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

en: Data plot of the field station (NEF)

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

en: Site photo of the forest station (NEB)

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

mimetype: image/jpeg

en: Data plot of the forest station (NEB)

en: Site photo of the field station (NEF)

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

en: Site aerial photo (Neunkirch)

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

en: Field station data (NEF)

en: Data file.

en: Forest station data (NEB)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Neunkirch in Switzerland where one station is located within a natural deciduous forest stand (NEB) with European beech (_Fagus sylvatica_; 160 yrs), sycamore maple (_Acer pseudoplatanus_; 160 yrs) and lime trees (_Tilia sp._; 110 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NEF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Neunkirch is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

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


maintainer: Beat Frey

en: database conservation soil-organisms info-fauna fungi fauna lichens red-list soil-quality soil-dependency eco-fauna ecosystem-protection soil-biodiversity

state: active

type: dataset

en: Raw dataset of species and their soil dependency as well as endangerment status.

format: XLS

en: Overview over the soil dependency and endangerment status of different groups of organisms.

en: Proportion of endangered species grouped by the four soil dependency classes.

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

Paper Citation:
Frey B., Maurer C., Schneider K. (2023) Informationen zum Boden anhand der Gebundenheit der Rote-Liste Arten BAFU. Bundesamt für Umwelt BAFU. 26 p.

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

Soil is the habitat and basis of life for countless creatures. The great importance of soil organisms for the fertility of our soils is widely recognized, but information is sparse on their distribution and possible endangerment in Switzerland. In the present project, target species, i.e. species that are closely linked to the soil, were identified, and for the the first time, information was gathered on the degree of endangerment of the soil ecosystem in Switzerland on the basis of Red List species. For eleven Red List groups, the species' soil dependency was determined, i.e. whether their life cycle includes stages in or on the soil. The assessment of soil dependency was made using four classes, which enabled the classification not only of insects but also of vertebrates, land snails, lichens and fungi.

conservation

display_name: conservation

database

display_name: database

eco-fauna

display_name: eco-fauna

ecosystem-protection

display_name: ecosystem-protection

fauna

display_name: fauna

fungi

display_name: fungi

info-fauna

display_name: info-fauna

lichens

display_name: lichens

red-list

display_name: red-list

soil-biodiversity

display_name: soil-biodiversity

soil-dependency

display_name: soil-dependency

soil-organisms

display_name: soil-organisms

soil-quality

display_name: soil-quality

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Information on soil based on the soil dependency of the FOEN red list species


maintainer: Milojevic

en: reservoir-inflow return-periods low-flow alpine-precipitation extreme-values

en: Daily Luzzone Reservoir Inflow 01-01-2000 to 31-12-2019

en: Daily total water inflow to Luzzone reservoir. Natural inflow (LUZ_inflow_raw) is back-calculated based on daily water level in reservoir (removing hydropower inputs and spills). Linear interpolation between daily values is used to bring all inflow values into positive space (LUZ_inflow_intrp).

format: SERVICE

en: R codes for Extreme Values Analysis

en: Zip file containing R codes for running extreme value analyses for high precipitation and inflow events as well as code for determining return levels of low inflow and low precipitation events

en:
 Summary

* Dataset of daily inflow to Luzzone reservoir in Ticino, Switzerland

* R scripts used to generate return levels for low reservoir inflow, low precipitation, high inflow, and extreme high precipitation based on various methods from extreme value analysis

 Data
The dataset included here is the "natural" reservoir inflow for the Luzzone reservoir.
Additional analyses were conducted on daily total precipitation of 6 meteorological stations (abbreviations: TIOLI, TIOLV, COM, VRN, VLS, ZEV). These precipitation data are freely available for teaching and research from the MeteoSwiss IDAweb portal (https://www.meteoswiss.admin.ch/services-and-publications/service/weather-and-climate-products/data-portal-for-teaching-and-research.html).

