en: aws cryowrf antarctica python consistent

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

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

The following data sources are taken into account:

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

Important notes

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

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

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

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

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

aws

display_name: aws

consistent

display_name: consistent

cryowrf

display_name: cryowrf

python

display_name: python

envidat

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

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


maintainer: Elisabeth Hafner

en: hazard-mapping avalanche natural-hazard satellite snow

state: active

type: dataset

en: SPOT6 Avalanche Outlines 16 January 2019

media_type: SHP

en: Shapefile of the manually mapped avalanche outlines. Please only use this data together with the example key document provided with this dataset and within the limitations of the dataset described in the publications.

format: SHP

en:
Outlines of 6'041 avalanches mapped from SPOT6 satellite data over the Swiss Alps on 16 January 2019. The dataset was acquired following a period with very high avalanche danger. The outlines have different attributes described in the data example key (ExampleKey_AvalMapping16012019.pdf)

The generation of the data is described in:

Bühler, Y., E. D. Hafner, B. Zweifel, M. Zesiger, and H. Heisig (2019), Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps, The Cryosphere, 13(12), 3225-3238, doi:10.5194/tc-13-3225-2019.

The data was comprehensivly validated in a subset area in

Hafner, E.D.; Techel, F.; Leinss, S.; Bühler, Y., 2021: Mapping avalanches with satellites - evaluation of performance and completeness. Cryosphere, 15, 2: 983-1004. doi: 10.5194/tc-15-983-2021

avalanche

display_name: avalanche

hazard-mapping

display_name: hazard-mapping

natural-hazard

display_name: natural-hazard

satellite

display_name: satellite

snow

display_name: snow

education

fr: Education, science

de: Bildung, Wissenschaft

en: Education and science

it: Formazione e scienza

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: SPOT6 Avalanche outlines 16 January 2019


maintainer: EnviDat Support

en: ice-nucleation clouds aerosol backward-trajectories raclets

media_type: text/csv

format: CSV

mimetype: text/csv

en: This dataset contains number concentrations of ice-nucleating particles active at -15 °C observed at Weissfluhjoch during February and March 2019, as well as complementary data (measured aerosol number concentrations and modelled total precipitation along air mass trajectories). This data formed the basis of our paper with the title “Towards parameterising atmospheric concentrations of ice-nucleating particles active at moderate supercooling”.

aerosol

display_name: aerosol

backward-trajectories

display_name: backward-trajectories

clouds

display_name: clouds

ice-nucleation

display_name: ice-nucleation

raclets

display_name: raclets

en: Ice nucleating particle concentrations active at -15 °C at Weissfluhjoch


en: water-scarcity reservoirs climate-change water-supply water-demand

en: Shapefile of 307 catchments in Switzerland

media_type: application/zip

en: Shapefile of 307 catchments for which estimates of water supply, demand, and reservoir volumes are provided. The OBJECTID entry corresponds to the catchment ID which is used as a reference in the other datasets provided.

format: ZIP

mimetype: application/zip

en: Reservoir volumes

en: Reservoir volume for the 307 medium-sized catchments for different storage categories: natural lakes, artificial reservoirs, reservoirs for artificial snow production, drinking water reservoirs, and usable volumes of natural lakes. Unit: Mio m³. One column per catchment (see shapefile).

en: Water demand version 2 (corrected version)

en: Water supply

en: Simulated discharge series for a reference period 1981-2010 and till the end of the century. Data are provided for 39 climate model chains and a control run based on the observed meteorology. Data are provided at daily resolution (mm/d).

en: Water demand (deprecated)

en:
Warning: please use the corrected version 2, since this file contains errors for certain categories such as Livestock, Industry or Houshold.

Water demand estimates for the 307 medium-sized catchments for different categories for current and future conditions. One text file is provided per demand category. One column corresponds to one catchment (IDs compare to shapefile), The data are provided at monthly resolution in mm/d and can be converted to volumes by using the catchment area. E.g.
Q[mm/d] * catchment area [m²] /1000 = Q[m³/d]
or to obtain discharge in m³/s
Q[mm/d] * catchment area [m²]/1000/24/3600 = Q [m³/s].
Future irrigation and hydropower water demand are provided per climate model chain.

en:
The dataset Hydro-CH2018 reservoirs provides estimates of current and future water supply, water demand, and storage volumes for 307 medium-sized catchments in Switzerland.
Water supply for current (1981-2010) and future (2070-2099) climate conditions was simulated using the hydrological model PREVAH. For modeling current water supply, observed meteorological time series were used as input, while simulated meteorological time series derived from 39 model chains of the CH2018 initiative were used as an input for simulating future climate conditions.

Water demand was estimated for six categories:

- 1) Drinking water (households and tourism),
- 2) industry (second and third sector),
- 3) artificial snow production,
- 4) agriculture (irrigation and livestock feeding),
- 5) ecology (residual flows), and
- 6) hydropower.

Future estimates consider changes in demand related to population growth and changes in the hydrological conditions.
Storage volumes are provided for natural lakes (storage capacities and usable volumes), artificial reservoirs, reservoirs for artificial snow production, and drinking reservoirs.

A detailed description of the simulation and estimation procedures can be found in

*  Brunner, M.I., Björnsen Gurung, A., Zappa, M., Zekollari, H., Farinotti, D., Stähli, M., 2019. Present and future water scarcity in Switzerland: Potential for alleviation through reservoirs and lakes. Sci. Total Environ. 666, 1033–1047. https://doi.org/10.1016/j.scitotenv.2019.02.169.

This dataset contains the following information:

1.	Shapefile with the 307 medium-sized Swiss catchments.
2.   Textfiles with the water supply simulations for the control run and the 39 climate model chains (one file per chain) at daily resolution for the 307 catchments.
3.	Textfiles with the current and future demand estimates per category at monthly resolution for the 307 catchments.
4.	Textfiles with the storage volumes per category and catchment.

climate-change

display_name: climate-change

reservoirs

display_name: reservoirs

water-demand

display_name: water-demand

water-scarcity

display_name: water-scarcity

water-supply

display_name: water-supply

en: Hydro-CH2018 reservoirs


maintainer: Lisa Hülsmann

en: empirical-mortality-models tree-mortality forest-reserves tree-growth inventory-data

en: Calibration data

en:
A set of three consecutive inventories was used to generate records for the calibration of mortality models based on trees that were alive in the first and second inventory and either dead or alive in the third inventory.

 Meta data
Reserve: Name of the forest reserve

Plot: Permanent plot number

 Tree data
Species: Latin species name (or species group, cf. _Quercus_ spp.)

y: Status of the tree with 0 = alive and 1 = dead

dt: Length of the mortality period (years)

DBH: Diameter at breast height (mm) of the second inventory

relBAI: Annual relative basal area increment (-) calculated based on the first and the second DBH measurement as the compound annual growth rate of the trees basal area

 Stand data
_All stand variables were calculated for the second inventory._

mDBH: Mean diameter at breast height (mm)

iqrDBH: Interquartile range of diameter at breast height (mm)

BA: Basal area per hectare (m2 ha-1)

N: Number of trees per hectare (ha-1)

 Climate data
_Both climate variables were calculated for the period between the second and the third inventory._

P: Mean annual precipitation sum (mm)

mT: Mean annual air temperature (°C)

en:
The dataset comprises > 90 000 records from inventories in 54 strict forest reserves in [Switzerland](https://www.wsl.ch/de/wald/biodiversitaet-naturschutz-urwald/naturwaldreservate.html) and [Lower Saxony / Germany](http://naturwaelder.de/) along a considerable environmental gradient. It was used to develop parsimonious, species-specific mortality models for 18 European tree species based on tree size and growth as well as additional covariates on stand structure and climate.

 Inventory data
Measurements had been conducted repeatedly on up to 14 permanent plots per reserve for up to 60 years with re-measurement intervals of 4 - 27 years. The permanent plots vary in size between 0.03 and 3.47 ha. The inventories provide diameter measurements at breast height (DBH) and information on the species and status (alive or dead) of trees with DBH ≥ 4 cm for Switzerland and ≥ 7 cm for Germany.

