en: survey municipality participation switzerland energy-transition

en: The dataset contains data from a survey, which was conducted in a periurban region close to Berne, Switzerland. The survey was conducted in Fall 2018 and contained opinion questions about the energy transition. Additionally, spatial data was collected using a PPGIS. While the opinion data is included in the data set, the spatial data is not. For more explanation, please consider the information sheet, the related publications or to contact the authors.

energy-transition

display_name: energy-transition

municipality

display_name: municipality

participation

display_name: participation

survey

display_name: survey

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Implementing the energy transition at municipal level in Switzerland: A regional survey


en: forest-transportation-systems trucks forest-access-roads trafficability timber-harvesting

state: active

type: dataset

media_type: application/pdf

en:
Article describing the dataset, as well as its acquisition and potential uses.
To access the data please contact Fabrizio Cioldi.

format: PDF

en:
In 2013–2014, a survey was conducted in Switzerland to update the Forest Access Roads geo-dataset within the framework of the Swiss National Forest Inventory (NFI). The resulting nationwide dataset contains valuable information on truck-accessible forest roads that can be used to transport wood. The survey involved interviewing staff from the approximately 800 local forest services in Switzerland and recording the data first on paper maps and then in digitized form. The data in the NFI on the forest roads could thus be updated and additional information regarding their trafficability for specific categories of truck included. The information has now been attached to the geometries of the Roads and Tracks of the swissTLM3D (release 2012) of the Federal Office of Topography swisstopo. The resulting data are suitable for statistical analyses and modeling, but further (labour-intensive) validation work would be necessary if they are to be used as a basis for applications requiring more spatial accuracy, such as navigation systems. The data are managed at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) and are available for third parties for non-commercial use provided they have purchased a TLM license.

Related Publication: [doi: 10.3188/szf.2016.0136](http://dx.doi.org/10.3188/szf.2016.0136)

forest-access-roads

display_name: forest-access-roads

forest-transportation-systems

display_name: forest-transportation-systems

timber-harvesting

display_name: timber-harvesting

trafficability

display_name: trafficability

trucks

display_name: trucks

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

it: Istruzione, cultura e sport

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

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: Forest Access Roads 2013


maintainer: EnviDat Support

en: ritigraben permos permafrost borehole ground-temperature

en: PERMOS DATA POLICY

en: Map of PERMOS borehole network

format: SERVICE

en: DATA ACCESS

en:
_ENVIDAT NOTE: Data currently unvailable and measures are being taken to recover and restore the data files._

Processed ground temperature measurements at the Ritigraben permafrost borehole (RIT_0102) in canton Valais, Switzerland. The borehole is located at 2690 m asl on a flat site. The surface material is coarse blocks and borehole depth is 30 m. Thermistors used YSI 44006. Year of drilling 2002. This borehole is part of the Swiss Permafrost network, PERMOS (www.permos.ch). Contact phillips@slf.ch for details of processing applied.

borehole

display_name: borehole

ground-temperature

display_name: ground-temperature

permafrost

display_name: permafrost

permos

display_name: permos

ritigraben

display_name: ritigraben

en: RIT1: Processed permafrost borehole data (2690  m asl), Ritigraben, Switzerland


dischmex-meteorological-measurements

maintainer: Rebecca Mott

title_for_slug: dischmex-meteorological-measurements

en: dischmex meteorological-measurements meteo-station dischma meteorological-data

en: Meteorological measurement 2014

media_type: application/zip

en: data

format: ZIP

mimetype: application/zip

en: Meteorological measurements 2014

en: Overview sensors

format: XLS

en: Meteorological measurements 2016

en: Meteorological measurement 2015

en: Location of the meteorological stations

mimetype: application/pdf

en:
Meteorological measurements recorded in the Dischma valley from 2014-2016. In 2014 and 2015 we used 11 mobile weather stations from sensorscope to record meteorological parameter in the upper Dischma valley in the closer surroundings of the Gletschboden area. The meteorological stations are eqiupped with at least air temperature/humidity, wind velocity and wind direction sensors. Some stations are additionally equipped with precipitation, shortwave radiation and snow surface temperature sensors.
Three transects were installed at different aspects and were equipped with air temperature/humidity and wind sensors. Transect 1 (stations 2-4) provides meteorological Information on an east-north-east facing slope at elevations ranging between 2100 m and 2500 m. Transect 2 (stations 5-7) provides meteorological Information on a south-west slope and transect 3 (stations 8-10) on a north-west slope. Station 1 is fully equipped with meteorological sensors (temperature/humidity, wind, IR, up and downwand shortwave radiation and precipitation).
In 2016, mobile stations from sensorscope were replaced with six permanent meteorological stations. Meteorological stations 1-3 are equipped with an air temperature/humidity sensor, two wind speed sensors, a wind direction sensor and an incoming and outgoing shortwave radiation sensor. Stations 4 and 6 are equipped with an air temperature/humidity sensor and a wind speed/direction sensor. Station 5 is a equipped with an air temperature/humidity sensor, a wind speed/direction sensor, a snow surface temperature sensor, an incoming and outgoing shortwave radiation sensor and an incoming longwave radiation sensor.

dischma

display_name: dischma

dischmex

display_name: dischmex

meteo-station

display_name: meteo-station

meteorological-data

display_name: meteorological-data

meteorological-measurements

display_name: meteorological-measurements

en: DISCHMEX - Meteorological measurements


en: consistency avalanche-bulletin switzerland avalanche-forecast danger-description avalanche avalanche-danger

media_type: text/csv

en:
The data contains the following information:
- districtId - the ID of the danger region
- validFromDate - the date and time when the forecast was published
- problem - whether the danger refers to dry or wet conditions as a major avalanche problem
- dangerlevel - the issued danger level
- danger_description_dt - the German danger description describing the major avalanche problem

format: CSV

en: The data set contains the danger descriptions (German) of the avalanche forecasts published at 5 pm between 27-Nov-2012 and 13-Feb-2020.

avalanche

display_name: avalanche

avalanche-bulletin

display_name: avalanche-bulletin

avalanche-danger

display_name: avalanche-danger

avalanche-forecast

display_name: avalanche-forecast

consistency

display_name: consistency

danger-description

display_name: danger-description

switzerland

display_name: switzerland

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: How is avalanche danger described in textual descriptions in avalanche forecasts in Switzerland?


en: shrubs soil-water-matrix-potential swiss-forest-lab long-term-forest-ecosystem-research icp-forests

en: Data Request

en:
Continuous measurement of soil matrix potential at 15, 50 and 100 cm depth with Decagon MPS-2 sensors 1 m N, SE and SW from the stem of 3 threes within much and 3 trees within few shrubs

 Purpose:

Explore the effect of shrubs on the water availability for pine trees in Visp.

