title_for_slug: total-stem-number-by-type-of-damage

en:
Number of all living and dead trees and shrubs starting at 12 cm dbh where a particular type of damage (including no damage, dead or lying) was observed. One tree may have more than one type of damage, which means it may contribute to the total number of stems for several different types of damage.

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

accessible-forest-without-shrub-forest

display_name: accessible-forest-without-shrub-forest

biogeographical-region

display_name: biogeographical-region

canton

display_name: canton

damage-of-tree

display_name: damage-of-tree

economic-region

display_name: economic-region

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

total-stem-number

display_name: total-stem-number

total-stem-number-by-type-of-damage

display_name: total-stem-number-by-type-of-damage

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: Total stem number by type of damage


maintainer: Giulia Mazzotti

en: forest-snow snow-distribution forest-structure snow-modelling snow-energy-balance

state: active

type: dataset

en: Snow depth data open sites

en: Contains all snow detph data accquired at open sites nearby the forest stands during the same period.

format: XLS

en: Grid point nomenclature

en: The grid structure of each forest site and the respective nomenclature applied to label individual points within each grid are outlined here.  Each point marks the location of snow depth measurements and co-located canopy structure parameters.

en: Snow depth data forest sites

en: Contains all snow depth data acquired at all forest sites during winters 2012/13, 2013714 and 2014/15.

en: Canopy data

en: Includes all canopy structure metrics computed at all surveyed locations

en: Coordinates data

en: Includes coordinate information of each surveyed point (7 x 276 = 1932 forest points, 2 open areas)

en: Meteo data to drive FSM2 model

media_type: application/zip

en: Includes meteorological driving data used to run the forest snow model FSM2, processed and parsed in text files that match the format required by FSM2. Separate files are available for each field area (Laret, Drusatscha and Ischlag) and season (2012/13, 2013/14, 2014/15). Each file includes data for the period from October 1st to May 31st at hourly resolution.

format: ZIP

mimetype: application/zip

en:
This dataset contains all snow, canopy and meteorological data presented and used in the publication: Mazzotti, G., Essery, R., Moeser, D. & Jonas T. (2020) 'Resolving spatial variability of forest snow using an energy-balance model with a 1-layer canopy'. Water Resources Research, https://doi.org/10.1029/2019WR026129.
This publication must be cited when using this dataset.

forest-snow

display_name: forest-snow

forest-structure

display_name: forest-structure

snow-distribution

display_name: snow-distribution

snow-energy-balance

display_name: snow-energy-balance

snow-modelling

display_name: snow-modelling

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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: Snow depth, canopy structure and meterorological datasets from the Davos area, Switzerland, Winters 2012/13-2014/15, used for high-resolution forest snow modelling


phosphatase-leaching

title_for_slug: phosphatase-leaching

en: forest-topsoil phosphate phoshporus-forms phosphomonoesterase-activity dissolved-organic-phosphorus

en:
Data on phosphomonoesterase activity in forest topsoil leachates and soil extracts as well as P forms in the leachate.
Leachate samples were taken in Feb./Mar. and July 2019 with zero-tension lysimeters at two sites in Germany
of contrasting phosphorus availability from the litter, the Oe/Oa, and the A horizon in beech forest.
Soil samples were taken in July 2020. For methods see publication.

en:
Data on phosphomonoesterase activity in forest topsoil leachates and soil extracts as well as P forms in the leachate.
Leachate samples were taken in Feb./Mar. and July 2019 with zero-tension lysimeters at two sites in Germany
of contrasting phosphorus availability from the litter, the Oe/Oa, and the A horizon in beech forest.
Soil samples were taken in July 2019. For methods see publication.

dissolved-organic-phosphorus

display_name: dissolved-organic-phosphorus

forest-topsoil

display_name: forest-topsoil

phoshporus-forms

display_name: phoshporus-forms

phosphate

display_name: phosphate

phosphomonoesterase-activity

display_name: phosphomonoesterase-activity

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: Phosphatase leaching


maintainer: Roksolana Petrovska

en: lar acer fagus mortality-rate regeneration shade-tolerance dominance

en: Mortality of regeneration

media_type: text/csv

en: Acer platanoides, Acer pseudoplatanus and Fagus sylvatica living and dead juveniles (0-500 cm in height) grown in the deep shade of a prime beech-dominated forest.

format: CSV

en: The R-script of survival analysis for accelerated failure time model

format: SERVICE

en:
 One individual per species, vitality class (low and high) and height class (eight classes: 0–10, 11–20, 21–35, 36–60, 61–90, 91–130, 131–200 and 201–500 cm) was randomly selected and harvested in each of the six plots. This resulted in a sample of 82, 80 and 89 living individuals of A. platanoides, A. pseudoplatanus and F. sylvatica, respectively.

 Additionally stems of dead Acer spp. and F. sylvatica trees that had died within the last three years (2015–2018) were randomly harvested, matching the height classes of the harvested living trees wherever possible. In total, 179 dead young trees (60 A. platanoides, 72 A. pseudoplatanus and 47 F. sylvatica) were collected.

 Variables:
* species_code: a_pla - Acer platanoides, a_pse - Acer pseudoplatanus, f_syl - Fagus sylvatica
* species: as above
* dummy: 0 - living individual, 1 - dead individual
* LAR_cm2_g: leaf area ratio or ratio of leaf area to total plant biomass, [cm2/g]
* tree_age: in years
* avg_ring_micron: average width of the last 5 rings in tree life excluding the last ring
* dry_mass_g: aboveground and belowground biomass
* DLI: direct light index (measured only under living individuals)
* BLI: diffuse light index (measured only under living individuals)
* GLI: global light index

acer

display_name: acer

dominance

display_name: dominance

fagus

display_name: fagus

lar

display_name: lar

mortality-rate

display_name: mortality-rate

regeneration

display_name: regeneration

shade-tolerance

display_name: shade-tolerance

en: Mortality of regeneration: Acer spp. and Fagus sylvatica


lwf-jussy-long-term-research-site

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

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

en: Jussy Meteorological Data LWF

en: LWF plot Jussy. Photo: Anne Thimonier Rickenmann, 2019

en:
![alt text](https://www.envidat.ch/dataset/465d852d-af24-415f-9db2-48fe31d6dc20/resource/a39a0e35-efdf-4bef-bab1-4b0532442b34/download/lwf_jussy.jpg "LWF Jussy")