 Data selection
We excluded three permanent plots where at least 80 % of the trees died during an interval of 10 years, and mortality could be clearly assigned to a disturbance agent. Mortality in the remaining stands was rather low, with a mean annual mortality rate of 1.5 % and strong variation between plots from 0 to 6.5 % (assessed for trees of all species with DBH ≥ 7 cm).
We only used data from permanent plots with at least 20 trees per species to obtain reliable plot-level mortality rates even for species with low mortality rates (about 5 % during 10 years), and selected tree species occurring on at least 10 plots to cover sufficient ecological gradients. This led to a dataset of 197 permanent plots and 18 tree or shrub species: _Abies alba_ Mill., _Acer campestre_ L., _Acer pseudoplatanus_ L., _Alnus incana_ Moench., _Betula pendula_ Roth, _Carpinus betulus_ L., _Cornus mas_ L., _Corylus avellana_ L., _Fagus sylvatica_ L., _Fraxinus excelsior_ L., _Picea abies_ (L.) Karst, _Pinus mugo_ Turra, _Pinus sylvestris_ L., _Quercus pubescens_ Willd., _Quercus_ spp. (_Q. petraea_ Liebl. and _Q. robur_ L.; not properly differentiated in the Swiss inventories), _Sorbus aria_ Crantz, _Tilia cordata_ Mill. and _Ulmus glabra_ Huds..

 Predictors of tree mortality
We considered tree size and growth as key indicators for mortality risk. Radial stem growth between the first and second inventory and DBH at the second inventory were used to predict tree status (alive or dead) at the third inventory. To this end, the annual relative basal area increment (relBAI) was calculated as the compound annual growth rate of the trees basal area. Additional covariates on stand structure and climate comprise mean annual precipitation sum (P), mean annual air temperature (mT), the mean and the interquartile range of DBH (mDBH, iqrDBH), basal area (BA) and the number of trees (N) per hectare.

 Further information
For further information, refer to Hülsmann _et al_. (in press) How to kill a tree – Empirical mortality models for eighteen species and their performance in a dynamic forest model. _Ecological Applications_.

empirical-mortality-models

display_name: empirical-mortality-models

forest-reserves

display_name: forest-reserves

inventory-data

display_name: inventory-data

tree-growth

display_name: tree-growth

tree-mortality

display_name: tree-mortality

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: Calibration data for empirical mortality models of 18 European tree species


maintainer: Matthias Haeni

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

en: Data plot of the field station (CLF)

media_type: application/pdf

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

format: PDF

mimetype: application/pdf

en: Data plot of the forest station (CLB)

en: Data file.

en: Site aerial photo (Celerina)

media_type: image/jpeg

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

format: SERVICE

mimetype: image/jpeg

en: Site photo of the field station (CLF)

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

en: Site photo of the forest station (CLB)

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

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 Celerina in Switzerland where one station is located within a natural coniferous forest stand (CLB) with Swiss pine (_Pinus cembra_; 210-250 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, CLF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Celerina is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

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


maintainer: Leo Gallus Bont

en: qgis seilaplan forest openstreetmap geoprocessing accessible-forest

en: Digitised sample perimeter

en: DEM (for the area of the sample forest range)

media_type: image/tiff

format: TIFF

mimetype: image/tiff

en: Juypter Notebook for Data Preparation (online)

media_type: ipynb

en: Juypter Notebook for Data Preparation

en: Power lines vector data (close to the area of the sample forest range)

en: Preview

media_type: image/png

format: PNG

mimetype: image/png

en: Railways vector data (close to the sample forest range)

en: Forest vector data (for sample forest range)

en:
This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of München, at the edge of Bavarian Alps).
The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin [Seilaplan]( https://doi.org/10.16904/envidat.software.1) for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting.
The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019.

Additional notes for vector data:
Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API - https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any perimeter or area can be digitised freely using the QGIS editing toolbar.

The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are:

- Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service “with funding by the European Union” based on SRTM and ASTER GDEM)
- Digitales Geländemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung – www.geodaten.bayern.de –and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif

This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed.

Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range.

This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.

accessible-forest

display_name: accessible-forest

forest

display_name: forest

geoprocessing

display_name: geoprocessing

openstreetmap

display_name: openstreetmap

qgis

display_name: qgis

seilaplan

display_name: seilaplan

education

en: Education and science

en: Sample Geodata and Software for Demonstrating Geospatial Preprocessing for Forest Accessibility and Wood Harvesting at FOSS4G2019


maintainer: Alexandre Buttler

en: soil silvopastoral-systems transplantation-experiment biogeochemisty climate-change

en: Dataset

en: Zip archive of all datasets detailed in study.

en:
Silvopastoral systems are highly productive and combine long-term wood production with annual provision of forage for livestock. In the Swiss Jura Mountains these systems are a key component of the landscape. As in other cold biomes, climate change can potentially accelerate landscape change within these historically sustainable systems.

In order to anticipate the evolution of subalpine wooded pasture ecosystems under future climate and land-use changes, this project focused on the interplay between soil, vegetation and climate. It was aimed at providing experimental evidence for chief ecosystem processes, with emphasis on the quality of the ecosystem services provided. The main interest was placed on vegetation turf resistance to climate change along an unwooded – sparsely wooded - densely wooded pasture gradient (land-use intensity), where plant productivity, diversity and succession along with rates of carbon cycling and microbial activity provided measures of ecosystem functioning at both plot and landscape level.

Experimental transplantation of monolith soil turfs to lower altitudes allowed to simulate soil warming and reduced annual precipitation. In order to simulate a year-round warmer and drier climate the natural climate variation along an altitudinal gradient was used as a proxy. The aim was to simulate realistic climate change scenarios for the second half of the 21st century predicted by the IPCC report and downscaled for Switzerland providing regionalized interpolated projections integrating therein trends for temperature increase and precipitation decrease. By using permanent meteorological stations within the network of the Federal Office of Meteorology and Climatology (MeteoSwiss), we obtained high resolution regional data on the variation of mean annual temperature (MAT) and mean annual precipitation (MAP) in relation to altitude in the Swiss Jura Mountains. We observed a general increase of +0.5 K in MAT and a decrease of -20 % MAP for each 100 m decrease in altitude along the SE slope of the Swiss Jura Mountains. These relationships served for the selection of the transplantation sites such that in comparison to a control site at 1350 m a.s.l. (Combe des Amburnex, N 46°54’, E 6°23’) a +2 K MAT and -20 % MAP was achieved at 1010 m a.s.l. (Saint-George, N 46°52’, E 6°26’), a +4 K MAT and -40 % MAP at 570 m a.s.l., (Arboretum d’Aubonne, N 46°51’, E 6°37’), and a +5 K MAT and -50 % MAP at 395 m a.s.l. (Les Bois Chamblard, N 46°47’, E 6°41’). The two stations at 1010 m a.s.l. and 570 m a.s.l. corresponded to the IPCC scenario A1B for a moderate increase in greenhouse gas emissions and to scenario A2 for a high increase in greenhouse gas emissions, respectively. The station at 395 m a.s.l. was chosen to represent an extreme scenario with climate variables lying at the positive tail distribution of model predictions under the A2 scenario.

Soil cores were assembled into rectangular PVC boxes of 60  80 cm2 size and of 35 cm height. All mesocosms were dug down to surface level into previously prepared trenches in the ground thus preventing lateral heat exchange with the atmosphere. Since at each site the mesocosms were placed in a common garden with no light interception, mesocosms with turfs from the two wooded pastures were shaded from direct sun light to simulate the natural light conditions in the corresponding habitats. Each mesocosm was equipped with a drainage system and was connected to a water tank thus representing a zero potential lysimeter collecting soil solution and precipitation/snowmelt runoff. ECH2O EC-TM sensor probes coupled to Em50 data-loggers (Decagon Devices, Inc., USA) recorded soil temperature and volumetric water content in each mesocosm at the top-soil (0 to -3 cm) every minute and data were averaged over one hour intervals. Climate parameters at each transplantation site were monitored continuously throughout the experiment by means of automated weather stations (Sensor Scope Sàrl, Switzerland), measuring rain precipitation (non-heated tipping bucket gauges) and air temperature and humidity 2 m above the ground surface at one minute intervals.

A list of above- and belowground variables were measured to assess the resilience of biogeochemical processes, plant productivity, tree regeneration, and carbon sequestration for each respective land-use practice. Furthermore, the experimental data were used to improve on (parameterization) the existing spatially explicit, dynamic model WoodPaM and refine the modelʼs climatic and land-use variables so that different scenarios of climate change and land use change could be simulated. Natural and management induced disturbance patterns were incorporated into the model.
The data have been made available within the project CCES Mounted. The climate stations Sensorscope are still in use within the project CLIMARBRE (Wald und Klimawandel, WSL/BAFU).

