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

en: data extract 20191113

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.

biodiversity

display_name: biodiversity

lichen

display_name: lichen

lichen-conservation

display_name: lichen-conservation

lichen-distribution

display_name: lichen-distribution

national-data-centre

display_name: national-data-centre

swisslichens

display_name: swisslichens

switzerland

display_name: switzerland

en: SwissLichens - Distribution of lichens in Switzerland


maintainer: Mahdi Jafari

en: openfoam numerical-simulation vapor-transport snow-density-profile convection-in-snow

state: active

type: dataset

en: snowpackBuoyantPimpleFoam

media_type: application/zip

en:
This zipped folder includes (1) the source code files, (2) an example case for simulation, and (3) an instruction how to compile and use the new solver.

Note that the most updated version of snowpackBuoyantPimpleFoam can be found in the WSL/SLF GitLab repository: https://gitlabext.wsl.ch/snow-models/snowpackbuoyantpimplefoam

format: ZIP

en: snowpackBuoyantPimpleFoam is a two-phase solver implemented to model convection of water vapor with phase change in snowpacks. This new solver is based on the standard solver of buoyantPimpleFoam in the open-source fluid dynamics software, OpenFOAM 5.0 (www.openfoam.org).

convection-in-snow

display_name: convection-in-snow

numerical-simulation

display_name: numerical-simulation

openfoam

display_name: openfoam

snow-density-profile

display_name: snow-density-profile

vapor-transport

display_name: vapor-transport

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: snowpackBuoyantPimpleFoam: an OpenFOAM Eulerian–Eulerian two-phase solver for modelling convection of water vapor in snowpacks


maintainer: EnviDat Support

en: atmospheric-conditions precipitation wind radiation temperature-profiles

en: Latest data from Stillberg climate station

en: This dataset is outdated and has been replaced with an updated version (see description).

format: SERVICE

en: Site photo 1

en: This site photo is outdated and has been replaced with an updated version (see description).

en: Overview plot

media_type: image/png

en: This overview plot is outdated and has been replaced with an updated version (see description).

format: PNG

en: DATA ACCESS

en: This data access is outdated and has been replaced with an updated version (see description).

en: STB2 CSV

media_type: text/csv

format: CSV

mimetype: text/csv

en: README

media_type: text/plain

en: This description is outdated and has been replaced with an updated version (see description).

format: TXT

en: Site photo 3

en: Site photo 2

en:
 Important

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

atmospheric-conditions

display_name: atmospheric-conditions

precipitation

display_name: precipitation

radiation

display_name: radiation

temperature-profiles

display_name: temperature-profiles

wind

display_name: wind

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


maintainer: Matthias Haeni

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

en: Site aerial photo (Bettlachstock)

en: Site aerial photo from both Bettlachstock stations. Source: Google. 2016. Site aerial photo from both Bettlachstock stations. Retrieved November 11, 2016, from https://www.google.ch/maps

mimetype: image/jpeg

en: Site photo of the forest station (BTB)

en: Site photo of the forest station from the side.

en: Data plot of the field station (BTF)

media_type: application/pdf

en: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

format: PDF

mimetype: application/pdf

en: Data plot of the forest station (BTB)

en: Data file.

en: Site photo of the field station (BTF)

en: Site photo of the field station from the side.

en: Field station data (BTF)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Bettlachstock in Switzerland where one station is located within a natural mixed forest stand (BTB) with European beech (_Fagus sylvatica_; 170-190 yrs), European silver fir (_Abies alba_; 190 yrs) and Norway spruce (_Picea abies_; 200 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, BTF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Bettlachstock is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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


en: insect-decline long-term switzerland time-series biodiversity

en: Climate data

en: Additional climate data on climate trend

en: Body size lookup-table

en: Syrphidae

en: Climate data of the respective sampling years

en: Data extract from WSL Fauna Database containing arthropod species and abundance data from 1987, 1997, 2019

en: Sites and traps

en: Lookup table of trap and site locations

en: Coleoptera

en: Species incidence per year

en: Total abundance of species per year

en: Fused catching data 1987, 1997, 2019

en: Complete dataset of species and abundances per year an trap

en: Master thesis of Regula Gygax, 1999, at University of Zürich

en: The study is comparing in 1997 the trapping efficiency of a newly developed combination trap with traditional window traps and yellow pan traps. Study locations in the Limpach valley matched with a subset from a larger study, which was conducted in 1987.

en: Input data - species list and trophic guilds

en: List of found species and associated trophic guild

en: Neuropteroidae

en: Staphylinidae

en: R code

en: R code for data analyses

en: Capture data with size and mass

en: Complete capture data including body sizes and biomass values

en: Aculeata

en: R code for data editing

en: Species number and abundances per trap

en: Sum of species and abundances listed per trap

en: SQL query executed on WSL Fauna Database to retrieve the data

en:
Recent publications about declines in arthropod biomass, abundance and species diversity
raise concerns and call for measures. Agricultural intensification is likely one cause for the negative trends. But rare long-term arthropod surveys conceal trends in arthropod communities in agricultural land. Here, we report about a standardized sampling of arthropod fauna in a Swiss agricultural landscape, repeated over 32 years (1987, 1997 and 2019). We sampled 8 sites covering 4 semi-natural and agricultural habitat types. Four trap types were used to capture a wide range of flying and ground dwelling arthropods between May and July. Over the three sampling periods, 58’255 specimens of 1’343 species were analysed. Mean arthropod biomass, abundance and species richness per trap was significantly higher in 2019 than in prior years and gamma diversity of the study area was highest in 2019. Biomass and abundance increased stronger in the flight traps than in the pitfall traps. The implementation of agri-environmental schemes has improved habitat quality since 1993, while landscape composition and pesticide and fertilizer use remained stable over the study period, both contributing to the findings. The results of this study contrast with outcomes of comparable investigations and highlight the importance of further long-term investigations on arthropod dynamics.

Data are provided on request to contact person against bilateral agreement.

en: Arthropod biomass, abundance and species richness trends over 32 years in the agricultural Limpach valley, Switzerland


en: snow-melt snow-water-equivalent snow-cover-fraction snow-climatology operational-snow-hydrological-service

en: Data package

en: This dataset comprises the climatology on gridded data of snow water equivalent and snow melt runoff spanning 1998-2022, with a spatial resolution of 1 km and daily temporal resolution. This data was produced with the conceptual OSHD model (Temperature Index Model).

operational-snow-hydrological-service

display_name: operational-snow-hydrological-service

snow-climatology

display_name: snow-climatology

snow-cover-fraction

display_name: snow-cover-fraction

snow-melt

display_name: snow-melt

snow-water-equivalent

display_name: snow-water-equivalent

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

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: Climatological snow data since 1998, OSHD


greenland-climate-network-gc-net-data

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

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

en: 22-Petermann ELA

en: GC Net Config for Stations

media_type: application/json

en: Stations Config from the static bucket

format: JSON

en: Access the latest L1 data

en:
The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).

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

en: GC Net Chart Parameters

en: Stations parameters from static bucket

en: Archived GC-Net Data (deprecated)

en:
The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).

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

en:
In Memory of Dr. Konrad (Koni) Steffen

Update October 2022: The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).
Starting October 3, 2022, the access to the latest versions of the "ready to use" L1 data has been migrated to GEUS. Future data versions will be available at: [https://doi.org/10.22008/FK2/VVXGUT](https://doi.org/10.22008/FK2/VVXGUT)
 Background
Starting with a single station in 1991, the Greenland Climate Network (commonly known as GC-Net) is a set of Automatic Weather Stations (AWS) set up and managed by the late Prof. Dr. Konrad (Koni) Steffen, and spanning the Greenland Ice Sheet (GrIS). This first station was "Swiss Camp" or the "ETH-CU" camp (GC-Net station 01) which was used as a field science and education site by Koni for years. The GC-Net was expanded with multiple NASA, NOAA, and NSF grants throughout the years, and then supported by WSL in the later years. These data (see "C-file" below) were previously hosted by the Cooperative Institute for Research in Environmental Sciences (CIRES) in Boulder, Colorado.

 Overview
Provided in this dataset are the 16 longest running stations in the network, which are spread over a significant area of the GrIS and the majority of the unique climatic zones. From the South Dome high point in the South, to the Western Jakobshavn ablation region in the west, to the Petermann glacier in the North across east of the Northeast Greenland Ice Stream to the east, GC-Net is the longest running climatological record of Greenland.

