en:
Observational data used to quantitatively describe the key elements describing avalanche danger: snowpack stability, the frequency distribution of snowpack stability, and avalanche size.
The data set consists of
- Rutschblock test results (Switzerland)
- Extended Column Test results (Switzerland, Norway)
- Avalanche occurrence data (Switzerland, Norway).
The data were extracted from the respective operational databases of the national avalanche warning services in Switzerland (WSL Institute for Snow and Avalanche Research SLF Davos, Switzerland) and Norway (The Norwegian Water Resources and Energy Directorate NVE).
For further information regarding the data, please refer to the publication or contact the author.

en: Stability tests, avalanche observations, Switzerland, Norway

en: norway extended-column-test avalanche-danger switzerland avalanche-observations rutschblock-test

de:
Resource Title: ECT data (Norway, Switzerland)
    Size: 241792 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ab401e00-0c6e-40c0-a24d-9e8716cb23da/resource/7618bcce-8c14-463d-8c4d-1dc4f5365085/download/ect_data_used_in_manuscript.csv
    Description:

de:
Resource Title: Rutschblock data (Switzerland)
    Size: 221029 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ab401e00-0c6e-40c0-a24d-9e8716cb23da/resource/30e5287d-6bc2-4810-bdc2-422976023486/download/rb_data_used_in_manuscript.csv
    Description:

de:
Resource Title: avalanche occurrence data (Norway, Sitzerland) - avalanche sizes per day and warning region
    Size: 1514094 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ab401e00-0c6e-40c0-a24d-9e8716cb23da/resource/23823196-1d56-47d1-b6bd-cb0add4dbd7b/download/avalsize_used_in_manuscript.csv
    Description:

de:
Resource Title: avalanche occurrence data (Norway, Sitzerland) - largest avalanche size per day and warning region
    Size: 256120 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ab401e00-0c6e-40c0-a24d-9e8716cb23da/resource/accef652-f988-4d75-8aff-4c4c06c24a74/download/maxavalsize_used_in_manuscript.csv
    Description:

avalanche-danger

display_name: avalanche-danger

avalanche-observations

display_name: avalanche-observations

extended-column-test

display_name: extended-column-test

norway

display_name: norway

rutschblock-test

display_name: rutschblock-test

switzerland

display_name: switzerland


$oid: OBSOLETE

en:
This repository contains all WRF model outputs and observational data sets used for the paper:
Georgakaki, P., Sotiropoulou, G., Vignon, É., Billault-Roux, A.-C., Berne, A., and Nenes, A.: Secondary ice production processes in wintertime alpine mixed-phase clouds, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-760, in review, 2021.

en: ice-water-content liquid-water-content blowing-snow wrf secondary-ice-production swiss-alps ice-crystal-number-concentration jungfraujoch

envidat

approval_status: approved

fr: EnviDat est le portail de données du WSL; il offre un accès uniformisé et réglementé aux données de surveillance et de recherche en matière d’environnement. Ce portail est capable d’héberger et de publier des ensembles de données précieuses et uniques, qui sont partagées aux quatre coins de la planète. Ce faisant, EnviDat favorise la recherche et contribue à l’avancement des sciences environnementales.

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

de: EnviDat ist das Datenportal der WSL für einen einheitlichen und verwalteten Zugang zu Umweltdaten aus Monitoring- und Forschungsprojekten. Auf dem Portal können Datensätze gehostet und veröffentlicht werden. Durch die weltweite Bereitstellung einzigartiger und wertvoller Datensätze leistet EnviDat einen Beitrag zur Forschung im Bereich der Umweltwissenschaften.

it: EnviDat è il portale dati del WSL che fornisce un accesso unificato e gestito ai dati di monitoraggio ambientale e di ricerca. Il portale ha la capacità di ospitare e pubblicare serie di dati preziosi e unici che vengono condivisi in tutto il mondo. Così facendo, EnviDat promuove la ricerca e contribuisce al progresso della scienza ambientale.

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

state: active

type: organization

wsl

capacity: public

de: EnviDat

en: Data for the publication "Secondary ice production processes in wintertime alpine mixed-phase clouds"

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

de:
Resource Title: WRF outputs
    Size: 741751014 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/a014f39f-34c3-499f-9627-0efde504d2e1/resource/45b96d72-d1ef-4208-8b22-6c6ab546208b/download/wrf-outputs.zip
    Description: The .zip file contains all the NetCDF outputs produced by the mesoscale Weather Research and Forecasting (WRF) model (version: 4.0.1). Two case studies were simulated in this study, between 25-28 January 2014 (00:00 UTC) and 29 January - 1 February 2014 (00:00 UTC), respectively. The former is denoted as "NW" and the latter as "SE", owing to the prevailing wind conditions at the Jungfraujoch (JFJ) station.

The outputs come from the innermost domain of the simulations with a spatial resolution of 1 km and are extracted from the nearest to JFJ model grid point (46.55°N 7.98°E). The dimensions of each model variable is [145,100], which corresponds to time and height, respectively. The frequency of the outputs is 30 min. Note that the first 24 hours of each simulation (i.e., the first 48 outputs) were considered spin up time.

The following sets of sensitivity simulations were presented in this study:

* CNTRL_NW.nc, CNTRL_SE.nc : Sensitivity simulations performed with the default version of WRF
* DS_PHILL_NW.nc, DS_PHILL_SE.nc : Sensitivity simulations including a description of the droplet shattering process following Phillips et al. (2018)
* DS_SULL_NW.nc, DS_SULL_SE.nc : Sensitivity simulations including a description of the droplet shattering process following Sullivan et al. (2018)
* PHILL_NW.nc, PHILL_SE.nc : Sensitivity simulations including a description of the ice-ice collisional break-up process following Phillips et al. (2017)
* TAKAH_NW.nc, TAKAH_SE.nc : Sensitivity simulations including a description of the ice-ice collisional break-up process following Sullivan et al. (2018) based on Takahashi et al. (1995)
* TAKAHrim_NW.nc, TAKAHrim_SE.nc : Same set-up as before but applied only to collisions between rimed ice particles
* BIPS_Xk0_DY_NW.nc, BIPS_Xk0_DY_SE.nc : Sensitivity simulations including a source of Xk0 (either 10.0 or 100.0 L⁻¹) ice crystals with sizes of DY (either 10 or 100 μm) in the first atmospheric level of the model
* BIPS100_PHILL_NW.nc, BIPS100_PHILL_SE.nc : Sensitivity simulations where the combined effect of blowing snow and ice-ice collisional break-up is examined

format: ZIP

media_type: application/zip

de:
Resource Title: Observational data
    Size: 4448 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/a014f39f-34c3-499f-9627-0efde504d2e1/resource/4ea9d0c6-106e-424a-9d61-9fc1dc99f576/download/observations.zip
    Description: The .zip file contains 6 .csv files including all microphysical measurements taken at the Jungfraujoch station during the two periods of interest:

* 25-28 January 2014 : northwesterly-winds (NW)
* 29 January - 1 February 2014 : southeasterly-winds (SE)

The total Ice Crystal Number Concentration (ICNC; reported in L⁻¹) is measured by the two-dimensional stereo hydrometeor spectrometer (2D-S; Lawson et al., 2006) over the size range 10-1280 μm. The total ice water content (IWC; reported in gm⁻³) is also derived by the 2D-S data using the Brown and Francis (1995) mass-diameter relationship. Finally, the quantification of the liquid water content (LWC; reported in gm⁻³) is based on the liquid droplet size distribution data derived from a DMT cloud droplet probe (CDP; Lance et al., 2010) over the size range 2-50 μm. The frequency of the measurements in the .csv files is 30 min, to match the output frequency of the model. Datetime is also reported in the files (DD/MM/YYYY h:mm) in UTC.

__Acknowledgment__: Collection and pre-processing of the microphysical measurements has been performed by Gary Lloyd (gary.lloyd@manchester.ac.uk). More details about the instrumentation during the CLACE2014 campaign can be found in Lloyd et al. (2015).