#References

1. Puissant, J., Cécillon, L., Mills, R.T.E., Robroek, B.J.M. Gavazov, K., De Danieli, S., Spiegelberger, T., Buttler, A., Brun, J.J. 2015. Seasonal influence of climate manipulation on microbial community structure and function in mountain soils. Soil Biology and Biochemistry 80: 296–305.
2. Mills, R., K. Gavazov, T. Spiegelberger, D. Johnson and A. Buttler 2014. Diminished soil functions occur under simulated climate change in a sup-alpine pasture, but heterotrophic temperature sensitivity indicates microbial resilience. Science of the Total Environment, vol. 473–474(0): 465-472.
3. Gavazov, K., Spiegelberger, T. and Buttler, A. 2014. Transplantation of subalpine wood-pasture turfs along a natural climatic gradient reveals lower resistance of unwooded pastures to climate change compared to wooded ones. Oecologia (174) : 1425-1435.
4. Peringer A., Siehoff S., Chételat J., Spiegelberger T., Buttler A. & Gillet F. 2013. Past and future landscape dynamics in pasture-woodlands of the Swiss Jura Mountains under climate change. Ecology and Society, 18, 3: 11. DOI: 10.5751/ES-05600-180311. [online] URL: http://www.ecologyandsociety.org/vol18/iss3/art11/
5. Gavazov, K. S., A. Peringer, A. Buttler, F. Gillet and T. Spiegelberger. 2013. Dynamics of Forage Production in Pasture-woodlands of the Swiss Jura Mountains under Projected Climate Change Scenarios. Ecology and Society 18 (1): 38. [online] URL: http://www.ecologyandsociety.org/vol18/iss1/art38/

biogeochemisty

display_name: biogeochemisty

climate-change

display_name: climate-change

silvopastoral-systems

display_name: silvopastoral-systems

soil

display_name: soil

transplantation-experiment

display_name: transplantation-experiment

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: Biogeochemical data from a transplantation experiment of monolith soil turfs along an altitudinal gradient to simulate climate change scenarios


wind-lidar-davos-wolfgang

title_for_slug: wind-lidar-davos-wolfgang

en: wolfgangpass wind profiles raclets lidar

en: Wind LIDAR data Wolfgang

en: A Leosphere WidCube 100S was deployed at Davos Wolfang, measuring wind profiles as well as performing PPI and RHI scans. Time format is in UTC with highest time resolution of 5 seconds.

en: Scanning wind Lidar from Meteoswiss was installed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577) and measured from 200 m above ground to 8100 m. The time resolution is up to 5 seconds. The Lidar was measuring wind profiles but also performed plan position indicator (PPI) and range height indicator (RHI) scans.

lidar

display_name: lidar

profiles

display_name: profiles

raclets

display_name: raclets

wind

display_name: wind

wolfgangpass

display_name: wolfgangpass

en: Wind LIDAR Davos Wolfgang


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

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: 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:
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


maintainer: Sofia Pagliarin

en: milan imperviousness barcelona urban-atlas copernicus soil-sealing

media_type: mpk

en: Shareable project in Esri ArcGIS format including the HRL IMD data in raster format for each of the territorial boundaries as specified in letter a)-h), see Dataset description

en: Information on data sources

en:
Dataset description

This dataset is a recalculation of the Copernicus 2015 high resolution layer (HRL) of imperviousness density data (IMD) at different spatial/territorial scales for the case studies of Barcelona and Milan. The selected spatial/territorial scales are the following:

 * a)	Barcelona city boundaries
 * b)	Barcelona metropolitan area, Àrea Metropolitana de Barcelona (AMB)
 * c)	Barcelona greater city (Urban Atlas)
 * d)	Barcelona functional urban area (Urban Atlas)
 * e)	Milan city boundaries
 * f)	Milan metropolitan area, Piano Intercomunale Milanese (PIM)
 * g)	Milan greater city (Urban Atlas)
 * h)	Milan functional urban area (Urban Atlas)

In each of the spatial/territorial scales listed above, the number of 20x20mt cells corresponding to each of the 101 values of imperviousness (0-100% soil sealing: 0% means fully non-sealed area; 100% means fully sealed area) is provided, as well as the converted measure into squared kilometres (km2).

 Dataset composition

The dataset is provided in .csv format and is composed of:

_IMD15_BCN_MI_Sources.csv_: Information on data sources

 _IMD15_BCN.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Barcelona:
 * a)	Barcelona city boundaries (label: bcn_city)
 * b)	Barcelona metropolitan area, Àrea metropolitana de Barcelona (AMB) (label: bcn_amb)
 * c)	Barcelona greater city (Urban Atlas) (label: bcn_grc)
 * d)	Barcelona functional urban area (Urban Atlas) (label: bcn_fua)

 _IMD15_MI.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Milan:
 * e)	Milan city boundaries (label: mi_city)
 * f)	Milan metropolitan area, Piano intercomunale milanese (PIM) (label: mi_pim)
 * g)	Milan greater city (Urban Atlas) (label: mi_grc)
 * h)	Milan functional urban area (Urban Atlas) (label: mi_fua)

_IMD15_BCN_MI.mpk_: the shareable project in Esri ArcGIS format including the HRL IMD data in raster format for each of the territorial boundaries as specified in letter a)-h).

Regarding the territorial scale as per letter f), the list of municipalities included in the Milan metropolitan area in 2016 was provided to me in 2016 from a person working at the PIM.

In the IMD15_BCN.csv and IMD15_MI.csv, the following columns are included:

 * Level: the territorial level as defined above (a)-d) for Barcelona and e)-h) for Milan);
 * Value: the 101 values of imperviousness density expressed as a percentage of soil sealing (0-100%: 0% means fully non-sealed area; 100% means fully sealed area);
 * Count: the number of 20x20mt cells corresponding to a certain percentage of soil sealing or imperviousness;
 * Km2: the conversion of the 20x20mt cells into squared kilometres (km2) to facilitate the use of the dataset.

 Further information on the Dataset

This dataset is the result of a combination between different databases of different types and that have been downloaded from different sources. Below, I describe the main steps in data management that resulted in the production of the dataset in an Esri ArcGIS (ArcMap, Version 10.7) project.

 1. The high resolution layer (HRL) of the imperviousness density data (IMD) for 2015 has been downloaded from the official website of Copernicus. At the time of producing the dataset (April/May 2021), the 2018 version of the IMD HRL database was not yet validated, so the 2015 version was chosen instead. The type of this dataset is raster.

 2. For both Barcelona and Milan, shapefiles of their administrative boundaries have been downloaded from official sources, i.e. the ISTAT (Italian National Statistical Institute) and the ICGC (Catalan Institute for Cartography and Geology). These files have been reprojected to match the IMD HRL projection, i.e. ETRS 1989 LAEA.

 3. Urban Atlas (UA) boundaries for the Greater Cities (GRC) and Functional Urban Areas (FUA) of Barcelona and Milan have been checked and reconstructed in Esri ArcGIS from the administrative boundaries files by using a Eurostat correspondence table. This is because at the time of the dataset creation (April/May 2021), the 2018 Urban Atlas shapefiles for these two cities were not fully updated or validated on the Copernicus Urban Atlas website. Therefore, I had to re-create the GRC and FUA boundaries by using the Eurostat correspondence table as an alternative (but still official) data source. The use of the Eurostat correspondence table with the codes and names of municipalities was also useful to detect discrepancies, basically stemming from changes in municipality names and codes and that created inconsistent spatial features. When detected, these discrepancies have been checked with the ISTAT and ICGC offices in charge of producing Urban Atlas data before the final GRC and FUA boundaries were defined.

Steps 2) and 3) were the most time consuming, because they required other tools to be used in Esri ArcGIS, like spatial joins and geoprocessing tools for shapefiles (in particular dissolve and area re-calculator in editing sessions) for each of the spatial/territorial scales as indicated in letters a)-h).

