Find research datasets worth reusing

opencc-by-4.0Mar 2024View details →

Seismic moment tensor solutions of Mw > 3.4 earthquakes occurred between 2002 and 2023 in the Southeastern Alps

Seismic moment tensor solutions of 63 earthquakes with 3.4≤ Mw≤ 5.1 occurring from 2002 to 2023 in the Southeastern Alps and strict surroundings (latitude 45°N-47.5°N and longitude 10°E-15°E). The seismograms have been recorded and acquired by the OGS - North-Eastern Italy Seismic and Deformation Network (<a href="https://doi.org/10.7914/SN/OX">https://doi.org/10.7914/SN/OX</a>).  For more details: Saraò A., Sugan M., Bressan G., Renner G., and Restivo A.: A focal mechanism catalogue of earthquakes that occurred in the southeastern Alps and surrounding areas from 1928–2019, Earth Syst. Sci. Data, 13, 2245–2258, https://doi.org/10.5194/essd-13-2245-2021, 2021.

opencc-by-4.0Dec 2021View details →

Data and code for the manuscript "From white to green: Snow cover loss and increased vegetation productivity in the European Alps"

Data and code used for the manuscript "From white to green: Snow cover loss and increased vegetation productivity in the European Alps" by Rumpf et al., submitted December 2021 to Science See file ReadMe.txt for a description of the content and the original publication for further explanations. You are free to use these data and code for scientific purposes but are obliged to cite the above-mentioned publication. For further questions, contact sabine.rumpf@unibas.ch

opencc-by-4.0May 2019View details →

The potential of low-cost UAVs and open-source photogrammetry software for high-resolution monitoring of alpine glaciers: A case study from the Kanderfirn (Swiss Alps)

This dataset contains high-resolution orthophotos (5 x 5 cm) and digital surface models (25 x 25 cm) of the Kandernfirn Glacier located in the Swiss Alps. Aerial images were aquired with a self-developed fixed-wing Unmanned Aerial Vehicle during ten surveys on five different days in 2017 and 2018. The open-source photogrammetry software OpenDroneMap (version 0.4.1) was used for image processing. The orthophotos and digital surface models were validated through dGNSS point measurements of ground control points. Please refer to the corresponding paper for information on the horizontal and vertical accuracy of the files.

opencc-by-4.0Apr 2021View details →

Hydrological, physicochemical and metabolic activity data for streams in the Japanese Alps

A series of files with hydrological, physicochemical and metabolic activity data from a study investigating the environmental dynamics of six stream systems in the Japanese Alps.

opencc-by-4.0Dec 2022View details →

Atmosphere-cryosphere interactions during the last phase of the LGM (21 ka BP) in the European Alps

