en:
Two raster maps (10m resolution) of: I) the most suitable extraction method for wood in the Swiss forest, and II) the overall suitability of the Swiss forest for economic wood extraction and transport.
A modern forest road system is important for the efficient management of forests. In order to assess the current forest accessibility in Switzerland on a comprehensive basis, the entire Swiss forest was investigated using a consistent methodology. In our model, wood extraction from the stand to the road and on-road transport are analysed in combination.
Suitable extraction methods for each forest parcel (10m x 10m) were determined using an approach in which ground-based, cable-based and air-based transport are distinguished. First, the areas for ground- and cable-based extraction were delineated. The trafficability of the forest areas was assessed based on the terrain and soil characteristics; trafficable areas also had to be connected to a forest road. To evaluate the suitability for cable-yarding (up to a maximum distance of 1500 m), terrain and possible obstacles (e.g., power lines) were considered. The remaining forest area, which was not suitable for either ground-based or cable-based methods, was assigned to the "helicopter" category. As a result of this analysis, a map of the most suitable skidding method for each plot could be created. When several methods were possible for a parcel, the priority was ground-based over cable-based over air-based.
Road transport was investigated using network analysis, based on the data set "Forest access roads 2013" from the Swiss National Forest Inventory (NFI), which contains information on width and weight limits of roads in the forest and up to the superordinate main road network. Thus, in addition to the distance, the largest type of vehicle allowed on the respective removal route could also be taken into account.
Based on the extraction method and the weight limits for on-road transport, the forest area was divided into three categories: 1) meets the requirements for efficient forest management (all forest parcels with ground-based extraction method or mobile cable-yarding, transport weight limit at least 28 tons); 2) limited suitability for efficient forest management; and 3) not suitable for efficient forest management (forest parcels in the "helicopter" category or transport with trucks under 26 tons).
The resulting maps cannot provide an accurate classification for each forest parcel. Missing or incorrect roads in the road dataset, insufficient information on ground trafficability or other local factors, the limitation to only three possible extraction systems, and failure to account for anchor trees, extraction methods changing over small distances, and unrealistically short cable-yarding distances can cause the model results to deviate from the assessment by an expert with knowledge of the local conditions. Also, protected areas were not excluded and harvesting intensity was not taken into account. The advantage of the method is that consistent criteria are used for the entire Swiss forest, making the results comparable throughout Switzerland.
The data are managed at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) and are available to third parties on request. (NFI data policy: https://www.lfi.ch/dienstleist/daten.php)
Input data used:
- Forest road dataset of the NFI4 (only truck roads from 3.0 m width and 26 t carrying capacity) (2016).
- NFI forest mask, 10 m resolution (2015)
- Digital elevation model, 10m resolution (based on swissALTI3D 2016)
- Slope map, 10m resolution (based on swissALTI3D 2016)
- Soil suitability map, 10m resolution (based on soil suitability map BFS 2000)
- Obstacles for cable lines, 10m resolution (buildings, major roads, power lines, railroads, based on swissTLM3D 2016)

en: economic-efficiency wood-harvesting timber-transport forest switzerland forest-accessibility wood-extraction forest-roads

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: Accessibility of the Swiss forest for economic wood extraction (2021)

en:
Resource Title: Data Request "Forest Accessibility"
    Size: 23228715 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/47871f44-7c5c-452e-b755-ff5643abb853/resource/b1a225d8-9db7-4a62-9d6a-224673f88b01/download/mapguete_merge_lv95_cog2.tif
    Description: Raster map (10m resolution) of the overall suitability of the Swiss forest for economic wood extraction and transport

en:
Resource Title: Data Request "Extraction Method"
    Size: 28660858 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/47871f44-7c5c-452e-b755-ff5643abb853/resource/e57bf80a-9c2b-40d5-8938-5b9dad24549b/download/maperntemethoden_merge_lv95_cog2.tif
    Description: Raster map (10m resolution) of the most suitable extraction method for wood in the Swiss forest

economic-efficiency

display_name: economic-efficiency

forest

display_name: forest

forest-accessibility

display_name: forest-accessibility

forest-roads

display_name: forest-roads

switzerland

display_name: switzerland

timber-transport

display_name: timber-transport

wood-extraction

display_name: wood-extraction

wood-harvesting

display_name: wood-harvesting


$oid: OBSOLETE

en:
This dataset contains simulation data for *"Influence of Air Flow Features on Alpine Wind Energy Potential" (Kristianti et al., 2024).*
•	The WRF_dia2702 and WRF_dia1103 folders contain the namelist.wps, namelist.input, and output files from the simulation in the Diablerets region on 27/02/2021 and 11/03/2021, respectively.
•	The WRF_luk2310 and WRF_luk0912 folders contain the namelist.wps, namelist.input, and output files from the simulation in the Lukmanier region on 23/10/2020 and 09/12/2020, respectively.
•	The postprocessing file contains Python codes used to create figures in Kristianti et al. (2024).
**Notes:**
•	Topography input is provided by Gerber et al. (2021).
•	Simulations are run using the COSMO-WRF model developed by Gerber et al. (2018).
•	Visualization by Python code uses wrf-python provided by Ladwig et al. (2017).
*This dataset was produced by the EDGE consortium sponsored by the Swiss Federal Office of Energy's SWEET programme, Swiss National Science Foundation (SNSF): Grant 179130, and the Swiss National Supercomputing Centre (CSCS) projects s938, s1115 and s1242.*

en: grayzone wrf numerical-simulation complex-terrain cosmo-wrf

envidat

approval_status: approved

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

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

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

de: EnviDat: das Umwelt-Datenportal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

state: active

type: organization

wsl

capacity: public

de: EnviDat

en: COSMO-WRF Dataset for Swiss Alps Simulations in Gray-Zone Resolution

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

en:
Resource Title: WRF_dia1103.zip
    Size: 9126805504 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.501/WRF_dia1103.zip
    Description:

format: ZIP

media_type: application/zip

en:
Resource Title: postprocessing.zip
    Size: 4301 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.501/postprocessing.zip
    Description:

en:
Resource Title: WRF_luk2310.zip
    Size: 9985798963 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.501/WRF_luk2310.zip
    Description:

en:
Resource Title: WRF_dia2702.zip
    Size: 8482560410 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.501/WRF_dia2702.zip
    Description:

en:
Resource Title: WRF_luk0912.zip
    Size: 10844792422 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.501/WRF_luk0912.zip
    Description:

complex-terrain

display_name: complex-terrain

cosmo-wrf

display_name: cosmo-wrf

grayzone

display_name: grayzone

numerical-simulation

display_name: numerical-simulation

wrf

display_name: wrf


photogrammetric-drone-data-gruenbodeli

en:
We conducted various drone flights at Grüenbödeli near Davos with the Sony RX1R II mounted on a Wingtra drone during 2020/21/22. The data was processed with the Agisoft Metashape Professional Software.
The following products are available for download:
- DSM 10cm resolution
- Orthomosaic 3cm/25mm resolution
- Snow Raster 10cm resolution
- original RGB images

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

en: Photogrammetric Drone Data Grüenbödeli

title_for_slug: photogrammetric-drone-data-gruenbodeli

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

uav

display_name: uav

wingtra

display_name: wingtra


en: To map and assess snow depth on different dates, 9 flights were conducted in the winter season of 2020/21 at the Latschüelfurgga in Davos. The data was captured with a Sony RX1R II mounted on a Wingtra drone and was processed with the Agisoft Metashape software. High-resolution DSMs, orthomosaics and snow height rasters, as well as the original RGB images from each flight are available.

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

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: Photogrammetric Drone Data Latschuelfurgga


en:
snowBedFoam 1.0. is a snow transport solver implemented in the computational fluid dynamics software OpenFOAM. It is adapted from the standard multi-phase flow solver DPMFoam for application in snow-influenced environments.
To simulate aeolian snow transport, snowBedFoam 1.0. handles coupled Eulerian–Lagrangian phases, which involve a finite number of particles (snow) spread in a continuous phase (air). The snow erosion and deposition are modelled through physics-based equations similar to the ones employed in the well-established LES-Lagrangian Stochastic Model (Comola and Lehning, 2017 ; Sharma et al., 2018 ; Melo et al., 2022). This modelling approach is computationally intensive and thus adapted to simulate snow movement and distribution on small scale terrain.
First, snowBedFoam 1.0. was applied to topographical data collected on Arctic sea ice during the MOSAiC expedition (Clemens-Sewall, 2021). Together with atmospheric data from the MOSAiC Met City (Shupe et al., 2021) used for the fluid forcing, the model was able to accurately simulate the zones of erosion and deposition of snow along a complex ice ridge structure (Hames et al., 2022).
Second, snowBedFoam 1.0. was used to simulate the snow distribution around the German Antarctic research station Neumayer Station III. The effect of snow properties, fluid forcing and aerodynamic structures on the snow accumulation were assessed.
snowBedFoam 1.0 was implemented in 2 different OpenFOAM versions, namely OpenFOAM-2.3.0 and OpenFOAM-5.0. The latter offers more options for turbulence models and boundary conditions. The fundamental model equations were not changed from one implementation to the other, thus both still correspond to snowBedFoam 1.0. The two branches are called snowBedFoam-v1-2.3.0 (OpenFOAM-2.3.0)  and snowBedFoam-v1-5.0 (OpenFOAM-5.0). The core codes of snowBedFoam 1.0. are directly accessible on the WSL/SLF GitLab repository (more details in the Resources section).

en: snow-deposition mosaic arctic-sea-ice snow-transport antarctica openfoam

en: EnviDat: the Environmental Data Portal

en: snowBedFoam: an OpenFOAM Eulerian-Lagrangian solver for modelling snow transport

en:
Resource Title: MOSAiC Met City preliminary (Level 2) wind data
    Size: 37737 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/8bdc13ec-e59e-4f08-aa0d-646745fb76a6/resource/a9b2c876-ac90-4aa5-ab56-a06e82221894/download/mosaic_mettowerdata_level2.zip
    Description: Extract from the preliminary MOSAiC atmospheric data (Level 2) for the time period 6th to 13th of November, 2019.
This data was used to set up the fluid forcing parameters in the snowBedFoam simulations on MOSAiC sea ice. It is being published in this repository with the kind permission from Matthew Shupe and his team.
The final processed and quality controlled version of this dataset (Level 3) has not been published yet. A complete reference will be added in this description once it is released.

en:
Resource Title: snowBedFoam-v1-5.0
    Size: 40167324 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/8bdc13ec-e59e-4f08-aa0d-646745fb76a6/resource/87b34f08-3ae1-44d3-bd5a-aa502f8e83c3/download/snowbedfoam-v1-5.0.zip
    Description: OpenFOAM-5.0 scripts containing the implemented Eulerian-Lagrangian snow transport model.