Once the databases for both Barcelona and Milan as described in points 2) and 3) were ready (uploaded in Esri ArcGIS, reprojected and their correctness checked), they have been ‘crossed’ (i.e. clipped) with the IMD HRL as described in point 1) and a specific raster for each territorial level has been calculated. The procedure in Esri ArcGIS was the following:

 * Clipping: Arctoolbox > Data management tools > Raster > Raster Processing > Clip. The ‘input’ file is the HRL IMD raster file as described in point 1) and the ‘output’ file is each of the spatial/territorial files. The option "Use Input Features for Clipping Geometry (optional)” was selected for each of the clipping.
 * Delete and create raster attribute table: Once the clipping has been done, the raster has to be recalculated first through Arctoolbox > Data management tools > Raster > Raster properties > Delete Raster Attribute Table and then through Arctoolbox > Data management tools > Raster > Raster properties > Build Raster Attribute Table; the "overwrite" option has been selected.

 Other tools used for the raster files in Esri ArcGIS have been the spatial analyst tools (in particular, Zonal > Zonal Statistics). As an additional check, the colour scheme of each of the newly created raster for each of the spatial/territorial attributes as per letters a)-h) above has been changed to check the consistency of its overlay with the original HRL IMD file. However, a perfect match between the shapefiles as per letters a)-h) and the raster files could not be achieved since the raster files are composed of 20x20mt cells.

The newly created attribute tables of each of the raster files have been exported and saved as .txt files. These .txt files have then been copied in the excel corresponding to the final published dataset.

barcelona

display_name: barcelona

copernicus

display_name: copernicus

imperviousness

display_name: imperviousness

milan

display_name: milan

soil-sealing

display_name: soil-sealing

urban-atlas

display_name: urban-atlas

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: Soil sealing Barcelona and Milan different territorial levels


zone-moderee

maintainer: Frédéric Ducry, Responsable de diffusion

title_for_slug: zone-moderee

name: Service de la mobilité et de l’aménagement des espaces publics Ville de Lausanne, Gestionnaire de la donnée

label: Métadonnée sur geocat.ch

label: Métadonnée complète sur viageo.ch

fr: Zone modérée (SHP)

fr: Télécharger sur viageo.ch

fr: Guichet cartographique de Lausanne

media_type: SERVICE

fr: Guichet en ligne : Guichet cartographique de Lausanne

fr: Zone modérée (CSV)

fr:
Données des zones modérées de la commune de Lausanne.
Contient les zones 30, piétonnes, et de rencontres.

spatial: Lausanne

geography

fr: Géographie

de: Geographie

en: Geography

it: Geografia

mobility

fr: Mobilité et transports

de: Mobilität und Verkehr

en: Mobility and Transport

it: Mobilità e trasporti

lausanne

fr: Ville de Lausanne

fr: Le service de la mobilité et de l’aménagement des espaces publics (MAP) de la Ville de Lausanne publie ses géodonnées sous forme de fichiers à télécharger. Toutes les géodonnées actuellement disponibles et soumises aux conditions générales d'aquisition et d'utilisation des géodonnées de base en libre accès sont listées ici.

political_level: commune

value: Stadt Lausanne - DCAT Harvester

fr: Zone modérée


en: precipitation soil-water-balance relative-humidity solar-radiation temperature growing-season-length growing-degree-days

en: CHELSA-BIOCLIM+ Technical Documentation

media_type: PDF

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

en: CHELSA-BIOCLIM+

media_type: geotiff

en: Global extented set of bioclimatic variables at different temporal (e.g. monthly, annual, long term normals) resolution in Geotiff format.

format: GeoTIFF

en: A multitude of physical and biological processes on which ecosystems and human societies depend are governed by climatic conditions. Understanding how these processes are altered by climate change is central to mitigation efforts. Based on mechanistically downscaled climate data, we developed a set of climate-related variables at yet unprecedented spatiotemporal detail as a basis for environmental and ecological analyses. We created gridded data for near-surface relative humidity (hurs), cloud area fraction (clt), near-surface wind speed (sfcWind), vapour pressure deficit (vpd), surface downwelling shortwave radiation (rsds), potential evapotranspiration (pet), climate moisture index (cmi), and site water balance (swb), at a monthly temporal and 30 arcsec spatial resolution globally starting 1980 until 2018. At the same spatial resolution, we further estimated climatological normals of frost change frequency (fcf), snow cover days (scd), potential net primary productivity (npp), growing degree days (gdd), and growing season characteristics for the periods 1981-2010, 2011-2040, 2041-2070, and 2071-2100, considering three shared socioeconomic pathways (SSP126, SSP370, SSP585) and five Earth system models. Time-series variables showed high accuracy when validated against observations from meteorological stations. Climatological normals were also highly correlated to observations although some variables showed notable biases, e.g., snow cover days (scd). Together, the data sets presented here allow improving our understanding of patterns and processes that are governed by climate, including the impact of recent and future climate changes on the world’s ecosystems and associated services to societies.

growing-degree-days

display_name: growing-degree-days

growing-season-length

display_name: growing-season-length

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

soil-water-balance

display_name: soil-water-balance

solar-radiation

display_name: solar-radiation

temperature

display_name: temperature

en: CHELSA-BIOCLIM+ A novel set of global climate-related predictors at kilometre-resolution


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

en: Data plot of the forest station (JUB)

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

en: Site aerial photo (Jussy)

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

en: Data plot of the field station (JUF)

en: Data file.

en: Site photo of the field station (JUF)

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

en: Site photo of the forest station (JUB)

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

en: Forest station data (JUB)

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 Jussy in Switzerland where one station is located within a natural broad-leaved forest stand (JUB) with sessile oak (_Quercus petrea_; 90 yrs), aspen (_Populus tremula_; 60 yrs) and European hornbeam (_Carpinus betulus_; 60 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, JUF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Jussy 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

swiss-forest-lab

display_name: swiss-forest-lab

wind-speed

display_name: wind-speed

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


horodateur

maintainer: Service de la mobilité et de l’aménagement des espaces publics Ville de Lausanne, Gestionnaire de la donnée

title_for_slug: horodateur

name: Frédéric Ducry, Responsable de diffusion

fr: Horodateur (CSV)

fr: Horodateur (SHP)

fr: Données des horodateurs de la commune de Lausanne. Contient les emplacements géographiques des horodateurs.

geography

en: Geography

mobility

en: Mobility and Transport

fr: Horodateur


en: place-attachment landscape sense-of-place urban-ecology electrodermal-activity

en: 02. Questionnaire Data

media_type: ZIP

en: 01. EDA Processed Data

en: 04. Scripts

en: 00. EDA Raw Data

en: 03. Publication Ready Data

en: 05. Visuals

en: This repository contains data on EDA measurements of visitors with different cultural backgrounds in virtual urban park settings. The parks are a Persian garden (Shiraz, Iran) and a historical park in Zurich, Switzerland. The cultural background of the visitors is Persian and Central European. The repository contains raw data from EDA, processed time series and statistical procedures.

electrodermal-activity

display_name: electrodermal-activity

landscape

display_name: landscape

place-attachment

display_name: place-attachment

sense-of-place

display_name: sense-of-place

urban-ecology

display_name: urban-ecology

education

fr: Education, science

en: Education and science

envidat

fr: EnviDat: le portail de données environnementales

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: Electrodermal Activity (EDA) of Bi-cultural Visitors In Virtual Park Settings


stationnement

title_for_slug: stationnement

fr: Stationnement (CSV)

fr: Stationnement (SHP)

fr:
Données du stationnement public de la commune de Lausanne.
Contient les places de parcs et les zones macarons.

fr: Stationnement


marquage-routier

title_for_slug: marquage-routier

fr: Marquage routier (SHP)

fr: Télécharger sur viageo.ch

fr: Marquage routier (CSV)

fr: Guichet cartographique de Lausanne

fr: Guichet en ligne : Guichet cartographique de Lausanne

fr:
Données du marquage routier de la commune de Lausanne.
Contient le balisage au sol.

fr: Marquage routier


mobilier-urbain

title_for_slug: mobilier-urbain

fr: Mobilier urbain (CSV)

fr: Mobilier urbain (SHP)

fr:
Données du mobilier urbain de la commune de Lausanne.
Contient les emplacements géographiques des bancs publics.

geography

fr: Géographie

mobility

fr: Mobilité et transports

lausanne

fr: Ville de Lausanne

fr: Le service de la mobilité et de l’aménagement des espaces publics (MAP) de la Ville de Lausanne publie ses géodonnées sous forme de fichiers à télécharger. Toutes les géodonnées actuellement disponibles et soumises aux conditions générales d'aquisition et d'utilisation des géodonnées de base en libre accès sont listées ici.

