wasserschloss-europas

en:
*english description below
Die Schweiz als „Wasserschloss Europas“ ist eine etablierte Bezeichnung im öffentlichen Diskurs, die sich im Laufe der Zeit zu einer identitätsstiftenden Metapher entwickelt hat. Die Zeitungsanalyse der letzten 100 Jahre zeigt, wie sich die Verwendung dieser Metapher unter dem Einfluss äusserer Faktoren gewandelt hat. Das Jahr mit den meisten Zeitungsartikeln, in denen die Metapher verwendet wurde, war der Hitzesommer 2003. Weitere Jahre mit häufigem Vorkommen der Metapher fallen mit internationalen Ausstellungen (Expos) zusammen, bei denen sich die Schweiz als „Wasserschloss Europas“ präsentierte oder entsprechende Bezüge herstellte. Dabei diente die Metapher einerseits als Symbol für den Wasserreichtum des Landes, andererseits unterstrich sie die Rolle der Schweiz im Umweltschutz, deren Verantwortung gegenüber den Unterliegern; zudem diente sie der touristischen Vermarktung der Naturlandschaft. Lange Zeit galt der Wasserreichtum in der Schweiz als selbstverständlich, angesichts der rasanten Klimaerwärmung und des damit einhergehenden steigenden Nutzungsdrucks auf die Ressource Wasser und die aquatischen Lebensräume erscheint die Verwendung dieser beschaulichen Metapher als überholt.
Switzerland, as the “Wasserschloss Europas”, is an established term in public discourse that has evolved over time into an identity-shaping metaphor. A newspaper analysis spanning the last 100 years shows how the use of this metaphor has changed in response to external factors. The year with the highest number of newspaper articles employing the metaphor was the heatwave summer of 2003. Other years with high numbers of occurrences coincide with international exhibitions (Expos), where Switzerland presented itself as the “Wasserschloss Europas” or made related references. The metaphor served as a symbol of the country’s abundance of water and, highlighted Switzerland’s role in environmental protection and its responsibility towards downstream countries; it also supported the tourist marketing of its natural landscapes. For a long time, Switzerland’s water wealth was taken for granted. In view of rapid climate warming and the resulting increasing pressure on water resources and aquatic habitats, the use of this idyllic metaphor now seems outdated.

en: wasserressourcen metapher zeitungsartikelanalyse wasserschloss wasserwirtschaft

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: Wasserschloss Europas

title_for_slug: wasserschloss-europas

envi

en: Environment

en:
Resource Title: Kodierungsschema_Wasserschloss_Europas.pdf
    Size: 56023 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/83825ceb-c5ca-4c11-882e-235fd121734b/resource/1ff678b7-aa77-47ae-8fa7-7f78fbc5af34/download/kodierungsschema_wasserschloss_europas.pdf
    Description: Kodierungsschema für die Zeitungsartikelanalyse.

en:
Resource Title: Wasserschloss_Europas.csv
    Size: 128150 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/83825ceb-c5ca-4c11-882e-235fd121734b/resource/73611293-12d3-496b-a721-85b5d0eba126/download/wasserschloss_europas.csv
    Description:

en:
Resource Title: Wasserschloss_Europas.xlsx
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    Description: Sammlung der 226 verwendeten Zeitungsartikel.

metapher

display_name: metapher

wasserressourcen

display_name: wasserressourcen

wasserschloss

display_name: wasserschloss

wasserwirtschaft

display_name: wasserwirtschaft

zeitungsartikelanalyse

display_name: zeitungsartikelanalyse


$oid: OBSOLETE

en: These datasets and code support research on the community assemblage of aquatic macroinvertebrates in ephemeral microhabitats, specifically water-filled tree holes (WTH), in three biogeographically and climatically distinct old-growth forests: Temperate-Mediterranean (France), Neotropical (Brazil) and Palaeotropical (India). By integrating standardised biological sampling, detailed environmental measurements and spatial analysis, the dataset allows for an in-depth examination of how local habitat conditions and spatial configuration influence taxonomic and functional beta-diversity patterns. The data capture the variation in species composition and ecological traits in 35 WTH per forest site and are particularly important for understanding how environmental filtering and spatial processes shape beta-diversity in natural forest ecosystems.

en: taxonomic-diversity community-assembly environmental-factors functional-diversity beta-diversity metacommunity water-filled-tree-holes spatial-factors

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.