The code file provided here is not updated. The most recent version of the script can be found on the WSL/SLF GitLab repository: https://gitlabext.wsl.ch/atmospheric-models/snowbedfoam-v1-0.git

en:
Resource Title: Snowdrift measurements around Neumayer Station III
    Size: 32270 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/8bdc13ec-e59e-4f08-aa0d-646745fb76a6/resource/a6d100a5-ab97-41f4-b45a-dbed9fb30619/download/nm3-measurements.zip
    Description: Files containing the topographical data barometrically measured in June 2009 around the German Antarctic Research station Neumayer III. Those measurements are compared to the snowBedFoam simulations to assess the ability of the model to reproduce real snow distribution observations.

en:
Resource Title: snowBedFoam-v1-2.3.0
    Size: 38674582 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/8bdc13ec-e59e-4f08-aa0d-646745fb76a6/resource/056080d7-e68d-4cb8-9b63-34ebf5998fe7/download/snowbedfoam-v1-0-main.zip
    Description: OpenFOAM-2.3.0. scripts containing the implemented Eulerian-Lagrangian snow transport model.

The code file provided here is not updated. The most recent version of the script can be found on the WSL/SLF GitLab repository: https://gitlabext.wsl.ch/atmospheric-models/snowbedfoam-v1-0.git

en:
Resource Title: Neumayer III simulations
    Size: 296665509 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/8bdc13ec-e59e-4f08-aa0d-646745fb76a6/resource/6bce1ab3-5fab-4fe6-afb0-7aa90760e9d2/download/nm3-simulations.zip
    Description: Snow distribution results around Neumayer Station III obtained for the sensitivity analysis and comparison to measurements. The file contains several sub-folders, namely:
-COMP: simulation output for the comparison to measurements;
-FLOW: simulation output for the study of wind forcing impact;
-simulation_exampleCase: example case of a snowBedFoam simulation with the reference Neumayer Station mesh;
-SNOW: simulation output for the study of snow properties impact;
-STRUCT: simulation output for the study of structure properties impact.

en:
Resource Title: Paper Draft
    Size: 3025070 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/8bdc13ec-e59e-4f08-aa0d-646745fb76a6/resource/0c5b7c6e-afa2-4a08-bb12-351648d1e1f9/download/snowbedfoam_mosaicseaice.pdf
    Description: This is the initial draft of the paper where the snowBedFoam 1.0. results are tested against Terrestrial Laser Scan (TLS) measurements of snow dynamics on Arctic sea ice (MOSAiC expedition). It highlights the numerical limitations of our model as well as the measurement uncertainties, which may occur in such extreme environments.

format: PDF

media_type: application/pdf

antarctica

display_name: antarctica

arctic-sea-ice

display_name: arctic-sea-ice

mosaic

display_name: mosaic

openfoam

display_name: openfoam

snow-deposition

display_name: snow-deposition

snow-transport

display_name: snow-transport


en: We present forecasts of land-use/land-cover (LULC) change for Switzerland for three time-steps in the 21st century under the representative concentration pathways 4.5 and 8.5, and at 100-m spatial and 14-class thematic resolution. We modelled the spatial suitability for each LULC class with a neural network (NN) using -200 predictors and accounting for climate and policy changes. We used data augmentation to increase performance for underrepresented classes, resulting in an overall quantity disagreement of 0.053 and allocation disagreement of 0.15, which indicate good model performance. These class-specific spatial suitability maps outputted by the NN were then merged in a single LULC map per time-step using the CLUE-S algorithm, accounting for LULC demand for the future and a set of LULC transition rules. As the first LULC forecast for Switzerland at a thematic resolution comparable to available LULC  maps for the past, this product lends itself to applications in land-use planning, resource management, ecological and hydraulic modelling, habitat restoration and conservation.

en: land-use landscape machine-learning land-cover modelling landscape-change

en: High resolution land use forecasts for Switzerland in the 21st century

envi

en: Environment

en:
Resource Title: swiss_lulc_forecast_21th_century.zip
    Size: 16438707 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/69945cd4-3dfc-4b1d-bdd4-e88cc577c3c9/resource/347fffe5-f3cb-4309-914b-9057a30a32db/download/swiss_lulc_forecast_21th_century.zip
    Description: Compressed (.zip) file containing six georeferenced raster files in the GeoTIFF format with embedded coordinate reference system, and a table (comma separated values, .csv) encoding a colour palette for plotting (HEX colour codes for each class). GeoTIFF file names include the time-step and the RCP (e.g. “LULC_2020_2049_RCP45.tiff”). Each raster file has 3484x2207 square pixels (i.e. 7.7 megapixels) and is projected in the Swiss coordinate system CH1903+/LV95 (EPSG:2056).

en:
Resource Title: Code.zip
    Size: 407530 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/69945cd4-3dfc-4b1d-bdd4-e88cc577c3c9/resource/593cc9c4-deb1-4637-a328-9d68f0e15cd6/download/code.zip
    Description: Code used to produce the Land-use/land-cover forecasts for Switzerland across the 21st century at 100-m resolution.

The script is based on input data which are not included as they are not freely available, therefore it will not run as it is. These input data are described in the accompanying publication on Data Science access to them can be requested to their owners. Alternatively the code can be modified to run on other data.

land-cover

display_name: land-cover

land-use

display_name: land-use

landscape

display_name: landscape

landscape-change

display_name: landscape-change

machine-learning

display_name: machine-learning

modelling

display_name: modelling


en:
This dataset provides distribution data of lichens in Switzerland of the National Data and Information Centre, called Swiss Lichens. SwissLichens is a partner of InfoSpecies, the network of Swiss data and information centres for fauna, flora and fungi. One of its main objectives is to document the spatial and temporal distribution of species in Switzerland.
The SwissLichens database currently contains more than 120’000 georeferenced lichen observations, distributed throughout Swizerland. The oldest observations date back to 1790. A large portion of the records dtae from the last two decades of the last century to the present day. The database is continuously updated with new findings. The data have been validated and originate from national inventories, from research projects, from floristic observations by volunteers as well as from private and public herbaria and from the literature.
Each record consists of the species name, information of the location (Swiss Coordinates, precision of the coordinates, elevation above sea level, municipality and canton), the date of observation, the ecotype (epiphytic, terricol, lignicol, saxicol), as well as information on the conservation status of the species (red list status, conservation priority status, status in the Nature and Cultural Heritage Act). Information on the ecology (habitat, substrate) is partially available.
They are free of charge for research projects and can be requested from InfoSpecies using a form.
Licence under www.infospecies.ch. Data is free of charge for research projects and available on request.

en: switzerland biodiversity national-data-centre lichen-conservation swisslichens lichens lichen-distribution

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: SwissLichens - Distribution of lichens in Switzerland

format: XLS

biodiversity

display_name: biodiversity

lichen-conservation

display_name: lichen-conservation

lichen-distribution

display_name: lichen-distribution

lichens

display_name: lichens

national-data-centre

display_name: national-data-centre

swisslichens

display_name: swisslichens

switzerland

display_name: switzerland


en:
This dataset contains all data on which the following publication below is based.
Paper Citation:
Frey B., Maurer C., Schneider K. (2023) Informationen zum Boden anhand der Gebundenheit der Rote-Liste Arten BAFU. Bundesamt für Umwelt BAFU. 26 p.
Please cite this paper together with the citation for the datafile.
Soil is the habitat and basis of life for countless creatures. The great importance of soil organisms for the fertility of our soils is widely recognized, but information is sparse on their distribution and possible endangerment in Switzerland. In the present project, target species, i.e. species that are closely linked to the soil, were identified, and for the the first time, information was gathered on the degree of endangerment of the soil ecosystem in Switzerland on the basis of Red List species. For eleven Red List  groups, the species' soil dependency was determined, i.e. whether their life cycle includes stages in or on the soil. The assessment of soil dependency was made using four classes, which enabled the classification not only of insects but also of vertebrates, land snails, lichens and fungi.

en: ecosystem-protection lichens soil-dependency info-fauna fungi red-list soil-organisms soil-quality eco-fauna conservation soil-biodiversity database fauna

envidat

en: EnviDat: the Environmental Data Portal

en: Information on soil based on the soil dependency of the FOEN red list species

en:
Resource Title: soil_dependency.xlsx
    Size: 1367091 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/006ebf36-0cbf-415f-bd0c-0261bc32b1f5/resource/73beef16-17c8-46e4-a77b-adc5ec694918/download/soil_dependency.xlsx
    Description: Overview over the soil dependency and endangerment status of different groups of organisms.

en:
Resource Title: percentages_endangerment-categories.xlsx
    Size: 14873 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/006ebf36-0cbf-415f-bd0c-0261bc32b1f5/resource/a79f2c66-cf27-4427-b7ad-829931cb175a/download/percentages_endangerment-categories.xlsx
    Description: Proportion of endangered species grouped by the four soil dependency classes.

en:
Resource Title: raw_data.xlsx
    Size: 422329 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/006ebf36-0cbf-415f-bd0c-0261bc32b1f5/resource/b2f4f53a-4ffa-4a44-8290-0c3c9c6b1a82/download/raw_data.xlsx
    Description: Raw dataset of species and their soil dependency as well as endangerment status.