fr: Mobilier urbain


fr: Réseau routier du 30km/h de nuit (SHP)

fr:
Données du réseau routier du 30km/h de nuit de la commune de Lausanne.
Contient les axes des voies de communication, hors zones modérées déjà existantes, impactés par les restrictions nocturne de vitesse.

fr: Réseau routier du 30km/h de nuit


amenagement-cyclable

title_for_slug: amenagement-cyclable

fr: Aménagement cyclable (SHP)

fr: Télécharger sur viageo.ch

fr: Aménagement cyclable (CSV)

fr:
Données des aménagements cyclables de la commune de Lausanne.
Contient les aménagements, les emplacements en libre-service, les pompes et les parkings pour vélos.

fr: Aménagement cyclable


fr: oreb zonenplan bauzone grundplan flachennutzungsplan

label: geocat.ch permalink

fr: WFS-Dienst des Kantons Schwyz

de: WFS-Dienst des Kantons Schwyz

en: WFS-Dienst des Kantons Schwyz

it: WFS-Dienst des Kantons Schwyz

format: WFS

fr: ÖREB: Nutzungsplanung kommunal, Muotathal, Sondernutzungsplanung

format: WMS

fr: ÖREB Sondernutzungsplanung Gemeinde Muotathal

fr: Die Gemeinden können Sondernutzungspläne erlassen und damit die allgemeine Nutzungsplanung in einem konkreten, klar umgrenzten Areal weiter präzisieren.

spatial: Muotathal

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


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

en: Distance to target results

media_type: XLSX

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)

format: XLS

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: 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: 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: 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: 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:
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

education

fr: Education, science

envidat

fr: EnviDat: le portail de données environnementales

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


label: WebGIS Kanton Schwyz, Thema einblenden

en: WFS-Dienst des Kantons Schwyz

fr: Lärmempfindlichkeitsstufen

de: Lärmempfindlichkeitsstufen

en: Lärmempfindlichkeitsstufen

it: Lärmempfindlichkeitsstufen

fr: Lärmempfindlichkeitsstufen (in Nutzungszonen). Geobasisdatensatz Nr. 145 gemäss KGeoiV Kanton Schwyz (SRSZ 214.111). Der Datensatz ist auch Bestandteil des Katasters der öffentlich-rechtlichen Eigentumsbeschränkungen (ÖREB)

spatial: Kanton Schwyz (SZ)


fr: zonenplan bauzone grundplan nutzungsplan flachennutzungsplan

fr: Nutzungsplanung kommunal Gemeinde Sattel

fr: Nutzungsplanung kommunal. Geobasisdatensätze Nr. 73D - 73F gemäss KGeoiV Kanton Schwyz (SRSZ 214.111).

spatial: Sattel


de: zonenplan bauzone grundplan nutzungsplan flachennutzungsplan

fr: Nutzungsplanung kommunal Gemeinde Wangen

de: Nutzungsplanung kommunal. Geobasisdatensätze Nr. 73D - 73F gemäss KGeoiV Kanton Schwyz (SRSZ 214.111).

spatial: Wangen (SZ)


fr: nutzungsplan zonenplan bauzone grundplan flachennutzungsplan

fr: Nutzungsplanung kommunal Gemeinde Gersau

en: Nutzungsplanung kommunal. Geobasisdatensätze Nr. 73D - 73F gemäss KGeoiV Kanton Schwyz (SRSZ 214.111).

spatial: Gersau


abfall-und-rohstoffe

title_for_slug: abfall-und-rohstoffe

fr: abbaustelle bausperrgut rohstoffgewinnung biogener-abfall sonderabfall bauabfalle rekultivierung abfallablagerung kompostierbarer-abfall altmetall

fr: Abfall/Rohstoffe: Steinbruch

de: Abfall/Rohstoffe: Steinbruch

en: Abfall/Rohstoffe: Steinbruch

it: Abfall/Rohstoffe: Steinbruch

fr: Abfall/Rohstoffe: Altmetalle

de: Abfall/Rohstoffe: Altmetalle

en: Abfall/Rohstoffe: Altmetalle

it: Abfall/Rohstoffe: Altmetalle

fr: Abfall/Rohstoffe: Grüngut

de: Abfall/Rohstoffe: Grüngut

en: Abfall/Rohstoffe: Grüngut

it: Abfall/Rohstoffe: Grüngut

fr: A011a: Abfall und Rohstoffe

de: A011a: Abfall und Rohstoffe

en: A011a: Abfall und Rohstoffe

it: A011a: Abfall und Rohstoffe

fr: Abfall/Rohstoffe: Deponie Typ B

de: Abfall/Rohstoffe: Deponie Typ B

en: Abfall/Rohstoffe: Deponie Typ B

it: Abfall/Rohstoffe: Deponie Typ B

fr: Abfall/Rohstoffe: Altfahrzeuge

de: Abfall/Rohstoffe: Altfahrzeuge

en: Abfall/Rohstoffe: Altfahrzeuge

it: Abfall/Rohstoffe: Altfahrzeuge

fr: Abfall/Rohstoffe: Sonderabfälle aus Gewerbe

fr: Abfall/Rohstoffe: Kiesgrube, Sandgrube

de: Abfall/Rohstoffe: Kiesgrube, Sandgrube

en: Abfall/Rohstoffe: Kiesgrube, Sandgrube

it: Abfall/Rohstoffe: Kiesgrube, Sandgrube

fr: Abfall/Rohstoffe: Deponie Typ A

de: Abfall/Rohstoffe: Deponie Typ A

en: Abfall/Rohstoffe: Deponie Typ A

it: Abfall/Rohstoffe: Deponie Typ A

fr: Als Ergänzung zu www.abfall.ch werden in der Karte „Abfall und Rohstoffe“ die Abfallanlagen für Bauabfälle und -sperrgut, biogene Abfälle, Altmetalle, gewerbliche Sonderabfälle sowie die Rohstoffgewinnung und Rekultivierungsstellen geografisch dargestellt. Primär soll die Karte einen Überblick über die einzelnen Standorte verschaffen.

fr: Standorte Abfall und Rohstoffe

de: Standorte Abfall und Rohstoffe

en: Standorte Abfall und Rohstoffe

it: Standorte Abfall und Rohstoffe


fr: bauzone zonenplan grundplan nutzungsplan flachennutzungsplan

fr: WFS-Dienst des Kantons Schwyz

fr: Nutzungsplanung kommunal Gemeinde Muotathal

it: Nutzungsplanung kommunal. Geobasisdatensätze Nr. 73D - 73F gemäss KGeoiV Kanton Schwyz (SRSZ 214.111).


fr: zonenplan bauzone grundplan nutzungsplan flachennutzungsplan

fr: Nutzungsplanung kommunal Gemeinde Vorderthal

fr: Nutzungsplanung kommunal. Geobasisdatensätze Nr. 73D - 73F gemäss KGeoiV Kanton Schwyz (SRSZ 214.111).

spatial: Vorderthal


fr: nutzungsplanung strasse kanton oreb flachennutzungsplan

fr: OeREB Nutzungsplanung kantonal Teil Strasse, Anschluss Steinerstrasse Schwyz, Grundnutzung

fr: OeREB Nutzungsplanung kantonal Teil Strasse, Anschluss Steinerstrasse Schwyz, linienbezogene Festlegung

fr: OeREB Nutzungsplanung kantonal Teil Strasse, Anschluss Steinerstrasse Schwyz, überlagernde Festlegung

fr: Nutzungsplanung kantonal Teil Strasse. Geobasisdatensatz Nr. 73B gemäss KGeoiV Kanton Schwyz (SRSZ 214.111)

fr: ÖREB Nutzungsplanung kantonal Teil Strasse Anschluss Steinerstrasse Schwyz


spatial: Innerthal

fr: ÖREB Nutzungsplanung kommunal Gemeinde Innerthal


fr: oreb zonenplan bauzone grundplan flachennutzung

fr: ÖREB: Nutzungsplanung kommunal, Freienbach, Sondernutzungsplanung

fr: ÖREB Sondernutzungsplanung Gemeinde Freienbach

fr: Die Gemeinden können Sondernutzungspläne erlassen und damit die allgemeine Nutzungsplanung in einem konkreten, klar umgrenzten Areal weiter präzisieren.