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

shrubs

display_name: shrubs

soil-water-matrix-potential

display_name: soil-water-matrix-potential

swiss-forest-lab

display_name: swiss-forest-lab

en: MPS-2 on LWF Visp to survey 2017 mortality wave


large-wood-event-database

title_for_slug: large-wood-event-database

en: large-wood flood geomorphology natural-hazards wood-transport event-analysis empirical-data mountain-stream dead-wood

en: large wood event database

en: In the context of the WoodFlow project (https://woodflow.wsl.ch), an extensive database was developed which documents recruited and transported quantities of large wood (woody debris) together with the associated catchment and flood-specific parameters. Transported large wood volumes were related to catchment area, forest cover, stream length, peak discharge, runoff volume, sediment load, and precipitation. The dataset covers flood events mostly from Switzerland, but also from other alpine catchments in Germany, Italy France and Japan.

dead-wood

display_name: dead-wood

empirical-data

display_name: empirical-data

event-analysis

display_name: event-analysis

flood

display_name: flood

geomorphology

display_name: geomorphology

large-wood

display_name: large-wood

mountain-stream

display_name: mountain-stream

natural-hazards

display_name: natural-hazards

wood-transport

display_name: wood-transport

en: Large wood event database


en: elevation switzerland insects butterflies insect-decline infospecies occupancy-models biogeographical-region

en: Mean annual occupancy estimates

en:
This file contains mean annual occupancy for all 390 species, study years (1970/1980-2020) and study regions (9 bioclimatic zones) as estimated from occupancy-detection models (MCMC: 4 chains, 1000 warmup each, 1000 sampling each). Each row corresponds to a unique combination of species, year and zone. The following columns are included:

-  **species**: Species name used in analyses

-  **year**: Year of the observation

-  **biogeo_elevation**: Bioclimatic zone of the observation, which is a combination of 5 biogeographic regions (Jura, Plateau, Nothern Alps, Central Alps, Southern Alps) and 2 elevational classes (high: above 1000 m. asl, low: below 1000 m. asl)

-  **n_squares**: Number of 1 km x 1 km squares of the respective zone, which were included in the analyses

-  **occ_m\_i[....]**: Mean annual occupancy estimates. Each column corresponds to one iteration of the MCMC sampling (4000 columns in total).

en:
This dataset contains all data, on which the following publication below is based.

**Paper Citation**:

Neff, F., Korner-Nievergelt, F., Rey, E., Albrecht, M., Bollmann, K., Cahenzli, F., Chittaro, Y., Gossner, M. M., Martínez-Núñez, C., Meier, E. S., Monnerat, C., Moretti, M., Roth, T., Herzog, F., Knop, E. 2022. Different roles of concurring climate and regional land-use changes in past 40 years' insect trends. Nature Communications, DOI: [10.1038/s41467-022-35223-3](https://doi.org/10.1038/s41467-022-35223-3)

Please cite this paper together with the citation for the datafile. Please also refer to this publication for details on the methods.

 Summary

Mean annual occupancy estimates for 390 insect species (215 butterflies [Papilionoidea, incl. Zygaenidae moths], 103 grasshoppers [Orthoptera], 72 dragonflies [Odonata]) for nine bioclimatic zones in Switzerland. Covers the years 1970-2020 (for butterflies) and 1980-2020 (for grasshoppers and dragonflies). Mean occupancy denotes the average number of 1 km x 1 km squares in a zone occupied by the focal species. Occupancy estimates stem from occupancy-detection models run with species records data hosted and curated by [info fauna](http://www.infofauna.ch). Data on the level of single MCMC iterations of model fitting are included (4000 sampling iterations). The nine bioclimatic zones were defined based on biogeographic regions and two elevation classes (square above or below 1000 m. asl)

biogeographical-region

display_name: biogeographical-region

butterflies

display_name: butterflies

elevation

display_name: elevation

infospecies

display_name: infospecies

insect-decline

display_name: insect-decline

insects

display_name: insects

occupancy-models

display_name: occupancy-models

en: Mean insect occupancy 1970–2020


lwf-neunkirch-long-term-research-site

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

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

en: Neunkirch Meteorological Data LWF

en: LWF plot Neunkirch. Photo: Anne Thimonier Rickenmann, 2011

en:
![alt text](https://www.envidat.ch/dataset/86603cf3-979c-4b28-a63e-9852bfb969bd/resource/69a58300-d372-4487-8596-5403254d539d/download/lwf_neunkirch.jpg "LWF Neunkirch")

LWF Plot Neunkirch

- Community: Neunkirch / canton SH
- Date of installation: 14 July 1995
- Size of the plot: 2 ha
- Altitude (m): 554-609
- Mean slope: 58%
- Geology (in German): Tafeljura, oberer Malmkalk; Malmhängeschutt
- Soil types (WSL) : Rendzic Leptosols
- Woodland association after EK72: 13: Cardamino-Fagetum tilietosum
- Main tree species: Fagus sylvatica
- Management system: former coppices w. standards
- Silvicultural system: reserve
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 56.5 cm
- Number of trees BHD >= 12 cm (2011): 442
- Maximum tree age: Fagus sylvatica 160 yr - Acer pseudoplatanus 160 yr - Tilia sp. 110 yr

More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/neunkirch.html

forest

display_name: forest

infrastructure

display_name: infrastructure

lwf

display_name: lwf

meteorological-data

display_name: meteorological-data

meteorological-measurements

display_name: meteorological-measurements

plot

display_name: plot

swiss-forest-lab

display_name: swiss-forest-lab

educ

en: Education, culture and sport

en: LWF Neunkirch long-term research site


en: number-of-caught-persons avalanche-accidents avalanche-fatalities number-of-dead-persons avalanche-accident-statistics

en: Fatal avalanche accidents in Switzerland since 1970

en:
**When using this data, please consider and adhere to the associated [Terms of Use](https://www.slf.ch/en/services-and-products/data-and-monitoring/slf-data-service.html)**.

This data collection contains information concerning all known accidents by snow avalanches in Switzerland with at least one person involved (caught). The data set commences on 01/10/1970. After the completion of a hydrological year, the new data is added.