blowing-snow

display_name: blowing-snow

ice-crystal-number-concentration

display_name: ice-crystal-number-concentration

ice-water-content

display_name: ice-water-content

jungfraujoch

display_name: jungfraujoch

liquid-water-content

display_name: liquid-water-content

secondary-ice-production

display_name: secondary-ice-production

swiss-alps

display_name: swiss-alps

wrf

display_name: wrf


en:
Data associated to the publication "Environmental DNA sequences from aquatic insects indicate freshwater ecosystem state"
 Authors:
Flurin Leugger, Martina Lüthi, Meret Jucker, Virginie Marques, Sarah Thurnheer, Zacharias Kontarakis and Loïc Pellissier
 Contact:
flurinleugger@gmail.com
loic.pellissier@usys.ethz.ch
 Project description:
In this project, we aimed to compare kick-sampling-derived taxa lists and ecosystem state classifications (IBCH: Indece biologique Suisse) with eDNA metabarcoding and CRISPR-Dx. Additionally, we calculated the association with environmental variables and ecosystem state.
We retrieved the kick-sampling-derived data from Haberthür et al. (2021) and Keck et al. (2023). We collected eDNA samples in 36 sites in the Rhine catchment in Switzerland (lowland) which are used for the national monitoring of freshwater in Summer 2024. We extracted the samples in the clean lab at ETH using the protocol described in Leugger et al. (2025). We used the 16S Ins primer (Elbrecht et al. 2016) for metabarcoding and for CRISPR analysis. To select indicator eDNA metabarcoding sequences, we applied a machine learning approach combining LASSO and rpart decision trees. For CRISPR-Dx, we used the rules of Leski et al. (2023) to in silicon predict detections based on the metabarcoding sequences and used the same machine learning approach as for the indicator metabarcoding eDNA sequences (see publication for more details). The data for the environmental variables is from the SWECO25 database (https://zenodo.org/communities/sweco25/records?q=&l=list&p=1&s=10&sort=newest; Külling et al. 2024).
 Folders:
 catchments\:
Catchments in Switzerland, downloaded from https://data.geo.admin.ch/browser/index.html/collections/ch.bafu.wasser-einzugsgebietsgliederung/items/wasser-einzugsgebietsgliederung?.language=en&.asset=asset-wasser-einzugsgebietsgliederung_2056-gpkg-zip
The subfolder upstream_catchments includes the catchment for each buffer size tested.
comparison_classification_methods\:
File with the ecosystem state classification based on kick-sampling, indicator eDNA metabarcoding sequences (using the rpart decision tree) and CRISPR-Dx (using predicted, lab and retrained rpart trees).
 eDNA\:
 Folder metabarcoding\:
Raw and cleaned metabarcoding data (ASVs) per environmental extract (EVE), including taxa information from Ecotag reference data base. Additionally, the rpart trees for indicator sequences only from Ephemeroptera and all EPT are provided.
Columns which might not be self explanatory
taxon: identified taxon
rank: rank of identified taxon
nb_reads: read numbers
EVS and metabarcoding: key columns to link with metadata files
ASV_cleaned_by_site.csv: Read count by site (after cleaning)
ASV_raw_by_site.csv: Read count by site (before cleaning), column 'NC' is for the negative controls
sequence_count_per_site.csv: Comparison of reads per site before and after cleaning/filtering. 'NC' refers again to Negative Controls.
 Folder CRISPR\:
crispr_by_site.csv: Lab-based detection of the guide per site. Maximum number of detections is 6, as each site had two environmental extracts (EVE), and the PCR replicates were combined in 3 pools each. GRN refers to lab internal numbering of CRISPR-Dx assays.
detection_matrix_Ephemeroptera_28_site.csv: Predicted detection based on eDNA metabarcoding sequences and the model of Leski et al 2023 per site.
rpart_model_Ephemeroptera.rds: best performing rpart model for ecosystem state classification using the predicted CRISPR-Dx detections
rpart_model_retrained.rds: best performing rpart model for ecosystem state classification using the actual lab-based CRISPR-Dx detections (thus "retrained" based on actual detections).
 IBCH\:
IBCH_classification_filtered_and_extended.csv: includes IBCH_cat (ecosystem category from kick-sampling with IBCH 2019 categorization).
IBCH_taxa_kick-sampling.csv: contains taxa detect per site (site_code as column name to link with metadata or eDNA data)
kicknet_Trend_IBCH_sites.csv: taxa detected by site, raw data
 metadata\:
Folder containing files with overview of site names, extract number and coordinates. Columns named "metabarcoding" (and "EVS") are keys to link rows from the different tables.
 SWECO25\:
Folder containing raster stack from SWECO25 data (Külling et al. 2024) used in the article and a table with the extracted variable value per site/catchment with the different upstream buffer sizes tested.
site_code: Code for the sampling site, see also metadata to link to EVS
Temperature_avg: annual mean temperature in upstream catchment [°C]
Precip_annual: annual precipitation [mm]
Population_density: mean population density in 25m cells
Traffic_noise: noise levels
EVI, NDVI and LAI: each standard deviation (sd) and average (avg) value per upstream catchment
Natural rivers: proportion of upstream river length which is classified as natural
Little-disturbed_rivers: proportion of upstream river length which is classified as little disturbed
Heavily-disturbed_rivers: proportion of upstream river length which is classified as heavily disturbed
unnatural_rivers: proportion of upstream river length which is classified as unnatural
culverted_rivers: proportion of upstream river length which is culverted
Forest_edges: proportion of forest edges in upstream catchment
Forest: proportion of forest in upstream catchment
Crops: proportion of crops in upstream catchment
Built_environment: proportion of built environment in upstream catchment
buffer: buffer width for upstream catchment area [m]
catchment_area_m2: upstream catchment area in square meters
 References:
Elbrecht, V., Taberlet, P., Dejean, T., Valentini, A., Usseglio-Polatera, P., Beisel, J. N., Coissac, E., Boyer, F., & Leese, F. (2016). Testing the potential of a ribosomal 16S marker for DNA metabarcoding of insects. PeerJ, 2016(4). https://doi.org/10.7717/peerj.1966
Haberthür, M. (2021). Ergebnisse der 3. Erhebung NAWA-Trend Los 2, Makrozoobenthos. https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://www.bafu.admin.ch/dam/bafu/de/dokumente/wasser/externe-studien-berichte/nawa_trend_biologie_2019-teil_makrozoobenthos.pdf.download.pdf/nawa_trend_biologie_2019-teil_makrozoobenthos.pdf&ved=2ahUKEwjI5crG-IaPAxUF_gIHHQisBZEQFnoECBgQAQ&usg=AOvVaw1QL3GRZdTllPsHLQ6hQtlP
Külling, N., Adde, A., Fopp, F., Schweiger, A. K., Broennimann, O., Rey, P. L., Giuliani, G., Goicolea, T., Petitpierre, B., Zimmermann, N. E., Pellissier, L., Altermatt, F., Lehmann, A., & Guisan, A. (2024). SWECO25: a cross-thematic raster database for ecological research in Switzerland. Scientific Data, 11(1). https://doi.org/10.1038/s41597-023-02899-1
Leski, T. A., Spangler, J. R., Wang, Z., Schultzhaus, Z., Taitt, C. R., Dean, S. N., & Stenger, D. A. (2023). Machine learning for design of degenerate Cas13a crRNAs using lassa virus as a model of highly variable RNA target. Scientific Reports, 13(1), 6506. https://doi.org/10.1038/s41598-023-33494-4
Leugger, F., Lüthi, M., Schmidlin, M., Kontarakis, Z., & Pellissier, L. (2025). Rapid field-based detection of a threatened and elusive species with environmental DNA and CRISPR-Dx. Global Ecology and Conservation, 59, e03518. https://doi.org/10.1016/j.gecco.2025.e03518

en: landuse conservation biodiversity-monitoring river-restoration biomonitoring edna aquatic-invertebrates dna-metabarcoding crispr-dx

en: Assessing ecosystem state with environmental DNA of aquatic insects

de:
Resource Title: IBCH.zip
    Size: 13178 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/3c77b20b-7b1f-4ccc-9ece-aca9b4d680b8/resource/02849ade-2de9-43ce-b2bb-c99b0749f52f/download/ibch.zip
    Description:

de:
Resource Title: catchments.zip
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    Format: application/zip
    URL: https://www.envidat.ch/dataset/3c77b20b-7b1f-4ccc-9ece-aca9b4d680b8/resource/352b1914-eab0-44eb-af50-1629b7c59060/download/catchments.zip
    Description:

de:
Resource Title: metadata.zip
    Size: 18080 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/3c77b20b-7b1f-4ccc-9ece-aca9b4d680b8/resource/2596f08b-959e-45d1-9aaa-6694b81e43c2/download/metadata.zip
    Description:

de:
Resource Title: Readme.txt
    Size: 6898 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/3c77b20b-7b1f-4ccc-9ece-aca9b4d680b8/resource/f477b2e4-59f5-495d-b7fa-7a611161fb36/download/readme.txt
    Description:

format: TXT

media_type: text/plain

de:
Resource Title: comparison_classification_methods.zip
    Size: 1165 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/3c77b20b-7b1f-4ccc-9ece-aca9b4d680b8/resource/a64df406-4654-423e-b5d5-a3835fb1ddc1/download/comparison_classification_methods.zip
    Description:

de:
Resource Title: eDNA.zip
    Size: 885782 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/3c77b20b-7b1f-4ccc-9ece-aca9b4d680b8/resource/d1e0c8df-b744-44f4-9721-e95e144bd7cc/download/edna.zip
    Description:

aquatic-invertebrates

display_name: aquatic-invertebrates

biodiversity-monitoring

display_name: biodiversity-monitoring

biomonitoring

display_name: biomonitoring

conservation

display_name: conservation

crispr-dx

display_name: crispr-dx

dna-metabarcoding

display_name: dna-metabarcoding

edna

display_name: edna

landuse

display_name: landuse

river-restoration

display_name: river-restoration


en:
The SwissRad10 dataset contains light availability maps at 10 m and hourly resolution across an entire annual cycle with wall-to-wall coverage across the whole of Switzerland.
Areas below and within 25 m of forests include light availability calculated for both leaf-on and leaf-off canopy structure, and terrain only data is available across the full domain.
The variables sky-view factor and direct-beam transmissivity are both included, allowing estimation of both direct and diffuse light at the ground surface.
The dataset was calculated using the detailed synthetic hemispheric image-based ray-tracing Canopy Radiation Model (CanRad) with the latest airborne lidar and terrain surface data from the swissSURFACE3D dataset.

en: terrain solar-energy light-availability shortwave-radiation transmissivity lidar solar-irradiance radiation-transfer sky-view-fraction forests

en: SwissRad10 Hourly light availability maps at 10 m resolution over Switzerland

forests

display_name: forests

lidar

display_name: lidar

light-availability

display_name: light-availability

radiation-transfer

display_name: radiation-transfer

shortwave-radiation

display_name: shortwave-radiation

sky-view-fraction

display_name: sky-view-fraction

solar-energy

display_name: solar-energy

solar-irradiance

display_name: solar-irradiance

terrain

display_name: terrain

transmissivity

display_name: transmissivity


en: Countrywide vegetation height models (VHM) were generated for Armenia based on Copernicus Sentinel-2 imagery within the framework of the FORACCA project. “The Forest Restoration and Climate Change in Armenia” (FORACCA) project is funded by the Swiss Agency for Development and Cooperation (SDC). The project is implemented by the Swiss Federal Institute for Forest, Snow, and Landscape Research (WSL) in collaboration with Zoï Environment Network as well as the Forest Alliance of Armenia. A Convolutional Neural Network (CNN) model was trained in Switzerland to estimate the maximum vegetation height at the spatial resolution of the Sentinel-2 pixel of 10 m. Vegetation heights from the spatially higher-resolved VHM Lidar NFI were used as reference data for the CNN training. Then, the model was spatially transferred and VHMs for Armenia were generated annually based on available Sentinel-2 imagery from May – September of the respective year. Further details on the dataset and model to create the Sentinel-2 VHMs can be found in the paper Jiang et al. (2023, https://doi.org/10.1016/j.srs.2023.100099). Contains modified Copernicus Sentinel data.