 The standard GC-Net station consists of:
* Air temperature measurements at 2 heights above the surface
* Temperature and humidity measurements at 2 heights above the surface
* Wind speed and direction measured at 2 heights above the surface
* Sonic distance sounder measurements for 2 snow height and distance of instruments to surface
* Incoming shortwave radiation measurement
* Reflected shortwave radiation measurement
* Net broadband radiation (long- and short-wave) measurement
* Air pressure measurement
Data have often been repatriated in near-real time using one of either the GOES geostationary satellite or the ARGOS polar orbiting satellite transmission system. The stations were visited typically every 1-2 years for maintenance and service, and to download full uncorrupted data directly from the dataloggers. GC-Net stations were visited by Twin Otter equipped with snow skids to land directly on the open-ice at the AWS locations, or by helicopter near the west coast. The AWSs operate on solar and battery power and occasionally lost power during the dark and cold winter months, particularly when the batteries were aging.

 Dataset
This dataset consists of 2 main data levels; Level 0 and Level 1.
Level 0 is the raw data from the dataloggers, historical processing codes, satellite transmissions, and Koni’s personal data archive. Level 0 data (.zip) directories contain subdirectories:
* “C file” - contains the historical processed datafile for each station.
* “Campbell logger files” - contains the raw csv datafiles from the stations’ Campbell Scientific dataloggers since the CR1000 era (~2007-2008 for most stations).
* “Photos” - contains photographs of the station when available marked by year.
Level 1 is the appended, calibrated, cleaned, and quality flagged data. The full processing scheme is open-source and publicly available on the following GitHub repository (please also check GitHub for the latest L1 data):
[GC-Net L1 data on GitHub](https://github.com/GEUS-Glaciology-and-Climate/GC-Net-level-1-data-processing "GC-Net-level-1-data-processing")
Level 1 data is provided in the newly described csv-compatible [NEAD format](https://www.envidat.ch/#/metadata/nead "NEAD format").

 Additional Details
Dataset description publication will be forthcoming.
The Geological Survey of Denmark and Greenland (GEUS) has been imperative in the reprocessing and continuity mission of GC-Net. Multiple GC-Net stations have been replaced with updated and upgraded AWS hardware at the same coordinates by GEUS. This effort will ensure continuity of the GC-Net dataset into the future.

air-temperature

display_name: air-temperature

automatic-weather-station

display_name: automatic-weather-station

aws

display_name: aws

climate

display_name: climate

greenland

display_name: greenland

net-radiation

display_name: net-radiation

en: Greenland Climate Network (GC-Net) Data


maintainer: Avalanche Warning Service SLF

en: Avalanche fatalities per year in Switzerland since 1936/1937

en:
Attention: this data is not updated after 2022 anymore.
This dataset contains the statistics on the number of avalanche fatalities per hydrological year in Switzerland. The data set commences with the beginning of the hydrological year 1936/37 on 01/10/1936. After the completion of a hydrological year, the new data is appended to the existing dataset.
The following information is contained (by column and column title):
 - hydrological year
 - number of fatalities in the backcountry (=tour)
 - number of fatalities in terrain close to ski areas (=offpiste)
 - number of fatalities on transportation corridors including ski runs, roads, railway lines (=transportation.corridors)
 - number of fatalities in or around buildings or in settlements (= buildings)
 - sum (of all four categories)
The definition for these four categories as described in the guidelines to the avalanche accident database:
**tour**:
activities include back-country ski, snowboard or snow-shoe touring
**offpiste**:
access from ski area, generally from the top of a skilift with short hiking distances
**transportation.corridors** ([Techel et al., 2016](http://www.geogr-helv.net/71/147/2016/ )):
people travelling or recreating on open or temporarily closed
transportation corridors (e.g. a road user or a skier on a ski run) and people working on open or closed transportation corridors (e.g. maintenance crews on roads, professional rescue teams)
**buildings** ([Techel et al., 2016](http://www.geogr-helv.net/71/147/2016/ )):
people inside or just outside buildings, and workers on high alpine building
sites

avalanche-accident-statistics

display_name: avalanche-accident-statistics

avalanche-accidents

display_name: avalanche-accidents

avalanche-fatalities

display_name: avalanche-fatalities

fatalities-in-or-around-buildings

display_name: fatalities-in-or-around-buildings

fatalities-in-settlements

display_name: fatalities-in-settlements

fatalities-in-the-backcountry

display_name: fatalities-in-the-backcountry

fatalities-on-transportation-corridors

display_name: fatalities-on-transportation-corridors

en: Number of avalanche fatalities per hydrological year in Switzerland since 1936-1937


en: streamflow alptal stable-water-isotopes runoff groundwater

en: Deuterium rainfall event B

en:
This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date (year-month-day) and time (hours:minutes:seconds) in UTC
P d.D: Deuterium composition of the rainfall samples (per mil)
X: X coordinate (CH1903 LV03)
Y: Y coordinate (CH1903 LV03)

en: Precipitation and discharge event A

en:
This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
With any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date (year-month-day) and time (hours:minutes:seconds) in UTC
Q: Discharge in mm per hour at catchment outlet (697234,210472)
P: Rainfall in mm per hour averaged for two rain gauges (697494,210675) and (698004, 210680)

en: Deuterium rainfall extra event

en: Deuterium streamwater event B

en:
This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date and time of sample collection (UTC; year-month-day, hours:minutes)
Q d.D: Deuterium composition (per mil) of streamwater sampled at the catchment outlet (697234,210472); the first sample is used as pre-event streamflow sample

en: Deuterium rainfall event A

en: Deuterium streamwater event C

en: Deuterium streamwater event A

en: Deuterium rainfall event C

en: Deuterium groundwater and baseflow October 2016

en:
This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

Date: Date (year-month-day)
Location: Well number or outlet
X: X coordinate (CH1903 LV03)
Y: Y coordinate (CH1903 LV03)
riparian: riparian wells (i.e. from the riparian-like cluster as presented in Kiewiet et al. (2019)) are marked a one, all other wells are marked a zero
d.D (per mil): Deuterium composition (per mil)

en: Precipitation and discharge extra event

en: Deuterium groundwater and baseflow October 2017

en: Deuterium streamwater extra event

en: Deuterium groundwater and baseflow August 2017

en: Precipitation and discharge event B

en: Precipitation and discharge event C

en:
This dataset includes discharge and rainfall measurements and deuterium compositions of streamflow, rainfall and groundwater, for four rainfall events and three baseflow snapshot campaigns in the Studibach (Alptal, Switzerland). More specifically, we present the following data:

- Specific discharge at the catchment outlet at 5-minute resolution (mm per hour);
- Rainfall at 5-minute resolution (mm per hour);
- Rainfall deuterium composition (‰);
- Stormflow deuterium composition (‰);
- Groundwater and baseflow deuterium compositions (‰).

For the files containing rainfall and discharge timeseries (QP), and rainfall and streamwater deuterium compositions ("Deuterium_Rainfall" and "Deuterium_Streamwater"), we added the corresponding event identifier (A, B, C or extra) to the file names. For the files containing the groundwater and baseflow deuterium values ("Deuterium_Snapshot") we added the sample collection date to the file name. We included the X and Y coordinates for each data point (coordinate system: CH1903 LV3) as well as the date and time (UTC).

More information on the data collection and preparation can be found in Kiewiet et al. (in review). A detailed description of the baseflow snapshot campaigns can also be found in Kiewiet et al., 2019.

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: Discharge, rainfall, and deuterium compositions of streamwater, rainwater and groundwater, for four rainfall events in the Studibach, Alptal, Switzerland


maintainer: Henning Löwe

en: discrete-element-modelling fast-sintering angle-of-repose granular-snow grain-shape

mimetype: text/plain

en: Angle of repose and 3D CT images

en:
5 Snow types were examined and glass beads as a cohesion-less reference, named as follows:

RG: rounded grains (sieved with 0.7 - 1.4 mm)

FC: faceted grains (1.4 - 2 mm)

FCS: small faceted grains (0.7 - 1.4 mm)

IB: ice beads (4 mm)

IBS: small ice beads (2 - 2.2 mm)

GB: glass beads(2 mm)

en:
Angle of repose experiments were performed with different snow types at temperatures between -2 and -40°C. They were used to examine granular snow dynamics on the grain-scale with focus on the role of grain shape and cohesion. The angle of repose was observed by sieving snow onto a round, freestanding base until a stationary heap was formed.

This dataset consists of 1) the images of the experimental heaps that were taken to determine the angle of repose, 2) one binary 3D micro computed tomography image of each snow type. The CT images were taken with the SLF micro-CT40 to characterize snow properties and grain shape.

