maintainer: Frank Techel

en: avalanche-accident-statistics long-term-trends avalanche-accident avalanche-fatalities avalanche-statistics

en: Data avalanche fatalities 1969/1970 - 2014/2015

media_type: text/plain

en: The data comprises avalanche fatality data from the Alpine regions in the seven countries Austria, France, Germany, Italy, Liechtenstein, Slovenia, and Switzerland.

en:
During the last 45 years, about 100 people lost their lives in avalanches in the European Alps each year. Avalanche fatalities in settlements and on transportation corridors have considerably decreased since the 1970s. In contrast, the number of avalanche fatalities during recreational activities away from avalanche-secured terrain doubled between the 1960s and 1980s and has remained relatively stable since, despite a continuing strong increase in winter backcountry recreational activities.

Data complementing Figure 2 in:
_"Avalanche fatalities in the European Alps: long-term trends and statistics"_, by Techel, F., Jarry, F., Kronthaler, G., Mitterer, S., Nairz, P., Pavšek, M., Valt, M., and Darms, G.

Data description:
please refer to section 2 (Data and Methods) in the mentioned publication

avalanche-accident

display_name: avalanche-accident

avalanche-accident-statistics

display_name: avalanche-accident-statistics

avalanche-fatalities

display_name: avalanche-fatalities

avalanche-statistics

display_name: avalanche-statistics

long-term-trends

display_name: long-term-trends

education

en: Education and science

en: EnviDat: the Environmental Data Portal

en: Avalanche fatalities in the European Alps (1969/1970 - 2014/2015)


maintainer: EnviDat Support

en: snow-stratigraphy ice-layers snow-temperature water-front weissfluhjoch snowmicropen

state: active

type: dataset

en: WFJ_ICE_LAYERS dataset

media_type: application/zip

format: ZIP

mimetype: application/zip

en: README

format: TXT

mimetype: text/plain

en: The WFJ_ICE_LAYERS dataset contains multi-instrument snowpack measurements at high temporal resolution, which enable to monitor the formation of deep ice layers due to preferential water flow, at the Weissfluhjoch research site, Davos, Switzerland. It covers the winter 2016/2017, with a focus on the early melting season. This dataset includes traditional snowpack profiles (weekly resolution, 15/11/2016-29/05/2017), SnowMicroPen penetration resistance profiles (daily resolution, 01/02/2017-19/04/2017), snow temperatures measured at different heights in the snowpack (half-hourly resolution, 01/03/2017-15/04/2017) and the water front height derived from an upward-looking ground penetrating radar (3-hour resolution, 04/03/2017-08/04/2017). The measurements are complemented by initialization files for SNOWPACK model simulations with the ice reservoir parameterization at Weissfluhjoch for the winter 2016/2017.

ice-layers

display_name: ice-layers

snow-stratigraphy

display_name: snow-stratigraphy

snow-temperature

display_name: snow-temperature

snowmicropen

display_name: snowmicropen

water-front

display_name: water-front

weissfluhjoch

display_name: weissfluhjoch

fr: Education, science

de: Bildung, Wissenschaft

it: Formazione e scienza

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: WFJ_ICE_LAYERS: Multi-instrument data for monitoring deep ice layer formation in an alpine snowpack


en: warning-regions danger-level avalanche-forecast snow european-alps

en: shape file warning regions Alps

en: Forecast avalanche danger level

media_type: text/csv

en: contains the highest forecast danger level (dangerLevelMax) for each day and warning region

format: CSV

mimetype: text/csv

en:
This dataset contains the data used in the publication by Techel et al., 2018 _Spatial consistency and bias in avalanche forecasts - a case study in the European Alps_ (Nat Haz Earth Syst Sci).
For details on the data please refer to this publication.
The dataset contains the following:
- shape files for the warning regions in the Alps
- highest forecast danger level for each warning region and day

avalanche-forecast

display_name: avalanche-forecast

danger-level

display_name: danger-level

european-alps

display_name: european-alps

snow

display_name: snow

warning-regions

display_name: warning-regions

en: Forecast avalanche danger level European Alps 2011 - 2015


en: consistent python antarctica cryowrf aws

media_type: .zip

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.

format: SERVICE

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

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


maintainer: Thorsten Bartels-Rausch

en: solution reaction metamorphism heterogeneous antarctica arctic chemistry brine snow laboratory-study alpine interface ozone

en: Acetone data

media_type: dat

en:
intensity of the acetone-water-cluster and water-cluster (as derived by a chemical ionisation mass spectrometer) with time in the carrier gas downstream of packed bed flow tube holding the snow sample or the by-pass, respectively. Time "0" denotes the time when the mass spectrometer data acquisition was started. When the carrier
gas with acetone was passed via the packed bed flow tube, that is switched from by-pass to pass the snow, acetone concentration in the carrier gas rapidly dropped due to surface adsorption and slowly recovered as the adsorption sites saturate. From this, the surface area can be obtained.

en: data list

en: Lists file names, snow sample properties, and flow settings for each data-set.

en: Ozone data

en: Trend of ozone mixing ratio ( _O3mixingRatio_ppb_ with time ( _time_s_ ) as measured downstream of the snow sample during the experiments. Time "0" denotes the time when data acquisition with ozone analyser was started. And, ozone (_nOzone_molec_per_s_ ) loss for each experiment with duration of ozone exposure ( _duration_s_ ) of ozone exposure, as derived from the ozone trends shown above. Time "0" denotes the time when the carrier gas with ozone was passed via the snow sample.

en: Data Figure 1

en: Data of published Figure

en: Matlab Data

media_type: mat

en: All data as Matlab mat file

en: Matlab Script to view and plot data

media_type: .m

en: Readme file

media_type: application/pdf

en: This file shows data, code, and plots.

format: PDF

mimetype: application/pdf

en: Earth’s snow cover is very dynamic on diurnal time scales. The changes to the snow structure during this metamorphism have wide ranging impacts such as on avalanche formation and on the capacity of surface snow to exchange trace gases with the atmosphere. Here, we investigate the influence of dry metamorphism, which involves fluxes of water vapor, on the chemical reactivity of bromide in the snow. For this, the heterogeneous reactive loss of ozone at a concentration of 5-6E12 molecules cm-3 is investigated in artificial, shock-frozen snow samples doped with 6.2 uM sodium bromide and with varying metamorphism history. The oxidation of bromide in snow is one reaction initiating polar bromine releases and ozone depletions.

alpine

display_name: alpine

arctic

display_name: arctic

brine

display_name: brine

chemistry

display_name: chemistry

heterogeneous

display_name: heterogeneous

interface

display_name: interface

laboratory-study

display_name: laboratory-study

metamorphism

display_name: metamorphism

ozone

display_name: ozone

reaction

display_name: reaction

solution

display_name: solution

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

en: Data set on bromide oxidation by ozone in snow during metamorphism from laboratory study


maintainer: Christoph Marty

en: Aare input

media_type: application/x-tar

en: Grid input data for the 'Aare' CH2014 simulation.

mimetype: application/x-tar

en: CH2014 REPORT

en:
CH2014-Impacts: Toward Quantitative Scenarios of Climate Change Impacts in Switzerland

The CH2014-Impacts report presents the results of a concerted effort of 15 Swiss research institutions to describe impacts of climate change in Switzerland quantitatively. This dataset contributes to the Crosphere/Snowcover section.

en: Dataset

en: Datasets are grouped under experiment "Aare" and "Graubunden".Data is also made available in netCDF format. A range of tools are available to [read netCDF](http://www.unidata.ucar.edu/software/netcdf/software.html).

en: Graubunden input

en: Grid input data for the 'Graubunden' CH2014 simulation.

en:
 Overview

The CH2014-Impacts initiative is a concerted national effort to describe impacts of climate change in Switzerland quantitatively, drawing on the scientific resources available in Switzerland today. The initiative links the recently developed Swiss Climate Change Scenarios CH2011 with an evolving base of quantitative impact models. The use of a common climate data set across disciplines and research groups sets a high standard of consistency and comparability of results. Impact studies explore the wide range of climatic changes in temperature and precipitation projected in CH2011 for the 21st century, which vary with the assumed global level of greenhouse gases, the time horizon, the underlying climate model, and the geographical region within Switzerland. The differences among climate projections are considered using three greenhouse gas scenarios, three future time periods in the 21st century, and three climate uncertainty levels (Figure 1). Impacts are shown with respect to the reference period 1980-2009 of CH2011, and add to any impacts that have already emerged as a result of earlier climate change.

 Experimental Setup

Future snow cover changes are simulated with the physics-based model Alpine3D (Lehning et al., 2006). It is applied to two regions: The canton of Graubünden and the Aare catchment. These domains are modeled with a Digital Elevation Model (DEM) with a resolution of 200 m × 200 m. This defines the simulation grid that has to be filled with land cover data and downscaled meteorological input data for each cell for the time period of interest at hourly resolution. The reference data set consists of automatic weather station data. All meteorological input parameters are spatially interpolated to the simulation grid. The reference period comprises only thirteen years (1999–2012), because the number of available high elevation weather stations for earlier times is not sufficient to achieve unbiased distribution of the observations with elevation. The model uses projected temperature and precipitation changes for all greenhouse gas scenarios (A1B, A2, and RCP3PD) and CH2011 time periods (2035, 2060, and 2085).