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

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: Spatial factors shape taxonomic and functional beta-diversity in water-filled tree holes in different biogeographical regions

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

fr: Environnement

it: Ambiente

en:
Resource Title: Fig_1_Data.csv
    Size: 637 bytes
    Format: text/csv
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.673/Fig_1_Data.csv
    Description: This dataset contains values of taxonomic and functional β-diversity for each study site, along with their respective components: species richness difference vs. turnover (taxonomic), and functional richness difference vs. functional turnover (functional). Used to generate Fig. 1.

format: CSV

media_type: text/csv

en:
Resource Title: EnvironmentalVariables.csv
    Size: 12854 bytes
    Format: text/csv
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.673/EnvironmentalVariables.csv
    Description: This dataset includes physical and environmental characteristics of water-filled tree holes (WTHs), such as water depth, height, opening size, hole origin and orientation, circumference at breast height, WTH type, and water volume. It also contains physico-chemical parameters, nutrient concentrations (ammonium, nitrogen, phosphate, sulfate, chloride), CDOM, Chlorophyll-A, turbidity, and dry organic matter (FPOM, CPOM, total).

en:
Resource Title: Beta_diversity_analysis_across_sites_code.R
    Size: 43312 bytes
    Format: text/x-r
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.673/Beta_diversity_analysis_across_sites_code.R
    Description: Code to do the analysis and figures of the paper.

en:
Resource Title: CommunitiesFinal.csv
    Size: 12550 bytes
    Format: text/csv
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.673/CommunitiesFinal.csv
    Description: This dataset describes the animal community composition sampled from 35 water-filled tree holes (WTHs) per site. It includes abundance and taxonomic identification (to morphospecies level) of aquatic macroinvertebrates and Chordata.

en:
Resource Title: TraitBrazil.csv
    Size: 2078 bytes
    Format: text/csv
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.673/TraitBrazil.csv
    Description: This dataset contains trait information used to characterize the macroinvertebrates in our study, for the Brazilian forest plot. The dataset includes seven trait groups: aquatic developmental stage, maximum body size, locomotion, dispersal mode, respiration mode, feeding group, and cohort production interval. These trait groups are further divided into 32 distinct categories.

en:
Resource Title: CoordinatesIndia.csv
    Size: 1027 bytes
    Format: text/csv
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.673/CoordinatesIndia.csv
    Description: This dataset provides the geographic coordinates (latitude and longitude) of each sampled water-filled tree hole (WTH) for the Indian forest plot.

en:
Resource Title: Metadata.xlsx
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    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.673/Metadata.xlsx
    Description: This Excel file contains metadata that provides detailed information about all other files included here. It describes the ten accompanying datasets, outlining the type of data each contains, the variables used, their descriptions, and the units of measurement (where applicable).

format: XLS

en:
Resource Title: TraitFrance.csv
    Size: 1123 bytes
    Format: text/csv
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.673/TraitFrance.csv
    Description: This dataset contains trait information used to characterize the macroinvertebrates in our study, for the French forest plot. The dataset includes seven trait groups: aquatic developmental stage, maximum body size, locomotion, dispersal mode, respiration mode, feeding group, and cohort production interval. These trait groups are further divided into 32 distinct categories.

en:
Resource Title: Fig_2_Data.csv
    Size: 3183 bytes
    Format: text/csv
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.673/Fig_2_Data.csv
    Description: This dataset includes results from  variation partitioning analysis used to assess the relative contributions of environmental and spatial factors to taxonomic and functional β-diversity and their components across biogeographic regions. It contains site-level values for pure and shared effects of predictors. Used to generate Fig_2.

en:
Resource Title: CoordinatesBrazil.csv
    Size: 1010 bytes
    Format: text/csv
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.673/CoordinatesBrazil.csv
    Description: This dataset provides the geographic coordinates (latitude and longitude) of each sampled water-filled tree hole (WTH) for the Brazilian forest plot.

en:
Resource Title: CoordinatesFrance.csv
    Size: 983 bytes
    Format: text/csv
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.673/CoordinatesFrance.csv
    Description: This dataset provides the geographic coordinates (latitude and longitude) of each sampled water-filled tree hole (WTH) for the French forest plot.

