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

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:
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.

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: 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: 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

depth-hoar

display_name: depth-hoar

microstructure

display_name: microstructure

slab-avalanche

display_name: slab-avalanche

snow

display_name: snow

snow-mechanics

display_name: snow-mechanics

strength

display_name: strength


$oid: OBSOLETE

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

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

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

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

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

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

format: ZIP

media_type: application/zip

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

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

format: CSV

media_type: text/csv

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

habitat-heterogeneity

display_name: habitat-heterogeneity

hengduan-mountains

display_name: hengduan-mountains

hypervolume

display_name: hypervolume

model-residuals

display_name: model-residuals

phytodiversity

display_name: phytodiversity


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

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

format: XLS

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: Gao_Drought resistance and resilience of rhizosphere communities_13C-PLFA

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

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

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: FSM2trans snowpack simulations with HICAR input

en: snow-hydrology fsm2 fsm2trans hicar snowpack dischma

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: 2016.12_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: 2017.06_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

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: 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: 2017.05_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_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: 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: 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: 2017.01_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: 2017.01_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: 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: 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: 2017.01_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: 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: 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: 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: 2017.07_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: 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: 2017.03_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: 2016.12_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: 2017.04_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_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: 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: 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: 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: 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: 2017.02_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: 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: 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: 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: 2017.03_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: 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: 2016.10_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: 2017.05_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: 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: 2017.03_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_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: 2017.06_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: 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_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


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: Distributed Acoustic Sensing Brienz

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

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

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: Wildfire events and drivers across Europe

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

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


bats-in-beaver-systems

en:
This dataset contains processed bat acoustic data collected to evaluate the ecological impacts of beaver-engineered ponds on bat species richness, activity, and feeding activity. It includes nightly activity and feeding call counts identified to species or species group level, site and treatment information (beaver pond vs. control areas along the same stream), and associated environmental covariates such as deadwood volume, arthropod abundance, and vegetation structure.
Methods: Bats were recorded using passive acoustic detectors placed at paired beaver-influenced and control sites. The resulting data was processed in the software Batscope. Arthropod abundance was estimated using flight-interception traps. The amount of deadwood and vegetation cover was measured in the field. Canopy cover and canopy heterogeneity were modelled from GIS models.
Temporal and Spatial Coverage:
Data were collected from eight sites in Switzerland between June and July 2022, each site sampled for 16 nights.

en: Bats in beaver systems

en: ecosystem-engineer castor wetland nature-conservation flying-arthropods beaver chiroptera deadwood habitat-heterogeneity

title_for_slug: bats-in-beaver-systems

en:
Resource Title: sampling dates.xlsx
    Size: 10360 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/a7d16ee7-974d-42e4-a16c-36351a0ee09c/resource/6f9bce17-7b4b-4176-a3b8-8a32a0475b60/download/sampling-dates.xlsx
    Description: For the time information about the research data, created (2022-09-23 to 2022-09-23) refers to the GIS data, collected (2022-06-13 to 2022-07-31) to the field sampling. Details can be found in the csv “sampling_dates”.

sampling_dates with columns
"site" (sampling site),
"bats_1" (first period of bat survey in June),
"bats_2" (second period of bat survey in June),
"bats_3" (first period of bat survey in July),
"bats_4" (second period of bat survey in July),
"flying_arthropods_1" (arthropod sampling in June),
"flying_arthropods_2" (arthropod sampling in July),
"deadwood survey" (date of deadwood survey),
"vegetation survey" (date of vegetation survey)

en:
Resource Title: dead_wood.csv
    Size: 40298 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/a7d16ee7-974d-42e4-a16c-36351a0ee09c/resource/3985310a-835b-4fdf-a66e-5ec0a92ab490/download/dead_wood.csv
    Description: dead_wood for deadwood with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"type" (deadwood type),
"height_length" (the height or lenghth of a piece of deadwood in m),
"DBH_diamlarge" (diameter at breast height or diameter of larger end if lying in m),
"diamsmall (diameter of smaller end if lying in m",
"volume" (calculated volume in m3)

en:
Resource Title: bats_pool_size.R
    Size: 7169 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/a7d16ee7-974d-42e4-a16c-36351a0ee09c/resource/664b4516-22a3-4509-bec6-ccbd57f4ee8c/download/bats_pool_size.r
    Description: R script bats_pool_size to test the beaver pool size on bat richness, activity and feeding activity