 Codes
R scripts used to determine return levels of the data set are included for both extreme high events and low events. The scripts include the following methods for calculating return levels:

* GEV (Generalized Extreme Value)

* GPD and GPDd (Generalized Pareto Distribution including declustered version)

* eGPD (extended Generalized Pareto Distribution)

* MEV (Metastatistical Extreme Value)

alpine-precipitation

display_name: alpine-precipitation

extreme-values

display_name: extreme-values

low-flow

display_name: low-flow

reservoir-inflow

display_name: reservoir-inflow

return-periods

display_name: return-periods

en: Data and Code on Extreme Inflow and Lowflow Analysis for Alpine Reservoirs


maintainer: Felix Gugerli

en: genetic-structure genetic-diversity conservation-genetics melanargia-galathea monitoring

media_type: text/csv

format: CSV

en:
Individual genotypes assessed at six nuclear microsatellite loci (two alleles per locus) for individuals of the butterfly Melanargia galathea (Marbled white) that were collected throughout Switzerland, along the regular grid of the Biodiversity Monitoring (BDM) of Switzerland. For each individual, the sampling site (number) and the genotype are given. Note that due to privacy restrictions, the original geographic coordinates remain disclosed. For original coordinates, data holders should be contacted (Swiss Federal Office for the Environment; Biodiversity Monitoring Switzerland).
The data refer to the following publication: Terzer et al., Distinct spatial patterns of genetic structure and diversity in the butterfly Marbled White (Melanargia galathea) inhabiting fragmented grasslands. Conservation Genetics, https://doi.org/0.1007/s10592-023-01593-4.

conservation-genetics

display_name: conservation-genetics

genetic-diversity

display_name: genetic-diversity

genetic-structure

display_name: genetic-structure

melanargia-galathea

display_name: melanargia-galathea

monitoring

display_name: monitoring

en: Nuclear microsatellite genotypes of the butterfly Melanargia galathea


maintainer: EnviDat Support

en: snow-height snow-water-equivalent gcos snow snow-profile

en: STATION METADATA

media_type: text/plain

format: TXT

mimetype: text/plain

en: DATA ACCESS

en:
This dataset contains long-term snow water equivalent and corresponding snow depth data 11 observer sites in Switzerland between 1200 and 2500 m a.s.l. compiled for the Global Climate Observing System (GCOS) and supported by MeteoSwiss. Snow depth (cm) and snow water equivalent (mm) are manually recorded every 2 weeks since the 1947 (depending on station). The attached metadata file gives details for each station.
The measurement series agree with GCOS objectives according to the GCOS Implementation Plan: This inlcudes:
• Raw data are archived in the snow and avalanche database at SLF.
• Measuring techniques are traceable and documented as snow depth and snow water equivalent have in general remained the same since beginning up to now. When planning new systems or changes of existing systems in the future, their impact will be assessed prior to implementation.
• Historical data of these 11 stations have been digitized and all data have been quality controlled.
• Detailed metadata (location of measurements) are available.
• Public availability of the data has been ensured by publishing the data on the Envidat portal (https://www.envidat.ch/dataset/gcos-swe-data).

gcos

display_name: gcos

snow

display_name: snow

snow-height

display_name: snow-height

snow-profile

display_name: snow-profile

snow-water-equivalent

display_name: snow-water-equivalent

en: GCOS SWE data from 11 stations in Switzerland


maintainer: Dominique Weber

en: use-intensity management landsat land-use mowing sentinel-2 remote-sensing grassland

en: Preview number of grassland management events 2020

media_type: image/png

en: Spatial distribution of the number of management events for Switzerland and for three zoomed-in regions (a, b, c). The three zoomed-in regions show different grassland-use intensities, which are reflected in the distribution of management frequency (Weber et al., 2023; IN REVIEW)

format: PNG

en: Readme

en: Metadata and description of the maps and webcam-based reference data.

en: Grassland-use intensity map 2020

en: Raster stack with information on the number and timing of grassland-management events (i.e. mowing, grazing) at a spatial resolution of 10 m × 10 m for the whole of Switzerland for 2020. See readme file for more details.