spatial: Freienbach


en: zonenplan bauzone grundplan nutzungsplan flachennutzungsplan

fr: Nutzungsplanung kommunal Gemeinde Lachen

spatial: Lachen


fr: kanton flachmoor biotop

de: kanton flachmoor biotop

en: kanton flachmoor biotop

it: kanton flachmoor biotop

fr: WFS-Dienst des Kantons Schwyz

fr: ÖREB: Kantonale Nutzungsplanung: Naturschutzgebiete, linienbezogene Festlegung

fr: ÖREB: Kantonale Nutzungsplanung: Naturschutzgebiete, Grundnutzung

fr: ÖREB: Kantonale Nutzungsplanung: Naturschutzgebiete, überlagernde Festlegung

fr: ÖREB: Kantonale Nutzungsplanung: Naturschutzgebiete, objektbezogene Festlegung

fr: Rechtsgültige Darstellung aller kantonalen Nutzungspläne im Bereich Natur- und Heimatschutz

fr: Nutzungsplanung kantonal, Bereich Natur- und Heimatschutz ÖREB


fr: oreb zonenplan bauzone grundplan flachennutzungsplan

fr: ÖREB: Nutzungsplanung kommunal, Unteriberg, Sondernutzungsplanung

fr: ÖREB Sondernutzungsplanung Gemeinde Unteriberg

de: Die Gemeinden können Sondernutzungspläne erlassen und damit die allgemeine Nutzungsplanung in einem konkreten, klar umgrenzten Areal weiter präzisieren.

spatial: Unteriberg


fr: zonenplan bauzone grundplan nutzungsplan flachennutzungsplan

de: WFS-Dienst des Kantons Schwyz

fr: Nutzungsplanung kommunal. Geobasisdatensätze Nr. 73D - 73F gemäss KGeoiV Kanton Schwyz (SRSZ 214.111).

spatial: Rothenthurm

fr: ÖREB Nutzungsplanung kommunal Gemeinde Rothenthurm


it: zonenplan bauzone grundplan nutzungsplan flachennutzungsplan

amt-geoinformation-sz

de: Amt für Geoinformation Kanton Schwyz

fr: ÖREB Nutzungsplanung kommunal Gemeinde Vorderthal


fr: zonenplan grundplan bauzone nutzungsplan flachennutzungsplan

spatial: Tuggen

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: ÖREB Nutzungsplanung kommunal Gemeinde Tuggen


fr: Nutzungsplanung kommunal Gemeinde Morschach

spatial: Morschach


fr: Solarkataster

de: Solarkataster

en: Solarkataster

it: Solarkataster

fr: WFS-Dienst des Kantons Schwyz

fr: Dieses Projekt stellt einerseits die im Kanton Schwyz eingeführte Pflicht der Eigenstromerzeugung (Photovoltaik) in Form einer Vollzugsfläche dar. Andererseits werden die Globalstrahlungsdaten dargestellt, auf welchen die Berechnung der Vollzugsfläche beruht.


de: zonenplan bauzone grundplan nutzungsplan flachennutzungsplan

spatial: Altendorf

fr: ÖREB Nutzungsplanung kommunal Gemeinde Altendorf


fr: nutzungsplan zonenplan bauzone grundplan flachennutzungsplan

fr: Nutzungsplanung kommunal Gemeinde Steinen

de: Nutzungsplanung kommunal. Geobasisdatensätze Nr. 73D - 73F gemäss KGeoiV Kanton Schwyz (SRSZ 214.111).

spatial: Steinen


fr: kanton flachmoor oreb nutzungsplanung biotop

fr: OeREB Nutzungsplanung kantonal, Bereich Natur- und Heimatschutz, Bätzimatt, überlagernde Festlegung

fr: OeREB Nutzungsplanung kantonal, Bereich Natur- und Heimatschutz, Bätzimatt, linienbezogene Festlegung

fr: OeREB Nutzungsplanung kantonal, Bereich Natur- und Heimatschutz, Bätzimatt, Grundnutzung

fr: OeREB Nutzungsplanung kantonal, Bereich Natur- und Heimatschutz, Bätzimatt, objektbezogene Festlegung

fr: Naturschutzgebiet Bätzimatt Rechtsgültige Darstellung des kantonalen Nutzungsplanes im Bereich Natur- und Heimatschutz

fr: ÖREB Nutzungsplanung kantonal Bereich Natur- und Heimatschutz Bätzimatt


fr: grundplan zonenplan bauzone nutzungsplan flachennutzungsplan

fr: Nutzungsplanung kommunal Gemeinde Wollerau

fr: Nutzungsplanung kommunal. Geobasisdatensätze Nr. 73D - 73F gemäss KGeoiV Kanton Schwyz (SRSZ 214.111).

spatial: Wollerau


fr: zonenplan bauzone grundplan nutzungsplan flachennutzungsplan

spatial: Feusisberg

fr: ÖREB Nutzungsplanung kommunal Gemeinde Feusisberg


fr: WFS-Dienst des Kantons Schwyz

spatial: Steinerberg

fr: ÖREB Nutzungsplanung kommunal Gemeinde Steinerberg


fr: Darstellung der Imkerstände und Sperrkreise bei Tierseuchenausbrüchen der vier Kantone NW, OW, SZ, UR auf dem WebGis des Kantons Schwyz.

fr: Der Veterinärdienst der Urkantone liefert die Grunddaten der Imker an das Amt für Geoinformation. Mit Hilfe der Grunddaten werden in einem ersten Schritt sämtliche erfassten Bienenstände auf dem WebGIS des Kantons Schwyz dargestellt. Bei einem Tierseuchenausbruch meldet der VdU dem Amt für Geoinformationen den betroffenen Stand. Je nach Seuche (Sauerbrut = 1 km, Faulbrut = 2 km) wird der Sperrkreis auf dem WebGIS SZ eingezeichnet. Via Link auf der Homepage des Labors der Urkantone können interessierte, involvierte Imker nachschauen, wo überall Bienenstände sind und welche Gebiete allenfalls wegen einer Tierseuche gesperrt sind.

spatial: Kanton Schwyz (SZ)

name: Veterinärdienst der Urkantone (VdU), Laboratorium der Urkantone


fr: Nutzungsplanung kommunal Gemeinde Schübelbach

spatial: Schübelbach


fr: bauzone zonenplan grundplan nutzungsplan flachennutzungsplan

fr: ÖREB Nutzungsplanung kommunal Gemeinde Unteriberg


fr: kanton flachmoor oreb nutzungsplanung biotop

fr: OeREB Nutzungsplanung kantonal, Bereich Natur- und Heimatschutz, Schwantenau, objektbezogene Festlegung

fr: OeREB Nutzungsplanung kantonal, Bereich Natur- und Heimatschutz, Schwantenau, überlagernde Festlegung

fr: OeREB Nutzungsplanung kantonal, Bereich Natur- und Heimatschutz, Schwantenau, Grundnutzung

fr: OeREB Nutzungsplanung kantonal, Bereich Natur- und Heimatschutz, Schwantenau, linienbezogene Festlegung

fr: Naturschutzgebiet Schwantenau Rechtsgültige Darstellung des kantonalen Nutzungsplanes im Bereich Natur- und Heimatschutz

fr: ÖREB Nutzungsplanung kantonal Bereich Natur- und Heimatschutz Schwantenau


fr: Nutzungsplanung kommunal Gemeinde Arth

de: Nutzungsplanung kommunal Gemeinde Arth

en: Nutzungsplanung kommunal Gemeinde Arth

it: Nutzungsplanung kommunal Gemeinde Arth

fr: Nutzungsplanung kommunal. Geobasisdatensätze Nr. 73D - 73F gemäss KGeoiV Kanton Schwyz (SRSZ 214.111).

spatial: Arth


baustellen

title_for_slug: baustellen

fr: Baustellen

de: Baustellen

en: Baustellen

it: Baustellen

fr: Baustellen, welche mehr als 5 Tage dauern, werden erfasst.