The following information is provided:

 * avalanche identifier
 * date of the accident
 * accuracy of the date in range of days before and after
 * canton
 * municipality
 * start zone point latitude
 * start zone point longitude
 * start zone point accuracy (in meters)
 * start zone point elevation (in meteres above sea level)
 * slope aspect (main orientation of start zone)
 * slope inclination (in degree, steepest point within start zone)
 * forecasted avalanche danger level 1 (first danger)
 * forecasted avalanche danger level 2 (second danger)
 * accident within the core zone (most dangerous aspect and elevation as mentioned in the forecast)
 * number of dead persons
 * number of caught persons
 * number of fully buried persons
 * activity/location of the accident party at the time of the incident

avalanche-accident-statistics

display_name: avalanche-accident-statistics

avalanche-accidents

display_name: avalanche-accidents

avalanche-fatalities

display_name: avalanche-fatalities

number-of-caught-persons

display_name: number-of-caught-persons

number-of-dead-persons

display_name: number-of-dead-persons

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: Avalanche accidents in Switzerland since 1970/71


en: snow-depth vertical-wind-motion saltation snow-cover boundary-layer-winds

en: Data Compilation

en: Compressed TXT files

en:
The data correspond to the experiments presented and discussed in a paper regarding the interaction between turbulent wind fluctuations and snow saltation mass-fluxes (Paterna, 2016). Each of the nine data files corresponds to a different experiment presented in the paper and conducted in the winter 2014/2015 in the WSL/SLF cold wind tunnel in Davos. For each file the five columns indicate the time from the beginning of the experiment, the streamwise (u’) and the vertical (w’) wind velocity fluctuations, the streamwise (qx) and the vertical (qz) snow mass-flux components. From these time-series the scales of the snow saltation and of the turbulent flow are obtained with respect to the eddy-cycles and snow saltation cycles. From spectral analysis of the time-series a decoupling of the snow saltation from the turbulence forcing reveals two regimes of interaction: a turbulence-dependent regime occurring with weak saltation, and a turbulence-independent regime with strong saltation. Further details can be found at the link below.

Publication http://onlinelibrary.wiley.com/doi/10.1002/2016GL068171/abstract

boundary-layer-winds

display_name: boundary-layer-winds

saltation

display_name: saltation

snow-cover

display_name: snow-cover

snow-depth

display_name: snow-depth

vertical-wind-motion

display_name: vertical-wind-motion

en: Wind tunnel measurement data of drifting snow and turbulent wind fluctuations


en: ips-typographus predators bivoltine antagonists spruce parasitoids scolytinae univoltine

en: Bark beetle infestation spots

en: see ReadMe

en: In an expanding bark beetle (Ips typographus) infestation spot emergence traps were installed on the stems of newly infested spruce trees capturing all emerging insects during several consecutive years. Two locations were sampled on elavations with univoltine and bivoltine generations, respectively. Bark beetles and their insect predators and parasitoids were identified to species level by specialists.

antagonists

display_name: antagonists

bivoltine

display_name: bivoltine

ips-typographus

display_name: ips-typographus

parasitoids

display_name: parasitoids

predators

display_name: predators

scolytinae

display_name: scolytinae

spruce

display_name: spruce

univoltine

display_name: univoltine

en: Emergence dynamics of natural enemies of spruce bark beetles


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

en: Site photo of the field station (NEF)

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

mimetype: image/jpeg

en: Data plot of the forest station (NEB)

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

en: Site aerial photo (Neunkirch)

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

en: Field station data (NEF)

en: Data file.

mimetype: text/csv

en: Site photo of the forest station (NEB)

en: Site photo of the forest station from below (viewing upwards the slope).

en: Data plot of the field station (NEF)

en: Forest station data (NEB)

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 Neunkirch in Switzerland where one station is located within a natural deciduous forest stand (NEB) with European beech (_Fagus sylvatica_; 160 yrs), sycamore maple (_Acer pseudoplatanus_; 160 yrs) and lime trees (_Tilia sp._; 110 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NEF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Neunkirch 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

precipitation

display_name: precipitation

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

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


en: ensemble-modelling shrub-forest modelling milieux-naturels random-forest typoch lebensraumkarte grassland remote-sensing wetlands habitat forest lebensraum

en: The Habitat Map of Switzerland VD v1 20220316, Canton Vaud, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Vaud, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland NW v1 20220316, Canton Nidwalden, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Nidwalden, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland UR v1 20220316, Canton Uri, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Uri, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland OW v1 20220316, Canton Obwalden, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to the borders of canton Obwalden, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert)

en: The Habitat Map of Switzerland GL v1 20220316, Canton Glarus, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Glarus, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland TI v1 20220316, Canton Ticino, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Ticino, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland SH v1 20220316, Canton Schaffhausen, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Schaffhausen, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland AG v1 20220316, Canton Aargau, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Aargau, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland ZG v1 20220316, Canton Zug, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Zug, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland SO v1 20220316, Canton Solothurn, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Solothurn, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland AI v1 20220316, Canton  Appenzell Innerrhoden, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Appenzell Innerrhoden, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland NE v1 20220316, Canton Neuchatel, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Neuchatel, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland GE v1 20220316, Canton Geneva, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Geneva, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland TG v1 20220316, Canton Thurgau, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Thurgau, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland CH v1 20220316, file geodatabase

en:
The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert).
V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland LU v1 20220316, Canton Lucerne, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Lucerne, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland GR v1 20220316, Canton Grisons, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Grisons, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland BE v1 20220316, Canton Bern, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Bern, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland BL v1 20220316, Canton Basel-Landschaft, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Basel-Landschaft, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland ZH v1 20220316, Canton Zurich, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Zurich, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland FR v1 20220316, Canton Fribourg, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Fribourg, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: Data Description

en: Metadata document describing the methodology used to develop the dataset

en: The Habitat Map of Switzerland BS v1 20220316, Canton Basel-Stadt, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Basel-Stadt, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland VS v1 20220316, Canton Valais, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Valais, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland SG v1 20220316, Canton St Gallen, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton St Gallen, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland JU v1 20220316, Canton Jura, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Jura, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland SZ v1 20220316, Canton Schwyz, file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Schwyz, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en: The Habitat Map of Switzerland AR v1 20220316, Canton Appenzell Ausserrhoden , file geodatabase (zipped)

en: The Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only. Available here is the dataset clipped to a 2km buffer around the borders of canton Appenzell Ausserrhoden, as a zipped folder containing a file geodatabase (.gdb, ArcGIS), the data description document (pdf) and symbology layer (lyr, lyrx, qlr) for the legend as the data appears on the WSL's virtual data centre (https://vdc.wsl.ch/vdc_expert). V1 20220316 has been improved (from V1 20211118) to repair geometry errors, and simplify geometry to reduce data size

en:
Lebensraumkarte der Schweiz/La carte des milieux naturels de Suisse
The FOEN funded project ‘Developing a Habitat Map of Switzerland’ conducted at the WSL, has produced a map of Swiss habitats according to the TypoCH classification (Delarze et al. 2015) wall-to-wall across the whole of Switzerland, to at least the classification’s 2nd level of detail (where possible to the 3rd level of detail). The implementation of the Habitat Map of Switzerland is a vector data set, where each polygon of the dataset is classified to one habitat type only.
Habitats are mapped through a variety of approaches that can be grouped as either:
1: Derived from the existing Swiss-wide high quality landcover mapping from Swisstopo’s Topographical Landscape Model (TLM), 2: Modelled within the project using Random Forest or Ensemble Modelling techniques to model the spatial distribution of individual habitat types, 3: Combining existing species distribution models to determine habitat types, or 4: Classification with relatively simple rule-sets based on auxiliary spatial datasets, i.e. vegetation height models, the digital terrain model, the normalised difference vegetation index (NDVI) derived from aerial imagery and/or time-series of growing season Sentinel-2 satellite imagery. Further detail on the methodology can be found within the README document.

ensemble-modelling

display_name: ensemble-modelling

forest

display_name: forest

grassland

display_name: grassland

habitat

display_name: habitat

lebensraum

display_name: lebensraum

lebensraumkarte

display_name: lebensraumkarte

milieux-naturels

display_name: milieux-naturels

modelling

display_name: modelling

random-forest

display_name: random-forest

remote-sensing

display_name: remote-sensing

shrub-forest

display_name: shrub-forest

typoch

display_name: typoch

wetlands

display_name: wetlands

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: multi-stage balanced-sampling vegetation unequal-probability habitat

en: data

en: R script

en:
License: GPL-v2

The R script presents an advanced sampling approach for monitoring biodiversity on agricultural land by combining multiple objectives and integrating environmental and geographic space.

