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1,604 results for “Wintering”
Raw Data of the Surveys conducted in the Advanced Inorganic Lab Course in the Winter Terms 2020/2021, 2021/2022, and 2022/2023 at RWTH Aachen University
<p>In the advanced inorganic lab course at RWTH Aachen University, the undergraduate students are asked to use the electronic laboratory notebook (ELN) Chemotion and thus become aware of and familiar with research data management (RDM) at an early stage in their studies. To map the implementation of research data management and the Chemotion ELN in the lab course, a survey was conducted in the winter terms 2020/2021, 2021/2022, and 2022/2023 to ask the students to share their experiences and criticism on these topics.</p> <p>In this data publication, the underlying raw data of the surveys (as received from the survey software SoSci Survey<sup>1</sup>) are available as .csv-files separated into data, values, and variables for the respective winter terms. Additionally, the evaluated data are summarized in .xlsx-files which are also part of this data publication. As the principal language of the inorganic lab course is German, the survey and it's evaluation are primarily in German language, too. For further information, please have a look at the 01_Read-me.txt file.</p> <p>The survey and the related results and interpretations are available as a journal publication elsewhere.</p> <p><strong>Literature:</strong></p> <p>1. Leiner, D. J. <em>SoSci Survey (Version 3.2.12 and newer) [Computer software]</em>. 2020.<strong> </strong><a href="https://www.soscisurvey.de">https://www.soscisurvey.de</a><em> </em>(accessed 2023-07-26).</p> <p> </p>
Thermal profiles in ponds and shallow lakes during summer to winter shoulder season
Autumn is an important transition time for freshwater ecosystems where many lakes turnover, going from thermally stratified to mixed in a short time. Ponds are more globally abundant than lakes, yet, the seasonal transition of ponds is poorly understood. To evaluate the mixing regimes of ponds, we examined summer into autumn thermal dynamics in 37 ponds and shallow lakes across temperate North America and Europe. This dataset provides a time series dataset of water temperatures across the water column along with characteristics of each study waterbody, including some physical, chemical, and biological parameters. Data from four waterbodies (Eddy, Tumbledown, Cranberry, Horns) have more extensive datasets published in Gavin et al. (2025). Gavin, A.L., J.E. Saros, R. Hovel, S. Birkel, S. Nelson, W.H. McDowell, and J. Daly. 2025. Sub-Alpine Lake (>600 m) High-Frequency Water Temperature, DOC (2007-2021), and Weather Station (Fall 2023) Dataset, Maine, USA. ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/6c6286abeccc90448af0f73251843407 (Accessed 2025-09-16).
Continuous soil temperature measurements at 10 cm depth from 3 month-long deployments in summer and winter within the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) site in 2017 and 2018
To understand the influence of marsh elevation and flooding on soil temperature in Spartina alterniflora marsh, we measured soil temperature at 10 cm depth along two transects that spanned a marsh edge to interior gradient. We then associated those measurements with elevation, creek water height and vegetation characteristics. Soil temperature was logged every 15 min with a Hobo Onset Tidbit Pendant Temperature probe in Spartina alterniflora-dominated marsh near the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) eddy covariance flux tower. Measurements were collected along two transects of approximately 250 m in length from 18 plots (transect 1) or 20 plots (transect 2) and over 3 sample deployments of approximately 1 month in length: 27 Jul – 31 Aug 2017 (transect 1), 8 Jan – 13 Feb 2018 (transect 1) and 23 Aug – 18 Sep 2018 (transect 2). Plot elevations along each transect were measured with a Trimble R6 RTK after probes were installed in the marsh. Creek water heights were estimated with the pressure transducer associated with the GCE-LTER eddy covariance flux tower data. Spartina alterniflora height forms were measured for each sample station during August as the mean of all stem heights within 0.25 m quadrants centered over each soil probe location. While these data are 24 hr soil temperature measurements, during all three deployments, we found that daily mean soil temperature was negatively correlated with marsh elevation on the marsh platform during low tide conditions, which represented the majority of the observations.
Chlorophyll determined by extraction of samples taken approximately weekly from seawater intake starting at Palmer Station by station personnel including during winter-over period, 1991-2024.
Chlorophyll a (Chl a) is the principal photosynthetic pigment of phytoplankton, and is used as a proxy measurement for estimating phytoplankton biomass in water samples. Chl a concentrations reflect the distribution of active phytoplankton spatially and with depth in the water column and their changes over time. Chlorophyll a is determined weekly year-round at the laboratory seawater intake (SWI), from a depth of 6 meters. Concentrations are typically very low (< 1 µg Chl a per liter) in winter (April-October), and higher (1-30 µg/L) following the initiation of the annual spring-summer phytoplankton bloom in November - January.