LWF Plot Jussy

- Community: Jussy / canton GE
- Date of installation: 31 May 1995
- Size of the plot: 1.99 ha
- Altitude: 496-506 m
- Mean slope: 3%
- Geology (in German): Quartär; tonreiche würmeiszeitliche Grundmoräne
- Soil types (WSL) : Stagnic Luvisols
- Woodland association after EK72: 35: Galio silvatici-Carpinetum
- Main tree species: Quercus species
- Management system: former coppices w. standards
- Silvicultural system: unmanaged / group selection
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 36.6 cm
- Number of trees BHD >= 12 cm (2011): 1278
- Maximum tree age: Carpinus betulus 60 yr - Populus tremula 60 yr - Quercus petrea 90 yr

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

en: LWF Jussy long-term research site


maintainer: Franziska Gerber

en: python cryowrf aws antarctica consistent

en: Scripts to preprocess (and download for some databases) atmospheric weather station data. Furthermore, a list of stations and a map can be created to have an overview.

en:
There are many sources providing atmospheric weather station data for the Antarctic continent. However, variable naming, timestamps and data types are highly variable between the different sources. The published python code intends to make processing of different AWS sources from Antarctica easier. For all datasets that are taken into account variables are renamed in a consistent way. Data from different sources can then be handled in one consistent python dictionary.

The following data sources are taken into account:

* AAD: Australian Antarctic Division (https://data.aad.gov.au/aws)
* ACECRC: Antarctic Climate and Ecosystems Cooperative Research Centre by the Australian Antarctic Division
* AMRC: Antarctic Meteorological Research Center (ftp://amrc.ssec.wisc.edu/pub/aws/q1h/)
* BAS: British Antarctic Survey (ftp://ftp.bas.ac.uk/src/ANTARCTIC_METEOROLOGICAL_DATA/AWS/; https://legacy.bas.ac.uk/met/READER/ANTARCTIC_METEOROLOGICAL_DATA/)
* CLIMANTARTIDE: Antarctic Meteo-Climatological Observatory by the italian National Programme of Antarctic Research (https://www.climantartide.it/dataaccess/index.php?lang=en)
* IMAU: Institute for Marine and Atmospheric research Utrecht (Lazzara et al., 2012), https://www.projects.science.uu.nl/iceclimate/aws/antarctica.ph
* JMA: Japan Meteorological Agency (https://www.data.jma.go.jp/antarctic/datareport/index-e.html)
* NOAA: National Oceanic and Atmospheric Administration (https://gml.noaa.gov/aftp/data/meteorology/in-situ/spo/)
* Other/AWS_PE: Princess Elisabeth (PE), KU Leuven, Prof. N. van Lipzig, personal communication
* Other/DDU_transect: Stations D-17 and D-47 (in transect between Dumont d’Urville and Dome C, Amory, 2020)
* PANGAEA: World Data Center (e.g. König-Langlo, 2012)

Important notes

* Information about data sources is available. Some downloading scripts are included in the provided code. However, users should make sure to comply with the data providers terms and conditions.
* Given changing download options of the differnent institutions the above links may not permanently work and data has to be retrieved by the user of this dataset.
* No quality control is applied in the provided preprocessing software - quality control is up to the user of the datasets. Some dataset are quality controlled by the owner.

Acknowledgements
--------------------------
We thank all the data providers for making the data publicly available or providing them upon request. Full acknowledgements can be found in Gerber et al., submitted.

References
---------------
Amory, C. (2020). “Drifting-snow statistics from multiple-year autonomous measurements in Adélie Land, East
Antarctica”. The Cryosphere, 1713–1725. doi: 10.5194/tc-14-1713-2020

Gerber, F., Sharma, V. and Lehning, M.: CRYOWRF - a validation and the effect of blowing snow on the Antarctic SMB, JGR - Atmospheres, submitted.

König-Langlo, G. (2012). “Continuous meteorological observations at Neumayer station (2011-01)”. Alfred Wegener
Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, PANGAEA, doi: 10.1594/PANGAEA.
775173

antarctica

display_name: antarctica

aws

display_name: aws

consistent

display_name: consistent

cryowrf

display_name: cryowrf

python

display_name: python

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Preprocessing Antarctic Weather Station (AWS) data in python


disdrometer-data-laret

maintainer: Daniel Wolfensberger

title_for_slug: disdrometer-data-laret

en: visibility precipitation disdrometer raclets hydrometeors

en: Disdrometer Data Laret

en: The zip folder contains one netCDF file per day for Laret from February 7 to March 29 2019 with a temporal resolution of 30 seconds (time in UTC). The naming convention for the files is Laret_YYYYMMDD.nc and the files include the following: characteristics of hydrometeors (e.g. diameter, fall velocity, amount per diameter class,...), precipitation rate, radar reflectivity, visibility range, weather codes and instrument performance.

en:
A laser optical disdrometer (Parsivel² , OTT Hydromet) was used to measure hydrometeors by extinction when passing a laser beam. The instrument can classify eight different kinds of precipitation, including rain, hail, snow, drizzle, and hybrid forms.
The dataset contains information on precipitation amount and type for the period of February 7 to March 29 2019 in Laret.

disdrometer

display_name: disdrometer

hydrometeors

display_name: hydrometeors

precipitation

display_name: precipitation

raclets

display_name: raclets

visibility

display_name: visibility

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.


maintainer: EnviDat Support

en: wind-velocity anemometer wind wind-direction raclets wind-speed

en: Sonic wind component w

en:
The data contains values for w direction of the wind in a sampling frequency of 20 Hz.
Erroneour spikes were filterd in two steps.

1. Define moving window uₓ = u(i-3:i+3)
2. If u(i)-mean(uₓ) > 2*std(uₓ):u(i) = NaN

Frozen detectors resulted in continuous zero values (not filtered).
The wind speed and direction were calculated as follows:

wind speed = sqrt(u² + v²)
wind direction = 180° + (180° / PI)* arctan2 (-u, -v)

en: Sonic wind component u

en:
The data contains values for u direction of the wind in a sampling frequency of 20 Hz.
Erroneour spikes were filterd in two steps.

1. Define moving window uₓ = u(i-3:i+3)
2. If u(i)-mean(uₓ) > 2*std(uₓ):u(i) = NaN

Frozen detectors resulted in continuous zero values (not filtered).
The wind speed and direction were calculated as follows:

wind speed = sqrt(u² + v²)
wind direction = 180° + (180° / PI)* arctan2 (-u, -v)

en: Sonic temperature

en: The data set contains values of temperature (°C) measured with the anemometer at a sampling frequency of 20 Hz.

en: Sonic time

en: The data set contains the time in YYYY-mm-dd HH:MM:SS.sss for the anemometer data. The time is given in UTC+1.

en: Sonic wind component v

en:
The data contains values for v direction of the wind in a sampling frequency of 20 Hz.
Erroneour spikes were filterd in two steps.