conservation

display_name: conservation

database

display_name: database

eco-fauna

display_name: eco-fauna

ecosystem-protection

display_name: ecosystem-protection

fauna

display_name: fauna

fungi

display_name: fungi

info-fauna

display_name: info-fauna

red-list

display_name: red-list

soil-biodiversity

display_name: soil-biodiversity

soil-dependency

display_name: soil-dependency

soil-organisms

display_name: soil-organisms

soil-quality

display_name: soil-quality


en:
What are the research data files about:
Raw data on various beech (Fagus sylvatica) leaf traits. Beech leaf chemistry (primary and specialized metabolites), leaf damage measurements from various herbivore feeding guilds and crown health assessment
Which methods were used:
Field surveys on trees that showed either prematured defoliation due to drought-stress or showed not pre-mature defoliation. .
When and where was the data collected/analyzed:
Field data was collected in 2019, 2020
Data was analyzed in 2021-2023

en: climate-change drought-legacy fagus-sylvatica herbivory phytochemistry

en: Legacy effects of premature defoliation in response to an extreme drought event modulate phytochemical profiles with subtle consequences for leaf herbivory in European beech

en:
Resource Title: Specialized_metabolome.xlsx
    Size: 3876111 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/57776282-fb31-43b3-be8a-42c4b8d25c71/resource/f1b46aa1-9647-4371-8c2c-442f78fe8831/download/specialized_metabolome.xlsx
    Description:

en:
Resource Title: Overview_chemistry_herbivory.xlsx
    Size: 43689 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/57776282-fb31-43b3-be8a-42c4b8d25c71/resource/6283a18b-69af-4fd1-bbd0-294ee24ec06b/download/overview_chemistry_herbivory.xlsx
    Description:

en:
Resource Title: Primary_metabolome.xlsx
    Size: 259529 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/57776282-fb31-43b3-be8a-42c4b8d25c71/resource/45798cb9-3888-4e89-8178-0f52330c08a8/download/primary_metabolome.xlsx
    Description:

climate-change

display_name: climate-change

drought-legacy

display_name: drought-legacy

fagus-sylvatica

display_name: fagus-sylvatica

herbivory

display_name: herbivory

phytochemistry

display_name: phytochemistry


soil-property-maps-for-the-swiss-forest

en:
We used 2071 forest soil profiles to model a wide range of soil properties for the forested area of Switzerland. The spatial prediction is based on the principle of «digital soil mapping». This involves linking soil profiles with soil forming factors using statistical or machine learning methods.
A quantile regression forest (QRF) approach was applied to predict the following soil properties at six depth ranges: clay, gravel, sand, fine earth density, SOC. The depth ranges correspond to the standard depths of the [GlobalSoilMap.Net](https://www.isric.org/) specification: 0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm. In addition, the total soil depth down to a non-root-permeable layer or solid rock soil thick was predicted. To quantify the uncertainty for each predicted pixel, the upper and lower limit of the 90% prediction interval derived from QRF was calculated. More details on the methods and results are described in Baltensweiler et al. 2021 and Baltensweiler et al 2022.
The soil property maps, and the uncertainty maps are provided as a GeoTIFF files at 25 m resolution. The excel file (xlsx) provides a short description of the raster layers.
**The soil and the uncertainty maps can be viewed in a simple web-GIS application available at:**
[www.wsl.ch/soilmaps](https://www.wsl.ch/soilmaps).

en: forest switzerland soil-ph soil-depth soc soil soil-texture soil-properties dsm

en: Soil property maps for the Swiss forest

title_for_slug: soil-property-maps-for-the-swiss-forest

envi

en: Environment

en:
Resource Title: depth_map.zip
    Size: 37183065 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/7c58ee27-6410-41bc-b533-1c3842032878/download/depth_map.zip
    Description:

en:
Resource Title: gravel_uncertainty_maps.zip
    Size: 89436761 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/d7b44437-7fc2-4750-a322-e26887a830c6/download/gravel_uncertainty_maps.zip
    Description:

en:
Resource Title: sand_uncertainty_maps.zip
    Size: 116700070 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/25bdf03f-226b-4274-8867-6553b2c12bd3/download/sand_uncertainty_maps.zip
    Description:

en:
Resource Title: pH_maps.zip
    Size: 129172696 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/639353b5-92a5-43d1-b0ec-4ce8ca7773c0/download/ph_maps.zip
    Description:

en:
Resource Title: density_maps.zip
    Size: 172162688 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/d5a87cce-64aa-40db-aa02-589fb89c7984/download/density_maps.zip
    Description:

en:
Resource Title: clay_maps.zip
    Size: 55978125 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/1616b34f-542a-4622-9bdd-e454500393c8/download/clay_maps.zip
    Description:

en:
Resource Title: soc_uncertainty_maps.zip
    Size: 222671274 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/a214d4d2-72bd-451d-8fd5-a461b3a78b28/download/soc_uncertainty_maps.zip
    Description:

en:
Resource Title: SoilMaps_description.xlsx
    Size: 11429 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/5f08172f-967c-4aa3-beaf-a81338290f9e/download/soilmaps_description.xlsx
    Description:

en:
Resource Title: pH_uncertainty_maps.zip
    Size: 192523204 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/c5bea000-de46-4b93-8de8-f246c6135b9f/download/ph_uncertainty_maps.zip
    Description:

en:
Resource Title: gravel_maps.zip
    Size: 80316578 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/c799d7c4-ae83-402d-976d-f23ef7021ad7/download/gravel_maps.zip
    Description:

en:
Resource Title: depth_uncertainty_maps.zip
    Size: 69351779 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/b4dc7b7b-e800-45d4-8d03-997968710b56/download/depth_uncertainty_maps.zip
    Description:

en:
Resource Title: clay_uncertainty_maps.zip
    Size: 105083064 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/1059745c-d83e-4f31-b723-07cce9fb2104/download/clay_uncertainty_maps.zip
    Description:

en:
Resource Title: density_uncertainty_maps.zip
    Size: 143926943 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/5886ee25-bfb6-44a2-97a9-00d5c968d1c9/download/density_uncertainty_maps.zip
    Description:

en:
Resource Title: soc_maps.zip
    Size: 144898851 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/d80078ce-cf36-4590-8f01-e79de67f8b2f/download/soc_maps.zip
    Description:

en:
Resource Title: sand_maps.zip
    Size: 65109556 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/9a0b1daa-a4e0-4c62-b348-ec23354105c6/resource/1a2addd8-1b83-4502-b015-43face6c519b/download/sand_maps.zip
    Description:

dsm

display_name: dsm

forest

display_name: forest

soc

display_name: soc

soil

display_name: soil

soil-depth

display_name: soil-depth

soil-ph

display_name: soil-ph

soil-properties

display_name: soil-properties

soil-texture

display_name: soil-texture

switzerland

display_name: switzerland


en:
Data from a 17-year-long irrigation experiment (Pfynwald, Switzerland) in a naturally dry forest dominated by 100-year-old pine trees (Pinus sylvestris). This dataset includes measure tree-ring width and tree-ring isotope chronologies (stable isotope ratios of non-exchangeable hydrogen δ2H,  oxygen δ18O, and carbon δ13C), for the Pfynwald experiment, including control trees, irrigation and irrigation-stop treatments until the year 2019.
This dataset contains all data on which the following publication below is based.
Please cite this paper together with the citation for the datafile.

en: dendrochronology tree-rings tree-ring-cellulose isotopes pfynwald scots-pine pinus-sylvestris

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: Pfynwald 2019 - Dendrochronological and tree-ring isotope dataset

en:
Resource Title: Read me file.docx
    Size: 14171 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/17c0a7f0-d19e-4e18-aaa8-96e9f1a1585c/resource/90e7d0c0-b205-4c92-bb0b-eb1757fdd755/download/read-me-file.docx
    Description:

en:
Resource Title: ChronoData_treeRingWidthIsotopes.csv
    Size: 80920 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/17c0a7f0-d19e-4e18-aaa8-96e9f1a1585c/resource/a64e928b-b690-4a5a-8c26-afcacff9d5bc/download/chronodata_treeringwidthisotopes.csv
    Description:

format: CSV

media_type: text/csv

en:
Resource Title: All_tissues_Isotopes_Pfynwald2019.csv
    Size: 43235 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/17c0a7f0-d19e-4e18-aaa8-96e9f1a1585c/resource/14910e94-cf63-44dc-9539-aabade0801a0/download/all_tissues_isotopes_pfynwald2019.csv
    Description: Isotope measurements of tree-level tissues for the vegetative season 2019.

en:
Resource Title: ChronoData_treeRingWidthIsotopes.csv
    Size: 80920 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/17c0a7f0-d19e-4e18-aaa8-96e9f1a1585c/resource/fc48bd90-0a2e-4c70-a822-0e93e88ea7ad/download/chronodata_treeringwidthisotopes.csv
    Description:

dendrochronology

display_name: dendrochronology

isotopes

display_name: isotopes

pfynwald

display_name: pfynwald

pinus-sylvestris

display_name: pinus-sylvestris

scots-pine

display_name: scots-pine

tree-ring-cellulose

display_name: tree-ring-cellulose

tree-rings

display_name: tree-rings


en:
This study examines the siting scenarios for renewable energy infrastructure (REI) in Switzerland, incorporating the external costs of ecosystem services and, innovatively, social preferences. This approach challenges the prevalent techno-economic siting paradigm, which often overlooks these externalities. To minimize the external costs of the scenarios while maximizing energy yield, Marxan, an optimization software, was employed.
MARXAN was run for 2 versions: a) without ground-mounted open space PV infrastructure (excl. OS) and b) with ground-mounted open space PV infrastructure (incl. OS). In each version optimization was done using ecological costs (ECUess) or social costs (ECUsoc) in a regular grid of 4x4km (planning unit).
File:
PU_data.shp: compressed Shapefile (ESRI) from the MARXAN optimization with 2216 rows (objects) and 18 columns (variables) with sf (simple feature) and data frame classes.
Headers are described below:
X01 = Planning_units (PUs)
X02 = incl.OS. number of times pu was selected in MARXAN  when optimized for ecological costs
X03 = incl.OS. number of times pu was selected in MARXAN when optimized for social  costs
X04 = incl.OS. ecological costs (ECUess) of pu when realizing the total energy (normalized)
X05 = incl.OS. social costs (ECUsoc) of pu when realizing the total energy (normalized)
X06 = incl.OS. total energy (MWh/a)
X07 = incl.OS. energy from roof-mounted PV infrastructure (MWh/a)
X08 = incl.OS. energy from wind turbines (MWh/a)
X09 = incl.OS. energy from ground-mounted PV infrastructure (MWh/a)

