Find research datasets worth reusing

MAR-ERA5 European Alps (1981-2020)

This deposit contains MAR simulations over the European Alps domain (7 kilometers resolution) forced by the ERA-5 reanalysis. The version of the MAR model used is v.3.10, model set-up is described in detail in Beaumet et al., 2021 (https://doi.org/10.1007/s10113-021-01830-x) Contact person : Julien Beaumet (beaumetjulien@gmail.com), Martin Menegoz (martin.menegoz@univ-grenoble-alpes.fr) The simulations cover the period covered by ERA5 reanalysis : 1981-2020 (1979-1980=Spin-up years) Data are available at the daily frequency, with one variable (10 years of data) per file. The available variables in this deposit are : LWD: Surface downward longwave radiation, [W/m2] LWU: Surface upward longwave radiation, [W/m2] MB:  Total snow water equivalent, [mm.We] MBrr: Daily rainfall, [mm.We] (5) MBsf: Daily snowfall, [mm.We] (5) QQz:  Near-surface specific humidity at constant height, [g/kg]  (2) SWD: Surface downward shortwave radiation, [W/m2] SWU: Surface upward shortwave radiation, [W/m2] TTmax: Near-surface maximum air temperature for the first model level above the surface (constant sigma), [C] TTmin: Near-surface minimum air temperature for the first model level above the surface (constant sigma),[C] TTz: Near-surface mean air temperature at constant-height, [C] UUz: Near-surface zonal component of wind speed at constant height, [m/s] VVz: Near surface Meridional component of wind speed at constant height, [m/s] ZN3: Total snow height, [m] Other variables are available upon request (see email above). AL : Surface albedo, [0-1] CC: Cloud cover, [0-1] CD: Low level Cloud cover, [0-1] CM: Middle level Cloud cover, [0-1] CU: High level Cloud cover, [0-1] SP:  Surface pressure, [hPa] ST:  Surface temperature, [C] TT:  Near-surface mean air temperature for the first three model level above the surface (constant sigma), [C] (1) TTp: Constant pressure-level mean air temperature, [C] (4) ZZ:  Surface geopotential for the first three model level above the surface (constant sigma), [m] SHF: Surface sensible heat flux, [W/m2]  LHF: Surface latent heat flux, [W/m2] * Latitude(LAT),longitude(LON)and surface elevation (SH) of each grid point can be read in MARgrid_EUl.nc file (1) For variable TT, ZZ model constant sigma level of 0.9997479 (2) For variables TTz, QQz constant height level at 2m (3) For variables UUz, VVz constant height level at 10m (4) Variables TTp, UUp, VVp available at pressure level : 925, 850, 800, 700, 600, 500, 200 hPa (5) Snow height and snow water equivalent are available for three sectors which corresponds to three different vegetation type : The three vegetation type used can be readen in the file MARgrid_EUy.nc, with the variable VEG and their respectibe fraction for each grid point is given by the variable FRV. The third vegetation type (sector=3) mostly corresponds to bare soil or low crops by default, but sometimes its fraction=0, which gives unrealistic low values of snow height. In this case, using the max. value on the axis sector often gives the best results. Legend of the vegetation type for the VEG variables : 0:NO_VEGETATION   1:CROPS_LOW   2:CROPS_MEDIUM   3:CROPS_HIGH   4:GRASS_LOW   5:GRASS_MEDIUM   6:GRASS_HIGH   7:BROADLEAF_LOW   8:BROADLEAF MEDIUM   9:BROADLEAF_HIGH  10:NEEDLELEAF_LOW  11:NEEDLELEAF MEDIUM  12:NEEDLELEAF_HIGH  13:City

MAR-ERA-20C European Alps (1902-2010)