1. Define moving window uₓ = u(i-3:i+3)
2. If u(i)-mean(uₓ) > 2*std(uₓ):u(i) = NaN

Frozen detectors resulted in continuous zero values (not filtered).
The wind speed and direction were calculated as follows:

wind speed = sqrt(u² + v²)
wind direction = 180° + (180° / PI)* arctan2 (-u, -v)

en: A Young 81000 sonic anemomenter was deployed at Gotschnagrat (LON: 46.859 LAT: 9.849) to record three components of the wind velocity (u, v, w in [m s‾ ¹]) and air temperature (Ts in [°C]). The anemomenter was mounted in direction North at a height of 1.5 m above snow surface at the beginning. The time within each data set is given in UTC+1. Instrument specifications can be found [here](http://www.youngusa.com/Manuals/81000-90(I).pdf) .

anemometer

display_name: anemometer

wind

display_name: wind

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

wind-velocity

display_name: wind-velocity

en: Snow Drift Station - 3D Ultrasonic


en: salinity freshwater-flux physical-oceanography cryospheric-science antartic melting satellite salt freezing polar climate-change forcing transport sea-ice sea-ice-volume sea-ice-divergence mass-balance southern-ocean

en: Link to Related Publication

en: Data in NetCDF4 CF-1.6 format

en: Explanatory Document

media_type: application/pdf

format: PDF

en:
This data set provides estimates of annual fresh water fluxes related to sea-ice formation from ocean freezing and snow-ice formation, sea-ice melting, lateral transport of sea ice in the Southern Ocean over the period 1982 to 2008.It is derived from a mass balance calculation of local sea-ice volume change and divergence from satellite data and sea-ice reconstructions. The mass balance is calculated on a daily basis and fluxes are then integrated over the entire year, where a year is defined from March to February of the next year (i.e. from March 1982 to February 2009). This approach combines multiple products of sea-ice concentration (Cavalieri & Parkinson, 2008;Comiso, 1986; Meier et al., 2013), sea-ice thickness (Kurtz & Markus, 2012; Massonnet et al., 2013; Worby et al., 2008), and sea-ice drift (Fowler et al., 2013; Kwok 2005; Schwegmann et al., 2011). For a detailed description of the method see Haumann et al. (2016). The data set is derived to estimate large-scale (regional to basin-scale) fluxes on an annual basis. Our confidence is reduced on a grid cell basis, such as for single coastal polynyas, where the method and underlying data induce large, unknown uncertainties.

_Disclaimer: This data set is free to use for any non-commercial purpose at the risk of the user and the authors do not take any liability on the use of the data set. The authors assembled the data set carefully and assessed accuracy, errors, and uncertainties. Please contact the authors if you find any issues._

Related publication: http://www.nature.com/nature/journal/v537/n7618/full/nature19101.html (doi:10.1038/nature19101)

Disclaimer: This data set is free to use for any non-commercial purpose at the risk of the user and the authors do not take any liability on the use of the data set. The authors assembled the data set carefully and assessed accuracy, errors, and uncertainties. Please contact the authors if you find any issues.

antartic

display_name: antartic

climate-change

display_name: climate-change

cryospheric-science

display_name: cryospheric-science

forcing

display_name: forcing

freezing

display_name: freezing

freshwater-flux

display_name: freshwater-flux

mass-balance

display_name: mass-balance

melting

display_name: melting

physical-oceanography

display_name: physical-oceanography

polar

display_name: polar

salinity

display_name: salinity

salt

display_name: salt

satellite

display_name: satellite

sea-ice

display_name: sea-ice

sea-ice-divergence

display_name: sea-ice-divergence

sea-ice-volume

display_name: sea-ice-volume

southern-ocean

display_name: southern-ocean

transport

display_name: transport

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Antarctic sea-ice freshwater fluxes associated with freezing, transport, and melting


length-of-forest-edge

title_for_slug: length-of-forest-edge

en:
Length of the forest edge calculated on the basis of the forest boundary lines determined in the aerial photo.

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

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

biogeographical-region

display_name: biogeographical-region

canton

display_name: canton

economic-region

display_name: economic-region

length-of-forest-edge

display_name: length-of-forest-edge

lower-higher-altitudinal-zones

display_name: lower-higher-altitudinal-zones

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

total-area

display_name: total-area

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: Length of forest edge


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

en: Site aerial photo (Lausanne)

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

mimetype: image/jpeg

en: Forest station data (LAB)

en: Data file.

mimetype: text/csv

en: Site photo of the field station (LAF)

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

en: Data plot of the forest station (LAB)

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

mimetype: application/pdf

en: Field station data (LAF)

en: Data plot of the field station (LAF)

en: Site photo of the forest station (LAB)

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

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 Lausanne in Switzerland where one station is located within a natural mixed forest stand (LAB) with European beech (_Fagus sylvatica_; 160-170 yrs), European silver fir (_Abies alba_; 160-170 yrs) and Norway spruce (_Picea abies_; 160-170 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, LAF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Lausanne 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

en: Lausanne, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1996 onwards


en: wrf cosmo-wrf static-data cryowrf topography landuse

en:
Example GEOGRID.TBL to use with the 1s static data;
Note about naming:
* 121 -> 1-2-1 topography smoothing applied to dataset
* 1 -> one smoothing cycle applied to the data (needs to be changed depending on the chosen resolution to avoid too steep slope angles)

en: Static data for WRF - Soiltype top

en: Documentation

en: Documentation of the dataset

en: Static data for WRF - Soiltype bottom

en: Example namelist.wps to use with the 1s static data

en: Static data for WRF - Landuse

en: Static data for WRF - Topography

en: Static input data (topography, landuse and soiltype) for the WRF preprocessing system WPS is provided for Switzerland and its neighboring countries between 45-49 N and 4-12 E. The data is provided at a resolution of 1 s. Topography is based on the Aster dataset, while landuse is extracted from the Corine landuse dataset. Soil type is set to silty clay loam for the entire domain. This static input data is valid for WRF and CRYOWRF.

cosmo-wrf

display_name: cosmo-wrf

cryowrf

display_name: cryowrf

landuse

display_name: landuse

static-data

display_name: static-data

topography

display_name: topography

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: High resolution static data for WRF over Switzerland


en: wrf topography antarctica landuse cryowrf

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

en: Scripts

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

en: WPSinput

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

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

antarctica

display_name: antarctica

en: REMA topography and AntarcticaLC2000 for WRF


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

en: SPSS-Dataset

en: Questionnaire Italian

en: Codebook to SPSS-file

en: Dataset in csv-format

en: Questionnaire German

en: Questionnaire French

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

adults-and-teenagers

display_name: adults-and-teenagers

nfi-photos

display_name: nfi-photos

online-questionnaire-survey

display_name: online-questionnaire-survey

perception-of-ecosystem-services

display_name: perception-of-ecosystem-services

visual-attractiveness

display_name: visual-attractiveness

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


en: dendroecology southern-switzerland drought-stress spei grapevine-yellows

en: Dataset 2. Ring width.