X10 = excl.OS. number of times pu was selected in MARXAN  when optimized for ecological costs
X11 = excl.OS. number of times pu was selected in MARXAN when optimized for social  costs
X12 = excl.OS. ecological costs (ECUess) of pu when realizing the total energy (normalized)
X13 = excl.OS. social costs (ECUsoc) of pu when realizing the total energy (normalized)
X14 = excl.OS. total energy (MWh/a)
X15 = excl.OS. energy from roof-mounted PV infrastructure (MWh/a)
X16 = excl.OS. energy from wind turbines (MWh/a)
X17 = excl.OS. energy from ground-mounted PV infrastructure (MWh/a)
geometry = Simple feature XY geometry (SFC_POINT) each representing the center of a 4x4km planning unit (PU) in EPSG 21781 (CRS CH1903 / LV03).
For MARXAN optimizations (for meaning of standard files in MARXAN see https://scholar.google.ch/scholar_url?url=https://courses.washington.edu/cfr590/software/Marxan1810/marxan_manual_1_8_2.pdf&hl=de&sa=X&ei=NAWHZI6JJ8PFmAGMo47oDQ&scisig=AGlGAw-DBrsV4kUzkR6GkN1jtG66&oi=scholarr), pu_data.shp was used to generate MARXAN files input.dat, pu.dat, puvfeat.dat, spec.dat.
Coordinate Reference System: CH1903 / LV03. EPSG: 21781.

en: spatial-planning optimization wind-power social-acceptance marxan photovoltaic energy landscape ecosystem-services

en: Optimizing renewable energy siting in the Swiss landscape

envi

en: Environment

ecosystem-services

display_name: ecosystem-services

energy

display_name: energy

landscape

display_name: landscape

marxan

display_name: marxan

optimization

display_name: optimization

photovoltaic

display_name: photovoltaic

social-acceptance

display_name: social-acceptance

spatial-planning

display_name: spatial-planning

wind-power

display_name: wind-power


en: This dataset comprised 254 historical (1937 – 1948) and resurveyed (2019 – 2022) vegetation relevés of beech-dominated forests sites in the Swiss Jura mountains. The data contains species lists as well as site information.

en: ground-vegetation air-pollution climate-change fagus-sylvatica forest-management forest

en: Resurveyed vegetation releves of beech forests in the Swiss Jura Mountains

en:
Resource Title: Taxa Data
    Size: 1350953 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/2bde3c93-9309-40e0-964e-d3b4f6f29804/resource/5e5dd383-4a1d-42a0-af8d-4c4aa5fbbbe3/download/species_data.xlsx
    Description: This table contains information about the individual taxa found in the historic and current vegetation relevés in Scherrer et al. 2024.

en:
Resource Title: Site Information
    Size: 34729 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/2bde3c93-9309-40e0-964e-d3b4f6f29804/resource/dfe9da2a-1d8e-4342-a4d7-82d5552e25c6/download/site_information.xlsx
    Description: This table contains information about the 254 forest sites used in the publication of Scherrer et al. 2024.

air-pollution

display_name: air-pollution

climate-change

display_name: climate-change

fagus-sylvatica

display_name: fagus-sylvatica

forest

display_name: forest

forest-management

display_name: forest-management

ground-vegetation

display_name: ground-vegetation


en:
Short videos showing scenarios of potential landscape development in a 4°C warmer climate for a Swiss mountain region and a region on the Swiss Plateau. The videos are in German or French with subtitles.
The videos' key message is: Climate change will have an impact on the Swiss landscapes. However, according to the strategy chosen to adapt to climate change, society and politics will also have a strong influence on the future landscapes and the services they will provide. If no action is taken to adapt to climate change today, then society will have to react in a hurry at a later point in time. This can often result in resource-intensive technical solutions. Conversely, if society takes action in an anticipatory way already today, nature-based solutions can be implemented more successfully and humans can benefit more from the landscape services..
These short videos are teasers that should make the viewers curious to visit the panorama pictures (https://viergrad.envidat.ch/).

en: landscape-change visualization policy-action scenario climate-change

en: Teaser videos Swiss landscapes under climate change

en:
Resource Title: Seeland subtitles DE
    Size: 38419246 bytes
    Format: video/mp4
    URL: https://www.envidat.ch/dataset/c19ad933-34e0-4184-ba30-eabec0bcdb51/resource/e5b088dc-780e-4244-939d-46b0d7565084/download/seeland_subtitles_de.mp4
    Description: Scenarios of landscape change under climate change in the Swiss lowlands.
Trailer in German with subtitles.

en:
Resource Title: Seeland subtitles FR
    Size: 38467901 bytes
    Format: video/mp4
    URL: https://www.envidat.ch/dataset/c19ad933-34e0-4184-ba30-eabec0bcdb51/resource/23cb4c0e-d84a-4fee-b271-6c267f0f638c/download/seeland_subtitles_fr.mp4
    Description: Scenarios of landscape change under climate change in the Swiss lowlands.
Trailer in French with subtitles.

en:
Resource Title: Entremont subtitles DE
    Size: 36993864 bytes
    Format: video/mp4
    URL: https://www.envidat.ch/dataset/c19ad933-34e0-4184-ba30-eabec0bcdb51/resource/60dc7982-5b1a-4b8a-a21c-4ecdd4a5bccb/download/entremont_subtitles_de.mp4
    Description: Scenarios of landscape change under climate change in the Swiss mountains.
Trailer in German with written subtitles.

en:
Resource Title: Entremon subtitles FR
    Size: 38930364 bytes
    Format: video/mp4
    URL: https://www.envidat.ch/dataset/c19ad933-34e0-4184-ba30-eabec0bcdb51/resource/79ff754a-4390-4c71-81a6-482a5b395582/download/entremont_subtitles_fr.mp4
    Description: Scenarios of landscape change under climate change in the Swiss mountains. Trailer in French with subtitles.

landscape-change

display_name: landscape-change

policy-action

display_name: policy-action

scenario

display_name: scenario

visualization

display_name: visualization


en:
This dataset contains the output and reference data published in the paper "Automated snow avalanche release area delineation - validation of existing algorithms and proposition of a new object-based approach for large scale hazard indication mapping" Yves Bühler, Daniel von Rickenbach, Andreas Stoffel, Stefan Margreth, Lukas Stoffel, Marc Christen (2018) Natural Hazards And Earth System Sciences.
Abstract:
Snow avalanche hazard is threatening people and infrastructure in all alpine regions with seasonal or permanent snow cover around the globe. Coping with this hazard is a big challenge and during the past centuries, different strategies were developed. Today, in Switzerland, experienced avalanche engineers produce hazard maps with a very high reliability based on avalanche cadastre information, terrain analysis, climatological datasets and numerical modelling of the flow dynamics for selected avalanche tracks that might affect settlements. However, for regions outside the considered settlement areas such area-wide hazard maps are not available mainly because of the too high cost, in Switzerland and in most mountain regions around the world. Therefore, hazard indication maps, even though they are less reliable and less detailed, are often the only spatial planning tool available. To produce meaningful and cost-effective avalanche hazard indication maps over large regions (regional to national scale), automated release area delineation has to be combined with volume estimations and state-of-the-art numerical avalanche simulations.
In this paper we validate existing potential release area (PRA) delineation algorithms, published in peer-reviewed journals, that are based on digital terrain models and their derivatives such as slope angle, aspect, roughness and curvature. For validation, we apply avalanche cadastre data from three different ski resorts in the vicinity of Davos, Switzerland, where experienced ski-patrol staff mapped most avalanches in detail since many years. After calculating the best fit input parameters for every tested algorithm, we compare their performance based on the reference datasets. Because all tested algorithms do not provide meaningful delineation between individual potential release areas (PRA), we propose a new algorithm based on object-based image analysis (OBIA). In combination with an automatic procedure to estimate the average release depth (d0), defining the avalanche release volume, this algorithm enables the numerical simulation of thousands of avalanches over large regions applying the well-established avalanche dynamics model RAMMS. We demonstrate this for the region of Davos for two hazard scenarios, frequent (10 – 30 years return period) and extreme (100 – 300 years return period). This approach opens the door for large scale avalanche hazard indication mapping in all regions where high quality and resolution digital terrain models and snow data are available.

en: avalanche hazard-mapping snow ramms hazard-indication-mapping release

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: Automated Avalanche Release Area (PRA) Delineation Davos

en:
Resource Title: RAMMS simulations NHESS
    Size: 24222947 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/1a8ed871-a483-4900-b528-95e901b3910e/resource/88c5334b-40d3-47df-b8c7-d300ba46d19c/download/ramms_sim.zip
    Description: Simulated maximum pressures for the scenarios frequent and extreme taking the forest layer into account

en:
Resource Title: Potential Release  Area  NHESS
    Size: 4379100 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/1a8ed871-a483-4900-b528-95e901b3910e/resource/b0aeb914-2c95-4db3-a257-3c51ff0b2e97/download/pra_nhess.zip
    Description: This folder contains the output of the different investigated algorithms plus the reference dataset, used for training and validation.

avalanche

display_name: avalanche

hazard-indication-mapping

display_name: hazard-indication-mapping

hazard-mapping

display_name: hazard-mapping

ramms

display_name: ramms

release

display_name: release

snow

display_name: snow


en:
A rule-based algorithm [(Schwieder et al., 2022)](https://doi.org/10.1016/j.rse.2021.112795) was used to produce annual maps for 2018–2021 of grassland-management events, i.e. mowing and/or grazing, for Switzerland using Sentinel-2 and Landsat 8 satellite time series. All satellite images were processed with the [FORCE](https://force-eo.readthedocs.io) framework.
The resulting maps provide information on the number and timing of grassland-management events at a spatial resolution of 10 m × 10 m for the whole of Switzerland. For the final maps, permanent grasslands were masked using a variety of land-use layers, according to [Huber et al. (2022)](https://doi.org/10.1002/rse2.298) but replacing the crop mask with the agricultural-use data from the cantons.
We assessed the detection of management events based on independent reference data, which we acquired from daily time series of publicly available webcams that are widely distributed across Switzerland. We further tested the ecological relevance of the generated intensity measures in relation to nationwide biodiversity data (see [Weber et al., 2023](https://doi.org/10.1002/rse2.372)).
The webcam-based reference data used for verification was subsequently added on 14.02.2024.

en: management use-intensity remote-sensing grassland mowing landsat sentinel-2 land-use

en: Grassland-use intensity maps for Switzerland

envi

en: Environment

en:
Resource Title: Grassland-use intensity map 2019
    Size: 2202043962 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/889019e0-7c66-406c-a061-b2e4ab96e5ad/resource/fdfed95b-c1c7-4842-9329-896bbbfe1c17/download/grassland-use_intensity_2019_ch_10m_32632_v1_0.tif
    Description: Raster stack with information on the number and timing of grassland-management events (i.e. mowing, grazing) at a spatial resolution of 10 m × 10 m for the whole of Switzerland for 2019. See readme file for more details.