This folder contains MAR simulations over the European Alps domain (7 kilometers resolution) forced by the ERA-20C reanalysis The version of the MAR model used is v.3.10, model set-up is described in detail in Beaumet et al., 2021 (https://doi.org/10.1007/s10113-021-01830-x) Contact person : Julien Beaumet (beaumetjulien@gmail.com), Martin Menegoz (martin.menegoz@univ-grenoble-alpes.fr) The simulations cover the period covered by ERA-20C reanalysis : 1902-2010 (1901=Spin-up year) Data are available at the daily frequency, with one variable per file (10 years of data per file). The available variables are : LWD: Surface downward longwave radiation, [W/m2] LWU: Surface upward longwave radiation, [W/m2] MB:  Total snow water equivalent, [mm.We] MBrr: Daily rainfall, [mm.We] (5) MBsf: Daily snowfall, [mm.We] (5) QQz:  Near-surface specific humidity at constant height, [g/kg]  (2) SWD: Surface downward shortwave radiation, [W/m2] SWU: Surface upward shortwave radiation, [W/m2] TTmax: Near-surface maximum air temperature for the first model level above the surface (constant sigma), [C] TTmin: Near-surface minimum air temperature for the first model level above the surface (constant sigma),[C] TTz: Near-surface mean air temperature at constant-height, [C] UUz: Near-surface zonal component of wind speed at constant height, [m/s] VVz: Near surface Meridional component of wind speed at constant height, [m/s] ZN3: Total snow height, [m] Other variables are available upon request (see email above). AL : Surface albedo, [0-1] CC: Cloud cover, [0-1] CD: Low level Cloud cover, [0-1] CM: Middle level Cloud cover, [0-1] CU: High level Cloud cover, [0-1] LHF : Surface latent heat flux, [W/m2] SHF : Surface sensible heat flux, [W/m2] SP:  Surface pressure, [hPa] ST:  Surface temperature, [C] TT:  Near-surface mean air temperature for the first three model level above the surface (constant sigma), [C] (1) TTp: Constant pressure-level mean air temperature, [C] (4) ZZ:  Surface geopotential for the first three model level above the surface (constant sigma), [m] * Latitude(LAT),longitude(LON)and surface elevation (SH) of each grid point can be read in MARgrid_EUe.nc file (1) For variable TT, ZZ model constant sigma level of 0.9997479 (2) For variables TTz, QQz constant height level are : 2m (3) For variables UUz, VVz constant height level are : 10m (4) Variables TTp, UUp, VVp available at pressure level : 925, 850, 800, 700, 600, 500, 200 hPa (5) Snow height and snow water equivalent are available for three sectors which corresponds to three different vegetation type : The three vegetation type used can be readen in the file MARgrid_EUy.nc, with the variable VEG and their respectibe fraction for each grid point is given by the variable FRV. The third vegetation type (sector=3) mostly corresponds to bare soil or low crops by default, but sometimes its fraction=0, which gives unrealistic low values of snow height. In this case, using the max. value on the axis sector often gives the best results. Legend of the vegetation type for the VEG variables : 0:NO_VEGETATION   1:CROPS_LOW   2:CROPS_MEDIUM   3:CROPS_HIGH   4:GRASS_LOW   5:GRASS_MEDIUM   6:GRASS_HIGH   7:BROADLEAF_LOW   8:BROADLEAF MEDIUM   9:BROADLEAF_HIGH  10:NEEDLELEAF_LOW  11:NEEDLELEAF MEDIUM  12:NEEDLELEAF_HIGH  13:City

MAR-EC-Earth3 HIST (1961-2014) and SSP245 European Alps (2015-2100)