en:
Ring width measured with cellSens from slides.
Measures expressed as [μm] and arranged per ID (see dataset 1) and year.

en: Dataset 1

en: Sample identification, symptomatology (symptomatic vs. asymptomatic), coordinates (CH1903), phloem disarrangement class, drought-sensitivity, slope sector.

en:
Dataset collected from dendroecological study on trunks of grapevines ('Chardonnay' cv.) infected by the "Flavescence dorée" phytoplasma (FDp) in Origlio (southern Switzerland) in 2019-2020. Ring widths were measured with cellSens (Olympus Corporation). Calculations and analysis were conducted within R.
The Flavescence dorée phytoplasma (FDp) causes a severe grapevine (Vitis vinifera) disease. Anatomical modification due to FDp infections are known to occur but research so far focused on stems and leaf tissues and, in particular, on their phloem structure. In this paper, we applied dendrochronological techniques on wood rings and analysed the anatomical structures of the trunk of the susceptible grapevine cultivar ‘Chardonnay’ in order to verify their response to FDp infections.
In this study, we tested the impact of FDp and drought stress on xylem ring width and also described phloem anomalies inside the trunk of grapevines. We concluded that drought and FDp infection both have a significant effect on ring width reductions and that FDp supersedes the effect of drought conditions (calculated after the SPEI index) in infected specimens.

dendroecology

display_name: dendroecology

drought-stress

display_name: drought-stress

grapevine-yellows

display_name: grapevine-yellows

southern-switzerland

display_name: southern-switzerland

spei

display_name: spei

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: Impact of the “Flavescence dorée” phytoplasma on xylem growth and phloem anomalies in trunks of ‘Chardonnay’ grapevines (Vitis vinifera)


photogrammetric-drone-data-gruenbodeli

title_for_slug: photogrammetric-drone-data-gruenbodeli

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

en: Data Access

en:
- ortho (3 cm/25 mm resolution)
- DSM (10 cm resolution)
- HS (10 cm resolution)
- RGB images
- Agisoft Metashape Project Report

All products are also available as a cloud optimized geotiff.

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

drone-data

display_name: drone-data

dsm

display_name: dsm

orthophoto

display_name: orthophoto

photogrammetry

display_name: photogrammetry

snow-depth-mapping

display_name: snow-depth-mapping

uav

display_name: uav

wingtra

display_name: wingtra

en: Photogrammetric Drone Data Grüenbödeli


en: swiss-forest-lab icp-forests deadwood long-term-forest-ecosystem-research coarse-woody-debris

en: Data Request

en:
Assessment of coarse woody debris on LWF plots using the line intersect method

 Purpose:

Assessing the amount of coarse woody debris

 Manual Citation:

 * Matthias Dobbertin, Nathalie Bretz Guby, 1997: Totholz. In: Peter Brang (ed.) LWF Aufnahmeanleitung. Langfristige Waldökosystem-Forschung LWF, Eidg. Forschungsanstalt WSL, Birmensdorf, 5 S.

 Paper Citation:

 * Bretz Guby, N.A., Dobbertin, M., 1996. Quantitative estimates of coarse woody debris and standing dead trees in selected Swiss forests. Glob. Ecol. Biogeogr. Lett. 5, 327-341.

coarse-woody-debris

display_name: coarse-woody-debris

deadwood

display_name: deadwood

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

swiss-forest-lab

display_name: swiss-forest-lab

en: Deadwood survey LWF 1995 - line intersect


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

media_type: text/plain

format: TXT

en: R-Script

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

en: GenPSUm

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

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

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

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

en: SNOWPACK climate change simulation files

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

en: Precipitation datasets

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

*Updated documentation 24 August 2020:*

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

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

This includes:

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

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

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

climate-change-impact

display_name: climate-change-impact

depth-of-snowfall

display_name: depth-of-snowfall

ground-temperature

display_name: ground-temperature

meteo-station

display_name: meteo-station

swiss-alps

display_name: swiss-alps

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


en: Script diameter and species modelling

en: This R script can be used to derive the uni- and bimodal Weibull log-likelihood regression models (in short: uni- and bimodal model), the species composition model and the main results presented in Mey et al. 2021 using data from the fourth Swiss NFI ('data_NFI4_diameter_modelling.rds' and 'data_NFI4_species_modelling.rds') and the R functions stored in 'functions_diameter_modelling.R'.

en: Data Figures 2 and 4

en:
This csv file contains the data required to reproduce Figure 2 and Figure 4.

* 'stand_type' states whether a row corresponds to NFI4 average summary statistics describing a generalised beech-dominated, spruce-dominated or an unspecified forest stand in Switzerland in its unimodal or bimodal form (e.g. 'beech.uni' refers to a generalised unimodal beech-dominated stand)
* 'md' refers to the mean diameter
* 'dq10' refers to the 10% quantile of the diameter
* 'dq90' refers to the 90% quantile of the diameter
* 'spr' refers to the spread between minimal and maximal diameter
* 'nph' refers to the number of stems per hectare
* 'o.qmd' refers to the overstorey quadratic mean diameter
* 'rho.emp' refers to proportion parameter (rho) determined by breakpoint setting
* 'ba' refers to the basal area per hectare
* 'drel' refers to the relative diameter ('drel' = (d - min(d)) / (max(d) - min(d)))
* 'temp' refers to the mean winter temperature (1988-2017, Dec-Feb)
* 'dev' refers to the development stage of the stand (e.g. 'Young growth')
* 'ftyp' refers to the potential climax forest type (e.g. 'Beech' refers to a beech-dominated stand)
* 'str' refers to the stand structure
* 'tillering' indicates whether the observed tree species on the NFI plot correspond to 'ftyp' ('TRUE') or not ('FALSE')
* 'prodreg' refers to the Swiss production region (ecoregion)
* 'fowner' refers to the forest ownership ('Public' or ' Private')

en: Data species modelling

en:
This rds file contains a data frame with single-tree and stand data required to reproduce the species composition model:

* 'clnr' refers to the plot number
* 'spec' refers to the main tree species group (12 groups)
* 'ba' refers to the basal area per hectare
* 'nph' refers to the number of stems per hectare
* 'drel' refers to the relative diameter ('drel' = (d - min(d)) / (max(d) - min(d)))
* 'temp' refers to the mean winter temperature (1988-2017, Dec-Feb)
* 'dev' refers to the development stage of the stand (e.g. 'Young growth')
* 'ftyp' refers to the potential climax forest type (e.g. 'Beech' refers to a beech-dominated stand)
* 'str' refers to the stand structure
* 'tillering' indicates whether the observed tree species on the NFI plot correspond to 'ftyp' ('TRUE') or not ('FALSE')
* 'prodreg' refers to the Swiss production region (ecoregion)
* 'fowner' refers to the forest ownership ('Public' or ' Private')

en: Functions diameter modelling

en: This R file contains the functions used to produce the generalised diameter distributions in Mey et al. 2021.

en: Data diameter modelling

en:
This rds file contains a list of 5085 data frames required to reproduce the uni- and bimodal diameter model. Each data frame comprises single-tree diameters ('dbh') and assigned representation factors ('repfa') at a single Swiss NFI4 plot.