en:
Resource Title: TraitIndia.csv
    Size: 1932 bytes
    Format: text/csv
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.673/TraitIndia.csv
    Description: This dataset contains trait information used to characterize the macroinvertebrates in our study, for the Indian forest plot. The dataset includes seven trait groups: aquatic developmental stage, maximum body size, locomotion, dispersal mode, respiration mode, feeding group, and cohort production interval. These trait groups are further divided into 32 distinct categories.

beta-diversity

display_name: beta-diversity

community-assembly

display_name: community-assembly

environmental-factors

display_name: environmental-factors

functional-diversity

display_name: functional-diversity

metacommunity

display_name: metacommunity

spatial-factors

display_name: spatial-factors

taxonomic-diversity

display_name: taxonomic-diversity

water-filled-tree-holes

display_name: water-filled-tree-holes


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

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: Sentinel-2 Vegetation Height Model Armenia

envi

en: Environment

en:
Resource Title: S2-VHM_2023_max.tif
    Size: 3189013217 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/d5dd3a3d-093e-4206-9e0b-76c6f21c9be1/resource/692904ba-e575-4e69-8950-288ffe11a952/download/s2-vhm_2023_max.tif
    Description:

format: TIFF

media_type: image/tiff

en:
Resource Title: S2-VHM_2020_max.tif
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    Format: image/tiff
    URL: https://www.envidat.ch/dataset/d5dd3a3d-093e-4206-9e0b-76c6f21c9be1/resource/1ccbef32-82b2-463e-a109-80f4f40037cd/download/s2-vhm_2020_max.tif
    Description:

en:
Resource Title: S2-VHM_2025_max.tif
    Size: 3167538381 bytes
    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:

en:
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:

en:
Resource Title: S2-VHM_2021_max.tif
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    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:

en:
Resource Title: S2-VHM_2019_max.tif
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    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:

en:
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:

en:
Resource Title: S2-VHM_2017_max.tif
    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:

en:
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:

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:
This repository hosts the experimental data accompanying our publication "The influence of snow microstructure on the compressive mechanical properties of weak snowpack layers" feautured in Acta Materialia.
- Experimental Data and microstructural descriptors:
Full dataset used for the main analysis of the paper. Contains the measured mechanical properties, µCT parameters, further analysis, etc. The column "ID" (e.g. 363)  links this data with the 3D µCT dataset  (e.g. e0000363).
- 3D Data of the segmented µCT scans:
Full dataset containing the raw segmented µCT scans in the MetaIO format. They can be opened using the SimpleITK package (`pip install SimpleITK`).
Note: the Files can be idenfied by their scan number in the filename, (e.g, e0000363)
 `import SimpleITK as sitk `
 `img = sitk.ReadImage("e0000363_crop_seg.mhd") `
 `array = sitk.GetArrayFromImage(img) `  shape: [z, y, x]
`array_xyz = np.transpose(array, (2, 1, 0))`  (Optional) for array axes in [x, y, z]
 `spacing = img.GetSpacing() `                  voxel size (x,y,z)

en: snow-microstructure snow-avalanche snow lab-experiment snow-mechanics

en:
Resource Title: 3D_CT_files
    Size: 6442450944 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.648%2F
    Description: MetaIO_files

en:
Resource Title: Dataset_v1.csv
    Size: 64099 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/e3ac64e4-3204-4edb-8b10-b458373d9e1f/resource/5b516f93-a87b-4315-9a02-325e2400ee81/download/dataset_v1.csv
    Description: Dataset containing both the experimental results and microstructural parameters.

lab-experiment

display_name: lab-experiment

snow

display_name: snow

snow-avalanche

display_name: snow-avalanche

snow-mechanics

display_name: snow-mechanics

snow-microstructure

display_name: snow-microstructure


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

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

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 repository hosts the experimental data accompanying our publication, “On the compressive strength of weak snow layers of depth hoar,” featured in the Journal of Glaciology.
-Compressive Strength Data:
Measurements of the compressive strength for 92 artificially grown weak snow samples.
-Microstructural CT Data:
CT-derived microstructural information for each sample, including:
Density
Specific surface area
Connectivity density
Correlation lengths
Anisotropy
-Additional CT Data:
Parent Sample Variability: CT data used to assess the variability of the parent samples.
Temporal Evolution: CT data capturing the evolution within the artificial weak layers.
Reference Data: Information on the reference CT data sourced from the RHOSSA and MOSAiC campaigns.