format: OCTET

en:
Resource Title: bats_age.R
    Size: 6554 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/a7d16ee7-974d-42e4-a16c-36351a0ee09c/resource/0e7ad52e-0545-4eec-98bc-ee80e4d27a90/download/bats_age.r
    Description: R script bats_age to test the beaver pool age effect on bat richness, activity and feeding activity

en:
Resource Title: bats_biodiv.day.csv
    Size: 16681 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/a7d16ee7-974d-42e4-a16c-36351a0ee09c/resource/56b11b90-7932-434b-a089-f3ff14b3f7d7/download/bats_biodiv.day.csv
    Description: bats_biodiv.day with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"community" (species group),
"date" (sampling date),
"month" (sampling month),
"feeding" (number of feeding buzzes summed per 5 minutes and identification level), "abundance" (sequences per 5 minutes and identification level),
"richness" (species richness),
"richness_feeding" (species richness of feeding bats)

en:
Resource Title: bats_data_exploration_Figure_nighly.R
    Size: 65739 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/a7d16ee7-974d-42e4-a16c-36351a0ee09c/resource/d59bb90b-29b3-4908-a692-17fdd8e79486/download/bats_data_exploration_figure_nighly.r
    Description: R script bats_data_exploration_Figure_nighly to explore the data and display nightly species richness, activity and feeding activity

en:
Resource Title: bats_biodiv.day.guilds.csv
    Size: 53549 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/a7d16ee7-974d-42e4-a16c-36351a0ee09c/resource/78d66f28-1f5a-43f9-8cfe-e5edf12055d8/download/bats_biodiv.day.guilds.csv
    Description: bats_biodiv.day.guilds with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"community" (species group),
"date" (sampling date),
"month" (sampling month),
"guild" (which feeding guild information),
"feeding" (number of feeding buzzes summed per 5 minutes and identification level), "abundance" (sequences per 5 minutes and identification level),
"richness" (species richness),
"richness_feeding" (species richness of feeding bats)

en:
Resource Title: bats_data_cleaning.R
    Size: 28317 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/a7d16ee7-974d-42e4-a16c-36351a0ee09c/resource/9c5e13fb-5c1a-4990-af1d-576832120846/download/bats_data_cleaning.r
    Description: R script bats_data_cleaning to clean the raw data

en:
Resource Title: arthropods_flying.csv
    Size: 1822 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/a7d16ee7-974d-42e4-a16c-36351a0ee09c/resource/22214e46-c662-4398-91da-197fa844c656/download/arthropods_flying.csv
    Description: arthropods_flying the arthropod abundance with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"community" (species group),
"month" (sampling month),
"abundance" (number of flying arthropods)

en:
Resource Title: README.txt
    Size: 6009 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/a7d16ee7-974d-42e4-a16c-36351a0ee09c/resource/03d87d3e-c9f0-4e53-b1fc-531dd2087e8f/download/readme.txt
    Description: README text file providing context for the data. Please do not hesitate to contact us if you have any questions

format: TXT

media_type: text/plain

en:
Resource Title: bats_raw_values_overview.R
    Size: 8975 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/a7d16ee7-974d-42e4-a16c-36351a0ee09c/resource/f7d9750d-6dcd-4fa9-856e-c810d08c9708/download/bats_raw_values_overview.r
    Description: bats_raw_values_overview to produce raw value summaries and tables

en:
Resource Title: bats.environment.xlsx
    Size: 10844 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/a7d16ee7-974d-42e4-a16c-36351a0ee09c/resource/ad5c3240-15c9-4330-9677-9786ef447662/download/bats.environment.xlsx
    Description: bats.environment with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"canopy" (modelled tree canopy cover, averaged across radius of 100 m, unitless),
"structure_ruggedness" (mean ruggedness of the total vertical structure > 3 m, averaged across radius of 100 m, unitless), "vegetation_ruggedness" (mean ruggedness of the canopy > 3 m, later called canopy_heterogenneity, averaged across radius of 100 m, unitless),
"pool_size" (maximum width of beaver pool in m),
"area_deadwood_monitoring" (total area of deadwood moonitoring in m3),
"age" (beaver presence in years)

en:
Resource Title: gewichtungsfaktor.csv
    Size: 9392 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/a7d16ee7-974d-42e4-a16c-36351a0ee09c/resource/b3f59232-46fa-4068-ad06-15947d042c16/download/gewichtungsfaktor.csv
    Description: gewichtungsfaktor with columns
"Project" (sampling site),
"Treatment" (sampling location within site, with levels ctrl for Control and pool for Pool),
"date" (sampling date),
"gew" (weighting factor to standardize night length)