ECOBREED WP2 T2.1 Winter common wheat (Triticum aestivum) - Late maturity group
<p>Description of the winter common wheat (Triticum aestivum) late maturity group nursery. Tested within T2.1 in Germany (by Secobra), Czech Republic (by Selgen) and Slovakia (by NPPC) in 2019/2020.</p>
ECOBREED WP2 T2.1 Winter durum wheat (Triticum durum) nursery
<p>Description of the winter durum wheat (Triticum durum) nursery. Tested within T2.1 in Austria (by BOKU), Hungary (by MTA-ATK) and Italy (by UNITUS). Results included from the season 2018/19.</p>
Water column changes under ice during diferent winters in a mid-latitude Mediterranean high mountain lake - Dataset
<p>Dataset of the research article <em>Water column changes under ice during diferent winters in a mid-latitude Mediterranean high mountain lake.</em></p> <p>Granados, I., Toro, M., Giralt, S., Camacho, A., Montes, C., 2020. Water column changes under ice during different winters in a mid-latitude Mediterranean high mountain lake. Aquatic Sciences 82, 30. <a href="https://doi.org/10/ggmkhv">https://doi.org/10/ggmkhv</a></p> <p> </p>
Data: Breeding progress for pathogen resistance is a second major driver for yield increase in German winter wheat at contrasting N levels
<p>This is the experimental data set of Zetzsche, et. al. (2020, Scientific Reports: doi.org/10.1038/s41598-020-77200-0) based on a three-year field trial (2014/15, 2015/16, 2016/7) of 178 German elite winter wheat cultivars.</p> <p>The table (QLB_BRIWECS_WW_fieldtrial_adjustMeans_treatments.csv) subsumes the adjusted mean values of four fungal disease scores (average ordinates) and six yield-related traits investigated at four treatments (T1: 110 kg N ha<sup>-1</sup>, no fungicides; T2: 110 kg N ha<sup>-1</sup> + fungicide; T3: 220 kg N ha<sup>-1</sup>, no fungicides; T4: 220 kg N ha<sup>-1</sup> + fungicide) of two replicates each over three years. Data of each trait are considered independent for all four treatments. Details of the plant material, the experimental site, the trail design as well as the phenotyping of the diseases and agronomical traits are given in the material and methods section of the related publication. Further metadata on the plant material and the trial design are provided in the Supplementary information of the publication.</p>
Nonlinearity corrections and bad pixel masks for the WINTER sensors
<p>Nonlinearity corrections and bad pixel masks for the WINTER sensors to be used with https://github.com/winter-telescope/winternlc. </p>
O_VLIELAND - Eurasian oystercatchers (Haematopus ostralegus, Haematopodidae) breeding and wintering on Vlieland (the Netherlands)
<p><em>O_VLIELAND - Eurasian oystercatchers (Haematopus ostralegus, Haematopodidae) breeding and wintering on Vlieland (the Netherlands)</em> is a bird tracking dataset published by the <a href="https://nioo.knaw.nl">Netherlands Institute of Ecology (NIOO-KNAW)</a>, <a href="http://www.sovon.nl">Sovon</a>, <a href="http://www.ru.nl">Radboud University</a>, the <a href="https://ibed.uva.nl">University of Amsterdam</a> and the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal tracking data collected during <a href="https://chirpscholekster.nl">CHIRP</a> (Cumulative Human Impact on biRd Populations) for the study <strong>O_VLIELAND</strong> using trackers developed by the University of Amsterdam Bird Tracking System (UvA-BiTS, <a href="http://www.uva-bits.nl">http://www.uva-bits.nl</a>). The study was operational from 2016 to 2021. In total 103 individuals of Eurasian oystercatchers (<em>Haematopus ostralegus</em>) have been tagged either as a breeding bird or while overwintering on the Wadden island Vlieland (the Netherlands), mainly to study how they respond to disturbances from aircraft. Data are uploaded from the UvA-BiTS database to Movebank and from there archived on Zenodo (see <a href="https://github.com/inbo/bird-tracking">https://github.com/inbo/bird-tracking</a>). No new data are expected.</p> <p>See van der Kolk et al. (2022, <a href="https://doi.org/10.3897/zookeys.1123.90623">https://doi.org/10.3897/zookeys.1123.90623</a>) for a more detailed description of this dataset.</p> <h2>Files</h2> <p>Data in this package are exported from Movebank study <a href="https://www.movebank.org/cms/webapp?gwt_fragment=page=studies,path=study1605802367">1605802367</a>. Fields in the data follow the <a href="http://vocab.nerc.ac.uk/collection/MVB">Movebank Attribute Dictionary</a> and are described in <code>datapackage.json</code>. Files are structured as a <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Package</a>. You can access all data in R via <code>https://zenodo.org/records/10053988/files/datapackage.json</code> using <a href="https://frictionlessdata.github.io/frictionless-r/">frictionless</a>.</p> <ul> <li><strong>datapackage.json</strong>: technical description of the data files.</li> <li><strong>O_VLIELAND-reference-data.csv</strong>: reference data about the animals, tags and deployments.</li> <li><strong>O_VLIELAND-gps-yyyy.csv.gz</strong>: GPS data recorded by the tags, grouped by year.</li> <li><strong>O_VLIELAND-acceleration-yyyy.csv.gz</strong>: acceleration data recorded by the tags, grouped by year.</li> <li><strong>O_VLIELAND-accessory-measurements-yyyy.csv.gz</strong>: behaviour categories derived from acceleration data, grouped by year.</li> </ul> <h2>Acknowledgements</h2> <p>These data were collected by the Netherlands Institute of Ecology (NIOO-KNAW), in collaboration with Sovon, Radboud University and the University of Amsterdam (UvA) for the CHIRP (Cumulative Human Impact on biRd Populations) project. Funding was provided by the Applied and Engineering Sciences domain of the Netherlands Organisation for Scientific Research (NWO-TTW 14638) and co-funding via NWO-TTW by Royal Netherlands Air Force, Birdlife Netherlands, NAM gas exploration and Deltares. The dataset was published with funding from Stichting NLBIF - Netherlands Biodiversity Information Facility.</p>
