en:
This repository contains extensive data for the European Alps:
   - Observations of ~3,500 plant species
   - Climate (1-km), soil (100-m) and land cover predictors (1km); current and future scenarios (28 CMIP6-GCMs, 2 land cover change and 3 dispersal scenarios i.e., unlimited, no and realistic vegetation dispersal)
   - Flora migration rates (categorical) and ecological preferences (continuous indicator values)
   - Regional maps of barriers to migration and water bodies at 100-m resolution
   - Sampling effort, distance to roads and cities predictors at 100-m resolution
   - Present and future abundances over the study region at 1-km resolution (~2,000 species)
   - Present and future multifaceted and uniqueness of the European Alps' Flora at  1-km resolution
   - Present and future conservation recommendations at 1-km resolution (26 current and future strategies)
   - Phylogenetic data and functional traits of ~2,000 plants (raw data and classification trees)
   - All scripts, data and plots used for the analyses, including a singularity container (mini-linux) to run them

en: functional-diversity taxonomic-diversity species-distribution-models beta-diversity phylogenetic-diversity migclim plants land-use-change zonation conservation european-alps global-change

en: Present and future multifaceted plant diversity, uniqueness and conservation in the European Alps

envi

en: Environment

de:
Resource Title: Data Access
    Size: 85899345920 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.371%2F
    Description:

de:
Resource Title: Updated SDM outputs
    Size: 10242439935 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/7b947fcf-73a0-46a7-be03-804797c9c3d7/resource/4838348a-6050-4774-83f1-cab5efe7b244/download/sdm_outputs_updated.zip
    Description: Refined scenarios of plants distribution in the European Alps

beta-diversity

display_name: beta-diversity

conservation

display_name: conservation

european-alps

display_name: european-alps

functional-diversity

display_name: functional-diversity

global-change

display_name: global-change

land-use-change

display_name: land-use-change

migclim

display_name: migclim

phylogenetic-diversity

display_name: phylogenetic-diversity

plants

display_name: plants

species-distribution-models

display_name: species-distribution-models

taxonomic-diversity

display_name: taxonomic-diversity

zonation

display_name: zonation


$oid: OBSOLETE

en: The dataset contains for three variables (snow water equivalent, surface water input and liquid precipitation) 50 realizations of current and future climate periods for two time horizons (mid end end of century), two emission senarions (RCP 4.5 and 8.5) and 10 climate model chains (all EUR11 chains within CH2018). To quantify natural climate variability for projections of snow conditions and resulting rain-on-snow (ROS) flood events, a weather generator was applied to simulate inherently consistent climate variables for multiple realizations of current and future climates at 100 m spatial and hourly temporal resolution over a 12 x 12 km high-altitude study area in the Swiss Alps. The output of the weather generator was used as input for subsequent simulations with an energy balance snow model. The data was extracted in 2021 from original model output.

en: uncertainty snow-water-equivalent rain-on-snow climate-change-impact cryosphere natural-climate-variability

envidat

approval_status: approved

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

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

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

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

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

state: active

type: organization

wsl

capacity: public

de: EnviDat

en: Multiple realizations of daily snow water equivalent, surface water input and liquid precipitation projections for mid- and late-century

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

fr: Environnement

it: Ambiente

climate-change-impact

display_name: climate-change-impact

cryosphere

display_name: cryosphere

natural-climate-variability

display_name: natural-climate-variability

rain-on-snow

display_name: rain-on-snow

snow-water-equivalent

display_name: snow-water-equivalent

uncertainty

display_name: uncertainty


en:
This dataset consist of simulated hourly power production from an Enercon E82 Turbine at 100 m hub-height. It describes the hourly power output a 1MW turbine would produce in each 0.01° grid cell for the years 2016 and 2017.
100 m wind speed data was taken from the COSMO-1 model (Consortium for Small-scale Modeling 2017), which has a 0.01° horizontal resolution. The domain covered is the whole of Switzerland, with the exclusion of lakes. As such, the number of 0.01◦ pixels within Switzerland amounts to 48657.
Conversion to power output was done based on the power curve of the Enercon E82 Turbine. As power output is lower at altitude due to lower air density, we corrected for this effect as described in (Kruyt et al. 2017).
Please cite the following paper in connection with the dataset:
Paper Citation:
 - _Bert Kruyt, Jérôme Dujardin, and Michael Lehning: Improvement of wind power assessment in complex terrain: The case of COSMO-1 in the Swiss Alps, Front. Energy Res., [doi:10.3389/fenrg.2018.00102] (https://doi.org/10.3389/fenrg.2018.00102)_

en: Dataset on Cosmo-1 based Energy Potential in Swiss Alps

de:
Resource Title: Data Source
    Size: 3144305336 bytes
    Format: application/x-r-data
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.51/dataset-on-wind-fields-and-energy-potential-in-swiss-alps/unit_pow_16_17.rdata
    Description: Hourly Gridded Wind Power Time Series

de:
Resource Title: Description
    Size: 42299 bytes
    Format: application/pdf
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.51/dataset-on-wind-fields-and-energy-potential-in-swiss-alps/envidat_cosmo-desc.pdf
    Description: Full Data Description

format: PDF

media_type: application/pdf

de:
Resource Title: Coordinates of Grid Points
    Size: 255151 bytes
    Format: application/x-rds
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.51/dataset-on-wind-fields-and-energy-potential-in-swiss-alps/gridch.rds
    Description: Coordinates of Grid Points

mountain-wind-potential

display_name: mountain-wind-potential

topography

display_name: topography

wind-energy

display_name: wind-energy


en:
The data archive contains site specific geographical data such as DEM and orthophoto as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 46, 200, 800 and 2670 kg of mass.
Additionally available are all the reconstructed data sets for all trajectories with videogrammetry installed comprising StoneNode data streams for rocks equipped with a sensor. The data set consists of:
 Resources (individual zip-archives)
ExperimentalRuns:  Archive with all available StoneNode data streams and its respective figure (.mat files)
Input: Archive containing folders
* GNSS: 182 Deposition points of all different weight and shape classes, shape files for release point, cliff and scree line,
* UAS: UAS generated pre- and post-experimental digital surface models and orthophoto of the four most important experimental days and
* VG_Coord: Reconstruction input: Videogrammetry based coordinate list along side with the corresponding sensor/video times
EOTA: Point cloud of cubic EOTA(111) and platy EOTA(221) rock as input for RAMMS::ROCKFALL or other suitable rockfall simulation codes incorporating complex shape files.
Output: Reconstruced trajectory information for all 82 reconstructed trajectories
Video: available video streams for all runs
 Further information
Preceeding publications concering the deployed sensors and the reconstruction methods are found in the subsequent references:
A. Caviezel et al., Design and Evaluation of a Low-Power Sensor Device for Induced Rockfall Experiments, IEEE Transactions on Instrumentation and Measurement, 2018, 67, 767-779, http://ieeexplore.ieee.org/document/8122020/
P. Niklaus et al., StoneNode: A low-power sensor device for induced rockfall experiments, 2017 IEEE Sensors Applications Symposium (SAS), 2017, 1-6, http://ieeexplore.ieee.org/document/7894081/
Caviezel, A., Demmel, S. E., Ringenbach, A., Bühler, Y., Lu, G., Christen, M., Dinneen, C. E., Eberhard, L. A., von Rickenbach, D., and Bartelt, P.: Reconstruction of four-dimensional rockfall trajectories using remote sensing and rock-based accelerometers and gyroscopes, Earth Surf. Dynam., 7, 199–210, https://doi.org/10.5194/esurf-7-199-2019, 2019

en: dem rockfall-experiments stonenode natural-hazard earth-surface-dynamics rockfall-runout stonenodedata induced-rockfall rockfall

en: Induced Rockfall Dataset #2 (Chant Sura Experimental Campaign), Flüelapass, Grisons, Switzerland

de:
Resource Title: EOTA
    Size: 419 bytes
    Format: application/x-7z-compressed
    URL: https://www.envidat.ch/dataset/71e65311-b0dd-48df-acfc-791c99cb0662/resource/eaa94cfe-a85f-4c26-ad2a-2f53b9e4af56/download/eota.7z
    Description: Cubic EOTA(111) and platy EOTA(221) point cloud.

de:
Resource Title: Video Archive
    Size: 5368709120 bytes
    Format: application/x-7z-compressed
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.174/caviezel_et_al_2020/Video.7z
    Description:

de:
Resource Title: Output Archive
    Size: 4736837 bytes
    Format: application/x-7z-compressed
    URL: https://www.envidat.ch/dataset/71e65311-b0dd-48df-acfc-791c99cb0662/resource/a2e218f8-cffe-4c6a-8515-01a0ff704524/download/output.7z
    Description: Data under embargo: Trajectory reconstruction tables

de:
Resource Title: Input Archive
    Size: 11811160064 bytes
    Format: application/x-7z-compressed
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.174/caviezel_et_al_2020/Input.7z
    Description:

de:
Resource Title: Experimental Runs Archive
    Size: 538660719 bytes
    Format: application/x-7z-compressed
    URL: https://www.envidat.ch/dataset/71e65311-b0dd-48df-acfc-791c99cb0662/resource/d9c77b83-fd15-414c-a257-461e6d2b2545/download/experimentalruns.7z
    Description: Compressed archive with the StoneNode experimental runs and the respective figures. Matlab format.

dem

display_name: dem

earth-surface-dynamics

display_name: earth-surface-dynamics

induced-rockfall

display_name: induced-rockfall

natural-hazard

display_name: natural-hazard

rockfall

display_name: rockfall

rockfall-experiments

display_name: rockfall-experiments

rockfall-runout

display_name: rockfall-runout

stonenode

display_name: stonenode

stonenodedata

display_name: stonenodedata


en:
This dataset includes all data and simulation files presented in the publication: Bender et al. 2020. Changes in climatology, snow cover and ground temperatures at high alpine locations, DOI: 10.3389/feart.2020.00100.
This includes:
* meteorological forcing,
* climate change timeries and
* simulation files together with
* snow depth
* ground temperature
Please refer to the following publication for further details which should be cited when using this dataset:
Bender et al. 2020. Changes in climatology, snow cover and ground temperatures at high alpine locations, DOI: 10.3389/feart.2020.00100.

en: depth-of-snowfall swiss-alps climate-change-impact meteo-station ground-temperature

en: Changes in climatology, snow cover and ground temperatures at high alpine locations in Switzerland (Bender et al. 2020)

de:
Resource Title: GenPSUm
    Size: 14668578 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/10658b8c-a7ff-4433-94f5-ebdf16d5304f/resource/a747974a-30ee-46d8-95fa-8b3bd4123d79/download/genpsum-20200405t154030z-001.zip
    Description: GenPSUm contains Files to get the complete precipitation set:

1. MeteoData.ini: Ini file for MeteoIO to get the met. input data. It has to be run two times, to get prec. Data from Imis 	stations and from ANETZ regressions.