This deposit contains MAR simulations over the European Alps domain (7 kilometers resolution) forced by the EC-EARTH3 GCM (CMIP6 version) The version of the MAR model used is v.3.10, model set-up is described in detail in Beaumet et al., 2021 (https://doi.org/10.1007/s10113-021-01830-x) Contact person : Julien Beaumet (beaumetjulien@gmail.com), Martin Menegoz (martin.menegoz@univ-grenoble-alpes.fr) The realization used is r25i1p1f1 and simulation was done for the historial (1961-2014), SSP245 scenario (2015-2100). For information about the EC-EARTH3 simulation, contact Eduardo Moreno-Chamarro (eduardo.moreno@bsc.es). Data are available at the daily frequency, with one variable per file (10 years of data per file) CC: Cloud cover, [0-1] MB:  Total snow water equivalent, [mm.We] MBrr: Daily rainfall, [mm.We] (5) MBsf: Daily snowfall, [mm.We] (5) QQz:  Near-surface specific humidity at constant height, [g/kg]  (2) TTmax: Near-surface maximum air temperature for the first model level above the surface (constant sigma), [C] TTmin: Near-surface minimum air temperature for the first model level above the surface (constant sigma),[C] TTz: Near-surface mean air temperature at constant-height, [C] UUz: Near-surface zonal component of wind speed at constant height, [m/s] VVz: Near surface Meridional component of wind speed at constant height, [m/s] ZN3: Total snow height, [m] Other variables are available upon request (see email above). AL : Surface albedo, [0-1] CD: Low level Cloud cover, [0-1] CM: Middle level Cloud cover, [0-1] CU: High level Cloud cover, [0-1] SP:  Surface pressure, [hPa] ST:  Surface temperature, [C] TT:  Near-surface mean air temperature for the first three model level above the surface (constant sigma), [C] (1) TTp: Constant pressure-level mean air temperature, [C] (4) ZZ:  Surface geopotential for the first three model level above the surface (constant sigma), [m] LWD: Surface downward longwave radiation, [W/m2] LWU: Surface upward longwave radiation, [W/m2] SWD: Surface downward shortwave radiation, [W/m2] SWU: Surface upward shortwave radiation, [W/m2] SHF: Surface sensible heat flux, [W/m2]  LHF: Surface latent heat flux, [W/m2] * Latitude(LAT),longitude(LON)and surface elevation (SH) of each grid point can be read in MARgrid_EUe.nc file (1) For variable TT, ZZ model constant sigma level of 0.9997479 (2) For variables TTz, QQz constant height level are : 2m (3) For variables UUz, VVz constant height level are : 50m (4) Variables TTp, UUp, VVp available at pressure level : 925, 850, 800, 700, 600, 500, 200 hPa (5) Snow height and snow water equivalent are available for three sectors which corresponds to three different vegetation type : The three vegetation type used can be readen in the file MARgrid_EUy.nc, with the variable VEG and their respective fraction for each grid point is given by the variable FRV. The third vegetation type (sector=3) mostly corresponds to bare soil or low crops by default, but sometimes its fraction=0, which gives unrealistic low values of snow height. In this case, using the max. value on the axis sector often gives the best results. Legend of the vegetation type for the VEG variables : 0:NO_VEGETATION   1:CROPS_LOW   2:CROPS_MEDIUM   3:CROPS_HIGH   4:GRASS_LOW   5:GRASS_MEDIUM   6:GRASS_HIGH   7:BROADLEAF_LOW   8:BROADLEAF MEDIUM   9:BROADLEAF_HIGH  10:NEEDLELEAF_LOW  11:NEEDLELEAF MEDIUM  12:NEEDLELEAF_HIGH  13:City

MAR-MPI-ESM1-2-HR SSP585 European Alps (2015-2100)