format: TIFF

media_type: image/tiff

en:
Resource Title: Grassland-use intensity map 2021
    Size: 2201662465 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/889019e0-7c66-406c-a061-b2e4ab96e5ad/resource/9d8f0f98-b7f9-434a-b711-70e864ec7703/download/grassland-use_intensity_2021_ch_10m_32632_v1_0.tif
    Description: Raster stack with information on the number and timing of grassland-management events (i.e. mowing, grazing) at a spatial resolution of 10 m × 10 m for the whole of Switzerland for 2021. See readme file for more details.

en:
Resource Title: Preview number of grassland management events 2020
    Size: 2192445 bytes
    Format: image/png
    URL: https://www.envidat.ch/dataset/889019e0-7c66-406c-a061-b2e4ab96e5ad/resource/58dfd5b4-86d2-434b-932e-8857dcfacdf2/download/preview_number_of_management_events_2020.png
    Description: Spatial distribution of the number of management events for Switzerland and for three zoomed-in regions (a, b, c). The three zoomed-in regions show different grassland-use intensities, which are reflected in the distribution of management frequency (Weber et al., 2023; IN REVIEW)

format: PNG

media_type: image/png

en:
Resource Title: Readme
    Size: 3433 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/889019e0-7c66-406c-a061-b2e4ab96e5ad/resource/06c9bd5e-ae9c-4c7f-9b29-2849a3aa971b/download/readme.txt
    Description: Metadata and description of the maps and webcam-based reference data.

format: TXT

media_type: text/plain

en:
Resource Title: Grassland-use intensity map 2018
    Size: 2244278154 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/889019e0-7c66-406c-a061-b2e4ab96e5ad/resource/a5da6789-de78-42d5-be38-c1d9daff7cff/download/grassland-use_intensity_2018_ch_10m_32632_v1_0.tif
    Description: Raster stack with information on the number and timing of grassland-management events (i.e. mowing, grazing) at a spatial resolution of 10 m × 10 m for the whole of Switzerland for 2018. See readme file for more details.

en:
Resource Title: Preview time of 1st grassland management event 2020
    Size: 2634652 bytes
    Format: image/png
    URL: https://www.envidat.ch/dataset/889019e0-7c66-406c-a061-b2e4ab96e5ad/resource/eeb08550-52de-4be6-aac9-d8daed43a51f/download/preview_time_of_1st_management_event_2020.png
    Description: Spatial distribution of the timing (day of year; DOY) of the first management event for Switzerland and for three zoomed-in regions (a, b, c). The three zoomed-in regions show different grassland-use intensities, which are reflected in the timing of the first event of the year (Weber et al., 2023; IN REVIEW)

en:
Resource Title: Webcam-based reference data
    Size: 57563 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/889019e0-7c66-406c-a061-b2e4ab96e5ad/resource/7561f6b0-7e0b-4b99-ad30-bd247546cf3a/download/webcam_reference_data.zip
    Description: Webcam-based reference data from 2020 and 2021 (see readme).

en:
Resource Title: Grassland-use intensity map 2020
    Size: 2252728445 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/889019e0-7c66-406c-a061-b2e4ab96e5ad/resource/2a301048-a736-442d-980e-852006dc61bb/download/grassland-use_intensity_2020_ch_10m_32632_v1_0.tif
    Description: Raster stack with information on the number and timing of grassland-management events (i.e. mowing, grazing) at a spatial resolution of 10 m × 10 m for the whole of Switzerland for 2020. See readme file for more details.

grassland

display_name: grassland

land-use

display_name: land-use

landsat

display_name: landsat

management

display_name: management

mowing

display_name: mowing

remote-sensing

display_name: remote-sensing

use-intensity

display_name: use-intensity


en:
This dataset documents the analyses and the plot generation for the manuscript **Long-term biomass dynamics in temperate forests in Europe after cessation of management**.
We analyse biomass development in an extensive network of 224 plots in 37 natural forest reserves (NFR) covering a wide environmental gradient with mean annual temperatures ranging from 1 to 10.4 °C and mean annual precipitation ranging from 901 to 2317 mm. Forest inventories have been conducted approximately every 10 years during the last 60 years.
It consists of the following files:
+ **Long_term_biomass_script.Rmd**: R markdown document to reproduce the analysis and the plot generation.
+ **dat_biomass.RDS**: Biomass values for each permanent plot at each inventory. This dataset is used for data exploration and for the Biomass model, and to create Figure 2 and Figure 3.
+ **dat_biomass_change.RDS**: Biomass change values between inventories for each permanent plot. It includes the explanatory variables as well. This dataset is used for the Biomass change model and to create Figure 4 and Figure 5.
+ **NFI_climspace_netCDF.RDS**: Climatic space covered by the Swiss National Forest Inventory (NFI) that is used in Figure 5.

en: carbon-storage forest-inventory environmental-gradients biomass-dynamics forest-reserves

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: Long-term biomass dynamics of temperate forests in Europe after cessation of management

en:
Resource Title: dat_biomass_change.RDS
    Size: 38176 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/81ea91cb-998e-447d-aa21-d190065a31d2/resource/91298b80-c946-417a-b7e0-388040d2859d/download/dat_biomass_change.rds
    Description: Biomass change values between inventories for each permanent plot. It includes the explanatory variables as well. This dataset is used for the Biomass change model and to create Figure 4 and Figure 5.

format: OCTET

en:
Resource Title: Long_term_biomass_script.Rmd
    Size: 19732 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/81ea91cb-998e-447d-aa21-d190065a31d2/resource/49c24a6e-5eb6-4870-ad14-0cc350d69e07/download/long_term_biomass_script.rmd
    Description: R markdown document used to download the data files, reproduce the analysis and plot generation.

en:
Resource Title: dat_biomass.RDS
    Size: 18677 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/81ea91cb-998e-447d-aa21-d190065a31d2/resource/67190b79-d5fc-4db4-8c3f-b7d97d32244e/download/dat_biomass.rds
    Description: Biomass values for each permanent plot at each inventory. This dataset is used for data exploration and for the Biomass model, and to create Figure 2 and Figure 3.

en:
Resource Title: NFI_climspace_netCDF.RDS
    Size: 578 bytes
    Format: application/x-rds
    URL: https://www.envidat.ch/dataset/81ea91cb-998e-447d-aa21-d190065a31d2/resource/0cc704a3-8f8f-4eae-a9ca-8dd1cd8c9da9/download/nfi_climspace_netcdf.rds
    Description: Climatic space covered by the Swiss National Forest Inventory (NFI) that will be used in Figure 5.

biomass-dynamics

display_name: biomass-dynamics

carbon-storage

display_name: carbon-storage

environmental-gradients

display_name: environmental-gradients

forest-inventory

display_name: forest-inventory

forest-reserves

display_name: forest-reserves


en: This dataset contains the Fire Weather Index for Hydrological Bavaria from 1980 - 2099 as stated in the paper "Climate change impacts on regional fire weather in heterogeneous landscapes of Central Europe" published in Natural Hazards and Earth System Sciens (NHESS) 2023. The dataset contains daily Fire Weather Index values for all 50 members (subfolders of the dataset) of the CRCM5-LE (11 km spatial resolution) from 1980 to 2099 over the domain of Hydrological Bavaria.  Please cite this dataset as the publication:  Miller, J., Böhnisch, A., Ludwig, R., & Brunner, M. I. (2023). Climate change impacts on regional fire weather in heterogeneous landscapes of Central Europe. Natural Hazards and Earth System Sciences Discussions, 1-25. doi: 10.5194/nhess-2023-51.

en: meteorological-data europe european-alps indicator-values mountains climate fire climate-indicators forest-fire climate-change

envidat

en: EnviDat: the Environmental Data Portal

en: Fire Weather Index for Hydrological Bavaria from 1980-2099 derived from the 50 member CRCM5-LE

envi

en: Environment

climate

display_name: climate

climate-change

display_name: climate-change

climate-indicators

display_name: climate-indicators

europe

display_name: europe

european-alps

display_name: european-alps

fire

display_name: fire

forest-fire

display_name: forest-fire

indicator-values

display_name: indicator-values

meteorological-data

display_name: meteorological-data

mountains

display_name: mountains


en:
The study aims to determine the effective elastic properties of snow, firn, and bubbly ice based on microstructural quantities. Anisotropy, one of these quantities (the other being ice volume fraction) in snow and ice, has two types: geometrical and crystallographic, resulting in snow's macroscopic anisotropic elastic behavior. The research focuses on the impact of geometrical anisotropy on potential ice volume fractions in snow and ice. 391 micro-CT images from various locations, including laboratories, the Alps, the Arctic, and Antarctica, were analyzed to achieve this.
 The analysis involved microstructure-based finite element simulations, which inherently consider microstructure and calculate the elasticity tensor. Hashin-Shtrikman bounds were utilized to predict the elastic properties of the microstructure samples. These bounds effectively captured the nonlinear interplay between geometrical anisotropy, captured by the Eshelby tensor and density. HS bounds have the advantage of the correct limiting behavior for low to high-ice volume fractions.
 We derived parameterization for five transversely isotropic elasticity tensor components, requiring only two free parameters. This parameterization was valid for ice volume fractions ranging from 0.06 to 0.93. The analysis employing the Thomsen parameter highlighted the dominance of geometrical anisotropy until an ice volume fraction of 0.7. However, to fully comprehend the elasticity of bubbly ice, a comprehensive approach is necessary to integrate coupled elastic theories that account for both geometrical and crystallographic anisotropy.
This dataset includes a Jupyter notebook with all the necessary functions required to predict the elasticity tensor of snow for the given ice volume fraction and anisotropy. Also, the code contains the least squares optimization function to compute the elasticity tensor for the six components of stress and strain. For example, we consider our dataset to calculate the samples' elasticity tensor and reproduce Fig. 7 from the paper. We take the stress and strain values obtained from load states as input for this example.
Also, a .csv file contains all the microstructural information: ice volume fraction, anisotropy, correlation functions, voxels size, and no. of voxels of the samples and the elasticity tensor obtained from finite element simulations and from present work parameterization.