The experiments with natural snow types (rounded and faceted grains) and spherical model snow allowed for an examination of the differences in granular properties between natural grain shapes and spherical particles in view of Discrete Element Modelling. With the chosen temperatures, the effect of sintering could be observed that increases the angle of repose with increasing temperature.

angle-of-repose

display_name: angle-of-repose

discrete-element-modelling

display_name: discrete-element-modelling

fast-sintering

display_name: fast-sintering

grain-shape

display_name: grain-shape

granular-snow

display_name: granular-snow

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

en: Angle of repose experiments with natural and spherical snow


maintainer: Leo Gallus Bont

en: swiss-geodata digital-elevation-model seilaplan cable-yarding digital-planning swiss-geo-downloader geoinformation-system qgis-plugin open-source

en: View video on youtube!

en: Herunterladen des DHM mit SwissGeoDownloader

en:
In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. The plugin ‘Swiss Geo Downloader’, which is available for the open source geoinformation software QGIS, allows freely available Swiss geodata to be downloaded and displayed directly within QGIS. It was developed in 2021 by Patricia Moll in collaboration with the Swiss Federal Institute for Forest, Snow and Landscape Research WSL. In this tutorial we describe how to download the high accuracy elevation model ‘swissALTI3D’ with the help of the ‘Swiss Geo Downloader’ and how to use it for digital planning of a cable line with the plugin ‘Seilaplan’. Please note that the tutorial language is German!

Link to the Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader

Link to Seilaplan website: https://seilaplan.wsl.ch

*********************

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. Das Plugin Swiss Geo Downloader, welches für das Open Source Geoinformationssystem QGIS zur Verfügung steht, ermöglicht frei verfügbare Schweizer Geodaten direkt innerhalb von QGIS herunterzuladen und anzuzeigen. Es wurde 2021 von Patricia Moll in Zusammenarbeit mit der eidgenössischen Forschungsanstalt Wald, Schnee und Landschaft WSL entwickelt. In diesem Tutorial beschreiben wir, wie man mit Hilfe des Swiss Geo Downloaders das hochgenaue Höhenmodell swissALTI3D herunterladen und für die Seillinienplanung mit dem Plugin Seilaplan verwenden kann.

Link zum Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader

Link zur Seilaplan-Webseite: https://seilaplan.wsl.ch

cable-yarding

display_name: cable-yarding

digital-elevation-model

display_name: digital-elevation-model

digital-planning

display_name: digital-planning

geoinformation-system

display_name: geoinformation-system

open-source

display_name: open-source

qgis-plugin

display_name: qgis-plugin

seilaplan

display_name: seilaplan

swiss-geo-downloader

display_name: swiss-geo-downloader

swiss-geodata

display_name: swiss-geodata

en: Seilaplan Tutorial: DTM download with SwissGeoDownloader


en: tree-ring-width dendrochronology swiss-forest-lab tree-selection-effects icp-forests long-term-forest-ecosystem-research

en: Data Request

en:
Two stem core samples of all trees of a subplot of approximately 1 a. Basal Area, Wood volume increment per area estimation

 Purpose:

Investigation of the relation between dendroparameters of dominance/surpression and stem growth. Abgleich Jahrringdaten mit Inventurdaten.

 Paper Citation:

 * Nehrbass-Ahles C, Babst F, Klesse S, Nötzli M, Bouriaud O, Neukom R, Dobbertin M, Frank D (2014) The influence of sampling design on tree-ring-based quantification of forest growth. Global Change Biology, 20 (9): 2867–2885. [doi: 10.1111/gcb.12599](http://doi.org/10.1111/gcb.12599)

 * Klesse S, Etzold S, Frank D (2016) Integrating tree-ring and inventory-based measurements of aboveground biomass growth: research opportunities and carbon cycle consequences from a large snow breakage event in the Swiss Alps. European Journal of Forest Research, 135 (2): 297-311.

dendrochronology

display_name: dendrochronology

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

swiss-forest-lab

display_name: swiss-forest-lab

tree-ring-width

display_name: tree-ring-width

tree-selection-effects

display_name: tree-selection-effects

en: Dendrochronological analyses of tree core samples (subplot) LWF


en: swiss-forest-lab long-term-forest-ecosystem-research icp-forests nitrogen base-cations atmospheric-deposition acidity

en:
Throughfall (precipitation under forest canopy) is a major pathway in forest nutrient cycling, and its quantification is necessary to establish both water and nutrient budgets. Furthermore, parallel sampling of throughfall and precipitation in the open field (bulk precipitation), together with assumptions about the canopy exchange processes (uptake and leaching of nutrients), allow the atmospheric deposition of nutrients and pollutants to be quantifed. Bulk precipitation and throughfall have been sampled since 1994 or later on 15 LWF plots using 3 (in the open) and 16 (in the forest) funnel-type precipitation collectors. These collectors are replaced by 1 (open area) and 4 (forest stand) snow buckets in winter on plots with abundant precipitation in the form of snow. The length of sampling intervals is usually 14 days.

 Purpose:

To assess a major flux of the water and nutrient budget in forests, and to quantify the atmospheric deposition of nitrogen, sulphur and other nutrients. Atmospheric deposition is one of the key factors in the causal chain between emission of air pollutants and acidifying or eutrophying effects in forest ecosystems.

 Manual Citation:

 * Thimonier, A., Brang, P., Wenger, K., 1997. Kapitel C4. Atmosphärische Deposition: Freiland- und Bestandesniederschläge, in: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Flächen der Langfristigen Waldökosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-48.

 * Clarke N, Žlindra D, Ulrich E, Mosello R, Derome J, Derome K, König N, Lövblad G, Draaijers GPJ, Hansen K, Thimonier A, Waldner P, 2016: Part XIV: Sampling and Analysis of Deposition. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Thünen Institute  of  Forest  Ecosystems,  Eberswalde,  Germany,  32  p.  +  Annex  [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)

 Paper Citation:

 * Thimonier A, Schmitt M, Waldner P, Rihm B (2005) Atmospheric deposition on Swiss Long-term Forest Ecosystem Research (LWF) plots. Environmental Monitoring and Assessment, 104: 81-118. [doi: 10.1007/s10661-005-1605-9](http://doi.org/10.1007/s10661-005-1605-9)

 * Thimonier A, Kosonen Z, Braun S, Rihm B, Schleppi P, Schmitt M, Seitler E, Waldner P, Thöni L (2019) Total deposition of nitrogen in Swiss forests: Comparison of assessment methods and evaluation of changes over two decades. Atmospheric Environment, 198: 335-350. [doi: 10.1016/j.atmosenv.2018.10.051](http://doi.org/10.1016/j.atmosenv.2018.10.051)

acidity

display_name: acidity

atmospheric-deposition

display_name: atmospheric-deposition

base-cations

display_name: base-cations

nitrogen

display_name: nitrogen

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: Atmospheric deposition (throughfall and bulk deposition) LWF


maintainer: Sebastian Schlögl

en: stable-boundary-layer atmosphere turbulent-sensible-heat-fluxes atmospheric-turbulence snow

en: Turbulence data AA00

en: sensor in 25 m height

mimetype: application/zip

en: Turbulence data GR00

en: sensor in 1 m height

en: Turbulence data WFJ07

en: upper sensor

en: Turbulence data PM07

en: NWW

en: Turbulence data WFJ11

en: sensor in 0.2 m height

en: lower sensor

en: SEE

en: sensor in 2 m height

en:
We used five different atmospheric turbulence datasets from four test sites, with these sites showing differences in their topographical characteristics. We chose one typical alpine test site with high topographical complexity (Weissfluhjoch, Davos, Switzerland) and three test sites consisting of one glacier site (Plaine Morte, Crans-Montana, Switzerland) and two polar sites (Greenland and Antarctica) representing a quasi-ideal site with homogeneous surface and quasi infinite fetch in all directions.
The turbulent sensible heat flux was calculated using the eddy-covariance method. Note that the sonic temperature fluctuations have been converted into virtual temperature fluctuations.
Three-dimensional wind velocity and air temperature were processed using a linear detrending (Rannik and Vesala, 1999) and a planar fit approach (Massmann and Lee, 2002) to rotate the coordinate system. Air temperature, relative humidity and air pressure from weather stations were used to calculate air properties, which are required for the data processing. The weather stations are located in the immediate vicinity of the turbulence tower and are affected by the same air masses. Turbulence data were averaged to 30-min intervals, whilst changing to a 15-min time interval marginally affects the heat fluxes at the Weissfluhjoch test site (Mott et al., 2011). Note that we define a negative sensible heat flux as being directed towards the snow surface and a positive sensible heat flux as being directed upwards.
The selected datasets and corresponding test sites are briefly introduced below:

Weissfluhjoch 2007 (WFJ07): A vertical set-up of two three-dimensional ultrasonic anemometers (CSAT3, Campbell Scientific, Inc.) was used at the traditional field site Weissfluhjoch (2540 m asl.) to measure three-dimensional wind velocity and air temperature at a frequency of 20 Hz. The sensors were mounted 3 m and 5 m above the ground and provided reliable data for 50 days between 11 February 2007 and 24 April 2007. Further information on the field campaign can be found in Stössel et al. (2010) and Mott et al. (2011).