en:
Resource Title: bats_SEM.R
    Size: 69411 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/a7d16ee7-974d-42e4-a16c-36351a0ee09c/resource/d682ac4b-8266-4fc8-ad06-0cbc03709b47/download/bats_sem.r
    Description: R script bats_SEM to test the influence of the beaver on bat richness, activity and feeding activity through feeding and habitat heterogeneity

en:
Resource Title: vegetation_characteristics.csv
    Size: 2769 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/a7d16ee7-974d-42e4-a16c-36351a0ee09c/resource/f60cdb2f-d4bc-454a-9fed-058c86234b5e/download/vegetation_characteristics.csv
    Description: vegetation_characteristics data on vegetation_cover and cumulative_cover with columns "laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"Vegetation_cover" (estimated vegetation cover over 5x1 m plot in %),
"Cumulative cover" (summed up cover over tree, shrub and herb layer in %),
"Cumulative cover trees" (tree cover level, higher than 3 metres in %),

en:
Resource Title: data_bats.xlsx
    Size: 7453297 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/a7d16ee7-974d-42e4-a16c-36351a0ee09c/resource/bab1affd-1f46-41bd-b405-364af91da886/download/data_bats.xlsx
    Description: Raw data data_bats with columns:
“Categories” (empty or "feeding", indicating that call sequence contains feeding buzz)
“Sequence” (sequence number)
“Timestamp” (recording time)
“Temperature” (temperature recorded by batlogger in degree celcius)
“Serial” (serial number of bat logger)
“Duration” (duration of call sequence in seconds)
“Call.Count” (call count number, how many calls could be detected)
“Verification.1” (bat sequence verification)
“Notes” (remarks for identification)
“treatment” (treatment ctrl = Control and Pool)
“slot” (slot number, 4 slots, two in June, two in July)
“Laufnummer” (unique identifier per sampling point)

en:
Resource Title: bat_guilds.CSV
    Size: 2607 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/a7d16ee7-974d-42e4-a16c-36351a0ee09c/resource/9dbe3429-5669-4b2c-a83c-a643411b06b5/download/bat_guilds.csv
    Description: bat_guilds with columns
"SP" (identification level),
"EndGuilde" (information on feeding guild)

en:
Resource Title: site_info.csv
    Size: 4452 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/a7d16ee7-974d-42e4-a16c-36351a0ee09c/resource/0a4317c7-3251-4c37-8519-26f542ed56a5/download/site_info.csv
    Description: site_info contains columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"location" (sampling location row within site),
"sample_nr" (sampling number within location)

en:
Resource Title: bats_glmm.R
    Size: 44503 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/a7d16ee7-974d-42e4-a16c-36351a0ee09c/resource/ba5c25fd-9c4b-480f-8f89-29449bbddfd7/download/bats_glmm.r
    Description: R script bats_glmm to test the influence of beavers on bat richness, activity and feeding activity

beaver

display_name: beaver

castor

display_name: castor

chiroptera

display_name: chiroptera

deadwood

display_name: deadwood

ecosystem-engineer

display_name: ecosystem-engineer

flying-arthropods

display_name: flying-arthropods

habitat-heterogeneity

display_name: habitat-heterogeneity

nature-conservation

display_name: nature-conservation

wetland

display_name: wetland


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

en: radiation temperature-profiles wind atmospheric-conditions precipitation

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

envi

en: Environment

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: 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: 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).

format: jpeg

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).

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).

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 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: 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).

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: Reproducibility Dataset for CRYOWRF v1.0

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

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.

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.

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: Snow and the water cycle in a changing climate

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

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

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

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 castanea-sativa torymus-sinensis pest-invasion southern-switzerland population-evolution dryocosmus-kuriphilus

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

envi

en: Environment

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: IRKIS Soil moisture measurements Davos

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

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 SWE data from 11 stations in Switzerland

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

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:

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

gcos

display_name: gcos

snow-height

display_name: snow-height

snow-profile

display_name: snow-profile

snow-water-equivalent

display_name: snow-water-equivalent


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

en: Alpine3D simulations of future climate scenarios CH2014

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

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

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

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

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

climate-change

display_name: climate-change

snow-depth

display_name: snow-depth


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: Snow Drift Station - Micro Rain Radar

en: radar precipitation snow raclets rain mountain

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.

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

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.

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

mountain

display_name: mountain

precipitation

display_name: precipitation

raclets

display_name: raclets

radar

display_name: radar

rain

display_name: rain

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

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

envidat

en: EnviDat: the Environmental Data Portal

envi

en: Environment

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: 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: 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: 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: 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

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.