Data from: "Correlates of mid-winter pregnancy and early reproductive outcomes in a reintroduced elk (Cervus canadensis) population"
<p>Raw and processed datasets used for analysis in "Correlates of mid-winter pregnancy and early reproductive outcomes in a reintroduced elk (<em>Cervus canadensis</em>) population" by Hooven et al., published in <em>Mammalian Biology</em>. Datasets are as follows:</p> <p>Pregnancy.csv - Raw dataset detailing year and date of capture, individual identifier, and measured intrinsic variables, along with confirmed or predicted pregnancy/calf viability status.</p> <p>Pregnancy_final_mass.csv - Raw dataset after body mass estimation for individuals that were not weighed. </p> <p>all_confirmed_preg.csv - Subset of raw data for all individuals with confirmed pregnancy status (via lab PSPB assay).</p> <p>preg_ageclass.csv - Subset of all_confirmed_preg dataset including all individuals with general age classification (e.g., adult or subadult).</p> <p>preg_numeric.csv - Subset of all_confirmed_preg dataset including all individuals with numeric age value (from incisiform canine cementum annuli).</p> <p>all_fns.csv - Subset of dataset including all individuals with confirmed or predicted fetal/early neonatal survival ("offspring viability") status.</p> <p>fns_ageclass.csv - Subset of all_fns.csv including all individuals with general age classification.</p> <p>fns_numeric.csv - Subset of all_fns.csv including all individuals with numeric age values.</p> <p>parameter_est.csv - Parameter estimates from top-performing generalized linear mixed models for both pregnancy and offpsinrg viability, for plotting.</p>
Dataset of vehicle emission measurements in real-world subfreezing winter conditions
<p>Dataset of vehicle emission measurements in real-world subfreezing winter conditions. Measured by chasing the measured vehicle. See Info.txt for description of the data.</p>
Summer and winter invertebrate and physicochemical data from the Coweeta Hydrologic Lab
<p>This resource contains data for aquatic invertebrates collected from leaf litterbags, which were deployed in 11 streams at the Coweeta Hydrologic Lab (Macon County, North Carolina, USA) during winter and summer months in 2017-2018. Litterbags consisted of fine-mesh bags (250µm) attached to coarse-mesh bags (5mm), each containing <em>Rhododendron maximum</em> leaf litter. The litterbags were deployed for two-month periods, which were as follows: 19 October - 11 or 18 December, 2017; 15 November - 5 January 2017-2018; 9 May - 5 July 2018; and 5 July - 31 August 2018. We collected invertebrate samples from the >1mm size fraction from 3 coarse-fine litterbag pairs incubated in each of our streams during the aforementioned 2-month periods. Invertebrates were preserved in ethanol, identified, and classified into functional feeding groups based on classifications in Merritt et al. 2019. We identified invertebrates in the "shredder" functional feeding group to genus and all other insects to family (Merritt et al. 2019). We also measured invertebrate lengths in mm and converted these lengths to masses using information from Benke 1999. This resource also contains daily temperature and discharge data, litter breakdown data from the coarse-mesh bags associated with the invertebrate data, and weekly nutrient concentration data from the streams during the study period. Discharge data was provided by the USFS Coweeta Hydrologic Lab and can also be found here: https://www.fs.usda.gov/rds/archive/catalog/RDS-2016-0025-2</p> <p>Discharge data citation:</p> <p>USFS Coweeta Hydrologic Laboratory. 2023. Daily streamflow data for gauged watersheds at Coweeta Hydrologic Laboratory, North Carolina. 2nd Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2016-0025-2</p>
Supplementary data: Winter cover cropping: Effect on soybean and synergistic implications on soil microbiome
<p>Supplementary data: (i) Agronomic and quality data of soybean (2 varieties) grown in 2 years (2020 & 2021) in two management systems (organic & low-input) with different cover crops; (ii) Soil microbiome analysis of the soybean field trials.</p>
Estimated field scale sediment loss on the North Wyke Farm Platform in typical and extreme wet winters
<p>Based on monitored runoff and turbidity at 15-minute intervals from the North Wyke Fam Platform - a UK National Bioscience Research Infrastructure (NBRI), sediment loss during both typical and more extreme wet winters (December - February, inclusive) over the past decade (2012-2013, 2013-2014, 2015-2016, 2019-2020 and 2023-2024) from 5 grassland field catchments and 5 recently converted arable field catchments was estimated, including uncertainty ranges. Daily rainfall totals for the corresponding winter periods are also included.</p>
Profitability and investment risk of Texan power system winterization