This dataset refers to: Del Gobbo, C., Colucci, R. R., Monegato, G., Žebre, M., and Giorgi, F.: Atmosphere-cryosphere interactions at 21 ka BP in the European Alps, Clim. Past Discuss. [preprint], https://doi.org/10.5194/cp-2022-43, in review, 2022.    We used the regional climate model RegCM4 to investigate the physical processes sustaining the glacier extent during the Last Glacial Maximum (LGM) and pre-industrial time (PI) over the European Alps. After a bias-correction of precipitation and temperature data, we reconstructed the environmental equilibrium line altitude (envELA) of the Alpine glaciers, which resulted consistent with geological records.  #----------------------------------------------------------   LGM in the file names referes to 21 ka BP PI refers to pre-industrial #----------------------------------------------------------   This dataset contains: NetCDF files ------------------------------------------------------------------------------------   <ul> <li>Monthly mean  TAS and PR <ul> <li>variables = <ul> <li>RegCM4 monthly mean near-surface air temperature (TAS)</li> <li>RegCM4 monthly mean precipitation (PR)</li> <li>model topography (topo)</li> </ul> </li> <li>units = TAS [°C], PR [mm/day], topo [m a.s.l.]</li> <li>model = RegCM4 (ICTP)</li> <li>method  = RCM forced with MPI-ESM-P</li> <li>remapped = no</li> <li>resolution = 12 km</li> <li>files = <ul> <li>LGM_PR_TAS_monmean.nc</li> <li>PI_PR_TAS_monmean.nc</li> </ul> </li> </ul> </li> </ul>   <ul> <li>Bias-corrected monthly mean TAS and PR <ul> <li>variables = <ul> <li>model topography (topo)</li> <li>Bias-corrected RegCM4 monthly mean precipitation (PR)</li> <li>Bias-corrected RegCM4 monthly mean near-surface air temperature (TAS)</li> </ul> </li> <li>units  = TAS [°C], PR [mm/day], topo [m a.s.l.]</li> <li>model = RegCM4 (ICTP)</li> <li>method  = bias-correction based on HISTALP (TAS) and LAPrec (PR) of RegCM4 data</li> <li>remapped = onto HISTALP grid</li> <li>resolution = 5 arcmin</li> <li>files= <ul> <li>LGM_PR_TAS_monmean_BC.nc</li> <li>PI_PR_TAS_monmean_BC.nc</li> </ul> </li> </ul> </li> </ul>   <ul> <li>ELA <ul> <li>variables = <ul> <li>ELA </li> <li>average RegCM-HISTALP-LAPrec topography</li> </ul> </li> <li>units = m a.s.l.</li> <li>data = calculated from bias-corrected RegCM4 data</li> <li>method = Zebre et al. (2020)</li> <li>remapped = on HISTALP grid</li> <li>resolution = 5 arcmin</li> <li>files = <ul> <li>LGM_ELA.nc</li> <li>PI_ELA.nc</li> </ul> </li> </ul> </li> </ul> csv files ------------------------------------------------------------------------------------ * dates refer to model dates, not real ones!!! tj_700_hpa_pr_lgm   : Tagliamento glacier daily wind and precipitation at the 21 ka BP tj_700_hpa_pr_pi       : Tagliamento glacier daily wind and precipitation at the PI db_700_hpa_pr_lgm : Dora Baltea glacier daily wind and precipitation at 21 ka BP db_700_hpa_pr_pi    : Dora Baltea glacier daily wind and precipitation at the PI r_700_hpa_pr_lgm    : Rhine glacier  daily wind and precipitation at 21 ka BP r_700_hpa_pr_pi        : Rhine glacier daily wind and precipitation at the PI ist_700_hpa_pr_lgm : Inn-Salzach-Traun glacier  daily wind and precipitation at 21 ka BP ist_700_hpa_pr_pi     : Inn-Salzach-Traun glacier  daily wind and precipitation at the PI

opencc-by-4.0Jun 2023View details →

Soil and meteorological data, and finite element simulation framework for heat transfer through shrubs in winter near Lautaret pass, French Alps

The data allow the calculation using finite element modeling of heat transfer through shrub branches and snow between the atmosphere and the soil. The shrubs are green alders (Alnus viridis). The site where they are found is called Alnus-Nivus (45.034750°N, 6.413630°E, 2034 m asl) near Col du Lautaret, French Alps. The soil data consist in temperature and volumetric liquid water content at 5 and 15 cm depths. One spot is near the alder collar (ALNUS), the other spot is 6 m away, under grass (GRASS). The meteorological data were obtained from the FR-Clt station, 750 m away (45.041278°N, 6.410611°E, 2046 m asl). See (Gupta et al., 2023) for details. Only the data relevant for heat transfer simulations are given. The simulation framework gives the alder mesh used in the heat transfer simulations. Typical simulations use a wood thermal conductivity of 1 W m-1 K-1 and a snow thermal conductivity of 0.1 W m-1 K-1. Based on observations, the snow height at Alnus-Nivus is likely to be at least twice the value at FR-Clt.  Forcing uses the snow surface temperature, derived from upwelling longwave radiation using an emissivity of 1.  The data allow testing thermal bridging through shrub branches. These data are used in a publication in preparation: Domine, Fourteau, Choler, Exploration of Thermal Bridging Through Shrub Branches in Alpine Snow. Reference Gupta, A., Reverdy, A., Cohard, J. M., Hector, B., Descloitres, M., Vandervaere, J. P., Coulaud, C., Biron, R., Liger, L., Maxwell, R., Valay, J. G., and Voisin, D.: Impact of distributed meteorological forcing on simulated snow cover and hydrological fluxes over a mid-elevation alpine micro-scale catchment, Hydrol. Earth Syst. Sci., 27, 191-212, 2023.