format: GeoTIFF

en: Grassland-use intensity map 2019

en: Raster stack with information on the number and timing of grassland-management events (i.e. mowing, grazing) at a spatial resolution of 10 m × 10 m for the whole of Switzerland for 2019. See readme file for more details.

en: Preview time of 1st grassland management event 2020

en: Spatial distribution of the timing (day of year; DOY) of the first management event for Switzerland and for three zoomed-in regions (a, b, c). The three zoomed-in regions show different grassland-use intensities, which are reflected in the timing of the first event of the year (Weber et al., 2023; IN REVIEW)

en: Grassland-use intensity map 2018

en: Raster stack with information on the number and timing of grassland-management events (i.e. mowing, grazing) at a spatial resolution of 10 m × 10 m for the whole of Switzerland for 2018. See readme file for more details.

en: Webcam-based reference data

en: Webcam-based reference data from 2020 and 2021 (see readme).

en: Grassland-use intensity map 2021

en: Raster stack with information on the number and timing of grassland-management events (i.e. mowing, grazing) at a spatial resolution of 10 m × 10 m for the whole of Switzerland for 2021. See readme file for more details.

en:
A rule-based algorithm [(Schwieder et al., 2022)](https://doi.org/10.1016/j.rse.2021.112795) was used to produce annual maps for 2018–2021 of grassland-management events, i.e. mowing and/or grazing, for Switzerland using Sentinel-2 and Landsat 8 satellite time series. All satellite images were processed with the [FORCE](https://force-eo.readthedocs.io) framework.

The resulting maps provide information on the number and timing of grassland-management events at a spatial resolution of 10 m × 10 m for the whole of Switzerland. For the final maps, permanent grasslands were masked using a variety of land-use layers, according to [Huber et al. (2022)](https://doi.org/10.1002/rse2.298) but replacing the crop mask with the agricultural-use data from the cantons.

We assessed the detection of management events based on independent reference data, which we acquired from daily time series of publicly available webcams that are widely distributed across Switzerland. We further tested the ecological relevance of the generated intensity measures in relation to nationwide biodiversity data (see [Weber et al., 2023](https://doi.org/10.1002/rse2.372)).

The webcam-based reference data used for verification was subsequently added on 14.02.2024.

grassland

display_name: grassland

land-use

display_name: land-use

landsat

display_name: landsat

management

display_name: management

mowing

display_name: mowing

remote-sensing

display_name: remote-sensing

use-intensity

display_name: use-intensity

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Grassland-use intensity maps for Switzerland


en: geology landscape-evolution endemism flora madagascar

en: Data v1.0

media_type: application/zip

format: ZIP

en: Escarpment evolution drives the diversification of the Madagascar flora

en: This upload contains the data (species checklist, species range maps, time-calibrated phylogenies, topographic change, etc.) generated, analyzed, and presented in the paper .

mimetype: application/zip

en: Although much of the endemic biodiversity of Madagascar can be attributed to its isolation as an island in the Indian Ocean, the high rates of speciation throughout its geologic history suggest an influence of local-scale landscape dynamics. The topographic evolution of Madagascar is dominated by the formation of high-relief continental rift escarpment and we argue that the erosion and landward retreat of this topography creates habitat heterogeneity that has served as a speciation pump for the island. The highest plant richness is found along the escarpment and is characterized by steady diversification rates over the last 45 Ma. Modeled landscape evolution by escarpment retreat demonstrates opportunities for allopatric speciation by transient habitat fragmentation through multiple mechanisms, including catchment expansion, isolation of highland remnants and formation of topographic and river barriers The segregation of floral phylogenetic turnover parallel to the escarpment is consistent with these mechanisms and indicates the importance of erosion-driven landscape dynamics on speciation.

endemism

display_name: endemism

flora

display_name: flora

geology

display_name: geology

landscape-evolution

display_name: landscape-evolution

madagascar

display_name: madagascar


maintainer: Stefan Klesse

en: mxd temperature-reconstruction tree-ring paleoclimate wood-anatomy

en:
This dataset contains data presented in Figure 2 in the paper "New Prospects for Temperature Reconstructions from Relict Wood Using Tree-Ring Anatomy".