en: depth-hoar snow snow-mechanics microstructure slab-avalanche strength

envidat

en: EnviDat: the Environmental Data Portal

envi

en: Environment

en:
Resource Title: TimeSeriesData.csv
    Size: 4202 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/f428c8e6-191b-45d4-8b92-239b5f056338/resource/f100da36-5f10-49ba-a38d-140aed84c664/download/timeseriesdata.csv
    Description: Time series data: Contains the CT data of upper slab, weak layer and lower slab used to evaluate the temporal evolution within the artificial samples.

en:
Resource Title: ReferenceDataCT.csv
    Size: 8841 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/f428c8e6-191b-45d4-8b92-239b5f056338/resource/e8f6bc95-aff3-437e-a5a2-b04cb020f330/download/ct_references.csv
    Description: CT Reference Data: Contains sample names, positions, evaluated regions and microstructural parameters based on data from the RHOSSA and MOSAiC campaigns.

MOSAiC:
https://mosaic-portal.org/data/open-mcs/vessel/polarstern/sn_microct/sn_microct/exdata/
https://doi.org/10.1594/PANGAEA.952794

RHOSSA:
https://doi.org/10.16904/envidat.151

en:
Resource Title: ExperimentalData.csv
    Size: 5928 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/f428c8e6-191b-45d4-8b92-239b5f056338/resource/adb3a453-f03a-44cc-b74b-ff9cb12878ec/download/experimentaldata.csv
    Description: Experimental Data: Contains results of the compression experiments and microstructural parameters.

en:
Resource Title: ParentSampleVariability.csv
    Size: 1406 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/f428c8e6-191b-45d4-8b92-239b5f056338/resource/83b74fbf-3d28-4a2e-b856-92573f39977c/download/parent_sample_variability.csv
    Description: Parent sample variability: Contains the CT data used to estimate the parent sample variability.

depth-hoar

display_name: depth-hoar

microstructure

display_name: microstructure

slab-avalanche

display_name: slab-avalanche

strength

display_name: strength


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

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

en:
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

format: ZIP

media_type: application/zip

en:
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

en:
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

en:
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

en:
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

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

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

active-layer

display_name: active-layer

arctic

display_name: arctic

bacteria

display_name: bacteria

biobank

display_name: biobank

biodiversity

display_name: biodiversity

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

envidat

en: EnviDat: the Environmental Data Portal

en: Gao_Drought resistance and resilience of rhizosphere communities_13C-PLFA

envi

en: Environment

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: snowpack fsm2 snow-hydrology fsm2trans hicar dischma

en: FSM2trans snowpack simulations with HICAR input

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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

en:
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


distributed-acoustic-sensing-brienz

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 machine-learning landslide natural-hazard rockfall seismic

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: Distributed Acoustic Sensing Brienz

title_for_slug: distributed-acoustic-sensing-brienz

envi

en: Environment

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: event-analysis forest-fire europe driver observations mountains

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:
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: radiation atmospheric-conditions wind precipitation temperature-profiles

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

en:
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).

en:
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).

format: jpeg

en:
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

en:
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).

en:
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).

en:
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).

en:
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).

format: TXT

media_type: text/plain

en:
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).

atmospheric-conditions

display_name: atmospheric-conditions

precipitation

display_name: precipitation

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

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: Reproducibility Dataset for CRYOWRF v1.0

en:
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.

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

en: Snow and the water cycle in a changing climate

envi

en: Environment

en:
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:

format: PDF

media_type: application/pdf

en:
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 the population evolution of a pest and its biocontrol agent in terms of presence proportion at gall level and absolute number of insects.
The study area extends from the Cuneo region (Piedmont, Italy) to southern Switzerland. In order to provide a complete range of data covering the entire process from the pest arrival to complete biological control by its natural enemy T. sinensis, a space-for-time substitution approach has been adopted so as to create a temporal gradient of the epidemic stages over the whole study area.
The southernmost Swiss sites roughly represent the arrival and establishment of the pest without the presence of the natural enemy, the central ones the early epidemic stage and the epidemic peak, whereas the northern ones the end of the epidemic with the beginning of the biocontrol. The Italian ones represent the beginning of the equilibrium between the two population as well as the situation with stable T. sinensis populations on the long term.
These data are used in the paper entitled: Torymus sinensis local and regional early population dynamics in the Insubrian and Piedmont regions