2 GenPSUM.ini: Inifile for Snowpack (in research mode), to get winter precipitation calculated by snowpack out of the snow hight measurements.
	In:_sno contain the soil profils for the station
	In_met contains the meterological input data. It's from the data base and MeteoData.ini is used to get it.

3. The folder R_programms contains the R sripts to generate the precipitation data set, it reads in IMIS, ANETZ and SNOWPACK precipi and makes one complet data set out of it.  (it contains the function- script plus a script to use the function)

format: ZIP

media_type: application/zip

de:
Resource Title: R-Script
    Size: 81604064 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/10658b8c-a7ff-4433-94f5-ebdf16d5304f/resource/d5ad28b3-5a3c-4fbf-be06-f3c4c2f91902/download/rscripts-20200405t154017z-001.zip
    Description: The Folder R- Script contains, the R files to create the Climate Change input and the Data for the WFJ- Modeling.

de:
Resource Title: SNOWPACK climate change simulation files
    Size: 1104566162 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.144/gtmodeling_snowpack.zip
    Description: This folder contains SNOWPACK INI-files for snowpack, soil profiles, and input data for climate change modeling.

de:
Resource Title: Readme.txt
    Size: 2048 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/10658b8c-a7ff-4433-94f5-ebdf16d5304f/resource/2c74496f-2c9a-4f87-9ebc-095bcece4623/download/readme.txt
    Description:

format: TXT

media_type: text/plain

de:
Resource Title: Precipitation datasets
    Size: 39685479 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/10658b8c-a7ff-4433-94f5-ebdf16d5304f/resource/4d951298-4a0f-4d8f-bbdd-2915f7b33666/download/precipitationdatasets-20200405t154024z-001.zip
    Description: This folder contains 16 ideal precipitation data sets for IMIS stations.

*Updated documentation 24 August 2020:*

1. Datasets have been filtered for unphysical values using a MeteoIO min/max tool, however spikes still exist that pass this filter yet appear to be outliers, no further manual filtering was applied.
2. Data prior to 2009 can have large data gaps in certain stations. This is documented in the accompanying publication and was not used for simulations.
3. GUT2 was erroneously filtered during summer periods. This does not affect published snow modelling results but summer (Jun-Oct) values of 8.25 should not be used and filtered prior to use. This affects 4759 summer values or 1.2% of the time-series.

climate-change-impact

display_name: climate-change-impact

depth-of-snowfall

display_name: depth-of-snowfall

ground-temperature

display_name: ground-temperature

meteo-station

display_name: meteo-station

swiss-alps

display_name: swiss-alps


en:
Environmental pressures on species can cascade within food webs and even extend beyond individual ecosystems to interconnected systems at large spatial scales. To facilitate the exploration of these dynamics, we constructed trophiCH: a data-based national trophic meta-food web (henceforth the metaweb), that includes vertebrates, invertebrates, and vascular plants within Switzerland's national boundaries, and drawing from literature published between 1862 and 2024. Our comprehensive dataset catalogues 1,112,073 trophic interactions involving 23,151 species and 125 feeding guilds (e.g., detritivores, fungivores, etc). Interactions were primarily documented at the species level from the literature. Additional species-level interactions were inferred by resolving coarser taxonomic records (e.g., using “species A feeds on genus B” to infer interactions between species A and species within genus B) using taxonomic and habitat co-occurrence information.
We provide seven datasets: 1) the metaweb, 2) the taxa checklist, 3) the data source meta-dataset, 4) the list of generalist basal and predator families and polylectic species with citations and 5) a dataset with citations for the inferences of missing predators, 6) a dataset with citations for the parallel inference of diets from similar species and 7) a list of other existing metawebs. The empirical dataset is 00_empirical_metaweb.csv. To reproduce the final metaweb (01_final_metaweb.csv), start the R Project file: trophich.Rproj. We provide five scripts, accompanying functions, and the raw data required to run these scripts to reproduce the taxonomic expansion and validation of the datasets. In the first script (01_inferring_interactions.R), we infer interactions using genus and family level interactions and for basal feeding groups (see Methods: Taxonomic expansion). In the second script (02_inferring_interactions_special_cases.R), we infer further interactions for a few special cases with detailed explanations. In the third script (03_metaweb_comparisons.Rmd), we provide the statistical comparisons between our metaweb and other empirical metawebs as an R Markdown document. We additionally provide a a Python Jupyter Notebook document, outlining the error validation of the data extraction process (04_error_validation.ipynb and an accompanying .html file). Finally, we provide a script to reproduce figure 1 from the associated data paper (05_metaweb_summary_figure_1.R).
This is a first step towards a comprehensive food web for Switzerland, but data gaps remain (see Reji Chacko et al 2024, Fig.1C). We encourage addition of new datasets to this metaweb and welcome any collaborations to contruibute to future versions of this dataset. Please contact: merin.rejichacko@gmail.com.

en: food-webs switzerland metaweb vascular-plants trophic-networks vertebrates invertebarates

envidat

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

en: trophiCH v1 - a food web for Switzerland

de:
Resource Title: trophiCH 1.0
    Size: 33123041 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/4f9547a1-3018-46f1-a399-09d65a3223f3/resource/6d7f57cc-a98f-4513-9c65-c0230a15bba7/download/trophich.zip
    Description: Most current version of the dataset, after revision round 1 at Scientific Data.

de:
Resource Title: trophich 0.1
    Size: 34142639 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/4f9547a1-3018-46f1-a399-09d65a3223f3/resource/fe802436-f927-4243-b199-0829a3e0d21b/download/trophich.zip
    Description: Deprecated version from before the first round of review at Scientific Data

food-webs

display_name: food-webs

invertebarates

display_name: invertebarates

metaweb

display_name: metaweb

switzerland

display_name: switzerland

trophic-networks

display_name: trophic-networks

vascular-plants

display_name: vascular-plants

vertebrates

display_name: vertebrates


en:
The WFJ_DailySMP dataset contains daily SnowMicroPen (SMP) measurements conducted throughout the winter seasons 2015-2025 (ongoing) at the Weissfluhjoch research site, Davos, Switzerland. The measurement program DailySMP started in winter 2015/2016 during which the measurement protocol was established as part of the RHOSSA campaign, described in the related publication.
Since then, measurements have been repeated every winter as part of the standard snow monitoring program by the PhD students at SLF. All published SMP files have undergone manual quality control by an expert, including a supervised detection of the snow surface, which is included in the dataset.
In addition to the raw data, we provide a rudimentary post-processing workflow in which the derived SSA and density profiles are adjusted by the total snow height obtained from the adjacent IMIS snow station (https://www.slf.ch/en/avalanche-bulletin-and-snow-situation/measured-values/description-of-automated-stations/) and their evolution is plotted over time. Please refer to the README for detailed information on how to get started.

en: snow-measurements snow-density weisfluhjoch microstructure snow-stratigraphy wfj-research-site weissfluhjoch snowmicropen snow snow-ssa

envidat

de: EnviDat: das Umwelt-Datenportal

de: EnviDat

en: WFJ_DailySMP - High-Resolution Snow Stratigraphy at Weissfluhjoch -- Davos 2015-ongoing

envi

de: Umwelt

en: Environment

microstructure

display_name: microstructure

snow

display_name: snow

snow-density

display_name: snow-density

snow-measurements

display_name: snow-measurements

snow-ssa

display_name: snow-ssa

snow-stratigraphy

display_name: snow-stratigraphy

snowmicropen

display_name: snowmicropen

weisfluhjoch

display_name: weisfluhjoch

weissfluhjoch

display_name: weissfluhjoch

wfj-research-site

display_name: wfj-research-site


photogrammetric-drone-data-dorfberg

en: The data was collected with a Wingtra Gen II drone and a Sony RX1R II sensor. In total, 10 flights were conducted at different dates, both in summer and winter. A DSM, an orthophoto, a snow depth raster and the original drone images from every flight are available at a high resolution (10cm and 3cm, respectively).

en: snow-depth-mapping orthophoto dsm snow-distribution drone-data photogrammetry snow uav