* 'dbh' refers to the diameter at breast height (130 cm)
* 'repfa' refers to the representation factor used to scale up to one hectare

en:
The files refer to the data and R code used in Mey et al. "From small forest samples to generalised uni- and bimodal stand descriptions" (2021) _Methods in Ecology and Evolution_.

Generalised stand descriptions are coming from the simultaneous examination of samples that are representative for a specific target area (here, Switzerland) and link available information about forest stand attributes. They combine the modelling of uni- or bimodal diameter distributions and species compositions, i.e. the shares of stems of individual species. Generalised stand descriptions may be used to interpret tree species diversity, regeneration and harvest potentials on a plot-level basis, and to initialise forest models with representative stand data.

The data stored here were derived from the fourth campaigns of the Swiss National Forest Inventory (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 file 'Data Figures 2 and 4' is publicly available and contains the data used to produce the Figures 2 and 4 published in the paper.

The files 'Data diameter modelling' and 'Data species modelling' contain all the data required to reproduce the diameter and species model building. The access to these two files is restricted as they contain raw data from the fourth Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement.

The files 'Script diameter and species modelling' and 'Functions diameter modelling' are publicly available and provide the R code used to derive the generalised stand descriptions from the Swiss NFI data.

forest-model-initialisation

display_name: forest-model-initialisation

forest-stand-description

display_name: forest-stand-description

national-forest-inventory

display_name: national-forest-inventory

small-forest-samples

display_name: small-forest-samples

tree-species-composition

display_name: tree-species-composition

weibull-function

display_name: weibull-function

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: Generalised stand descriptions in Switzerland


en: downscaling rcm toposcale cryosphere cordex

en: TopoCLIM v1.0

en: Archive of Github repository as of 10 June 2021 including license and documentation and examples.

en: Github link to TopoCLIM project

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

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

License GPL v.3

cordex

display_name: cordex

cryosphere

display_name: cryosphere

downscaling

display_name: downscaling

rcm

display_name: rcm

toposcale

display_name: toposcale

en: TopoCLIM v1.0 code


en: orography precipitation paleo-climate last-glacial-maximum air-temperature glaciers

en: Data Access via S3

en:
High resolution, downscaled climate model data are used in a wide variety of applications in environmental sciences. Here we present the CHELSA-TraCE21k downscaling algorithm to create global monthly climatologies for temperature and precipitation at 30 arcsec spatial resolution in 100 year time steps for the last 21,000 years. Paleo orography at high spatial resolution and at each timestep is created by combining high resolution information on glacial cover from current and Last Glacial Maximum (LGM) glacier databases with the interpolation of a dynamic ice sheet model (ICE6G) and a coupling to mean annual temperatures from CCSM3-TraCE21k. Based on the reconstructed paleo orography, mean annual temperature and precipitation was downscaled using the CHELSA V1.2 algorithm.

The data is published under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.

format: GeoTIFF

en: Technical documentation

en: Technical documentation containing file metadata and variable descriptions.

air-temperature

display_name: air-temperature

glaciers

display_name: glaciers

last-glacial-maximum

display_name: last-glacial-maximum

orography

display_name: orography

paleo-climate

display_name: paleo-climate

precipitation

display_name: precipitation

en: CHELSA-TraCE21k: Downscaled transient temperature and precipitation data since the last glacial maximum


en: avalanche snow-instability danger-level snow avalanche-danger avalanche-forecasting

en: Read-me file

en: This read-me file contains the descriptions of the variables.

en: Field observations of snow instabilities

en:
This data set includes 589 snow profile observations including a rutschblock test, observations of signs of instability and an assessment of the local avalanche danger level, mainly recorded in the region of Davos (eastern Swiss Alps) during the winter seasons 2001-2002 to 2018-2019.

These data were analyzed and results published by Schweizer et al. (2021). They characterized the avalanche danger levels based on signs of instability (whumpfs, shooting cracks, recent avalanches), snow stability class and new snow height.

The data are provided in a csv file (589 records); the variables are described in the corresponding read-me file.

These data are the basis of the following publication: Schweizer, J., Mitterer, C., Reuter, B., and Techel, F.: Avalanche danger level characteristics from field observations of snow instability, Cryosphere, 15, 3293-3315, https://doi.org/10.5194/tc-15-3293-2021, 2021.

 Acknowlegements
Many of the data were recorded by SLF observers and staff members, among those Roland Meister, Stephan Harvey, Lukas Dürr, Marcia Phillips, Christine Pielmeier and Thomas Stucki. Their contribution is gratefully acknowledged.

avalanche

display_name: avalanche

avalanche-danger

display_name: avalanche-danger

avalanche-forecasting

display_name: avalanche-forecasting

danger-level

display_name: danger-level

snow

display_name: snow

snow-instability

display_name: snow-instability


maintainer: Dirk Nikolaus Karger

en: climate-change climatology climate bioclim climate-layers free-climate-data precipitation temperature

en: CHELSA V2.1 (current)

en: CHELSA Version 2.1 based on ERA5 forcings and CMIP6

en: CHELSA V1.2 (deprecated)

en: CHELSA Version 1.2 based on ERA-Interim forcings and CMIP5

en: CHELSA Technical Documentation

en: Technical documentation document including the variable names, units, scaling, offset, and background.

en:
High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth’s land surface areas) data of downscaled temperature and precipitation to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction.

CHELSA data published in EnviDat includes the deprecated version 1.2 (originally published under 10.5061/dryad.kd1d4). Please use the current 2.1 version.