 Data

Snow cover changes are projected to be relatively small in the near term (2035) (Figure 5.1 top), in particular at higher elevations above 2000 m asl. As shown by Bavay et al. (2013) the spread in projected snow cover for this period is greater between different climate model chains (Chapter 3) than between the reference period and the model chain exhibiting the most moderate change. In the 2085 period much larger changes with the potential to fundamentally transform the snow dominated alpine area become apparent (Figure 5.1 bottom). These changes include a shortening of the snow season by 5–9 weeks for the A1B scenario. This is roughly equivalent to an elevation shift of 400–800 m. The slight increase of winter precipitation and therefore snow fall projected in the CH2011 scenarios (with high associated uncertainty) can no longer compensate for the effect of increasing winter temperatures even at high elevations. In terms of Snow Water Equivalents (SWE), the projected reduction is up to two thirds toward the end of the century (2085). A continuous snow cover will be restricted to a shorter time period and/or to regions at increasingly high elevation. In Bern, for example, the number of days per year with at least 5 cm snow depth will decrease by 90% from now 20 days to only 2 days on average.

climate-change

display_name: climate-change

snow-depth

display_name: snow-depth

snow-water-equivalent

display_name: snow-water-equivalent

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: Alpine3D simulations of future climate scenarios CH2014


disdrometer-data-laret

maintainer: Daniel Wolfensberger

title_for_slug: disdrometer-data-laret

en: hydrometeors visibility disdrometer raclets precipitation

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: European Snow Booklet public metadata

en:
The core of the European Snow Booklet (ESB), the country reports of 38 European countries, are based on information and metadata on snow measurements collected through a comprehensive survey, the ESB Questionnaire, between August 2017 and March 2018. The station metadata (for example location, elevation, measurement start date) from each country was provided by one or several institutions responsible for snow observations. By answering the questionnaire the contacts agreed that the information and metadata provided could be presented in the ESB. For cases where institutions indicated that information on station locations could be made publicly available, the metadata for the variables snow depth (HS), depth of snowfall (HN) and water equivalent of the snow cover (SWE), is additionally provided here for download (METADATA.xlsx).
Please note that not all institutions gave permission to publish their station metadata. This metadata has to be requested from the non-personal institution contacts provided in the METADATA.xlsx file. In some cases, however, no institutional contact is available and thus the contact of the country representative who filled in the questionnaire is used.
For current, updated station metadata we refer to the (non-personal) institutional contacts provided in METADATA.xlsx and/or the institution webpages. Note, however, that we cannot guarantee for completeness of the dataset for all countries because operational networks are prone to changes.

format: XLS

en: COST European Snow Booklet (ESB) - DORA Link

media_type: HTML

en: DORA Link of the COST European Snow Booklet (ESB).

format: HTML

en: List of authors and list of the editorial board of the European Snow Booklet

en:
Here you can find the list of all lead authors of the different chapters of the European Snow Booklet (ESB).
In addition a list of the editorial board that reviewed the ESB is given.

en: COST European Snow Booklet (ESB)

en: Please find here the web version of the COST European Snow Booklet (ESB).

en: European Snow Booklet unpublished metadata

media_type: XLSX

en: The entire station metadata of all European institutions that contributed data is stored in METADATA_unpublished.xlsx. This file, however, cannot be accessed, since not all institutions gave permission to publish their station metadata. If you wish to access the metadata, please send a request to marty@slf.ch stating clearly why you want to access these metadata. NOTE: in this file ONLY station metadata (Lat/Lon/Altitude) is given, NO measured snow depth data.

en:
The European Snow Booklet (ESB) is a book of reference for snow measurements that has been produced through collaboration with many European snow practitioners and snow scientists in the framework of the European Cooperation in Science and Technology (COST) Action ES1404 “A European network for a harmonised monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction (HarmoSnow)”. The ESB provides a unique collection of information about operational snow observations in the European countries and the methods used to perform basic measurements of snow on the ground: snow depth (HS), depth of snowfall (HN), water equivalent of the snow cover (SWE) and presence of snow on the ground (PSG). Information and station metadata (for example location, elevation) for these basic snow variables were collected through a comprehensive survey, the ESB questionnaire between August 2017 and March 2018. Numerous institutions of 38 European countries provided detailed information describing the status of the operational snow observations and the methods used at the time of the survey. Based on the information provided, a country report was written for each European country. Similarities and differences among the countries, that is, the choice of snow variables to be measured, the measurement principles applied, the number of stations, or the spatial and elevational station distribution are pointed out. Thus the collection of country reports demonstrates the relevance of snow measurements for each country. Thus, the intention of the ESB is to foster better knowledge transfer regarding snow measurements between the snow science and operational communities and to improve the communication of information to the general public.

For detailed information on the European countries, we refer to the ESB, which can be downloaded here (envidat.ch). Please note that the ESB is not a living document and information and station metadata are from August 2017 till March 2018, except for Latvia (metadata updated in December 2018).

depth-of-snowfall

display_name: depth-of-snowfall

harmonisation-of-snow-measurements

display_name: harmonisation-of-snow-measurements

snow-measurement-methods

display_name: snow-measurement-methods

water-equivalent-of-the-snow-cover

display_name: water-equivalent-of-the-snow-cover

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

en: European Snow Booklet – an Inventory of Snow Measurements in Europe


maintainer: James W. Kirchner

en: deuterium chloride stable-water-isotopes plynlimon precipitation streamflow catchment tracer

en: Data Set Documentation

en: This file contains a documentation of the data sets, including references to other helpful documentations.

en: 7-hourly stable water istopes

en: Data set of 7-hourly stable water isotope data measured at Carreg Wen (precipitation) and Upper Hafren (streamflow).

en: Weekly stable water isotopes

en: Data set of weekly stable water isotope data collected at Carreg Wen (precipitation) and Lower Hafren and Tanllwyth (streamflow).

en: The data base contains timeseries of stable water isotopes in precipitation and streamflow at Plynlimon, Wales, UK. One data set contains weekly stable water isotope data from the Lower Hafren and Tanllwyth catchments, and the other data set contains 7-hourly stable water isotope data from Upper Hafren. Both data sets also include chloride concentrations in precipitation and streamflow.

catchment

display_name: catchment

chloride

display_name: chloride

deuterium

display_name: deuterium

plynlimon

display_name: plynlimon

stable-water-isotopes

display_name: stable-water-isotopes

streamflow

display_name: streamflow

tracer

display_name: tracer

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: Stable water isotopes in precipitation and streamflow at Plynlimon, Wales, UK


en: snow-profiles failure-layer snow-stratigraphy snowpack snowpack-simulation rutschblock snow-instability

en: SWISS data set: Properties of observed snow profiles and simulated failure layers

media_type: .csv

en: For each profile pair from the SWISS data set, this file contains one row with information on the observed snow stability and all extracted features describing the simulated failure layers. Please note that only those profile pairs which fulfilled the similarity criteria (column similarity_obs_sim = 1, 121 profiles) were used to validate the RF model. The content of the columns is explained in Table 1 of the ReadMe file.

en: Observed snow profiles

en: Observed snow stratigraphy in form of one *.csv-file per snow profile from the DAV and SWISS data sets (in respective subfolders). Each file is named after the profile ID which allows identifying the corresponding simulated profile in the "simulated_profiles" folder. One row corresponds to one observed snow layer. The content of the columns is described in Table 2 of the ReadMe file.

en: Simulated snow profiles

en: Simulated snow stratigraphy in form of one *.csv-file per snow profile from the DAV and SWISS data sets (in respective subfolders). Each file is named after the profile ID which allows identifying the corresponding observed profile in the "observed_profiles" folder. One row corresponds to one simulated snow layer. The content of the columns 1-7 is described in Table 3 of the ReadMe file. The remaining columns (8-31) contain all features that were used in the development of the RF model and are described in Table 1 of the ReadMe file.

en: ReadMe

en: ReadMe file explaining data structure and content

en: DAV data set: Properties of observed snow profiles and simulated failure layers

media_type: CSV

en:
For each profile pair from the DAV data set, this file contains one row with information on the observed snow stability and all extracted features describing the simulated failure layers. The content of the columns is explained in Table 1 of the ReadMe file. Only those profile pairs which fulfilled predefined similarity criteria (column
"similarity_obs_sim" = 1) and which belonged to the stability classes "stable" and "unstable" (column "stability_class"
= 0 or 1) were used to train the RF model. Please note that for each of the two snow profiles with RB score 7 (profile
IDs 7228 and 9526), three additional layers were included in this data set to obtain a balanced set of training data (73
unstable and 73 stable profiles).

en:
This data set includes information on all observed and simulated snow profiles that were used to train and validate the
random forest model described in Mayer et al. (2022). The RF model was trained to assess snow instability from simulated snow stratigraphy. The data set contains observed snow profiles from the region of Davos (DAV subset, 512 profiles) and from all over Switzerland (SWISS subset, 230 profiles). For each observed snow profile, there is a corresponding simulated profile which was obtained using meteorological input data for the numerical snow cover model SNOWPACK. The information on the observed snow profile contains a Rutschblock test result including the depth of the failure interface. As part of the study described in Mayer et al. (2022), each observed snow profile was manually compared to its simulated counterpart and the simulated layer corresponding to the Rutschblock failure layer was identified.