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: Photogrammetric Drone Data Dorfberg

title_for_slug: photogrammetric-drone-data-dorfberg

drone-data

display_name: drone-data

dsm

display_name: dsm

orthophoto

display_name: orthophoto

photogrammetry

display_name: photogrammetry

snow-depth-mapping

display_name: snow-depth-mapping

snow-distribution

display_name: snow-distribution

uav

display_name: uav


en:
We developed a monitoring system using low-cost lidar (Livox Avia) and optical sensors, to measure snow depth variations in an avalanche release area at a high spatiotemporal scale (centimeter to low decimeter spatial resolution and hourly temporal resolution). A detailed description of the setup and a first data analysis is published in:
[Ruttner-Jansen, P., Voordendag, A., Hartmann, T., Glaus, J., Wieser, A., & Bühler, Y. (2024). Monitoring snow depth variations in an avalanche release area using low cost lidar and optical sensors. EGUsphere, 2024, 1-20.](https://nhess.copernicus.org/articles/25/1315/2025/)
This dataset contains hourly 3D lidar data of the snow surface at the test site Braemabuel in Davos, Switzerland from the winter season 2023-2024 (23.11.2023 - 14.04.2024). Braema1 is installed at 2191 m a.s.l. in the slope, looking up into the avalache release area, and Braema2 is installed at 2255 m a.s.l at the ridge, looking down. Both scans together cover an area of about 15000 m².
The data is in format ".las" and contains the points in XYZ [m], in the scanner own coordinate system
- scalar fields: intensity, return number, timestamp, tag, label
-- tag: binary code from Livox (see [Livox Avia Manual](https://terra-1-g.djicdn.com/65c028cd298f4669a7f0e40e50ba1131/Download/Avia/Livox%20Avia%20User%20Manual%20202204.pdf))
-- label: marking outliers (filtered by distance and SOR to get snow particles in the air)

en: lidar snow low-cost avalanche monitoring wind-drifted-snow

en: Snow depth mapping by lidar station Braemabuel

de:
Resource Title: Braema1
    Size: 22441204122 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.581/braema1_23_24_v2.zip
    Description:

de:
Resource Title: Braema2
    Size: 20937965568 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.581/braema2_23_24.zip
    Description:

avalanche

display_name: avalanche

lidar

display_name: lidar

low-cost

display_name: low-cost

monitoring

display_name: monitoring

wind-drifted-snow

display_name: wind-drifted-snow


en: Site description of the Seehornwald Davos (CH-DAV) research site. The material provided offers information on the biomass and growth of trees in CH-DAV, including supporting materials such as maps, geolocations and analyses. CH-DAV is part of networks such as ICOS, LWF, TreeNet, ICPForests and is dedicated to forest monitoring and research.

en: forest-biomass dendrometer biomass davos forest climate-change atmospheric-conditions dbh water-relations icp-forests lwf picea-abies eddy-covariance-measurements stem-growth carbon-sequestration forest-ecosystems automated-monitoring seehornwald flux carbon-cycle tree-stem forest-area metadata ecosystem-functioning carbon-dioxide sap-flow ilter eddy-covariance twd carbon-storage icos treenet open-data forest-canopy forest-function subalpine co2 forest-monitoring forest-dynamics climate-growth forest-characteristics ecophysiology

en: Site description Davos Seehornwald CH-DAV

de:
Resource Title: Readme
    Size: 2476 bytes
    Format: application/rtf
    URL: https://www.envidat.ch/dataset/4d332ffa-d5f5-413c-ba1c-5f73ceebbed6/resource/7f13f634-76b5-4d56-aa5a-61140240f616/download/readme.rtf
    Description:

format: rtf

de:
Resource Title: CH-DAV_Site_2024.zip
    Size: 34214528 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4d332ffa-d5f5-413c-ba1c-5f73ceebbed6/resource/73875879-bec0-4e15-a805-e6cd9d4fe596/download/ch-dav_site_2024.zip
    Description: Collection of files with information about the research site Seehornwald Davos CH-DAV

de:
Resource Title: CH-DAV_Site_2024_V2.zip
    Size: 27267396 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4d332ffa-d5f5-413c-ba1c-5f73ceebbed6/resource/41e3d87c-7907-4a01-bf5e-36a0d4457c40/download/ch-dav_site_2024_v2.zip
    Description: Collection of files with information about the research site Seehornwald Davos CH-DAV

atmospheric-conditions

display_name: atmospheric-conditions

automated-monitoring

display_name: automated-monitoring

biomass

display_name: biomass

carbon-cycle

display_name: carbon-cycle

carbon-dioxide

display_name: carbon-dioxide

carbon-sequestration

display_name: carbon-sequestration

carbon-storage

display_name: carbon-storage

climate-change

display_name: climate-change

climate-growth

display_name: climate-growth

davos

display_name: davos

dbh

display_name: dbh

dendrometer

display_name: dendrometer

ecophysiology

display_name: ecophysiology

ecosystem-functioning

display_name: ecosystem-functioning

eddy-covariance

display_name: eddy-covariance

eddy-covariance-measurements

display_name: eddy-covariance-measurements

flux

display_name: flux

forest

display_name: forest

forest-area

display_name: forest-area

forest-biomass

display_name: forest-biomass

forest-canopy

display_name: forest-canopy

forest-characteristics

display_name: forest-characteristics

forest-dynamics

display_name: forest-dynamics

forest-ecosystems

display_name: forest-ecosystems

forest-function

display_name: forest-function

forest-monitoring

display_name: forest-monitoring

icos

display_name: icos

icp-forests

display_name: icp-forests

ilter

display_name: ilter

lwf

display_name: lwf

metadata

display_name: metadata

open-data

display_name: open-data

picea-abies

display_name: picea-abies


en: The data results from a forest visitor survey that was conducted in June 2022 in the Waldlabor on Hönggerberg in Zurich. This questionnaire’s aim was to investigate how visitors perceived the different local forest management forms and to gain insights into the influence of visitors’ forest preferences, reasons for the visit and performed activities. The visual perceived forest attractiveness was collected based on six different survey locations featuring different forest management forms respectively different stages. This study was supported by the Waldlabor Zürich.

en: forest-recreation coppice-with-standards forest-management forest-attractiveness forest-characteristics

en: The influence of different forest management forms on the visual perceived attractiveness of forests for recreational use

de:
Resource Title: Questionnaire German
    Size: 214415 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/76529e56-b3ba-43a5-9433-e2eb76b57718/resource/52eb9892-e63b-4daf-be96-f70c08d6d460/download/fragebogen.pdf
    Description:

de:
Resource Title: SPSS-Dataset
    Size: 108035 bytes
    Format: application/x-spss-sav
    URL: https://www.envidat.ch/dataset/76529e56-b3ba-43a5-9433-e2eb76b57718/resource/fec0b2ad-27cb-4fe4-afcd-02cb20a06d89/download/spss_datensatz_bewirtschaftungsformen-waldlabor.sav
    Description: The survey's dataset collected in the Waldlabor Hönggerberg, Zurich in SPSS-format.

de:
Resource Title: Codebook to SPSS-file
    Size: 130582 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/76529e56-b3ba-43a5-9433-e2eb76b57718/resource/ad1e39f1-6797-4f57-9d33-713f544f7ffe/download/codebook_variablen.pdf
    Description:

de:
Resource Title: Dataset in Excel-format
    Size: 148438 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/76529e56-b3ba-43a5-9433-e2eb76b57718/resource/d7be3d25-5199-49b6-8de0-7bfd62e3821e/download/spss_datensatz_bewirtschaftungsformen-waldlabor.xlsx
    Description: The survey's dataset collected in the Waldlabor Hönggerberg, Zurich in Excel-format.

format: XLS

coppice-with-standards

display_name: coppice-with-standards

forest-attractiveness

display_name: forest-attractiveness

forest-management

display_name: forest-management

forest-recreation

display_name: forest-recreation


en:
Starter kit for the TreeMig framework that allows for an easy application of the forest landscape model TreeMig for simulating forest dynamics in space under changing environmental and land use conditions.
The starter kit consists of a set of climate, land-use, topography and soil property input data for Switzerland, a script for installing the framework and running the GUI of the framework with the link to the input data, a tutorial covering a range of simulation examples for Switzerland and a transect through Europe, and an associated example simulation environment containing the model executables and other model inputs.

en: forest-dynamics spatial-modelling species-interaction forest-model gui land-use bioclimatic-variables forest treemig r-package species-migration

en: Starter Kit for the TreeMig framework v1 for easy simulation of spatio-temporal forest dynamics

de:
Resource Title: TreeMig_Tutorial_v2024_12_04.pdf
    Size: 6400462 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/d4c15251-917c-4102-b172-256fecd39161/download/treemig_tutorial_v2024_12_04.pdf
    Description:

de:
Resource Title: TreeMig Simulation Environment and Input Data
    Size: 1612103870 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/5eef16d1-80af-442b-9da2-f64b4fa94671/download/tmenvandinputdata.zip
    Description: The zip file consists of a R-script to provide the framework, an example simulation environment and a set of input data.
- The R-script installAndRunTreeMigWrapperAndGUI.R installs the R-package, links to the input data and starts the graphical user interface.
- The gridded input datasets in TMInputData are specifically tailored to Switzerland and have the same coordinate reference system and a congruent 200m spatial resolution. They consist of climate, topographic, land use and soil data.  For details see TMInputDataDetails.pdf.
- TMEnvironment is an example simulation environment, i.e. a set of hierarchically structured folders.  For the first example simulation of the tutorial it contains the
   - the TreeMig executables for MacOS, Linux and Windows
   - simulation control files that define the setup and input files of the simulation (in folder C)
   - species parameters (in folder P)
   - bioclimatic data  (in folder E)
   - folder R for results
   - folder Plots for plots of the simulation results.
   - This environment can be also used for the subsequent simulations of the tutorial.
- Some additional R-scripts demonstrate more complex applications described in the tutorial, e.g. coupling to another model.