The example demonstrates the first-stage selection of squares (km2) in the ALL-EMA sampling design using modern sampling techniques such as unequal probability sampling with fixed sample size, balanced sampling, stratified balancing and geographic spreading. Sampling is done with unequal probabilities and weights defined by power allocation to give equal weight to extrapolations to the total agricultural area of Switzerland and two stratifications of predefined interest (regions and agricultural production zones). Calibration is used to limit the distribution of the sampling weights. The sample sizes are almost fixed within the strata and evenly distributed across the years of a temporal rotation plan, which is favourable for the organisation of the field survey. Sampling also ensures an optimal (annual) distribution across geographic space, including altitude. Despite the complexity of the sampling, estimation based on probability theory is straightforward.

Ecker, K.T., Meier, E.S. & Tillé, Y. 2023. Integrating spatial and ecological information into comprehensive biodiversity monitoring on agricultural land. Environmental Monitoring and Assessment 195.

balanced-sampling

display_name: balanced-sampling

multi-stage

display_name: multi-stage

unequal-probability

display_name: unequal-probability

vegetation

display_name: vegetation

educ

en: Education, culture and sport

en: R script and input data for "ALL-EMA sampling design"


sensitivity-of-modeled-snow-instability

maintainer: Bettina Richter

title_for_slug: sensitivity-of-modeled-snow-instability

en: snow-instability global-sensitivity-analysis snowpack snow-cover-modeling numerical-avalanche-forecasting

en: Reference run

en: Reference run for the sensitivity analysis at Weissfluhjoch field site for winter season 2016-2017. Meteorological data from the automatic weather stations are used. No biases were introduced to the input data.

en: Case ALL

en: Extracted output data for case ALL. Here biases were introduced to meteorological input data during the whole simulation period. Data include quasi random biases, which were introduced, extracted output of simulated snow profiles and sobol indices for each output variable.

en: Case WL

en: Extracted output data for case ALL. Here biases were introduced to meteorological input data during the period of weak layer formation. Data include quasi random biases, which were introduced, extracted output of simulated snow profiles and sobol indices for each output variable.

en: Manually observed snow profiles

en: Manually observed snow profiles at Weissfluhjoch field site for winter season 2016-2017, including layer depth, hand hardness, grain size and grain shape for each layer.

en: Case SL

en: Extracted output data for case SL. Here biases were introduced to meteorological input data during the period of slab formation. Data include quasi random biases, which were introduced, extracted output of simulated snow profiles and sobol indices for each output variable.

en: We investigated the sensitivity of modeled snow instability to meteorological input data using SNOWPACK. We therefore used input data from the automatic weather station at the Weissfluhjoch field site for the year 2016-2017. We investigated three scenarios and performed 14'000 simulations for each scenario. The dataset contains extracted output data from modeled SNOWPACK simulations, including setup files to reproduce the simulations. For further information read the README file.

global-sensitivity-analysis

display_name: global-sensitivity-analysis

numerical-avalanche-forecasting

display_name: numerical-avalanche-forecasting

snow-cover-modeling

display_name: snow-cover-modeling

snow-instability

display_name: snow-instability

snowpack

display_name: snowpack

en: Sensitivity of modeled snow instability


en: atree stakeholder-analysis forest-sink carbon-sequestration wood-sink social-network-analysis

en: Questionnaire for ATREE SNA survey Lucerne

en: SNA survey data stakeholder analysis Lucerne of the ATREE project

en:
The survey was conducted online in a non-anonymized manner, however, the data provided here is anonymized, hence only the name and function of the stakeholder organization are contained but not the name of the person that answered the survey. Only one answer per organization has been collected. The data contains only the answers to the standardized questions (choice of predefined answer categories) but not those answers to open questions (comments). These are not relevant for interpretation of the rest of the data and may contain indications about the author.
The data is available as an SPSS Data file. It can be imported into R with:

library(foreign)
 ?read.spss explains options that might be adjusted, particularly regarding the value labels of variables
data <- read.spss("ATREE_SNA_Lucerne.sav",to.data.frame=T)
 variable labels contain the original question as posed in the questionaire
#?attributes
attributes(data)

en: In January 2020 a social network analysis survey was conducted among forest policy stakeholders (at the organizational level) from the Canton of Lucerne as well as the national level. The aim was to elicit positions relative to a set of policy options currently discussed with respect to carbon mitigation and sequestration services of the forest, i.e. forest management and to establish information and collaboration network relations in order to identify actor coalitions as inspired by the "actor coalition framework" approach to policy analysis. Of the 66 questionnaires sent out, 51 were answered (77%). Only one additional organization was indicated as being missing from the provided list of stakeholder organizations.

atree

display_name: atree

carbon-sequestration

display_name: carbon-sequestration

forest-sink

display_name: forest-sink

social-network-analysis

display_name: social-network-analysis

stakeholder-analysis

display_name: stakeholder-analysis

wood-sink

display_name: wood-sink

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: ATREE Social Network Analysis survey on policy options regarding CO2 mitigation and sequestration in wood and forest


hymenoptera

title_for_slug: hymenoptera

en: agricultural hautfluger arthropoda urban forests hymenoptera

media_type: text/plain

en: Data extract in TAB-delimted format. First row is header, first column is counter.

format: TXT

en: Data query used on Fauna Database to retrieve the submitted data.

en:
Hymenopteran data from all historic up to the recent projects (29.10.2019) of WSL, collected with various standardized methods in landscapes of different types.