The data are provided in the following form: one file each per observed and simulated snow profile (2x512 files DAV, 2x230 files SWISS), two files (1 file DAV, 1 file SWISS) containing the observed information on snow instability, the allocation between observed and simulated failure layer, and all features extracted from the simulated weak layers that were used to develop the RF model.

failure-layer

display_name: failure-layer

rutschblock

display_name: rutschblock

snow-instability

display_name: snow-instability

snow-profiles

display_name: snow-profiles

snow-stratigraphy

display_name: snow-stratigraphy

snowpack

display_name: snowpack

snowpack-simulation

display_name: snowpack-simulation

en: Observed and simulated snow profile data


en: norway switzerland avalanche-observations rutschblock-test avalanche-danger extended-column-test

en: avalanche occurrence data (Norway, Sitzerland) - largest avalanche size per day and warning region

en: Rutschblock data (Switzerland)

en: ECT data (Norway, Switzerland)

en: avalanche occurrence data (Norway, Sitzerland) - avalanche sizes per day and warning region

en:
Observational data used to quantitatively describe the key elements describing avalanche danger: snowpack stability, the frequency distribution of snowpack stability, and avalanche size.
The data set consists of
- Rutschblock test results (Switzerland)
- Extended Column Test results (Switzerland, Norway)
- Avalanche occurrence data (Switzerland, Norway).
The data were extracted from the respective operational databases of the national avalanche warning services in Switzerland (WSL Institute for Snow and Avalanche Research SLF Davos, Switzerland) and Norway (The Norwegian Water Resources and Energy Directorate NVE).
For further information regarding the data, please refer to the publication or contact the author.

avalanche-danger

display_name: avalanche-danger

avalanche-observations

display_name: avalanche-observations

extended-column-test

display_name: extended-column-test

norway

display_name: norway

rutschblock-test

display_name: rutschblock-test

switzerland

display_name: switzerland

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

en: Stability tests, avalanche observations, Switzerland, Norway


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

en: Technical documentation

media_type: .pdf

en: Technical documentation containing file metadata and variable descriptions.

en: Data Access via S3

media_type: geotiff

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

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


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

en: Field observations of snow instabilities

en: Read-me file

media_type: PDF

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

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, in review, 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-forecasting

display_name: avalanche-forecasting

danger-level

display_name: danger-level

snow

display_name: snow

snow-instability

display_name: snow-instability


maintainer: Zhao

en: driving-factors urban-land-use planning austin land-dynamics

en: Potential driving factors of urban transformations of Austin over 25 years

media_type: ZIP

en: In this study, the Austin metropolitan area, Texas, U.S., one of the fastest urban transformations and transformations regions, is selected to test the hypothesis that spatial planning and policies are important factors of urban transformations. Despite ample previous work in understanding the interactions between human and urban form transformation at specific areas, the actual interventions and outcomes of planning and policies (e.g., ‘smart growth’) on urban forms have been poorly measured. In this study, the potential influencing factors of urban transformations of Austin over 25 years were selected and collected.

austin

display_name: austin

driving-factors

display_name: driving-factors

land-dynamics

display_name: land-dynamics

planning

display_name: planning

urban-land-use

display_name: urban-land-use


en: precipitation hydrology snowmelt discharge climate-change soil-moisture

en: Daily simulated data for the future period

en: Daily simulated discharge, precipitation, snowmelt, and soil moisture for the period 1981-2100 in mm for the 307 catchments.

en: Shapefile of 307 catchments in Switzerland

en: Shapefile of the 307 medium-sized catchments in Switzerland. The "gridcode" provides the ID of the catchment used in the datafiles.

en: Daily simulated data for the reference period

en: Daily simulated discharge, precipitation, snowmelt, and soil moisture for the reference period 1981-2017 in mm.

en:
Daily discharge and the related hydro-meteorological variables precipitation, snowmelt, and soil moisture are provided for current (1981-2017) and for future climate conditions (1981-2100) for 307 medium-sized catchments in Switzerland. The catchments have a median catchment area of 117 km². The 307 catchments together form a set representative of the climatological conditions and runoff characteristics in Switzerland. The four variables were simulated at a daily resolution using the hydrological model PREVAH. PREVAH is a conceptual process-based model that was run in this study in its fully distributed version on a 500 m grid (Viviroli et al. 2009a). For the calibration, runoff time series from 140 mesoscale catchments covering the different runoff regimes were used. The model calibration was conducted over the period 1993-1997. Verification was performed on the period 1983-2005 using (i) volumetric deviation (Viviroli et al. 2007) and (ii) benchmark efficiency (Schäfli et al 2007) as objective functions. The calibration and validation procedures are described in detail in Köplin et al. (2010). The parameters for each model grid cell were derived by regionalizing the parameters obtained for the 140 catchments with a procedure based on ordinary kriging (Viviroli et al. 2009b, Köplin et al. 2010).
The calibrated and validated model was then driven with transient meteorological data (precipitation, temperature, radiation, and wind) representing both reference (1981-2017) and future climate conditions (2018-2099). The data were derived from the CH2018 climate scenarios (NCCS 2018) provided by the Swiss National Centre for Climate Services (NCCS). They were obtained from climate experiments produced with different climate modeling chains, consisting of a global and a regional circulation model each, within EUROCORDEX for three representative concentration pathways (RCP) emission scenarios. Downscaled output of ten climate model chains derived by quantile mapping were considered. The focus was on the chains of the EUR-11 domain with a horizontal resolution of 0.11 degrees (roughly 12.5 km). The climate model chains (GCM, RCM, RCP, and grid resolution) used are listed below:

- ICHEC-EC-EARTH	DMI-HIRHAM5	2.6	EUR-11
- ICHEC-EC-EARTH	DMI-HIRHAM5	4.5	EUR-11
- ICHEC-EC-EARTH	DMI-HIRHAM5	8.5	EUR-11
- ICHEC-EC-EARTH	SMHI-RCA4	2.6	EUR-11
- ICHEC-EC-EARTH	SMHI-RCA4	4.5	EUR-11
- ICHEC-EC-EARTH	SMHI-RCA4	8.5	EUR-11
- MOHC-HadGEM2-ES	SMHI-RCA4	4.5	EUR-11
- MOHC-HadGEM2-ES	SMHI-RCA4	8.5	EUR-11
- MPI-M-MPI-ESM-LR	SMHI-RCA4	4.5	EUR-11
- MPI-M-MPI-ESM-LR	SMHI-RCA4	8.5	EUR-11

*References*:

-	Köplin, N., D. Viviroli, B. Schädler, and R. Weingartner (2010), _How does climate change affect mesoscale catchments in Switzerland? - A framework for a comprehensive assessment_, Advances in Geosciences, 27, 111-119, doi:10.5194/adgeo-27-111-2010.
-	National Centre for Climate Services (2018), CH2018 - _Climate Scenarios for Switzerland_, Tech. rep., NCCS, Zurich.
-	Schäfli, B., and H. V. Gupta (2007), _Do Nash values have value?_, Hydrological Processes, 21, 2075-2080, doi:10.1002/hyp.6825.
-	Viviroli, D., J. Gurtz, and M. Zappa (2007), _The hydrological modelling system PREVAH. Part II - Physical model description_, Geographica Bernensia, 40, 1-89.
-	Viviroli, D., M. Zappa, J. Gurtz, and R. Weingartner (2009a), _An introduction to the hydrological modelling system PREVAH and its pre- and post-processing-tools_, Environmental Modelling & Software, 24, 1209-1222, doi:10.1016/j.envsoft.2009.04.001.
-	Viviroli, D., H. Mittelbach, J. Gurtz, and R. Weingartner (2009b), _Continuous simulation for flood estimation in ungauged mesoscale catchments of Switzerland-Part II: Parameter regionalisation and flood estimation results_, Journal of Hydrology, 377 (1), 208-225, doi:10.1016/j.jhydrol.2009.08.022.

climate-change

display_name: climate-change

discharge

display_name: discharge

hydrology

display_name: hydrology

snowmelt

display_name: snowmelt

soil-moisture

display_name: soil-moisture

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

en: Simulated future discharge and climatological variables for medium-sized catchments in Switzerland


en: snow sea-salt-aerosol polar arctic cryosphere laboratory-study antartic

en: Experimental Results: Uptake coefficients vs. temperature and ozone concentration

en:
* Final Data to Figure 3: Uptake coefficients as a function of temperature at 5E12 molecules per cm3 ozone concentration.
* Final Data to Figure 6: Uptake coefficients at different temperatures as a function of gas- phase ozone concentration.

The average ozone uptake coefficients of the three cycles between the bypass and the flow tube per repeat is taken as the mean uptake coefficient for each experiment. Gamma was corrected for gas phase diffusion limitation following the Cooney–Kim– Davis (CKD) method52 (see supporting material of publication for details).

en: Experimental results of uptake coefficient vs. temperature and ozone concentration

en:
Data to Figure 3: Measured uptake coefficients as a function of temperature at 5E12 molecules per cm3 ozone concentration.

Data to Figure 6: Measured uptake coefficients at different temperatures as a function of gas- phase ozone concentration.

en: The reaction of ozone with bromide in polar regions results in the formation of reactive bromide species with impacts on ozone budget and the oxidative capacity of the lower atmosphere. Here, we present a data investigating the temperature dependence of bromide oxidation by ozone using a coated wall flow tube reactor coated with an aqueous mixture of citric acid and sodium bromide, a proxy for sea salt aerosol in snow or the free troposphere. Thus study shows the effect of of organic species at relatively mild temperatures between the freezing point and eutectic temperature as typical for Earth's cryosphere.

antartic

display_name: antartic

arctic

display_name: arctic

cryosphere

display_name: cryosphere

laboratory-study

display_name: laboratory-study

polar

display_name: polar

sea-salt-aerosol

display_name: sea-salt-aerosol

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: Kinetic experiments on the oxidation of bromide by ozone from 289-245 K.