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

de:
Resource Title: TreeMig_Tutorial_v2024_12.pdf
    Size: 3399819 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/5188a2e1-470c-4ed7-83c6-26dc72bd18ab/download/treemig_tutorial_v2024_12.pdf
    Description: Tutorial for using the TreeMig framework; updated

de:
Resource Title: TreeMig Simulation Environment and Input Data
    Size: 1602386904 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/c2ae78fd-418a-4cdb-a511-0914eb46224f/download/tmenvandinputdata.zip
    Description: The zip file consists of a R-script to provide the framework, an example simulation environment and a set of input data.
- The R-script installAndRunTreeMigWrapperAndGUI.R installs the R-package, links to the input data and starts the graphical user interface.
- The gridded input datasets in TMInputData are specifically tailored to Switzerland and have the same coordinate reference system and a congruent 200m spatial resolution. They consist of climate, topographic, land use and soil data.  For details see TMInputDataDetails.pdf.
- TMEnvironment is an example simulation environment, i.e. a set of hierarchically structured folders.  For the first example simulation of the tutorial it contains the
   - the TreeMig executables for MacOS, Linux and Windows
   - simulation control files that define the setup and input files of the simulation (in folder C)
   - species parameters (in folder P)
   - bioclimatic data  (in folder E)
   - folder R for results
   - folder Plots for plots of the simulation results.
   - This environment can be also used for the subsequent simulations of the tutorial.
- Some additional R-scripts demonstrate more complex applications described in the tutorial, e.g. coupling to another model.

de:
Resource Title: TreeMig_Tutorial_2025-05-02.pdf
    Size: 2107648 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/a1dec65a-30ba-4688-90ac-50df5db73d4c/download/treemig_tutorial_2025-05-02.pdf
    Description: Tutorial for using the TreeMig framework; updated

de:
Resource Title: TreeMig Tutorial
    Size: 2046576 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/8e6ac324-ab2b-40bc-a12f-70960c8f4bd1/download/treemig_tutorial.pdf
    Description: Tutorial for using the TreeMig framework

bioclimatic-variables

display_name: bioclimatic-variables

forest

display_name: forest

forest-model

display_name: forest-model

gui

display_name: gui

land-use

display_name: land-use

r-package

display_name: r-package

spatial-modelling

display_name: spatial-modelling

species-interaction

display_name: species-interaction

species-migration

display_name: species-migration

treemig

display_name: treemig


en:
This dataset contains all processed data, simulation outputs, and R/Python scripts used to assess the robustness of regional multi-habitat food webs in Switzerland under various non-random species extinction scenarios. It accompanies the manuscript submitted to Communications Biology, and available as a preprint here:
Reji Chacko, M,  Albouy, C., Altermatt, F., et al. Decreases in the robustness of regional food webs to sequential species extinctions following habitat loss, 04 December 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-5225132/v1]
Please note that the most up-to-date version of the simulations code is available here: https://github.com/mrejichacko/casCHades

en: food-webs trophich network-analysis metaweb habitat-fragmentation robustness trophic-networks

envidat

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

de: EnviDat: das Umwelt-Datenportal

de: EnviDat

en: Simulation data and analyses of regional food web robustness under habitat loss

envi

de: Umwelt

en: Environment

food-webs

display_name: food-webs

habitat-fragmentation

display_name: habitat-fragmentation

metaweb

display_name: metaweb

network-analysis

display_name: network-analysis

robustness

display_name: robustness

trophic-networks

display_name: trophic-networks

trophich

display_name: trophich


en:
Description: Fish environmental DNA data set collected in 2023 in the Calanques National Park
The eDNA samples were collected in 2023 in two locations (Moyades, M−FPA and “Impérial du large”, I-LPA), during the winter (January- February) season, with the sampling dates depending on the weather conditions. For the M−FPA, samples were collected between Moyades island and Riou island, while for the I-LPA, they were collected on the south side of the “Impérial du large” island. To account for the existing bathymetry, in the M−FPA the samples were taken at two sampling sites with depths of 20 and 40 m, while in the I-LPA they were taken at two sites with depths of 20 and 80 m.
At each site, in-situ filtration of seawater was performed using a double-head submersible pump (Subspace, Geneva, Switzerland; nominal flow of ca. 1 L/min) strapped to an underwater scooter with 2 VigiDNA 0.20 µm filtration capsules (SPYGEN, le Bourget du Lac, France), along with disposable sterile tubing. The samples were collected along two horizontal transects (up to 400 m in length) during each closed-circuit rebreather dive, enabling the filtration of a water volume of 15 L/filter per depth, as close as possible to the substrate. Two filter replicates were collected by two divers at each sampling site, except in two cases where bad weather conditions or logistical issues meant that only one replicate was sampled.
After the filtration, the remaining seawater was emptied from the capsule back on the boat and replaced by a 80 mL CL1 conservation buffer (SPYGEN, le Bourget du Lac, France). To prevent any contamination, a strict protocol was followed during the entire process, requiring disposable gloves and single-use filtration equipment. Finally, the samples were stored at room temperature. We followed a strict contamination control protocol in both field and laboratory stages. Each water sample processing included the use of disposable gloves and single-use filtration equipment to avoid any risk of contamination. Libraries were prepared with ligation using the MetaFast protocol (Fasteris).
Data content:
	* rawdata/: contains the raw reads for each individual sample. One archive contains the paired-end reads specified by the _R1 or _R2 suffix as well as individually tagged PCR replicates (if available) together with an archive containing all extraction and PCR blank samples of the library. Reads have been demultiplexed using cutadapt but not trimmed, individual demultiplexing tags and primers remain present in the sequences.
	* taxadata/: contains the table with all detected taxonomy for each sample after bioinformatic processing (see Polanco et al. 2020 for details; https://doi.org/10.1002/edn3.140) and associated field metadata.
	* metadata/: contains two metadata files, one related to the data collected in the field for each filter, and the second related to the sequencing process in the lab (including the tag sequence, library name, and marker information for each sample)

en: mediterraneaen fish-composition marine teleo edna marseille

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: Environmental DNA Marine France Calanques 2023

edna

display_name: edna

fish-composition

display_name: fish-composition

marine

display_name: marine

marseille

display_name: marseille

mediterraneaen

display_name: mediterraneaen

teleo

display_name: teleo


lwf-alptal-long-term-research-site

en:
![alt text](https://www.envidat.ch/dataset/49729a45-f5bf-4bc0-afdd-77123894d3bb/resource/aa505753-198c-49dc-a33e-c4c8e4fcb611/download/lwf_alptal.jpg "LWF Alptal")
LWF plot Alptal
- Community: Alpthal / canton SZ
- Date of installation: 31 May 1995
- Size of the plot: 0.6 ha
- Altitude: 1149-1170 m
- Mean slope: 23%
- Geology (in German): Nordpenninikum; obere Kreide-unteres Eozän, Wägitaler Flysch
- Soil types (WSL) : Mollic Gleysols, Gleyic Cambisols
- Woodland association after EK72: 49: Equiseto-Abietetum
- Main tree species: Picea abies
- Management system: high forest
- Silvicultural system: selection
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 39.3 cm
- Number of trees BHD -= 12 cm (2011): 321
- Maximum tree age: Picea abies 180-230 yr
More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/alptal.html

en: lwf infrastructure swiss-forest-lab meteorological-data forest plot meteorological-measurements

en: LWF Alptal long-term research site

title_for_slug: lwf-alptal-long-term-research-site

de:
Resource Title: LWF plot Alptal
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    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/49729a45-f5bf-4bc0-afdd-77123894d3bb/resource/aa505753-198c-49dc-a33e-c4c8e4fcb611/download/lwf_alptal.jpg
    Description: Photo: Flurin Sutter, 2013

format: jpeg

de:
Resource Title: Alpthal Meteorological Data LWF
    Size: 1 bytes
    Format: No Info
    URL: https://doi.org/10.16904/envidat.94
    Description:

de:
Resource Title: Site_image
    Size: 123357 bytes
    Format: image/jpeg
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    Description: LWF plot Alptal Photo: P.Waldner

forest

display_name: forest

infrastructure

display_name: infrastructure

lwf

display_name: lwf

meteorological-data

display_name: meteorological-data

meteorological-measurements

display_name: meteorological-measurements

plot

display_name: plot

swiss-forest-lab

display_name: swiss-forest-lab


en:
This dataset accompanies the publication "How Flat is Flat? Investigating the spatial variability of snow surface temperature and roughness on landfast sea ice using UAVs in McMurdo Sound, Antarctica".
The dataset consists of 1) processed orthomosaic of RGB values, snow topography (digital elevation model), and surface temperature, 2) topography-dependent irradiance map, and 3) point data of measured radiation and atmospheric parameters in the McMurdo Sound, October-December 2022.  The data was collected as part of the New Zealand Marsden Fund Research Grant 21-VUW-103 "Can Snow Change the Fate of Antarctic Sea Ice?". Unprocessed data associated with this field campaign are published in 10.16904/envidat.633.
For the airborne measurements, we used a DJI Matrice 30T, a multi-rotor (quadcopter) UAV with self-heating batteries, suitable for operations in polar conditions (temperature range from -20 to +50 °C). The UAV has a wide-angle camera (12 megapixels) for RGB images and a thermal camera (Uncooled Vox Microbolometer, long-wave infrared spectrum 8-14 µm, 1.31 megapixels) for TIR images with an accuracy of ±2 °C or ± 2 %.
GNSS data includes high-precision location information (latitude, longitude, ellipsoidal height, receiver clock offset, and timestamps formatted in decimal hours and day-of-year notation).
WE calculated the distribution of received solar irradiance over the sea-ice surface using a terrain-corrected radiative transfer model and measured shortwave radiation components, accounting for surface roughness, aspect, slope, and solar position during the flight, while applying terrain shading and measured diffuse radiation.
The broadband radiation station data consists of shortwave (Sw) and longwave (Lw) radiation measurements (Kipp & Zonen CMP22 pyranometers and CGR4 pyrgeometers).
Surface temperature readings from cold, sediment, and hot targets were recorded using Apogee SI-121-SS sensors.
This dataset also contains automated weather station (AWS) data (air and snow surface temperatures, relative humidity, wind direction, and wind speed).
For detailed information, please use the README file.