en: remote-sensing forest armenia sentinel-2 cnn foracca vegetation-height

en: Sentinel-2 Vegetation Height Model Armenia

de:
Resource Title: S2-VHM_2025_max.tif
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    Format: image/tiff
    URL: https://www.envidat.ch/dataset/d5dd3a3d-093e-4206-9e0b-76c6f21c9be1/resource/f1b1e699-6f6c-4bb0-89f6-89d8c455c5a2/download/s2-vhm_2025_max.tif
    Description:

format: TIFF

media_type: image/tiff

de:
Resource Title: S2-VHM_2018_max.tif
    Size: 3167538381 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/d5dd3a3d-093e-4206-9e0b-76c6f21c9be1/resource/cf0e2bc7-a73a-477a-86bc-6ed8e7cd8e8d/download/s2-vhm_2018_max.tif
    Description:

de:
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    URL: https://www.envidat.ch/dataset/d5dd3a3d-093e-4206-9e0b-76c6f21c9be1/resource/1ccbef32-82b2-463e-a109-80f4f40037cd/download/s2-vhm_2020_max.tif
    Description:

de:
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    URL: https://www.envidat.ch/dataset/d5dd3a3d-093e-4206-9e0b-76c6f21c9be1/resource/692904ba-e575-4e69-8950-288ffe11a952/download/s2-vhm_2023_max.tif
    Description:

de:
Resource Title: S2-VHM_2019_max.tif
    Size: 3178275799 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/d5dd3a3d-093e-4206-9e0b-76c6f21c9be1/resource/7bde832d-dcba-4df3-b2ff-e01941712029/download/s2-vhm_2019_max.tif
    Description:

de:
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    Size: 3178275799 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/d5dd3a3d-093e-4206-9e0b-76c6f21c9be1/resource/511abee2-19eb-45e9-b28c-23a3f8cada46/download/s2-vhm_2017_max.tif
    Description:

de:
Resource Title: S2-VHM_2021_max.tif
    Size: 2834678415 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/d5dd3a3d-093e-4206-9e0b-76c6f21c9be1/resource/5b72d532-7a61-45a2-a09a-d83dfa61f9f2/download/s2-vhm_2021_max.tif
    Description:

de:
Resource Title: S2-VHM_2022_max.tif
    Size: 3178275799 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/d5dd3a3d-093e-4206-9e0b-76c6f21c9be1/resource/f0b468d3-1f4b-454d-93dc-d01a72902001/download/s2-vhm_2022_max.tif
    Description:

de:
Resource Title: S2-VHM_2024_max.tif
    Size: 3189013217 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/d5dd3a3d-093e-4206-9e0b-76c6f21c9be1/resource/98667a97-fe5d-4d46-a0a0-93a0e951c7e4/download/s2-vhm_2024_max.tif
    Description:

armenia

display_name: armenia

cnn

display_name: cnn

foracca

display_name: foracca

forest

display_name: forest

remote-sensing

display_name: remote-sensing

vegetation-height

display_name: vegetation-height


en:
The available datasets are snow depth maps with a spatial resolution of 0.5 m derived from images of the survey camera Vexcel Ultracam mounted on a piloted airplane. Image acquisition was carried out during the approximately peak of winter (time when the thickest snowpack is expected) in spring. The snow depth maps are calculated by the subtraction of a summer-DTM from the processed winter- DSM of the corresponding date. The summer-DTM used was derived from a point cloud of an airborne laser scanner from 2020.
Due to the occurrence of inaccuracies of the calculated snow depth values caused by the photogrammetric method, we applied different masks to significantly increase the reliability of the snow depth maps. We masked out settled areas, high-frequented streets and technical constructions, pixels with high vegetation (height - 0.5 m) , outliers and unrealistic snow depth values. In addition, we modified the snow depth values of snow-free pixels to 0.
The information on buildings and infrastructure comes from the exactly classified ALS point cloud and the TLM dataset from Swisstopo (https://www.swisstopo.admin.ch/de/geodata/landscape/tlm3d.htmllinks). High vegetation is also derived from the classification and the calculated object height from the point cloud.
Outliers and unrealistic snow depth values are defined as negative snow depth values and snow depths exceeding 10 m. The classification of each pixel of the corresponding orthophoto into snow-covered or snow-free is based on the application of a threshold of the NDSI or manually determined ratios of the RGB values.
An extensive accuracy assessment proves the high accuracy of the snow depth maps with a root mean square error of 0.25 m for the year 2017 and 0.15 m for the following snow depth maps.
The work is published in:

en: avalanches photogrammetry davos snow-hydrology dischma snow-depth-distribution

envidat

de: EnviDat ist das Datenportal der WSL für einen einheitlichen und verwalteten Zugang zu Umweltdaten aus Monitoring- und Forschungsprojekten. Auf dem Portal können Datensätze gehostet und veröffentlicht werden. Durch die weltweite Bereitstellung einzigartiger und wertvoller Datensätze leistet EnviDat einen Beitrag zur Forschung im Bereich der Umweltwissenschaften.

de: EnviDat: das Umwelt-Datenportal

de: EnviDat

en: Snow depth mapping by airplane photogrammetry (2017 -  ongoing)

envi

de: Umwelt

avalanches

display_name: avalanches

davos

display_name: davos

dischma

display_name: dischma

photogrammetry

display_name: photogrammetry

snow-depth-distribution

display_name: snow-depth-distribution

snow-hydrology

display_name: snow-hydrology


en: This research data is used to re includes 1 km resolution seed plant species range maps for the Hengduan mountain region (southwest China), elevation information for each species, family-level plant richness data, and habitat heterogeneity data at both Eurasia and Hengduan mountain regions. This research data includes 1 km resolution seed plant species range maps for the Hengduan mountain region (southwest China), elevation information for each species, family-level plant richness data, and habitat heterogeneity data at both Eurasia and Hengduan mountain regions.

en: phytodiversity model-residuals habitat-heterogeneity hypervolume hengduan-mountains

en: Phytodiversity is associated with habitat heterogeneity from Eurasia to Hengduan

de:
Resource Title: HDM heterogeneity scales
    Size: 210551963 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/736a30b8-2b01-40b0-b97f-aba77c2272e6/resource/092b3ec7-afab-4692-9547-1aacba54f89e/download/hdm_heterogeneity_scales.zip
    Description: The Heterogeneity matrics in the Hengduan mountain scale

de:
Resource Title: Eurasia plant family richness map
    Size: 1149466779 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/736a30b8-2b01-40b0-b97f-aba77c2272e6/resource/8b94b856-31b3-42e3-bfb3-e77d7a54e209/download/eurasia_fam.zip
    Description: This is the plant family level richness map at the Eurasia scale calculated from https://www.nature.com/articles/s41597-021-00997-6

de:
Resource Title: Eurasia SR and heteorgeneity
    Size: 33861 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/736a30b8-2b01-40b0-b97f-aba77c2272e6/resource/f80b00d6-7381-4789-873c-c2bb4d2aa932/download/sr_eurasia_revision.csv
    Description: This table is used for the Eurasia scale analyses, including both family level species richness map and different heterogeneity predictors

format: CSV

media_type: text/csv

de:
Resource Title: Plant range map at Hengduan scale
    Size: 635180593 bytes
    Format: application/x-7z-compressed
    URL: https://www.envidat.ch/dataset/736a30b8-2b01-40b0-b97f-aba77c2272e6/resource/95b657e6-4f16-4d44-aea1-6014d6560b54/download/three_models.7z
    Description: This is the plant range map at the Hengduan mountain scale based on species distribution model, elevation, and county level distribution information

de:
Resource Title: Hengduan plant family richness map
    Size: 159254007 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/736a30b8-2b01-40b0-b97f-aba77c2272e6/resource/fd56e712-502c-4c52-9ec4-fe7f0b461393/download/hengduan-plant-richness.zip
    Description: This is the family level plant richness map in the Hengduan mountain scale at 1km resoution

habitat-heterogeneity

display_name: habitat-heterogeneity

hengduan-mountains

display_name: hengduan-mountains

hypervolume

display_name: hypervolume

model-residuals

display_name: model-residuals

phytodiversity

display_name: phytodiversity


en:
The TreeMig framework allows for an easy application of the forest landscape model TreeMig for simulating forest dynamics in space under changing environmental and land use conditions.
Here, case study examples in Switzerland are given that simulate the dynamics and spatial spread of competing tree species in a region in Switzerland, the invasion of a hypothetical invasive tree species, and the control of this species via a management model coupled to TreeMig.  The datasets consist of the installation- and GUI starting-script,  example environmental input data for Switzerland, the simulation environment, and further R-scripts for running the simulations and plotting the simulation results directly from R and for coupling TreeMig to the forest management model.

en: bioclimatic-variables climate-change land-use spatial-modelling forest r-package treemig example-applications species-migration forest-management forest-dynamics

envidat

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: EnviDat: the Environmental Data Portal

en: Case study applications demonstrating the use and potential of the TreeMig framework v1

de:
Resource Title: readme_descriptioninputdata.pdf
    Size: 269312 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/3db8ca57-2838-4b26-82a9-9770b75b7096/download/readme_descriptioninputdata.pdf
    Description: Where the environmental and land use input data come from and how they were processed

format: PDF

media_type: application/pdf

de:
Resource Title: installAndRunTreeMigWrapperAndGUI_std.R
    Size: 553 bytes
    Format: text/x-r
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/916c8a50-1d57-4e28-97c8-f50b8a9f7bf7/download/installandruntreemigwrapperandgui_std.r
    Description: R-script to install the TreeMig framework and start the graphical user interface

de:
Resource Title: InstructionsForRunningTheSimulations.pdf
    Size: 68995 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/830a45db-7985-42f6-8864-4961fe5ef5a2/download/instructionsforrunningthesimulations.pdf
    Description: A condensed instruction for running the presented simulations.

de:
Resource Title: InstructionsForRunningTheSimulations.pdf
    Size: 127302 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/32c74611-c61e-48be-998b-6722bbb697c8/download/instructionsforrunningthesimulations.pdf
    Description: A condensed instruction for running the presented simulations.

de:
Resource Title: ExampleApplicationsTreeMig.zip
    Size: 1676301558 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/609e85c8-98d0-4043-be91-48bd32c162ea/download/exampleapplicationstreemig.zip
    Description: 1) Simulation environment TMEnvironmentExampleApplications for the simulation of the case study applications. It contains a) simulation settings (folder C),  b) species parameters (folder P), c) folders S, R and P for the state files and results and plots to be produced, and the binaries of the TreeMig model.
2) Input data for Switzerland in TMInputData (Climate, soil, land cover, DEM)
3) Several R scripts (folder Scripts) to run and plot the simulations directly from R without having to use the graphical user interface.
For learning how to use TreeMig we recommend the starter kit https://www.doi.org/10.16904/envidat.533.

de:
Resource Title: ExampleApplicationsTreeMig.zip
    Size: 1686005000 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/0f59997f-3e90-4b25-9e5b-37b641d9769b/download/exampleapplicationstreemig.zip
    Description: 1) Simulation environment TMEnvironmentExampleApplications for the simulation of the case study applications. It contains a) simulation settings (folder C),  b) species parameters (folder P), c) folders S, R and P for the state files and results and plots to be produced, and the binaries of the TreeMig model.
2) Input data for Switzerland in TMInputData (Climate, soil, land cover, DEM)
3) Several R scripts (folder Scripts) to run and plot the simulations directly from R without having to use the graphical user interface.
For learning how to use TreeMig we recommend the starter kit https://www.doi.org/10.16904/envidat.533.