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

agricultural

display_name: agricultural

arthropoda

display_name: arthropoda

forests

display_name: forests

hautfluger

display_name: hautfluger

hymenoptera

display_name: hymenoptera

urban

display_name: urban

educ

en: Education, culture and sport

en: Hymenoptera


latent-reserves-within-the-swiss-nfi

title_for_slug: latent-reserves-within-the-swiss-nfi

en: switzerland national-forest-inventory tree-mortality forest-reserves near-natural-forest

en: Data for mortality analyses

en:
This table contains the tree- and plot-level data necessary to conduct the tree mortality analyses:

* "DEAD" takes the value '0' when the tree was alive at NFI3 and '1' when it was observed dead
* "SPECIES" refers to which species group the tree belongs to
* "relBAL" is the relative basal area of larger trees
* "DBH" is the diameter at breast height
* "relBAI" is the relative basal area increment of the tree
* "BA" is the basal area in squared meters per hectare (stand-level)
* "STAND_DENSITY_LOG" is the logarithm of the stand density in number of stems per hectare
* "BEERS_ASPECT_CORR" is the Beers aspect corrected for slope of the plot
* "SLOPE_SQRT" is the squared root of the slope of the plot in percentage
* "ELEVATION" is the elevation of the plot in meters above sea level
* "AWC" is the available soil water-holding capacity in millimeters
* "VEGYEAR" is the number of vegetation years between the measurements of the plots at NFI2 and NFI3.

en:
This table contains the tree-level information required to reproduce the charaterisation of 'latent reserves' and comparison to managed forests:

* "FOREST_TYPE" states whether a row corresponds to managed forests or to 'latent reserves' with a given number of years without human intervention (for example, 'LATENT_RESERVE40' are the 'latent reserves' with at least 40 years without human intervention)
* "SOURCE" differentiate inventoried trees ("W", DBH greater or equal to 12 cm) and young-growth trees measured on subplots ("JW").
* "SPECIES" refers to the species code of the Swiss NFI.
* "SPECIES_GROUP" refers to which species group a tree belongs to (spruce, fir, pine, larch, stone pine, other conifers, beech, maple, ash, oak, chestnut, other broadleaves)
* "ALIVE_NFI2" takes the value '1' when the tree was alive at the previous inventory (NFI2). Trees with no value (NA) were not measured at NFI2. This variable should be used to calculate mortality rates, as only trees that were present at NFI2 can be used to calculate the mortality rate between NFI2 and NFI3.
* "DEAD" takes the value '1& when a tree was observed dead at NFI3, and the value '0' when it was observed alive.
* "INCLUSION_PROB" represents the inclusion probability of a tree i.e. the number of trees that a given tree represents per hectare. This number depends on the DBH of the tree and on whether the plot area was reduced by a boundary (e.g. a forest edge). This variable should be used to weight the trees in some calculations.
* "DBH" is the diameter at breast height of a tree in centimeters.
* "LIGHT", "TEMPERATURE", "MOISTURE" refer to the value of the Ellenberg indicators for light, temperature and moisture that are species-specific. 'NA' is attributed when the value of an indicator is not available for a given species.

en: Data figure 7

en:
This file contains the data required to reproduce Figure 7:

- "SPECIES_GROUP" refers to the species groups used in the mortality analyses (spruce, fir, pine, larch, stone pine, beech, maple and ash, oak, chestnut, other broadleaves)
- "VARIABLE" refers to the explanatory variables used in the model and presented in the figure: DBH (diameter at breast height) and relBAI (relative basal area increment).
- "VALUE" refers to the the values of the variables for which predictions were made.
- "PRED_MORTALITY_PROB" refers to the predicted mortality probability for a given species group and value of either DBH or relBAI.

en: Data figure 4

en:
This file contains the data required to reproduce Figure 4:

- "FOREST_TYPE" states whether a row corresponds to managed forests or to 'latent reserves' with a given number of years without human intervention (for example, 'LATENT_RESERVE40' are the 'latent reserves' with at least 40 years without human intervention)
- "ELEV_VEG_ZONE" refers to the elevation vegetation zone (colline and submontane, montane or subalpine)
- "SPECIES_GROUP" refers to all the trees of a given species group (spruce, fir, pine, larch, stone pine, other conifers, beech, maple, ash, oak, chestnut, other broadleaves)
- "DBH_CL" refers to the DBH (diameter at breast height) class to which a tree belongs to, in centimetres.
- "DENSITY" refers to the proportion of trees (corrected for the inclusion probability of each tree) within each combination of forest type, elevation vegetation zone, species group and DBH class.

The inclusion probability of each tree was accounted for in the density calculation.

en:
This table contains the plot-level information required to reproduce the charaterisation of 'latent reserves' and comparison to managed forests:

* "FOREST_TYPE" states whether a row corresponds to managed forests or to 'latent reserves' with a given number of years without human intervention (for example, 'LATENT_RESERVE40' are the 'latent reserves' with at least 40 years without human intervention)
* "ELEV_VEG_ZONE" refers to the elevation vegetation zone (colline and submontane, montane or subalpine)
* "DOMINANT_SPECIES" refers to the dominant species in a given plot (spruce, fir, other conifers, beech, other broadleaves)
* "TIME_SINCE_INTERVENTION" refers to the numbers of years since the last human intervention on the plot. This variable should be used to set the threshold of time since last intervention one wants to use to define 'latent reserves'.
* "VEGYEAR" refers to the number of vegetation years between the measurements of a plot for NFI2 and NFI3.
* "BA" provides the basal area in squared meters per hectare of a plot calculated from trees having a DBH of at least 12 cm.
* "BA_WITH_YG" provides the basal area in squared meters per hectare of a plot calculated from trees having a DBH of at least 12 cm as well as young growth trees.
* "STAND_DENSITY" provides the stand density in number of stems per hectare of a plot calculated from trees having a DBH of at least 12 cm.
* "STAND_DENSITY_WITH_YG" provides the stand density in number of stems per hectare of a plot calculated from trees having a DBH of at least 12 cm as well as young growth trees.
* "SITE_INDEX" refers to the site index of a plot, i.e. the total biomass productivity calculated according to Keller (1976).
* "DEADWOOD" refers to the amount of deadwood of a plot in tons per hectare.

******

Reference
Keller, W. (1976). Einfacher ertragskundlicher Bonitätsschlüssel für Waldbestände in der Schweiz. Zurich, Switzerland: ETH Zurich.

en: Data figure 2

en:
This file contains the data required to reproduce Figure 2:

- "FOREST_TYPE" states whether a row corresponds to managed forests or to 'latent reserves' with a given number of years without human intervention (for example, 'LATENT_RESERVE40' are the 'latent reserves' with at least 40 years without human intervention)
- "ELEV_VEG_ZONE" refers to the elevation vegetation zone (colline and submontane, montane or subalpine)
- "SPECIES_GROUP" refers to all the trees of a given species group (spruce, fir, pine, larch, stone pine, other conifers, beech, maple, ash, oak, chestnut, other broadleaves)
- "DENSITY" refers to the proportion of trees (corrected for the inclusion probability of each tree) within each combination of forest type, elevation vegetation zone and species group.