fr: zonenplan grundplan bauzone nutzungsplan flachennutzungsplan

fr: WFS-Dienst des Kantons Schwyz

en: WFS-Dienst des Kantons Schwyz

it: WFS-Dienst des Kantons Schwyz

fr: Nutzungsplanung kommunal Gemeinde Freienbach


fr: zonenplan bauzone grundplan nutzungsplan flachennutzungsplan

spatial: Lauerz

fr: ÖREB Nutzungsplanung kommunal Gemeinde Lauerz


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


flughindernisse-kanton-schwyz

title_for_slug: flughindernisse-kanton-schwyz

fr: Flughindernisse Kanton Schwyz

de: Flughindernisse Kanton Schwyz

en: Flughindernisse Kanton Schwyz

it: Flughindernisse Kanton Schwyz

fr: Linienförmige Lufthindernisse im Kanton Schwyz mit Maximalhöhen von 25 m über Boden. Es handelt sich vorwiegend um Seilanlagen der Landwirtschaft wie Transport- und Heuseile. Die Daten sollen in erster Linie dazu dienen, die Flugsicherheit bei Helikoptereinsätzen zu erhöhen. Gewähr auf Vollständigkeit und Haftungsansprüche sind ausgeschlossen. Auch bezüglich Genauigkeit erheben wir nicht den Anspruch auf Geometerqualität. Die Nutzung dieser Daten erfolgt auf eigene Verantwortung. Hinweise, die zur Aktuellhaltung dieses Datensatzes beitragen, werden gerne entgegengenommen. Diese Daten ergänzen die Datenbank des Bundesamtes für Zivilluftfahrt BAZL, welches für die - meldepflichtigen - Luftfahrthindernisse über 25 m Höhe zuständig ist.


fr: kanton flachmoor oreb nutzungsplanung biotop

fr: OeREB Nutzungsplanung kantonal, Bereich Natur- und Heimatschutz, Frauenwinkel, überlagernde Festlegung

fr: OeREB Nutzungsplanung kantonal, Bereich Natur- und Heimatschutz, Frauenwinkel, objektbezogene Festlegung

fr: OeREB Nutzungsplanung kantonal, Bereich Natur- und Heimatschutz, Frauenwinkel, Grundnutzung

fr: OeREB Nutzungsplanung kantonal, Bereich Natur- und Heimatschutz, Frauenwinkel, linienbezogene Festlegung

fr: Naturschutzgebiet Frauenwinkel Rechtsgültige Darstellung des kantonalen Nutzungsplanes im Bereich Natur- und Heimatschutz

fr: ÖREB Nutzungsplanung kantonal Bereich Natur- und Heimatschutz Frauenwinkel


de: kanton flachmoor oreb nutzungsplanung biotop

fr: WFS-Dienst des Kantons Schwyz

fr: OeREB Nutzungsplanung kantonal, Bereich Natur- und Heimatschutz, Hopfräben, linienbezogene Festlegung

fr: OeREB Nutzungsplanung kantonal, Bereich Natur- und Heimatschutz, Hopfräben, Grundnutzung

fr: OeREB Nutzungsplanung kantonal, Bereich Natur- und Heimatschutz, Hopfräben, objektbezogene Festlegung

fr: OeREB Nutzungsplanung kantonal, Bereich Natur- und Heimatschutz, Hopfräben, überlagernde Festlegung

fr: Naturschutzgebiet Hopfräben Rechtsgültige Darstellung des kantonalen Nutzungsplanes im Bereich Natur- und Heimatschutz

fr: ÖREB Nutzungsplanung kantonal Bereich Natur- und Heimatschutz Hopfräben


geschwindigkeitsbereiche

title_for_slug: geschwindigkeitsbereiche

fr: erlaubte Geschwindigkeiten auf Kantonsstrassen

fr: Abbildung der Geschwindigkeit auf den Kantonsstrassen des Kantons Schwyz

fr: Geschwindigkeitsbereiche

de: Geschwindigkeitsbereiche

en: Geschwindigkeitsbereiche

it: Geschwindigkeitsbereiche


fr: Revitalisierung Gewässer: Ökomorphologische Klassierung

fr: Ökomorphologie Fliessgewässer

de: Ökomorphologie Fliessgewässer

en: Ökomorphologie Fliessgewässer

it: Ökomorphologie Fliessgewässer

fr: A026b: Ökomorphologie

de: A026b: Ökomorphologie

en: A026b: Ökomorphologie

it: A026b: Ökomorphologie

fr: Darstellung des ökomorphologischen Zustandes der Fliessgewässer im Kanton Schwyz (Ökomorphologie, Stufe F). Die strukturelle Beschaffenheit eines Fliessgewässers hat neben der Gewässerqualität massgeblichen Einfluss auf dessen ökologische Funktionsfähigkeit. Die Ökomorphologiekartierung dient daher der Identifizierung strukturell stark beeinträchtiger Gewässerabschnitte und als Planungsgrundlage für zukünftige Revitalisierungen. Andere Datengrundlagen: - Digitales Gewässernetz der Landeskarten 1:25’000 - Primärflächen 1:25’000 - Gemeinde, Bezirks- und Kantonsgrenzen 1:25’000 - Höhenmodell RIMINI (Auflösung 250 m in der Horizontalen) - Digitale Landeskarten 1:25’000 - Digitale Landeskarten 1:100’000 - Fischgewässer-Karte des Kantons Schwyz 2020 wurden die bestehenden Daten auf das neue Refrenz-Fliessgewässernetz des Kanton Schwyz referenziert.


waldabstandslinien

title_for_slug: waldabstandslinien

fr: waldabstand waldgrenze oreb

de: waldabstand waldgrenze oreb

en: waldabstand waldgrenze oreb

it: waldabstand waldgrenze oreb

fr: Waldabstandslinien

de: Waldabstandslinien

en: Waldabstandslinien

it: Waldabstandslinien

fr: Waldabstandslinien gemäss §67 PBG "Bauten und Anlagen" haben gegenüber Wäldern einen Mindestabstand von 15 m ab Waldgrenze einzuhalten. Weitergehenden Bestimmungen bzw. Ausnahmeregelungen finden sich in den Baureglementen zu den Nutzungsplänen der Gemeinden. Der Datensatz enthält nur diejenigen Waldabstandslinien, die aufgrund einer Sonderbestimmung im Baureglement der Gemeinde gegenüber dem gesetzlich festgelegten Waldabstand von 15 m einen Minderabstand zulassen. Der Datensatz ist auch Bestandteil des Katasters der öffentlich-rechtlichen Eigentumsbeschränkungen (ÖREB)


ortstafeln

title_for_slug: ortstafeln

fr: WFS-Dienst des Kantons Schwyz

de: WFS-Dienst des Kantons Schwyz

fr: Ortstafeln

de: Ortstafeln

en: Ortstafeln

it: Ortstafeln

fr: Abbildung der Ortstafeln auf den Kantonsstrassen des Kantons Schwyz. Die Daten werden bei Bedarf nachgeführt.


fr: belasteter-standort altstandort grundwasserschutz altlast

fr: Kataster der belasteten Standorte (KbS)

de: Kataster der belasteten Standorte (KbS)

en: Kataster der belasteten Standorte (KbS)

it: Kataster der belasteten Standorte (KbS)

fr: Bei den im Kataster aufgenommen Standorten handelt es sich um Standorte mit begrenzter Ausdehnung, bei denen feststeht oder mit grosser Wahrscheinlichkeit zu erwarten ist, dass sie mit Abfällen belastet sind (Art. 5 Abs. 3 Altlasten-Verordnung). Nicht in den Kataster eingetragen werden (insbesondere über den Luftpfad verursachte) grossflächige diffuse Belastungen sowie Areale mit ausschliesslich belasteter Gebäudesubstanz (z. B. durch Asbest). Beim Kataster der belasteten Standorte handelt es sich um ein dynamisches Arbeitsinstrument, bei dem abhängig von den aktuellen Erkenntnissen Standorte aufgenommen, verändert oder wieder gelöscht werden. Der Kataster gibt somit immer nur den aktuellen Wissensstand wieder, eine Gewähr für die Richtigkeit (z. B. dass ein nicht eingetragenes Grundstück unbelastet ist) besteht nicht. Insbesondere im Rahmen von Bauvorhaben oder Nutzungsänderungen können altlastenrechtliche oder abfallrechtliche Massnahmen notwendig werden. Der Datensatz ist auch Bestandteil des Katasters der öffentlich-rechtlichen Eigentumsbeschränkungen (ÖREB)

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.


