovon 2016). </p> <p> </p> <p><strong>White-tailed Eagle</strong></p> <p>Krüger et al. (2010) published demographic data on White-tailed Eagles in Schleswig-Holstein, Germany, over the period 1947 till 2008. Following these authors, and based on the two matrices in COMADRE v.2.0.1 (Salguero-Gómez et al., 2016), we used separate matrix models for the early period (stable population dynamics) and from 1975 onwards (population growth). These eagles start reproducing at age five. </p> <p> </p> <p>Here we describe the archived files:</p> <p> </p> <p><strong>matrices.R</strong></p> <p>This annotated R file details how the vital rate estimates are used to construct age-structured, post-breeding-census, one-year-timestep population matrix models. In these so-called post-breeding census models the birds in the first class were 0 years old (Caswell 2001).</p> <p> </p> <p><strong>commonstarling19602012.csv</strong></p> <p>Mean survival and reproductive rates for the whole Dutch breeding population of Common Starlings for the time period 1960-2012. </p> <p>year = start year</p> <p>juvSurv = first-year survival of fledgelings</p> <p>adultSurv = annual survival of older birds</p> <p>fec = number of fledgelings per pair (which have a 1:1 sex ratio)</p> <p> </p> <p><strong>blacktailedgodwit20112016.csv</strong></p> <p>Mean survival and reproduction rates of the Black-tailed Godwit in southwestern Fryslân (populations Skriezekrite and Kuststrook) over four to five annual transitions in the period 2011-2016. </p> <p>pop = population</p> <p>startYear = start year</p> <p>adultSurv = annual survival of older birds</p> <p>chickSurv = first-year survival of chicks</p> <p>nestSuc = probability that a nest is successful</p> <p> </p> <p><strong>marshharrier19972015.csv</strong></p> <p>Mean vital rates of the Dutch breeding population of Western Marsh Harriers for 1997-2015.</p> <p>year = start year</p> <p>r = number of fledgelings per pair</p> <p>s1 = first-year survival of fledgelings</p> <p>s2 = annual survival of older birds</p> <p> </p> <p><strong>marshharrierreproduction.csv</strong></p> <p>Western Marsh Harrier nest record data of in the Netherlands.</p> <p>year = year</p> <p>clutchSize = number of eggs</p> <p>young = number of chicks (if known)</p> <p>fledgelings = number of fledgelings</p> <p> </p> <p> </p> <p> </p> <p><strong>marshharriersurvival.csv</strong></p> <p>Ringing and resighting data (using EURING coding) on Western Marsh Harriers in the Netherlands. </p> <p>ringID = ring identifier</p> <p>date = observation date</p> <p>metalRingInformation</p> <p>1 = Metal ring added (where no metal ring was present), position (on tarsus or above) unknown or unrecorded.</p> <p>2 = Metal ring added (where no metal ring was present), definitely on tarsus.</p> <p>3 = Metal ring added (where no metal ring was present), definitely above tarsus.</p> <p>4 = Metal ring is already present.</p> <p>condition </p> <p>0 = Condition completely unknown.</p> <p>1 = Dead but no information on how recently the bird had died (or been killed).</p> <p>2 = Freshly dead – within about a week.</p> <p>3 = Not freshly dead – information available that it had been dead for more than about a week.</p> <p>4 = Found sick, wounded, unhealthy etc. and known to have been released (including ring or other mark identified on a bird in poor condition without the bird having being caught).</p> <p>5 = Found sick, wounded, unhealthy etc. and not released or not known if released.</p> <p>6 = Alive and probably healthy but taken into captivity.</p> <p>7 = Alive and probably healthy and certainly released (including ring or other mark identified on a healthy bird without the bird having being caught).</p> <p>8 = Alive and probably healthy and released by a ringer (including ring or other mark identified on the bird by a ringer without the bird having being caught). </p> <p>ageReported </p> <p>0 = Age unknown, i.e. not recorded.</p> <p>1 = Pullus: nestling or chick, unable to fly freely, still able to be caught by hand.</p> <p>2 = Full-grown: able to fly freely but age otherwise unknown.</p> <p>3 = First-year: full-grown bird hatched in the breeding season of this calendar year.</p> <p>4 = Afer first-year: full-grown bird hatched before this calendar year; year of hatching otherwise unknown.</p> <p>5 = 2<sup>nd</sup> year: a bird hatched last calendar year and now in its second calendar year.</p> <p>6 = Afer 2<sup>nd</sup> year: full-grown bird hatched before last calendar year; year of hatching otherwise unknown.</p> <p>7 = 3<sup>rd</sup> year: a bird hatched two calendar years before, and now in its third calendar year.</p> <p>8 = Afer 3<sup>rd</sup> year: a full-grown bird hatched more than three calendar years ago (including present year as one); year if bird otherwise unknown.</p> <p>9 = 4<sup>th</sup> year: a bird hatched three calendar years before, and now in its fourth calendar year.</p> <p>A = Afer 4<sup>th</sup> year: a bird older than category 9 – age otherwise unknown.