Young-growth trees are not included in the density calculation.

en: Data figure 8

en:
This file contains the data required to reproduce Figure 8:

* "SPECIES_GROUP" refers to the species groups used in the mortality analyses (spruce, fir, pine, larch, stone pine, beech, maple and ash, oak, chestnut, other broadleaves)
* "BASAL_AREA" refers to the values of the basal area in squared meters per hectare for which predictions were made
* "STAND_DENSITY" refers to the values of stand density in number of stems per hectare for which predictions were made
* "NORM_PRED_MORTALITY_PROB" refers to the normalised predicted mortality probability corresponding to a given species group and values of basal area and stand density.

en: Data figure 5

en:
This file contains the data required to reproduce Figure 5:

- "FOREST_TYPE" states whether a row corresponds to managed forests or to 'latent reserves' with a given number of years without human intervention (for example, 'LATENT_RESERVE40' are the 'latent reserves' with at least 40 years without human intervention)
- "DOMINANT_SPECIES" refers to the dominant species in a given plot (spruce, fir, other conifers, beech, other broadleaves)
- "VARIABLE" refers to the attributes described in the figure: basal area in squared meters per hectare, median DBH in cm, stand density in number of stems per hectare, deadwood in tons per hectare, site index in tons per hectare per year. Basal area, median DBH and stand density are given both with and without including young-growth trees.
- "mean" refers to the mean value of each attribute for each combination of forest type and dominant species (corrected for the inclusion probability of each tree).
- "se" corresponds to the standard error.

For these calculations, site index was constrained to the range observed in ‘latent reserves’ with at least 40 years without intervention. Additionally, only NFI sample plots from the montane and subalpine zones were used as colline and submontane NFI plots from ‘latent reserves’ and managed forests are located in different geographical regions.

en: Data figure 3

en:
This file contains the data required to reproduce Figure 3:

- "FOREST_TYPE" states whether a row corresponds to managed forests or to 'latent reserves' with a given number of years without human intervention (for example, 'LATENT_RESERVE40' are the 'latent reserves' with at least 40 years without human intervention)
- "ELEV_VEG_ZONE" refers to the elevation vegetation zone (colline and submontane, montane or subalpine)
- "VARIABLE" refers to the indicators described in the figure: Ellenberg indicators for light, temperature and moisture, as well as species richness.
- "mean" refers to the mean value of each indicator for each combination of forest type and elevation vegetation zone (corrected for the inclusion probability of each tree).
- "se" corresponds to the standard error.

For these calculations, site index was constrained to the range observed in 'latent reserves' with at least 40 years without intervention.

en: Data figure 6

en:
This file contains the data required to reproduce Figure 6:

- "FOREST_TYPE" states whether a row corresponds to managed forests or to 'latent reserves' with a given number of years without human intervention (for example, 'LATENT_RESERVE40' are the 'latent reserves' with at least 40 years without human intervention)
- "SPECIES_GROUP" refers to all the trees of a given species group (spruce, fir, pine, larch, stone pine, other conifers, beech, maple, ash, oak, chestnut, other broadleaves)
- "MORTALITY_RATE" refers to the mean annual mortality rate of each species group calculated according to Sheil and May (1996)
- "LOWER_CI" and "UPPER_CI" refer to the lower and upper confidence intervals on the mortality rates, calculated according to Sheil and May (1996).

Reference
Sheil, D., & May, R. M. (1996). Mortality and recruitment rate evaluations in heterogeneous tropical forests. Journal of Ecology, 84(1), 91–100. doi: 10.2307/2261703

en:
The files refer to the data used in Portier et al. "‘Latent reserves’: a hidden treasure in National Forest Inventories" (2020) *Journal of Ecology*.

**'Latent reserves'** are defined as plots in National Forest Inventories (NFI) that have been free of human influence for >40 to >70 years. They can be used to investigate and acquire a deeper understanding of attributes and processes of near-natural forests using existing long-term data. To determine which NFI sample plots could be considered ‘latent reserves’, criteria were defined based on the information available in the Swiss NFI database:

* Shrub forests were excluded.
* Plots must have been free of any kind of management, including salvage logging or sanitary cuts, for a minimum amount of time. Thresholds of 40, 50, 60 and 70 years without intervention were tested.
* To ensure that species composition was not influenced by past management, plots where potential vegetation was classified as deciduous by Ellenberg & Klötzli (1972) had to have an observed proportion of deciduous trees matching the theoretical proportion expected in a natural deciduous forest, as defined by Kienast, Brzeziecki, & Wildi (1994).
* Plots had to originate from natural regeneration.
* Intensive livestock grazing must never have occurred on the plots.

The tables stored here were derived from the first, second and third campaigns of the Swiss NFI. The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php).

****

The files 'Data figure 2' to 'Data figure 8' are publicly available and contain the data used to produce the figures published in the paper.

The files 'Plot-level data for characterisation of 'latent reserves' and 'Tree-level data for characterisation of 'latent reserves' contain all the data required to reproduce the section of the article concerning the characterisation of 'latent reserves' and the comparison to managed forests. The file 'Data for mortality analyses' contains the data required to reproduce the section of the article concerning tree mortality in 'latent reserves'. The access to these three files is restricted as they contain some raw data from the Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement.

forest-reserves

display_name: forest-reserves

national-forest-inventory

display_name: national-forest-inventory

near-natural-forest

display_name: near-natural-forest

switzerland

display_name: switzerland

tree-mortality

display_name: tree-mortality

en: 'Latent reserves' within the Swiss NFI


maintainer: Paraskevi Georgakaki

en: ice-crystal-number-concentration ice-water-content wrf swiss-alps jungfraujoch secondary-ice-production blowing-snow liquid-water-content

en: Observational data

en:
The .zip file contains 6 .csv files including all microphysical measurements taken at the Jungfraujoch station during the two periods of interest:

* 25-28 January 2014 : northwesterly-winds (NW)
* 29 January - 1 February 2014 : southeasterly-winds (SE)

The total Ice Crystal Number Concentration (ICNC; reported in L⁻¹) is measured by the two-dimensional stereo hydrometeor spectrometer (2D-S; Lawson et al., 2006) over the size range 10-1280 μm. The total ice water content (IWC; reported in gm⁻³) is also derived by the 2D-S data using the Brown and Francis (1995) mass-diameter relationship. Finally, the quantification of the liquid water content (LWC; reported in gm⁻³) is based on the liquid droplet size distribution data derived from a DMT cloud droplet probe (CDP; Lance et al., 2010) over the size range 2-50 μm. The frequency of the measurements in the .csv files is 30 min, to match the output frequency of the model. Datetime is also reported in the files (DD/MM/YYYY h:mm) in UTC.

Acknowledgment: Collection and pre-processing of the microphysical measurements has been performed by Gary Lloyd (gary.lloyd@manchester.ac.uk). More details about the instrumentation during the CLACE2014 campaign can be found in Lloyd et al. (2015).

en: WRF outputs

en:
The .zip file contains all the NetCDF outputs produced by the mesoscale Weather Research and Forecasting (WRF) model (version: 4.0.1). Two case studies were simulated in this study, between 25-28 January 2014 (00:00 UTC) and 29 January - 1 February 2014 (00:00 UTC), respectively. The former is denoted as "NW" and the latter as "SE", owing to the prevailing wind conditions at the Jungfraujoch (JFJ) station.