This deposit contains MAR simulations over the European Alps domain (7 kilometers resolution) forced by the MPI-ESM1-2-HR GCM (CMIP6 version) for the SSP585 projection (2015 to 2100). The version of the MAR model used is v.3.10, model set-up is described in detail in Beaumet et al., 2021 (https://doi.org/10.1007/s10113-021-01830-x) Contact person : Julien Beaumet (beaumetjulien@gmail.com), Martin Menegoz (martin.menegoz@univ-grenoble-alpes.fr) The realization used is r1i1p1f1. Data are available at the daily frequency, with one variable per file (10 years of data per file). The available variables in this deposit are : CC: Cloud cover, [0-1] MB:  Total snow water equivalent, [mm.We] MBrr: Daily rainfall, [mm.We] (5) MBsf: Daily snowfall, [mm.We] (5) QQz:  Near-surface specific humidity at constant height, [g/kg]  (2) TTmax: Near-surface maximum air temperature for the first model level above the surface (constant sigma), [C] TTmin: Near-surface minimum air temperature for the first model level above the surface (constant sigma),[C] TTz: Near-surface mean air temperature at constant-height, [C] UUz: Near-surface zonal component of wind speed at constant height, [m/s] VVz: Near surface Meridional component of wind speed at constant height, [m/s] ZN3: Total snow height, [m] Other variables are available upon request (see email above). AL : Surface albedo, [0-1] CD: Low level Cloud cover, [0-1] CM: Middle level Cloud cover, [0-1] CU: High level Cloud cover, [0-1] SP:  Surface pressure, [hPa] ST:  Surface temperature, [C] TT:  Near-surface mean air temperature for the first three model level above the surface (constant sigma), [C] (1) TTp: Constant pressure-level mean air temperature, [C] (4) ZZ:  Surface geopotential for the first three model level above the surface (constant sigma), [m] LWD: Surface downward longwave radiation, [W/m2] LWU: Surface upward longwave radiation, [W/m2] SWD: Surface downward shortwave radiation, [W/m2] SWU: Surface upward shortwave radiation, [W/m2] SHF: Surface sensible heat flux, [W/m2]  LHF: Surface latent heat flux, [W/m2] * Latitude(LAT),longitude(LON)and surface elevation (SH) of each grid point can be read in MARgrid_EUy.nc file (1) For variable TT, ZZ model constant sigma level of 0.9997479 (2) For variables TTz, QQz constant height level is : 2m (3) For variables UUz, VVz constant height level are : 10m (4) Variables TTp, UUp, VVp available at pressure level : 925, 850, 800, 700, 600, 500, 200 hPa (5) Snow height and snow water equivalent are available for three sectors which corresponds to three different vegetation type : The three vegetation type used can be readen in the file MARgrid_EUy.nc, with the variable VEG and their respectibe fraction for each grid point is given by the variable FRV. The third vegetation type (sector=3) mostly corresponds to bare soil or low crops by default, but sometimes its fraction=0, which gives unrealistic low values of snow height. In this case, using the max. value on the axis sector often gives the best results. Legend of the vegetation type for the VEG variables : 0:NO_VEGETATION   1:CROPS_LOW   2:CROPS_MEDIUM   3:CROPS_HIGH   4:GRASS_LOW   5:GRASS_MEDIUM   6:GRASS_HIGH   7:BROADLEAF_LOW   8:BROADLEAF MEDIUM   9:BROADLEAF_HIGH  10:NEEDLELEAF_LOW  11:NEEDLELEAF MEDIUM  12:NEEDLELEAF_HIGH  13:City ~

opencc-by-4.0Oct 2022View details →

Processed model output of the climate simulation in the study: The effects of diachronous surface uplift of the European Alps on regional climate and the isotopic composition of precipitation (δ18Op) [Boateng et al.]

The geodynamic evolution of the Alps suggests that the Alps did not rise monotonically due to the different post-collisional processes such as slab break-off. However, understanding such subsurface dynamics would require adequate knowledge about its surface uplift history. Stable isotope paleoaltimetry methods are widely used to infer past surface elevation using geologic archives. However, its accurate interpretation relies on attributing the extracted isotopic signal from proxies to surface uplift despite other influences such as climate. To resolve this issue, topographic sensitivity experiments across the Alps are used to investigate the impacts of the diachronous surface uplift on regional climate and δ18Op. The Atmospheric General Circulation Model ECHAM5 with water isotope tracking capabilities (ECHAM5-wiso) is used to simulate the climate with varied topographic scenarios. We present the processed (long-term means) model output of the relevant climate variables (i.e δ18Op, near-surface temperature, precipitation amount, near-surface meridional and zonal winds, mean sea level pressure, and elevation) in response to the changes in topography. The file names are representative of the topographic scenarios used for the simulations. For example, the file “W2E1.nc” is the model output produced by a topographic scenario in which the topography across the west-central Alps was set to 200% of its modern height, and the Eastern Alps were kept at 100%. The “CTL.nc” file contains model output from the control simulation that uses present-day topography. The datasets for instance can be used to select far-field sampling points for the δ-δ paleoaltimetry method that are not significantly affected by the topographic changes.