bioclimatic-variables

display_name: bioclimatic-variables

climate-change

display_name: climate-change

example-applications

display_name: example-applications

forest-dynamics

display_name: forest-dynamics

forest-management

display_name: forest-management

land-use

display_name: land-use

r-package

display_name: r-package

spatial-modelling

display_name: spatial-modelling

species-migration

display_name: species-migration

treemig

display_name: treemig


en:
Comparing environmental DNA metabarcoding and underwater visual census to monitor tropical reef fishes, the Gayraca Bay data set.
The Gayraca bay is located in the Tayrona National Natural Park along the continental Colombian Caribbean coast bordering the Sierra Nevada de Santa Marta. Tayrona Park has a heterogeneous coastal topography composed of metamorphic rocks, with numerous rocky headlands, islets, and bays (Garzón-Ferreira & Díaz, 2003). Coral and other hard-bottom communities are distributed along the coast, mainly as fringing reefs, while seagrass beds, mangroves, and coral reefs have developed to some extent in sheltered conditions within the bays (Garzón-Ferreira & Cano, 1991). The study was carried out in Gayraca Bay, where corals on the exposed side exhibit mainly massive to encrusting growth forms with colonies and a reef-like structure. In Gayraca Bay, we sampled two filtration replicates from each of six stations, for a total of 12 water samples, from 23 to 26 October 2018. We recorded the GPS coordinates at the start and end of the transect. We conducted eDNA sampling by using a filtration device composed of an Athena® peristaltic pump (Proactive Environmental Products LLC, Bradenton, Florida, USA; nominal flow of 1.0 L/min), a VigiDNA® 0.20 μM cross-flow filtration capsule (SPYGEN, le Bourget du Lac, France) and disposable sterile tubing for each filtration capsule. We performed two filtration replicates in parallel on each side of a boat, at each station, for 30 min corresponding to a volume of ~30 L of water filtered by each capsule. At the end of each filtration, the water inside the capsules was emptied, and we filled the capsules with 80 ml of CL1 Conservation buffer (SPYGEN, le Bourget du Lac, France) and stored at room temperature. We followed a strict contamination control protocol in both field and laboratory stages (Valentini et al., 2016). Each water sample processing included the use of disposable gloves and single-use filtration equipment to avoid any risk of contamination. Libraries were prepared with ligation using the MetaFast protocol (Fasteris).
Data content:
	* rawdata/: contains the raw reads for each individual sample. One archive contains the paired-end reads specified by the _R1 or _R2 suffix as well as individually tagged PCR replicates (if available) together with an archive containing all extraction and PCR blank samples of the library. Reads have been demultiplexed using cutadapt but not trimmed, individual demultiplexing tags and primers remain present in the sequences.
	* taxadata/: contains the table with all detected taxonomy for each sample after bioinformatic processing (see Polanco et al. 2020 for details; https://doi.org/10.1002/edn3.140) and associated field metadata.
	* metadata/: contains two metadata files, one related to the data collected in the field for each filter, and the second related to the sequencing process in the lab (including the tag sequence, library name, and marker information for each sample)

en: marine biomonitoring teleo biodiversity vert01 chon01 edna reef-fish environmental-dna caribbean-region

en: Environmental DNA Marine Colombia Gayraca 2018

envi

en: Environment

biodiversity

display_name: biodiversity

biomonitoring

display_name: biomonitoring

caribbean-region

display_name: caribbean-region

edna

display_name: edna

environmental-dna

display_name: environmental-dna

marine

display_name: marine

reef-fish

display_name: reef-fish

teleo

display_name: teleo


en:
The cold habitats of the Swiss Alps and the Arctic are undergoing change and are at risk of disappearing completely or partially in the future. Along with them, a poorly known diversity of microorganisms that have adapted to life in these supposedly hostile places. By cryopreserving microorganisms from these habitats, some of today's biodiversity can be preserved in biobanks.
The collection contains cryopreserved bacteria and fungi isolated from permafrost and active layer of Muot da Barba Peider and Val Lavirun, from the glacier forefield and glacier toe of the Damma glacier, from active layer in northern Greenland and from plastic waste in Svalbard, as well as isolates from plastic incubations in Muot da Barba Peider, northern Greenland and arctic Russian soils.
Project overview: https://www.wsl.ch/en/projects/life-at-chilly-temperatures-a-collection-of-microorganisms-from-extreme-habitats/

en: biobank collections extreme-habitat biodiversity bacteria swiss-alps active-layer conservation fungi plastisphere glaciers permafrost arctic

en: Life at chilly temperatures - A collection of microorganisms from extreme habitats

format: XLS

active-layer

display_name: active-layer

arctic

display_name: arctic

bacteria

display_name: bacteria

biobank

display_name: biobank

collections

display_name: collections

conservation

display_name: conservation

extreme-habitat

display_name: extreme-habitat

fungi

display_name: fungi

glaciers

display_name: glaciers

permafrost

display_name: permafrost

plastisphere

display_name: plastisphere

swiss-alps

display_name: swiss-alps


en: Data of 13C-pulselabeling experiments in a young beech model ecosystem and a mature pine forest exposed to drought and rewetting. Data include microclimate during the experiment, total PLFA contents, d13C signatures in PLFA , microbial biomass, soil-respired CO2 measure during 30 days following 3-4 hour long pulse labeling with highly 13C-enriched CO2.

en: drought microbial-biomass forest biogeochemical-cycling c13-labeling plfa

en: Gao_Drought resistance and resilience of rhizosphere communities_13C-PLFA

biogeochemical-cycling

display_name: biogeochemical-cycling

drought

display_name: drought

forest

display_name: forest

microbial-biomass

display_name: microbial-biomass

plfa

display_name: plfa


en:
DESCRIPTION
The dataset is used to run snowpack simulations with various forcing data at different resolutions with the Flexible Snow Model (FSM2oshd). A dynamical downscaling model (HICAR) and a semi-statistical downscaling approach (COSD) are used to downscale COSMO data to resolutions of 250 m, 100 m and 50 m. Simulations are run with the operational snow cover model and a model extension including wind- and gravity-induced snow redistribution (FSM2trans).
 Input Data
This paragraph describes the input data used to run simulations with FSM2trans
 HICAR
The HICAR input data is created by the High-resolution Intermediate Complexity Atmospheric Research model. The input data is available for different resolutions (250m, 100m and 50m) for a domain covering complex terrain in the Swiss Alps (Dischma)
 COSD
The COSD data was statistically downscaled within the OSHD framework to different resolutions (250m, 100m, 50m) for a domain covering complex terrain in the Swiss Alps (Dischma)
 Simulation files
For the Simulations, a landuse file and a namelist file with the parametrizations need to be supplied to the model
 simulation output
This simulation output that is used for the analysis of the study is provided.

en: dischma snow-hydrology snowpack hicar fsm2trans fsm2

en: FSM2trans snowpack simulations with HICAR input

de:
Resource Title: 2016.11_HICAR_50.zip
    Size: 1186232457 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/5581cabf-14c4-464b-9c4f-b4067d14f051/download/2016.11_hicar_50.zip
    Description: Input data November 2016 HICAR 50m resolution

de:
Resource Title: FSM_sntrans.zip
    Size: 117802989 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/4a6c39e8-ef52-439e-9f68-ac4f17423188/download/fsm_sntrans.zip
    Description: Available input files for running with FSM2trans

de:
Resource Title: 2017.03_COSD.zip
    Size: 288438593 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/f71d4daa-b6ac-40e8-aff0-2bffd45ce846/download/2017.03_cosd.zip
    Description: Input data March 2017 COSD

de:
Resource Title: 2017.06_COSD.zip
    Size: 294775835 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/14c5c921-6d52-409b-a542-04e46024f319/download/2017.06_cosd.zip
    Description: Input data June 2017 COSD

de:
Resource Title: 2016.10_COSD.zip
    Size: 278982217 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/f8e29340-2c65-40c8-97c0-d72389c3f6e7/download/2016.10_cosd.zip
    Description: input data october 2016 COSD

de:
Resource Title: 2017.05_HICAR_50.zip
    Size: 2561728437 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/d0e98236-f5fc-495d-a6e2-2ed08fd62f2f/download/2017.05_hicar_50.zip
    Description: Input data May 2017 HICAR 50m resolution

de:
Resource Title: 2016.10_HICAR_100.zip
    Size: 577152229 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/d1950e5f-834f-43af-8b77-faa0645830cb/download/2016.10_hicar_100.zip
    Description: Input data october 2016 HICAR 100m resolution

de:
Resource Title: 2017.07_COSD.zip
    Size: 301251879 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/8a792b31-ff97-439d-8eb3-2515cf2499c6/download/2017.07_cosd.zip
    Description: Input data July 2017 COSD

de:
Resource Title: FSM.zip
    Size: 33570817 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/969d40cd-9460-4500-8a49-3ec7d0e13b70/download/fsm.zip
    Description: Available input files for running with FSM2

de:
Resource Title: output_time_series.zip
    Size: 81853 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/2ac4c04e-5ea3-452e-9805-6acda2dc3ea4/download/output_time_series.zip
    Description: time series of daily averages of the whole domain for each simulation run
- rainfall
- snowfall
- runoff
- snowmelt
- temperature

de:
Resource Title: 2017.05_HICAR_100.zip
    Size: 590876781 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/c80ce909-8175-4c97-83b9-d26b26965151/download/2017.05_hicar_100.zip
    Description: Input data May 2017 HICAR 100m resolution

de:
Resource Title: 2016.12_COSD.zip
    Size: 266020075 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/3a541855-fcd5-4308-904e-279f26c08079/download/2016.12_cosd.zip
    Description: input data january 2016 COSD

de:
Resource Title: 2017.07_HICAR_100.zip
    Size: 588530697 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/38eb4592-9dc7-4128-8b10-5c7bab86e461/download/2017.07_hicar_100.zip
    Description: Input data July 2017 HICAR 100m resolution

de:
Resource Title: 2017.04_HICAR_250.zip
    Size: 338504059 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/8b697f7e-d86b-42ec-b514-b1cf61afae87/download/2017.04_hicar_250.zip
    Description: Input data April 2017 HICAR 250m resolution

de:
Resource Title: 2016.12_HICAR_250.zip
    Size: 314433953 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/5f573752-59a4-42d6-952b-2c01b9cf1d06/download/2016.12_hicar_250.zip
    Description: Input data December 2016 HICAR 250m resolution