en: biological-control pest-invasion torymus-sinensis dryocosmus-kuriphilus castanea-sativa southern-switzerland population-evolution

biological-control

display_name: biological-control

castanea-sativa

display_name: castanea-sativa

dryocosmus-kuriphilus

display_name: dryocosmus-kuriphilus

pest-invasion

display_name: pest-invasion

population-evolution

display_name: population-evolution

southern-switzerland

display_name: southern-switzerland

torymus-sinensis

display_name: torymus-sinensis


irkis-soil-moisture-measurements-davos

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

en: IRKIS Soil moisture measurements Davos

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

en: GCOS SWE data from 11 stations in Switzerland

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

en:
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


snow-drift-station-micro-rain-radar

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: radar raclets precipitation snow mountain rain

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: Snow Drift Station - Micro Rain Radar

title_for_slug: snow-drift-station-micro-rain-radar

en:
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.

en:
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).

en:
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.

en:
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.

en:
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.

mountain

display_name: mountain

precipitation

display_name: precipitation

raclets

display_name: raclets

radar

display_name: radar

rain

display_name: rain

snow

display_name: snow


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

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

envi

en: Environment

en:
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.

en:
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.

en:
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.

en:
Resource Title: Measurement data from micro-meteorological station
    Size: 402248169 bytes
    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

en:
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).

en:
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.

en:
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

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 wind-crust kinect

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: Wind crust formation: Microsoft Kinect data

en:
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

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

en:
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

kinect

display_name: kinect

wind-crust

display_name: wind-crust

wind-packing

display_name: wind-packing

wind-tunnel

display_name: wind-tunnel


wind-crust-formation-snowmicropen-data

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: snow snowmicropen wind-packing wind-crust smp

en: Wind crust formation: SnowMicroPen data

title_for_slug: wind-crust-formation-snowmicropen-data

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

en:
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

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

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

envi

en: Environment

en:
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

en:
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


en:
Overview
We present GROUNDEYE, a new model of radiative transfer over mountainous terrain, which considers for the first time the forward scattering properties of snow.
Embedded in the surface process model Alpine3D, the new terrain radiation model GROUNDEYE receives interpolated real weather data with diffuse and direct broadband shortwave radiation for each pixel as well as a spatially variable plane albedo from the module SNOWPACK.
 Format
The GROUNDEYE model is written in c++, as is the entire environment of Alpine3d. The input and output data sets are .xlsx or .txt format, pre- and postprocessing including the generation of all figures is in .R format.
 Structure
In Data_Forward_Scattering.zip you will find all necessary data and model details to
reproduce the results of the JGR publication "How forward-scattering snow and terrain change the Alpine radiation balance with application to solar panels"
 - 	Model Input Data contains the meteorological and topographic input data sets, the BRDF, and preprocessing scripts.
 -  Model Code contains the full model Alpine3d including the radiative transfer module GROUNDEYE.
 -  Model Output Data contains the results of the simulation of terrain irradiance and irradiance of solar panels; hourly resolution, 1. Sptember 2017 - 31. August 2018.
 -  Measurements Solar Testsite contains information and measurements of the solar testsite at the Totalp near Davos, Switzerland.
 -  Postprocessing contains all R-Scripts used for the analysis and
plotting of the corresponding data.
In each of these folders you will find detailed information in
the file 'About this Folder.txt'.

en: shortwave-radiation photovoltaic snow winter-energy-production albedo

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: Data, Code Link and Metadata on Forward Scattering of Snow at Totalp

albedo

display_name: albedo

photovoltaic

display_name: photovoltaic

shortwave-radiation

display_name: shortwave-radiation

snow

display_name: snow

winter-energy-production

display_name: winter-energy-production


en:
This is the simulation dataset from _"Response of snow cover and runoff to climate change in high Alpine catchments of Eastern Switzerland"_, M. Bavay, T. Grünewald, M. Lehning, Advances in Water Resources 55, 4-16, 2013
A model study on the impact of climate change on snow cover and runoff has been conducted for the Swiss Canton of Graubünden. The model Alpine3D has been forced with the data from 35 Automatic Weather Stations in order to investigate snow and runoff dynamics for the current climate. The data set has then been modified to reflect climate change as predicted for the 2021-2050 and 2070-2095 periods from an ensemble of regional climate models.
The predicted changes in snow cover will be moderate for 2021-2050 and become drastic in the second half of the century. Towards the end of the century the snow cover changes will roughly be equivalent to an elevation shift of 800 m. Seasonal snow water equivalents will decrease by one to two thirds and snow seasons will be shortened by five to nine weeks in 2095.
Small, higher elevation catchments will show more winter runoff, earlier spring melt peaks and reduced summer runoff. Where glacierized areas exist, the transitional increase in glacier melt will initially offset losses from snow melt. Larger catchments, which reach lower elevations will show much smaller changes since they are already dominated by summer precipitation.