en: snow-roughness irra drone snow uav sea-ice antarctica snow-temperature

en: Snow On Antarctic Sea Ice - McMurdoSound 2022 - UAV Retrievals of Snow Topography and Snow Surface Temperature

antarctica

display_name: antarctica

drone

display_name: drone

irra

display_name: irra

sea-ice

display_name: sea-ice

snow

display_name: snow

snow-roughness

display_name: snow-roughness

snow-temperature

display_name: snow-temperature

uav

display_name: uav


greenland-climate-network-gc-net-data

en:
In Memory of Dr. Konrad (Koni) Steffen
-br /-
-br /-
Update October 2022: The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).
Starting October 3, 2022, the access to the latest versions of the "ready to use" L1 data has been migrated to GEUS. Future data versions will be available at:  [https://doi.org/10.22008/FK2/VVXGUT](https://doi.org/10.22008/FK2/VVXGUT)
 Background
Starting with a single station in 1991, the Greenland Climate Network (commonly known as GC-Net) is a set of Automatic Weather Stations (AWS) set up and managed by the late Prof. Dr. Konrad (Koni) Steffen, and spanning the Greenland Ice Sheet (GrIS). This first station was "Swiss Camp" or the "ETH-CU" camp (GC-Net station 01) which was used as a field science and education site by Koni for years. The GC-Net was expanded with multiple NASA, NOAA, and NSF grants throughout the years, and then supported by WSL in the later years. These data (see "C-file" below) were previously hosted by the Cooperative Institute for Research in Environmental Sciences (CIRES) in Boulder, Colorado.
 Overview
Provided in this dataset are the 16 longest running stations in the network, which are spread over a significant area of the GrIS and the majority of the unique climatic zones. From the South Dome high point in the South, to the Western Jakobshavn ablation region in the west, to the Petermann glacier in the North across east of the Northeast Greenland Ice Stream to the east, GC-Net is the longest running climatological record of Greenland.
 The standard GC-Net station consists of:
* Air temperature measurements at 2 heights above the surface
* Temperature and humidity measurements at 2 heights above the surface
* Wind speed and direction measured at 2 heights above the surface
* Sonic distance sounder measurements for 2 snow height and distance of instruments to surface
* Incoming shortwave radiation measurement
* Reflected shortwave radiation measurement
* Net broadband radiation (long- and short-wave) measurement
* Air pressure measurement
Data have often been repatriated in near-real time using one of either the GOES geostationary satellite or the ARGOS polar orbiting satellite transmission system. The stations were visited typically every 1-2 years for maintenance and service, and to download full uncorrupted data directly from the dataloggers. GC-Net stations were visited by Twin Otter equipped with snow skids to land directly on the open-ice at the AWS locations, or by helicopter near the west coast. The AWSs operate on solar and battery power and occasionally lost power during the dark and cold winter months, particularly when the batteries were aging.
 Dataset
This dataset consists of 2 main data levels; Level 0 and Level 1.
Level 0 is the raw data from the dataloggers, historical processing codes, satellite transmissions, and Koni’s personal data archive. Level 0 data (.zip) directories contain subdirectories:
* “C file” - contains the historical processed datafile for each station.
* “Campbell logger files” - contains the raw csv datafiles from the stations’ Campbell Scientific dataloggers since the CR1000 era (~2007-2008 for most stations).
* “Photos” - contains photographs of the station when available marked by year.
Level 1 is the appended, calibrated, cleaned, and quality flagged data. The full processing scheme is open-source and publicly available on the following GitHub repository (please also check GitHub for the latest L1 data):
[GC-Net L1 data on GitHub](https://github.com/GEUS-Glaciology-and-Climate/GC-Net-level-1-data-processing "GC-Net-level-1-data-processing")
Level 1 data is provided in the newly described csv-compatible [NEAD format](https://www.envidat.ch//metadata/nead "NEAD format").
-br /-
 Additional Details
Dataset description publication will be forthcoming.
The Geological Survey of Denmark and Greenland (GEUS) has been imperative in the reprocessing and continuity mission of GC-Net. Multiple GC-Net stations have been replaced with updated and upgraded AWS hardware at the same coordinates by GEUS. This effort will ensure continuity of the GC-Net dataset into the future.

en: air-temperature net-radiation automatic-weather-station aws climate greenland

en: Greenland Climate Network (GC-Net) Data

title_for_slug: greenland-climate-network-gc-net-data

de:
Resource Title: GC Net Chart Parameters
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    URL: https://frontend-static.s3-zh.os.switch.ch/gcnet/stationparameters.json
    Description: Stations parameters from static bucket

format: JSON

media_type: application/json

de:
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de:
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de:
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de:
Resource Title: 10-Saddle
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de:
Resource Title: Archived GC-Net Data (deprecated)
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    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.1%2Fgcnet%2Fdata%2Fwsl-geus-cooperation%2F
    Description: The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).

Starting October 3, 2022, the access to the latest versions of the "ready to use" L1 data has been migrated to GEUS, (doi:10.22008/FK2/VVXGUT). Future versions will be available from GEUS, while the initial versions of L0 and L1 data have been archived.

de:
Resource Title: 00-SwissCamp10m
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de:
Resource Title: GC Net Config for Stations
    Size: 101376 bytes
    Format: application/json
    URL: https://frontend-static.s3-zh.os.switch.ch/gcnet/stationsconfig.json
    Description: Stations Config from the static bucket

de:
Resource Title: 01-SwissCamp
    Size: 80269102 bytes
    Format: No Info
    URL: https://dataverse.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/VVXGUT/DZSKEM
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de:
Resource Title: 05-Humboldt
    Size: 63987342 bytes
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    URL: https://dataverse.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/VVXGUT/BUOC7Y
    Description:

de:
Resource Title: Access the latest L1 data
    Size: 1181116006 bytes
    Format: application/xhtml+xml
    URL: https://dataverse.geus.dk/dataset.xhtml?persistentId=doi:10.22008/FK2/VVXGUT
    Description: The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).

Starting October 3, 2022, the access to the latest versions of the "ready to use" L1 data has been migrated to GEUS, (doi:10.22008/FK2/VVXGUT). Future versions will be available from GEUS, while the initial version of L1 data has been archived.

format: xhtml+xml

de:
Resource Title: 11-SouthDome
    Size: 105216949 bytes
    Format: No Info
    URL: https://dataverse.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/VVXGUT/4ONGVQ
    Description:

air-temperature

display_name: air-temperature

automatic-weather-station

display_name: automatic-weather-station

aws

display_name: aws

climate

display_name: climate

greenland

display_name: greenland

net-radiation

display_name: net-radiation


en:
Background information
Climate change-induced range expansion of treeline populations depends on their successful recruitment, which requires dispersal of viable seeds followed by successful establishment of individual propagules. The Global Treeline Range Expansion Experiment (G-TREE) is a global initiative involving researchers from Europe, North America, Australia and New Zealand (Brown et al., 2013). At 15 alpine and Arctic treeline sites worldwide the mechanisms determining the elevational and latitudinal distribution of tree populations are studied using a standardized experimental approach. In summer 2013, a multifactorial seedling recruitment experiment has been established at the Stillberg ecological treeline research site. The aim of this experiment, is to quantify the effect of multiple abiotic and biotic drivers on emergence, survival, and growth of *Larix decidua* and *Picea abies* seedlings in replicated plots along an elevation gradient with three sites below (1930 m a.s.l.), at (2090 m a.s.l.), and above treeline (2410 m a.s.l.; Frei et al., 2018). All plots have been surveyed annually to count seedlings and to measure their total height. Additional environmental factors, such as soil temperature, have been recorded.
 Experimental design
The Stillberg research area is located in the Eastern Swiss Alps near Davos, Switzerland. The site has been used for several long-term monitoring as well as experimental studies for the last four decades. Our G-TREE experiment consists of a lowest site located in a subalpine Larch-Spruce forest (*Larici-Picetum*) dominated by *Larix decidua* and *Picea abies* (1930 m a.s.l.), a transition zone site dominated by alpine shrubs (2100 m a.s.l.), and an uppermost site in an alpine meadow with some dwarf shrubs (2390 m a.s.l.). The three experimental sites were set up following the standard protocol of the global G-TREE initiative (Brown et al., 2013). In a split-plot design, 20 plots (224 cm × 45 cm) were established at each site, which were randomly assigned to the 2 × 2 treatment combinations of the main factors seeding and scarification (i.e. seeding and scarification, seeding only, scarification only, and full control), resulting in five replications per main treatment combination. Each plot was divided into 16 split-plots (22.5 cm × 28 cm), to which treatment combinations of four additional two-level factors species (larch and spruce), provenance (low- and high-elevation), herbivore exclosure (with and without exclosure), and seeding year (2013, 2014) were randomly assigned, which resulted in a total of 960 split-plots (Details see Frei et al. 2018).
 Data description
All plots have been surveyed annually to count seedlings and to measure their total height. Seedling height was assessed with a hand ruler as the total length from the original emerging point to the apical meristem (Details see Frei et al. 2018). Additionally, soil temperature at each site, has been continuously recorded since 2013. Here, we present data from eight years (2013–2021).

en: stillberg swiss-alps treeline g-tree forest climate-change seedling-recruitment picea-abies larix-decidua

envidat

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

de: EnviDat: das Umwelt-Datenportal

de: EnviDat

en: G-TREE: Global Treeline Range Expansion Experiment Davos, Switzerland

envi

de: Umwelt

climate-change

display_name: climate-change

forest

display_name: forest

g-tree

display_name: g-tree

larix-decidua

display_name: larix-decidua

picea-abies

display_name: picea-abies

seedling-recruitment

display_name: seedling-recruitment

stillberg

display_name: stillberg

swiss-alps

display_name: swiss-alps

treeline

display_name: treeline


en:
This dataset groups numerical simulation outputs and validation measurement data produced in the context of the following [publication]: _not published yet_
The snow transport model [snowBedFoam] was used to analyse snow deposition around a specific type of Alpine PV structures named HELIOPLANT®.
The results of a sensitivity analysis of multiple key parameters that govern the spatial organisation of these structures are provided here. Measurements of snow distribution taken from the test-site of the [Gondosolar] project, are provided too.

To reproduce the results of the aforementioned publication, follow the instructions on this [repository].
[publication]: https://
[snowBedFoam]: https://www.doi.org/10.16904/envidat.223
[Gondosolar]: https://www.gondosolar.ch
[repository]: https://github.com/frischwood/snowbed-helio.git

en: photovoltaic preferential-deposition renewable-energy alpine snow-transport

en: Drifting and blowing snow distribution around structures for Alpine PV applications

envi

en: Environment

de:
Resource Title: Browse simulation files
    Size: 1024 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.589%2Fsimulations%2F
    Description:

de:
Resource Title: simulations.zip
    Size: 11811160064 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.589/simulations.zip
    Description: This file contains the snowBedFoam simulations carried out to produce the results presented in: _article not published yet_.

## Content

### 1. /simulations
Groups all simulations folders organised by parameter (azimuth, groupsize, height, interspace) and by type of experiment (single_ref, group_ref).