de:
Resource Title: 2017.03_HICAR_50.zip
    Size: 1238243000 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/71eb5579-653e-44d0-a76e-102ae9616b3e/download/2017.03_hicar_50.zip
    Description: Input data March 2017 HICAR 50m resolution

de:
Resource Title: 2017.02_COSD.zip
    Size: 251637401 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/2b9d9c27-0e23-47c5-8e88-bc6406fcb01f/download/2017.02_cosd.zip
    Description: Input data February 2017 COSD

de:
Resource Title: 2016.12_HICAR_100.zip
    Size: 529826318 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/99285212-c956-46c7-b286-2138e9e0a0b3/download/2016.12_hicar_100.zip
    Description: Input data December 2016 HICAR 100m resolution

de:
Resource Title: 2017.05_COSD.zip
    Size: 301461076 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/fc51e6dc-0217-4260-acdd-4774487412d4/download/2017.05_cosd.zip
    Description: Input data May 2017 COSD

de:
Resource Title: 2016.10_HICAR_50.zip
    Size: 1249412855 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/de489e7c-ea5e-425a-9028-df8f4eaa43c6/download/2016.10_hicar_50.zip
    Description: Input data october 2016 HICAR 50m resolution

de:
Resource Title: 2017.07_HICAR_50.zip
    Size: 1277440392 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/34e952ac-5973-4da0-997f-ab88dfcbd564/download/2017.07_hicar_50.zip
    Description: Input data July 2017 HICAR 50m resolution

de:
Resource Title: 2017.01_HICAR_100.zip
    Size: 556488789 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/44b7b48e-3424-4725-9d46-ae5b80d06357/download/2017.01_hicar_100.zip
    Description: Input data January 2017 HICAR 100m resolution

de:
Resource Title: 2017.02_HICAR_50.zip
    Size: 1097463760 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/35a80379-3a54-4792-aa57-30d77f5cab02/download/2017.02_hicar_50.zip
    Description: Input data February 2017 HICAR 50m resolution

de:
Resource Title: 2017.02_HICAR_250.zip
    Size: 303548366 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/ec1636b8-1bce-4cff-b1f1-a38069ac89c1/download/2017.02_hicar_250.zip
    Description: Input data February 2017 HICAR 250m resolution

de:
Resource Title: 2017.01_COSD.zip
    Size: 272140667 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/b9490aeb-da86-494f-877c-9eb6be651d8c/download/2017.01_cosd.zip
    Description: Input data January 2017 COSD

de:
Resource Title: output_validation_LiDAR.zip
    Size: 3626428 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/a24da30f-627b-413c-9ed9-aad3acaef5e2/download/output_validation_lidar.zip
    Description: snowheight output of validation days (LiDAR days) for all simulation runs
- 20.03.2027
- 31.03.2017
- 17.05.2017

de:
Resource Title: 2017.02_HICAR_100.zip
    Size: 507866833 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/177e8244-a298-498e-b2c2-613a34e43768/download/2017.02_hicar_100.zip
    Description: Input data February 2017 HICAR 100m resolution

de:
Resource Title: 2017.04_HICAR_50.zip
    Size: 2446451988 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/f3b1699c-2b57-4461-9a06-f9d2d2b6f68e/download/2017.04_hicar_50.zip
    Description: Input data April 2017 HICAR 50m resolution

de:
Resource Title: 2017.06_HICAR_50.zip
    Size: 1248134155 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/06001feb-2337-46e6-884d-8d1f6b27472e/download/2017.06_hicar_50.zip
    Description: Input data June 2017 HICAR 50m resolution

de:
Resource Title: 2016.10_HICAR_250.zip
    Size: 342373326 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/b9b3253d-0e25-4051-b9c3-3156a532850b/download/2016.10_hicar_250.zip
    Description: Input data October 2016 HICAR 250m resolution

de:
Resource Title: 2016.11_COSD.zip
    Size: 264224065 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/b4507205-372a-42fb-a9c1-e8d92552d040/download/2016.11_cosd.zip
    Description: input data november 2016 COSD

de:
Resource Title: 2017.04_HICAR_100.zip
    Size: 565969532 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/b9ec85e4-e78a-4c97-9fd6-1a2dede5e261/download/2017.04_hicar_100.zip
    Description: Input data April 2017 HICAR 100m resolution

de:
Resource Title: 2016.12_HICAR_50.zip
    Size: 1146348392 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/085643a0-d8a9-4051-9b40-dc31f36b90a5/download/2016.12_hicar_50.zip
    Description: Input data December 2016 HICAR 50m resolution

de:
Resource Title: 2017.05_HICAR_250.zip
    Size: 352928870 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/f6a4bef4-46e0-479c-a0d5-641881a0f6f7/download/2017.05_hicar_250.zip
    Description: Input data May 2017 HICAR 250m resolution

de:
Resource Title: 2017.06_HICAR_100.zip
    Size: 573560621 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/78ed5fc9-112a-48dd-996a-f84b26b3263c/download/2017.06_hicar_100.zip
    Description: Input data June 2017 HICAR 100m resolution

de:
Resource Title: 2017.01_HCIAR_50.zip
    Size: 1204154523 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/ccdeef06-535f-4735-a626-8d641701e305/download/2017.01_hciar_50.zip
    Description: Input data January 2017 HICAR 50m resolution

de:
Resource Title: 2017.04_COSD.zip
    Size: 288051313 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/4aae6922-c820-4e83-9387-c1d810896431/download/2017.04_cosd.zip
    Description: Input data April 2017 COSD

de:
Resource Title: 2017.03_HICAR_250.zip
    Size: 340805883 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/0b313459-557e-4e48-aaa5-3f3ecea99ef1/download/2017.03_hicar_250.zip
    Description: Input data March 2017 HICAR 250m resolution

de:
Resource Title: 2016.11_HICAR_250.zip
    Size: 326225322 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/87223396-acb5-4f85-8969-e9c1e37be822/download/2016.11_hicar_250.zip
    Description: Input data November 2016 HICAR 250m resolution

de:
Resource Title: 2017.03_HICAR_100.zip
    Size: 571345359 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/0cbd4ca1-2bb7-4e95-a68f-aa1cab01552c/download/2017.03_hicar_100.zip
    Description: Input data March 2017 HICAR 100m resolution

de:
Resource Title: 2016.11_HICAR_100.zip
    Size: 549614230 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/668af1cb-d429-4163-82ed-175818ba5870/download/2016.11_hicar_100.zip
    Description: Input data november 2016 HICAR 100m resolution

de:
Resource Title: 2017.01_HICAR_250.zip
    Size: 331441414 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/190d102a-93f1-4a8e-9373-3442681efe51/download/2017.01_hicar_250.zip
    Description: Input data January 2017 HICAR 250m resolution

de:
Resource Title: 2017.06_HICAR_250.zip
    Size: 340448153 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/a0d70e21-c08f-4bf8-9043-8c061a63473c/download/2017.06_hicar_250.zip
    Description: Input data June 2017 HICAR 250m resolution

de:
Resource Title: 2017.07_HICAR_250.zip
    Size: 349289674 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f9eb2878-7c48-443c-82bf-f86908dea39a/resource/520f9b5e-953b-4ac2-b14d-774b3802e6ca/download/2017.07_hicar_250.zip
    Description: Input data July 2017 HICAR 250m resolution


en:
This dataset contains the Distributed Acoustic Sensing (DAS), radar detection data used for training and result analysis in the GRL paper titled `Automatic Monitoring of Rock-Slope Failures Using Distributed Acoustic Sensing and Semi-Supervised Learning`.
The DAS dataset (both waveform and cross-spectral density matrices), extracted features, labeled dataset, two trained models (feature extraction model and xgboost classification model), scripts to reproduce the whole training and classification processes, and a notebook to replicate the result analysis part are provided under the MIT license. To provide a reasonable data size, we chunked the raw data to a few hundred channels which we used in our project.
Abstract:
Effective use of the wealth of information provided by Distributed Acoustic Sensing (DAS) for mass movement monitoring remains a challenge. We propose a semi-supervised neural network tailored to screen DAS data related to a series of rock collapses leading to a major failure of approximately 1.2 million cubic meters on 15 June 2023 in Brienz, Eastern Switzerland. Besides DAS, the dataset from 16 May to 30 June 2023 includes Doppler radar data for partially ground-truth labeling. The proposed algorithm is capable of distinguishing between rock-slope failures and background noise, including road and train traffic, with a detection precision of over 95%. It identifies hundreds of precursory failures and shows sustained detection hours before and during the major collapse. Event size and signal-to-noise ratio (SNR) are the key performance dependencies. As a critical part of our algorithm operates unsupervised, we suggest that it is suitable for general monitoring of natural hazards.

en: early-warning landslide natural-hazard seismic rockfall machine-learning

envidat

de: EnviDat ist das Datenportal der WSL für einen einheitlichen und verwalteten Zugang zu Umweltdaten aus Monitoring- und Forschungsprojekten. Auf dem Portal können Datensätze gehostet und veröffentlicht werden. Durch die weltweite Bereitstellung einzigartiger und wertvoller Datensätze leistet EnviDat einen Beitrag zur Forschung im Bereich der Umweltwissenschaften.

de: EnviDat: das Umwelt-Datenportal

de: EnviDat

en: Distributed Acoustic Sensing Brienz

envi

de: Umwelt

early-warning

display_name: early-warning

landslide

display_name: landslide

machine-learning

display_name: machine-learning

natural-hazard

display_name: natural-hazard

rockfall

display_name: rockfall

seismic

display_name: seismic


en:
This dataset contains the fire events, drivers and auxillery datasets analyzed in the study "Compounding preconditions of wildfires vary in time and space within Europe" of Miller et al. (2025) (DOI: 10.1038/s43247-025-02955-1).
A Jupyter-Notebook explaining the fire event datasets and code to reproduce the figures of Miller et al. (2025) are available on gitlab (see https://gitlab.com/jumi26/wildfire-drivers-in-europe).