en: fungi nematodes soil plants soil-biology bacteria soil-chemistry biodiversity topsoil-removal restoration grassland soil-biodiversity

en: Soil properties

en:
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)
N.pool	Soil nitrogen pool (June 2017); corrected for bulk density and 12 cm sampling depth [kg ha-1 12cm-1]
Corg.pool	Soil organic carbon pool (June 2017); corrected for bulk density and 12 cm sampling depth [kg ha-1 12cm-1]
pH	soil pH [CaCl2]
BD	Bulk density [g cm-3]
FC	Field capacity [%]
Veg.1	principle coordinate axis 1 (vegetation)
Veg.1	principle coordinate axis 2 (vegetation)
Nem.1	principle coordinate axis 1 (nematoda)
Nem.2	principle coordinate axis 2 (nematoda)
Fun.1	principle coordinate axis 1 (fungi)
Fun.2	principle coordinate axis 2 (fungi)
Prok.1	principle coordinate axis 1 (prokaryota)
Prok.2	principle coordinate axis 2 (prokaryota)
Veg.H	Shannon diversity (vegetation)
Nem.H	Shannon diversity (nematoda)
Fun.H	Shannon diversity (fungi)
Prok.H	Shannon diversity (prokaryota)

en: Networks

en:
Treatment	Treatment groups; Y.4-18 = restored grasslands; Initial = intensively managed grasslands; Target = species-rich semi-wet to semi-dry grasslands
Correlation	non-parametric Spearman rank correlations
Vertex.1+2	Comparison between network components; N.pool = total soil nitrogen pool [kg ha-1 12 cm-1]; Corg.pool = soil organic carbon pool [kg ha-1 12 cm-1], pH = soil pH [CaCl2]; BD = bulk density [g cm-3]; FC = field capacity [%]
	PCoA 1 + 2 biotic communities [Veg = vegetation; Nem = nematoda; Fun = fungi; Prok = prokaryota]
Type	Interactions type; A.A = abiotic.abiotic; A.B = abiotic.biotic; B.B = biotic.biotic

en: Ecosystem coupling_interactions

en:
Treatment	Treatment groups; Y.4-18 = restored grasslands; Initial = intensively managed grasslands; Target = species-rich semi-wet to semi-dry grasslands
Type	Interactions type; A.A = abiotic.abiotic; A.B = abiotic.biotic; B.B = biotic.biotic
Correlation	non-parametric Spearman rank correlations between biotic and abiotic parameters (ecological interactions)

en: Nematode data

en:
Sample.data
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)

Taxonomy
ASV.ID	unit number.letter code per amplicon sequence variant (in total 904)
Taxonomic rank	Phylum, Class, Order, Family, Genus, Species

Abundance
columns	Plot.ID (see Sample.data)
rows	ASV.ID (see Taxonomy)

en: Distance to target results

en:
Community	biotic community (vegetation, nematoda, fungi, prokaryota)
Treatment	Treatment groups; Y.4-30= restored grasslands (time since restoration); Initial=intensively managed grasslands (I)
Comparison	within- or between-community Bray-Curtis dissimilarity distance comparison
Label	within-community distances of initial/restored grasslands: treatment abbreviation; between-communities distances of initial/restored-to-target grasslands: treatment abbreviation plus Target (T; species-rich grasslands)
mean	Mean distance to Target (6 plots per treatment group)
sd	Standard deviation of distances to Target (6 plots per treatment group)

en: Fungi data

en:
Sample.data
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)

Taxonomy
OTU.ID	consecutive numbers for operational taxonomic units (OTUe 1-7794)
Taxonomic rank	Domain, Phylum, Class, Order, Family, Genus, Species

Abundance
columns	Plot.ID (see Sample.data)
rows	OTU.ID (see Taxonomy)

en: Vegetation data

en:
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)
column I-LW	Plant abundance per species (in total 327 plant species) expressed as cover percentage estimation according to van der Maarel (vdM 1979; 7 classes: 1-7); nomenclature according to Lauber & Wagner (1996)

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

Paper Citation:

_Resch, M.C., Schütz, M., Ochoa-Hueso, R., Buchmann, N., Frey, B., Graf, U., van der Putten, W.H., Zimmermann, S., Risch, A.C. (in review). Long-term recovery of above- and belowground interactions in restored grassland after topsoil removal and seed addition. Journal of Applied Ecology_

Please cite this paper together with the citation for the datafile.

Study area and experimental design
The study was conducted in and around two nature reserves, Eigental and Altläufe der Glatt, which were located approximately 5 km apart (47°27´ to 47°29´ N, 8°37´ to 8°32´ E, 417 to 572 m a.s.l., Canton of Zurich, Switzerland; Figure S1 and S2, Table S1). Mean annual temperature and precipitation are 9.8 ± 0.6 °C and 990 ± 168 mm (Kloten climate station 1988-2018; MeteoSchweiz, 2019). TFor this study, we used a space-for-time approach based on eight restoration sites that were between 3 and 32 years old. We measured recovery and restoration success by comparing the restored grasslands with intensively managed and semi-natural grasslands. Using a space-for-time approach requires high similarities in historical properties of the site, such as soil conditions and management regimes, to assure that temporal processes are appropriately represented by spatial patterns (Walker et al., 2010). This was the case in our study. The restored sites had similar soil conditions (i.e., soil type, structure, water availability) as the targeted semi-natural grasslands, while they shared the same agricultural legacy with intensively managed grasslands, i.e., biomass harvest and fertilization (manure and/or slurry) three to five times a year as well as tillage. We randomly established three 5 m x 5 m (25-m2) plots for plant identification and three 2 m x 2 m (4-m2) subplots for soil biotic and abiotic data collection at least 2 m away from the 25-m2 plots in each restoration site. Sites of similar age were grouped into four age classes: Y.4 (3 & 4 years after restoration), Y.18 (17 & 19 years), Y.24 (23 & 25 years), and Y.30 (27 & 32 years). Six intensively managed (Initial) and six semi-natural grassland (Target) sites complemented the experimental set-up, for a total of 36 plots. All plots were sampled under similar conditions, i.e., day of the year, air temperature, soil moisture, and time since last rain event, in June/July 2017 (intensively managed and semi-natural plots) and 2018 (restored plots).

Collection of plants and selected soil biota data
Plant species cover (in %) was visually estimated in each 25-m2 plot in mid-June (Braun-Blanquet, 1964; nomenclature: Lauber & Wagner, 1996). We calculated Shannon diversity and assessed plant community structure. We included soil microbial (fungi, procaryotes) and nematodes in our study as they represent the majority of soil biotic diversity and abundance (Bardgett & van der Putten, 2014), cover various trophic levels of the soil food web (Bongers & Ferris, 1999), and play key roles in soil functioning and ecosystem processes (Bardgett & van der Putten, 2014). In particular, soil nematodes were found to be well suited belowground indicators to evaluate recovery/development after restoration (e.g. Frouz, et al. 2008; Kardol et al., 2009; Resch et al., 2019). We randomly collected ten soil cores (2.2 cm diameter x 12 cm depths; sampler from Giddings Machine Company, Windsor, USA) in the 4-m2 subplots to assess soil nematode and microbial (fungal, prokaryotic) diversities and community structures. For soil nematodes, eight of the soil cores were combined and gently homogenized, placed in coolers and stored at 4 °C and transported to the laboratory (Netherlands Institute of Ecology, NIOO, Wageningen, Netherlands) within three days after collection. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriators (Oostenbrink, 1960). After extraction, each sample was divided into three subsamples, two for molecular identification and one to determine nematode abundance (see Resch et al., 2019). For the molecular work, two subsamples were stored in 70% ethanol (final volume 10 mL each) and transported to the laboratory at the Swiss Federal Research Institute WSL (Birmensdorf, Switzerland). Each subsample was reduced to roughly 200 μL by centrifugation and removal of the supernatant. The remaining ethanol was vaporized (65 °C for 3 h). Thereafter, 180 μL ATL buffer solution (Qiagen, Hilden, Germany) was immediately added and samples were stored at 4 °C until further processing. From these samples, nematode metagenomic DNA was extracted using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) according to the manufacturer`s protocol, except for the incubation step which was run at 56 °C for 4 h. PCR amplification of the V6-V8 region of the eukaryotic small-subunit (18S) was performed with 7.5 μL of genomic DNA template (ca. 1 ng/μL) in 25 μL reactions containing 5 μL PCR reaction buffer, 2.5 mM MgCL2, 0.2 mM dNTPs, 0.8 μM of each primer (NemF: Sapkota & Nicolaisen, 2015; 18Sr2b: Porazinska et al., 2009), 0.5 μL BSA, and 0.25 μL GoTaq G2 Hot Start Polymerase (Promega Corporation, Madison, USA). Amplification was using an initial DNA denaturation step of 95 °C for 2 min, followed by 35 cycles at 94 °C for 40 sec, 58 °C for 40 sec, 72 °C for 1 min, and a final elongation step at 72 °C for 10 min. Filtering, dereplication, sample inference, chimera identification, and merging of paired-end reads was implemented using the DADA2 pipeline (v.1.12; Callahan et al., 2016) to finally assign amplicon sequence variants (ASVs) as taxonomic units.
We combined and homogenized the remaining two soil cores to assess soil microbes, placed them in coolers (4 °C) and transported them to the laboratory at WSL. Metagenomic DNA was extracted from 8 g sieved soil (2 mm) using the DNAeasy PowerMax Soil Kit (Qiagen, Hilden, Germany) according to the manufacturer´s protocol. PCR amplification of the V3-V4 region of the small-subunit (16S) of prokaryotes (i.e., bacteria and archaea) and the ribosomal internal transcribed spacer region (ITS2) of fungi was performed with 1 ng of template DNA using PCR primers and conditions as previously described (Frey et al., 2016). PCRs were run in triplicates, pooled and sent to the Genome Quebec Innovation Centre (Montreal, QC, Canada) for barcoding using the Fluidigm Access Array technology (Fluidigm) and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, USA). Quality filtering, clustering into operational taxonomic units (OTUs, 97% similarity cutoffs) and taxonomic assignment were performed as previously described (Resch et al., 2021).Taxonomic classification of nematode, prokaryotic and fungal sequences was conducted querying against the most recent versions of PR2 (v.4.11.1; Guillou et al., 2013), SILVA (v.132; Quast et al., 2013), and UNITE (v.8; Nilsson et al., 2019) reference sequence databases. Taxonomic assignment cutoffs were set to confidence rankings ≥ 0.8 (below ranked as unclassified). Prokaryotic OTUs assigned to mitochondria or chloroplasts as well as OTUs or ASVs assigned to other than Fungi or Nematoda were manually removed prior to data analysis. The three datasets were filtered to discard singletons and doubletons. Taxonomic abundance matrices were rarefied to the lowest number of sequences per community to achieve parity of the total number of reads between samples (Prokaryotes: 10,929 reads; Fungi: 18,337 reads; Nematodes: 6,662 reads). We calculated Shannon diversity and assessed community structures for soil nematodes, prokaryotes and fungi based on their relative abundances of ASV or OTU at the taxon level.