opencc-by-4.0Aug 2023View details →

Coupled climate-glacier modelling of the last glaciation in the Alps: modelling data

This dataset contains key distributed 2D variables resulting from the modelling of the Alpine Ice Field over the last glacial cycle from Jouvet and al. (2023, 10.1017/jog.2023.74), including basal surface topography, ice thickness, pressure-adjusted basal temperature, basal and surface ice flow speeds. The results are given on a raster grid in UTM system of coordinate with a spatial resolution of 2 km and a temporal resolution of 100 year. The data are compiled in netCDF. As explained in the paper, the model was designed to match LGM evidence. Modelled results related to intermediate states and to the Holocene must be interpreted with caution considering the relative coarse resolution (2 km). Small ice caps aside the main Alpine Icefield (except the Jura) were excluded.

opencc-by-4.0Apr 2020View details →

Neolithic/Copper Age radiocarbon dates from West Carpathian basin and East Alps

Radiocarbon dates used in this study were collected from original publications including grey literature.  The resulting database was cross-referenced with available radiocarbon databases to ensure the quality of data.  We used the latest revision (2019) of CalPal database by Bernhardt Weninger (B. Weninger et al. 2019), EuroEvol database (Manning et al.2016), C14.sk database (Barta et al. 2013) and RADON database (Martin Hinz et al. 2012).    Database structure consist of ten columns, from  site name (Site), unique site code (Scode), site longitude and latitude (Lon and Lat) in degrees, country (Country),  laboratory code (Labcode), the conventional radiocarbon date (Date) in years before present, standard deviation (SD), dated material (Mat) and bibliographic reference (Cit).  Where possible, we referenced the radiocarbon database where the date is recorded, otherwise, the publication where the date is reported or referenced is cited.    Many thanks to Bernhardt Weninger for kindly providing the latest version of CalPal database.   References    B. Weninger, Jöris O., Danzeglocke U. 2019. CalPal-2007. Cologne Radiocarbon & Palaeoclimatic Research Package. http:\\www.calpal.de Manning K., Colledge S., Crema E., Shennan S., Timpson A. 2016. The Cultural Evolution of Neolithic Europe. EUROEVOL Dataset 1: Sites, Phases and Radiocarbon Data. Journal of Open Archaeological Data 5:e2. DOI: http://doi.org/10.5334/joad.40 Martin Hinz M.F., Johannes Müller, Dirk Raetzel-Fabian, Rinne C., Sjögren K.-G., Wotzka H.-P. 2012. RADON - Radiocarbon dates online 2012. Central European database of 14C dates for the Neolithic and Early Bronze Age. http:\\www.jungsteinsite.de Barta P., Demján P., Hladíková K., Kmeťová P., Piatničková K. 2013. Database of radiocarbon dates measured on archaeological samples from Slovakia, Czechia, and adjacent regions. http://www.c14.sk