Weissfluhjoch 2011-13 (WFJ11): Three-dimensional wind velocity and air temperature were recorded at 5 m above the ground at a frequency of 10 Hz with a three-dimensional ultrasonic anemometer (CSAT3). The analysis was conducted for data obtained between February and March in the years 2011-13.

Plaine Morte 2007 (PM07): Two three-dimensional ultrasonic anemometers (CSAT3) were installed on a horizontal boom facing opposite directions (west-north-west vs. east-south-east) at 3.75 m above the ground to measure air temperature and three-dimensional wind velocity at 20 Hz. The data were collected at the almost flat field site on the Plaine Morte glacier (2750 m asl.) near Crans-Montana, Switzerland from February to April 2007. High quality meteorological data were additionally recorded and used to force the model. A detailed description about the set-up at the Plaine Morte glacier can be found in Huwald et al. (2009) and Bou-Zeid et al. (2010).

Greenland 2000 (GR00): High-frequency three-dimensional ultrasonic anemometer measurements (CSAT3) were recorded at 50 Hz at the Summit Camp (72.3 °N, 38.8 °W, 3208 m asl.) located on the northern dome of the Greenland ice sheet. Data were collected at 1 m and 2 m above the snow surface during summer in 2000 and 2001. Additionally, meteorological measurements were obtained for the post processing and used to force the model. More information about the field campaign can be found in Cullen et al. (2007, 2014).

Antarctica 2000 (AA00): A set-up of three vertical three-dimensional ultrasonic anemometers (DA-600, Kaijo Denki) were installed at Mizuho Station (70°42' S, 44°20' E, 2230 m asl.) in Eastern Antarctica at 0.2, 1 and 25 m and recorded turbulence data at a frequency of 100 Hz from October to November 2000. Longwave and shortwave radiation, relative humidity, air and snow surface temperature were additionally measured and used to force the model. More information about the field campaign can be found in Nishimura and Nemoto (2005).

atmosphere

display_name: atmosphere

atmospheric-turbulence

display_name: atmospheric-turbulence

snow

display_name: snow

stable-boundary-layer

display_name: stable-boundary-layer

turbulent-sensible-heat-fluxes

display_name: turbulent-sensible-heat-fluxes

en: How do stability corrections perform in the stable boundary layer over snow?


en: dsm photogrammetry ground-classification drone-data uav

en: Geodata and Ground Classification Wolfgang Arelen

en: Original RGB images and generated products.

format: GeoTIFF

en: We conducted two drone flights with the Wingtra and DJI Phantom 4 RTK drones in Davos Wolfgang Arelen, on 25.08.2021. The data was processed with the Agisoft Metashape Professional Software.The Wingtra point cloud was further processed to derive a ground classification in individual LASTools and Terrasolid workflows.

drone-data

display_name: drone-data

dsm

display_name: dsm

ground-classification

display_name: ground-classification

photogrammetry

display_name: photogrammetry

uav

display_name: uav

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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 and derived Ground Classification Wolfgang Arelen


en: wind-disturbance damaged-wood chablis wood-market bois-endommage scieries stockage-humide wet-storage marche-du-bois sawmills

en: Impact des événements météorologiques extrêmes sur l'économie forestière suisse: le point de vue des scieries

format: XLS

en:
Extreme events impact on the Swiss forest economy: the sawmill perspective

Supplementary Information
This survey aimed at answering three main questions: (i) What are the Swiss sawmills challenges and actions taken after a large storm/windthrow?, (ii) How do these challenges and actions vary across sawmill size and location?, and (iii) is adaptation from the sawmills to extreme events possible, with regards to wood type, products and required infrastructure?

Informations supplémentaires
Cette enquête visait à répondre à trois questions principales : (i) Quels sont les défis et les mesures prises par les scieries suisses après une grosse tempête ou un coup de vent ? (ii) Comment ces défis et ces mesures varient-ils selon la taille et l'emplacement de la scierie ? et (iii) l'adaptation des scieries aux événements extrêmes est-elle possible, en ce qui concerne le type de bois, les produits et l'infrastructure requise ?

Ziel dieser Umfrage war die Beantwortung von drei Hauptfragen: (i) Welche
s sind die Herausforderungen und Massnahmen der Schweizer Sägewerke nach einem grossen Sturm/Windwurf?, (ii) Wie unterscheiden sich diese Herausforderungen und Massnahmen je nach Grösse und Standort des Sägewerks? und (iii) Ist eine Anpassung der Sägewerke an Extremereignisse möglich, in Bezug auf Holzart, Produkte und erforderliche Infrastruktur?

bois-endommage

display_name: bois-endommage

chablis

display_name: chablis

damaged-wood

display_name: damaged-wood

marche-du-bois

display_name: marche-du-bois

sawmills

display_name: sawmills

scieries

display_name: scieries

stockage-humide

display_name: stockage-humide

wet-storage

display_name: wet-storage

wind-disturbance

display_name: wind-disturbance

wood-market

display_name: wood-market


en: wild-bee gardens species-composition functional-diversity community-assembly functional composition environmental-heterogeneity garden niche ground-beetles allotments allotment trait ground-beetle diversity species urban-ecology functional-traits bettergarden wild-bees bettergardens ecology urbanization niche-breadth

en: Urban carabid and bee species city of Zurich Switzerland

en: Carabid beetle and wild bee occurrences in the city of Zurich, Switzerland, from the years (project) 2009 (BiodiverCity), 2011 (ENHANCE) and 2016 (BetterGardens).

en: Arthropod-databank query used to access data.

en:
Carabid beetle and wild bee occurrences in the city of Zurich, Switzerland.

Dataset available upon request (An agreement between the data provider and the data recipient is necessary).

allotment

display_name: allotment

allotments

display_name: allotments

bettergarden

display_name: bettergarden

bettergardens

display_name: bettergardens

community-assembly

display_name: community-assembly

composition

display_name: composition

diversity

display_name: diversity

ecology

display_name: ecology

environmental-heterogeneity

display_name: environmental-heterogeneity

functional

display_name: functional

functional-diversity

display_name: functional-diversity

functional-traits

display_name: functional-traits

garden

display_name: garden

gardens

display_name: gardens

ground-beetle

display_name: ground-beetle

ground-beetles

display_name: ground-beetles

niche

display_name: niche

niche-breadth

display_name: niche-breadth

species

display_name: species

species-composition

display_name: species-composition

trait

display_name: trait

urban-ecology

display_name: urban-ecology

urbanization

display_name: urbanization

wild-bee

display_name: wild-bee

wild-bees

display_name: wild-bees

en: The origin of urban communities: from the regional species pool to community assemblages in city


en: tree-ring-width dendrochronology stable-isotopes long-term-forest-ecosystem-research dominant-trees icp-forests swiss-forest-lab

en: Data Request

en:
Two stem core samples of 10 trees outside the plot of each main species in the plot were taken at breast height (1.3 m above ground) with a SUUNTO corer. Tree ring width and density were determined with the instruments CATRAS and TSAP, the Densitometer DENDRO 2003 and a stereo-microscope. The selected trees included at least 10 pre-dominant or dominant trees.

 Purpose:

N and C stable isotope signals

 Paper Citation:

 * Tomlinson, G., Siegwolf, R.T.W., Buchmann, N., Schleppi, P., Waldner, P., Weber, P., 2014. The mobility of nitrogen across tree-rings of Norway spruce (Picea abies L.) and the effect of extraction method on tree-ring d15N and d13C values. Rapid Commun. Mass Spectrom. 28, 1258-1264.

dendrochronology

display_name: dendrochronology

dominant-trees

display_name: dominant-trees

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

stable-isotopes

display_name: stable-isotopes

swiss-forest-lab

display_name: swiss-forest-lab

tree-ring-width

display_name: tree-ring-width

en: Dendrochronological analyses of tree core samples (IsoN) adjacent to LWF sites


maintainer: Armin Sigmund

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

en: Data

en: Modeled data including the data shown in the figures of the related research article

en: Python scripts for data visualization

en: Scripts for generating the figures of the related research article and generating the data files (csv) from the model output

en: Model code

en: Model Sublimation and Model Air Parcel (python scripts)

en: Readme

en: Meta data

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

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

educ

en: Education, culture and sport

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


foliar-analyses-lwf

title_for_slug: foliar-analyses-lwf

en: swiss-forest-lab foliar-analyses long-term-forest-ecosystem-research tree-nutrition icp-forests

en:
Foliage has been sampled every two years since 1995/1997 on 5-6 trees of the main species on all LWF plots. Concentrations of macronutrients (N, P, K, Ca, Mg, S), carbon (C) and micronutrients are determined on leaves and current and previous year needles. The dry mass of 100 leaves or 1000 needles is determined as well.