Collection of soil physical and chemical properties
We randomly collected one undisturbed soil core (5 cm diameter, 12 cm depth) per 4-m2 subplot using a steel cylinder that fit into the soil corer. The cylinders were capped to avoid disturbance during transport and used to measure field capacity, rock content and fine earth density as previously described (Resch et al., 2021). We randomly collected another three soil cores (5 cm diameter, 12 cm depths) in each 4-m2 subplot to determine soil chemical properties. The cores were pooled, dried at 60 °C for 48 h and passed through a 2 mm sieve. We measured soil pH (CaCl2) on dried samples, total nitrogen (N) and organic carbon (C) concentration on dried and fine-ground samples (≤ 0.5 mm; for details see Resch et al., 2021). We calculated total N and organic C pools after correcting its concentration for soil depth, rock content and fine earth density.

bacteria

display_name: bacteria

biodiversity

display_name: biodiversity

fungi

display_name: fungi

grassland

display_name: grassland

nematodes

display_name: nematodes

plants

display_name: plants

restoration

display_name: restoration

soil

display_name: soil

soil-biodiversity

display_name: soil-biodiversity

soil-biology

display_name: soil-biology

soil-chemistry

display_name: soil-chemistry

topsoil-removal

display_name: topsoil-removal

en: Long-term recovery of above-and belowground interactions in restored grasslands


maintainer: Marcia Phillips

en: borehole ritigraben meteo-station permos permafrost

en: DATA ACCESS

media_type: SMET

en: Meterological station at the [Ritigraben permafrost borehole](http://www.envidat.ch/dataset/rit1) (RIT_0102) in canton Valais, Switzerland. The station is located at 2690 m asl on a flat site.

borehole

display_name: borehole

meteo-station

display_name: meteo-station

permafrost

display_name: permafrost

permos

display_name: permos

ritigraben

display_name: ritigraben

education

en: Education and science

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: RIT2: Meteorological station at Ritigraben borehole site


maintainer: Matthias Haeni

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

en: Site photo of the field station (ISF)

media_type: image/jpeg

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

mimetype: image/jpeg

en: Forest station data (ISB)

en: Data file.

en: Data plot of the forest station (ISB)

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

en: Site aerial photo (Isone)

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

en: Site photo of the forest station (ISB)

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

en: Data plot of the field station (ISF)

en: Field station data (ISF)

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 Isone in Switzerland where one station is located within a natural broad-leaved forest stand (ISB) with European beech (_Fagus sylvatica_; 70-100 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, ISF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Isone 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: Isone, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards


en: ground-temperature sdms aridity precipitation frost-change-velocity soil

media_type: TIFF

format: TIFF

en: Soil pH modified from SoilGrids

en: Soil clay content modified from SoilGrids

media_type: tif

en: Precipitation seasonality from CHELSA

en: Mean annual temperature modified from CHELSA

en: Soil organic carbon content modified from SoilGrids

en: Frost change frequency modified from CHELSA

en: Mean diurnal range modified from CHELSA

en: Precipitation of driest quarter modified from CHELSA

en: The dataset contains seven environmental layers (average annual temperature, aridity [annual precipitation divided by annual potential evapotranspiration], frost change frequency, precipitation in the driest quarter, mean diurnal temperature range, and precipitation seasonality) modified from CHELSA (https://chelsa-climate.org/) and three soil layers (soil organic matter content, pH water, and clay content) modified from SoilGrids (https://soilgrids.org/).

aridity

display_name: aridity

frost-change-velocity

display_name: frost-change-velocity

ground-temperature

display_name: ground-temperature

sdms

display_name: sdms

soil

display_name: soil

education

en: Education and science

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: Environmental layers for SDM simulations (GDPlants)


en: microstructure snow-density-profile snow-density snowmicropen antarctica

en: Python code exported from the Jupyter notebook for processing the SnowMicroPen files

media_type: py

media_type: ipynb

en: Jupyter notebook for processing the SnowMicroPen files.

en: Manual snow pits and other snow density measurements

media_type: .txt

en: SnowMicroPen pts files and processed txt files

en: Zip archive containing the acquired *.pts files (binary files recorded by the SnowMicroPen), and the converted *.txt files using the SMP.ipynb jupyter notebook (see the python code in the other resources in this dataset)

en: List of SnowMicroPen measurements

en: List of all SnowMicroPen measurements, including date and location, and processing status. This list has been generated by the python code (see other resources in this dataset).

en: Documentation SnowMicroPen measurements

media_type: .zip

en: This *.zip archive contains all available documentation of the SnowMicroPen measurements, including the mapping of the SMP measurements in the terrestrial laser scan field at PEA (see documentation for 20161220) and at the Hammarryggen Ice Rise (see documentation for 20190104 and 20190111).

en: SnowMicroPen (SMP) measurements and manual snowpits from Dronning Maud Land, East Antarctica. Measurements were taken in the vicinity of the Belgium Princess Elisabeth Station (PEA), in a transect towards the coast, and on the Lokeryggen and Hammarryggen Ice Rises near the coast. Measurements were taken during 3 individual campaigns in the 2016-2017, 2018-2019 and 2019-2020 field seasons.

antarctica

display_name: antarctica

microstructure

display_name: microstructure

snow-density

display_name: snow-density

snow-density-profile

display_name: snow-density-profile

snowmicropen

display_name: snowmicropen

en: SnowMicroPen measurements and manual snowpits from Dronning Maud Land, East Antarctica


grundwasserkarte

title_for_slug: grundwasserkarte

fr: mittelwasserstand grundwasservorkommen fassung quellhorizont fliessrichtung isohypse grundwasser quelle trinkwasser

label: geocat.ch permalink

spatial: Kanton Schwyz (SZ)

fr: Die Grundwasserkarte dient als wichtiges Planungs- und Beurteilungsinstrument und zeigt die Grundwasservorkommen in Lage, Ausdehnung und Mächtigkeit. Daneben werden Flächen mit Isohypsen, Fliessrichtungen sowie punktbezogene Geodaten und Messwerte dargestellt.

amt-geoinformation-sz

de: Amt für Geoinformation Kanton Schwyz

de: Das Amt für Geoinformation verwaltet und veröffentlicht die Geodaten des Kantons Schwyz als OGC-Geodienste und als Datei-Download. Alle aktuell verfügbaren Geodaten, welche dem OGD unterliegen, sind hier aufgeführt.

political_level: canton

value: SZ-Kt Geoinformation - Geocat Harvester

fr: Grundwasserkarte

de: Grundwasserkarte

en: Grundwasserkarte

it: Grundwasserkarte


en: cable-yarding open-source swiss-geodata geoinformation-software qgis-plugin digital-elevation-model digital-planning seilaplan swisstopo

en: Herunterladen des DHM von swisstopo Website

en: View video on youtube!

en:
In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. As an alternative to using the ‘Swiss Geo Downloader’ plugin, the DTM can be obtained directly from Swisstopo. In this tutorial we explain step by step how to download the necessary DTM from the Swisstopo Website, and how to use it in QGIS for the digital planning of a cable line using the plugin ‘Seilaplan’. Please note that the tutorial language is German!

Link to the elevation model on the swisstopo website: https://www.swisstopo.admin.ch/de/geodata/height/alti3d.html#technische_details

Link to the rope map website: https://seilaplan.wsl.ch

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

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. Als Alternative zum Swiss Geo Downloader erklären wir in diesem Tutorial Schritt für Schritt, wie man das nötige Höhenmodell von der Swisstopo Webseite herunterladen und in QGIS zur Seillinienplanung verwenden kann.

Link zum Höhenmodell auf der swisstopo Webseite: https://www.swisstopo.admin.ch/de/geodata/height/alti3d.html#technische_details

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

cable-yarding

display_name: cable-yarding

digital-elevation-model

display_name: digital-elevation-model

digital-planning

display_name: digital-planning

geoinformation-software

display_name: geoinformation-software

open-source

display_name: open-source

qgis-plugin

display_name: qgis-plugin

seilaplan

display_name: seilaplan

swiss-geodata

display_name: swiss-geodata

swisstopo

display_name: swisstopo

education

en: Education and science

en: Seilaplan Tutorial: DTM download from swisstopo website


fr: Die Gefahrenkarte macht flächendeckend Aussagen zur Gefährdung oder Nichtgefährdung eines Gebietes, der Art des gefährlichen Prozesses (Gefahrenart) sowie der Intensität und der Eintretenswahrscheinlichkeit bzw. Häufigkeit desselben. Die Bestimmung der Gefahrenstufe (rot, blau, gelb, gelb-weiss) erfolgt anhand der Kriterien Intensität und Wahrscheinlichkeit eines Gefahrenprozesses. Die Gefahrenkarte mit den Gefahrenstufen beschränkt sich auf Gebiete grosser Bearbeitungstiefe (Siedlungsgebiet und Einzelobjekte).