The outputs come from the innermost domain of the simulations with a spatial resolution of 1 km and are extracted from the nearest to JFJ model grid point (46.55°N 7.98°E). The dimensions of each model variable is [145,100], which corresponds to time and height, respectively. The frequency of the outputs is 30 min. Note that the first 24 hours of each simulation (i.e., the first 48 outputs) were considered spin up time.

The following sets of sensitivity simulations were presented in this study:

* CNTRL_NW.nc, CNTRL_SE.nc : Sensitivity simulations performed with the default version of WRF
* DS_PHILL_NW.nc, DS_PHILL_SE.nc : Sensitivity simulations including a description of the droplet shattering process following Phillips et al. (2018)
* DS_SULL_NW.nc, DS_SULL_SE.nc : Sensitivity simulations including a description of the droplet shattering process following Sullivan et al. (2018)
* PHILL_NW.nc, PHILL_SE.nc : Sensitivity simulations including a description of the ice-ice collisional break-up process following Phillips et al. (2017)
* TAKAH_NW.nc, TAKAH_SE.nc : Sensitivity simulations including a description of the ice-ice collisional break-up process following Sullivan et al. (2018) based on Takahashi et al. (1995)
* TAKAHrim_NW.nc, TAKAHrim_SE.nc : Same set-up as before but applied only to collisions between rimed ice particles
* BIPS_Xk0_DY_NW.nc, BIPS_Xk0_DY_SE.nc : Sensitivity simulations including a source of Xk0 (either 10.0 or 100.0 L⁻¹) ice crystals with sizes of DY (either 10 or 100 μm) in the first atmospheric level of the model
* BIPS100_PHILL_NW.nc, BIPS100_PHILL_SE.nc : Sensitivity simulations where the combined effect of blowing snow and ice-ice collisional break-up is examined

en:
This repository contains all WRF model outputs and observational data sets used for the paper:
Georgakaki, P., Sotiropoulou, G., Vignon, É., Billault-Roux, A.-C., Berne, A., and Nenes, A.: Secondary ice production processes in wintertime alpine mixed-phase clouds, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-760, in review, 2021.

blowing-snow

display_name: blowing-snow

ice-crystal-number-concentration

display_name: ice-crystal-number-concentration

ice-water-content

display_name: ice-water-content

jungfraujoch

display_name: jungfraujoch

liquid-water-content

display_name: liquid-water-content

secondary-ice-production

display_name: secondary-ice-production

swiss-alps

display_name: swiss-alps

wrf

display_name: wrf

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: Data for the publication "Secondary ice production processes in wintertime alpine mixed-phase clouds"


en: permafrost temperature permos ground borehole

en: DATASET & PLOTS

en: PERMOS DATA POLICY

en: Map of PERMOS borehole network

en: Processed ground temperature measurements at the Fluelapass permafrost borehole A (FLU_0102) in canton Graubunden, Switzerland. The borehole is located at 2394 m asl on a moderate (26°) North-east slope (45°). The surface material is talus and borehole depth is 23 m. Thermistors used YSI 44006. Year of drilling 2002. This borehole is part of the Swiss Permafrost network, PERMOS (www.permos.ch). Contact phillips@slf.ch for details of processing applied.

borehole

display_name: borehole

ground

display_name: ground

permafrost

display_name: permafrost

permos

display_name: permos

temperature

display_name: temperature

en: Processed permafrost borehole data (2394 m asl), Fluelapass A, Switzerland


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

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.

format: GeoTIFF

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 4.3.6 (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 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 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 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: 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: 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: 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 4.5.3 (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: 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 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 2.3.1 (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:
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 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 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 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: 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 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 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 4.5.4 (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 2.3.2/3 (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 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:
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: unmanned-aerial-vehicle-laser-scanning 3d-point-cloud structure-from-motion forest-inventory terrestrial-laser-scanning terrestrial-photogrammetry mobile-laser-scanning lidar

en:
Close Range Remote Sensing Benchmark for different LiDAR and photogrammetric Sensors in a mixed temperate forest.
Benchmarks are needed to evaluate the performance of different close-range remote sensing devices and approaches, both in terms of efficiency as well as accuracy. In this study we evaluate the performance of two terrestrial (TLS), one handheld mobile (PLS) and two drone based (UAVLS) laser scanning systems to detect trees and extract the diameter at breast height (DBH) in three plots with a steep gradient in tree and understorey vegetation density. As a novelty, we also tested the acquisition of 3D point-clouds using a low-cost action camera (GoPro) in conjunction with the Structure from Motion (SfM) technique and compared its performance with those of the more costly LiDAR devices.

forest-inventory

display_name: forest-inventory

lidar

display_name: lidar

mobile-laser-scanning

display_name: mobile-laser-scanning

structure-from-motion

display_name: structure-from-motion

terrestrial-laser-scanning

display_name: terrestrial-laser-scanning

terrestrial-photogrammetry

display_name: terrestrial-photogrammetry

unmanned-aerial-vehicle-laser-scanning

display_name: unmanned-aerial-vehicle-laser-scanning

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: Ramerenwald Close Range Remote Sensing Benchmark


maintainer: Christian G. Sommer

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

en: README

en: Describes the dataset in detail

mimetype: text/plain

en: Processing scripts

en: Contains the Matlab- and R-scripts used to process the Kinect data

en: Kinect data

en: Links to the dataset

en:
This data sets contains the Microsoft Kinect data from 15 wind tunnel experiments on wind-packing / wind crust formation. These experiments were performed in the winter 2016/17. The Kinect measures distributed snow depth.

The Kinect data is organized per experiment. Each experiment subfolder contains the processed Kinect depth images and some additional files.

Please refer to the README for more details on the data. The processing scripts are available for download as well. The scripts are mainly provided as documentation and would need to be adjusted to be used.