### 2. /simulations/[parameter]/
For each parameter multiple simulations were carried out. Each simulation folder follows the same naming systematic:

`helioplant_n#_h#_ARL#_BL#_az#_windDir#`
- n: number of Helioplant units
- h: height-above-surface
- sp: regular distance between the units
- ARL: additional refinement level
- BL: boundary layer first cell height
- windDir: wind direction
- stag: staggered arangement (suffix)

Here is an example of a folder content for a specific parameter
(simulations/azimuth/):
- `helioplant_n9_h07_sp10_ARL45_BL0015_az30_windDir5`: following the naming systematic this folder corresponds to a simulation with 9 aligned Helioplant units with 0.7m of height-above-surface, an inter-unit distance of 10m an azimuth of 30° and a wind direction of 5°.

### 3. /simulations/[parameter]/[helioplant_n#_h#_ARL#_BL#_az#_windDir#]
Each simulation folder contains multiple sub-folders (0/, 100/, 300/, constant/ etc.) and files that define the experiment. For each simulation, the files used in the processing [scripts] are the following:
- 0/Cx: contains the x coordinates of sampling grid cells and of the Helioplants structures.
- 0/Cy: contains the y coordinates of sampling grid cells and of the Helioplants structures.
- 300/kinematicCloud-massCheckPatterns: contains the snow mass distribution values [kg/m2] for each grid cell.

[scripts]: https://github.com/frischwood/snowbed-helio.git

alpine

display_name: alpine

photovoltaic

display_name: photovoltaic

preferential-deposition

display_name: preferential-deposition

renewable-energy

display_name: renewable-energy

snow-transport

display_name: snow-transport


en:
Forests cover about a third of the European land area and provide a multitude of ecosystem services. They are subject to constant change and increasing demands with respect to the ecosystem services they provide. As well as provision of timber, non-timber forest goods (e.g. berries and mushrooms), clean water, carbon sequestration, and protection against natural disasters, there is increasing emphasis being placed on conservation and promotion of biodiversity within forests. However, the state of biodiversity in European forests varies widely. Because of the different biogeographic, sociocultural, political, and climatic conditions at small scales, there is a wide variety of situations, approaches, and legacies across the continent.
This book strives to show the challenges that forest managers are faced with to fulfil the societal demands with regard to forests, and especially to integrate the promotion of biodiversity. In a first section (12 chapters), 44 authors from science, policy, and practice describe  the driving factors of forest management; these factors include national laws and legacies, ownership structures, forest history, and socio-structural conditions. In a second section, 113 authors present 32 case examples from 19 European countries that demonstrate the different approaches to integrate the locally requested ecosystem services. These experiences are synthesised in a final section, and a toolbox of integrative measures is presented that is intended to support forest owners and managers to choose appropriate measures for targeted integrated management in their forests.
Copyright reserved WSL and EFI.

en: biodiversity multifunctional-forestry integrative-forest-management europe timber-production

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: How to balance forestry and biodiversity conservation? - A view across Europe

de:
Resource Title: How to balance forestry and biodiversity conservation? A view across Europe.
    Size: 213891396 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/9ed59d04-de36-484f-ac2c-71dbc91adaa3/resource/97860f83-945b-43b7-ad41-fe9fa5ce2929/download/schuck-2020-how_to_balance_forestry_and-published_version.pdf
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de:
Resource Title: Link to DORA WSL
    Size: 1 bytes
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    URL: https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:25581
    Description:

biodiversity

display_name: biodiversity

europe

display_name: europe

integrative-forest-management

display_name: integrative-forest-management

multifunctional-forestry

display_name: multifunctional-forestry

timber-production

display_name: timber-production


swiss-fluxnet-site-lageren

en:
The Swiss FluxNet Site Lägeren is a managed mixed deciduous mountain forest located on the steep Lägeren mountain (NW of Zurich, Swiss Plateau). The forest is highly diverse, dominated by beech, but also including ash, maple, spruce and fir trees. Eddy covariance flux measurements were started in April 2004. The site was part of the international CarboEurope IP network and the National Air Pollution Monitoring Network (NABEL). In addition to Swiss FluxNet, the site is part of the Long-term Forest Ecosystem Research (LWF) of WSL and the biological drought and growth indicator network (TreeNet) of WSL.
Measurements
- Ecosystem flux measurements of CO2, H2O vapour are performed with the eddy-covariance method. This method is based on measurements of trace gas mixing ratios, using infrared gas analyzers (for CO2, H2O vapor), combined with wind speed and wind direction measurements, using 3D sonic anemometers. To resolve the short-term turbulent fluctuations in the atmosphere, very fast measurements are needed: we measure at 10-20 Hz, i.e., 10-20 times per second. To assess the energy budget of each ecosystem, also radiation sensors and soil climate profiles are installed at the site.
- Sub-canopy eddy fluxes (CO2, H2O), soil respiration campaigns
- Continuous CO2 profile measurements.
- Auxiliary micrometeorology and soil climate measurements.
Data availability
All data are available from the European Fluxes Database Cluster, but are also part of Fluxnet2015 dataset.
Data policy
ICOS data license:
[https://www.icos-cp.eu/data-services/about-data-portal/data-license](https://www.icos-cp.eu/data-services/about-data-portal/data-license)
Detailed site info:
[https://www.swissfluxnet.ethz.ch/index.php/sites/ch-lae-laegeren/site-info-ch-lae//](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-lae-laegeren/site-info-ch-lae/)

en: swiss-fluxnet flux swiss-forest-lab eddy-covariance greenhouse-gas forest meteorological-measurements

en: Swiss FluxNet Site Lägeren

title_for_slug: swiss-fluxnet-site-lageren

de:
Resource Title: Data access
    Size: 1024 bytes
    Format: No Info
    URL: http://www.europe-fluxdata.eu/home/site-details?id=CH-Lae
    Description: The flux and meteo data of the Swiss FluxNet site Lägeren are openly available from the European Fluxes Database Cluster.

de:
Resource Title: Site description
    Size: 512219 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4666c33f-b2af-495c-ac49-4fdec3e26bd1/resource/3e9fc3ba-afc4-41f9-b8ca-488ca6810669/download/swissfluxnet_lae_sitedescription.pdf
    Description:

eddy-covariance

display_name: eddy-covariance

flux

display_name: flux

forest

display_name: forest

greenhouse-gas

display_name: greenhouse-gas

meteorological-measurements

display_name: meteorological-measurements

swiss-fluxnet

display_name: swiss-fluxnet

swiss-forest-lab

display_name: swiss-forest-lab


treenetai

en:
A Toolbox designed around neural networks for time series analysis of TreeNet data. The software containing the machine learning models is stored as a repository on GitHub (https://github.com/treenet/treenetai) and licensed under GNU GENERAL PUBLIC LICENSE version 3.
TreeNet (treenet.info) is an international monitoring and research network in which automated tree stem radius fluctuations measured with point dendrometers are analyzed in terms of forest ecosystem responses to climate change. A continuous stream of microclimate and tree physiology data provides realtime information on tree water relations and tree growth.
TreeNet aims to link research results from carbon flux sites with dendrometer data to entire landscapes. Further it provides online-tools to its partners to automatically interpret stem radius fluctuations in terms of tree water deficit, wood growth and related indicators for forest ecosystem carbon sink and drought stress.
The project was initiated by Roman Zweifel, WSL and Werner Eugster, ETHZ in 2009.

en: tree-growth time-series stem-radius-changes drought-stress dendrometer software treenet machine-learning tree-water-deficit

en: TreeNetAI

title_for_slug: treenetai

de:
Resource Title: TreeNetAI
    Size: 1024 bytes
    Format: No Info
    URL: https://github.com/treenet/treenetai
    Description:

de:
Resource Title: treenetai-main.zip
    Size: 1103745 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/cc075630-946c-4b19-9f5e-d081a683494b/resource/c7a7ff59-403e-4faa-a848-8f65ad2cffb9/download/treenetai-main.zip
    Description: main branch from 06.03.2026

dendrometer

display_name: dendrometer

drought-stress

display_name: drought-stress

machine-learning

display_name: machine-learning

software

display_name: software

stem-radius-changes

display_name: stem-radius-changes

time-series

display_name: time-series

tree-growth

display_name: tree-growth

tree-water-deficit

display_name: tree-water-deficit

treenet

display_name: treenet


swiss-canopy-crane-ii-research-site

en:
![alt text](https://www.envidat.ch/dataset/fc69e369-eee9-42ab-8486-d2c38cff317d/resource/68fa7065-de32-4343-aa26-71094c5254ae/download/sccii.jpg "Swiss Canopy Crane II")
This research site is located near Hölstein in Canton Basel-Landschaft in a mature temperate forest that harbours more than 400 trees from 14 different species. The 1.6 ha site is equipped with the latest infrastructure, including 60 automated point dendrometers, automated soil respiration chambers, 72 ceramic suction cups at various locations and depths across the site, and a range of automated environmental sensors in the soil, the forest floor and in the canopy. A key piece of infrastructure is the new Swiss Canopy Crane II (SCC II), a 50 m tall crane with a 50 m jib that provides canopy access to 250 trees from 12 different species.

en: forest climate-change ecosystem-functions infrastructure drought damage-of-tree swiss-forest-lab

en: Swiss Canopy Crane II research site

title_for_slug: swiss-canopy-crane-ii-research-site

de:
Resource Title: Swiss Canopy Crane II
    Size: 552040 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/fc69e369-eee9-42ab-8486-d2c38cff317d/resource/68fa7065-de32-4343-aa26-71094c5254ae/download/sccii.jpg
    Description:

de:
Resource Title: Research site metadata
    Size: 14985 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/fc69e369-eee9-42ab-8486-d2c38cff317d/resource/f2c3b300-e834-438a-84cb-a93b7a273fdd/download/meta-data-sccii-sfl_env.xlsx
    Description:

climate-change

display_name: climate-change

damage-of-tree

display_name: damage-of-tree

drought

display_name: drought

ecosystem-functions

display_name: ecosystem-functions

infrastructure

display_name: infrastructure


data-analysis-toolkits

en:
These are condensed notes covering selected key points in data analysis and statistics.  They were developed by James Kirchner for the course "Analysis of Environmental Data" at Berkeley in the 1990's and 2000's.  They are not intended to be comprehensive, and thus are not a substitute for a good textbook or a good education!
License: These notes are released by James Kirchner under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.