fr: Gefahrenkarte

de: Gefahrenkarte

en: Gefahrenkarte

it: Gefahrenkarte


risikokataster

title_for_slug: risikokataster

label: WebGIS Kanton Schwyz, Thema einblenden

format: WFS

format: WMS

fr: Risikokataster

de: Risikokataster

en: Risikokataster

it: Risikokataster

fr: Zur übersichtlichen Darstellung der Gefahren und Risiken wurde der Kantonale Risikokataster realisiert. Dieser gibt Auskunft, an welchen Standorten gefährliche Stoffe in grösseren Mengen oder besonders gefährliche Mikroorganismen vorhanden sind und auf welchen Verkehrswegen sie transportiert werden. Er zeigt auch, wo es im Kanton Schwyz zu Ereignissen mit Auswirkungen ausserhalb des Betriebsareals (Konsulationsbereiche) kommen könnte. Neben den chemischen Gefahrenpotenzialen sind auch die möglichen Schäden und die Risiken erfasst, die durch Betriebe oder durch Gefahrguttransporte verursacht werden können.


grundwasserschutzareal-oereb

title_for_slug: grundwasserschutzareal-oereb

fr: gewasserschutzbereich grundwasserschutzareal schutzzone gewasser flachennutzungsplanung grundwasserschutz grundwasser gewasserschutz

fr: ÖREB: Grundwasserschutzareal

de: ÖREB: Grundwasserschutzareal

en: ÖREB: Grundwasserschutzareal

it: ÖREB: Grundwasserschutzareal

fr: Die Grundwasserschutzareale sind neben den Grundwasserschutzzonen und den besonders gefährdeten Gewässerschutzbereichen in der Gewässerschutzkarte enthalten. Die Karte dient der nachhaltigen Bewirtschaftung der natürlichen Wasserressourcen. Sie ist damit das entscheidende Instrument für den planerischen Gewässerschutz und wird von den Kantonen erstellt.

fr: Grundwasserschutzareal ÖREB

de: Grundwasserschutzareal ÖREB

en: Grundwasserschutzareal ÖREB

it: Grundwasserschutzareal ÖREB


en: wetlands conservation occupancy-models amphibians landscape

en: Covariate data 2

en: This file contains covariate data that changes over time for 856 ponds in 21 years (long format): whether or not a pond exists in year _t_, the age of constructed ponds in year _t_, structural connectivity of the pond in year _t_.

en: Original raw data

en: This file contains the original raw monitoring data as provided by the canton of Aargau. Access is restricted and only considered upon request and in consultation with the canton of Aargau.

en: Covariate data 1

en: This file contains covariate data that does not change over time for 856 ponds: the region the pond is in, whether the pond is old or new (constructed since 1991), elevation, water surface area, fluctuations of the water table (yes, no), the percent forest cover in 100m radius around the pond, the area or large (>6m wide) roads in 1km radius around the pond.

en: Readme

en: Description of methods and datasets.

en: Model code

media_type: Rmd

en: This R markdown file contains all code and descriptions necessary to load and format the provided data and to fit the dynamic occupancy models described in the associated publication, including JAGS code for the fitted models.

en: Model code, description

en: This is the output of the R markdown file that provides JAGS model code and describes how to format the provided data and fit the dynamic occupancy models described in the associated publication, for a quick overview.

en: Observation data

en: This file contains detection/non-detection data for the 12 amphibian species between 1999 and 2019 for 3 annual visits. Detectable denotes whether or not a visit j was made to pond i in year t. ObservedID gives a unique ID for the observer collecting the data during visit j to pond i in year t.

en:
In the canton of Aargau, hundreds of new ponds have been constructed since the 1990s to benefit declining amphibian populations. This dataset consists of monitoring data for all 12 pond-breeding amphibian species in the canton of Aargau from 1999 to 2019 in 856 ponds, and environmental variables that describe the ponds and the landscape surrounding the ponds. Species observation data is detection/non-detection data from repeat visits during survey years, during which all potentially suitable ponds in an area were surveyed. Environmental variables describing the ponds are whether the pond has been newly constructed since 1991 or not, pond age (if constructed), elevation a.s.l., the water surface area, and whether the water table fluctuates or not. Environmental variables describing the surroundings of the ponds are the percent area of forest within a circular buffer of radius 100m around the pond, the area of large (width ≥6m) roads within a circular buffer of radius 1km around the pond, and structural connectivity.

The canton of Aargau is the owner of the monitoring data; the original datafile is only disclosed upon request and in consultation with the canton of Aargau.

The edited dataset contains cleaned observation data for the 12 amphibian species, as well as compiled and edited covariate data and code to fit dynamic occupancy models.

amphibians

display_name: amphibians

conservation

display_name: conservation

landscape

display_name: landscape

occupancy-models

display_name: occupancy-models

wetlands

display_name: wetlands

envidat

en: EnviDat: the Environmental Data Portal

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

en: Amphibian observation and pond data (Aargau, Switzerland)


hinweiskarte-bodenverwertung

title_for_slug: hinweiskarte-bodenverwertung

fr: landwirtschaftszone boden

de: landwirtschaftszone boden

en: landwirtschaftszone boden

it: landwirtschaftszone boden

fr: Aufwertungsflächen

de: Aufwertungsflächen

en: Aufwertungsflächen

it: Aufwertungsflächen

fr: Die Hinweiskarte Bodenverwertung ist das Resultat einer luftbildgestützten Erfassungsaktion und einer Umfrage bei Landwirtinnen und Landwirten. In der Hinweiskarte werden anthropogen veränderte Flächen dargestellt, welche sich je nach Bewertung für Bodenverwertungsprojekte oder FFF-Kompensationen eigenen.

fr: Hinweiskarte Bodenverwertung

de: Hinweiskarte Bodenverwertung

en: Hinweiskarte Bodenverwertung

it: Hinweiskarte Bodenverwertung


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

en: Site photo of the forest station (VSB)

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

en: Data plot of the forest station (VSB)

media_type: application/pdf

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

en: Forest station data (VSB)

media_type: text/csv

en: Data file.

en: Data plot of the field station (VSF)

en: Field station data (VSF)

en: Site photo of the field station (VSF)

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

en: Site aerial photo (Visp)

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

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 Visp in Switzerland where one station is located within a natural mixed forest stand (VSB) with Scots pine (_Pinus sylvestris_; 40-80 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, VSF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Visp 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

education

fr: Education, science

en: Education and science

envidat

fr: EnviDat: le portail de données environnementales

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


fr: Kantonsstrassen mit Kilometrierung

de: Kantonsstrassen mit Kilometrierung

en: Kantonsstrassen mit Kilometrierung

it: Kantonsstrassen mit Kilometrierung

fr: Strassenachsen Kantonsstrassen mit Kilometrierung


fr: schutzzone grundwasserschutz gewasserschutz grundwasser trinkwasser

fr: Grundwasserschutzzbereiche sind Teil des planerischen Gewässerschutzes. Sie dienen als Grundlage für Schutzmassnahmen dem Schutz vor Beeinträchtigungen des Grundwassers. Sie umfassen Schutzbereiche der unterirdischen sowie oberflächlichen Gewässer, welche der Trinkwasserversorgung dienen.

fr: Gewässerschutzbereiche

de: Gewässerschutzbereiche

en: Gewässerschutzbereiche

it: Gewässerschutzbereiche


fr: landwirtschaftliche-nutzflache-ln biodiversitat landwirtschaftliche-bewirtschaftung landwirtschaftliche-nutzung lebensraume-und-biotope

fr: Landw. Bewirtschaftung: Biodiversitätsförderflächen Qualitätsstufe II und Vernetzung 2022

fr: Die „Biodiversitätsförderflächen Qualitätsstufe II und Vernetzung“ überlagern die „Nutzungsflächen“. Die Daten enthalten die Elemente für die Qualitätsstufe II sowie für die Vernetzung des Beitragsjahres 2022, welche Bundesbeiträge erhalten. Biodiversitätsförderflächen Qualitätsstufe II und Vernetzung sind mit Ausnahme der artenreiche Grün- und Streueflächen im Sömmerungsgebiet eine Untermenge der Biodiversitätsförderflächen Qualitätsstufe I und können nicht grösser sein als diese selber. Qualitätsstufe II 153.3 und Vernetzung 153.4 sind in zwei separaten Ebenen geführt und können sich überlagern.


geringdurchlassige-deckschicht

title_for_slug: geringdurchlassige-deckschicht

fr: Grundwasservorkommen: schlecht durchlässige Deckschicht

fr: Der Datensatz beschreibt schlecht durchlässige Deckschichten von nutzbaren Grundwasservorkommen. Diese haben eine besondere Schutzwirkung, veringern jedoch die Grundwasserneubildung.

fr: Geringdurchlässige Deckschicht

de: Geringdurchlässige Deckschicht

en: Geringdurchlässige Deckschicht

it: Geringdurchlässige Deckschicht


grundwasserleiter-lockergestein

title_for_slug: grundwasserleiter-lockergestein

fr: gewasserschutz grundwasser trinkwasser

de: gewasserschutz grundwasser trinkwasser

en: gewasserschutz grundwasser trinkwasser

it: gewasserschutz grundwasser trinkwasser

fr: Grundwasservorkommen

de: Grundwasservorkommen

en: Grundwasservorkommen

it: Grundwasservorkommen

fr: Lockergesteins-Grundwasserleiter sind unverfestigte Gesteinskörper, in welchen der zusammenhängende Porenraum vollständig mit Wasser gesättigt ist.

fr: Grundwasserleiter Lockergestein

de: Grundwasserleiter Lockergestein

en: Grundwasserleiter Lockergestein

it: Grundwasserleiter Lockergestein


perimeter

title_for_slug: perimeter

fr: Naturgefahrenkarten sind eine Momentaufnahme. Bedrohungen durch Naturgefahren können sich im Laufe der Zeit ändern. Neue Erkenntnisse, neue Schutzbauten oder rege Bautätigkeiten können eine Überarbeitung der Gefahrenkarte erfordern («Perimeter in Überarbeitung»). Aufgrund fehlender Schadenpotenziale (Sachwerte und Personen) sind einzelne Gebiete im Kanton Schwyz nicht beurteilt. Solche Gebiete sind im «Perimeter nicht beurteilt» einsehbar.