en: elasticity fem geometrical microstructure x-ray-tomography anisotropy crystallographic

en: Effective, anisotropic elasticity tensor of snow, firn, and bubbly ice

anisotropy

display_name: anisotropy

crystallographic

display_name: crystallographic

elasticity

display_name: elasticity

fem

display_name: fem

geometrical

display_name: geometrical

microstructure

display_name: microstructure

x-ray-tomography

display_name: x-ray-tomography


en:
(English version below)
Ziel dieser Faktenblätter ist es Informationen zu den Schätzmethoden, Hinweise zur Parametrisierung und eine grobe Einschätzung der Güte der Resultate in übersichtlicher Form bereitzustellen. Ein zeitaufwändiges Zusammensuchen dieser Informationen aus unterschiedlichen Quellen entfällt daher weitgehend. Die Bearbeitenden sollen damit in der Lage sein, sich innerhalb weniger Minuten die wichtigsten Hintergrundinformationen zu jeder der vorgestellten Methoden in Erinnerung zu rufen. Der Leitfaden unterstützt bei der Plausibilisierung und bei der Mittelung der Ergebnisse der einzelnen Schätzmethoden zu einem repräsentativen Endergebnis. Vorausgesetzt wird Grundwissen und Erfahrung in der Thematik.
Der Leitfaden führt die Fachperson anhand eines Entscheidungsbaums in acht Schritten durch den Arbeitsablauf und unterstützt mit einem Fragenkatalog bei der Plausibilisierung der Ergebnisse.
Auf den Faktenblättern sind für jede Schätzmethode alle relevanten Informationen stichwortartig und grafisch übersichtlich auf einer DIN-A4 Seite zusammengefasst. Jedes Faktenblatt ist einheitlich gegliedert.
The aim of these fact sheets is to provide information on the flood estimation methods, notes on their parameterization and a rough assessment of the quality of the results in a clear form. A time-consuming research for this information from different sources is therefore largely eliminated. The users should be able to recall the most important background information on each of the methods presented within a few minutes. The guide provides support in checking plausibility and averaging the results of the individual estimation methods to produce a representative final result. Basic knowledge and experience in the subject are assumed.
The guide leads the expert through the workflow in eight steps using a decision tree and supports the plausibility check of the results with a list of questions.
On the fact sheets, all relevant information for each estimation method is summarized with key words and graphics on one DIN A4 page. Each fact sheet has a standardized structure.

en: hydrology engineering decision-making flood estimation-methods

en: Faktenblätter und Leitfaden für Hochwasserschätzmethoden in der Praxis

decision-making

display_name: decision-making

engineering

display_name: engineering

estimation-methods

display_name: estimation-methods

flood

display_name: flood

hydrology

display_name: hydrology


en:
This dataset contains the raw environmental DNA data associated with the publication *Environmental drivers of eukaryotic plankton and fish biodiversity in an Arctic fjord* in the journal Polar Biology (2023).
 Methods

**Sampling**
We sampled the Lilliehöök fjord on the west coast of Spitsbergen (Svalbard, Norway) over 3 days from 3 to 5 of August 2021. Samples were taken from the glacier front up to the fjord mouth of the Krossfjorden system, around 30 km long, after the Lilliehöök fjord merged with the mouth of Möller fjord. The fjord’s maximum depth has been recorded at 373 m (Svendsen et al. 2002) and has no sill at its entrance, thereby facilitating water exchange with the open ocean of the West Spitsbergen Current. We used a research vessel to sample 5 sites for a total of 15 samples, sampling 3 depths per site (3-m, chlorophyll a maximum and 85-m, unless sea floor was shallower). Shallow and intermediate samples between 3-m and 12-m represent ~35-L of water filtered in-situ using long tubing and a peristaltic pump, and all other deeper samples were taken from a total of 3 Niskin bottles (General Oceanics), representing 22-L of water sampled per sample. Water was filtered through a VigiDNA filtration capsule (SPYGEN) with a 0.20-µm pore size using an Athena peristaltic pump (Proactive Environmental Products, Bradenton, Florida) with a flow rate of ~1-L/min. Each sample was handled with single use tubing and gloves.
**Molecular**
To perform the amplification, we used two sets of primers: teleo (forward: ACACCGCCCGTCACTCT, reverse: CTTCCGGTACACTTACCATG; Valentini et al. 2016) and the universal eukaryotic 1389F/1510R primer pair, amplifying the V9-18S rDNA gene (Amaral-Zettler et al. 2009) (forward: TTGTACACACCGCCC, reverse: CCTTCYGCAGGTTCACCTAC).
 Data content:
+ Metabarcoding data: This zip file contains the 2 sequencing libraries filtered to only retain the samples used in the present study.
+ Code, data and figure: This zip file contains all data and code to reproduce the figures and the analysis in the study, with an associated README explaining the content of each folder.
 Additional informations
For more details, please see the Methods in the associated publication: DOI: 10.1007/s00300-023-03187-9.

en: plankton biodiversity environmental-dna fjord marine fish dna-metabarcoding

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: Data: Environmental drivers of eukaryotic plankton and fish biodiversity in an Arctic fjord

en:
Resource Title: Metabarcoding data
    Size: 621901827 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/2a7f749a-430f-443b-bbac-073b166c2361/resource/1e6638c8-fb7e-4049-8fee-effa8d64177b/download/metabarcoding-data.zip
    Description: Raw metabarcoding data for both primers (teleo and 18S) and corresponding file to perform the demultiplexing and primer trimming

en:
Resource Title: Code, data and figures
    Size: 63570143 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/2a7f749a-430f-443b-bbac-073b166c2361/resource/ce6111f5-09b4-4d7a-8a5d-21529c2da40b/download/code_data_figures.zip
    Description: Here there are all the data and code to reproduce the figures and analysis presented in the paper, with a README.md file

biodiversity

display_name: biodiversity

dna-metabarcoding

display_name: dna-metabarcoding

environmental-dna

display_name: environmental-dna

fish

display_name: fish

fjord

display_name: fjord

marine

display_name: marine

plankton

display_name: plankton


en:
The data set contained in this repository was used in the analysis by Mayer et al. (2023):
Mayer, S. I., Techel, F., Schweizer, J., and van Herwijnen, A.: Prediction of natural dry-snow avalanche activity using physics-based snowpack simulations, EGUsphere, https://doi.org/10.5194/egusphere-2023-646, 2023.

en: natural-avalanches avalanche-size avalanche machine-learning snowpack-model

en: Data-set for prediction of natural dry-snow avalanche activity and avalanche size using physics-based snowpack simulations

en:
Resource Title: dataForValidationWithDangerLevels.csv
    Size: 34614625 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/28660c6d-68ce-484d-a7d7-24d7a612f392/resource/fac61ade-ba4c-40bc-bc90-f47c763e153c/download/dataforvalidationwithdangerlevels.csv
    Description: Data set DL was used for validation of the avalanche-day classifier and the avalanche-size estimator. The data set contains the quality-checked danger levels merged with the respective SNOWPACK simulations (Sections 2.3 and 3.3(2) in Mayer et al.).

en:
Resource Title: dataForAvalancheDayModelDevelopment.csv
    Size: 1584694 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/28660c6d-68ce-484d-a7d7-24d7a612f392/resource/961c07d9-5f7e-4c7b-9073-e2b31b6dab28/download/dataforavalanchedaymodeldevelopment.csv
    Description: Data set AV1 was used for training the avalanche-day classifier (Sections 2.1.1, 2.2, and 3.1 in Mayer et al.).

en:
Resource Title: dataForAvalancheSizeModelDevelopment.csv
    Size: 423741 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/28660c6d-68ce-484d-a7d7-24d7a612f392/resource/49e9e29b-683a-4784-a3ab-0c02d2e0902b/download/dataforavalanchesizemodeldevelopment.csv
    Description: Data set AV2 used for training the avalanche-size estimator (Sections 2.1.2 and 3.2 in Mayer et al.)

en:
Resource Title: dataForValidationDavos.csv
    Size: 74355 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/28660c6d-68ce-484d-a7d7-24d7a612f392/resource/38836706-8b98-47a5-9f94-a3b5e88553e2/download/davos_validation.csv
    Description: Data set AV3 was used for validation of the avalanche-day classifier (Sections 2.1.3 and 3.3(1) in Mayer et al.).

avalanche

display_name: avalanche

avalanche-size

display_name: avalanche-size

machine-learning

display_name: machine-learning

natural-avalanches

display_name: natural-avalanches

snowpack-model

display_name: snowpack-model


en:
Summary
* Dataset of daily inflow to Luzzone reservoir in Ticino, Switzerland
* R scripts used to generate return levels for low reservoir inflow, low precipitation, high inflow, and extreme high precipitation based on various methods from extreme value analysis
 Data
The dataset included here is the "natural" reservoir inflow for the Luzzone reservoir.
Additional analyses were conducted on daily total precipitation of 6 meteorological stations (abbreviations: TIOLI, TIOLV, COM, VRN, VLS, ZEV). These precipitation data are freely available for teaching and research from the MeteoSwiss IDAweb portal (https://www.meteoswiss.admin.ch/services-and-publications/service/weather-and-climate-products/data-portal-for-teaching-and-research.html).
 Codes
R scripts used to determine return levels of the data set are included for both extreme high events and low events. The scripts include the following methods for calculating return levels:
* GEV (Generalized Extreme Value)
* GPD and GPDd (Generalized Pareto Distribution including declustered version)
* eGPD (extended Generalized Pareto Distribution)
* MEV (Metastatistical Extreme Value)

en: low-flow reservoir-inflow alpine-precipitation return-periods extreme-values

envidat

en: EnviDat: the Environmental Data Portal

en: Data and Code on Extreme Inflow and Lowflow Analysis for Alpine Reservoirs

envi

en: Environment

en:
Resource Title: Daily Luzzone Reservoir Inflow 01-01-2000 to 31-12-2019
    Size: 261834 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/b138fae4-b287-4af6-b666-ef860ac593ff/resource/f3b117de-ba26-42dd-b958-e46e826222e4/download/luz.csv
    Description: Daily total water inflow to Luzzone reservoir. Natural inflow (LUZ_inflow_raw) is back-calculated based on daily water level in reservoir (removing hydropower inputs and spills). Linear interpolation between daily values is used to bring all inflow values into positive space (LUZ_inflow_intrp).