opencc-by-4.0Mar 2024View details →

Focal mechanisms of the Southeastern Alps and surroundings

We report the focal mechanisms (FPS) of earthquakes that occurred in the southeastern Alps and surrounding areas (latitude ~ 45°N-47.5°N and longitude ~ 10°E-15°E) from 1928 to 2023. The FPS have been collected and revised from literature or, depending on data availability, newly computed both by first polarities inversion or by means of seismic moment tensor. For more details about the catalogue (V 1.0, V 1.1) refer to the paper: Saraò, A., Sugan, M., Bressan, G., Renner, G., and Restivo, A.: A focal mechanism catalogue of earthquakes that occurred in the southeastern Alps and surrounding areas from 1928–2019, Earth Syst. Sci. Data, 13, 2245–2258, https://doi.org/10.5194/essd-13-2245-2021, 2021 Cite as: Sugan, M., Saraò, A., Magrin, A., Snidarcig, A., Bressan, G., Renner, G., Romano, M. A., Guidarelli, M., Santulin, M., Di Bartolomeo, P., & Restivo, A. (2024). Focal mechanisms of the Southeastern Alps and surroundings (2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10853582   V 2.0, March - doi: 10.5281/zenodo.10853582 <ul> <li>corrected some typos;</li> <li>added new FPS solutions for the period 2014-2023</li> </ul> V 1.1, April 2021 - doi: 10.5281/zenodo.4660412 <ul> <li>corrected some typos;  </li> <li>added priority criteria code</li> <li>added code to describe the changes applied with respect to the original solutions </li> </ul> V 1.0, November 2020 - doi: 10.5281/zenodo.4284971  <pre> </pre> <pre> </pre> <pre>                      </pre>

opencc-by-4.0Nov 2021View details →

LGM-Lateglacial 3D ice surface reconstructions of the Dora Baltea glacier system (western Italian Alps)

3D ice surface configurations of six LGM-Lateglacial ice stages of the Dora Baltea glacier system (western Italian Alps). Ice-configurations were obtained by combining existing and new chronological constraints from glacial and postglacial  landforms/deposits from the Dora Baltea catchment into 2D and 3D ice surface reconstructions, similar to the approach of the GlaRe ArcGIS toolbox (Pellitero et al., 2016). Mean position of the study area: 45.7412/7.3978 (°N/°E, WGS84)

opencc-by-4.0Sep 2021View details →

L'Alpe d'Huez: a dataset to benchmark topographic map generalisation

This dataset derives from the one used in a past EuroSDR benchmark (http://dx.doi.org/10.1016/j.compenvurbsys.2009.06.002), and should be used as a benchmark for topographic map generalisation techniques. It contains several topographic layers as shapefiles (roads, buildings, rivers, forests, contour lines...), a style description to display the data at the 1:50k scale, and a table of the generalisation constraints that should be respected in this 1:50k scale map. The initial data can considered as detailed for maps at the 1:15k scale. The projection of the data is "Lambert II Etendu", EPSG:27572. The area is 11*11 km large.

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

Fatiando a Terra Data: Alps - 3D GPS velocities

This is a compilation of 3D GPS velocities for the Alps. The horizontal velocities are reference to the Eurasian frame. All velocity components and even the position have error estimates, which is very useful and rare to find in a lot of datasets. Note: This is a processed and formatted version of the source dataset below. It's mean for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please cite the original authors when using this dataset. Changes made: Combined the data from 3 different files, keeping the 3-component velocities in the Eurasion frame, coordinates, uncertainties, and station ID; exported to a compressed CSV file. Source: Sánchez, Laura; Völksen, Christof; Sokolov, Alexandr; Arenz, Herbert; Seitz, Florian (2018): Present-day surface deformation of the Alpine Region inferred from geodetic techniques (data). PANGAEA, <a href="https://doi.org/10.1594/PANGAEA.886889">https://doi.org/10.1594/PANGAEA.886889</a> Source license: <a href="https://doi.org/10.1594/PANGAEA.886889">CC-BY-3.0</a> Repository: <a href="https://github.com/fatiando-data/alps-gps-velocity">https://github.com/fatiando-data/alps-gps-velocity</a>

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 ~

Supplementary material for "Long-term trends of reproductive success of black grouse Lyrurus tetrix in the southern Swiss Alps in relation to changes in climate and land use"