sportanlagen

title_for_slug: sportanlagen

fr: A102a: Sportanlagen

de: A102a: Sportanlagen

en: A102a: Sportanlagen

it: A102a: Sportanlagen

fr: Sportanlagen

de: Sportanlagen

en: Sportanlagen

it: Sportanlagen

fr: Übersicht aller Sportanlagen im Kanton Schwyz. Neben den Koordinaten und dem Typ der Sportanlage ist jeweils auch ein Link zum Betreiber enthalten.


panoramatafeln

title_for_slug: panoramatafeln

fr: Panoramatafeln

de: Panoramatafeln

en: Panoramatafeln

it: Panoramatafeln

fr: Der Kanton Schwyz ist zuständig für das Hauptwanderwegnetz und trägt dafür die Kosten für Planung, Bau, rechtliche Sicherung, Unterhalt und Markierung, soweit dafür nicht Dritte zuständig sind. Um die Attraktivität des Hauptwanderwegnetzes zu steigern, wurden an geeigneten Orten zur Erklärung der Bergwelt und Umgebung Panoramatafeln erstellt. Es gibt über den ganzen Kanton verteilt weitere Panoramatafeln, welche auf private Initiative oder von Gemeinden/Koporationen erstellt wurden und von diesen auch unterhalten werden. Diese Panoramatafeln sind in diesem Datensatz jedoch nicht enthalten.


wanderwegsperrungen

title_for_slug: wanderwegsperrungen

fr: wanderweg tourismus-und-freizeit

de: wanderweg tourismus-und-freizeit

en: wanderweg tourismus-und-freizeit

it: wanderweg tourismus-und-freizeit

fr: Wanderwegsperrungen

de: Wanderwegsperrungen

en: Wanderwegsperrungen

it: Wanderwegsperrungen

fr: Auf dem Schwyzer Wanderwegnetz kommt es immer wieder zu kürzeren oder längeren Sperrungen, welche aufgrund von Sanierungen, Holzereiarbeiten, Rutschung oder anderen Naturgefahren notwendig sind. Solche Sperrungen sollen dem Fachbereich Fuss- und Wanderwege gemeldet werden, damit die Sperrungen inklusive Infoblatt auf dem WebGIS publiziert und aktuell gehalten werden können. Damit dies möglich ist, muss auch das Ende jeder Sperrung gemeldet werden.


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: Grundwasserschutzareale ÖREB

de: Grundwasserschutzareale ÖREB

en: Grundwasserschutzareale ÖREB

it: Grundwasserschutzareale ÖREB


fr: rettungssystem herzkreislaufsystem

de: rettungssystem herzkreislaufsystem

en: rettungssystem herzkreislaufsystem

it: rettungssystem herzkreislaufsystem

fr: WFS-Dienst des Kantons Schwyz

en: WFS-Dienst des Kantons Schwyz

it: WFS-Dienst des Kantons Schwyz

fr: AED-Standorte

de: AED-Standorte

en: AED-Standorte

it: AED-Standorte

fr: Diese Datenebene zeigt die AED-Standorte (automatisierter externer Defibrillator) im Kanton Schwyz, die rund um die Uhr zugänglich sind.


maintainer: Kantonspolizei des Kantons Zürich, Abteilung Kriminalpolizeiliches Datenmanagement

de: kriminalitaet straftaten einbrueche strafbarehandlungen strafdelikte gemeinden

label: Vorbereiteter Code in R oder Python für jedes Dataset mit csv-Datei(en)

de: Anzahl Einbrüche nach Gemeinden und in den Stadtkreisen der Stadt Zürich

de: Das Total Einbrüche beinhaltet alle polizeilich bekannten, vollendeten und versuchten Einbruchs- und Einschleichdiebstähle. Die Bevölkerungszahlen sind der Einwohnerstatistik des Bundes am Ende des Vorjahres entnommen (bis 2010 ESPOP, ab 2011 STATPOP). Damit wird die Häufigkeitszahl berechnet (Anzahl registrierten Straftaten pro 1000 Einwohner).

de: Anzahl der Einbrüche wie sie in der Kriminalstatistik der Kantonspolizei Zürich erfasst werden. Aufgegliedert nach Gemeinden (im Fall der Stadt Zürich nach Stadtkreisen) und ergänzt mit Angaben zur Bevölkerungszahl. Die polizeiliche Kriminalstatistik (PKS) des Kantons Zürich wird jährlich publiziert. Die polizeiliche Kriminalstatistik verzeichnet nur die polizeilich bekannt gewordene Kriminalität - das sogenannte Hellfeld. Sie ist eine Ausgangsstatistik. D.h. die Straftaten erscheinen nicht zum Tatzeitpunkt in der Statistik. Ausschlaggebend für die Zählung in der PKS ist der Zeitpunkt, an dem die Polizei einen ersten Rapport zur Straftat abgeschlossen hat.

einbrueche

display_name: einbrueche

gemeinden

display_name: gemeinden

kriminalitaet

display_name: kriminalitaet

strafbarehandlungen

display_name: strafbarehandlungen

strafdelikte

display_name: strafdelikte

straftaten

display_name: straftaten

crime

fr: Criminalité, droit pénal

de: Kriminalität, Strafrecht

en: Crime, criminal justice

it: Criminalità, diritto penale

kantonspolizei-kanton-zuerich

de: Kantonspolizei Zürich

de: Im Auftrag der Regierung und damit der Bevölkerung übernimmt die Kantonspolizei Zürich Verantwortung für die Sicherheit im Kanton Zürich, indem sie vorausschauend handelt, entschlossen Hilfe leistet und Straftaten konsequent aufklärt. Dabei folgt sie den Grundsätzen der Verhältnismässigkeit und der Gesetzmässigkeit.

value: ZH-Kt Kantonspolizei - DCAT Harvester

de: Anzahl Einbrüche nach Gemeinden des Kantons Zürich


maintainer: Kantonspolizei des Kantons Zürich, Verkehrstechnische Abteilung

de: sachschaden verkehrsunfaelle motorraeder fahrzeuge langsamverkehr strassenverkehrsunfaelle motorfahrzeuge strassenverkehr

label: Unfallkarte der Schweiz (Geoportal des Bundes)

label: Polizeilich registrierte Verkehrsunfälle auf dem Stadtgebiet Zürich seit 2011 (Dienstabteilung Verkehr, Sicherheitsdepartement Stadt Zürich)

label: Unfalldaten der Schweiz, Statistiken nach Kantonen (Bundesamt für Strassen ASTRA)

de: Zu jedem Strassenverkehrsunfall sind der Unfallort (geokodiert), das Jahr, der Monat, der Wochentag, die Unfallstunde, die Strassenart, der Unfalltyp (ab 1. Juli 2015 inklusive Bagatellunfälle zB. Parkschäden), die Unfallbeteiligung ('Fussgänger', 'Velo (ohne E-Bikes)', 'Motorrad') und die Unfallschwerekategorie verfügbar. Detaillierte Definitionen der Variabeln sind in der Ressource "Minimales Geodatenmodell Strassenverkehrsunfallorte (ASTRA)" dokumentiert.

media_type: application/json

format: JSON

mimetype: application/json

de: Detaillierte Definitionen der enthaltenen Variabeln.

de: Die Verkehrsunfallstatistik des Kantons Zürich (VUSTA) enthält die Strassenverkehrsunfälle mit Personen- und Sachschäden, die durch die Kantonspolizei Zürich, die Dienstabteilung Verkehr der Stadt Zürich sowie die Stadtpolizei Winterthur registriert wurden. Sie wird einmal jährlich aktualisiert, jeweils gegen Ende des ersten Quartals des Folgejahres

fahrzeuge

display_name: fahrzeuge

langsamverkehr

display_name: langsamverkehr

motorfahrzeuge

display_name: motorfahrzeuge

motorraeder

display_name: motorraeder

sachschaden

display_name: sachschaden

strassenverkehr

display_name: strassenverkehr

strassenverkehrsunfaelle

display_name: strassenverkehrsunfaelle

verkehrsunfaelle

display_name: verkehrsunfaelle

mobility

fr: Mobilité et transports

de: Mobilität und Verkehr

en: Mobility and Transport

it: Mobilità e trasporti

health

fr: Santé

de: Gesundheit

en: Health

it: Sanità

de: Polizeilich registrierte Verkehrsunfälle im Kanton Zürich seit 2011