Sample ID = Sample ID
group = living or relict wood material
Bulk wood density = volumetric measured density (kg / m^3)
Mean Tree Ring Density	= Density derived via X-ray densitometry (kg / m^3)
Cellulose_to_wood ratio = Holo-cellulose-to-wood ratio
Anatomical density = Area of cell wall / (area of cell wall + lumen area)

en: This dataset contains the mean chronologies of MXD and QWA of the relict and living wood material presented in Figure 3. It also contains the JJA temperature record of the Salekhard Meteo station.

en: Data (Quantitative Wood Anatomy, Maximum Latewood Density, Volumetric Density, holo-cellulose-to-wood-ratio) used in the manuscript "New Prospects for Temperature Reconstructions from Relict Wood Using Tree-Ring Anatomy". Wood samples stem from Larix sibirica collected in the Yamal region, northeastern Siberia, Russia. We investigated wood properties of living trees and subfossil wood buried in riverbank sediments, dated to approximately -2760 to -2600 BCE. Our goal was to evaluate the integrity of climate proxies in sub-fossil wood and develop practical methods to detect wood degradation when reconstructing Late Holocene and Common Era climate conditions using tree-ring data.

mxd

display_name: mxd

paleoclimate

display_name: paleoclimate

temperature-reconstruction

display_name: temperature-reconstruction

tree-ring

display_name: tree-ring

wood-anatomy

display_name: wood-anatomy

en: Quantitative Wood Anatomy and Maximum Latewood Density from Yamal peninsula


maintainer: Frank Hagedorn

en: biogeochemical-cycling drought plfa microbial-biomass forest c13-labeling

en: Gao_Frank_C13 pulse labeling.xlsx

en:
Data of manuscript by Gao t al. : Drought resistance and resilience of rhizosphere communities in forest soils from the cellular to ecosystem scale – insights from 13C pulse labeling
Inludes data on 13C pulse-labeling experiment in young beech model ecosystems and a mature pine forest. Data of soil climate, total PLFA, 13C label strength in PLFA of pulse-labeling carried out under drought and after rewetting

en: Data of 13C-pulselabeling experiments in a young beech model ecosystem and a mature pine forest exposed to drought and rewetting. Data include microclimate during the experiment, total PLFA contents, d13C signatures in PLFA , microbial biomass, soil-respired CO2 measure during 30 days following 3-4 hour long pulse labeling with highly 13C-enriched CO2.

biogeochemical-cycling

display_name: biogeochemical-cycling

drought

display_name: drought

forest

display_name: forest

microbial-biomass

display_name: microbial-biomass

plfa

display_name: plfa

en: Gao_Drought resistance and resilience of rhizosphere communities_13C-PLFA


maintainer: Felix Kienast

en: optimization ecosystem-services social-acceptance marxan wind-power photovoltaic spatial-planning landscape energy

en:
This is the compressed Shapefile (ESRI) from the MARXAN optimization with 2216 rows (objects) and 18 columns (variables) with sf (simple feature) and data frame classes.
Column description:
X01 = Planning_units (PUs)
X02 = incl.OS. number of times pu was selected in MARXAN when optimized for ecological costs
X03 = incl.OS. number of times pu was selected in MARXAN when optimized for social costs
X04 = incl.OS. ecological costs (ECUess) of pu when realizing the total energy (normalized)
X05 = incl.OS. social costs (ECUsoc) of pu when realizing the total energy (normalized)
X06 = incl.OS. total energy (MWh/a)
X07 = incl.OS. energy from roof-mounted PV infrastructure (MWh/a)
X08 = incl.OS. energy from wind turbines (MWh/a)
X09 = incl.OS. energy from ground-mounted PV infrastructure (MWh/a)