<p><strong>Profitability and investment risk of Texan power system winterization</strong></p> <p>This data repository contains interim and final results of the <a href="https://www.nature.com/articles/s41560-022-00994-y">paper </a>“Profitability and investment risk of Texan power system winterization” published in Nature Energy. Code used to generate these results can be found at <a href="https://github.com/inwe-boku/texas-power-outages">github</a></p> <p><strong>Abstract</strong></p> <p>A lack of winterization of power system infrastructure resulted in significant rolling blackouts in Texas in 2021 though debate about the cost of winterization continues. Here, we assess if incentives for winterization on the energy only market are sufficient. We combine power demand estimates with estimates of power plant outages to derive power deficits and scarcity prices. Expected profits from winterization of a large share of existing capacity are positive. However, investment risk is high due to the low frequency of freeze events, potentially explaining under-investment, as do high discount rates and uncertainty about power generation failure under cold temperatures. As the social cost of power deficits is one to two orders of magnitude higher than winterization cost, regulatory enforcement of winterization is welfare enhancing. Current legislation can be improved by emphasizing winterization of gas power plants and infrastructure.</p> <p><strong>Date and time format</strong></p> <p>Please observe that we omit the date column from the description of columns below for all datasets. The ERA5 data in <strong>input/</strong> is in UTC, all other input datasets are in local Texas time (GMT-6). In <strong>interim</strong>, <em>temperatures/temppop/</em>, <em>temperatures/temp_gas_powerplant.csv</em>, <em>temperatures/temp_gas_outages.csv</em>, <em>temperatures/temp_coal_powerplant.csv</em>, <em>temperatures/temp_coal_outages.csv</em> and the wind power simulation output (<em>windpower/</em>) is in UTC. All other datasets are in local Texas time.</p> <p><strong>Data</strong></p> <p><strong>cache/</strong></p> <p>Data cache used by the scripts analyzing the extreme events: extreme temperatures, loss of load, their return periods, durations, maxima/minima (the cached files are not included, but can be generated with scripts/R/events.R)</p> <p><strong>figures/</strong></p> <p>Figures shown in the manuscript</p> <ul> <li><strong>raw_data</strong>: includes raw data for reproducing the figures in the main part of the manuscript</li> <li><strong>outage_model</strong>: figures representing the outage function as derived with our model</li> </ul> <p><strong>input/</strong></p> <p>Input data from external sources (with exception of orcd not included due to licensing issues)</p> <ul> <li><strong>ERA5_windspeeds_USA</strong>: available from the <a href="https://cds.climate.copernicus.eu/#!/home">CDS</a>. Download with scripts/download_era5_USA.py</li> <li><strong>gas_production</strong>: available from the Texas Railroad Commission in PDF format <a href="https://www.rrc.state.tx.us/media/qcpp3bau/2020-12-monthly-production-county-gas.pdf">here</a>. We extracted the data manually.</li> <li><strong>Load</strong>: available from ERCOT <a href="https://www.ercot.com/gridinfo/load">here</a></li> <li><strong>orcd</strong>: Scarcity prices as regulated by ERCOT. Manually extracted from <a href="https://doi.org/10.1016/j.enpol.2019.111143.334">J. Zarnikau et al.</a></li> <li><strong>outages</strong>: Outage Events from ERCOT with geo locations provided by Edgar Virguez <a href="https://bit.ly/EGOVADatabase">here</a> resulting from unit outage data provided by <a href="http://www.ercot.com/content/wcm/lists/226521/Unit_Outage_Data_20210312.xlsx">Ercot</a></li> <li><strong>population</strong>: population density data provided by arcgis <a href="https://www.arcgis.com/home/item.html?id=28bcaee42e2c4ace9fcb7c8b9ca524e7">here</a></li> <li><strong>powerplants</strong>: locations of power plants in Texas provided by the Energy Information Administration <a href="https://www.eia.gov/maps/layer_info-m.php">here</a></li> <li><strong>shp</strong>: shapefile of Texas state boundaries provided by arcgis <a href="https://gis-txdot.opendata.arcgis.com/datasets/texas-state-boundary-detailed">here</a></li> <li><strong>temperatures</strong>: available from the CDS <a href="https://cds.climate.copernicus.eu/#!/home">here</a>. Can be downloaded with script scripts/download_era5_TX_temp.py</li> <li><strong>USWTDB</strong>: US wind turbine data base provided by the US Geological Service <a href="https://eerscmap.usgs.gov/uswtdb/">here</a>. We used version: uswtdb_v3_3_20210114</li> <li><strong>GWA2</strong>: Global Wind Atlas Version 2.1 accessible <a href="https://silo1.sciencedata.dk/shared/cf5a3255eb87ca25b79aedd8afcaf570?path=%2FGWA2.1">here</a></li> </ul> <p><strong>interim/</strong></p> <p>Intermediary files from the analysis</p> <ul> <li><strong>bootstrap_year.csv</strong>: 30 randomly selected years between 1950 and 2021, 10,000 times used for bootstrapping<br> Generated by notebooks/outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb</li> <li><strong>bootstrap_year2020.csv</strong>: 30 randomly selected years between 1950 and 2020, 10,000 times used for bootstrapping without 2021 event<br> Generated by outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb</li> <li><strong>turbine_data.csv</strong>: turbine data for Texan wind turbines<br> Generated by scripts/prepare_TX_turbines.py<br> Columns: <ul> <li>capacity: turbine capacity (kW)</li> <li>height: turbine height (m)</li> <li>lon: longitude coordinate (°)</li> <li>lat: latitude coordinate (°)</li> <li>sp: specific power (W/m²)</li> <li>ind: running index</li> </ul> </li> </ul> <p><strong>interim/load/</strong></p> <p>Temperature dependent estimates of electricity load for Texas.