en: driver europe forest-fire observations event-analysis mountains

envidat

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: EnviDat: the Environmental Data Portal

en: Wildfire events and drivers across Europe

driver

display_name: driver

europe

display_name: europe

event-analysis

display_name: event-analysis

forest-fire

display_name: forest-fire

mountains

display_name: mountains

observations

display_name: observations


en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for one meteorological station in Alpthal in Switzerland which is located within a natural coniferous forest (ALB) with Norway spruce (_Picea abies_; 180-230 yrs) as dominant tree species. The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Alpthal is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

en: Alpthal, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards

en: climate-change precipitation meteorology relative-humidity par long-term wind-speed meteo swiss-forest-lab temperature

envi

en: Environment

de:
Resource Title: Forest station data (ALB)
    Size: 7164766 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/3bb25386-f2d8-4fda-b7fa-86ea4be16535/resource/5109ff84-b671-41af-a243-34f1eda1fc88/download/albforest.csv
    Description:

de:
Resource Title: Data plot of the forest station (ALB)
    Size: 277476 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/3bb25386-f2d8-4fda-b7fa-86ea4be16535/resource/50d91991-2164-4f48-9bff-7dd1ecd9f379/download/albplot.pdf
    Description: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

de:
Resource Title: Site aerial photo (Alpthal)
    Size: 7983732 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/3bb25386-f2d8-4fda-b7fa-86ea4be16535/resource/c331a75a-6b89-4924-8896-6582b74c7284/download/alfront.jpg
    Description: Site aerial photo from the Alpthal forest station. Source: Google. 2016. Site aerial photo from the Alpthal forest station. Retrieved November 11, 2016, from https://www.google.ch/maps

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed


en:
Important
This EnviDat entry is outdated. The most recent, usable version of the data can be found under the new EnviDat entry "Long-term meteorological station Stillberg, Davos, Switzerland at 2090 m a.s.l.." The entry can be found under this link -https://www.envidat.ch//metadata/meteo-stillberg-  and with this DOI -https://doi.org/10.16904/envidat.396-.

en: precipitation atmospheric-conditions temperature-profiles radiation wind

en: Long-term meteorological and snow station at 2090 m a.s.l., Stillberg, Davos, Switzerland (1975 - present)

de:
Resource Title: DATA ACCESS
    Size: 2684354560 bytes
    Format: application/x-smet
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.43/dps/STB2_L3.smet
    Description: This data access is outdated and has been replaced with an updated version (see description).

de:
Resource Title: Site photo 1
    Size: 2643479 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/938e0cc0-0f75-41da-a358-679f27c254d4/resource/17df9e78-39eb-453e-a69d-3b71de67ff48/download/046.jpg
    Description: This site photo is outdated and has been replaced with an updated version (see description).

format: jpeg

de:
Resource Title: Site photo 2
    Size: 3453733 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/938e0cc0-0f75-41da-a358-679f27c254d4/resource/93f3b3d4-955d-4cd8-a7ad-0fdea9f5a937/download/048.jpg
    Description: This site photo is outdated and has been replaced with an updated version (see description).

de:
Resource Title: README
    Size: 1383 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/938e0cc0-0f75-41da-a358-679f27c254d4/resource/0fa6c765-9e81-49b3-9c11-83a0043bf653/download/metadatastillberg.txt
    Description: This description is outdated and has been replaced with an updated version (see description).

de:
Resource Title: Latest data from Stillberg climate station
    Size: 1 bytes
    Format: No Info
    URL: https://doi.org/10.16904/envidat.396
    Description: This dataset is outdated and has been replaced with an updated version (see description).

de:
Resource Title: Site photo 3
    Size: 3044052 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/938e0cc0-0f75-41da-a358-679f27c254d4/resource/74d417de-a852-41d6-a6dc-194b0d144bee/download/050.jpg
    Description: This site photo is outdated and has been replaced with an updated version (see description).

de:
Resource Title: STB2 CSV
    Size: 65410290 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/938e0cc0-0f75-41da-a358-679f27c254d4/resource/2e3b9d07-fb9e-4668-a2d0-0d3f07c7128c/download/stb2.csv
    Description: This dataset is outdated and has been replaced with an updated version (see description).

de:
Resource Title: Overview plot
    Size: 440583 bytes
    Format: image/png
    URL: https://www.envidat.ch/dataset/938e0cc0-0f75-41da-a358-679f27c254d4/resource/81d220e5-4cc5-4ac8-8e9d-f5eb149fbfa5/download/stillbergclimate.png
    Description: This overview plot is outdated and has been replaced with an updated version (see description).

format: PNG

media_type: image/png

atmospheric-conditions

display_name: atmospheric-conditions

radiation

display_name: radiation

temperature-profiles

display_name: temperature-profiles

wind

display_name: wind


en:
This repository contains data required for reproducibility of the results to be published in the associated manuscript.
Apart from reproducibility, the attached datasets also serve as templates for new users to adopt CRYOWRF in their research.
The datasets consist of two folders organized in zip format:
1. REPRODUCIBILITY_SIMULATION:
    Consists of namelists for WPS, WRF and SNOWPACK to reproduce simulations published in the manuscript
    Additional files include datasets (from IMAU-FDM / RACMO, see "credits" below ) as well as helper python scripts to
    produce *.sno files
    which are used as initial conditions for SNOWPACK in CRYOWRF.
2. REPRODUCIBILITY_POSTPROCESSING:
     Includes outputs of CRYOWRF and python scripts used to prepare figures in the manuscript.
Each of the folders have their own readme files for more details.
 Code
citation: Varun Sharma. (2021, July 2). vsharma-next/CRYOWRF: CRYOWRF v1.0 (Version v1.0). Zenodo. http://doi.org/10.5281/zenodo.5060165
location: https://gitlabext.wsl.ch/atmospheric-models/CRYOWRF     (stable releases / institutional repo)
               https://github.com/vsharma-next/CRYOWRF                       (dev branches  / developer repo)

 Publication
**Introducing CRYOWRF v1.0: Multiscale atmospheric flow simulations with advanced snow cover modelling.**
Varun Sharma, Fraziska Gerber and Michael Lehning, Submitted to Geoscientific Model Development
 Acknowledgements
We thank Peter Kuipers Munneke (P.KuipersMunneke@uu.nl) for preparing and sharing outputs of IMAU-FDM and RACMO used for initial conditions for case Ia. The relevant citations for the methods through which these datasets were generated are:
* Kuipers Munneke, P., S. R. M. Ligtenberg, B. P. Y. Noël, I. M. Howat, J. E. Box, E. Mosley-Thompson, J. R. McConnell, K. Steffen, J. T. Harper, S. B. Das and M. R. van den Broeke. 2015. Elevation change of the Greenland ice sheet due to surface mass balance and firn processes, 1960-2014. The Cryosphere, 9, 2009-2025. doi:10.5194/tc-9-2009-2015
 * Ligtenberg, S. R. M., P. Kuipers Munneke, B. P. Y. Noël, and M. R. van den Broeke. 2018. Brief communication: Improved simulation of the present-day Greenland firn layer (1960-2016).  The Cryosphere, 12, doi:10.5194/tc-12-1643-2018

en: blowing-snow alps snow-atmosphere-interaction mesoscale-modelling antarctica

en: Reproducibility Dataset for CRYOWRF v1.0

de:
Resource Title: REPRODUCIBILITY_SIMULATION
    Size: 265305391 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/945c156b-42f0-4dd9-9d8d-40fd4170e766/resource/4369d742-f153-485e-8d89-788d545bae82/download/reproducibility_simulation.zip
    Description: REPRODUCIBILITY_SIMULATION.zip contains all the extraneous files required to perform the simulations in associated manuscript (see Citations #3 in README.txt). The purpose of sharing these files apart from purposes of reproducibility is to also serve as templates for users to learn the workflow of CRYOWRF and perform simulations for their own purposes.

de:
Resource Title: REPRODUCIBILITY_POSTPROCESSING
    Size: 265305391 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.232/REPRODUCIBILITY_POSTPROCESSING.zip
    Description: REPRODUCIBILITY_POSTPROCESSING.zip contains data and scripts used to produce all figures from for the associated manuscript submitted for peer review ( see section on Citations below )

The folders stand for the three case studies presented in the manuscript. Two case studies (case Ia and case Ib) are on simulations in Antarctica and one case study
case II is for simulations in the Alps.

The associated data in the form of netcdf files or text files were produced using CRYOWRF v1.0

Apart from reproducibility, the intention of sharing these scripts is also to make it easy for new users to adopt CRYOWRF and be efficient, in particular in processing snowpack outputs.

The scripts were tested on multiple machines (different OSes) with the attached YAML file (wrf_analysis.yml) to setup the conda environment. All scripts were run using spyder as the IDE.

alps

display_name: alps

antarctica

display_name: antarctica

blowing-snow

display_name: blowing-snow

mesoscale-modelling

display_name: mesoscale-modelling

snow-atmosphere-interaction

display_name: snow-atmosphere-interaction


en:
This report was prepared as one of the synthesis report chapters of the Hydro-CH2018 project of the Federal Office for the Environment (FOEN). An important feature of snow cover is the fact that its volume and duration is subject to large year-to-year fluctuations. As frozen precipitation, snow cover is nothing other than a natural water reservoir that delays precipitation to runoff and is thus of outstanding importance for the seasonal water balance in Switzerland. Over a whole year, approximately 40% (22 km3) of the annual runoff  currently comes from snow melting and only 1% from glacier melting. Typically, the snow cover in the Alpine region builds up over the autumn and winter months, reaches its maximum between February and May, depending on the altitude, and dominates the runoff processes during melting in the following spring and summer months.
Due to the great dependence on minus temperatures and precipitation, the snow cover reacts sensitively to temperatures above 0° Celsius and more or less precipitation. Due to climate change and the associated warming, the proportion of precipitation that falls as snow decreases measurably. In addition to this reduction in snowfall, the warmer temperatures also cause the snow cover to melt more quickly. The decline in snowfall has so far mainly affected lower altitudes, where winter temperatures often reach positive levels.
As climate change progresses, this trend is likely to continue and above all affect higher zones. Even at higher altitudes, the snow cover will then start later, melt away earlier and is increasingly no longer permanently present. This development will also have an effect on the water bodies. Today nival regimes, i.e. regimes shaped by snow, are shifting towards pluvial regimes, i.e. regimes dominated by rain. Overall, winter runoff increases, summer runoff decreases. By the end of the century, the proportion of runoff from snowmelt will decrease throughout Switzerland, albeit to a lesser extent than the proportion from glacier melt.

en: hydrology snowfall snow climate-change-impacts water-cycle

envidat

de: EnviDat ist das Datenportal der WSL für einen einheitlichen und verwalteten Zugang zu Umweltdaten aus Monitoring- und Forschungsprojekten. Auf dem Portal können Datensätze gehostet und veröffentlicht werden. Durch die weltweite Bereitstellung einzigartiger und wertvoller Datensätze leistet EnviDat einen Beitrag zur Forschung im Bereich der Umweltwissenschaften.

