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50 results for “Regionalisation”
Supplementary material to the manuscript: Regionalised Heat Demand and Power-To-Heat Capacities in Germany - An Open Data Set for Assessing Renewable Energy Integration
<p>This is the supplementary material for the manuscript:</p> <p>"Regionalised Heat Demand and Power-To-Heat Capacities in Germany - an Open Data Set for Assessing Renewable Energy Integration"</p> <p>Article DOI: <a href="https://doi.org/10.1016/j.apenergy.2019.114161">https://doi.org/10.1016/j.apenergy.2019.114161</a></p> <p>Open access preprint: <a href="https://arxiv.org/abs/1912.03763">https://arxiv.org/abs/1912.03763</a></p> <p> </p> <p><strong>DESCRIPTION OF THE DATASET AND LICENSES:</strong></p> <p>The subdirectory "04_results" contains the regionalised heat demand an power-to-heat capacity data on administrative district level (NUTS-3) for Germany. The subdirectories "01_census_special_evaluation_data" and "02_other_input_data" contain the utilised input data. The subdirectory "03_code" contains the developed and applied source code.</p> <p>The data in this repository are provided under open source licenses. For license information and other general information on the supplementary material, refer to the LICENSE files and README files in the respective subdirectories.</p> <p>For a detailed description of the approach developed by the author, the input data used and the generated results, refer to the manuscript "Regionalised Heat Demand and Power-To-Heat Capacities in Germany - an Open Data Set for Assessing Renewable Energy Integration".</p> <p><strong>METADATA:</strong></p> <p>Sector: Residential Buildings – Space Heating and Domestic Hot Water</p> <p>Geographical scope: Germany</p> <p>Geographical resolution: Administrative districts (NUTS-3)</p> <p>Temporal scope: 2011, three scenarios for 2030</p> <p>Temporal resolution: 15min</p> <p> </p> <p><strong>UNITS:</strong></p> <p>In the final results folders (04_results/01_installed_heating_p2h_capacity; 04_results/02_daily_time_series; 04_results/03_yearly_time_series) the units of the data are indicated in the file names or the column names, e.g. by "in_MW". In case of unit indication in the file name, the unit refers to all columns in the file.</p> <p>In the intermediate results folder (04_results/00_sql_tables_exported_to_csv) all units referring to power are "kW" and all units referring to energy are "kWh".</p> <p><strong>NEWS AND CONTACT:</strong></p> <p>This dataset will be used as part of the <a href="https://wiki.openmod-initiative.org/wiki/Region4FLEX">region4FLEX model</a>. We are currently enhancing the data by temporally and spatially resolved COP time series and determining load shifting potentials. If you wish to receive news or have general questions please contact: wilko.heitkoetter@dlr.de. </p>
NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in-situ snow depth time series and the regionalisation of the ΔSNOW model
<p>Time series of daily Snow Water Equivalent (SWE) and Snow Density over the Northern Hemisphere, based on in-situ station observations of snow depth converted to SWE using the ΔSNOW model (Winkler et al., 2021) and regionalised parameters. </p> <p>An extensive description of the dataset and the method to generate it can be found in the data descriptor manuscript published in the journal Earth System Science Data: <a href="https://essd.copernicus.org/preprints/essd-2023-31/">https://essd.copernicus.org/articles/15/2577/2023/essd-15-2577-2023</a> </p> <p><strong>Dataset:</strong> A total of 11,0071 time series of modelled SWE and estimated snow density at the point scale, spanning 1950-2022, at daily resolution.<em> "NH-SWE_dataset_MAP.png"</em> shows a Northern Hemisphere map with the location of all stations in the NH-SWE dataset and their elevation in meters. </p> <p><strong>Files: </strong>The dataset is provided in two different formats:</p> <ol> <li>Individual <em>.csv</em> files for each station in the NH-SWE dataset at <em>"NH_SWE_dataset_vector_files.zip"</em></li> <li>Full-dataset <em>.csv </em>matrices with dates as rows and NH-SWE stations as columns at <em>"NH_SWE_dataset_matrix_files.zip"</em></li> </ol> <p><strong>Metadata:<em> </em></strong><em>"NH_SWE_METADATA.csv"</em> Includes information on NH-SWE stations location (ID, country, station name, coordinates, elevation), data source, length of time series, model parameters and the climate variables used to estimate them, and average snow climatology such as average maximum snow depth, average peak SWE and average maximum snow cover duration. More details and units in the <em>"README_fileformats.txt"</em> file. </p> <p><strong>ΔSNOW model parameter regionalisation: </strong>The code to obtain the ΔSNOW model parameters based on climate variables for all the stations in the NH-SWE dataset is shared in<em><strong> </strong>"DeltaSNOW_parameter_regionalisation.zip"</em>. The method is extensively described in the data descriptor manuscript by Fontrodona-Bach et al., (2023) submitted to Earth System Science Data. More details in the <em>"README_regionalisation.txt"</em> file. </p> <p><strong>Data use: </strong>Free, provided adequate citation of both the data descriptor manuscript and the zenodo record. See <em>"README_datausage.txt"</em></p> <p><strong>Version history:</strong><br>v1: Initial upload. The ΔSNOW model regionalisation was missing.