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5,023 results for “landing”
Global Hourly, 5-km, All-sky Land Surface Temperature (GHA-LST) from 2011 - now
<p>GHA-LST is a global, hourly, 5-km, all-sky, gap-free, and all-weather land surface temperature (LST) dataset. The manuscript describing this dataset has been accepted by <em>Earth System Science Data (ESSD)</em> (<a href="https://doi.org/10.5194/essd-15-869-2023" target="_new" rel="noopener">https://doi.org/10.5194/essd-15-869-2023</a>). Due to storage limitations on Zenodo, the full dataset is available at <a href="http://glass.umd.edu/allsky_LST/GHA-LST" target="_new" rel="noopener">glass.umd.edu/allsky_LST/GHA-LST</a>. The dataset is updated annually. For further details, please contact Dr. Aolin Jia at <a rel="noopener">aolin@terpmail.umd.edu</a>.</p>
Datasets for figures in Implementation and evaluation of Wet Bulb Globe Temperature within non-urban environments in the Community Land Model version 5
<p>The files contain 4 scripts and 6 netcdf files. </p> <p>"laborCap_200400.ncl" uses "Lancet_LRF.nc" to create Figure 1.</p> <p>Script "world_plot_ensemble_Avg.I2000.csh", drives a NCL script, "plot_modern.I2000.WBGT.ncl" to make figures 3 and 4, using the netcdf files, "I2000_PR_22_x1_60_5.exceed.WBGT.20yrs.75_99.nc," "I2000_PR_22_x1_60_5.exceed.WBGT_BG_R.20yrs.75_99.nc," "I2000_PR_22_x1_60_5.exceed.WBGT_BC_R.20yrs.75_99.nc," and "I2000_PR_22_x1_60_5.exceed.WBGT_AC_R.20yrs.75_99.nc."</p> <p>"heatmap.wbgt.v4.org.ncl" uses netcdf "I2000_PR_23_Chicago_x1_60_1.11-17.Chicago.allvars.nc" to create figures 5-7. </p>
Land Cover Fraction Mapping with FORCE - Supplemental Data
<p> </p> <p>This upload contains data required to replicate a <a href="https://github.com/franzschug/force/blob/develop/docs/source/howto/lcf.rst">tutorial </a>that applies regression-based unmixing of spectral-temporal metrics for sub-pixel land cover mapping with synthetically created training data. The tutorial uses the <a href="https://github.com/davidfrantz/force">Framework for Operational Radiometric Correction for Environmental monitoring</a>.</p> <p>This dataset contains intermediate and final results of the workflow described in that tutorial as well as auxiliary data such as parameter files.</p> <p>Please refer to the above mentioned tutorial for more information.</p> <p> </p>
Italy, climate data analyst based on era5 land data
<p>These plots illustrate the results of a climatic analysis conducted in Italy using the ERA5 Land (Copernicus Climate Service), since 1950.</p>
Spatiotemporal dynamics in freshwater amphipod assemblages are associated with surrounding terrestrial land use type - Dataset
<p>Biological assemblages are the result of dynamic processes that have explicit temporal and spatial dimensions. While biodiversity patterns can be directly inferred from the structure of these assemblages, an assessment of changes through time and space is needed to understand how organisms initially assembled and how they are responding to local environmental and biotic factors. Small freshwater streams are particularly affected by contemporary anthropogenic activities and biological invasions, yet are commonly less studied, as studies often focus on lakes and large streams. Here, we conducted a spatially explicit analysis of keystone shredder assemblages across eight years in twelve replicated small tributary streams. In each stream, we monitored multiple sites per km stream length. By assessing temporal beta diversity dynamics, defined by the gain or loss of species or abundance-per-species at individual sites, we show that changes in amphipod assemblages occur within the context of the surrounding terrestrial matrix and reflect recent amphipod colonization history. While amphipod composition was mostly constant in streams located in forested catchments, streams embedded in catchments with more extensive agricultural land use displayed more pronounced temporal changes, either driven by colonization of unoccupied upstream locations, or by more pronounced but undirected fluctuations in gains and losses of species or abundance-per-species. Our study thus suggests that agricultural landscapes might destabilize aquatic amphipod assemblages, causing higher temporal changes in community structures, and highlighting the vulnerability of aquatic ecosystems to terrestrial land use drivers.</p>