opencc-by-4.0Apr 2024View details →

Interactive map of distribution of gene fragments indicative of cyanotoxin biosynthesis and cyanotoxins in the European Alps

Distribution of cyanotoxins and cyanotoxin biosynthesis genes in Alpine region determined by LC-MS/MS and (q)PCR. Cyanotoxins and cyanotoxin genes are mapped on separate layers, and two basemaps are available (simple and relief). Results can be filtered by location, sample type, water body type, cyanotoxins and cyanotoxin genes. Note that cyanotoxin analyses were not performed on all sampling points.

Data supporting 'Ice loss in the European Alps until 2050 using a fully assimilated, deep-learning-aided 3D ice-flow model'

The dataset supporting our publication 'Ice loss in the European Alps until 2050 using a fully assimilated, deep-learning-aided 3D ice-flow model' in Geophysical Research Letters. The main .zip archive contains a set of NetCDF files detailing: <ul> <li>Initial optimised glacier states (geology-optimized...)</li> <li>Simulation results (Prog20...)</li> </ul> Initial states and results are given by cluster (see Figure 1 in the paper), as shown in all filenames (C1 through to C12). Prognostic simulation filenames additionally distinguish between runs between 1999 and 2019 (Prog2020) and between 2020 and 2050 (Prog2050). 'NV'/'NoVel' and 'NT'/'NoThk' refer to simulations using the partial optimisation (optimisation without including velocity/thickness observations) as detailed in the paper. 'AV' at the end of the filename denotes the integrated area/volume results file, as opposed to the 2D raster results file. A 'V' before the cluster designation shows that the simulation used the variable SMB as opposed to the fixed SMB (see the paper for details). 'ID' before the cluster designation shows that the simulation was using extrapolated SMB based on the trend in SMB since 2000, instead of assuming the continuation of the current SMB. 'ID' on its own denotes linear extrapolation and 'IDQ' denotes quadratic extrapolation (not used in the published paper). 'SMBF' in the filename shows that the simulation used the SMB-elevation feedback. The additional .zip archive contains the code of IGM v1.0 used to produce the model results. For details on installing and using IGM, please see the Github page at <a href="https://github.com/jouvetg/igm.The">https://github.com/jouvetg/igm</a>. A further .zip archive (in version 3 - Sims2010-2022.zip) contains the simulations based on linear extrapolation of the observed trend in SMB between 2010 and 2022, following the same nomenclature as in the principal archive (see above). Version 4 contains an additional mosaicked DEM of the results for the whole Alps with the ice removed to give the complete basal topography (kindly processed by T. Léger at UNIL) using the Japan Aerospace Exploration Agency (2021) ALOS World 3D 30 meter DEM. V3.2, Jan 2021. Distributed by OpenTopography. <a title="https://doi.org/10.5069/G94M92HB" href="https://doi.org/10.5069/G94M92HB" target="_blank" rel="noreferrer noopener">https://doi.org/10.5069/G94M92HB</a>. Accessed: 2024-09-09.

opencc-by-4.0Oct 2020View details →

Snow cover in the European Alps: Station observations of snow depth and depth of snowfall