</p> <ul> <li><strong>load_est70_LR24_temptrend_Hook-8.csv</strong><br> Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb<br> Columns: <ul> <li>load_est: load estimated for the period 1950-2021 assuming an average load level as in 2021 (MWh)</li> <li>temp: population weighted temperature (°C)</li> </ul> </li> <li><strong>load_est10_LR24_temptrend_Hook-8.csv</strong><br> Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb<br> Columns: <ul> <li>load: observed load in period 2012-2021 as published by ERCOT (MWh)</li> <li>load_est: load estimated for period 2012-2021 considering time trend, i.e. this is a replication of the observed load without outages with our model for validation purposes (MWh)</li> </ul> </li> <li><strong>load_est9_LR24temptrend2021_Hook-8.csv</strong><br> Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb<br> Columns: <ul> <li>load: observed load in period 2004-2021/01 and load forecast 2021/02 as published by ERCOT (MWh)</li> <li>load_est: load estimated for the years 2012-2020 for cross validation of load model. For training, the years 2012-2021 (2021/02 forecast) were used, except the predicted year, i.e. this is a replication of the observed load with our model for validation purposes. (MWh)</li> </ul> </li> <li><strong>load_est17_crossvalidation_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb<br> Columns: <ul> <li>load: observed load 2004 - 2021/01 and load forecast 2021/02 as published by ERCOT (MWh)</li> <li>load_est: load estimated for cross validation for years 2004-2021, training years 2012-2020, trained with each year in traning period except modelled year with variable load level, i.e. this is a replication of the observed load with our model for validation purposes(MWh)</li> </ul> </li> <li><strong>load_est_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb<br> Columns: <ul> <li>load: observed load 2004 - 2021 as published by ERCOT (MWh)</li> <li>load_est: years 2004-2021 predicted with a model which was trained for the years 2012-2020 considering time trend, i.e. this is a replication of the observed load without outages with our temperature dependent model with our model for validation purposes (MWh)</li> </ul> </li> </ul> <p><strong>interim/outages</strong></p> <ul> <li><strong>outages.feather</strong> Outage by minute of all generation units in Texas in February 2021. Created by scripts/R/create-ercot-outage-timeseries.R In Texas local time.<br> Columns: <ul> <li>station: name of power plant</li> <li>unit: name of generation unit</li> <li>fullname: concatenated string of station and name</li> <li>dataset: ercot or edgar. ercot refers to the raw dataset provided by ERCOT, Edgar to the dataset provided by Edgar Virguez (for details see above in section <strong>input/</strong>)</li> <li>Longitude: Longitude of location of power plant</li> <li>Latitude: Latitude of location of power plant</li> <li>reduction: hourly reduction of capacity due to outage in this minute (MW)</li> <li>cap_available: available capacity in this minute (MW)</li> <li>cap_max: maximum capacity of unit (MW)</li> </ul> </li> <li><strong>outages-hourly.feather</strong> Hourly outages at all generation units in Texas in February 2021. Created by scripts/R/create-ercot-outage-timeseries.R In Texas local time.<br> Columns: <ul> <li>station: name of power plant</li> <li>unit: name of generation unit</li> <li>fullname: concatenated string of station and name</li> <li>dataset: ercot or edgar. ercot refers to the raw dataset provided by ERCOT, Edgar to the dataset provided by Edgar Virguez (for details see above in section <strong>input/</strong>)</li> <li>Longitude: Longitude of location of power plant</li> <li>Latitude: Latitude of location of power plant</li> <li>reduction: hourly reduction of capacity due to outage in this time step (MW)</li> <li>cap_available: hourly available capacity in this minute (MW)</li> <li>cap_max: maximum capacity of unit (MW)</li> </ul> </li> <li><strong>outages_reduction.csv</strong> Hourly outages per fuel (MW). We use these outages for COAL and GAS only in the analysis.<br> Generated by notebooks/prepare_outages_NSsplit.ipynb<br> Columns: <ul> <li>NG: natural gas power plants</li> <li>WIND: wind power plants</li> <li>SOLAR: solar power plants</li> <li>ESR: energy storage resource</li> <li>HYDRO: hydropower plants</li> <li>NUCLEAR: nuclear power plants</li> </ul> </li> <li><strong>outages_reductionNorth.csv</strong> Hourly outages for the Northern part of Texas (latitude > 30) (MW). We use these outages for WIND only in the analysis.<br> Generated by notebooks/prepare_outages_NSsplit.ipynb<br> Columns as above.</li> <li><strong>outages_reductionSouth.csv</strong> Hourly outages for the Southern part area of Texas (latitude <= 30) (MW). We use these outages for WIND only in the analysis.<br> Generated by notebooks/prepare_outages_NSsplit.ipynb<br> Columns as above.</li> </ul> <p><strong>interim/temperatures</strong></p> <ul> <li><strong>temppop</strong><br> Generated by scripts/calc_temppopC.py <ul> <li>contains population weighted temperatures for Texas, one file for each year (°C).