Mapping ecosystem types and land cover types in the Seychelles granitic islands, using Earth Engine and Sentinel-2
<p>We share here maps produced using Earth Engine: https://code.earthengine.google.com/?accept_repo=users/bsenterre/gis</p> <p>The maps include a land cover classification based on Sentinel-2, at 10m resolution, using an Object-Based Image Analysis approach, for the Seychelles granitic islands. Based on the land cover, landform (modeled using TauDEM), altitude and expert knowledge, we then derived a model of ecosystem types, with 3 maps: current distribution, potential distribution and prehuman distribution.</p> <p>A report exists (18th May 2022) that describes in detail the methodology, and it is being used for the preparation of a publication. The maps uploaded here are in raster format (geotif), crs=4326, and are accompanied by QGIS legend files (.qml), so they should load in QGIS with their legend automatically.</p>
A hybrid 100-m global land cover dataset with Local Climate Zones for WRF
<p>This hybrid 100-m CGLC-MODIS-LCZ global land cover dataset is produced for the Weather Research and Forecasting (WRF) model starting from version 4.5. It is based on 1) the Copernicus Global Land Service Land Cover (CGLC, Buchhorn et al., 2021) product resampled to MODIS IGBP classes (CGLC-MODIS), and 2) the global map of Local Climate Zones (LCZ, Demuzere et al., 2022a, b) that describes the urban and built-up land surface. Both the CGLC and LCZ products are available at a 100-m spatial resolution, are representative for the year 2018, and cover -180°W to 180°E and -60°S to 78°N. Remaining areas are filled with the MODIS land cover classes. This dataset has been implemented into the WRF Preprocessing System (WPS) as <a href="https://www2.mmm.ucar.edu/wrf/users/download/get_sources_wps_geog.html">tiled binary data files</a> with <a href="https://github.com/wrf-model/WPS/blob/develop/geogrid/GEOGRID.TBL.ARW_LCZ">a new GEOGRID table entry</a> to allow WRF/WPS users to flexibly use this dataset in their studies particularly for urban modeling applications.</p> <p>To display the dataset in QGIS, <em>cmap_Qgis_CGLC_MOD_LCZ.txt </em>can be used as a color scheme.</p> <p>For more details, please read the technical documentation: <a href="https://doi.org/10.5281/zenodo.7670792">https://doi.org/10.5281/zenodo.7670792</a>.<br> <br> References:</p> <p><em>Buchhorn, M., Smets, B., Bertels, L., De Roo, B., Lesiv, M., Tsendbazar, N.-E., Li, L., Tarko, A. Copernicus Global Land Service: Land Cover 100m: version 3 Globe 2015-2019: Product User Manual (Dataset v3.0, doc issue 3.4). Product User Manual; Zenodo, Geneve, Switzerland, September 2020; doi: 10.5281/zenodo.3938963<br> <br> Demuzere M, Kittner J, Martilli A, et al. A global map of local climate zones to support earth system modelling and urban-scale environmental science. Earth Syst Sci Data. 2022a;14(8):3835-3873. doi:10.5194/essd-14-3835-2022</em></p> <p><em>Demuzere M, Kittner J, Martilli A, et al. (2022). Global map of Local Climate Zones (2.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6364593</em></p>
Netcdf of LCN-PyCCS negative emission potentials as described in Werner et al., 2022 "Potential of land-neutral negative emissions through biochar sequestration"