<br>v2: Manuscript submission version. Updated dataset and includes the ΔSNOW model regionalisation code.</p> <p><strong>Reported errors:</strong><br>The dataset accidentally contains one station from the Southern Hemisphere (NH-SWE ID 500001), located in Antarctica (Country code AY). <br>The longitude of a few stations exceeds +180 decimal degrees. To obtain the correct value within the [-180,180] decimal degree longitude bounds, the value exceeding +180 needs to be added to -180 degrees (e.g. +181.0 degrees is actually -179.0 degrees).<br>Swedish stations have two different country codes, SE for the ECA&D stations, and SW for the GHCNd stations. <br>Japan country code is "JA" in the metadata, although the official country code should be JP. </p>
Long-Term Demographic Trends in Prehistoric Italy: climate impacts and regionalised socio-ecological trajectories (dataset and R script)
<p>The present digital archive is the outcome of the paper: <strong>Palmisano, A., Bevan, A., Kabelindde, A., Roberts, N., and Shennan, S., 2021. <a href="https://doi.org/10.1007/s10963-021-09159-3">Long-Term Demographic Trends in Prehistoric Italy: climate impacts and regionalised socio-ecological trajectories</a>. <em>Journal of World Prehistory, 34 (3)</em>, </strong>381-432<strong>.</strong></p> <p>The dataset included here provides a collection of <strong>4,010</strong> radiocarbon dates from <strong>947</strong> archaeological sites for a period spanning between 11,000 and 1500 BP. In addition, the digital archive related to this paper provides reproducible analyses in the form of one script written in R statistical computing language.</p> <p>List of versions:</p> <ul> <li><strong>1.0.</strong> 4 August 2021 - First public release of the dataset on Zenodo. </li> </ul>
Floristic characteristics and regionalisation of karst woody plants in China
Open the record for dataset details and reuse information.
Data from: A biogeographical regionalisation of Australian Acacia species
Aim: To develop a biogeographical regionalization of Australian Acacia species and to investigate their environmental correlates. Location: Australia. Methods: We used a previously published framework for delineating biogeographical regions. We calculated species turnover patterns of 1020 Australian Acacia species with distributions estimated from 171,758 georeferenced herbarium records aggregated to 100 km × 100 km cells (868 across Australia). An agglomerative cluster analysis using a matrix of pairwise Simpson's beta (βsim) dissimilarity values was applied. Eleven environmental variables at the same resolution as the aggregated herbarium records were used to explore the correlates of the βsim patterns using a non-metric multidimensional scaling (NMDS) analysis. We also used an ANOVA to test the significance of the environmental changes between each pair of biogeographical regions. Results: Five major Acacia biogeographical regions were proposed. These bioregions were broadly similar to the biomes of Australia. A new subdivision of the Eremaean biome was proposed for Acacia. The most influential environmental variables for the individual bioregions were: (1) temperature seasonality and topographic flatness for the south-western temperate bioregion; (2) precipitation during the coldest quarter of the year for the south-eastern temperate bioregion; (3) annual precipitation, annual mean temperature and precipitation seasonality for the monsoonal bioregion; and (4) percentage of sand in the top 30 cm of the soil, rock grain size, annual mean radiation and annual mean temperature for the Eremaean south and north regions. The NMDS analysis provided support for the observed biogeographical patterns. The statistical test showed a highly significant difference between the environments of the proposed bioregions. Climatic variables were consistent predictors across regions, whereas the influence of soils and topographic features varied among bioregions. Main conclusions: The major Acacia biogeographical regions correspond well to historical bioregionalizations, suggesting that the environmental drivers of diversification in Acacia are broadly similar to those that act on the flora as a whole. Climate seasonality combined with annual values and non-climatic factors provide support for the proposed biogeographical regionalization for Acacia.
Fig. 6 in Toward a terrestrial biogeographical regionalisation of the world: historical notes, characterisation and area nomenclature
Fig. 6. Verteilung der wichtigsten physiologischen Pflanzengruppen in den Vegetationsgebieten der Erde [Division of the important physiological plant groups in the vegetation of the earth] (Engler 1882, p. 387).
Fig. 9 in Toward a terrestrial biogeographical regionalisation of the world: historical notes, characterisation and area nomenclature
Fig. 9. Takhtajan's floristic kingdoms and regions of the earth (Takhtajan 1978, separate map). Red solid and dashed lines indicate the boundaries of kingdoms; green solid and dashed lines indicate the boundaries of the regions.