Auxiliary files, code, and data for paper published in The Cryosphere: Observed snow depth trends in the European Alps 1971 to 2019  <a href="https://doi.org/10.5194/tc-15-1343-2021">https://doi.org/10.5194/tc-15-1343-2021</a>   Auxiliary files: <ul> <li>aux_paper.zip: Auxiliary figures to the paper (time series showing the consistency of averaging monthly mean snow depth of stations within 500 m elevation bins; times of seasonal snow depth and snow cover duration indices).</li> <li>aux_paper_crocus_comparison.zip: Time series comparing spatial statistical gap filling from paper to gap filling using snow depth assimilation into Crocus snow model (only for subset of stations in the French Alps)</li> <li>aux_paper_monthly_time_series.zip: Plots of monthly time series of snow depth, for each station.</li> <li>aux_paper_spatial_consistency.zip: Aggregate results from spatial consistency (statistical simulation using neighboring stations), and time series of observed versus simulated monthly snow depths.</li> </ul>   Code (working copy, not cleaned, all written in R statistical software): code.zip <ul> <li>to read in the different data sources</li> <li>to do quality checks and data processing</li> <li>to perform statistical analyses as in paper</li> <li>to produce figures and tables as in paper</li> </ul>   Data: <ul> <li>> 2000 stations from Austria, Germany, France, Italy, Switzerland, and Slovenia</li> <li>Daily stations snow depth and depth of snowfall, as .zips, grouped by data provider. Information on column content is provided in "data_daily_00_column_names_content.txt".</li> <li>Monthly stations mean snow depth, sum of depth of snowfall, maximum snow depth, days with snow cover (1-100cm thresholds), as .zips, grouped by data provider. Information on column content is provided in "data_monthly_00_column_names_content.txt".</li> <li>Meta data (name, latitude, longitude, elevation) in "meta_all.csv", along with an interactive map "meta_interactive_map.html", and column information in "meta_00_column_names_content.txt".</li> <li>If you use the data you agree to adhere to the respective data provider's terms as listed in "00_DATA_LICENSE_AND_TERMS.PDF"</li> <li>The license terms especially (and additionally to any other terms of the single data providers) include: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. [from <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a>] </li> </ul>     Version history: v1.3: added maxHS and SCD (with various 1-100cm thresholds) to monthly data v1.2: uploaded data v1.1: changes to aux-paper.zip and code.zip as consequence from submitting a revised manuscript v1.0: initial upload

opencc-by-4.0Dec 2022View details →

Data from air, englacial and permafrost temperature measurements on Mt. Ortles (Eastern European Alps)

The *.csv files report the temperature data recorded between 2010 and 2016 and presented in the paper “Modern air, englacial and permafrost temperatures at high altitude on Mt. Ortles, (3905 m a.s.l.) in the Eastern European Alps” (Carturan et al., 2023, submitted). The data were used to display the time series reported in the paper, which details variable names, data quality flags, maintenance logs of field operations, and characteristics of measurement sites. The data files contain measurements of air temperature, englacial temperature, soil surface temperature and rockwall temperature. The file named ‘Ortles_Temperature_Metadata.pdf’ contains information regarding variable names, structure of data files, quality codes, geolocation, and topographic and geomorphological characteristics of sites instrumented for temperature measurements. Reference: Carturan, L., De Blasi, F., Dinale, R., Dragà, G., Gabrielli, P., Mair, V., Seppi, R., Tonidandel, D., Zanoner, T., Zendrini, T. L., and Dalla Fontana, G.: Modern air, englacial and permafrost temperatures at high altitude on Mt. Ortles, (3905 m a.s.l.) in the Eastern European Alps, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-164, in review, 2023.

opencc-by-4.0Nov 2019View details →

Daily MODIS snow cover maps for the European Alps from 2002 onwards at 250m horizontal resolution along with a nearly cloud-free version