</li> </ul> </li> <li><strong>temp_coal_outage.csv</strong><br> Generated by notebooks/temperatures_NSsplit.ipynb<br> Columns: <ul> <li>t2m: temperature weighted by coal power plants experiencing outages in February 2021 (°C)</li> </ul> </li> <li><strong>temp_coal_powerplant.csv</strong><br> Generated by notebooks/temperatures_NSsplit.ipynb<br> Columns: <ul> <li>t2m: temperature weighted by all coal power plants (°C)</li> </ul> </li> <li><strong>temp_gas_outage.csv</strong><br> Generated by notebooks/temperatures_NSsplit.ipynb<br> Columns: <ul> <li>t2m: temperature weighted by gaspower plants experiencing outages in February 2021 (°C)</li> </ul> </li> <li><strong>temp_gas_powerplant.csv</strong><br> Generated by notebooks/temperatures_NSsplit.ipynb<br> Columns: <ul> <li>t2m: temperature weighted by all gas power plants (°C)</li> </ul> </li> <li><strong>temp_gasfields.csv</strong><br> Generated by notebooks/temperatures_NSsplit.ipynb<br> Columns: <ul> <li>t2m: temperature weighted by all gasfields (°C)</li> </ul> </li> <li><strong>tempWP_NSsplit.csv</strong><br> Generated by notebooks/wp_temp_NSsplit.ipynb<br> Columns: <ul> <li>t2mSouth: temperatures weighted by all wind power plants in the South (°C)</li> <li>t2mNorth: temperatures weighted by all wind power plants in the North (°C)</li> </ul> </li> </ul> <p><strong>interim/thresholds</strong></p> <ul> <li><strong>thresh_total63.5GW.csv</strong><br> Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>total available capacity of gas, coal and wind considering outages, assuming gasfield temperatures for gas outages (GW)</li> </ul> </li> <li><strong>thresh_totalPP63.5GW.csv</strong><br> Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>total available capacity of gas, coal and wind, considering outages, assuming gas power plant temperatures for gas outages (GW)</li> </ul> </li> <li><strong>threshold_coal.csv</strong><br> Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>outages of coal power plants based on coal power plant temperatures (GW)</li> </ul> </li> <li><strong>threshold_gas.csv</strong><br> Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>outages of gas power plants based on gasfield temperatures (GW)</li> </ul> </li> <li><strong>threshold_gasPP.csv</strong><br> Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>outages of gas power plants based on gas power plant temperatures (GW)</li> </ul> </li> <li><strong>threshold_gas_coal63.5GW.csv</strong><br> Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>available capacity of gas and coal, considering outages, assuming gasfield temperatures for gas outages (GW)</li> </ul> </li> <li><strong>threshold_gas_coalPP63.5GW.csv</strong><br> Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>available capacity of gas and coal, considering outages, assuming gas power plant temperatures for gas outages (GW)</li> </ul> </li> </ul> <p><strong>interim/windpower</strong></p> <ul> <li><strong>cfTXh.csv</strong><br> Generated by notebooks/windpower_ERA5_GWA2_const_cap.ipynb<br> Columns: <ul> <li>Capacity factors of simulated Texan wind power (dimensionless)</li> </ul> </li> <li><strong>wpTXh.csv</strong><br> Generated by notebooks/windpower_ERA5_GWA2_const_cap.ipynb<br> Columns:</li> <li>simulated Texan wind power generation (kWh)</li> </ul> <p><strong>output/</strong></p> <ul> <li><strong>marginal_revenue_coal_10_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_reduced_bootstrap_10_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>Marginal revenues from winterization for coal (bn$/GW winterized) with discount rate of 10% and deficit events up to 2021</li> </ul> </li> <li><strong>marginal_revenue_coal_LR24temptrend_Hook-8.csv</strong><br> Generated by notebooks/outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>Marginal revenues from winterization of coal (bn$/GW winterized) with discount rate of 5% and deficit events up to 2021</li> </ul> </li> <li><strong>marginal_revenue_coal2020_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_reduced_bootstrap_2020_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>Marginal revenues from winterization of coal (bn$/GW winterized) with discount rate of 5% and deficit events up to 2020</li> </ul> </li> <li><strong>marginal_revenue_gas_10_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_reduced_bootstrap_10_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>Marginal revenues from winterization of gas (bn$/GW winterized) with discount rate of 10% and deficit events up to 2021</li> </ul> </li> <li><strong>marginal_revenue_gas_LR24temptrend_Hook-8.csv</strong><br> Generated by notebooks/outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>Marginal revenues from winterization of gas (bn$/GW winterized) with discount rate of 5% and deficit events up to 2021</li> </ul> </li> <li><strong>marginal_revenue_gas2020_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_reduced_bootstrap_2020_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>Marginal revenues from winterization of gas (bn$/GW winterized) with discount rate of 5% and deficit events up to 2020</li> </ul> </li> <li><strong>marginal_revenue_wind_north_10_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_reduced_bootstrap_10_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>Marginal revenues from winterization of Northern Wind in Texas (bn$/GW winterized) with discount rate of 10% and deficit events up to 2021</li> </ul> </li> <li><strong>marginal_revenue_wind_north_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>Marginal revenues from winterization of Northern Wind in Texas (bn$/GW winterized) with discount rate of 5% and deficit events up to 2021</li> </ul> </li> <li><strong>marginal_revenue_wind_north2020_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_reduced_bootstrap_2020_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>Marginal