Paper Citation:
 > _Karger DN. et al. Climatologies at high resolution for the earth’s land surface areas, Scientific Data, 4, 170122 (2017) [doi: 10.1038/sdata.2017.122](https://doi.org/10.1038/sdata.2017.122)._

bioclim

display_name: bioclim

climate

display_name: climate

climate-change

display_name: climate-change

climate-layers

display_name: climate-layers

climatology

display_name: climatology

free-climate-data

display_name: free-climate-data

temperature

display_name: temperature

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

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: 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 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 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 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 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 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: Data Description

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

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 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 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 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 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 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 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 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 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 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 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: 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 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 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 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:
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


maintainer: Yohann Chauvier

en: occurrence-records conservation ecoregions hard-limit species-observations accepted-name landsca synonyms r-package range-maps flags large-scale gbif filtering taxonomy species-distribution

en: gbif.range R package version 0.2 (deprecated)

en: wsl.gbif R package version 0.1 (deprecated)

en: Test version, not to be used.

en: gbif.range R package version 1.1 (current)

en: GitHub URL

en: Although species range may be obtained using expert maps or modeling methods, expert data is often species-limited and statistical models need more technical expertise as well as many species observations. When unavailable, such information may be extracted from the Global Biodiversity Information facility (GBIF), the largest public data repository inventorying georeferenced species observations worldwide. However, retrieving GBIF records at large scale may be tedious if users are unaware of specific tools and functions that need to be employed. Here we present *gbif.range*, an R library that contains automated methods to generate species range maps from scratch using in-house ecoregions shapefiles and an easy-to-use GBIF download wrapper. Finally, this library also offers a set of additional very useful parameters and functions for large GBIF datasets (generate doi, extract GBIF taxonomy, records filtering...). [gbif.range R project](https://github.com/8Ginette8/gbif.range)

accepted-name

display_name: accepted-name

conservation

display_name: conservation

ecoregions

display_name: ecoregions

filtering

display_name: filtering

flags

display_name: flags

gbif

display_name: gbif

hard-limit

display_name: hard-limit

landsca

display_name: landsca

large-scale

display_name: large-scale

occurrence-records

display_name: occurrence-records

r-package

display_name: r-package

range-maps

display_name: range-maps

species-distribution

display_name: species-distribution

species-observations

display_name: species-observations

synonyms

display_name: synonyms

taxonomy

display_name: taxonomy

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: gbif.range - An R package to generate species range maps based on ecoregions and a user-friendly GBIF wrapper


soil-matrix-chemistry-lwf

title_for_slug: soil-matrix-chemistry-lwf

en: swiss-forest-lab soil-matrix soil-chemistry long-term-forest-ecosystem-research icp-forests

en: Data Request

en:
Assessment of several chemical soil parameters at the beginning of the monitoring campaign. Most parameters were determined accordung to the manual of ICP-Integrated-Monitoring. The parameters were analysed for all genetic horizons in soil pits and additionally for fixed layers in the Intensive-Monitoring-Plots. In heterogeneous LWF-plots, several soil pits were analysed in order to assess the soil variability of the plot. The manual (in German) for soil sampling and soil analyses is available in www: http://e-collection.ethbib.ethz.ch/view/eth:25622?q=walthert. The results and data of the first soil survey are available (in German) in www: http://e-collection.ethbib.ethz.ch/view/eth:26275?q=walthert.

 Purpose:

The chemical characterisation of soil matrix down to the paraent material is realised with data from soil pits. The monitoring of the soil matrix in a frequency of roundly 15 years is effected with soil samples from Intensiv-Monitoring-Plots. For soil monitoring, pooled samples with 16 replicats are used down to a depth of 80 cm. The date of the second soil survey is not yet fixed.

 Manual Citation:

 * Walthert L, Lüscher P, Luster J, Peter B (2002) Langfristige Waldökosystem-Forschung LWF. Kernprojekt Bodenmatrix. Aufnahmeanleitung zur ersten Erhebung 1994-1999. ETHZ Zürich, e-collection , Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Vol. 269: 56 p. [10.3929/ethz-a-004375470](https://doi.org/10.3929/ethz-a-004375470)

 Paper Citation:

 * Walthert L, Blaser P, Lüscher P, Luster J, Zimmermann S (2003) Langfristige Waldökosystem-Forschung LWF. Kernprojekt Bodenmatrix. Ergebnisse der ersten Erhebung 1994-1999. ETHZ Zürich, e-collection , Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Vol. 276: 340 p.

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

soil-chemistry

display_name: soil-chemistry

soil-matrix

display_name: soil-matrix

swiss-forest-lab

display_name: swiss-forest-lab

en: Soil matrix chemistry LWF


en: snow-modelling energy-balance mass-balance upscaling forest-canopy

en: Hemispherical Photographs

en: Hemispherical images at the intersection points of the experimental plots at Laret (CH) and Sodankylä (FIN)

en: Canopy height model

en: Canopy height model of the lidar domain at Laret

en:
This dataset contains forest canopy structure data acquired in a spruce forest at Laret, Switzerland, and a pine forest at Sodankylä, Finland. Data include:

* Hemispherical photographs taken at transect intersection points of 13 experimental plots (40x40m each)
* a Canopy Height Model (tree height map) derived by rasterizing airborne LiDAR data, encompassing the entire simulation domain at Laret (150'000 m2)

These data provide the necessary basis for creating canopy structure datasets to be used as input to the forest snow snow model FSM2. These datasets, the model input derivatives and the radiation and snow modelling are described in detail in the following publication:

_Mazzotti, G., Webster, C., Essery, R., and Jonas, T. (2021) Improving the physical representation of forest snow processes in coarse-resolution models: lessons learned from upscaling hyper-resolution simulations. Water Resources Research 57, e2020WR029064. [doi: 10.1029/2020WR029064](https://doi.org/10.1029/2020WR029064)_

This publication must be cited when using the data.

 See also:
For additional information on the FSM2 model, see the corresponding [GitHub repository](https://github.com/GiuliaMazzotti/FSM2/tree/hyres_enhanced_canopy)

The datasets and the model have also been used in _Mazzotti et al. (2020) Process-level evaluation of a hyper-resolution forest snow model using distributed multi-sensor observations. [doi: 10.1029/2020WR027572](https://doi.org/10.1029/2020WR027572)

energy-balance

display_name: energy-balance

forest-canopy

display_name: forest-canopy

mass-balance

display_name: mass-balance

snow-modelling

display_name: snow-modelling

upscaling

display_name: upscaling

en: Forest canopy structure data for radiation and snow modelling (CH/FIN)