en:
Resource Title: R codes for Extreme Values Analysis
    Size: 35946 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/b138fae4-b287-4af6-b666-ef860ac593ff/resource/6647b3fe-62a5-461b-8b22-be7bcdfb51be/download/r_codes.zip
    Description: Zip file containing R codes for running extreme value analyses for high precipitation and inflow events as well as code for determining return levels of low inflow and low precipitation events

alpine-precipitation

display_name: alpine-precipitation

extreme-values

display_name: extreme-values

low-flow

display_name: low-flow

reservoir-inflow

display_name: reservoir-inflow

return-periods

display_name: return-periods


en:
Summary
This data set contains Python programming code and modeled data discussed in a related research article. We developed a simple isotope model to study the drivers of the particularly depleted vapour isotopic composition measured on the ship of the Antarctic Circumnavigation Expedition close to the outlet of the Mertz glacier, East Antarctica, in the 6-day period from 27 January 2017 to 1 February 2017. The model considers the stable water isotopologues H2(16O), H2(18O), and HD(16O). It uses data from the ERA5 reanalysis product with a spatial resolution of 0.25° x 0.25° (Hersbach et al., 2018) and 10-day backward trajectories for the location of the ship, published by Thurnherr et al. (2020a). Our data set includes the model code, Python scripts for visualizing the results, and data produced by the model including the results shown in the figures of the related research article. Here, we summarize the most important model characteristics while further details can be found in the readme.txt file and the related research article including its supporting information.
 Main model characteristics
The modeling approach consists of two steps called *Model Sublimation* and *Model Air Parcel*. The former estimates the isotopic compositions of the snow and sublimation flux across the Antarctic Ice Sheet using an Eulerian frame of reference while the latter models the vapour isotopic composition and specific humidity along air parcel trajectories using a Lagrangian frame of reference. The isotope effects of most phase changes are represented by equilibrium fractionation. Only for ocean evaporation, kinetic fractionation is additionally taken into account (original Craig-Gordon formula). For snow sublimation, two assumptions are tested: *Run E* assumes that sublimation is associated with equilibrium fractionation while *Run N* assumes that sublimation occurs without isotopic fractionation.
 Model Sublimation
Model Sublimation uses a simple one-dimensional mass-balance approach in each grid cell, considering snow accumulation due to snowfall and vapour deposition and snow ablation due to sublimation. The snowpack is represented by 100 layers of equal thickness (e.g., 1 cm) and density (350 kg m-3). The isotopic composition of snowfall is parameterized by generalizing a site-specific, empirical relationship between the daily mean air temperature and snowfall isotopic composition. In the case of vapour deposition, Model Sublimation assumes equilibrium fractionation and estimates the isotopic composition of the atmospheric vapour as the average value for two idealized situations: (i) locally sourced vapour which has the same isotopic composition as the sublimation flux; (ii) non-locally sourced vapour in isotopic equilibrium with snowfall. Model Sublimation is run with a time step of 1 h, independently of Model Air Parcel.
 Model Air Parcel
Every hour, an ensemble of trajectories arrives at different heights in the ABL above the ship. For each of these trajectories, we consider an air parcel with a constant volume of 1 x 1 x 1 m3. The air parcels are initialized at the first suitable time when the trajectories are located in the ABL, either over the ice-free ocean in conditions of evaporation or over snow (Antarctic Ice Sheet or sea ice). Subsequently, the masses of the water isotopologues in the air parcels are simulated with a time step of 3 h, considering vapour uptake or removal due to the moisture flux at the snow or liquid ocean surface (only if the parcel is in the ABL) and cloud/precipitation formation (if the saturation specific humidity is reached). Sea ice is taken into account in a very simplified way. We represent the sea ice by grid cells with a sea-ice cover of more than 90% and assume the isotopic composition of the sublimation flux to be identical to that in the nearest grid cell of the Antarctic Ice Sheet. The isotopic composition of the sublimation flux is taken from Model Sublimation whereas the isotopic composition of the vapour deposition flux (over snow) and condensation flux (over ice-free ocean) is simulated assuming an isotopic equilibrium with the air parcel. Isotope effects of cloud/precipitation formation are represented using the classic Rayleigh distillation model with equilibrium fractionation, where the cloud water is assumed to precipitate immediately after formation.
 References
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horanyi, A., Munoz Sabater, J.,... others (2018). *ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS)*. doi: 10.24381/cds.bd0915c6
Thurnherr, I., Wernli, H., & Aemisegger, F. (2020a). *10-day backward trajectories from ECMWF analysis data along the ship track of the Antarctic Circumnavigation Expedition in austral summer 2016/2017*. Zenodo. doi: 10.5281/zenodo.4031705

en: backward-trajectories isotopic-distillation snow-sublimation evaporation lagrangian-analysis antarctica stable-water-isotopes

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: Modeled Isotopic Composition of Water Vapour Along Air Parcel Trajectories in the Antarctic

en:
Resource Title: Model code
    Size: 38463 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/99ad307f-be6f-48d3-ac80-387bcb0aded7/resource/9d6a4ef1-c0fa-46bf-9479-43bbf1d13964/download/python_scripts_model.zip
    Description: Model Sublimation and Model Air Parcel (python scripts)

en:
Resource Title: Readme
    Size: 23594 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/99ad307f-be6f-48d3-ac80-387bcb0aded7/resource/8be31ec0-9d80-4995-be16-c6e1704144fb/download/readme.txt
    Description: Meta data

en:
Resource Title: Data
    Size: 2993639 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/99ad307f-be6f-48d3-ac80-387bcb0aded7/resource/6d387739-0552-425f-90ac-584c2f8330d0/download/data_processed.zip
    Description: Modeled data including the data shown in the figures of the related research article

en:
Resource Title: Python scripts for data visualization
    Size: 21542 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/99ad307f-be6f-48d3-ac80-387bcb0aded7/resource/acb8a9ee-2499-4892-a6ea-79d7381019d4/download/python_scripts_plotting.zip
    Description: Scripts for generating the figures of the related research article and generating the data files (csv) from the model output

antarctica

display_name: antarctica

backward-trajectories

display_name: backward-trajectories

evaporation

display_name: evaporation

isotopic-distillation

display_name: isotopic-distillation

lagrangian-analysis

display_name: lagrangian-analysis

snow-sublimation

display_name: snow-sublimation

stable-water-isotopes

display_name: stable-water-isotopes


en:
The files correspond to the data and R-script used for the analyses of the following paper "Feasting on the ordinary or starving for the exceptional: phenological synchrony between spongy moth and budburst of European trees in a warmer climate" published in Ecology and Evolution by Vitasse et al. 2023.
There are three zip files corresponding to the Temperature data, phenology/preference/performance tests and R-Scripts for the analyses.
Input data:
'Synchrony_Cuttings_Pheno.txt':

en: spongy-month chilling hatch budburst phenology

en: Synchrony between spongy moth hatching and leaf phenology of temperate trees

en:
Resource Title:
    Size: 19723 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/31166a90-ab95-46c4-b651-4f7d474e66a7/resource/6680e96d-e720-4e07-b844-ba19e2de693e/download/r-scripts.zip
    Description: 3 R-Scripts used for the analyses of the temperature, phenology and performance/preference tests

en:
Resource Title: Phenology and performance/preference tests Data
    Size: 7058 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/31166a90-ab95-46c4-b651-4f7d474e66a7/resource/464b7325-15da-49ed-8e50-9c1be08a3d20/download/phenology_performance_preference-tests_data.zip
    Description: -"Synchronex_Cuttings_Pheno.txt" provides the budburst date ('Stage 2') of each twig of the six studies species in the four different temperature treatments ("ambient", "a+5"=Ambient +5°C, 'Greenhouse-5'= Greenhouse at temperature >5°C, 'Greenhouse_10' = Greenhouse at temperature >10°C); 'Success' give the percentage of buds within the twig that succeeded to reach at least stage 2 during the experiment.

'Synchronex-Gypsy.txt' provides the number of eggs that have hatched at each monitoring date (Day of the year in columns); "DOY_hatch_1" corresponds to the first DOY when at least one egg has hatched; "Replicate_egg_mass" correspond to the egg mass replicate (1 to 5)

'PerformanceTest_GypsyMoth.txt' provides the biomass in mg of the caterpillars fed with fresh leaves of each tested tree species during one week. "Fresh_alive" is the biomass of only the living caterpillars after 1 week and "Freshpermoth" corresponds the weight per living caterpillar (Fresh alive/Nalive)

'PreferenceTest_All_species.txt' provides the total surface of leaf disc of each tested tree species remaining after exposing 5 caterpillars to these leaves during 24H. "Total_area" is the surface of the disc of each species measured after 24h exposed to the spongy moth first instars. For each species 'control' means the surface of the disc after 24H without any exposure to the larvae. 'EatenPercent correspond to the percentage surface loss compared to the control.

en:
Resource Title: Temperature data Spongy moth experiment
    Size: 84293 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/31166a90-ab95-46c4-b651-4f7d474e66a7/resource/3891fb6e-eb1d-4835-80bf-e8ae0c85164c/download/temperature.zip
    Description: 4 files with hourly temperature recorded at the height of buds in each of the four temperature treatments:
- Winter6: greenhouse where temperature was kept >6°C over winter
- Winter10: greenhouse where temperature was kept >10°C over winter
- OTC_Ambient: Open Top Chamber at Ambient temperature
- OTC_Warm: Open Top Chamber at +3.5°C warmer temperature

budburst

display_name: budburst

chilling

display_name: chilling

hatch

display_name: hatch

phenology

display_name: phenology

spongy-month

display_name: spongy-month


en:
The fate of plastic in the environment is of global concern, because its production recently has
increased strongly and it accumulates in terrestrial and aquatic ecosystems. Although some knowledge on
its role in aquatic and terrestrial ecosystems was gained in the recent decade, hitherto very little is known
about the impact of micro and nanoplastics in forest ecosystems.
The aim of this pioneering project was to explore if nanoplastics are taken up by forest trees species
through leaves or roots. In greenhouse experiments, we exposed leaves or roots of seedling of two forest
trees species to solutions with highly 13C-labelled polystyrene nanoparticles (13C-nPS, 99 atom%) and
examined if they were incorporated in different above- and belowground tissues.
Treated part of the trees for both species showed significant 13C-enrichment, indicating that trees take up nanoparticles. However, the overall 13C signal strength in tissues that were not exposed to the 13C label remained low (Δδ13C-1‰) and was confined to a few seedlings, leaving it ambiguous whether nanoplastic transport occurs or not.
We acknowledge that the new method developed might be not sensitive enough to unequivocally detect
mechanisms of nanoplastic uptake and transport at environmentally realistic concentrations.