en: switzerland snow climate-change alpine3d-simulations graubunden

en: Alpine3D simulations of future climate scenarios for Graubunden

en:
Resource Title: Configuration file
    Size: 2807 bytes
    Format: text/x-ini
    URL: https://www.envidat.ch/dataset/8bce53fb-3b5e-49fc-94ae-6331401315de/resource/e03efd3c-5966-491d-9d40-8f5366d9c4b7/download/io.ini
    Description:

en:
Resource Title: Simulation data
    Size: 0
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?prefix=doi/envidat.608/clisp_2012/
    Description: Reference data as well as the 2050 and 2099 scenarios for ARPEGE and ETHZ other RCMs available on demand)

climate-change

display_name: climate-change

graubunden

display_name: graubunden

switzerland

display_name: switzerland


en:
Acknowledgements: The CORE format was proudly inspired by the Cloud Optimized GeoTIFF ([COG](https://www.cogeo.org/)) format, by considering how to leverage the ability of clients issuing HTTP GET range requests for a time-series of remote sensing and aerial imagery (instead of just one image).
License: The Cloud Optimized Raster Encoding (CORE) specifications are released to the public domain under a Creative Commons 1.0 CC0 "No Rights Reserved" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions.
-----------------------
Summary:
The Cloud Optimized Raster Encoding (CORE) format is being developed for the efficient storage and management of gridded data by applying video encoding algorithms. It is mainly designed for the exchange and preservation of large time series data in environmental data repositories, while in the same time enabling more efficient workflows on the cloud. It can be applied to any large number of similar (in pixel size and image dimensions) raster data layers. CORE is not designed to replace COG but to work together with COG for a collection of many layers (e.g. by offering a fast preview of layers when switching between layers of a time series).
WARNING: Currently only applicable to RGB/Byte imagery. The final CORE specifications may probably be very different from what is written herein or CORE may not ever become productive due to a myriad of reasons (see also 'Major issues to be solved').
With this early public sharing of the format we explicitly support the Open Science agenda, which implies "shifting from the standard practices of publishing research results in scientific publications towards sharing and using all available knowledge at an earlier stage in the research process" (quote from: European Commission, Directorate General for Research and Innovation, 2016. Open innovation, open science, open to the world).
CORE Specifications:
1) a MP4 or WebM video digital multimedia container format (or any future video container playable as HTML video in major browsers)
2) a free to use or open video compression codec such as H.264, VP9, or AV1 (or any future video codec that is open sourced or free to use for end users)
Note: H.264 is currently recommended because of the wide usage with support in all major browsers, fast encoding due to acceleration in hardware (which is currently not the case for AV1 or VP9) and the fact that MPEG LA has allowed the free use for streaming video that is free to the end users. However, please note that H.264 is restricted by patents and its use in proprietary or commercial software requires the payment of royalties to [MPEG LA](https://www.mpegla.com/programs/avc-h-264/). However, when AV1 matures and accelerated hardware encoding becomes available, AV1 is expected to offer 30% to 50% smaller file size in comparison with H.264, while retaining the [same quality](https://trac.ffmpeg.org/wiki/Encode/AV1).
3) the encoding frame rate should be of one frame per second (fps) with each layer segmented in internal tiles, similar to COG, ordered by the main use case when accessing the data: either layer contiguous or tile contiguous;
Note: The internal tile arrangement should support an easy navigation inside the CORE video format, depending on the use case.
4) a CORE file is optimised for streaming with the moov atom at the beginning of the file (e.g. with -movflags faststart) and optional additional optimisations depending on the codec used (e.g. -tune fastdecode -tune zerolatency for H.264)
5) metadata tags inside the moov atom for describing and using geographic image data (that are preferably compatible with the [OGC GeoTIFF standard](https://www.ogc.org/standards/geotiff) or any future standard accepted by the geospatial community) as well as list of original file names corresponding to each CORE layer
6) it needs to encode similar source rasters (such as time series of rasters with the same extent and resolution, or different tiles of the same product; each input raster should be having the same image and pixel size)
7) it provides a mechanism for addressing and requesting overviews (lower resolution data) for a fast display in web browser depending on the map scale (currently external overviews)
Major issues to be solved:
- Internal overviews (similar to COG), by chaining lower resolution videos in the same MP4 container for fast access to overviews first); Currently, overviews are kept as separate files, as external overviews.
- Metadata encoding (how to best encode spatial extent, layer names, and so on, for each of the layer inside the series, which may have a different geographical extent, etc...; Known issues: adding too many tags with FFmpeg which are not part of the standard MP4 moov atom; metadata tags have a limited string length.
- Applicability beyond RGB/Byte datasets; defining a standard way of converting cell values from Int16/UInt16/UInt32/Int32/Float32/Float64/ data types into multi-band Byte values (and reconstructing them back to the original data type within acceptable thresholds)
Example
Notice: The provided CORE (.mp4) examples contain modified Copernicus Sentinel data [2018-2021].
For generating the CORE examples provided, 50 original Sentinel 2 (S-2) TCI data images from an area located inside Switzerland were downloaded from www.copernicus.eu, and then transformed into CORE format using ffmpeg with H.264 encoding using the [x264 library](https://www.videolan.org/developers/x264.html).
DISCLAIMER: Basic scripts are provided for the Geomatics peer review (in 2021) and kept as additional information for the dataset. Nevertheless, please note that software dependencies and libraries, as well as cloud storage paths, may quickly become deprecated over time (after 2021). For compatibility, stable dependencies and libraries released around 2020 should be used.