NOTE: We discovered some errors in the data for images after February 2019. They will be fixed in version >= 1.1.x, until then, usage of the data after Feb 2019 is not advised. The rest of the data is fine.   This is the data to the same-titled Data paper, which can be found at <a href="https://doi.org/10.3390/data5010001">https://doi.org/10.3390/data5010001</a>. Along with auxilary files for the <a href="https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal">cloudremoval package</a>, and example scripts on how to access chunks of the data. The files contain: <ol> <li>python-cloudremoval-aux-data.tar.gz : auxilary data (altitude, aspect, ...) to run the cloudremoval module which can be found at <a href="https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal">https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal</a></li> <li>python-example-data-access.html : Example script how to access parts of the data using python</li> <li>R-example-data-access.html : Example script how to access parts of the data using R</li> <li>zenodo_01_original.tar.gz : time series of snow cover maps, developed at the Institute for Earth Observation, Eurac Research, Bolzano, Italy. More information in same-title Data paper (<a href="https://doi.org/10.3390/data5010001">https://doi.org/10.3390/data5010001</a>), and for algorithm at <a href="https://doi.org/10.3390/rs5010110">https://doi.org/10.3390/rs5010110</a>.</li> <li>zenodo_02_cloudremoval.tar.gz : time series of cloud filtered maps, based on 2. above, using code mentioned in 1. More information in same-titled Data paper.</li> </ol>   The maps are GeoTIFF with integer based values: 0 = no data; 1 = snow; 2 = land; 3 = cloud; 4&5 = water bodies / nodata   Version history: 1.0.0 : initial upload 1.0.1 : changes after revision of Data paper 1.0.2 : added example scripts

opencc-by-4.0Jun 2022View details →

Maps of ecosystem multifunctionality and ecological connectivity for identifying Green Infrastructure networks in the European Alps

High resolution raster datasets (20 meters) containing the results of an ecological connectivity and an ecosystem multifunctionality assessment for identifying Green Infrastructure networks in 10 pilot regions of the European Alps, modelled as part of the LUIGI Interreg Alpine Space project. Pilot regions include: department of Isère (FR), departments of Savoie and Haute-Savoie (FR), Munich Metropolitan Region (DE), Central Area of Salzburg (AT), South Burgenland (AT), Gori&scaron;ka region (SI), South Tyrol (IT), canton of Grisons (CH), Metropolitan City of Milan (IT), and Metropolitan City of Turin (IT). For a preview of the data and the results available for each pilot region <a href="https://www.alpine-space.org/projects/luigi/en/project-results/d.t1.2.1-pilot-regions-policy-briefs">click here</a> Further information on the LUIGI project is available at: <a href="https://www.alpine-space.org/projects/luigi/en/home">https://www.alpine-space.org/projects/luigi/en/home</a> <a href="https://webassets.eurac.edu/31538/1661510408-luigi-wp1-technical-annex-mapping-a-green-infrastructure-network-in-the-alpine-space.pdf">https://webassets.eurac.edu/31538/1661510408-luigi-wp1-technical-annex-mapping-a-green-infrastructure-network-in-the-alpine-space.pdf </a> The datasets include: <ul> <li>a map for ecosystem service-based multifunctionality calculated out of the average of 11 standardized ecosystem service indicators: water provision, crop potential, timber production, fodder provision, pollination potential, carbon sequestration, nitrogen retention, natural hazard mitigation, runoff retention, outdoor recreation, and landscape aesthetics.</li> <li>a map of the modelled Ecological Network composed of core areas and ecological corridors. Corridors are modelled for medium-large forest mammal species and represent least-cost pathways connecting core areas. Different classes indicate areas with different levels of current ecological connectivity starting from core areas to areas in cities or anthropized land with no connectivity. Modeled corridors are presented in two classes to mirror different levels of prioritization and management actions.</li> <li>a map of the resistance of the landscape to the movement of forest mammal species. The landscape resistance raster has been developed by reclassifying and aggregating a high resolution (5m) land use and land cover map. Resistance values have been determined in relation to the naturalness of different land use and land cover classes. In this context, land use or landscape resistance is intended as the opposite of habitat suitability.</li> </ul>

opencc-by-4.0Dec 2023View details →

Fig. 1 in Sarcoptic mange in wild ungulates in the European Alps - A systematic review