 Purpose:

To assess the nutrient status of the forest stands and detect possible deficiencies or imbalances, which are often indicative of processes at the ecosystem level.

 Manual Citation:

 * Brang, P., Hug, C., Thimonier, A., Zehnder, U., 1997. Kapitel D1.5 Nährstoffversorgung von Nadeln und Blättern, in: Brang, P. (Ed.), Aufnahmeanleitungen aller Forschungsprojekte auf Flächen der Langfristigen Waldökosystem-Forschung (LWF). Eidg. Forschungsanstalt WSL, Birmensdorf, pp. 1-12.

 * Rautio P, Fürst A, Stefan K, Raitio H, Bartels U, 2016: Part XII: Sampling and Analysis of Needles and Leaves. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on  forests.  Thünen  Institute  of  Forest  Ecosystems,  Eberswalde,  Germany,  19  p.  +  Annex  [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual)

 Paper Citation:

 * Thimonier A, Graf Pannatier E, Schmitt M, Waldner P, Walthert L, Schleppi P, Dobbertin M, Kräuchi N (2010) Does exceeding the critical loads for nitrogen alter nitrate leaching, the nutrient status of trees and their crown condition at Swiss Long-term Forest Ecosystem Research (LWF) sites?. European Journal of Forest Research, 129 (3): 443-461. [doi: 10.1007/s10342-009-0328-9](http://doi.org/10.1007/s10342-009-0328-9)

foliar-analyses

display_name: foliar-analyses

tree-nutrition

display_name: tree-nutrition

envidat

en: EnviDat: the Environmental Data Portal

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: Foliar analyses LWF


en: magerrasen dry-grasslands sentinel milieux-naturels fens typoch distribution-models schweiz bogs remote-sensing fettwiesen lebensraum trockenrasen lebensraumkarte flachmoore

en: Map for TypoCH 2.3.1 (Molinion)

en:
Probability of occurrence for *Molinion* (TypoCH 2.3.1) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 4.5.3 (Cynosurion)

en:
Probability of occurrence for *Cynosurion* (TypoCH 4.5.3) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en:
Countrywide ensemble maps of the 20 grassland habitat types

(1) Median predicted probability of occurrence

(2) Standard deviation of predicted probability of occurrence

(3) Presence/Absence map

en: Map of the most likely grassland habitat type

en:
Final combined map of the most likely grassland habitat type (i.e., after regional corrections) at 10x10 m resolution

Coordinate System: CH1903+_LV95

Value:	    TypoCH     Name
1:           2.2.1       *Magnocaricion* + *Cladetium*
2:           2.2.2       *Caricion fuscae*
3:	 2.2.3	*Caricion davallianae*
4:	 2.3.1	*Molinion	*
5:	 2.3.2/3	*Calthion* + *Filipendulion*
6:	 2.4.1	*Sphagnion magellanici *
7:	 4.0.2	Re-seeded and heavy fertilized grassland/Kunstrasen
8:	 4.2.1/2/3	*Stipo-Poion*, *Xerobromion*, *Diplachnion*
9:	 4.2.4	*Mesobromion *
10:	 4.3.1	*Seslerion*
11:	 4.3.2/4	*Caricion firmae* + *Elynion*
12:	 4.3.3	*Caricion ferrugineae*
13:	 4.3.5	*Nardion*
14:	 4.3.6	*Festucion variae*
15:	 4.3.7	*Caricion curvulae*
16:	 4.5.1	*Arrhenatherion*
17:	 4.5.2	*Polygono-Trisetion*
18:	 4.5.3	*Cynosurion*
19: 4.5.4	*Poion alpinae*
20: 	 4.6	Fallow grassland/Grasbrachen

en: Map for TypoCH 2.3.2/3 (Calthion + Filipendulion)

en:
Probability of occurrence for *Calthion* (TypoCH 2.3.2) and *Filipendulion* (TypoCH 2.3.3) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 4.2.4 (Mesobromion)

en:
Probability of occurrence for *Mesobromion* (TypoCH 4.2.4) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en:
Map of the second most likely grassland habitat type at 10x10 m resolution; Coordinate System: CH1903+_LV95

Value:	 TypoCH  Name
1:  2.2.1  *Magnocaricion* + *Cladetium*
2:  2.2.2  *Caricion fuscae*
3:	 2.2.3	*Caricion davallianae*
4:	 2.3.1	*Molinion	*
5:	 2.3.2/3	*Calthion* + *Filipendulion*
6:	 2.4.1	*Sphagnion magellanici *
7:	 4.0.2	Re-seeded and heavy fertilized grassland/Kunstrasen
8:	 4.2.1/2/3	*Stipo-Poion*, *Xerobromion*, *Diplachnion*
9:	 4.2.4	*Mesobromion *
10:	 4.3.1	*Seslerion*
11:	 4.3.2/4	*Caricion firmae* + *Elynion*
12:	 4.3.3	*Caricion ferrugineae*
13:	 4.3.5	*Nardion*
14:	 4.3.6	*Festucion variae*
15:	 4.3.7	*Caricion curvulae*
16:	 4.5.1	*Arrhenatherion*
17:	 4.5.2	*Polygono-Trisetion*
18:	 4.5.3	*Cynosurion*
19: 4.5.4	*Poion alpinae*
20: 	 4.6	Fallow grassland/Grasbrachen

en: Map for TypoCH 4.3.1 (Seslerion)

en:
Probability of occurrence for *Seslerion* (TypoCH 4.3.1) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 2.2.3 (Caricion davallianae)

en:
Probability of occurrence for *Caricion davallianae* (TypoCH 2.2.3) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 4.2.1/2/3 (Stipo-Poion/Cirsio-Brachypodion/Xerobromion)

en:
Probability of occurrence for *Stipo Poion* (TypoCH 4.2.1.1), *Cirsio-Brachypodion* (TypoCH 4.2.1.2), *Xerobromion* (TypoCH 4.2.2) and *Diplachnion* (TypoCH 4.2.3) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 4.3.7 (Caricion curvulae)

en:
Probability of occurrence for *Caricion curvulae* (TypoCH 4.3.7) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 4.3.5 (Nardion)

en:
Probability of occurrence for *Nardion* (TypoCH 4.3.5) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Overview: individual grassland habitats in the combined map

en:
Overview of the occurrence of the individual habitat types in the combined map (M1F):

For each grassland habitat type, its predicted occurrence is shown per elevation, slope, aspect and biogeographic region.

en: Map for TypoCH 4.5.1 (Arrhenatherion)

en:
Probability of occurrence for *Arrhenatherion* (TypoCH 4.5.1) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 4.6 (Fallow/Abandoned grassland)

en:
Probability of occurrence for fallow/abandoned grasslands (TypoCH 4.6) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: List of modelled grassland habitat types

en:
List of the 20 grassland habitat types modelled

Attributes: TypoCH, Modelled, Latin, Français, Deutsch, Italiano, English

en: Map for TypoCH 4.5.2 (Polygono-Trisetion)

en:
Probability of occurrence for *Polygono-Trisetion* (TypoCH 4.5.2) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 2.3.2/4 (Caricion firmae + Elynion)

en:
Probability of occurrence for *Caricion firmae* (TypoCH 4.3.2) and *Elynion* (TypoCH 4.3.4) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 4.3.3 (Caricion ferrugineae)

en:
Probability of occurrence for *Caricion ferrugineae* (TypoCH 4.3.3) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 4.5.4 (Poion alpinae)

en:
Probability of occurrence for *Poion alpinae* (TypoCH 4.5.4) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 2.4.1 (Sphagnion magellanici)

en:
Probability of occurrence for *Sphagnion magellanici* (TypoCH 2.4.1) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 2.2.1 (Magnocaricion + Cladietum)

en:
Probability of occurrence for *Magnocaricion* and *Cladietum* (TypoCH 2.2.1) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 4.3.6 (Festucion variae)

en:
Probability of occurrence for *Festucion variae* (TypoCH 4.3.6) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en: Map for TypoCH 2.2.2 (Caricion fuscae)

en:
Probability of occurrence for *Caricion fuscae* (TypoCH 2.2.2) as predicted by the ensemble models

Values: 0-1000 corresponding to probabilities of occurrence from 0-1

Coordinate System: CH1903+_LV95

Spatial resolution: 10x10m

Remarks: No masking applied to remove non-grassland areas such as forested areas, etc.

en:
We modelled the spatial distribution of 20 permanent grassland habitats at the level of phytosociological alliances according to the Swiss habitat typology (TypoCH; Delarze et al. 2015) at 10x10 m resolution across Switzerland. The 20 grassland habitat types belong to the following habitat groups: fens, wet meadows, raised bogs, re-seeded and heavy fertilized grasslands, dry grasslands, nutrient-poor alpine and subalpine grasslands, nutrient-rich pastures and meadows as well as fallow grasslands.