fr: Perimeter

de: Perimeter

en: Perimeter

it: Perimeter


grundwassermessstellen

title_for_slug: grundwassermessstellen

fr: grundwasservorkommen gewasser grundwasser trinkwasser

fr: Grundwassermessstellen

de: Grundwassermessstellen

en: Grundwassermessstellen

it: Grundwassermessstellen

amt-geoinformation-sz

de: Amt für Geoinformation Kanton Schwyz


grundwasser-vorfluter-inflitrant

title_for_slug: grundwasser-vorfluter-inflitrant

fr: gewasser grundwasser

de: gewasser grundwasser

en: gewasser grundwasser

it: gewasser grundwasser

fr: Die Datensatz stellt dar, wie Oberflächengewässer als Vorfluter/Infiltrant für das Grundwasser wirken.

amt-geoinformation-sz

de: Das Amt für Geoinformation verwaltet und veröffentlicht die Geodaten des Kantons Schwyz als OGC-Geodienste und als Datei-Download. Alle aktuell verfügbaren Geodaten, welche dem OGD unterliegen, sind hier aufgeführt.

fr: Grundwasser-Vorfluter/Inflitrant

de: Grundwasser-Vorfluter/Inflitrant

en: Grundwasser-Vorfluter/Inflitrant

it: Grundwasser-Vorfluter/Inflitrant


fr: Landwirtschaftliche Produktionsstätten, Stand 2022

fr: Landw. Bewirtschaftung: Produktionsstätten 2022


fr: Strassenachsen Nationalstrassen mit Kilometrierung


gespanntes-grundwasservorkommen

title_for_slug: gespanntes-grundwasservorkommen

fr: Grundwasservorkommen mit Druckverhältnis gespannt und artesisch

fr: Der Datensatz gibt Auskunft über die Lage von artesisch gespanntem Grundwasser.

fr: Gespanntes Grundwasservorkommen

de: Gespanntes Grundwasservorkommen

en: Gespanntes Grundwasservorkommen

it: Gespanntes Grundwasservorkommen


fr: Gewässerschutzkarte

de: Gewässerschutzkarte

en: Gewässerschutzkarte

it: Gewässerschutzkarte

fr: Grundwasserschutzareale sind Teil des planerischen Gewässerschutzes. Sie dienen der Sicherstellung des Grundwassers bezüglich einer künftigen Grundwasserbewirtschaftung (Anreicherung oder Nutzung).

fr: Grundwasserschutzareale

de: Grundwasserschutzareale

en: Grundwasserschutzareale

it: Grundwasserschutzareale


grundwasserschutzzonen-oereb

title_for_slug: grundwasserschutzzonen-oereb

fr: gewasserschutzbereich grundwasserschutzareal schutzzone flachennutzungsplanung grundwasserschutz grundwasser gewasserschutz gewasser

fr: ÖREB: Grundwasserschutzzone

de: ÖREB: Grundwasserschutzzone

en: ÖREB: Grundwasserschutzzone

it: ÖREB: Grundwasserschutzzone

fr: Die Grundwasserschutzzonen sind neben den Grundwasserschutzarealen und den besonders gefährdeten Gewässerschutzbereichen in der Gewässerschutzkarte enthalten. Die Karte dient der nachhaltigen Bewirtschaftung der natürlichen Wasserressourcen. Sie ist damit das entscheidende Instrument für den planerischen Gewässerschutz und wird von den Kantonen erstellt.

fr: Grundwasserschutzzonen ÖREB

de: Grundwasserschutzzonen ÖREB

en: Grundwasserschutzzonen ÖREB

it: Grundwasserschutzzonen ÖREB


netzgebiete

title_for_slug: netzgebiete

fr: Netzgebiet: Netzebene 3

de: Netzgebiet: Netzebene 3

en: Netzgebiet: Netzebene 3

it: Netzgebiet: Netzebene 3

fr: Netzgebiet: Netzebene 7

de: Netzgebiet: Netzebene 7

en: Netzgebiet: Netzebene 7

it: Netzgebiet: Netzebene 7

fr: Netzgebiet: Netzebene 5

de: Netzgebiet: Netzebene 5

en: Netzgebiet: Netzebene 5

it: Netzgebiet: Netzebene 5

fr: Dieser kantonal erweiterte Datensatz basiert auf dem minimalen Geodatenmodell (MGDM) «Stromversorgungssicherheit: Netzgebiete» und teilt den Kanton für jede Netzebene in verschiedene Versorgungsgebiete ein. Diese Gebiete definieren, durch welche der jeweiligen Netzanbietenden die Anschluss- und Lieferpflicht wahrzunehmen ist.

fr: Netzgebiete

de: Netzgebiete

en: Netzgebiete

it: Netzgebiete


fr: Landwirtschaftliche Bewirtschaftung: Nutzungsflächen 2022

fr: Die Nutzungsflächen entsprechen den landwirtschaftlich genutzten Flächen des Beitragsjahr 2022 gemäss landwirtschaftlicher Begriffsverordnung (LVB), Direktzahlungsverordnung und der daraus abgeleiteten Definitionen der Nutzungen durch den Bund und die Kantone. Die Daten entsprechen dem MGDM ID 153 Nutzungsflächen.

fr: Landw. Bewirtschaftung: Nutzungsflächen 2022


fr: Landw. Bewirtschaftung: Perimeter LN- und Sömmerungsflächen 2021

fr: Der Perimeter landwirtschaftliche Nutzflächen (LN) und Sömmerungsflächen stellt die potenziell zu bewirtschaftende Landwirtschaftsfläche des Beitragsjahr 2021, ohne die landwirtschaftlich unproduktiven Flächen dar. Es sind dies die Flächen innerhalb der landwirtschaftlichen Nutzfläche LN (LBV Art. 14) und die Sömmerungsfläche SF (LBV Art. 24). Die Daten entsprechen dem MGDM Perimeter LN- und Sömmerungsflächen.


fr: Landw. Bewirtschaftung: Perimeter LN- und Sömmerungsflächen 2022

fr: Der Perimeter landwirtschaftliche Nutzflächen (LN) und Sömmerungsflächen stellt die potenziell zu bewirtschaftende Landwirtschaftsfläche des Beitragsjahr 2022, ohne die landwirtschaftlich unproduktiven Flächen dar. Es sind dies die Flächen innerhalb der landwirtschaftlichen Nutzfläche LN (LBV Art. 14) und die Sömmerungsfläche SF (LBV Art. 24). Die Daten entsprechen dem MGDM Perimeter LN- und Sömmerungsflächen.


badegewaesserqualitaet

maintainer: Païvi Rinta, BAFU

title_for_slug: Badegewässerqualität (ods)

fr: baignade e-coli enterocoques-intestinaux qualite-des-eaux

author: Bundesamt für Umwelt BAFU

format: ODS

fr: Les relevés de la qualité des eaux de baignade permettent d’évaluer la qualité hygiénique des eaux à l’aide des indicateurs que sont E. coli et les entérocoques intestinaux. Les informations fournies servent à déterminer l’efficacité des mesures de protection des eaux, notamment si les stations d’épuration remplissent leur fonction.

en: The assessment of bathing waters is made on the basis of hygienic quality using E.coli and intestinal enterococci. They provide indications on the effectiveness of water protection measures and in particular the extent to which waste water treatment plants are fulfilling their function.

de:
Die Beurteilung der Badegewässerqualität erfolgt anhand der Daten des kantonalen Gewässermonitorings zu den hygienischen Parametern E. coli und intestinale Enterokokken (Fäkalbakterien). Sie gibt Hinweise darauf, wie gut Gewässerschutzmassnahmen greifen, und insbesondere wieweit die Abwasserreinigungsanlagen ihre Funktion erfüllen.
Für die Beurteilung der Qualität gilt die EUA-Einstufung gemäss Anhang I der EU-Badegewässerrichtlinie 2006 (siehe Tabelle 3, Kapitel 7 der Empfehlung für die Beurteilung der Badegewässer). Die EUA-Einstufung kann nur berechnet werden, wenn mindestens 4 Proben pro Badesaison und Messresultate für mindestens 4 Jahre vorliegen.

it: I rilevamenti per la determinazione della qualità delle acque di balneazione valutano la qualità igienica delle acque in base all’E. coli e agli enterococchi intestinali. Forniscono informazioni sull’incisività delle misure di protezione delle acque, in particolare sull’efficienza degli impianti di depurazione delle acque di scarico.

badegewasserqualitat

display_name: badegewasserqualitat

baignade

display_name: baignade

e-coli

display_name: e-coli

enterocoques-intestinaux

display_name: enterocoques-intestinaux

enterokkoken-bakterien

display_name: enterokkoken-bakterien

qualite-des-eaux

display_name: qualite-des-eaux

fr: Qualité des eaux de baignade (ods)

de: Badegewässerqualität (ods)

en: Bathing water quality (ods)

it: Qualità delle acque di balneazione (ods)

health

fr: Santé

de: Gesundheit

en: Health

it: Sanità

territory

fr: Territoire et environnement

de: Raum und Umwelt

en: Territory and environment

it: Territorio e ambiente

bundesamt-fur-umwelt-bafu

fr: Office fédéral de l'environnement OFEV

de: Bundesamt für Umwelt BAFU

en: Federal Office for the Environment FOEN

it: Ufficio federale dell'ambiente UFAM

fr: L’autorité compétente pour les questions environnementales. Il est à ce titre chargé de gérer les ressources naturelles selon les principes du développement durable et de protéger l’homme contre les dangers naturels et l’environnement contre les pollutions.