<p><br> - This data includes three netcdfs providing the negative emission potentials<br> of land- and calorie-neutral Pyrogenic Carbon Capture and Storage (PyCCS) as<br> described in WERNER, C., LUCHT, W., GERTEN, D. & KAMMANN, C. 2022.<br> Potential of Land-Neutral Negative Emissions Through Biochar Sequestration.<br> Earth's Future, 10, e2021EF002583.</p> <p>- the negative emission potential is given in tonnes per grid cell<br> - the data ranges from 2020 to 2099 with a linear ramp up of the approach until 2025<br> - the negative emission potentials are given for each yield increase scenario,<br> 15%, 20% and 30% yield increase<br> - the LPJmL model source code and analysis script can be found in:<br> https://doi.org/10.5281/zenodo.6595002</p>
Model run and scenario data for study "Bioenergy-induced land-use change emissions with sectorally fragmented policies"
<p>This data archive contains model runs and data analysis files to the research article</p> <p><strong>Bioenergy-induced land-use change emissions with sectorally fragmented policies</strong></p> <p>by <em>Leon Merfort, Nico Bauer, Florian Humpenöder, David Klein, Jessica Strefler, Alexander Popp, Gunnar Luderer, Elmar Kriegler</em></p> <p>published in <em>Nature Climate Change </em>(2023).</p> <p><em><strong>ModelRuns_remind </strong></em>(directory) contains all REMIND model runs of the scenarios underlying the paper.</p> <p><em><strong>ModelRuns_magpie </strong></em>(directory) contains all MAgPIE model runs of the scenarios underlying the paper.</p> <p><em><strong>DataAnalysis </strong></em>(directory) contains an RStudio Project that was used for the data analysis and the generation of the figures of the paper. It additionally contains all figures and figure data that are shown in the paper.</p> <p><em><strong>ScenarioMapping.pdf</strong></em> contains the mapping from scenario names used in the paper to the model experiment names (in the model run directories).</p>
Systematic map of literature on land use conflicts: Dataset
<p>This dataset compiles the international, peer-reviewed literature on land use conflicts that was published between 2005 and 2020. It lists 306 publications and provides for each of them information regarding the research methods, research aims, covered land use types, covered geographical region, subject area, definition of "land use conflict", and conceptual approach. It can be used to identify publications on specified topics or combinations of topics, such as literature on land use conflicts in Africa regarding the land use type pastoralism. The ROSES for systematic map reports file provides information of how the systematic map was compiled and the list of unobtainable and excluded publications lists those publications that appeared in the initial database search but were not included in the mapping, with reasons for exclusion. More information on the dataset's creation, a detailed analysis, and interpretation are provided in the corresponding journal article:</p> <p>Fienitz, Meike. 2023. "Taking stock of land use conflict research: A systematic map with special focus on conceptual approaches." <em>Society & Natural Resources</em>. http://dx.doi.org/10.1080/08941920.2023.2199380</p>
Validation data set on land cover changes for RapidAI4EO project
<p>This is a reference data set collected for validation of the monthly land cover maps at a 3m and at a 10m resolution produced in the WP5. The reference data set has been collected by using Geo-Wiki toolbox for visual interpretation of very high-resolution images, including Planet data and Google maps. The data set has been collected over 3 AOIs. Each reference sample site corresponds to a 30m-by-30m box and includes information about monthly land cover type over the period 2018-2020. Land cover legend is the same as in ESA WorldCover map at a 10m resolution (https://worldcover2021.esa.int/).</p> <p>Fields:</p> <p>"rowid" – unique row identifier;</p> <p>"sampleid" – unique sample site identifier in the Geo-Wiki database;</p> <p>"samplegroupid" – group id with values 257(Portugal), 258 (Belgium), 259(Sicily);</p> <p>"x_min","x_max","y_min","y_max" – bounding box coordinates of each sample site (30m x 30m), in WGS84</p> <p>"X2018_1","X2018_2",…, "X2020_12" – dominant land cover class in each sample site in each month from January 2018 to December 2020;</p> <p>Land cover codes:</p> <p>10 – Tree cover</p> <p>20 - Shrubland</p> <p>30 - Grassland</p> <p>40 - Cropland</p> <p>50 – Urban/built-up</p> <p>60 - Bare/Sparse vegetation</p> <p>80 - Water</p> <p>90 - Wetland</p> <p>110 - Burnt</p> <p>120 – Not sure</p>