We followed a two-step approach:

(1) Ensemble models provide **distribution maps of the 20 individual grassland habitat types**, using training data from various sources. Predictors were Copernicus Sentinel satellite imagery and variables describing climate, soil and topography. The performance of these maps was assessed with the True Skill Statistics and split‐sampling of the data.

Available maps for each grassland habitat: (1) *Map of the median of predicted probability of occurrence*; (2) *Map of the standard deviation of the predicted probability of occurrence* (available upon request); (3) *Binary presence/absence map* (available upon request). For an overview, see *Overview: Maps of the individual grassland habitats*.

(2) **Combined maps**: The individual maps were combined into countrywide maps of the most and second most likely habitat type, respectively, using an expert‐based weighting approach. The performance of the combined map for the most likely habitat type was assessed via an independent testing dataset and a comparison of the predicted habitat‐type proportions with extrapolations from field surveys.

Available combined maps: Map of the most likely habitat type (M1F; after regional corrections); Map of the second most likely habitat type (M2); Map of the most likely habitat type without regional corrections (available upon request); Map of the weighted median of the predicted probability of occurrence for the most/second most likely habitat type, respectively (available upon request); map of the ratio of the probabilities of occurrence of the most and second most likely habitat types (available upon request)

bogs

display_name: bogs

distribution-models

display_name: distribution-models

dry-grasslands

display_name: dry-grasslands

fens

display_name: fens

fettwiesen

display_name: fettwiesen

flachmoore

display_name: flachmoore

lebensraum

display_name: lebensraum

lebensraumkarte

display_name: lebensraumkarte

magerrasen

display_name: magerrasen

milieux-naturels

display_name: milieux-naturels

remote-sensing

display_name: remote-sensing

en: Distribution maps of permanent grassland habitats for Switzerland


en: nfi-photos visual-attractiveness adults-and-teenagers online-questionnaire-survey perception-of-ecosystem-services

en: Dataset in csv-format

en: Questionnaire French

en: Codebook to SPSS-file

en: Questionnaire German

en: SPSS-Dataset

en: Questionnaire Italian

en: This questionnaire survey was conducted as an online survey and aimed at investigating the relationship between physical forest characteristics, visual attractiveness of forest and the perception of ecological and cultural ecosystem services in urban forests. Each participant was shown 6 photos out of a pool of 50 photos taken from the Swiss National Forest Inventory (NFI) database. Physical forest characteristics were derived from the photos. The study was conducted as part of the "WaMos meets LFI" (WML) project.

adults-and-teenagers

display_name: adults-and-teenagers

nfi-photos

display_name: nfi-photos

online-questionnaire-survey

display_name: online-questionnaire-survey

perception-of-ecosystem-services

display_name: perception-of-ecosystem-services

visual-attractiveness

display_name: visual-attractiveness

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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: Relationship between physical forest characteristics, visual attractiveness and perception of ecosystem services in urban forests


snow-drift-station-flowcapt

maintainer: Benjamin Walter

title_for_slug: snow-drift-station-flowcapt

en: flowcapt raclets acoustic-mass-flux wind-speed snowdrift snow-blowing

en: maximum mass flux

en: The data file contains the maximum mass flux of 10 minute intervalls. The duration of one cycle was 1 min and contained 6s observations and 54 s stand-by. The time is given in UTC+1.

en: minimum mass flux

en: The data file contains the minimum mass flux of 10 minute intervalls. The duration of one cycle was 1 min and contained 6s observations and 54 s stand-by. The time is given in UTC+1.

en: average wind speed

en: The data file contains the average wind speed in 10 minute intervalls. The duration of one cycle was 1 min and contained 6s observations and 54 s stand-by. The time is given in UTC+1.

en: avergaged mass flux

en: Average mass flux of 10 min measurement interval. The Cycle duration is 1 min which includes 6 s observation and 54 s stand-by. The time is given in UTC+1.

en: minimum wind spees

en: The data file contains the minimum wind speed in 10 minute intervalls. The duration of one cycle was 1 min and contained 6s observations and 54 s stand-by. The time is given in UTC+1.

en: maximum wind speed

en: The data file contains the maximum wind speed in 10 minute intervalls. The duration of one cycle was 1 min and contained 6 s observations and 54 s stand-by. The time is given in UTC+1.

en: The FlowCapt is an ultra-robust instrument measuring solid particle acoustic mass - flux intensities (g m‾ ² s‾ ¹) and wind speeds (m s‾ ¹). It was deployed at Gotschnagrat (LON: 46.859 LAT: 9.849). The vertical tube with a length of 1 m monitors snowdrift and snow-blowing; and is mounted at a height between 0.1 an 1.1 m above snow surface. The time within each data set is given in UTC+1.

acoustic-mass-flux

display_name: acoustic-mass-flux

flowcapt

display_name: flowcapt

raclets

display_name: raclets

snow-blowing

display_name: snow-blowing

snowdrift

display_name: snowdrift

wind-speed

display_name: wind-speed

en: Snow Drift Station - Flowcapt


en: rcm cryosphere software downscaling toposcale cordex

en:
Model code and documentation for the downscaling model TopoCLIM which provides methods to downscale climate timeseries from CORDEX RCM data. This scheme specifically addresses the need for hillslope scale atmospheric forcing timeseries for modeling the local impact of regional climate change projections on the land surface in complex terrain. The method has a global scope and is able to generate the full suite of model forcing variables required for hydrological and land surface modeling at hourly timesteps.

A working example is provided in this code archive but for full running of the scheme TopoSCALE is required https://doi.org/10.5194/gmd-7-387-2014 with code available at https://github.com/joelfiddes/tscaleV2. Standard library dependencies are given in the python requirements.txt of the archive with installation instructions in the README.md.

cordex

display_name: cordex

cryosphere

display_name: cryosphere

downscaling

display_name: downscaling

rcm

display_name: rcm

software

display_name: software

toposcale

display_name: toposcale

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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: TopoCLIM v1.1 code


en: picea-abies insect-outbreaks windthrow castanea-sativa pinus-sylvestris national-forest-inventory disturbance tree-recruitment switzerland drought vegetation-shift

en: Site Information

en:
File containing the information on parameters of the 5521 National forest inventory sites recorded during the NFI2 and NFI4.

CLNR = Number of site in the National Forest Inventory
INVNR = Information on which National Forest Inventory
Elevation = Elevation above sea level
Perc_Conifers = Proportional contribution of Conifer species to the total basal area of a site
DominantTreeSp = Dominant tree taxa of a site as defined by maximum basal area contribution
DMGTYP = Type of damage observed between NFI2 and NFI4 (up to four observations possible)
DMGYEAR = In case a DMGTYPE event was observed here is the corresponding year marked.
BioReg = Biogeographic region of the site
Ggrob = NaiS site type (Potential natural forest vegetation of a site)
DisturbanceType = Type of disturbance recorded on the site between NFI2 and NFI4.

en: R-Scripts for Figures and Data tables

en:
This archive contains the R-Scripts to reproduce the Figures 2-4, S1-S4 and the data tables S4-S10 in the associated publication.

Scherrer, D., Ascoli, D., Conedera, M., Fischer, C., Maringer, J., Moser, B., . . . T., W. (2021). Canopy disturbances catalyse tree species shifts in Swiss forests. Ecosystems, in revision.

en: ReadMe

en: A detailed description of the data.

en: Species-specific biomass per NFI site

en:
File containing the information about taxa-specific basal area, sapling density and polestage density for the different National Forest Inventory sites during NFI2 (1993-1995) and NFI4 (2009-2017).

INVNR = Information on which National Forest Inventory
CLNR = Number of site in the National Forest Inventory
Species = Information on the species
BA = basal area per hectare
Sapling = Sapling (< 4 cm DBH) density as stems per hectare
Polestage = Polestage (DBH 4 - 11.9 cm) density as stems per hectare

en:
The files refer to the data used in Scherrer et al. (2021) "Canopy disturbances catalyse tree species shifts in Swiss forests" in _Ecosystems_.

The two data files contain information about site factors (e.g. disturbance events, dominant tree species, elevation) and species-specific biomass of 5521 plots of the Swiss National Forest Inventory visited during the second (NFI2 1993-1995) and fourth (NFI4 2009-2017) inventory.
In addition, we provide all the R-scripts necessary to reproduce the Figures and data tables of the related publication.