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

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

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

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

bryophytes

display_name: bryophytes

extinction-debt

display_name: extinction-debt

habitat-area

display_name: habitat-area

habitat-fragmentation

display_name: habitat-fragmentation

nature-conservation

display_name: nature-conservation

species-richness

display_name: species-richness

vascular-plants

display_name: vascular-plants

wetlands

display_name: wetlands


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

en: climate realizations

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

climate-change-impact

display_name: climate-change-impact

cryosphere

display_name: cryosphere

natural-climate-variability

display_name: natural-climate-variability

rain-on-snow

display_name: rain-on-snow

snow-water-equivalent

display_name: snow-water-equivalent

uncertainty

display_name: uncertainty

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


maintainer: Ana Beatriz Pierri Daunt

en: resilience landscape-ecology landscape-services literature-review decision-making green-infrastructure multifunctionality sense-of-place pdfsearch socio-ecological-system landscape-perception planning landscape-planning ecosystem-services

en: 6. Protocol

en: 5. Planning steps.

en: 1. Frequency of early concepts.

en: 2. Frequency of additional concepts.

en: 4. Use of any additional concept.

en: 3. Use of any early concept.

en:
Table of content: 1. Frequency of early concepts; 2. Frequency of additional concepts; 3. Use of any early concept; 4. Use of any additional concept, 5. Planning steps; 6. Protocol.

The present dataset is part of the published scientific paper entitled “Landscape ecological concepts in planning: review of recent developments” (Hersperger et al., 2021). The goal of this research was to review recent publications to assess the use of landscape ecological concepts in planning. Specifically, we address the following research questions: Q1. Landscape ecological concepts: What are they? How frequently are they mentioned in current research? Q2. How are landscape ecological concepts integrated in landscape planning?

We analysed all empirical and overview papers that have been published in four key academic journals in the field of landscape ecology and landscape planning in the years 2015–2019 (n = 1918). Four key journals in the field of landscape ecology were selected to conduct the analysis, respectively Landscape Ecology (LE), Landscape Online (LO), Current Landscape Ecology Reports (CLER), and Landscape and Urban Planning (LUP). The title, abstract and keywords of all papers were read in order to identify landscape ecological concepts. Then, all 1918 papers went through a keyword search to identify the use of early and additional concepts. We used the “pdfsearch” package in R programming language and searched for singular and plural forms and different variations of the concepts (see Supplementary material 1, Table A). As a result, we provided four outputs:

  1. Frequency of early concepts. This data provides the total number of times each article used each early concept (Q1). This data was used to produce the Figure 2a at the original publication.

  2. Frequency of additional concepts. This data provides the total number of times each article used each additional concept (Q1). This data was used to produce the Figure 2b at the original publication.

  3. Use of any early concept. This data provides the total number of times each article used any early concept (Q1).
This data was used to produce the Figure 3a at the original publication.

  4. Use of any additional concept. This data provides the total number of times each article used any additional concept (Q1). This data was used to produce the Figure 3b at the original publication.

To address the second question (Q2), the title, abstract and keywords of the papers included in our sample (n=1918 articles) were screened to identify papers that might show how landscape ecological concepts are integrated into planning. We selected 52 empirical papers (see Supplementary material – 4 Integration of landscape ecological concepts into planning), and we provided two outputs:

  5. Planning steps. This data provides the number of times landscape ecological concepts were addressed in each
planning steps in 52 empirical papers analysed in detail (Q2). This data was used to produce the Figure 4 at the
original publication.

  6. Protocol for assessing the integration of landscape ecological concepts into planning. To systematically collect the
data, we used this protocol which addressed the following questions: (a) which type of planning is addressed by the
paper? (b) to which planning level does the paper refer to? (c) which concepts are integrated in any of the planning
steps described above?

decision-making

display_name: decision-making

ecosystem-services

display_name: ecosystem-services

green-infrastructure

display_name: green-infrastructure

landscape-ecology

display_name: landscape-ecology

landscape-perception

display_name: landscape-perception

landscape-planning

display_name: landscape-planning

landscape-services

display_name: landscape-services

literature-review

display_name: literature-review

multifunctionality

display_name: multifunctionality

pdfsearch

display_name: pdfsearch

planning

display_name: planning

resilience

display_name: resilience

sense-of-place

display_name: sense-of-place

socio-ecological-system

display_name: socio-ecological-system

en: The usage of landscape ecological concepts in the planning literature


en:
Volume of stemwood with bark of all dead trees and shrubs (standing and lying) starting at 12 cm dbh recorded according to the NFI1 method. In NFI1 only those dead trees were recorded whose wood could still be exploited. In addition, lying green trees were classified in NFI1 as deadwood.

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

accessible-forest-without-shrub-forest

display_name: accessible-forest-without-shrub-forest

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

biogeographical-region

display_name: biogeographical-region

canton

display_name: canton

conifers-broadleaves

display_name: conifers-broadleaves

economic-region

display_name: economic-region

lower-higher-altitudinal-zones

display_name: lower-higher-altitudinal-zones

main-tree-species

display_name: main-tree-species

ownership

display_name: ownership

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

tree-state-standing-lying

display_name: tree-state-standing-lying

en: Volume of dead wood NFI1


weissfluhjoch-dataset-for-esm-snowmip

maintainer: Nander Wever

title_for_slug: weissfluhjoch-dataset-for-esm-snowmip

en: esm-snowmip snow-models snow-depth snow weissfluhjoch

en: Dataset Weissfluhjoch for ESM-SnowMIP

en: Conversion script

en:
Use this script to convert WFJ_optimaldataset_v6.smet to wfj.smet, the file that is used for the ESM-SnowMIP project. It also requires the file: WFJ_AirPressure_Data.zip

Use: bash create_wfj_dataset.sh > input/wfj.smet

mimetype: text/x-sh

en: ini file

en: This ini file is required by data-converter from MeteoIO version 1887 (http://models.slf.ch/p/meteoio/)

en: This Weissfluhjoch dataset is a processed version of the Weissfluhjoch dataset version 6 from https://doi.org/10.16904/6. This dataset was specially created for the ESM-SnowMIP project. Here it is documented how this dataset has been created.

esm-snowmip

display_name: esm-snowmip

snow

display_name: snow

snow-depth

display_name: snow-depth

snow-models

display_name: snow-models

weissfluhjoch

display_name: weissfluhjoch

en: Weissfluhjoch dataset for ESM-SnowMIP


maintainer: Joan Casanelles Abella

en: angiospermae floral-inventories poznan paris urban zurich tartu antwerp plants floral-resources

en: Floral units, mean number of flowers per floral unit and floral abundance of the different plants counted in quadrats in the study sites in Zurich during the four sampling periods

en: List of plant species sampled in the five cities (Antwerp, Paris, Poznan, Tartu and Zurich) during the different sampling perionds (four for Zurich, three for the other cities) with their frequency (oc1-oc16) within a 100 m radius

en: Trait values for the sampled plants

en: Data of a survey of flowering plants in 80 sites in five European cities and urban agglomerations (Antwerp, Belgium; greater Paris, France; Poznan, Poland; Tartu, Estonia; and Zurich, Switzerland) sampled between April and July 2018.