Fig. 1. Map of the European Alps and sarcoptic mange outbreaks in wild ungulates reported in the literature (n = 27). Host species is indicated with colour, the number of reported animals is indicated with size, and the time period is indicated by opacity. Grey area: alpine regions. Thin black lines: administrative boundaries. Thick black lines: country borders. The names of the countries are given. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

opencc-by-4.0Nov 2022View details →

European Alps Receiver Function Database

This dataset contains the receiver function traces calculated using seismic waveform data from the <a href="http://www.alparray.ethz.ch/en/home/">AlpArray Seismic Network</a>, three other temporary seismic networks, and permanent stations in the broader European Alpine region. We use this dataset to compile a new Moho depth map for the broader European Alps. In particular, we include: <ol> <li>all the downloaded seismic waveform data (ZNE components; 120s before and after the theoretical P-wave arrival) prior to RF calculation apart from those from the PACASE seismic network that are under embargo.</li> <li>the radial receiver function traces in .SAC format (RRF.zip). One SAC trace per station and teleseismic earthquake.</li> <li>the transverse (TRF.zip) traces in .SAC format. </li> <li>the receiver function stacks from all the seismic stations plotted versus back-azimuth in .png format (RRF_plots.zip and TRF_plots.zip)</li> <li>moho_depth_picks.csv that contains the moho depth picks information shown in the main article Figure 8a. </li> <li>a CSV file with details of the teleseismic earthquakes.</li> <li>a list of all the seismic stations used here. </li> </ol> For details on how to read and process the receiver function traces have a look at the README file of the repository. For more details on this dataset, have a look at our <a href="https://essd.copernicus.org/preprints/essd-2022-397/">manuscript</a> entitled “Moho depths beneath the European Alps: a homogeneously processed map and receiver functions database” in Earth System Science Data (ESSD) journal and our <a href="https://github.com/kemichai/rfmpy">GitHub repository</a>.

Fig. 9 in Sex-ratio and body size plasticity in two cold-adapted ground beetles co-occurring in a periglacial area of the European Alps (Coleoptera: Carabidae)

Fig. 9 – Polynomial regression graph between elytra width of Nebria castanea females and the springtail abundance.

Fig. 7 in Sex-ratio and body size plasticity in two cold-adapted ground beetles co-occurring in a periglacial area of the European Alps (Coleoptera: Carabidae)

Fig. 7 – Boxplots of elytra width of Nebria castanea females as a function of landform. p-value of N. castanea morphometric analysis with Kruskal-Wallis test, that evaluate the presence of significant differences in body size between landforms with ice (active rock glacier) and without ice (fossil rock glacier and scree slope). Asterisk highlights significant values.

Fig. 3 in Sex-ratio and body size plasticity in two cold-adapted ground beetles co-occurring in a periglacial area of the European Alps (Coleoptera: Carabidae)

Fig. 3 – Boxplot of Nebria germarii body parameters as a function of sex (F=female; M=male). p-value of the Kruskal-Wallis tests for N. germarii body size as a function of sex. Asterisk highlights significant values.

Fig. 5 in Sex-ratio and body size plasticity in two cold-adapted ground beetles co-occurring in a periglacial area of the European Alps (Coleoptera: Carabidae)

Fig. 5 – PCA analysis graphs. Blue stars=active rock glacier specimens; Gold squares=fossil rock glacier specimens; Green dot=scree slope specimens.

Fig. 6 in Sex-ratio and body size plasticity in two cold-adapted ground beetles co-occurring in a periglacial area of the European Alps (Coleoptera: Carabidae)

Fig. 6 – Boxplot of head width of Nebria germarii females as a function of landform. p-value of N. germarii morphometric analysis with Kruskal-Wallis test, that evaluate the presence of significant differences in body size between landforms with ice (active rock glacier) and without ice (fossil rock glacier and scree slope). Asterisk highlights significant values.