For more detailed information about the data files please check the ReadMe.docx file.

castanea-sativa

display_name: castanea-sativa

disturbance

display_name: disturbance

drought

display_name: drought

insect-outbreaks

display_name: insect-outbreaks

national-forest-inventory

display_name: national-forest-inventory

picea-abies

display_name: picea-abies

pinus-sylvestris

display_name: pinus-sylvestris

switzerland

display_name: switzerland

tree-recruitment

display_name: tree-recruitment

vegetation-shift

display_name: vegetation-shift

windthrow

display_name: windthrow

en: Effects of canopy disturbance on Swiss forests


en: snow-avalanche experiment snow-mechanics dynamic-crack-propagation crack-propagation propagation-saw-test

en: SNOWPACK modelling

en: The snow cover model SNOWPACK (version 3.60) was used to simulate the evolving snow stratigraphy at our field site. The model was driven with data from an automatic weather station which was located 100m next to the field site (FLS_bunker_snowpack.smet). The SNOWPACK initiation file (FLS_bunker_snowpack.ini) contains the selected model parameters that result in the modeled snow stratigraphy (FLS_bunker_snowpack.pro).

en: Snowpack data and crack propagation characteristics

en: Snowpack data includes the elastic modulus (stiffness properties) of the slab, the elastic modulus of the weak layer, and the specific fracture energy of the weak layer determined with a homogeneous and a layered slab model. During crack propagation, we measured the crack speed, touchdown distance, crack arrest length and energy dissipation due to compaction and dynamic fracture.

en: Videos of crack propagation

en: The videos show the cracking process in the Propagation Saw Tests (flat field). The upper panel shows strain on top of the recorded movie frames. The second row shows vertical (w, blue line) and horizontal (u, orange line) displacement fields. The third and fourth row show the first and second time derivatives, namely the velocity and acceleration field.

en: Sustained crack propagation index

en: Index of self-sustained crack propagation SSP (ssp_measured.xlsx) with time. The self-sustained crack propagation index SSP was also modelled for simulated snow stratigraphy (ssp_modelled.xlsx) using crack speed and critical cut length parameterizations from Heierli (2005) and Richter et al. (2019), respectively.

en:
This data set includes material and results described in the related research article:

Bergfeld, B., van Herwijnen A., Bobillier, G., Rosendahl P., Weißgraeber P., Adam V., Dual, J., and Schweizer, J.: Temporal evolution of crack propagation characteristics in a weak snowpack layer: conditions of crack arrest and
sustained propagation, Natural Hazards and Earth System Sciences, 23, 293-315, https://doi.org/10.5194/nhess-23-293-2023, 2023.

We performed a series of propagation saw test experiments, up to ten meters long, over a period of 10 weeks and analyzed these using digital image correlation techniques. We derived the elastic modulus of the slab, the elastic modulus of the weak layer and the specific fracture energy of the weak layer with a homogeneous and a layered slab model. During crack propagation, we measured crack speed, touchdown distance and the energy dissipation due to compaction and dynamic fracture. Our data set provides unique insight and valuable data to validate models.

crack-propagation

display_name: crack-propagation

dynamic-crack-propagation

display_name: dynamic-crack-propagation

experiment

display_name: experiment

propagation-saw-test

display_name: propagation-saw-test

snow-avalanche

display_name: snow-avalanche

snow-mechanics

display_name: snow-mechanics

en: Time series data on dynamic crack propagation in long propagation saw tests


en: ecological-indicator-values landolt plants soil-mapping ecological-maps switzerland soil-moisture soil-ph

en: Description

en: Description of the predictors provided in this dataset

en: Predictors

en: Predictors generated and used in Descombes et al. (2020)

en:
Ecologically meaningful predictors are often neglected in plant distribution studies, resulting in incomplete niche quantification and low predictive power of species distribution models (SDMs). Because environmental data are rare and expensive to collect, and because their relationship with local climatic and topographic conditions are complex, mapping them over large geographic extents and at high spatial resolution remains a major challenge. Here, we derived environmental data layers by mapping ecological indicator values (EIVs) in space by using a large set of environmental predictors in Switzerland.

This dataset contains the predictors (raster layers) generated and used in the following publication (Descombes et al. 2020). Only predictors for which we have the rights to share them are provided. Other datasets and predictors can be accessed via the original data provider. Details on the predictors and sources are fully described in the publication. The predictors are provided as GeoTIFF files, at 93 m spatial resolution and Mercator projection ("+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"). The excel file (xlsx) provides a short description of the raster layers.

Paper Citation:

Descombes, P. et al. (2020). Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes. Ecography. (accepted)

ecological-indicator-values

display_name: ecological-indicator-values

ecological-maps

display_name: ecological-maps

landolt

display_name: landolt

plants

display_name: plants

soil-mapping

display_name: soil-mapping

soil-moisture

display_name: soil-moisture

soil-ph

display_name: soil-ph

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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.


mortality

title_for_slug: mortality

en:
Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh that died or disappeared between two inventories, but were not cut. *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted.

Citation:
 > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_

canton

display_name: canton

mortality-star

display_name: mortality-star

ownership

display_name: ownership

en: Mortality*


en: topography cryowrf antarctica wrf landuse

en: Documentation

en: Documentation about how the provided dataset was produced including details to reproduce the dataset.

en: WPSinput

en: Input files to run the Weather Research and Forecasting model (WRF) preprocessing system WPS with REMA topography and AntarcticaLC2000 landuse data (see Documentation for details).

en: Scripts

en: Scripts to run the preprocessing to produce WRF binary tiles from REMA and AntarcticaLC2000 for topography and landuse (see Documentation for procedure).

en:
Reference Elevation Model of Antarctica (REMA) topography and AntarcticaLC2000 landuse data
are now available as static data input for the Weather Research and Forecasting model (WRF). Topography
and landuse are made available at a spatial resolution of 1 km. This documentation describes the methods
applied to convert REMA and AntarcticaLC2000 to WRF readable format and shows how this improves
the representation of the Antarctic topography and landuse categories over coastal Antarctic regions.

antarctica

display_name: antarctica

cryowrf

display_name: cryowrf

landuse

display_name: landuse

topography

display_name: topography

wrf

display_name: wrf

en: REMA topography and AntarcticaLC2000 for WRF


en: species-richness bryophytes nature-conservation vascular-plants habitat-area extinction-debt habitat-fragmentation wetlands

en: Wetlands of the canton of Zürich (Switzerland): Data on species richness and recent and historic area and connectivity of 55 fens.

en:
The data provided in the Excel sheet contain the following information for 55 wetlands of the canton of Zürich (Switzerland):
-Species richness data for vascular plants and bryophytes classified as follows: (1) all vascular plant species; (2) wetlands specialists among vascular plants; (3) generalists, which were all non-specialist vascular plant species; (4) short-lived vascular plant specialists; (5) long-lived vascular plant specialists; (6) short-lived vascular plant generalists; (7) long-lived vascular plant generalists; (8) bryophyte species.
-Area of all wetlands in 1850, 1900, 1950 and 2000
-Wetland area within buffers with 2km radius with the center of the wetland as starting point
-Mean indicator values according to Landolt et al. (2010) and the standard deviation of these indicator values based on presence-absence data of vascular plants. Indicator values for temperature, light availability, moisture, acidity, nutrients, amount of humus and soil aeration were considered.

en:
This dataset includes data on species richness of vascular plants and bryophytes in 55 wetlands of the canton of Zürich (Switzerland) as well as recent and historic data on the area and connectivity of these 55 wetlands and was used for the paper Jamin A., Peintinger M., Gimmi U., Holderegger R., Bergamini A. (2020) Evidence for a possible extinction debt in Swiss wetland specialist plants. Ecology and Evolution.
Species richness data are available for vascular plants and bryophytes. The field survey was carried out between June 5 and August 10, 2012. The survey covered all wetland (fen) types in the canton of Zürich. For data collection, at least half a day per wetland was spent searching for species. Within each wetland all different vegetation types were covered until no new species were found to get as complete species lists as possible. In the Excel file information on species richness of the following groups is provided: (1) all vascular plant species; (2) wetlands specialists among vascular plants; (3) generalists, which were all non-specialist vascular plant species; (4) short-lived vascular plant specialists; (5) long-lived vascular plant specialists; (6) short-lived vascular plant generalists; (7) long-lived vascular plant generalists; (8) bryophyte species. Specialist vascular plant species included all characteristic species listed in Appendix 1a of the wetland inventory of Switzerland (BUWAL, 1990).
Based on the data of Gimmi et al. (2011), the area of all wetlands in 1850, 1900, 1950 and 2000 were determined as well as the wetland area within buffers 2km in radius with the center of the wetland as starting point. These data are also provided in the Excel sheet.
Moreover, for each wetland mean indicator values according to Landolt et al. (2010) and the standard deviation of these indicator values based on presence-absence data of vascular plants were calculated and are provided in Excel sheet. Indicator values for temperature, light availability, moisture, acidity, nutrients, amount of humus and soil aeration were considered.