de: Die Fachbehörde für die Umwelt. Es ist zuständig für die nachhaltige Nutzung der natürlichen Ressourcen sowie für den Schutz des Menschen vor Naturgefahren und den Schutz der Umwelt vor übermässigen Belastungen.

en: The federal environmental authority. It is responsible for ensuring that natural resources are used sustainably, that the public is protected against natural hazards, and that the environment is protected from unacceptable adverse impacts.

it: Il servizio specializzato in materia ambientale, è competente per quanto riguarda l'utilizzazione sostenibile delle risorse naturali, la protezione delle persone dai pericoli naturali e dell'ambiente da un inquinamento eccessivo.

political_level: confederation


gewasseruberwachung

title_for_slug: gewasseruberwachung

fr: Gewässerüberwachung

de: Gewässerüberwachung

en: Gewässerüberwachung

it: Gewässerüberwachung

fr: Die Karte zur Gewässerüberwachung gibt einen Überblick über die Messstellen zu den vier Messprogrammen DÜFUR, NAWA, Seen und Badewasserqualität innerhalb des Kantons Schwyz. - DÜFUR: Dauerüberwachung der Fliessgewässer in den Urkantonen - NAWA: Nationale Beobachtung der Oberflächengewässerqualität - Seen: Gewässerqualität der Seen - Badewasserqualität: Untersuchung der mikrobiologischen Qualität des Badewassers an Badeplätzen

amt-geoinformation-sz

de: Amt für Geoinformation Kanton Schwyz

de: Das Amt für Geoinformation verwaltet und veröffentlicht die Geodaten des Kantons Schwyz als OGC-Geodienste und als Datei-Download. Alle aktuell verfügbaren Geodaten, welche dem OGD unterliegen, sind hier aufgeführt.


elektrizitatsproduktionsanlagen

title_for_slug: elektrizitatsproduktionsanlagen

spatial: Schweiz

protocol: WWW:DOWNLOAD:Text (.csv), comma separated

de: Csv

media_type: Text (.csv), comma separated

protocol: WWW:DOWNLOAD:Geopackage (ogc)

de: Geopackage

media_type: Geopackage (ogc)

format: GPKG

de: Interlis

media_type: INTERLIS

format: INTERLIS

fr: Service WMS-IFDG, couche "Installations production électrique"

de: WMS-BGDI Dienst, Layer "Elektrizitätsproduktionsanlagen"

en: WMS-FSDI service, layer "Electricity production plants"

it: Servizio WMS-IFDG, strato "Impianti produzione elettrica"

fr: Map (Preview) Géoportail thématique - map.energie.admin.ch

de: Map (Preview) Fachportal - map.energie.admin.ch

en: Map (Preview) Thematic geoportal - map.energie.admin.ch

it: Map (Preview) Geoportale tematico - map.energie.admin.ch

de: Fachportal - map.energie.admin.ch

de: swisstopo REST API

format: API

fr: Une installation de production d’électricité est un dispositif technique destiné à produire de l’électricité. Il est dans l’intérêt de tous de disposer d’un aperçu géographique de l’ensemble des installations de production d’électricité de la Suisse. Le développement des installations produisant de l’électricité avec des énergies renouvelables doit notamment être présenté de manière transparente. Il importe aussi de montrer quelle est la proportion de ces installations par rapport à celles produisant de l’électricité avec des énergies non renouvelables. Cet aperçu doit permettre de faire la distinction entre l‘hydraulique, le photovoltaïque, l’éolien, la biomasse, la géothermie, l’énergie nucléaire, le pétrole, le gaz naturel et le charbon. Cette base de données comprend uniquement les installations de production d’électricité enregistrées dans le système suisse des garanties d’origine. Sont donc concernées d’une part les installations d’une puissance supérieure à 30 kilovoltampères (kVA) et les petites installations (à partir de 2 kilowatts (kW)) qui se sont enregistrées sur une base volontaire et d’autre part, les installations encouragées par une rétribution de l’injection, une rétribution unique, un financement des frais supplémentaires ou une contribution d’investissement (art. 19 et 24, LEne). Seules les installations de production d’électricité en service sont prises en compte.

de: Eine Elektrizitätsproduktionsanlage ist eine technische Einrichtung zur Erzeugung von Strom.Es ist von allgemeinem Interesse, eine räumliche Übersicht über sämtliche Elektrizitätsproduktionsanlagen der Schweiz zu haben. Insbesondere soll der Zubau an Produktionsanlagen, die Elektrizität aus erneuerbaren Energien produzieren, transparent dargestellt werden. Zudem kann das Verhältnis der Anlagen, die Elektrizität aus erneuerbaren Energien produzieren, zu den Anlagen, die Elektrizität aus nicht erneuerbaren Energien produzieren, sichtbar gemacht werden. Man unterscheidet zwischen Wasserkraft, Photovoltaik, Windenergie, Biomasse, Geothermie, Kernenergie, Erdöl, Erdgas und Kohle. Dieser Datenbestand enthält alle Elektrizitätsproduktionsanlagen, welche im Schweizerischen Herkunftsnachweissystem registriert sind. Darunter fallen zum einen alle Anlagen mit einer Leistung grösser 30 Kilovoltampere (kVA), sowie Kleinanlagen (grösser als 2 Kilowatt), welche freiwillig für die Ausstellung von Herkunftsnachweisen (HKN) registriert worden sind. Zum anderen enthält der Datenbestand Anlagen, die durch eine Einspeisevergütung, Einmalvergütung, Mehrkostenfinanzierung oder einen Investitionsbeitrag geförderten werden (gemäss Art. 19 und 24; EnG). Es sind nur Elektrizitätsproduktionsanlagen enthalten, welche in Betrieb sind.

en: An electricity production plant is a technical facility for generating electricity. It is in the public interest to have a spatial overview of all electricity production plants in Switzerland. In particular, the expansion of production plants that produce electricity from renewable energy should be presented transparently. The ratio of plants producing electricity from renewable energy to plants producing electricity from non-renewable energy can also be shown. A distinction is made between hydropower, photovoltaics, wind energy, biomass, geothermal energy, nuclear energy, oil, natural gas and coal. This database contains all electricity production plants that are registered in the Swiss proof of origin system and includes all plants with an output exceeding 30 kilovolt-amperes (kVA), as well as small plants (more than 2 kilowatts) that have been voluntarily registered to display proof of origin. The database also includes plants that are subsidised by a feed-in rate, a one-off payment, additional cost financing or an investment contribution (pursuant to Art. 19 and 24; EnA). Only electricity production plants that are in operation are included.

it: Un impianto di produzione di energia elettrica è un dispositivo tecnico per la produzione di energia elettrica. È di interesse generale avere una panoramica spaziale di tutti gli impianti di produzione di energia elettrica presenti in Svizzera. In particolare, l'espansione degli impianti di produzione che generano elettricità da fonti energetiche rinnovabili dovrebbe essere presentata in modo trasparente. Inoltre, è possibile rendere visibile il rapporto tra gli impianti che producono elettricità da fonti energetiche rinnovabili e quelli che producono elettricità da fonti energetiche non rinnovabili. Si distingue tra energia idroelettrica, fotovoltaica, eolica, biomassa, geotermica, nucleare, petrolio, gas naturale e carbone. Questo set di dati comprende tutti gli impianti di produzione di energia elettrica registrati nel sistema svizzero delle garanzie di origine. Da un lato, questo include tutti gli impianti con una capacità superiore a 30 kilovolt-ampere (kVA), così come i piccoli impianti (superiori a 2 kilowatt) che sono stati registrati a titolo volontario per il rilascio di garanzie di origine. Dall'altro lato, il set di dati include impianti che sono sovvenzionati tramite una rimunerazione per l'immissione di elettricità, un versamento unico, un finanziamento dei costi aggiuntivi o un contributo d'investimento (secondo gli art. 19 e 24; LEne). Sono inclusi solo gli impianti di produzione di elettricità che sono in funzione.

fr: Installations de production d'électricité

de: Elektrizitätsproduktionsanlagen

en: Electricity production plants

it: Impianti di produzione di energia elettrica

culture

fr: Culture, médias, société de l'information, sport

de: Kultur, Medien, Informationsgesellschaft, Sport

en: Culture, media, information society, sport

it: Cultura, media, società dell'informazione, sport

energy

fr: Energie

de: Energie

en: Energy

it: Energia

geography

fr: Géographie

de: Geographie

en: Geography

it: Geografia

bundesamt-fur-energie-bfe

fr: Office fédéral de l'énergie OFEN

de: Bundesamt für Energie BFE

en: Swiss Federal Office of Energy SFOE

it: Ufficio federale dell'energia UFE

fr: L'Office fédéral de l'énergie (OFEN) est le centre de compétences du Département fédéral de l'environnement, des transports, de l'énergie et de la communication (DETEC) pour les questions liées à l'approvisionnement en énergie et à son utilisation.

de: Das Bundesamt für Energie (BFE) ist das Kompetenzzentrum für Fragen der Energieversorgung und der Energienutzung im Eidgenössischen Departement für Umwelt, Verkehr, Energie und Kommunikation (UVEK).

en: The Swiss Federal Office of Energy (SFOE) is the country's competence centre for issues relating to energy supply and energy use at the Federal Department of the Environment, Transport, Energy and Communications (DETEC).

it: L'Ufficio federale dell'energia (UFE) è il centro di competenza in materia di approvvigionamento e di impiego dell'energia in seno al Dipartimento federale dell'ambiente, dei trasporti, dell'energia e delle comunicazioni (DATEC).