X10 = excl.OS. number of times pu was selected in MARXAN when optimized for ecological costs
X11 = excl.OS. number of times pu was selected in MARXAN when optimized for social costs
X12 = excl.OS. ecological costs (ECUess) of pu when realizing the total energy (normalized)
X13 = excl.OS. social costs (ECUsoc) of pu when realizing the total energy (normalized)
X14 = excl.OS. total energy (MWh/a)
X15 = excl.OS. energy from roof-mounted PV infrastructure (MWh/a)
X16 = excl.OS. energy from wind turbines (MWh/a)
X17 = excl.OS. energy from ground-mounted PV infrastructure (MWh/a)
geometry = Simple feature XY geometry (SFC_POINT) each representing the center of a 4x4km planning unit (PU) in EPSG 21781 (CRS CH1903 / LV03).

en:
This study examines the siting scenarios for renewable energy infrastructure (REI) in Switzerland, incorporating the external costs of ecosystem services and, innovatively, social preferences. This approach challenges the prevalent techno-economic siting paradigm, which often overlooks these externalities. To minimize the external costs of the scenarios while maximizing energy yield, Marxan, an optimization software, was employed.
MARXAN was run for 2 versions: a) without ground-mounted open space PV infrastructure (excl. OS) and b) with ground-mounted open space PV infrastructure (incl. OS). In each version optimization was done using ecological costs (ECUess) or social costs (ECUsoc) in a regular grid of 4x4km (planning unit).

File:
PU_data.shp: compressed Shapefile (ESRI) from the MARXAN optimization with 2216 rows (objects) and 18 columns (variables) with sf (simple feature) and data frame classes.

Headers are described below:
X01 = Planning_units (PUs)

X02 = incl.OS. number of times pu was selected in MARXAN when optimized for ecological costs
X03 = incl.OS. number of times pu was selected in MARXAN when optimized for social costs
X04 = incl.OS. ecological costs (ECUess) of pu when realizing the total energy (normalized)
X05 = incl.OS. social costs (ECUsoc) of pu when realizing the total energy (normalized)
X06 = incl.OS. total energy (MWh/a)
X07 = incl.OS. energy from roof-mounted PV infrastructure (MWh/a)
X08 = incl.OS. energy from wind turbines (MWh/a)
X09 = incl.OS. energy from ground-mounted PV infrastructure (MWh/a)

X10 = excl.OS. number of times pu was selected in MARXAN when optimized for ecological costs
X11 = excl.OS. number of times pu was selected in MARXAN when optimized for social costs
X12 = excl.OS. ecological costs (ECUess) of pu when realizing the total energy (normalized)
X13 = excl.OS. social costs (ECUsoc) of pu when realizing the total energy (normalized)
X14 = excl.OS. total energy (MWh/a)
X15 = excl.OS. energy from roof-mounted PV infrastructure (MWh/a)
X16 = excl.OS. energy from wind turbines (MWh/a)
X17 = excl.OS. energy from ground-mounted PV infrastructure (MWh/a)

geometry = Simple feature XY geometry (SFC_POINT) each representing the center of a 4x4km planning unit (PU) in EPSG 21781 (CRS CH1903 / LV03).

For MARXAN optimizations (for meaning of standard files in MARXAN see https://scholar.google.ch/scholar_url?url=https://courses.washington.edu/cfr590/software/Marxan1810/marxan_manual_1_8_2.pdf&hl=de&sa=X&ei=NAWHZI6JJ8PFmAGMo47oDQ&scisig=AGlGAw-DBrsV4kUzkR6GkN1jtG66&oi=scholarr), pu_data.shp was used to generate MARXAN files input.dat, pu.dat, puvfeat.dat, spec.dat.
Coordinate Reference System: CH1903 / LV03. EPSG: 21781.