en: Snow and the water cycle in a changing climate

de:
Resource Title: Synthesis report chapter
    Size: 1271280 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/b9bc50a3-b45a-4afb-8468-5ad7feda33d8/resource/d3f00a2f-f06a-463d-975a-a05ce0036bfa/download/snow-hydro-ch2018-synthesis-report-chapters-future-changes-in-hydrology.pdf
    Description:

de:
Resource Title: DORA Persistent URL
    Size: 1 bytes
    Format: No Info
    URL: https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:23389
    Description:

climate-change-impacts

display_name: climate-change-impacts

hydrology

display_name: hydrology

snow

display_name: snow

snowfall

display_name: snowfall

water-cycle

display_name: water-cycle


en:
This dataset contains long-term snow water equivalent and corresponding snow depth data 11 observer sites in Switzerland between 1200 and 2500 m a.s.l. compiled for the Global Climate Observing System (GCOS) and supported by MeteoSwiss. Snow depth (cm) and snow water equivalent (mm) are manually recorded every 2 weeks since the 1947 (depending on station). The attached metadata file gives details for each station.
The measurement series agree with GCOS objectives according to the GCOS Implementation Plan: This inlcudes:
• Raw data are archived in the snow and avalanche database at SLF.
• Measuring techniques are traceable and documented as snow depth and snow water equivalent have in general remained the same since beginning up to now. When planning new systems or changes of existing systems in the future, their impact will be assessed prior to implementation.
• Historical data of these 11 stations have been digitized and all data have been quality controlled.
• Detailed metadata (location of measurements) are available.
• Public availability of the data has been ensured by publishing the data on the Envidat portal (https://www.envidat.ch/dataset/gcos-swe-data).

en: snow-profile snow-height snow-water-equivalent gcos snow

envidat

de: EnviDat: das Umwelt-Datenportal

de: EnviDat

en: GCOS SWE data from 11 stations in Switzerland

envi

de: Umwelt

de:
Resource Title: DATA ACCESS
    Size: 343040 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?prefix=doi/15/GCOS/
    Description:

de:
Resource Title: STATION METADATA
    Size: 8049 bytes
    Format: text/plain
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/15/GCOS/metadata.txt
    Description:

gcos

display_name: gcos

snow-height

display_name: snow-height

snow-profile

display_name: snow-profile

snow-water-equivalent

display_name: snow-water-equivalent


en:
Meteorological and soil moisture measurements from soil moisture stations installed from October 2010 - October 2013 in the area surrounding Davos, in particular in the Dischma catchment.
There are in total 7 stations: 1202, 1203, 1204, 1205, 222, 333 and SLF2. For each of the stations, there is a:
* vwc_[stn].smet: containing the soil moisture measurements
* station_[stn].smet: in-situ measured meteorlogical parameters. Note, the quality of these measurements for stations 1202, 1203, 1204 and 1205 is very low, with data gaps. Use this data with care. For stations 222, 333 and SLF2, data quality is high and only the default cautiousness should be applied.
* interpolatedmeteo_[stn].smet contains per stations a dataset derived by interpolating from several stations in the Davos area to the stations location. This dataset was generated from the output of the Alpine3D model, of which simulations are presented in the Wever et al. (2017) manuscript.
At the soil moisture measurement sites, Decagon 10HS sensors were installed, at 10, 30, 50, 80 and 120 cm depth. Per depth 2 sensors were installed, labelled A and B in the datafiles. Note that at stations 1203, 1204 and 1205, sensors were only installed at 10, 30 and 50 cm depth.
The files follow the SMET format: https://models.slf.ch/docserver/meteoio/SMET_specifications.pdf and metadata for the stations can be found in the header of the smet files.
Please cite the Wever et al. (2017) reference when using this data in publications.
For a more detailed description, please refer to: Wever, N., Comola, F., Bavay, M., and Lehning, M.: Simulating the influence of snow surface processes on soil moisture dynamics and streamflow generation in an alpine catchment, Hydrol. Earth Syst. Sci., 21, 4053-4071, https://doi.org/10.5194/hess-21-4053-2017, 2017.

en: meteo-station soil-moisture davos meteorological-measurements dischma-catchment

envidat

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: EnviDat: the Environmental Data Portal

en: IRKIS Soil moisture measurements Davos

davos

display_name: davos

dischma-catchment

display_name: dischma-catchment

meteo-station

display_name: meteo-station

meteorological-measurements

display_name: meteorological-measurements

soil-moisture

display_name: soil-moisture


en: The instrument (MRR, Metek) was mounted at Gotschnagrat (LON: 46.859 LAT: 9.849) at a height of 1 m above snow surface (at the beginning of the campaign) with an orientation of 22° with respect to North and a horizontal viewing direction. The sampling time was either 5 s or 10 s, depending on the settings at the specific period.  The MRR produces standard outputs like radar reflectivity, doppler velocity, etc., and additional information can be found [here](https://metek.de/de/product/mrr-2/).

en: mountain precipitation radar raclets rain snow

en: Snow Drift Station - Micro Rain Radar

envi

en: Environment

de:
Resource Title: MRR data of period 3
    Size: 340845550 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.113/raclets/Period3.zip
    Description: The dataset contains measurements between March 8 and March 13, 2019, with time in UTC. The parameters and specifications are given in MRR_parameters.pdf. The zip folder contains hourly data files in netCDF format and a log file, reporting the instrument status.

de:
Resource Title: MRR data Period 2
    Size: 720723546 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.113/raclets/Period2.zip
    Description: The dataset contains measurements between March 5 and March 8, 2019, with time in UTC. The parameters and specifications are given in MRR_parameters.pdf. The zip folder contains hourly data files in netCDF format and a log file, reporting the instrument status.

de:
Resource Title: MRR data of period 4
    Size: 517071441 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.113/raclets/Period4.zip
    Description: The dataset contains measurements between March 13 and March 22, 2019, with time in UTC. The parameters and specifications are given in MRR_parameters.pdf. The zip folder contains hourly data files in netCDF format and a log file, reporting the instrument status.

de:
Resource Title: MMR Data Period 1
    Size: 2576980378 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.113/raclets/Period1.zip
    Description: The dataset contains measurements between March 1 and March 5, 2019, with time in UTC. The parameters and specifications are given in MRR_parameters.pdf. The zip folder contains hourly data files in netCDF format and a log file, reporting the instrument status.

de:
Resource Title: Parameter MRR
    Size: 93093 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/898d94a9-7a48-4fdc-9eda-43f536e39285/resource/cc39db24-81eb-4110-8a71-d7b04d5f56e6/download/mrr_parameters.pdf
    Description: The PDF contains additional information on parameters which were set for the certain time periods (time in UTC).

mountain

display_name: mountain

precipitation

display_name: precipitation

raclets

display_name: raclets

radar

display_name: radar

rain

display_name: rain


en:
Overview
The CH2014-Impacts initiative is a concerted national effort to describe impacts of climate change in Switzerland quantitatively, drawing on the scientific resources available in Switzerland today. The initiative links the recently developed Swiss Climate Change Scenarios CH2011 with an evolving base of quantitative impact models. The use of a common climate data set across disciplines and research groups sets a high standard of consistency and comparability of results. Impact studies explore the wide range of climatic changes in temperature and precipitation projected in CH2011 for the 21st century, which vary with the assumed global level of greenhouse gases, the time horizon, the underlying climate model, and the geographical region within Switzerland. The differences among climate projections are considered using three greenhouse gas scenarios, three future time periods in the 21st century, and three climate uncertainty levels (Figure 1). Impacts are shown with respect to the reference period 1980-2009 of CH2011, and add to any impacts that have already emerged as a result of earlier climate change.
 Experimental Setup
Future snow cover changes are simulated with the physics-based model Alpine3D (Lehning et al., 2006). It is applied to two regions: The canton of Graubünden and the Aare catchment. These domains are modeled with a Digital Elevation Model (DEM) with a resolution of 200 m × 200 m. This defines the simulation grid that has to be filled with land cover data and downscaled meteorological input data for each cell for the time period of interest at hourly resolution. The reference data set consists of automatic weather station data. All meteorological input parameters are spatially interpolated to the simulation grid. The reference period comprises only thirteen years (1999–2012), because the number of available high elevation weather stations for earlier times is not sufficient to achieve unbiased distribution of the observations with elevation. The model uses projected temperature and precipitation changes for all greenhouse gas scenarios (A1B, A2, and RCP3PD) and CH2011 time periods (2035, 2060, and 2085).
 Data
Snow cover changes are projected to be relatively small in the near term (2035) (Figure 5.1 top), in particular at higher elevations above 2000 m asl. As shown by Bavay et al. (2013) the spread in projected snow cover for this period is greater between different climate model chains (Chapter 3) than between the reference period and the model chain exhibiting the most moderate change. In the 2085 period much larger changes with the potential to fundamentally transform the snow dominated alpine area become apparent (Figure 5.1 bottom). These changes include a shortening of the snow season by 5–9 weeks for the A1B scenario. This is roughly equivalent to an elevation shift of 400–800 m. The slight increase of winter precipitation and therefore snow fall projected in the CH2011 scenarios (with high associated uncertainty) can no longer compensate for the effect of increasing winter temperatures even at high elevations. In terms of Snow Water Equivalents (SWE), the projected reduction is up to two thirds toward the end of the century (2085). A continuous snow cover will be restricted to a shorter time period and/or to regions at increasingly high elevation. In Bern, for example, the number of days per year with at least 5 cm snow depth will decrease by 90% from now 20 days to only 2 days on average.

en: Alpine3D simulations of future climate scenarios CH2014

de:
Resource Title: Dataset
    Size: 35433480192 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?prefix=doi/12/ch2014/
    Description: Datasets are grouped under experiment "Aare" and "Graubunden".Data is also made available in netCDF format. A range of tools are available to [read netCDF](http://www.unidata.ucar.edu/software/netcdf/software.html).

de:
Resource Title: Aare input
    Size: 360212 bytes
    Format: application/x-tar
    URL: https://www.envidat.ch/dataset/c8696023-5622-481d-952a-13f88c35e9fe/resource/ad886f54-bc18-4972-9fd0-db9bda3f7dd5/download/aare_input.tar.bz2
    Description: Grid input data for the 'Aare' CH2014 simulation.

de:
Resource Title: CH2014 REPORT
    Size: 0
    Format: No Info
    URL: http://ch2014-impacts.ch/
    Description: CH2014-Impacts: Toward Quantitative Scenarios of Climate Change Impacts in Switzerland