en: uptake nanoplastic polystyrene forest-trees 13c-labelling

envidat

en: EnviDat: the Environmental Data Portal

en: Nanoplastics in forests: Exploring the effects of nanoplastics on forest soils and tree physiology (NanoPlast)

envi

en: Environment

forest-trees

display_name: forest-trees

nanoplastic

display_name: nanoplastic

polystyrene

display_name: polystyrene

uptake

display_name: uptake


en: This dataset includes gridded data on snow depth (m), snow water equivalent (mm), runoff from snow melt (mm) and snow cover fraction for Swtzerland. The data is spanning the water years 2016-2022 at a high spatial resolution of 250 m. Data are stored as daily results.

en: oshd seasonal-snow-data snow-cover-fraction snow-depth-distribution snow-melt

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: Seasonal snow data for Switzerland OSHD - FSM2sohd

oshd

display_name: oshd

seasonal-snow-data

display_name: seasonal-snow-data

snow-cover-fraction

display_name: snow-cover-fraction

snow-depth-distribution

display_name: snow-depth-distribution

snow-melt

display_name: snow-melt


en:
The R script eemma.R, which implements Ensemble End-Member Mixing Analysis (EEMMA) to estimate source fractions in mixtures, exploiting information contained in time-series correlations among tracer time series.
A brief user's guide, a demonstration script, and a demonstration data set are also provided, to accompany Kirchner, J.W., Mixing models with multiple, overlapping, or incomplete end-members, quantified using time series of a single tracer, Geophysical Research Letters, 2023.
The user's guide is available for public use under Creative Commons CC-BY-SA.  Public use of the scripts is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/

en: end-member-mixing-analysis tracers eemma stable-isotopes emma mixing-models

en: eemma.R, an R script for Ensemble End-Member Mixing Analysis

en:
Resource Title: eemma.R
    Size: 46043 bytes
    Format: text/x-r
    URL: https://www.envidat.ch/dataset/48dff6cd-bfcb-4c5e-a30d-f909dc8d36d3/resource/af04514c-75bb-4fbb-a5d2-4b25fcadf32f/download/eemma.r
    Description: An R script that performs ensemble end-member mixing analysis

en:
Resource Title: eemma user's guide
    Size: 501164 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/48dff6cd-bfcb-4c5e-a30d-f909dc8d36d3/resource/8df18f5d-c902-4acc-a8ee-b94324a043a1/download/eemma-users-guide.pdf
    Description: A brief user's guide to EEMMA, with illustrations using a simple data set

en:
Resource Title: vignette data
    Size: 1659 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/48dff6cd-bfcb-4c5e-a30d-f909dc8d36d3/resource/f72873bb-b4f2-4cce-a408-8d53f54bdbec/download/vignette_data.txt
    Description: A simple data set for the vignette script

en:
Resource Title: eemma vignette script
    Size: 4457 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/48dff6cd-bfcb-4c5e-a30d-f909dc8d36d3/resource/a96e9b19-9fee-4b21-b36c-5f7f84a4fb37/download/eemma_vignette_script.r
    Description: An R script for the vignettes (simple data analysis demonstrations) shown in the user's guide

eemma

display_name: eemma

emma

display_name: emma

end-member-mixing-analysis

display_name: end-member-mixing-analysis

mixing-models

display_name: mixing-models

stable-isotopes

display_name: stable-isotopes

tracers

display_name: tracers


en:
What are the research data files about: Raw data on perfomance (dry weight, development and mortality) of emerald ash borer larvae used in published bioassays. Raw data on ash dieback leasion lenghts. Raw data on untargeted and targeted specialized ash metabolites.
Which methods were used: Bioassays in greenhouses and climate chambers to collect data on emerald ash borer and ash dieback perfomance. Phytochemical analyses on ash phloem for quantifiying specialized metabolites.
When and where was the data created / extracted: Summer 2020-2021

en: intraspecific-variation cross-resistance ash-dieback invasive-species defense-chemistry emerald-ash-borer fraxinus

en: Raw data-Publication cross-resistance in ash - New Phytologist

ash-dieback

display_name: ash-dieback

cross-resistance

display_name: cross-resistance

defense-chemistry

display_name: defense-chemistry

emerald-ash-borer

display_name: emerald-ash-borer

fraxinus

display_name: fraxinus

intraspecific-variation

display_name: intraspecific-variation

invasive-species

display_name: invasive-species


en:
The data in this repository was used for the calibration and validation of the
SWE2HS model in the following publication:
Aschauer, J., Michel, A., Jonas, T., & Marty, C. (2023). An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0. Geoscientific Model Development Discussions, 1-19. https://doi.org/10.5194/gmd-2022-258
Contains daily snow water equivalent and snow depth timeseries from stations in the European Alps.

en: snow-water-equivalent model-calibration-data snow-depth model-validation-data snow-density

envidat

en: EnviDat: the Environmental Data Portal

en: SWE2HS model calibration and validation data

model-calibration-data

display_name: model-calibration-data

model-validation-data

display_name: model-validation-data

snow-density

display_name: snow-density

snow-depth

display_name: snow-depth

snow-water-equivalent

display_name: snow-water-equivalent


en:
This dataset includes the processed data of the glide-snow avalanche activity and dynamics on Dorfberg (Davos, Switzerland) covering seasons 2008/09 to 2021/22. This dataset was described in the research article:
Fees, A., van Herwijnen A., Altenbach, M., Lombardo, M., Schweizer, J.: Glide-snow avalanche characteristics at different time-scales extracted from time-lapse photography, Annals of Glaciology, 91
We extracted the dynamics of opening glide-cracks and the glide-snow avalanche activity from time-lapse photographs. Glide-snow avalanches were separated into surface and interface events using the liquid water content which was simulated with SNOWPACK at 10 virtual stations on Dorfberg.

en: snowpack-simulation time-lapse-photography avalanche avalanche-activity glide-snow-avalanche

en: Glide-snow avalanche activity on Dorfberg, Davos, Switzerland

envi

en: Environment

en:
Resource Title: Glide distances for opening glide-cracks
    Size: 512266 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/d6c7a578-6317-49f3-8ba5-71f62b5b6610/resource/7ee75315-9399-48fe-b5b1-e3ef1d2f0774/download/metadata.zip
    Description: This folder contains all glide-cracks where glide-distances were extracted from time-lapse photographs. Glide-distances were calculated in x-direction (width) and y-direction (downslope, length) by georeferencing the images.

en:
Resource Title: SNOWPACK simulations
    Size: 719769784 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/d6c7a578-6317-49f3-8ba5-71f62b5b6610/resource/73dee7b2-a419-4fd2-ac0e-585bc0138d90/download/snowpack.zip
    Description: SNOWPACK simulations (.pro and .smet) covering season 2008/09 to 2021/22 at 10 virtual locations on Dorfberg. The locations correspond to the most common glide-snow avalanche paths.

en:
Resource Title: Glide-snow avalanche activity
    Size: 88583 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/d6c7a578-6317-49f3-8ba5-71f62b5b6610/resource/0aca99e1-7b3d-492f-a7bb-2756e5b74bbd/download/events.csv
    Description: Table that contains all glide-snow avalanche events that were detected in time-lapse photographs (season 2008/09 to 2021/22). The columns are explained in the README.txt

en:
Resource Title: README
    Size: 1996 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/d6c7a578-6317-49f3-8ba5-71f62b5b6610/resource/26a702a9-37e6-4dc9-9ce9-e7a223d73bff/download/readme.txt
    Description: README.txt explaining the dataset and columns of tables in more detail.

avalanche

display_name: avalanche

avalanche-activity

display_name: avalanche-activity

glide-snow-avalanche

display_name: glide-snow-avalanche

snowpack-simulation

display_name: snowpack-simulation

time-lapse-photography

display_name: time-lapse-photography


en: This data includes (1) Scripts to aggregate landscape resistance layers into squared and hexagonal grids (i.e., different representations and resolutions), (2) Input resistance layers and focal nodes in .txt format to run in Circuitscape (Python implementation v4.0.5). Circuitscape is a software tool for modeling and analyzing landscape connectivity, which simulates movement of organisms across landscapes by estimating resistance to movement across each point of the landscape. (3) Scripts for the ecological network analysis, and (4) environmental predictors for amphibian whole-life cycle habitats used to describe the local environment for BGI design (i.e. topographic, hydrologic, edaphic, vegetation, land-use derived, movement-ecology related).

en: urban-planning biodiversity ecosystem-services circuit-theory ecological-corridors network-analysis

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: Multi-Scale Prioritization framework for Urban Blue-Green Infrastructure Planning to Support Biodiversity: Data & Codes

en:
Resource Title:  Amphibian movement resistance layers and scripts for connectivity modeling - part 1
    Size: 247430561 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/dd7b7d6e-6fe8-455e-9743-adf791914a17/resource/e364cf62-5561-4b4d-8909-0977b3da60f3/download/1_resistance_layers.zip
    Description: The zipped folder includes the script to aggregate the resistance layers into squared and hexagonal grids. The subfolders “Boundaries”, “Layers”, “Reference_grids”, and “Resistance_from_SDMs” contain files either called by the code or created in the process.The subfolder “Inputs_for_Circuitscape” contains the inputs to run Circuitscape, after doing some subsequent processing steps in QGIS (listed in the R script) with the files created with the script.

en:
Resource Title:  Amphibian movement resistance layers and scripts for connectivity modelling - part 2
    Size: 20335272 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/dd7b7d6e-6fe8-455e-9743-adf791914a17/resource/1283e651-6999-46ad-b23d-0e81c593a90e/download/2_network-analysis.zip
    Description: The zipped folder includes the script to create and analyse the networks from Circuitscape outputs. The subfolders “Boundaries”, “Layers”, and “Reference_net” contain files either called by the code or created in the process. The subfolder “Outputs_from_Circuitscape” contains the outputs from Circuitscape including node and branch cumulative currents in a network format.

biodiversity

display_name: biodiversity

circuit-theory

display_name: circuit-theory

ecological-corridors

display_name: ecological-corridors

ecosystem-services

display_name: ecosystem-services

network-analysis

display_name: network-analysis

urban-planning

display_name: urban-planning