Extracellular polymeric substances are closely related to land cover, microbial communities, and enzyme activity in tropical soils
<p>These are datasets and R codes linked to the paper: Extracellular polymeric substances are closely related to land cover, microbial communities, and enzyme activity in tropical soils. </p>
Direct and indirect effects of climate and land use change on food webs in lakes and streams
<p>Here, we provide the data and code necessary to reproduce the workflow and analysis in: Barbosa and Siqueira. Direct and indirect effects of climate and land use change on food webs in lakes and streams. A preprint is available at https://doi.org/10.1101/2022.04.18.488700</p> <p>We compiled multicontinental data to investigate how climate and land use change are related to the structure of freshwater food webs, considering the inherent differences in lentic and lotic ecosystems. We analyzed the direct and indirect relationships between land use intensity, and temperature and precipitation changes, and food webs using multi-group structural equation modeling. Freshwater food webs were obtained from three sources: the Mangal interaction database, using the rmangal package in R, the GlobAl databasE of traits and food Web Architecture (GATEWAY) version 1.0, and the Interaction Web Data Base (IWDB). We also included food webs acquired from a search in the Web of Science Core Collection. Land use data was compiled from the global ESA CCI database, an annually generated land cover product at 300 m resolution for the period 1992 – 2015. Climate data was compiled from the TerraClimate database, a monthly generated product for climate and climatic water balance for global terrestrial surfaces at ~ 4 km for the period 1958 – 2015. </p>
High resolution and high cadence time series of land surface categories, land use land cover, and land use land cover changes
<p>A prototype of monthly, 10 m resolution land surface categories, land use land cover (LULC) cover, and LULC change maps derived from Sentinel-2 data over three areas within Belgium, Portugal, and Sicily for the period 2018-2020. The LULC and LULC change maps were independently validated by IIASA. All products were generated within the framework of the RapidAI4EO project, funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004356.</p> <p>The data description can be found below. The validation report of the LULC and LULC change maps can be found in validation_LULC.pdf and validation_change.pdf, respectively, and the validation dataset can be found in Lesiv <em>et al.</em> (2023).</p> <p><strong>Data description</strong></p> <p>Increasing the cadence of the land cover updates from the typical (multi-)annual to monthly cadence poses several challenges. First, several land cover types are difficult to discriminate without any knowledge of temporal dynamics. For instance, croplands are characterized by a dynamic of vegetation growth and a harvest period (i.e. cycles of bare soil, sparsely vegetated and vegetated periods). This contrasts with grasslands that often lack the harvest period resulting in a bare soil cover. Without this temporal information, it is difficult to distinguish a vegetated cropland field from grassland. Second, phenological changes may introduce a large intra-class variability and thus also confusion between classes. For example, the shedding of leaves during autumn or wilting of herbaceous vegetation in dry summer periods introduces spectral variability within land cover classes.</p> <p>To overcome these challenges, we developed a workflow with two main phases. The first phase aims to map land surface categories (LSC) at a monthly resolution. The next phase uses the resulting monthly LSC probability time series to classify land cover.