The CH2014-Impacts report presents the results of a concerted effort of 15 Swiss research institutions to describe impacts of climate change in Switzerland quantitatively. This dataset contributes to the Crosphere/Snowcover section.

de:
Resource Title: Graubunden input
    Size: 1208440 bytes
    Format: application/x-tar
    URL: https://www.envidat.ch/dataset/c8696023-5622-481d-952a-13f88c35e9fe/resource/41555ebb-435b-40a3-b338-826e1c3172e3/download/graubunden_input.tar.bz2
    Description: Grid input data for the 'Graubunden' CH2014 simulation.

climate-change

display_name: climate-change

snow-depth

display_name: snow-depth


en:
This dataset contains measurement and simulation data. The measurements characterize the standard meteorology, turbulence, and snow transport at the S17 site near Syowa Station in East Antarctica during an expedition in austral summer 2018/2019. Large-eddy simulations with sublimating particles provide additional insight into the latent and sensible heat exchange between snow and air in two example situations observed at the S17 site.
A part of the measurement data was recorded by an automatic measurement station from 10th January 2019 to 26th January 2019. This measurement station was equipped with standard meteorological sensors, a three-dimensional ultrasonic anemometer, an open-path infrared gas analyzer, a snow particle counter, an infrared radiometer for measurements of the surface temperature, and a sonic ranging sensor measuring changes in snow surface elevation.
At a horizontal distance of approximately 500 m, a Micro Rain Radar (MRR) was installed in a tilted configuration with an elevation angle of 7° for remote sensing of blowing snow between 25th December 2018 and 24th January 2019. In addition, near-surface in-situ measurements of snow transport were performed at the location of the MRR by deploying a snow particle counter from 27th December 2018 to 24th January 2019.
The simulations cover a 18 x 18 x 6 m³ domain and reproduce the steady-state conditions during a 10-min interval with significant snow transport and another 10-min interval with negligible snow transport. We provide the model source code and the post-processed simulation data, i.e., horizontally averaged quantities as a function of height and time.

en: antarctica eddy-covariance-measurements mass-flux blowing-snow large-eddy-simulations turbulence micro-rain-radar meteorological-data drifting-snow

envidat

de: EnviDat ist das Datenportal der WSL für einen einheitlichen und verwalteten Zugang zu Umweltdaten aus Monitoring- und Forschungsprojekten. Auf dem Portal können Datensätze gehostet und veröffentlicht werden. Durch die weltweite Bereitstellung einzigartiger und wertvoller Datensätze leistet EnviDat einen Beitrag zur Forschung im Bereich der Umweltwissenschaften.

en: Meteorology and snow transport at S17 near Syowa, Antarctica, in austral summer 2018/2019

de:
Resource Title: LES-LSM_code
    Size: 226304 bytes
    Format: application/zip
    URL: https://gitlabext.wsl.ch/atmospheric-models/les-lsm/-/archive/master/les-lsm-master.zip
    Description: Source code of the large-eddy simulations coupled with a Lagrangian stochastic model (LES-LSM), available on GitLab.

de:
Resource Title: Measurement data from snow particle counter at MRR site
    Size: 64061921 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/0d5870d2-19c1-42c0-b6c9-46a3db93a328/resource/ea337030-1803-40d7-b7b8-afd5d58a2395/download/spc-at-mrr-site.zip
    Description: Near-surface observations of drifting snow from a Snow Particle Counter (SPC) installed at the location of the Micro Rain Radar.

de:
Resource Title: Readme
    Size: 33528 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/0d5870d2-19c1-42c0-b6c9-46a3db93a328/resource/3da99719-199e-4db7-84bc-dcef72df1c14/download/readme.txt
    Description: Meta data

de:
Resource Title: Measurement data from micro-meteorological station
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    Format: application/zip
    URL: https://www.envidat.ch/dataset/0d5870d2-19c1-42c0-b6c9-46a3db93a328/resource/94aad4d7-ade0-4e12-90bd-54a37db679bb/download/micro-met-station.zip
    Description: Automatic in-situ measurements of micro-meteorology and snow transport

de:
Resource Title: Simulation data for a situation with negligible snow transport
    Size: 1991241 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/0d5870d2-19c1-42c0-b6c9-46a3db93a328/resource/bb6b912f-cc5c-4c21-81c2-e493004bf916/download/les-lsm_nodrift.zip
    Description: Results from two LES-LSM simulations called NoDrift_1 and NoDrift_2, which reproduce the conditions observed at the S17 site on 12th January 2019, 05:40 to 05:50 UTC.

de:
Resource Title: Measurement data from Micro Rain Radar (MRR)
    Size: 36829344563 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.237/MRR/MRR.zip
    Description: Remote sensing data of blowing snow from a Micro Rain Radar (MRR).

de:
Resource Title: Simulation data for a situation with significant snow transport
    Size: 3687791 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/0d5870d2-19c1-42c0-b6c9-46a3db93a328/resource/a1da4a3a-f9d6-4445-a542-8f09bc5e03ca/download/les-lsm_drift.zip
    Description: Data from two LES-LSM simulations called Drift_1 and Drift_2, which reproduce the conditions observed at the S17 site on 11th January 2019, 00:40 to 00:50 UTC.

antarctica

display_name: antarctica

blowing-snow

display_name: blowing-snow

drifting-snow

display_name: drifting-snow

eddy-covariance-measurements

display_name: eddy-covariance-measurements

large-eddy-simulations

display_name: large-eddy-simulations

mass-flux

display_name: mass-flux

meteorological-data

display_name: meteorological-data

micro-rain-radar

display_name: micro-rain-radar

turbulence

display_name: turbulence


en:
This data sets contains the Microsoft Kinect data from 15 wind tunnel experiments on wind-packing / wind crust formation. These experiments were performed in the winter 2016/17. The Kinect measures distributed snow depth.
The Kinect data is organized per experiment. Each experiment subfolder contains the processed Kinect depth images and some additional files.
Please refer to the README for more details on the data. The processing scripts are available for download as well. The scripts are mainly provided as documentation and would need to be adjusted to be used.
This dataset is the basis of the following publication:
Sommer C.G., Lehning M. & Fierz C. (2018). Wind Tunnel Experiments: Influence of Erosion and Deposition on Wind-Packing of New Snow. Front. Earth Sci. 6:4. doi: 10.3389/feart.2018.00004

en: wind-tunnel snow wind-packing kinect wind-crust

envidat

de: EnviDat: das Umwelt-Datenportal

de: EnviDat

en: Wind crust formation: Microsoft Kinect data

envi

de: Umwelt

de:
Resource Title: README
    Size: 967 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/d57b3332-804d-463b-bbf8-0ed38593e180/resource/76c0e984-004d-4524-9f2f-30005a955d83/download/readme.txt
    Description:  Describes the dataset in detail

de:
Resource Title: Processing scripts
    Size: 35366 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/d57b3332-804d-463b-bbf8-0ed38593e180/resource/28bfe352-df8a-423e-ae2c-d938b0c2a4c1/download/processingscripts.zip
    Description:  Contains the Matlab- and R-scripts used to process the Kinect data

de:
Resource Title: Kinect data
    Size: 3221225472 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?prefix=doi/22/windCrustFormation_kinectData/
    Description:

kinect

display_name: kinect

snow

display_name: snow

wind-crust

display_name: wind-crust

wind-packing

display_name: wind-packing

wind-tunnel

display_name: wind-tunnel


en:
This dataset contains the SnowMicroPen (SMP) data from 38 wind tunnel experiments on wind-packing / wind crust formation. These experiments were performed in the winters 2015/16 and 2016/17 and include more than 1000 SMP measurements.
The SMPs are organized per experiment. Each experiment subfolder contains the processed SMP profiles and some additional files.
Please refer to the README for more details on the data. The processing scripts are available for download as well. The scripts are mainly provided as documentation and would need to be adjusted to be used.
This dataset is the basis of the following publication:
Sommer C.G., Lehning M., & Fierz C. (2017). Wind tunnel experiments: Saltation is necessary for wind-packing. Journal of Glaciology, 63(242), 950-958. doi:10.1017/jog.2017.53

en: wind-crust snowmicropen snow smp wind-packing

en: Wind crust formation: SnowMicroPen data

de:
Resource Title: README
    Size: 2701 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/9918d77c-3176-453f-b34c-062ba979144a/resource/551fe7aa-af53-44a5-8d6f-615b5891a696/download/readme.txt
    Description: Describes the dataset in detail

de:
Resource Title: SMP data
    Size: 536870912 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?prefix=doi/21/windCurstFormation_smpData/
    Description:

de:
Resource Title: Processing scripts
    Size: 12976 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9918d77c-3176-453f-b34c-062ba979144a/resource/f4d23fd3-364b-4574-bc54-5fc07aae4d35/download/processingscripts.zip
    Description: Contains the R-scripts used to process the SMPs

smp

display_name: smp

snowmicropen

display_name: snowmicropen


en:
This dataset contains the data acquired during the expedition to Princess Elisabeth Antarctica Station in December 2016 and January 2017. The dataset consits of meterorological data, drifting snow mass flux data, SnowMicroPen data and Terrestrial Laser Scanning data.
Please refer to the README for more information about the data.
This dataset is the basis of the following publication:
Sommer, C. G., Wever, N., Fierz, C., and Lehning, M.: Wind-packing of snow in Antarctica, The Cryosphere Discuss., https://doi.org/10.5194/tc-2018-36, in review, 2018.

en: snowmicropen meteorological-data drifting-snow antarctica mass-flux terrestrial-laser-scanning

envidat

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

de: EnviDat ist das Datenportal der WSL für einen einheitlichen und verwalteten Zugang zu Umweltdaten aus Monitoring- und Forschungsprojekten. Auf dem Portal können Datensätze gehostet und veröffentlicht werden. Durch die weltweite Bereitstellung einzigartiger und wertvoller Datensätze leistet EnviDat einen Beitrag zur Forschung im Bereich der Umweltwissenschaften.

en: EnviDat: the Environmental Data Portal

en: Expedition to Princess Elisabeth Antarctica Station, 2016/2017

de:
Resource Title: README
    Size: 3534 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/b5c9438a-83c3-4afb-8f37-b3ec18d3db6e/resource/f5b4224d-6a68-4090-b0d6-24234f698bb5/download/readme.txt
    Description: Describes the dataset in detail

de:
Resource Title: Dataset
    Size: 697932186 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?prefix=doi/envidat.30/princessElisabethStation_2016_2017/
    Description:

terrestrial-laser-scanning

display_name: terrestrial-laser-scanning