en: gridded-data research-data-management core video-encoding time-series geodata sentinel-2 raster

en:
Resource Title: View publication in the Digital Object Repository at WSL (DORA)
    Size: 1 bytes
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    URL: https://www.dora.lib4ri.ch/wsl/islandora/object/wsl%3A28180
    Description:

en:
Resource Title: extract_S2A_CORE
    Size: 3147 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/387cd755-33df-4543-ae50-529df462b94f/resource/dec6f9ce-cef3-4da1-97a2-42e2d47315a6/download/extract_s2a_core.sh.zip
    Description: Disclaimer: provided for the Geomatics peer review in 2021 and left as additional information for the dataset. Software dependencies and libraries, as well as cloud storage paths, may change over time (after 2021).

en:
Resource Title: CORE Dataset (80 GB)
    Size: 85899345920 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?prefix=doi/envidat.230/CORE_S2A/
    Description:

en:
Resource Title: reproduce_S2A_CORE
    Size: 4313 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/387cd755-33df-4543-ae50-529df462b94f/resource/5d8926a7-e8b9-4f01-a197-7edd0968e5c8/download/reproduce_s2a_core.zip
    Description: Disclaimer: provided for the Geomatics peer review in 2021 and left as additional information for the dataset. Software dependencies and libraries, as well as cloud storage paths, may change over time (after 2021).

core

display_name: core

geodata

display_name: geodata

gridded-data

display_name: gridded-data

raster

display_name: raster

research-data-management

display_name: research-data-management

time-series

display_name: time-series

video-encoding

display_name: video-encoding


en:
In fall 2019, a new set of climate change scenarios has been released for
Switzerland, the CH2018 dataset (www.climate-scenarios.ch). The data are
provided at daily resolution. We produced from the CH2018 dataset a new
set of climate change scenarios temporally downscaled at hourly resolution.
In addition, we extended this dataset integrating the meteorological stations
from the Inter-Cantonal Measurement and Information System (IMIS) network,
an alpine network of automatic meteorological stations operated by the WSL
Institute for Snow and Avalanche Research SLF.
The extension to the IMIS network is obtained using a Quantile Mapping approach
in order to perform a spatial transfer of the CH2018 scenarios from the location
of the  MeteoSwiss stations to the location of the IMIS stations. The temporal
downscaling is performed using an enhanced Delta-Change approach. This approach
is based on objective criteria for assessing the quality of the determined delta
and downscaled time series. In addition, this method also fixes a flaw of common
quantile mapping methods (such as used in the CH2018 dataset for spatial downscaling)
related to the decrease of correlation between different variables. The idea behind
the delta change approach is to take the main seasonal signal (and mean) from
climate change scenarios at daily resolution and to map it to a historical time series
at hourly resolution in order to modify the historical time series. The obtained time
series exhibit the same seasonal signal as the original climate change time series,
while it keeps the sub-daily cycle from the historical time series.
The applied methods (Quantile Mapping and Delta-Change) have limitations in
correctly representing statistically extreme events and changes in the frequency
of discontinuous events such as precipitation. In addition, the sub-daily cycle in
the data is inherited from the historical time series, so there is no information
of the climate change signal in this sub-daily cycle. A careful reading of the paper
accompanying the dataset is necessary to understand the limitations and scope of
application of this new dataset.
This material is distributed under CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/legalcode).