bryophytes

display_name: bryophytes

extinction-debt

display_name: extinction-debt

habitat-area

display_name: habitat-area

habitat-fragmentation

display_name: habitat-fragmentation

nature-conservation

display_name: nature-conservation

species-richness

display_name: species-richness

vascular-plants

display_name: vascular-plants

wetlands

display_name: wetlands


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

en: GenPSUm

en:
GenPSUm contains Files to get the complete precipitation set:

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

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

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

en: SNOWPACK climate change simulation files

en: This folder contains SNOWPACK INI-files for snowpack, soil profiles, and input data for climate change modeling.

en: R-Script

en: The Folder R- Script contains, the R files to create the Climate Change input and the Data for the WFJ- Modeling.

en: Precipitation datasets

en:
This folder contains 16 ideal precipitation data sets for IMIS stations.

*Updated documentation 24 August 2020:*

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

en:
This dataset includes all data and simulation files presented in the publication: Bender et al. 2020. Changes in climatology, snow cover and ground temperatures at high alpine locations, DOI: 10.3389/feart.2020.00100.

This includes:

* meteorological forcing,
* climate change timeries and
* simulation files together with
* snow depth
* ground temperature

Please refer to the following publication for further details which should be cited when using this dataset:

Bender et al. 2020. Changes in climatology, snow cover and ground temperatures at high alpine locations, DOI: 10.3389/feart.2020.00100.

climate-change-impact

display_name: climate-change-impact

depth-of-snowfall

display_name: depth-of-snowfall

ground-temperature

display_name: ground-temperature

meteo-station

display_name: meteo-station

swiss-alps

display_name: swiss-alps

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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


ect-and-rb-data-switzerland

title_for_slug: ect-and-rb-data-switzerland

en: slope-stability stability-test ect snow-stability rutschblock

en: data set (update)

en:
This is the same data set as uploaded originally, except for the following three additional columns, which are required to replicate the respective classification of ECT results: *depthCriteriaDecisive_ectStabOrig*, *depthCriteriaDecisive_ectStabWinkler*, *depthCriteriaDecisive_ectStabNew*. These columns have the following values:
- 1 if the depth criteria as defined in the paper was decisive for rating the ECT result with stability class 4
- 0 if the depth of the fracture plane was not decisive for the stability classification

In addition, the stability class has been recoded for:
- *ectStabOrig* from [1, 2] to [1, 4]
- *ectStabWinkler* from [1, 2, 3] --> [1, 2.5, 4]
to be fully in line with the stability classes used in the paper.

en: data set

en:
The data set contains the following information. For details, refer to the publication:
Variables
- unique identifier for the snow pit location (profileID)
- unique identifier for the ECT stability test (stabTestID)
- ECT stability classifications
-- orginal (ectStabOrig)
-- Winkler and Schweizer (ectStabWinkler)
-- new (ectStabNew)
- ECT results
-- lowest number of taps (ectScoreMin) for the decisive propagation
-- propagation
- slope stability information (slopeStability 0 = stable, 1 = unstable)
- RB stability class
- forecast avalanche danger level (forecastDangerLevel)

en: The data set contains the data used in the publication "On snow stability interpretation of Extended Column Test results" by Techel et. al. (2020), published in Natural Hazards Earth System Sciences.

ect

display_name: ect

rutschblock

display_name: rutschblock

slope-stability

display_name: slope-stability

snow-stability

display_name: snow-stability

stability-test

display_name: stability-test

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: ECT and RB data Switzerland


high-resolution-climate-data-for-europe

title_for_slug: high-resolution-climate-data-for-europe

en: cordex daily-climate maximum-temperature eur11 minimum-temperature precipitation air-temperature europe

en: Data Access

en: CHELSA EUR11 Technical Documentation

en: As the CHELSA project is continuously expanded, certain new parameters, time scales etc. are not included anymore in the original publication (Karger et al. 2017). We are therefore provide this document as a guideline of the current climatic parameters available and will expand it as new parameters or time periods will become available. Please refer to the version history for changes made in the document.

en:
High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present downscaled climate data for the CORDEX EUR11 domain at a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperature lapse rates. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height. The resulting data consist of a daily temperature and precipitation timeseries.

The data is distributed under a: Creative Commons: Attribution 4.0 International (CC BY 4.0) license.

air-temperature

display_name: air-temperature

cordex

display_name: cordex

daily-climate

display_name: daily-climate

europe

display_name: europe

maximum-temperature

display_name: maximum-temperature

minimum-temperature

display_name: minimum-temperature

precipitation

display_name: precipitation

en: High resolution climate data for Europe


maintainer: Axel Volkwein

en: mass-movements experiments field-tests trajectory field-test natural-hazards rockfall experiment field-data

en: Jumps and impacts

en: This table contains data of more than 2200 of each jumps and impacts of the test blocks on a grassy slope. It gives there durations in combination with acting accerlartions and the rotational speed of the blocks. The data were retrieved using the block internal accelerometer and rotation sensors. The combination of these data with the measurement from the local positioning system allows the deduction of jump lengths, slope gradient etc.

en: These data contain the point clouds obtained from laser scanning the test slope and the six test blocks used for the experiments. The coordinates of the slope are given in CH-1903 format. The coordinates of the block point clouds are kept in a lokal x/y/z coordinate system.

en: Overview of all tests

en:
This table gives an overview on all tests performed including
- block used
- start & end position
- number of jumps
- etc.
-

en: Block trajectories recorded from local positioning system

en: This file contains more than 80 rockfall trajectories projected onto the terrain, i.e. the positions of the single blocks over time during the single tests that were recorded by the local positioning system with about 10Hz. It contains the detected positions as well as a smoothened path (see Volkwein et al., Geosciences, 2018). The table also contains the derived velocities and accelerations along the slope (attention: these accelerations are the once that occur during an impact).

en:
In summer 2014, 6 rock blocks between 20 and 80kg have been thrown in total 111 times down a slope at the Swiss Oberalppass close to the village Tschamut. The slope was mainly covered by grass and its lower part was flat and large enough to provide natural runouts of the single trajectories.

An extensive measurement program has been set up to measure the block trajectories: With surveyor's instruments the slope and the six used rock blocks were scanned and the start and end positions of each test were recorded. During the single events two cameras filmed the trajectories. A special sensor device located within the blocks recorded the acting accelerations and rotational speeds over time. Further, the device emitted a Wifi signal that got detected from eight receivers around the slope. Based on this signal the block position has been recorded over time.

The dataset contains all data that were gathered through above field campaign.

experiment

display_name: experiment

experiments

display_name: experiments

field-data

display_name: field-data

field-test

display_name: field-test

field-tests

display_name: field-tests

mass-movements

display_name: mass-movements

natural-hazards

display_name: natural-hazards

rockfall

display_name: rockfall

trajectory

display_name: trajectory

educ

en: Education, culture and sport

en: Repetitive trajectory testing in Tschamut 2014


swiss-canopy-crane-ii-research-site

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

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

en: Link to website

en: Research site metadata

en: Swiss Canopy Crane II

en:
![alt text](https://www.envidat.ch/dataset/fc69e369-eee9-42ab-8486-d2c38cff317d/resource/68fa7065-de32-4343-aa26-71094c5254ae/download/sccii.jpg "Swiss Canopy Crane II")

This research site is located near Hölstein in Canton Basel-Landschaft in a mature temperate forest that harbours more than 400 trees from 14 different species. The 1.6 ha site is equipped with the latest infrastructure, including 60 automated point dendrometers, automated soil respiration chambers, 72 ceramic suction cups at various locations and depths across the site, and a range of automated environmental sensors in the soil, the forest floor and in the canopy. A key piece of infrastructure is the new Swiss Canopy Crane II (SCC II), a 50 m tall crane with a 50 m jib that provides canopy access to 250 trees from 12 different species.

climate-change

display_name: climate-change

damage-of-tree

display_name: damage-of-tree

drought

display_name: drought

ecosystem-functions

display_name: ecosystem-functions

forest

display_name: forest

infrastructure

display_name: infrastructure

swiss-forest-lab

display_name: swiss-forest-lab

envidat

en: EnviDat: the Environmental Data Portal

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: Swiss Canopy Crane II research site