</p> <p><strong><em>Land surface category (LSC)</em></strong></p> <p>These LSC represent basic, observable bio-geophysical properties (categories) of the Earth surface that can be predicted directly from individual monthly composites. LSC classes contain a set of vegetated and non-vegetated surface categories.</p> <p>Discrete LSC classification legend:</p> <table> <tbody> <tr> <td> <p>Map code</p> </td> <td> <p>Land cover class</p> </td> </tr> <tr> <td> <p>11</p> </td> <td> <p>Tree (leaf-on)</p> </td> </tr> <tr> <td> <p>12</p> </td> <td> <p>Shrubland (leaf-on)</p> </td> </tr> <tr> <td> <p>13</p> </td> <td> <p>Grassland</p> </td> </tr> <tr> <td> <p>14</p> </td> <td> <p>Woody vegetation (leaf-off)</p> </td> </tr> <tr> <td> <p>15</p> </td> <td> <p>Wilted herbaceous vegetation</p> </td> </tr> <tr> <td> <p>21</p> </td> <td> <p>Bare/sparse vegetation</p> </td> </tr> <tr> <td> <p>22</p> </td> <td> <p>Water</p> </td> </tr> <tr> <td> <p>24</p> </td> <td> <p>Built-up</p> </td> </tr> </tbody> </table> <p>In order to predict the LSC, we trained a CatBoost model (Dorogush et al., 2018) using a DEM, spectral bands and vegetation indices, country, the timing (month) of the spectral data, and the pseudo-probability of a U-Net model trained to segment built-up surfaces as input. Labels were derived by post-processing the land cover labels of the ESA WorldCover product (Zanaga et al., 2021). Please note that the collection of these labels was suboptimal, likely having an impact on the LULC and change maps generated in the prototype.</p> <p><strong><em>Land use land cover</em></strong></p> <p>After predicting LSC over the three AOI’s, we trained a CatBoost model using the LSC probabilities over a window of one year, country, and the timing (month) as independent variable. The use of LSC probabilities over multiple months allows to incorporate information about dynamics, which is necessary to discriminate some classes (e.g. cropland and grassland or cropland and bare). Similar to the LSC labels, the LULC labels were derived from the ESA WorldCover product v100 (year 2020), resulting in a similar legend system.</p> <p>The use of a moving window approach to predict LULC allows to (i) incorporate temporal information that is necessary to discriminate land cover classes and (ii) is expected to lead to more consistent land cover maps. It however has the disadvantage that (i) no land cover predictions are available at the beginning and the end of the time series and (ii) the timing of the predicted land cover change is not always accurate. To resolve these issues, we applied a post-processing step that compares and integrates the LULC predictions and cleaned LSC predictions.</p> <p>Discrete LC classification legend:</p> <table> <tbody> <tr> <td> <p><strong>Map code</strong></p> </td> <td> <p><strong>Land cover class</strong></p> </td> </tr> <tr> <td> <p>10</p> </td> <td> <p>Tree cover</p> </td> </tr> <tr> <td> <p>20</p> </td> <td> <p>Shrubland</p> </td> </tr> <tr> <td> <p>30</p> </td> <td> <p>Grassland</p> </td> </tr> <tr> <td> <p>40</p> </td> <td> <p>Cropland</p> </td> </tr> <tr> <td> <p>50</p> </td> <td> <p>Built-up</p> </td> </tr> <tr> <td> <p>60</p> </td> <td> <p>Bare/sparse vegetation</p> </td> </tr> <tr> <td> <p>80</p> </td> <td> <p>Permanent water bodies</p> </td> </tr> <tr> <td> <p>90</p> </td> <td> <p>Herbaceous wetland</p> </td> </tr> </tbody> </table> <p><strong><em>Land use land cover change </em></strong></p> <p>Monthly change maps were finally derived from the land cover maps. The pixel values within the change maps represent the percentage of pixels that changed with respect to the previous month over an area of 90x90m. The maps contain values between 0-100, with larger values assigned to larger change patches. A value of 100 indicates that all pixels within an area of 90x90m around the pixel were flagged as change.