ecosystem-services

display_name: ecosystem-services

energy

display_name: energy

landscape

display_name: landscape

marxan

display_name: marxan

optimization

display_name: optimization

photovoltaic

display_name: photovoltaic

social-acceptance

display_name: social-acceptance

spatial-planning

display_name: spatial-planning

wind-power

display_name: wind-power

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Optimizing renewable energy siting in the Swiss landscape


maintainer: Ionuț Iosifescu Enescu

en: Bulk Data Access

en: Link to the S3 bucket where all the data resources can be downloaded in bulk

en: map preview 0.5MB

en:
The attached data are some large GIS raster files (GeoTIFFs) made with Natural Earth data. Natural Earth is a free vector and raster map data @ naturalearthdata.com. The data used for creating these large files was the "Cross Blended Hypso with Shaded Relief and Water". Data was concatenated to achieve larger and larger files. Internal pyramids were created, in order that the files can be opened easily in a GIS software such as QGIS or by a (future) GIS data visualisation module integrated in EnviDat.

Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com

gis

display_name: gis

large-files

display_name: large-files

natural-earth

display_name: natural-earth

qgis

display_name: qgis

en: Large GIS raster data derived from Natural Earth Data (Cross Blended Hypso with Shaded Relief and Water)


maintainer: Thorsten Bartels-Rausch

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

en: Matlab Script to view and plot data

en: Matlab Data

en: All data as Matlab mat file

en: Readme file

media_type: application/pdf

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

format: PDF

mimetype: application/pdf

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

alpine

display_name: alpine

antarctica

display_name: antarctica

arctic

display_name: arctic

brine

display_name: brine

chemistry

display_name: chemistry

heterogeneous

display_name: heterogeneous

interface

display_name: interface

laboratory-study

display_name: laboratory-study

metamorphism

display_name: metamorphism

ozone

display_name: ozone

reaction

display_name: reaction

snow

display_name: snow

solution

display_name: solution

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


maintainer: Izabela Bujak-Ozga

en: catchment stream-water hydrogen-isotopes groundwater oxygen-isotopes catchment-data solutes

en: This document provides a detailed description of the hydrochemical dataset, including, among other things, an overview of the dataset content, an explanation of the column headers, a description of the locations where samples were collected, and an explanation of the abbreviations used in the dataset.

en: This dataset includes mean concentrations of solutes (Calcium, Iron, Magnesium, Sodium, Potassium, Boron, Nitrate, Sulfate, and Chloride) and stable isotope values (hydrogen and oxygen) in water molecules. These were measured in precipitation, groundwater, soil water, and surface water samples collected from the Haute-Mentue catchment in Switzerland during Spring and Fall 2022. Detailed information is available in the accompanying documentation (documentation_for_the_hydrochemical_data_collected_during_spring_fall_2022_in_the_haute-mentue_catchment.pdf).

en: The dataset contains hydrochemical data collected from spring to fall 2022 in the Haute-Mentue catchment, Switzerland. This hydrochemical data includes solute concentrations and stable isotopes of oxygen and hydrogen in water molecules, measured in streamwater, soil water, groundwater, and rainfall samples. Samples were collected during eight rainfall events with higher temporal resolution (hourly) as well as at lower resolution between events. A detailed description of the dataset is provided in the documentation.

catchment

display_name: catchment

catchment-data

display_name: catchment-data

groundwater

display_name: groundwater

hydrogen-isotopes

display_name: hydrogen-isotopes

oxygen-isotopes

display_name: oxygen-isotopes

solutes

display_name: solutes

stream-water

display_name: stream-water

educ

en: Education, culture and sport

en: Hydrochemical Data Collected during Spring-Fall 2022 in the Haute-Mentue Catchment


maintainer: Rebecca Mott

en: snow-patch patchy-snow-covers dischmex eddy-covariance-measurements turbulent-sensible-heat-fluxes

en: Eddy-covariance measurement with an ultrasonic anemometer (R.M. Young Company) in 3.5 m.

en: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific Inc.) in 1.7 m on a daily basis.

en: Output explanation

en: Gletschboden setup

en: Background map based on data © 2014/2016 swisstopo (JD100042)

mimetype: image/png

en: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 2.2 m.