Abstract Breeding success of an Alpine black grouse Lyrurus tetrix population in southern Switzerland was monitored from 1981 to 2020. This long-term dataset allows exploring relationships of reproductive rates with climate and habitat, which have shown marked changes during this period. Over the 40 years, the average elevation of black grouse breeding sites increased by around 100 m in Central/Southern Ticino but showed only a slight increase in Northern Ticino, where black grouse occur at higher elevations. Average reproductive rates in Northern Ticino remained constant throughout the study period but declined in Central/Southern Ticino. Relationships between reproductive success and weather as well as habitat variables were analysed with a multiple regression model. Temperature during the early chick-rearing phase and the time of egg-laying was positively correlated with reproductive rate. Correlations between reproductive rates and precipitation were less clear, and only small proportions of the variance in reproductive rates could be explained by precipitation. Brush forest explained the greatest amount of variation in reproductive rate (6.2%). Forest, alpine agricultural areas, and unproductive vegetation all showed a positive relationship with reproductive rate, but the proportion of the variance explained was small. Year (5.1%) and its interaction with region (2.3%) explained considerable amounts of the variance. While in Northern Ticino reproductive success did not show a negative trend when correcting for weather and habitat changes, there remained a negative trend over the years in Central/Southern Ticino. Despite the positive correlations of reproductive rate with temperature, increasing temperatures do not appear to have improved reproductive success, likely as a result of habitat changes that forced black grouse towards higher elevations. Changes in reproductive success were limited to the southern region, indicating deteriorating conditions at the edge of the distribution range.

opencc-by-4.0May 2022View details →

Data from the field experiment on katabatic winds on a steep slope (Grand Colon, French Alps), February 2019

These are the data from the field experiment described in the paper 'Katabatic winds over steep slopes: overview of a field experiment designed to investigate slope-normal velocity and near surface turbulence' by CHARRONDIERE, C., BRUN, C., COHARD, J.M., SICART, J.E., OBLIGADO, M., BIRON, R., COULAUD, C. & GUYARD, H. (2022), Boundary-Layer Meteorol. 187, 29-54.

opencc-by-4.0Jun 2022View details →

Dataset for Heavy snowfall event over the Swiss Alps: Did wind shear impact secondary ice production?

The change in wind direction and speed with height, referred to as vertical wind shear, causes enhanced turbulence in the atmosphere. As a result, there are enhanced interactions between ice particles that break up during collisions in clouds which could cause heavy snowfall. For example, intense dual-polarization Doppler signatures in conjunction with strong vertical wind shear were observed by an X-band weather radar during a wintertime high-intensity precipitation event over the Swiss Alps. An enhancement of differential phase shift (Kdp > 1◦ km−1) around −15◦C suggested that a large population of oblate ice particles was present in the atmosphere. Here, we show that ice–graupel collisions are a likely origin of this population, probably enhanced by turbulence. We perform sensitivity simulations that include ice–graupel collisions of a cold frontal passage to investigate whether these simulations can capture the event better and whether the vertical wind shear had an impact on the secondary ice production (SIP) rate. The simulations are conducted with the Consortium for Small-scale Modeling (COSMO), at a 1km horizontal grid spacing in the Davos region in Switzerland. The rime splintering simulations could not reproduce the high ice crystal number concentrations, produced too large ice particles and therefore overestimated the radar reflectivity. The collisional-breakup simulations reproduced both the measured horizontal reflectivity and the ground-based observations of hydrometeor number concentration more accurately (&sim; 20L−1). During 14:30–15:45UTC, the vertical wind shear strengthened by 60% within the region favorable for SIP. Calculation of the mutual information between the SIP rate and vertical wind shear and updraft velocity suggests that the SIP rate is best predicted by the vertical wind shear rather than the updraft velocity. The ice–graupel simulations were insensitive to the parameters in the model that control the size threshold for the conversion from ice to graupel and snow to graupel.