</p> <p><strong><em>Files</em></strong></p> <p>The zip files contain the following data:</p> <ul> <li>lsc.zip: land surface category maps over the three AOI’s</li> <li>lc.zip: LULC maps over the three AOI’s</li> <li>change.zip: change maps over the three AOI’s</li> </ul> <p>These maps are generated for each month over the period 2018-2020 for each of the tiles (see tiles.gpkg for an overview of all tiles). The files names use the following naming convention: “<em>tile</em>-<em>year</em>-<em>month</em>.tif”.</p> <p><strong><em>References</em></strong></p> <p>Myroslava Lesiv, Halyna Bun, & Martina Duerauer. (2023). Validation data set on land cover changes for RapidAI4EO project [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7825963 </p> <p>Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363.</p> <p><em>Zanaga, D., </em><em>et al.</em><em>., 2021. ESA WorldCover 10 m 2020 v100. </em><a href="https://doi.org/10.5281/zenodo.5571936 "><em>https://doi.org/10.5281/zenodo.5571936 </em></a></p>
Map Of Land Cover Agreement - MOLCA
<p>Map Of Land Cover Agreement (MOLCA) is generated by reusing existing datasets for High-Resolution Land Cover (HRLC), but only select portions with unanimous agreement among multiple datasets. We combined multiple HRLCs using intersection methods, retaining only the areas where all datasets agree on the land cover classes and disregarding areas of disagreement. The following HRLCs were used:</p> <ul> <li>FROM-GLC 2017 (<a href="http://data.ess.tsinghua.edu.cn/">[1]</a><a href="https://doi.org/10.1080/01431161.2012.748992">[2]</a><a href="https://doi.org/10.1016/j.scib.2017.03.011">[3]</a><a href="https://doi.org/10.1016/j.scib.2019.03.002">[4]</a>),</li> <li>GL30 2020 (<a href="http://www.globallandcover.com/">[5]</a><a href="https://doi.org/10.1016/j.isprsjprs.2014.09.002">[6]</a>),</li> <li>GHS BU S1NODSM 2016 (<a href="https://doi.org/10.1080/20964471.2017.1397899">[7]</a><a href="https://doi.org/10.3390/rs8040299">[8]</a>),</li> <li>WSF 2019 (<a href="https://geoservice.dlr.de/web/maps/eoc:wsf2019">[9]</a><a href="https://doi.org/10.6084/M9.FIGSHARE.C.4712852.V1">[10]</a><a href="https://doi.org/10.1553/giscience2021_01_s33">[11]</a>),</li> <li>GSW 2019 (<a href="https://global-surface-water.appspot.com/">[12]</a><a href="https://doi.org/10.1038/nature20584">[13]</a>),</li> <li>FNF 2018 (<a href="https://earth.jaxa.jp/en/data/2555/index.html">[14]</a><a href="https://doi.org/10.1016/j.rse.2014.04.014">[15]</a>),</li> <li>MapBiomas 2019 (<a href="https://doi.org/10.3390/rs12172735">[16]</a><a href="https://mapbiomas.org/en/accuracy-statistics?cama_set_language=en">[17]</a>),</li> <li>CCI Africa Prototype 2016 (<a href="https://2016africalandcover20m.esrin.esa.int/">[18]</a><a href="https://iiasa.dev.local/">[19]</a>),</li> <li>ESA DUE GlobPermafrost 2016 (<a href="https://doi.org/10.13140/RG.2.2.30661.76007">[20]</a><a href="https://doi.org/10.1594/PANGAEA.897916">[21]</a></li> </ul> <p> MOLCA contains around 117 billion 10-meter pixels (covering about 11.7 million square kilometers) distributed across a total area of 19 million square kilometers. It is available for 3 macro-regions in Siberia, Africa, and Amazon. The land cover classes represented in MOLCA are Bareland, Built-up, Cropland, Forest, Grassland, Shrubland, Water, Wetland, and Permanent ice and snow, covering the period between 2016 and 2020 The accuracy estimate for MOLCA indicates an Overall Accuracy (OA) of 96%. </p> <p>The repository contains:</p> <ul> <li><strong>MOLCA_<em>nnxxx</em>_v1.tif </strong>x 2075: 2075 MOLCA tiles (893 in Africa, 658 in Amazon, 524 in Siberia). Tiles of MOLCA follow the Sentinel-2 Level-1C product tiling grid, and consequently, the identifier of MOLCA tiles (<em>nnxxx</em>) is the same as the corresponding Sentinel-2 Level-1C tile identifier. </li> <li><strong>MOLCA_tiles_with_statistics.gpkg</strong>: Vector of MOLCA tile extents with statistics (number of pixels per class, total number of pixels, proportion of valid values) of each tile in the attribute table.</li> <li><strong>MOLCA_legend.csv</strong>: Class code and labels of MOLCA</li> </ul> <p> </p> <p>The creation of the MOLCA dataset was done in the project Climate Change Initiative Extension (CCI+) Phase 1 New Essential Climate Variables (NEW ECVS) High Resolution Land Cover ECV (HR_LandCover_cci) funded by the European Space Agency (ESA) known under the abbreviation CCI HRLC or CCI+ HRLC <a href="https://climate esa int/en/projects/high-resolution-land-cover/">[22]</a>.</p>