en: statistics uncertainty error-propagation hypothesis-testing bayesian-inference data-analysis

en: Data analysis toolkits

title_for_slug: data-analysis-toolkits

de:
Resource Title: Toolkit 2: quantifying data distributions using descriptive statistics
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    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/90b055a6-f835-4029-b926-52d5b96c9022/download/toolkit-02-quantifying-distributions.pdf
    Description:

de:
Resource Title: Toolkit 4: confidence intervals
    Size: 155626 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/a06f6e74-1065-4dd3-af6b-9af0acf44f61/download/toolkit-04-confidence-intervals.pdf
    Description:

de:
Resource Title: Toolkit 12: weighted averages and their uncertainties
    Size: 115056 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/78995d64-cf7c-4564-96f2-72763bc2288b/download/toolkit-12-weighted-averages-and-uncertainties.pdf
    Description: There are two different ways to calculate the uncertainty of a weighted average.  Software packages often don't tell you which one they are reporting, and the resulting errors can be huge.

de:
Resource Title: Toolkit 10: linear regression
    Size: 695233 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/12fa9470-b900-4858-9ba9-ddf0978c2470/download/toolkit-10-linear-regression.pdf
    Description: An overview of the math, logic, and pitfalls of ordinary least-squares linear regression

de:
Resource Title: toolkit 6: averaging in nonlinear functions
    Size: 112578 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/35650811-6329-4025-9240-bad37169e31f/download/toolkit-06-nonlinear-averaging.pdf
    Description: The average of a nonlinear function will not equal the function evaluated at its average inputs (the mean of the function will differ from from the function of the mean).  This toolkit shows how to estimate that averaging bias.

de:
Resource Title: Toolkit 1: graphically displaying data distributions
    Size: 198277 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/40c94808-e96f-4826-812d-844de07dfe42/download/toolkit-01-graphing-distributions.pdf
    Description:

de:
Resource Title: Toolkit 9: Experimentwise error rates and statistical fishing expeditions
    Size: 106670 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/557b276f-7c37-4687-bb6b-665bb6e040eb/download/toolkit_09.pdf
    Description: Statistical fishing expeditions, and how to avoid being misled by them

de:
Resource Title: Toolkit 7: hypothesis testing, significance, and power
    Size: 180657 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/8c6c7021-0811-480f-ab52-00fbe3886591/download/toolkit-07-hypothesis-testing.pdf
    Description: An overview of basic concepts -- and common pitfalls -- in statistical hypothesis testing, statistical significance, and power analysis

de:
Resource Title: Toolkit 8: Hodges-Lehmann estimators
    Size: 87630 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/b796d0b3-535a-481c-8110-4947b8408186/download/toolkit-08-hodges-lehmann-estimators.pdf
    Description: A quick introduction to Hodges-Lehmann estimators (nonparametric measures of effect size)

de:
Resource Title: Toolkit 5: uncertainty analysis and error propagation
    Size: 308876 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/5074901f-3168-4b40-b8f0-20dc5acc6248/download/toolkit-05-error-propagation.pdf
    Description: Gaussian error propagation, monte carlo methods, and first-order, second-moment error propagation

de:
Resource Title: Toolkit 11: serial correlation
    Size: 284194 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/8ed5ef3a-173f-4089-b6f0-f5095c9d1b2d/download/toolkit-11-serial-correlation.pdf
    Description: Tools for detecting and correcting for serial correlation in analyses of time series

de:
Resource Title: Toolkit 3: tools for transforming data
    Size: 223799 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/bb3d4263-4c88-4886-bc74-ba48f0398144/download/toolkit-03-transforming-distributions.pdf
    Description: The ladder of powers and a few other cool transformations

bayesian-inference

display_name: bayesian-inference

data-analysis

display_name: data-analysis

error-propagation

display_name: error-propagation

hypothesis-testing

display_name: hypothesis-testing

statistics

display_name: statistics

uncertainty

display_name: uncertainty


en: Although much of the endemic biodiversity of Madagascar can be attributed to its isolation as an island in the Indian Ocean, the high rates of speciation throughout its geologic history suggest an influence of local-scale landscape dynamics. The topographic evolution of Madagascar is dominated by the formation of high-relief continental rift escarpment and we argue that the erosion and landward retreat of this topography creates habitat heterogeneity that has served as a speciation pump for the island. The highest plant richness is found along the escarpment and is characterized by steady diversification rates over the last 45 Ma. Modeled landscape evolution by escarpment retreat demonstrates opportunities for allopatric speciation by transient habitat fragmentation through multiple mechanisms, including catchment expansion, isolation of highland remnants and formation of topographic and river barriers The segregation of floral phylogenetic turnover parallel to the escarpment is consistent with these mechanisms and indicates the importance of erosion-driven landscape dynamics on speciation.

en: endemism madagascar landscape-evolution flora geology

envidat

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

de: EnviDat: das Umwelt-Datenportal

de: EnviDat

envi

de: Umwelt

en: Environment

de:
Resource Title: Data v1.0
    Size: 777360730 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/5fe6b561-d506-47f4-944b-a5fdc8fc3fe2/resource/b2aad61d-11f6-48ca-94d3-056c95916c7c/download/data.zip
    Description:

de:
Resource Title: Escarpment evolution drives the diversification of the Madagascar flora
    Size: 2087857142 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/5fe6b561-d506-47f4-944b-a5fdc8fc3fe2/resource/896d9a92-5cde-4259-a50b-3762698a1d61/download/data.zip
    Description: This upload contains the data (species checklist, species range maps, time-calibrated phylogenies, topographic change, etc.) generated, analyzed, and presented in the paper .

endemism

display_name: endemism

flora

display_name: flora

geology

display_name: geology

landscape-evolution

display_name: landscape-evolution

madagascar

display_name: madagascar


swiss-municipal-spatial-planning-survey

en: Survey of spatial planning instruments and the organization of land use planning in Swiss municipalities. In 2014, the survey was sent to all Swiss municipalities in letter and online form. The response rate of 69% (i.e. 1619 of 2352 municipalities at this time) results in a representative sample of Swiss municipalities. The survey contains questions on the implementation of 20 specific planning instruments and the decade they had been implemented at first, as well as details on the local planning regimes.

en: densification policy-analysis settlement-development spatial-planning urban-sprawl

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: Swiss Municipal Spatial Planning Survey

title_for_slug: swiss-municipal-spatial-planning-survey

de:
Resource Title: Glossary
    Size: 167531 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/a1032456-f016-42f4-a21f-849a26c5ce43/resource/0f76de0f-4c0e-4ed9-b302-6a39cea9cbd5/download/glossaire_fr.pdf
    Description: francais

de:
Resource Title: Municipal Planning Survey
    Size: 1309046 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/a1032456-f016-42f4-a21f-849a26c5ce43/resource/22d4c7c5-ed39-4f2a-a7d6-2957b9e3956b/download/municipal-planning-survey_de.pdf
    Description: german

de:
Resource Title: Municipal Planning Survey
    Size: 240214 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/a1032456-f016-42f4-a21f-849a26c5ce43/resource/ae5941d5-886d-43bb-a62e-90bb35145acc/download/municipal-planning-survey_fr.pdf
    Description: francais

de:
Resource Title: Municipal Planning Survey Data for SPSS
    Size: 2007126 bytes
    Format: No Info
    URL: https://www.envidat.ch/dataset/a1032456-f016-42f4-a21f-849a26c5ce43/resource/f9d0d695-36d9-47f0-a6f3-abc247e401ec/download/municipal-planning-survey_spss
    Description: SPSS

de:
Resource Title: Municipal Planning Survey Data for Stata
    Size: 980633 bytes
    Format: application/x-stata
    URL: https://www.envidat.ch/dataset/a1032456-f016-42f4-a21f-849a26c5ce43/resource/99851c4c-2bf3-48ae-8e4b-e41d7e2ffe35/download/municipal-planning-survey.dta
    Description: STATA 13

de:
Resource Title: Glossary
    Size: 167301 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/a1032456-f016-42f4-a21f-849a26c5ce43/resource/7b8f1dc3-57ec-4f6c-82d5-ee6ffaa5e885/download/glossar_dt.pdf
    Description: german

densification

display_name: densification

policy-analysis

display_name: policy-analysis

settlement-development

display_name: settlement-development

spatial-planning

display_name: spatial-planning

urban-sprawl

display_name: urban-sprawl


en:
![alt text](https://www.envidat.ch/dataset/39a232b5-c50e-490c-9bee-f04c2f697e14/resource/6d38da33-adc3-498e-aa48-7faf60a50a02/download/lwf_irrigation_experiment-pfynwald_2013.jpg "LWF experimental irrigation site Pfynwald")
As the largest contiguous pine forest in Switzerland, the Pfyn forest in Canton Valais (46° 18' N, 7° 36' E, 615 m ASL) offers the best conditions for such measurements.  In light of this, a WSL research team installed a long-term experiment of 20 years duration in the Pfyn forest.  The average temperature here is 9.2°C, the yearly accumulated precipitation is 657 mm (average 1961-1990). The pines in the middle of the forest are about 100 years old and 10.8 m high. The test area has 876 trees covering 1.2 ha divided into 8 plots of 1'000 m2 each. Between the months of April and October, four of these plots are irrigated by a sprinkler system providing an additional 700 mm of water, annually. In the other four plots, the trees grow under natural, hence relatively dry conditions.