This dataset is the basis of the following publication:

Sommer C.G., Lehning M. & Fierz C. (2018). Wind Tunnel Experiments: Influence of Erosion and Deposition on Wind-Packing of New Snow. Front. Earth Sci. 6:4. doi: 10.3389/feart.2018.00004

kinect

display_name: kinect

snow

display_name: snow

wind-crust

display_name: wind-crust

wind-packing

display_name: wind-packing

wind-tunnel

display_name: wind-tunnel

en: Wind crust formation: Microsoft Kinect data


maintainer: Marco Moretti

en: climate beta-diversity urbanization switzerland biodiversity community-indices protected-areas resource-availability elevation-gradient anthophila wild-bee predictive-modelling functional-diversity hymenoptera

en: Taxonomic and functional metrics in the community plots

en:
Response data used for the modeling.
Lat: latitude community plot
Lon: longitude community plot
Richness: species richness
Shannon: Shannon diversity index
FDis: functional dispersion
TOP: Trait Onion Peeling (functional richness)
TED: Trait Even Distribution (functional evenness)
LCBD_taxo: local community contributions to taxonomic beta diversity
LCBD_fun: local community contributions to functional beta diversity
ITD: community weighted means inter-tegula distance
feeding_specialization: community weighted means feeding specialization
tong_length: community weighted means tongue length
phenostart: community weighted means start of the phenology
phenoduration: community weighted means duration of the phenoloy
cleptoparasite: community weighted means cleptoparasites
belowgound: community weighted means proportion belowground
solitary: community weighted means proportion solitary

en: Variable importance, model evaluation, partial dependence plots

en: The compress file containes a set of txt files with the variable importance, the model evaluation metrics and the partial dependence plots of each of the responses used (alpha and beta taxonomic and functional diversity metrics; community weighted means of 8 functional traits)

en: Spatial environmental predictors and responses

en:
The .zip file contains the rasters of the environmental predictors and the predicted responses:
-  **Environmental predictors.**:

  -  The raster on the climate contains the PCA axes on the CHELSA variables.

  -  The raster on resource availability contains the PCA axes on plant communities

  -  The raster on land-use contains the land-use composition at 2500 m.

  -  The raster on beekeeping intensity contains thedensity of hives within 2500 m

-  **Responses**:
- The predicted responses without masking the waterbodies (Diversity_stack_revised_Selected_descriptors.tif).
- The predicted responses masking the waterbodies (masked.stack.all.diversity.tiff).

en: Elevation and waterbodies

en: Zip file containing two rasters: the digital elevation model of Switzerland and the waterbodies of Switzerland.

en: Wild bee trait data

en: Text file containing the scores of the 8 used traits. The data originates from the European trait database (compiler: Stuart Roberts; pollinator loss module of the EU- FP6 ALARM project).

en: Protected areas

en: Protected areas considered, divided in three groups and rasters: protected areas sensu stricto, protected areas sensu lato and all protected areas

en: Deprecated-Predictors

en:
The compress file includes the rster files of the predictors used in the analyses, including the climate, resource availability, land-use and beekeeping intensity metrics.

The raster on the climate contains the PCA axes on the CHELSA variables.
The raster on resource availability contains the PCA axes on plant communities
The raster on land-use contains the land-use composition at 2500 m.
The raster on beekeeping intensity contains thedensity of hives within 2500 m

en:
Raw data supporting the paper "Countrywide wild bee taxonomic and functional diversity reveal a spatial mismatch between alpha and beta-diversity facets across multiple ecological gradients". It contains taxonomic and functional metrics in 3343 community-plots distributed across Switzerland. The calculated metrics are:
- Alpha taxonomic community metrics: species richness and Shannon diversity
- Alpha functional community metrics: Functional richness (using the Trait Onion Peeling index, TOP), functional eveness (using the Trait Even Distribution index, TED) and the functional dispersion.
- Community weighted means of 8 functional traits
- The local community contributions on the functional and taxonomic beta diversity (LCBD).

The dataset also includes the following:
- The used predictors to model the spatial distribution of the community metrics (climate PCA, vegetation PCA, land-use metrics, beekeeping intensity).
-The three types of protected areas, defined according to the protective measures.
- The model evaluation, variable importance and partial dependece data.

anthophila

display_name: anthophila

beta-diversity

display_name: beta-diversity

biodiversity

display_name: biodiversity

climate

display_name: climate

community-indices

display_name: community-indices

elevation-gradient

display_name: elevation-gradient

functional-diversity

display_name: functional-diversity

hymenoptera

display_name: hymenoptera

predictive-modelling

display_name: predictive-modelling

protected-areas

display_name: protected-areas

resource-availability

display_name: resource-availability

switzerland

display_name: switzerland

urbanization

display_name: urbanization

wild-bee

display_name: wild-bee

en: Data on wild bee taxonomic and functional diversity in Switzerland


aerosol-data-davos-wolfgang

title_for_slug: aerosol-data-davos-wolfgang

en: raclets particles ice-nucleation inp aerosol drincz

en: SMPS Dataset Davos Wolfgang

en: The SMPS file contains aerosol data (particle concentration, total number concentration, median, mean, geometric mean, mode and geometric standard deviation of the size distribution) in a size range between 12 and 460 nm (electrical mobility diameter) as well as instrument characteristics (sample temperature, pressure, relative humidity, mean free path and gas viscosity), in a temporal resolution of 2 minutes, with time in UTC, measured at Davos Wolfgang.

mimetype: application/x-netcdf

en: OPC-N3 Dataset Davos Wolfgang

en: Particle concentrations in a size range of 0.35 μm to 40 μm were measured with an OPC-N3 (Alphasense, Inc.) in a resolution of 20 seconds (time in UTC). PM₁, PM₂,₅ and PM₁₀ were directly derived.

en: DRINCZ Dataset Davos Wolfgang

en: The DRINCZ file contains information on temperature (in °C) dependent frozen fractions, number of Ice Nuleating Particles (INP, per standard liter) and differential INP concentrations (per standard liter and Kelvin).

en: APS Dataset Davos Wolfgang

en: The APS file contains aerosol data (total number concentration, median, mean, geometric mean, mode and geometric standard deviation of the size distribution) for aerodynamic diameters between 0.5 μm and ~20 μm as well as instrument characteristics, in a temporal resolution of 20 seconds, with time in UTC, measured at Davos Wolfgang.

en: OPC GT-526S Dataset Davos Wolfgang

en: Particle counts were recorded in six size channels (0.3, 0.5, 0.8, 1.5, 2.5 and 5 μm) in a resolution of 20 seconds (time in UTC) by a particle counter (GT-526S, Met One Instruments, Inc.).

en:
Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs) and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room.

Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0).

Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³).

Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3788 , TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³).

Coriolis μ and DRINCZ: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l minˉ¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the DRoplet Ice Nuclei Counter Zurich (DRINCZ, ETH Zurich) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles.

aerosol

display_name: aerosol

drincz

display_name: drincz

ice-nucleation

display_name: ice-nucleation

inp

display_name: inp

particles

display_name: particles

raclets

display_name: raclets

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: Aerosol Data Davos Wolfgang


en: snow-height snow-onset-and-disappearance-dates parallel-measurements climate-indicators snow-depth

en: Sdur

en: Potential sunshine duration during MA and mean snow disappearance dates for a selection of station pairs.

en: snow climate indicators

en: Snow climate indicators derived from parallel manual snow measurements

en: ReadMe

en: File containing information about variables used in this data set

en: Data set consisting of snow climate indicators derived from parallel manual snow measurements in Switzerland.

climate-indicators

display_name: climate-indicators

parallel-measurements

display_name: parallel-measurements

snow-depth

display_name: snow-depth

snow-height

display_name: snow-height

snow-onset-and-disappearance-dates

display_name: snow-onset-and-disappearance-dates