Simulation output for seven simulations with CLM-FATES in the Land Sites Platform
<p>Supporting data for a manuscript involving simulation of vegetation with the<a href="https://www.cesm.ucar.edu/models/clm"> Community Land Model </a>and<a href="https://github.com/NGEET/fates"> FATES</a>. Simulations were run using the <a href="https://noresmhub.github.io/noresm-land-sites-platform/">Land Sites Platform </a>on two virtual linux machines provided by <a href="https://nrec.no/">NREC</a>. The manuscript is part of my (EL) PhD thesis. <br> <br> See the associated GitHub repository for notebooks and workflow documentation, and the thesis chapter or manuscript for further information.</p> <p>In this dataset:</p> <ul> <li>Concatenated model history files for the entire simulation period (e.g. "alp4-1500-cosmo-IA.0-1500.nc")</li> <li>Readme files per simulation specifying the variables passed to the Land Sites Platform to start the simulation (e.g. "readme.md")</li> <li>Zipped case folders, containing the standard CLM case folder structure. Individual monthly history files are found under /archive/lnd/hist. E.g. "2985cbd23a2e3d7b1ac057abd862abb7_alp4-1500-cosmo-ia.zip"</li> </ul>
Hourly ERA5-land data (T2m, SD and TP) from 2015-01-01 to 2022-12-31 for Troms and Finnmark (Norway)
<p>This dataset contains hourly values of 2m air temperature, snow depth and total precipitation from the ERA5-land reanalysis from 2015-01-01 to 2022-12-31.</p> <p>The geographical area of interest corresponds to the Troms and Finnmark counties in Norway.</p> <p>Along with 10.5281/zenodo.8142734 this is to be used as input to forecast vegetation browning in Troms and Finnmark using machine learning.</p>
Copernicus Global Land Cover data from 2015-01-01 to 2019-12-31 for Troms and Finnmark (Norway)
<p>This dataset contains 100m x 100m maps of cover fraction expressed in % ground cover per pixel for 10 base classes including moss & lichen for years 2015 to 2019.</p> <p>The geographical area of interest corresponds to the Troms and Finnmark counties in Norway.</p> <p>Along with 10.5281/zenodo.8142713 this is to be used as input to forecast vegetation browning in Troms and Finnmark using machine learning.</p>
Dataset for "Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects"
<p>Data and Code for 'Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects' by Graf et al. (Communications Earth and Environment)</p>
Data and code: Kuipers et al. (2023) Land use diversification may mitigate on-site land use impacts on mammal popultions and assemblages. Global Change Biology
<p>Zip folder conaining the data and code that support the findings of <em>Kuipers et al. (2023) Land use diversification may mitigate on-site land use impacts on mammal popultions and assemblages. Global Change Biology.</em></p> <p>The <em>Data_code.zip</em> folder contains four subfolders with the following files:</p> <ul> <li>Data_raw <ul> <li>AgriDiv_data.csv</li> <li>AgriDiv_metadata.docx</li> <li>Species_data.csv</li> <li>Species_metadata.docx</li> <li>Landscape_data.csv</li> <li>Landscape_metadata.docx</li> </ul> </li> <li>Data_derived <ul> <li>RIA_RSR_effect_sizes.csv</li> <li>MSA_effect_sizes.csv</li> </ul> </li> <li>Data_output <ul> <li>Response_estimation.csv</li> </ul> </li> <li>R_scripts <ul> <li>01_Effect_size_calculation.R</li> <li>02_Null_model_analysis.R</li> <li>03_Model_selection.R</li> <li>04_Model_analysis.R</li> <li>05_Response_estimation.R</li> <li>06_Figures.R</li> <li>README.md</li> </ul> </li> </ul>
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