revenues from winterization of Northern Wind in Texas (bn$/GW winterized) with discount rate of 5% and deficit events up to 2020</li> </ul> </li> <li><strong>marginal_revenue_wind_south_10_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_reduced_bootstrap_10_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>Marginal revenues from winterization of Southern Wind in Texas (bn$/GW winterized) with discount rate of 10% and deficit events up to 2021</li> </ul> </li> <li><strong>marginal_revenue_wind_south_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>Marginal revenues from winterization of Southern Wind in Texas (bn$/GW winterized) with discount rate of 5% and deficit events up to 2021</li> </ul> </li> <li><strong>marginal_revenue_wind_south2020_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_reduced_bootstrap_2020_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>Marginal revenues from winterization of Southern Wind in Texas (bn$/GW winterized) with discount rate of 5% and deficit events up to 2020</li> </ul> </li> <li><strong>sensitivity_analysis_temperature_thresholds_all_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_thresholds_sensitivity_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>delta_thresh_temp: change in outage temperature thresholds for all technologies (°C)</li> <li>delta_rec_temp: change in recovery temperature thresholds for all technologies (°C)</li> <li>number_of_events: estimated number of events within 7 decades</li> <li>after2004: years of events after 2004</li> <li>total_loss_TWh: total loss of load (TWh)</li> <li>max_loss_GW: maximum loss of load (GW)</li> <li>mean_loss_GW: average loss og load (GW)</li> <li>mean_revenue_mioUSD: average revenue (M$)</li> <li>rank_2021_event: rank of 2021 event in terms of loss of load</li> </ul> </li> <li><strong>sensitivity_analysis_temperature_thresholds_coal_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_thresholds_sensitivity_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>thresh_coal_temp: outage temperature thresholds for coal (°C)</li> <li>rec_coal_temp: recovery temperature thresholds for coal (°C)</li> <li>number_of_events: estimated number of events within 7 decades</li> <li>after2004: years of events after 2004</li> <li>total_loss_TWh: total loss of load (TWh)</li> <li>max_loss_GW: maximum loss of load (GW)</li> <li>mean_loss_GW: average loss og load (GW)</li> <li>mean_revenue_mioUSD: average revenue (M$)</li> <li>rank_2021_event: rank of 2021 event in terms of loss of load</li> </ul> </li> <li><strong>sensitivity_analysis_temperature_thresholds_gas_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_thresholds_sensitivity_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>thresh_gas_temp: outage temperature thresholds for gas (°C)</li> <li>rec_gas_temp: recovery temperature thresholds for gas (°C)</li> <li>number_of_events: estimated number of events within 7 decades</li> <li>after2004: years of events after 2004</li> <li>total_loss_TWh: total loss of load (TWh)</li> <li>max_loss_GW: maximum loss of load (GW)</li> <li>mean_loss_GW: average loss og load (GW)</li> <li>mean_revenue_mioUSD: average revenue (M$)</li> <li>rank_2021_event: rank of 2021 event in terms of loss of load</li> </ul> </li> <li><strong>sensitivity_analysis_temperature_thresholds_wind_north_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_thresholds_sensitivity_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>thresh_windn_temp: outage temperature thresholds for wind north (°C)</li> <li>rec_windn_temp:recovery temperature thresholds for wind north (°C)</li> <li>number_of_events: estimated number of events within 7 decades</li> <li>after2004: years of events after 2004</li> <li>total_loss_TWh: total loss of load (TWh)</li> <li>max_loss_GW: maximum loss of load (GW)</li> <li>mean_loss_GW: average loss og load (GW)</li> <li>mean_revenue_mioUSD: average revenue (M$)</li> <li>rank_2021_event: rank of 2021 event in terms of loss of load</li> </ul> </li> <li><strong>sensitivity_analysis_temperature_thresholds_wind_south_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_thresholds_sensitivity_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>thresh_winds_temp: outage temperature thresholds for wind south (°C)</li> <li>rec_winds_temp:recovery temperature thresholds for wind south (°C)</li> <li>number_of_events: estimated number of events within 7 decades</li> <li>after2004: years of events after 2004</li> <li>total_loss_TWh: total loss of load (TWh)</li> <li>max_loss_GW: maximum loss of load (GW)</li> <li>mean_loss_GW: average loss og load (GW)</li> <li>mean_revenue_mioUSD: average revenue (M$)</li> <li>rank_2021_event: rank of 2021 event in terms of loss of load</li> </ul> </li> </ul> <p> </p>
Data from: Radial stem growth of the clonal shrub Alnus alnobetula at treeline is constrained by summer temperature and winter desiccation and differs in carbon allocation strategy compared to co-occurring Pinus cembra