Fig. 3 in Toward a terrestrial biogeographical regionalisation of the world: historical notes, characterisation and area nomenclature
Fig. 3. Zoogeographic regions recognised by Wallace (1876a, 1876b) in The Geographical Distribution of Animals.
Fig. 13 in Toward a terrestrial biogeographical regionalisation of the world: historical notes, characterisation and area nomenclature
Fig. 13. Map of the terrestrial zoogeographic realms and regions of the world (Holt et al. 2013a, fig. 1).
Fig. 2 in Toward a terrestrial biogeographical regionalisation of the world: historical notes, characterisation and area nomenclature
Fig. 2. Tabula mundi geographico zoologica sistens quadrupedes hucusque notos sedibus suis adscriptos, second edition (Zimmermann 1783). The revised map in the 1783 German edition contains the newly discovered Sandwich Islands (Hawai'i) and the Seychelles, which were absent in the original 1777 edition (Ebach 2015, p. 31; source: National Library of Australia).
Fig. 5 in Toward a terrestrial biogeographical regionalisation of the world: historical notes, characterisation and area nomenclature
Fig. 5. Outline map of the world, showing the six regions of the geographical distribution of mammals (Sclater and Sclater 1899, map opposite p. 16).
Fig. 1 in Toward a terrestrial biogeographical regionalisation of the world: historical notes, characterisation and area nomenclature
Fig. 1. Representation of the distribution of mammals according to their zones and provinces by Wagner (1844). The southern boundary of the northern polar province is indicated by a line of a different colour, drawn somewhat further south than the equatorial border of the Arctic fox ([Vulpes] lagopus), although not so far in some places as the reindeer may descend there on their summer migrations. The southern polar province is not included in this map, because it is only in the process of discovery and, according to all previous experience, it does not harbour land mammals (Wagner 1846b, p. 241; Table 1).
Fig. 14 in Toward a terrestrial biogeographical regionalisation of the world: historical notes, characterisation and area nomenclature
Fig. 14. World biogeographical regionalisation, with indication of the regions and transition zones. (1) Nearctic region; (2) Palearctic region; (3) Neotropical region; (4) Ethiopian region; (5) Oriental region; (6) Andean region; (7) Australian region; (8) Antarctic region; (9) Mexican transition zone; (10) Chinese transition zone; (11) Saharo-Arabian transition zone; (12) South American transition zone; (13) Indo-Malayan transition zone.
Fig. 12 in Toward a terrestrial biogeographical regionalisation of the world: historical notes, characterisation and area nomenclature
Fig. 12. The six major biogeographical divisions of Kreft and Jetz (2010) are highlighted in the dendrogram with large coloured rectangles: Australian (orange); Neotropical (red); African (brown); Oriental (yellow); Palaearctic (blue); Nearctic (green). The first 30 groups in the dendrogram (small rectangles) and in the map are displayed in different colours. Additionally, the first 60 groups are indicated with black boundaries in the map (Kreft and Jetz 2010, fig 9).
Fig. 7 in Toward a terrestrial biogeographical regionalisation of the world: historical notes, characterisation and area nomenclature
Fig. 7. Biogeographische Gliederung der Kontinente [Biogeographic Classification of the Continents] (Arldt 1907, map 1). Shaded areas indicate regions; hatched lines indicate the boundaries of kingdoms; bold lines the boundaries of regions; and thin lines the boundaries of subregions.
Fig. 11 in Toward a terrestrial biogeographical regionalisation of the world: historical notes, characterisation and area nomenclature
Fig. 11. Biogeographic regionalisation of Rapoport (1968) recognising the Holarctic, Holotropical and Holantarctic belts.
Fig. 10 in Toward a terrestrial biogeographical regionalisation of the world: historical notes, characterisation and area nomenclature
Fig. 10. Simplified diagram of the main conventional zoogeographical subdivisions (Poynton 1959, fig. 1). The Palaearctic and Nearctic are treated as subregions within a larger Holarctic region.
Fig. 4 in Toward a terrestrial biogeographical regionalisation of the world: historical notes, characterisation and area nomenclature
Fig. 4. Zoogeographical areas illustrating the Distributions of Birds by R. Bowlder Sharpe (1893). The classification is loyal to Wallace (1876a, 1876b) because it clearly distinguishes the Nearctic from the Palaearctic.
FIGURE 11. Interim Biogeographic Regionalisation for Australia Version 6.1 in A history of biogeographical regionalisation in Australia
FIGURE 11. Interim Biogeographic Regionalisation for Australia Version 6.1 (IBRA, 1995). [Reproduced with permission of Australian Government].
FIGURE 10 in A history of biogeographical regionalisation in Australia
FIGURE 10. The avian endemic areas of Cracraft (1991). [Reproduced with permission of CSIRO Publishing, Australia.]
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