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 crust formation: Microsoft Kinect data

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

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:
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

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

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: Wind crust formation: SnowMicroPen data

en: wind-crust wind-packing smp snow snowmicropen

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: Expedition to Princess Elisabeth Antarctica Station, 2016/2017

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

envidat

en: EnviDat: the Environmental Data Portal

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:
This dataset contains the experimental results described in Bergfeld et al. (2025). It includes three Propagation Saw Test (PST) experiments, each approximately 9 m long, performed side-by-side on a 37° slope. For each PST, we provide the full field of view along the crack-propagation direction. For the second and third PSTs, we additionally provide close-up recordings focused on the weak layer where cracking occurred.
All data are supplied as netCDF files containing displacement and strain measurements derived from Digital Image Correlation (DIC) analysis. Metadata describing dimensions and units are stored directly within the netCDF files. We recommend using the xarray package in Python to read and work with these datasets.
All figures presented in Bergfeld et al. (2025) can be reproduced using the included Python scripts.
Information about the snowpack is provided in PDF, pickle, and CAAML file formats.

en: Sub-Rayleigh to supershear fracture transition in long Propagation Saw Tests

en: dry-snow-avalanche snow crack-speed crack-propagation weak-layer

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:
Resource Title: 202101191100.SimpleProfile.pkl
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    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/938ce90f-e917-4838-af40-e2e161a22122/resource/4e0e7212-86dd-427a-803d-f5ce2e99dc17/download/202101191100.simpleprofile.pkl
    Description: pickled version of the manual profile. read into python with pickle.  E.g. given the density is used in plotting scripts.

en:
Resource Title: models.py
    Size: 5763 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/938ce90f-e917-4838-af40-e2e161a22122/resource/584604ff-1cfb-4900-b429-652991451516/download/models.py
    Description: Some functions from this script are imported from the plotting scripts.

en:
Resource Title: PST1_full.nc
    Size: 4820520982 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/938ce90f-e917-4838-af40-e2e161a22122/resource/ff1a1d3b-4a55-4efc-ae5b-7f4f95a71a9c/download/pst1_full.nc
    Description: netCDF file of dispalcement and strain measurement of PST1 in full view perspective.

en:
Resource Title: PST2_closeup.nc
    Size: 5029545808 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/938ce90f-e917-4838-af40-e2e161a22122/resource/b37dfe4f-1477-4b3c-a979-a50891104ce8/download/pst2_closeup.nc
    Description: netCDF file of dispalcement and strain measurement of PST2 in closeup view.

en:
Resource Title: snowprofile_87828_de.pdf
    Size: 117573 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/938ce90f-e917-4838-af40-e2e161a22122/resource/13e725eb-cdcc-4741-b604-5f6e9b20dae5/download/snowprofile_87828_de.pdf
    Description: pdf Version of the manual profile taken next to the PSTs

en:
Resource Title: fig4.ipynb
    Size: 138010 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/938ce90f-e917-4838-af40-e2e161a22122/resource/f91414e4-48e5-4e0e-a3ff-c1757b83a77f/download/fig4.ipynb
    Description: Jupyter script to load the netcdf files and reproduce Figure 4 from the related publication.

en:
Resource Title: Emod_parametrizations.py
    Size: 3309 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/938ce90f-e917-4838-af40-e2e161a22122/resource/fb277441-199d-4cef-8822-2f83a98c1265/download/emod_parametrizations.py
    Description: Emodul parametrizations for snow. Some functions from this script are imported from the plotting scripts.

en:
Resource Title: 202101191100.SimpleProfile.caaml
    Size: 20239 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/938ce90f-e917-4838-af40-e2e161a22122/resource/11c65ad1-4d24-48f9-a4b4-d5ab446f0327/download/202101191100.simpleprofile.caaml
    Description: manual profile taken next to the PSTs. Open the caaml-file using niviz.org

en:
Resource Title: PST3_closeup.nc
    Size: 5029545770 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/938ce90f-e917-4838-af40-e2e161a22122/resource/53aad081-49f3-4524-81fd-5ad482a41d60/download/pst3_closeup.nc
    Description: netCDF file of displacement and strain measurement of PST3 in closeup view.

en:
Resource Title: img_ref.tif
    Size: 256122 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/938ce90f-e917-4838-af40-e2e161a22122/resource/eee898b4-1047-488b-a5a7-c9538f9fe39b/download/img_ref.tif
    Description: reference frame used for Digital image correlation of the video of PST1

format: TIFF

media_type: image/tiff

en:
Resource Title: fig3.ipynb
    Size: 217503 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/938ce90f-e917-4838-af40-e2e161a22122/resource/137c67a3-5ee2-4aa5-bd4b-8d2aedc29aad/download/fig3.ipynb
    Description: Jupyter script to load the netcdf files and reproduce Figure 3 from the related publication.