<p><strong>Data are documented in the following article:</strong></p> <p>Oberhuber W., G Wieser, F. Bernich, A. Gruber (2022) Radial stem growth of the clonal shrub <em>Alnus alnobetula</em> at treeline is constrained by summer temperature and winter desiccation and differs in carbon allocation strategy compared to co-occurring <em>Pinus cembra</em>. Forests 2022, 13, 440. doi: 10.3390/f13030440.</p> <p> </p> <p><strong>Summary:</strong></p> <p>Global change is affecting species areal distribution in many regions. A better understanding of how land-use change and climate warming affects shrub growth is essential for improved predictions of forest dynamics at the alpine treeline. Evaluation of radial stem growth of the clonal shrub <em>Alnus alnobetula</em> (= <em>Alnus viridis</em>) and the co-occurring tree species Swiss stone pine (<em>Pinus cembra</em>) within an alpine treeline ecotone revealed that mean ring width of nitrogen fixing <em>A. alnobetula</em> was about four times lower compared to <em>P. cembra</em>. Our findings are based on ring width data from <em>A. alnobetula</em> and <em>P. cembra</em> stems sampled at the alpine treeline ecotone on Mt. Patscherkofel (47°12’N, 11°27’E, Central European Alps, Austria, elevation range 2050 to 2190 m asl). Ring width time series include 86 radii from 51 stems of <em>A. alnobetula</em> (stems had mean age of 18±7 yrs) and 24 radii from 16 stems of <em>P. cembra </em>(18±4 yrs). We explain our findings by different carbon allocation strategies, i.e., preference of “vertical” stem growth in late successional <em>P. cembra</em> vs. favoring “horizontal” spread in the pioneer shrub<em> A. alnobetula.</em> By favouring clonal propagation over individual stem growth <em>A. alnobetula</em> is able to quickly spread at the alpine treeline ecotone.</p>
Data for: Wind-induced hypolimnetic upwelling between the multi-depth basins of Lake Geneva during winter: An overlooked deepwater renewal mechanism?
<p>Combining field observations, 3D hydrodynamic modeling and particle tracking, we investigated wind-driven interbasin exchange, and in particular hypolimnetic upwelling, between the deep <em>Grand Lac</em> (max. depth 309 m) and shallow <em>Petit Lac</em> (max. depth 75 m) basins of Lake Geneva (Switzerland/France) during the weakly stratified fall/winter period 2018-2019.</p> <p><br> The data include measurements from moored Acoustic Doppler Current Profilers (ADCPs) and vertical thermistor lines along with the corresponding 3D modeling and particle tracking results.</p> <p><br> The three-dimensional model used in this study is based on the MIT General Circulation Model (MITgcm, http://mitgcm.org/, https://doi.org/10.1029/96JC02775).</p> <p><br> The particle tracking code is based on ctracker (https://doi.org/10.5281/zenodo.1034118)</p>
Sea ice core temperature and salinity data collected during the 2019 SCALE Winter Cruise
<p>Temperature and salinity profiles of sea ice cores extracted from in situ sea ice floes and lifted pancakes were measured in the Atlantic sector of the Antarctic Marginal Ice Zone during the Southern oCean seAsonal Experiment (SCALE) winter cruise in 2019 (<a href="http://www.scale.org.za">www.scale.org.za</a>) aboard the SA Agulhas II.</p>
Data and code accompanying: A quantitative synthesis of and predictive framework for studying winter warming effects in reptiles
<p>This data and code were used to generate the publication "A quantitative synthesis of and predictive framework for studying winter warming effects in reptiles", doi: 10.1007/s00442-022-05251-3</p> <p>Please direct any queries or requests to use these datasets/code to: k.macleod@bangor.ac.uk</p> <p>Two datasets are presented in separate excel files: one contains meta-analytical data from experimental studies on winter warming effects on reptiles, and the other contains the same type of data from observational studies on the same.</p> <p>R code for analysis is in an R file; this should be openable in any text editing application.</p> <p>Manuscript abstract below:</p> <p><em>Increases in temperature related to global warming have important implications for organismal fitness. For ectotherms inhabiting temperate regions, ‘winter warming’ is likely to be a key source of the thermal variation experienced in future years. Studies focusing on the active season predict largely positive responses to warming in the reptiles; however, overlooking potentially deleterious consequences of warming during the inactive season could lead to biased assessments of climate change vulnerability. Here, we review the overwinter ecology of reptiles, and test specific predictions about the effects of warming winters, by performing a meta-analysis of all studies testing winter warming effects on reptile traits to date. We collated information from observational studies measuring responses to natural variation in temperature in more than one winter season, and experimental studies which manipulated ambient temperature during the winter season. Available evidence supports that most reptiles will advance phenologies with rising winter temperatures, which could positively affect fitness by prolonging the active season although effects of these shifts are poorly understood. Conversely, evidence for shifts in survivorship and body condition in response to warming winters was equivocal, with disruptions to biological rhythms potentially leading to unforeseen fitness ramifications. Our results suggest that the effects of warming winters on reptile species are likely to be important but highlight the need for more data and greater integration of experimental and observational approaches. To improve future understanding, we recap major knowledge gaps in the published literature of winter warming effects in reptiles and outline a framework for future research.</em></p>
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Annotated Behaviour and Observability Dataset (ABODe)
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