</p> <p>sexReported</p> <p>U = Unknown</p> <p>M = Male</p> <p>F = Female</p> <p> </p> <p><strong>eurasianspoonbill19942008.csv</strong></p> <p>For each year in the 1994-2008 period, age-specific (first-year, second-year, third-year, older) annual survival rates are given for the Dutch Spoonbill population.</p> <p>year = start year</p> <p>fled = number of fledgelings per breeding pair</p> <p>s1 = first-year survival rate</p> <p>s2 = second-year survival rate</p> <p>s3 = third-year survival rate</p> <p>s4 = older birds' annual survival rate</p> <p> </p> <p><strong>whitestork19772000.csv</strong></p> <p>Demographic data on White Storks in Switzerland from 1977 till 2000.</p> <p>year = start year</p> <p>fled = number of fledgelings per pair</p> <p>sj = first-year survival of fledgelings</p> <p>sa = annual survival of older birds</p> <p> </p> <p><strong>commontern19942009.csv</strong></p> <p>Mean vital rate estimates for the Common Tern for the Dutch Waddenzee population, including the Northern part of the IJsselmeer, between 2000 and 2010.</p> <p>year = start year</p> <p>r = number of daughter fledgelings per adult female</p> <p>s1 = first-year survival rate</p> <p>s2 = second-year survival rate</p> <p>sA = older birds' annual survival rate</p> <p> </p> <p><strong>whitetailedeaglepmat1.csv</strong></p> <p><strong>whitetailedeaglepmat2.csv</strong></p> <p><strong>whitetailedeaglefmat1.csv</strong></p> <p><strong>whitetailedeaglefmat2.csv</strong></p> <p>White-Tailed Eagle age-specific survival (Pmat) and reproduction (Fmat) matrices as found in COMADRE v.2.0.1, for Schleswig-Holstein, Germany, studied over the period 1947-2008. Period 1 lasts upto 1975, period 2 from 1975. </p>
In-Stream Energy by Tidal and Wind-Driven Currents: An Analysis for the Gulf of California
<p>Dataset associated with the submitted publication - "In-Stream Energy by Tidal and Wind-Driven<br> Currents: An Analysis for the Gulf of California" in Energies.</p> <p>Created: 10/15/2020 by Victor M. Godínez (CICESE). Ver. 1.0</p> <p>Authors: Vanesa Magar, Victor M. Godínez, Markus S. Gross, Manuel López-Mariscal, Anahí Bermúdez-Romero, Julio Candela and Luis Zamudio.<br> Project_info: This data base has been obtained during the project funded by the financial support of SENER-CONACyT grant 249795, within the project "CeMIE-Océano".</p> <p>License: The authors appreciate that users of these data: 1) Include the requested acknowledgment (cite using the DOI of this dataset) in any presentations or publications.</p> <p>Fig2: Variables:</p> <p> 'Time' 'Julian days'</p> <p> 'SEC Speed' 'm/s'</p> <p> 'HYCOM Speed' 'm/s'</p> <p> </p> <p>Fig3: Variables:</p> <p> 'Latitude' 'degrees'</p> <p> 'Longitude' 'degrees'</p> <p> 'U STM Speed' 'm/s'</p> <p> </p> <p>Fig5: Variables:</p> <p> 'Latitude' 'degrees'</p> <p> 'Longitude' 'degrees'</p> <p> 'TPD>50' 'W m<sup>-2</sup>'</p> <p> </p> <p>Fig6: Variables:</p> <p> 'Latitude' 'degrees'</p> <p> 'Longitude' 'degrees'</p> <p> '%Time TPD>50' '%'</p> <p> </p> <p>Fig7: Variables:</p> <p> 'Latitude' 'degrees'</p> <p> 'Longitude' 'degrees'</p> <p> 'AEP ' 'k Wh m<sup>-2</sup> yr<sup>-1</sup>'</p> <p> </p> <p>Fig8: Variables:</p> <p> 'Latitude' 'degrees'</p> <p> 'Longitude' 'degrees'</p> <p> 'Residual TPD ' 'W m<sup>-2</sup>'</p> <p> </p> <p>Fig9: Variables:</p> <p> 'Latitude' 'degrees'</p> <p> 'Longitude' 'degrees'</p> <p> '%AEP from total AEP '%'</p>
Data from: A bridge between oceans: Overland migration of marine birds in a wind energy corridor
Located at the shortest overland route between the Gulf of Mexico and the Pacific Ocean, Mexico's Tehuantepec Isthmus is a globally important migratory corridor for many terrestrial bird species. The Pacific coast of the Isthmus also contains a significant wetland complex that supports large multi-species aggregations of non-breeding waterbirds during the boreal winter. In recent years, extensive wind energy development has occurred in the plains bordering these wetlands, directly along the migratory flyway. Using recent studies of movement patterns of three marine-associated bird species—reddish egrets (Egretta rufescens), brown pelicans (Pelecanus occidentalis), and red knots (Calidris canutus)—from the northern Gulf of Mexico, we assess the use of the isthmus as a migratory corridor. Our data provide evidence that marine birds from the Gulf region regularly overwinter along the Pacific coast of Mexico and use the isthmus as a migratory corridor, creating the potential for interaction with terrestrial wind farms during non-breeding. This study is the first to describe migration by marine-associated bird species between the Gulf of Mexico and Pacific coast. These data contribute new information toward ongoing efforts to understand the complex migration patterns of mobile marine species, with the goal of informing integrated conservation efforts for species whose year-round habitat needs cross ecoregional and geopolitical boundaries.