en: forest plot experiment lwf irrigation infrastructure swiss-forest-lab

en: Pfynwald long-term experimental irrigation site

de:
Resource Title: LWF Irrigation Plot Pfynwald
    Size: 136857 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/39a232b5-c50e-490c-9bee-f04c2f697e14/resource/6d38da33-adc3-498e-aa48-7faf60a50a02/download/lwf_irrigation_experiment-pfynwald_2013.jpg
    Description: Photo: Maria Schmitt, 2013

de:
Resource Title: Tree measurements 2002-2016 from the long-term irrigation experiment Pfynwald, Switzerland
    Size: 1 bytes
    Format: No Info
    URL: https://doi.org/10.16904/11
    Description:

experiment

display_name: experiment

forest

display_name: forest

infrastructure

display_name: infrastructure

irrigation

display_name: irrigation

lwf

display_name: lwf

plot

display_name: plot

swiss-forest-lab

display_name: swiss-forest-lab


stable-isotope-research-lab-wsl

en:
![alt text](https://www.envidat.ch/dataset/6480bbef-06bf-4da8-8502-96f4def23358/resource/0a9d712c-38ad-4f55-842e-36b21a7e1b97/download/isotopelab_wsl.jpg "Isotope Laboratory WSL")
The lab uses stable isotope ratios of the light elements hydrogen, carbon, nitrogen and oxygen as a universal tool for studying physical, chemical and biological processes in forests and other ecosystems. Due to natural isotope fractionations, environmental changes leave unique fingerprints in organic matter, like tree-rings. It is, therefore, possible to detect the influence of ongoing climate changes on plant physiology. By applying isotopically labelled substrate, matter fluxes through plants and soil can be traced and better understood. The facility has isotope-Ratio mass-spectrometers and dedicated periphery for the analysis of organic matter, gas samples and water samples. With HPLC and GC we apply compound-specific isotope ratio analysis of sugars and organic acids. Additional isotope mass-spectrometers are operated by the Zentrallabor WSL.

en: forest swiss-forest-lab stable-isotopes infrastructure isotopes laboratory

en: Stable Isotope Research Lab WSL

title_for_slug: stable-isotope-research-lab-wsl

isotopes

display_name: isotopes

laboratory

display_name: laboratory

stable-isotopes

display_name: stable-isotopes


massimo

en:
MASSIMO is a distance-independent individual-tree simulator that represents demographic processes (regeneration, growth and mortality) with empirical models that have been parameterized with data from the Swiss NFI. Tree regeneration, growth and mortality are simulated on the regular grid of sample plots of the Swiss NFI, which allows for statistically representative simulations of forest development.
![alt text](https://www.envidat.ch/dataset/8fd996d1-aa7e-41b1-ae6d-1192582c62cc/resource/a12e2cfd-da45-4faf-8291-446c5763ac3c/download/massimo2swissforlab.png)

en: massimo timber-harvesting nfi biomass tree-growth forest model swiss-forest-lab forest-management

en: MASSIMO

title_for_slug: massimo

format: PNG

biomass

display_name: biomass

forest-management

display_name: forest-management

massimo

display_name: massimo

model

display_name: model

nfi

display_name: nfi

timber-harvesting

display_name: timber-harvesting

tree-growth

display_name: tree-growth


en:
This repository consists of the merged data from WaMos2 (2010) and WaMos3 (2020) and also includes both Corona-related surveys that have been conducted within the phase of WaMos3.
WaMos3 is the third assessment of the relationship of the Swiss population to the forest after 1997 and 2010 and was conducted in 2020. As in WaMos2 in 2010, the attitude of the population to the forest as a recreation area, to wood production and to the protective and ecological functions were examined. The topic of climate change was also included. In addition, the views of adolescents between 15 and 18 years of age were taken into account for the first time.
A detailed description of the provided data  can be found in accompanied file "WaMos_Metadatenbeschreibung_221027.pdf" which also contains explanations and examples of the merging process from WaMos2 to WaMos3 as well as sampling procedures. Further, the samples itself can be processed with the help of the provided R-file "EnviDat_WaMos_dataset.R".

en: switzerland corona forest-recreation ppgis questionnaire spatio-temporal-data survey forest-monitoring representative-online-panel

en: Dataset of the Socio-cultural Forest Monitoring Switzerland for WaMos2 and WaMos3

de:
Resource Title: WaMos data scripts and pre-processing information
    Size: 386948 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/1fed4aeb-4879-4a86-8106-d4ac084f4819/resource/2d769e5d-6367-44d0-98f2-6ea661d05ff3/download/wamos_sample_processing.zip
    Description:

de:
Resource Title: WaMos data
    Size: 22248789 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/1fed4aeb-4879-4a86-8106-d4ac084f4819/resource/bd3721d4-1966-4834-b09f-50bd393658e0/download/wamos_samples.zip
    Description:

de:
Resource Title: WaMos meta data description
    Size: 544437 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/1fed4aeb-4879-4a86-8106-d4ac084f4819/resource/72a4cba1-bc0f-4045-afe3-fd781903f9de/download/wamos_metadatenbeschreibung_221027.pdf
    Description:

corona

display_name: corona

forest-monitoring

display_name: forest-monitoring

forest-recreation

display_name: forest-recreation

ppgis

display_name: ppgis

questionnaire

display_name: questionnaire

representative-online-panel

display_name: representative-online-panel

spatio-temporal-data

display_name: spatio-temporal-data

survey

display_name: survey

switzerland

display_name: switzerland


en: These data were used to drive and evaluate Jules Investigation Model (JIM) snow simulations. The data provided are the forcing data used for the "deterministic" runs as described in Winstral et al., 2019. The bias-detecting ensemble (Winstral et al., 2019) used observed snow depths (HS) to detect biases in these deterministic simulations related to precipitation and energy inputs to JIM. Simulations that included the BDE evaluations substantially improved JIM simulations.

en: snow-depth snow jules-investigation-model jim snow-simulation

envidat

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

en: Meteorological data used to develop and validate the bias-detecting ensemble (BDE)

envi

en: Environment

de:
Resource Title: Station forcing data
    Size: 152284372 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/04470a99-93e5-499d-b869-6c30dffdc2f5/resource/282b7bc4-a607-4927-a2fd-858610a973c8/download/forcings.zip
    Description: All forcing data need to run JIM at HS and SWE stations. These are data downscaled directly from COSMO NWP data (WY14, WY15) and further enhanced by additional station observations of snow depth (HS) and air temperature (Ta) in WY17. Further information can be found in README file. Note that in WY14 and WY15, shortwave radiation was not split into direct and diffuse (not necessary for JIM) and that the default method in JIM for splitting precipitation phase was used. In WY 17, direct and diffuse were combined as input to JIM and JIM was forced with pre-processed liquid and solid precipitation phases.

de:
Resource Title: READ_ME
    Size: 1134 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/04470a99-93e5-499d-b869-6c30dffdc2f5/resource/30d1a503-fd18-43a5-9af9-e0e0f1855272/download/read_me.txt
    Description: File/data descriptor

de:
Resource Title: DEM
    Size: 568273 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/04470a99-93e5-499d-b869-6c30dffdc2f5/resource/705365e0-bdd9-4dca-966d-e8f9b9b7ede5/download/dem.txt
    Description: 1-km resolution DEM used for downscaling gridded NWP data to stations and for distributing BDE evaluations at HS-stations to SWE-stations

de:
Resource Title: HS and SWE observations
    Size: 1214806 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/04470a99-93e5-499d-b869-6c30dffdc2f5/resource/d1a89df0-4991-48a0-8fbc-35f72afb953e/download/validation.zip
    Description: Automated (HS) and manual (SWE) data used to evaluate model simulations

jim

display_name: jim

jules-investigation-model

display_name: jules-investigation-model

snow

display_name: snow

snow-depth

display_name: snow-depth

snow-simulation

display_name: snow-simulation


en:
Ontologies used for the case study in the publication.
Creative Commons (CC) license: CC BY-NC-SA -https://creativecommons.org/licenses/by-nc-sa/3.0/-

en: conflict-resolution meaning-negotiation ontology-authoring disease-control infectious-disease

envidat

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

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

de: EnviDat

envi

de: Umwelt

conflict-resolution

display_name: conflict-resolution

disease-control

display_name: disease-control

infectious-disease

display_name: infectious-disease

meaning-negotiation

display_name: meaning-negotiation

ontology-authoring

display_name: ontology-authoring


en:
This data-set contains Land Surface Albedo (LSA) data obtained via a UAV sytem with up and downlooking shortwave radiation sensors, as described in the JGR-Atmospheres paper "Effect of forest canopy structure on wintertime Land Surface Albedo: Evaluating CLM5 simulations with in-situ measurements", by Malle et al. (2021, under review). This publication must be cited when using the data.
Data was collected across a large range of forest structures and solar angles in Switzerland (Davos Laret) and in Finland (Sodankylä). For each waypoint location at each site, data includes measured LSA, incoming SWR, reflected SWR and sunlit snow-view fraction alongside zenith angle, azimuth angle and measurement time (local time). Please refer to the abovementioned article for more detailed explanation.

en: uav forest-snow snow-modelling albedo climate

en: Wintertime UAV-based land surface albedo data over forested environments

de:
Resource Title: Davos Laret 2018
    Size: 12033 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/e2f6c051-40b9-469d-9276-c8980b4b53e6/resource/0eb24bd9-413a-4e44-8c89-3fbc72616882/download/davos-laret.zip
    Description: UAV-based LSA data collected in Davos Laret between January and May 2018. The zip file contains one csv file per waypoint location, please refer to the following paper on further information on waypoint locations etc. (to be updated once published): Malle et al. (2021): Effect of forest canopy structure on wintertime Land Surface Albedo: Evaluating CLM5 simulations with in-situ measurements in Journal of Geophysical Research: Atmospheres (under review).

de:
Resource Title: Sodankylä (FIN) pine 2019
    Size: 8316 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/e2f6c051-40b9-469d-9276-c8980b4b53e6/resource/e4009b6d-1e1b-46ae-aa19-8a34be3f9cab/download/sodankylae_pine.zip
    Description: UAV-based LSA data collected in Sodankylae (FIN) between April and May 2019. The zip file contains one csv file per waypoint location, please refer to the following paper on further information on waypoint locations etc. (to be updated once published): Malle et al. (2021): Effect of forest canopy structure on wintertime Land Surface Albedo: Evaluating CLM5 simulations with in-situ measurements in Journal of Geophysical Research: Atmospheres (under review).

albedo

display_name: albedo

climate

display_name: climate

forest-snow

display_name: forest-snow

snow-modelling

display_name: snow-modelling

uav

display_name: uav