en:
Resource Title: PST2_full.nc
    Size: 95474900 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/938ce90f-e917-4838-af40-e2e161a22122/resource/05a3cbfe-c5cd-4357-8ae3-c93787d7262c/download/pst2_full.nc
    Description: netCDF file of dispalcement and strain measurement of PST2 in full view perspective.

en:
Resource Title: fig2.ipynb
    Size: 280950 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/938ce90f-e917-4838-af40-e2e161a22122/resource/19389651-d793-485f-883e-f9eac407d711/download/fig2.ipynb
    Description: Jupyter script to load the netcdf files and reproduce Figure 2 from the related publication.

en:
Resource Title: PST3_full.nc
    Size: 63991645 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/938ce90f-e917-4838-af40-e2e161a22122/resource/3fbea48d-4def-40a1-b80b-6298467c098a/download/pst3_full.nc
    Description: netCDF file of dispalcement and strain measurement of PST3 in full view perspective.

crack-propagation

display_name: crack-propagation

crack-speed

display_name: crack-speed

dry-snow-avalanche

display_name: dry-snow-avalanche

snow

display_name: snow

weak-layer

display_name: weak-layer


en:
The data presented here corresponds to the publication "A Close-Ridge Small-Scale Atmospheric Flow Field and its Influence on Snow Accumulation" (Gerber et al., 2017), which investigates an eddy-like structure in the vicinity of the Sattelhorn in the Dischma valley (Davos Switzerland)  and its influence on snow accumulation. The dataset contains:
* Alpine3D: Alpine3D snow depth grids (25 m resolution) for two simulations with and without snow redistribution.
* ARPS: 10 ARPS simulations (25 m horizontal resolution) with different model setups (wind direction, wind speed, stability).
* LiDAR: Processed LiDAR PPI (D2_PPI_1h) and RHI (D2_cross_1h) across the valley with a hourly resolution for the period 27 October 2015 01:00 - 29 October 2015c 21:00 (spatial resolution: 25 m).
* meteostations-dischma: Meteorological station data of two meteorological stations in the Dischma valley with 10 minute resolution for the period 28 October 2015 - 30 October 2015.
* TLS: Snow depth change data (m) between 28 October 2015 and 30 October 2015 based on terrestrial laser scans.
For more details about the simulation and observation data, see Gerber et al., 2017.
Publication: Gerber et al., 2017: A Close-Ridge Small-Scale Atmospheric Flow Field and its Influence on Snow Accumulation, submitted to JGR - Atmospheres.

en: DISCHMEX - Observations and simulations of the close-ridge small-scale atmospheric flow field and snow accumulation at Sattelhorn, Dischma valley, Davos, Switzerland.

en: preferential-deposition tls alpine3d lidar lee-side-flow-field

envidat

en: EnviDat: the Environmental Data Portal

envi

en: Environment

lee-side-flow-field

display_name: lee-side-flow-field

lidar

display_name: lidar

preferential-deposition

display_name: preferential-deposition

tls

display_name: tls


en:
This work presents the first extensive study of climate change impacts on rivers temperature in Switzerland. Results show that even for low emissions scenarios, water temperature increase will lead to adverse effect for both ecosystems and socioeconomic sectors (such as nuclear plant cooling) throughout the 21st century. For high emissions scenarios, the effect will be worsen. This study also shows that water warming in summer will be more important in Alpine regions than in lowlands.
This material is distributed under CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/legalcode).

en: Dataset for: Future water temperature of rivers in Switzerland under climate change investigated with physics-based models

en: water-temperature discharge hydrology model climate-change-impact

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.

climate-change-impact

display_name: climate-change-impact

discharge

display_name: discharge

hydrology

display_name: hydrology

model

display_name: model

water-temperature

display_name: water-temperature


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: Alpine3D simulations of future climate scenarios for Graubunden

en: graubunden switzerland alpine3d-simulations climate-change snow

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)

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:

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

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: 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: 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).

en:
Resource Title: View publication in the Digital Object Repository at WSL (DORA)
    Size: 1 bytes
    Format: No Info
    URL: https://www.dora.lib4ri.ch/wsl/islandora/object/wsl%3A28180
    Description:

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

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

envidat

en: EnviDat: the Environmental Data Portal

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