Mirror of "ENSPRESO - an open data, EU-28 wide, transparent and coherent database of wind, solar and biomass energy potentials"
<h2>Mirrored from Joint Research Centre Data Catalogue</h2><p><a href="https://data.jrc.ec.europa.eu/collection/id-00138#datasets">https://data.jrc.ec.europa.eu/collection/id-00138#datasets</a></p><blockquote><p>This collection contains datasets from ENSPRESO, an EU-28 wide, open dataset for energy models on renewable energy potentials, at national (NUTS0) and regional levels (NUTS2) for the 2010-2050 period. Within ENSPRESO, ENergy Systems Potential Renewable Energy SOurces, technical potentials are provided for wind, solar and biomass, based on coherent GIS-based land-restriction scenarios. For wind, resource evaluation also considers setback distances as well as high resolution geo-spatial wind speed data. For solar, potentials are derived from irradiation data and available area for solar applications. For biomass, agriculture, forestry and waste sectors are considered. The temporal resolution for wind and solar is both annual and year fractions (timeslices as used by JRC-EU-TIMES). ENSPRESO complements the EMHIRES collection, that provides meteorologically derived power time series at high temporal and spatial resolution. ENSPRESO can impact the results of any energy model by improving its analyses of the competition and complementarity of energy technologies.</p></blockquote><p><a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:RUIZ%20CASTELLO%20Pablo">RUIZ CASTELLO Pablo</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:NIJS%20Wouter">NIJS Wouter</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:TARVYDAS%20Dalius">TARVYDAS Dalius</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:SGOBBI%20Alessandra">SGOBBI Alessandra</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:ZUCKER%20Andreas">ZUCKER Andreas</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:PILLI%20Roberto">PILLI Roberto</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:CAMIA%20Andrea">CAMIA Andrea</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:THIEL%20Christian">THIEL Christian</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:HOYER-KLICK%20Carsten">HOYER-KLICK Carsten</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:DALLA%20LONGA%20Francesco">DALLA LONGA Francesco</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:KOBER%20Tom">KOBER Tom</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:BADGER%20Jake">BADGER Jake</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:VOLKER%20Patrick">VOLKER Patrick</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:ELBERSEN%20Berien">ELBERSEN Berien</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:BROSOWSKI%20Andre">BROSOWSKI Andre</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:THR%C3%84N%20Daniela">THRÄN Daniela</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:JONSSON%20Klas">JONSSON Klas</a></p><h3>How to cite</h3><p>Ruiz Castello, P., Nijs, W., Tarvydas, D., Sgobbi, A., Zucker, A., Pilli, R., Camia, A., Thiel, C., Hoyer-Klick, C., Dalla Longa, F., Kober, T., Badger, J., Volker, P., Elbersen, B., Brosowski, A., Thrän, D. and Jonsson, K., ENSPRESO - an open data, EU-28 wide, transparent and coherent database of wind, solar and biomass energy potentials, European Commission, 2019, JRC116900.</p><p>European Commission</p><p>JRC116900</p><h3>Remarks</h3><p>The originator of this mirror requires stable and reliable URLs due to an integration of the dataset into an automated workflow. The data catalogue has frequent outages.</p>
Skillful Seasonal Prediction of Wind Energy Resources in the contiguous United States
<p>This dataset contains data files used to replicate figures in a paper published in communications earth & environment. These datasets are in .mat format, which can be readable by Matlab software.</p>
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