angiospermae

display_name: angiospermae

antwerp

display_name: antwerp

floral-inventories

display_name: floral-inventories

floral-resources

display_name: floral-resources

paris

display_name: paris

plants

display_name: plants

poznan

display_name: poznan

tartu

display_name: tartu

urban

display_name: urban

zurich

display_name: zurich

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: Flowering Plants (Angiospermae) in Urban Green Areas in five European Cities


en: swiss-forest-lab icp-forests ambient-air-quality nitrogen-dioxide long-term-forest-ecosystem-research

en: Data Request

en:
NO₂ concentrations were measured at 11 LWF plots (1999/2000) with passive samplers (Palmes-type diffusion tubes) during one year. Three samplers per site and period (usually 14 days) were installed in the open area of the LWF plots and, at Beatenberg, Novaggio and Vordemwald, under the forest as well (weather station). In 2014, NO2 concentrations were measured again at 14 plots, using two samplers per site and period (usually 28 days). At Lausanne and Vordemwald, concentrations were also measured below the canopy.

 Purpose:

To assess air concentrations of nitrogen dioxide (NO2) and, using deposition velocities available from the literature, to quantify the dry deposition of NO2 (alternative method to the throughfall method).

ambient-air-quality

display_name: ambient-air-quality

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

nitrogen-dioxide

display_name: nitrogen-dioxide

swiss-forest-lab

display_name: swiss-forest-lab

en: Passive sampling of NO2 LWF


en: national-forest-inventory protection-forest biomass biodiversity volume carbon

en: Data on dead standing and downed trees for the NFI1

en: Variable definitions for the datasets NFI1, NFI2, NFI3, NFI4 and NFI5

en: Data on dead standing and downed trees, lying dead wood, and stumps for the NFI3

en: Artikel - Annals of Forest Science

en: Site Description

en: information on NFI sites throughout the five NFIs considered in this study

en: Files and contents

en: Data on dead standing and downed trees for the NFI2

en: Data on dead standing and downed trees, lying dead wood, and stumps for the NFI5

en: Data on dead standing and downed trees, lying dead wood, and stumps for the NFI4

en: Lanz 2014

en: A short overview over stump assessment (field protocol) in some European NFIs

en:
Based on the detailed tree stump inventory implemented in the Swiss NFI5 (https://www.lfi.ch/lfi/lfi.php), a study was conducted to obtain an accurate assessment of the stumps pool in the Swiss NFI over the last 30 years and to identify its significance for the total dead wood (DW) pool. The NFI5 includes a detailed stump inventory to improve accuracy and completeness of the above-ground DW- pool. Based on available data, stump volume estimates were derived at different accuracies to evaluate the contribution to the total DW-pool over time.
The study found that in Swiss Forests the contribution of stumps to total DW-pool is approximately 25%, and that applying simplifying assumptions to estimate stump volume can result in a significant underestimation of the true size of this pool. This study demonstrates that stumps can be a significant proportion of DW in forests, which should be accounted for in order to improve accuracy and completeness of NFI estimates and derived data such as C stocks for greenhouse gas reporting.

The study is published in Annals of Forest Science (2022) 79:34, https://doi.org/10.1186/s13595-022-01155-7 (open access).
The data can be obtained from the authors upon reasonable request.

biodiversity

display_name: biodiversity

biomass

display_name: biomass

carbon

display_name: carbon

national-forest-inventory

display_name: national-forest-inventory

protection-forest

display_name: protection-forest

volume

display_name: volume

en: Stumps as a dead wood resource in forests - data based on the Swiss National Forest Inventory


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

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: Sdur

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

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

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

en: Data Access

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

oshd

display_name: oshd

seasonal-snow-data

display_name: seasonal-snow-data

snow-cover-fraction

display_name: snow-cover-fraction

snow-depth-distribution

display_name: snow-depth-distribution

snow-melt

display_name: snow-melt

en: Seasonal snow data for Switzerland OSHD - FSM2sohd


en: environmental---history wetlands environmental--policy historical-carthography switzerland ecology

en: Wetlands 1850 - 2010 (shapefiles)

en:
 History of wetlands - GIS-Layers
 Location of wetlands based on signatures in topographic maps
* Basis 1850: original measuring table sheets Dufour created between the years 1834 and 1863.
* Basis 1900/ Basis 1950: The Siegfried maps were updated and published at regular intervals between 1870 and 1949. This long period of time made it possible to reconstruct wetlands for two periods based on the Siegfried Maps: the first edition (hereinafter referred to as "1900") and the last edition ("1950").
* Basis 2010: Map sheets from the National Map from 2006 to 2011 were used for the most recent time period of the reconstruction.
 Location of wetlands due to reconstructions
The reconstruction **must not** be interpreted in the sense of a surface-accurate separation, but shows without claim to location accuracy the approximate extent of the areas that would have corresponded to the mapping criteria for wetlands used in today's topographic maps around 1850, 1900 and 1950. The basics can be found in Stuber & Bürgi 2019, chapter 5.4.

en:
Naturally, large parts of the Swiss Plateau are characterised by wetlands
and meandering rivers. That this is no longer the case today is the result
of centuries of efforts to obtain dry land. But how did this process take
place? What were the relevant actors and what were their motivations? And
what can be said about the ecological consequences of this development?

In a research project on the history of wetlands in Switzerland since 1700,
we conducted (a) a historical analysis of the development of land use in
wetlands and the actors involved, (b) a historical-cartographic
reconstruction of wetland extent since 1850 and (c) an evaluation of
ecological effects of changes in wetlands on various organisms groups.

The series of GIS layers on wetland history stem from the second part of
the project. The area reconstruction is based on digitized and homogenized
signatures from national map series, as they have been available since
about 1850. Details about the digitalization process and the homogenization
procedures applied ("Rekonstruktionen") are included in Stuber & Bürgi
2019.

Book Citation:
 > Stuber M, Bürgi M (2019) Vom «eroberten Land» zum Renaturierungsprojekt. Geschichte der Feuchtgebiete in der Schweiz seit 1700. "Bristol Schriftenreihe", Band 59. Haupt Verlag, Bern, Stuttgart, Wien. 262 Seiten.

ecology

display_name: ecology

environmental---history

display_name: environmental---history

environmental--policy

display_name: environmental--policy

historical-carthography

display_name: historical-carthography

switzerland

display_name: switzerland

wetlands

display_name: wetlands

en: History of wetlands in Switzerland since 1850