en: meteoswiss climate-change-scenarios switzerland alpine-regions imis delta-change quantile-mapping temporal-downscaling

en: Dataset for: Climate change scenarios at hourly time-step over Switzerland from an enhanced temporal downscaling approach

envi

en: Environment

alpine-regions

display_name: alpine-regions

climate-change-scenarios

display_name: climate-change-scenarios

delta-change

display_name: delta-change

imis

display_name: imis

meteoswiss

display_name: meteoswiss

quantile-mapping

display_name: quantile-mapping

temporal-downscaling

display_name: temporal-downscaling


en:
Data of snow and sea ice in the McMurdo Sound, October-December 2022. The data was collected as part of the New Zealand Marsden Fund Research Grant 21-VUW-103 "Can Snow Change the Fate of Antarctic Sea Ice?"
The dataset includes raw data of the manual snow and sea ice measurements from snow pits and ice cores (temperature, density, salinity, dO18), measurements of snow water equivalent (SWE), spatial information of snow height (MagnaProbe) and sea ice thickness (EM-31), AWS (air temperature, wind speed, wind direction, relative humidity, pressure), radiations stations (shortwave, longwave, thermal IR, spectral shortwave), differential GPS data (3 fixed stations on different sea ice thicknesses, + 1 rover station for georeferencing UAV measurements), SIMBA buoy temperature (+heated temperature) data (3 buoys during November, 1 buoy for 15 months),  UAV data: RGB, thermal IR, broadband albedo, spectral albedo, Chlorophyll-a from ice cores (bottom 10 cm), NIR reflectivity data of snow at 850 nm, and 940 nm (snow surface, profile, ice surface), photographs (1. overview of field sites, 2. for Structure from Motion for surface roughness, 3. macrophotos of snow) surface impurity concentrations, microCT data of snow microstructure, Denoth probe (density) and InfraSnow (specific surface area - SSA).
See the README file in each dataset for detailed information.

en: snow drone-data spatial-variability sea-ice antarctica polar

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: Snow on Antarctic Sea Ice - McMurdo Sound 2022

en:
Resource Title: manual_ice_thickness_RAW.zip
    Size: 144489 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/75532410-dca7-49a0-813c-274b2b51fa05/resource/1e651935-ae61-4b66-a486-3284fe294595/download/manual_ice_thickness_raw.zip
    Description:

en:
Resource Title: EM31_RAW.zip
    Size: 1942229 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/75532410-dca7-49a0-813c-274b2b51fa05/resource/159f3a6b-658f-4656-81b1-73b48493f232/download/em31_raw.zip
    Description:

en:
Resource Title: magnaprobe_RAW.zip
    Size: 256517 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/75532410-dca7-49a0-813c-274b2b51fa05/resource/cd15f7d9-c236-43eb-be7c-e4002ac7831e/download/magnaprobe_raw.zip
    Description:

antarctica

display_name: antarctica

drone-data

display_name: drone-data

polar

display_name: polar

sea-ice

display_name: sea-ice

snow

display_name: snow

spatial-variability

display_name: spatial-variability


en:
Meteorological station at Gemstock (3021 m asl) in Canton Uri. The station includes in/out LW/SW and a snow height sensor.
Data from this station is managed by the permos.ch project. More information: https://www.permos.ch/permafrost-monitoring/field-sites

en: shortwave-radiation gemstock meteo-station snow-height longwave-radiation

en: GEM2: Meteorological and snow station at Gemsstock (3021 m asl), Canton Uri, Switzerland

gemstock

display_name: gemstock

longwave-radiation

display_name: longwave-radiation

meteo-station

display_name: meteo-station

shortwave-radiation

display_name: shortwave-radiation

snow-height

display_name: snow-height