en: Eddy-covariance measurement with an ultrasonic anemometer (R.M. Young Company) in 0.7 m.

en: Eddy-covariance measurement with an ultrasonic anemometer (R.M. Young Company) in 3.3 m.

en: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific Inc.) in 3.2 m on a daily basis.

en: Eddy-covariance measurement with an ultrasonic anemometer (DA-600, Kaijo-Denki) in 0.3 m on a daily basis.

en: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 2.6 m.

en:
This dataset contains eddy-covariance measurements in the ablation period of 2014-2016. Measurements were taken from two turbulence towers over a long-lasting snow patch, which are 5 m apart from each other (2014 and 2015). The turbulence towers were equipped with two YOUNG ultrasonic anemometers mounted 0.7 m (in 2014) and 3.3 m (in 2015) above snow-free ground, two ultrasonic anemometers (CSAT3, Campbell Scientific, Inc.) mounted at 2.6 m (in 2014) and 2.2 m (in 2015) above snow-free ground and one anemometer (DA-600, Kaijo Denki) mounted at 0.3 m above snow surface.

The measurement setup changed in 2016 and includes a measurement above the snow-free ground in upwind direction (Swiss coordinates: 790191/176689). The measurement tower is equipped with one ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 3.3 m above the snow-free ground. Additionally, one measurement tower is installed above the long-Lasting snow patch and equipped with the same setup as 2015.

Turbulence data were sampled at a frequency of 20 Hz. The processing of the data to quality controlled fluxes has been done with the Biomicrometeorology flux software (Thomas et al., 2009). The program applies plausibility tests and a despiking test after Vickers and Mahrt (1997) on the measured data. The routine further applies a time-lag correction and considers the deployment (e.g. the sonic azimuth). A frequency response correction (Moore, 1986) is done and a three-dimensional rotation is performed. Finally, quality assurance/quality control (QA/QC) flags after Foken et al., (2004) are issued and fast Fourier transform power and co-spectra are calculated. The change in snow height is considered in the post-processing for every measurement day. The turbulence data were averaged to 30 minute intervals.

dischmex

display_name: dischmex

eddy-covariance-measurements

display_name: eddy-covariance-measurements

patchy-snow-covers

display_name: patchy-snow-covers

snow-patch

display_name: snow-patch

turbulent-sensible-heat-fluxes

display_name: turbulent-sensible-heat-fluxes

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: DISCHMEX - Impact of extreme land-surface heterogeneity on micrometeorology over spring snow-cover


en: sustainability outdoor-lighting artificial-lighting alan ecosystem-services

en: R code for PERMANOVA Analysis of flight-active insects.

format: OCTET

en: Raw data for ground-dwelling arthropod NMDS analysis

en: Raw data for flying insects NMDS analysis

en: R code of ground-dwelling arthropod analysis.

en: Raw data Moran's I test

en: Raw data for flying insects analysis

en: R code for NMDS of ground-dwelling arthropods

en: Raw data for flying insects and ground-dwelling arthropod combined analysis

en: Raw data for ground-dwelling arthropod analysis

en: R code of flight-active insects analysis.

en: R code for PERMANOVA Analysis of ground-dwelling arthropods.

en: R code for NMDS of flight-active insects

en: R code of flight-active and ground-dwelling arthropod combined analysis.

en: Raw data and R Code to do all the analysis performed for the paper "Advancing sustainable LED solutions to mitigate light-pollution impacts on arthropods". Including data for both flight-active insects and ground-dwelling arthropods combined (PT_TRF_all.csv), flight-active insects alone (ALANeX_PT_all_clean_control.csv) and ground-dwelling insects alone (ALANeX_TRF_all_clean_control.csv). For the NMDS analysis, there is a data set for the flight-active insects (ALANeX_PT_NMDS.csv) and the ground-dwelling arthropods (ALANeX_TRF_NMDS.csv). For the Morans I use the 'ALANeX_MoransI.csv' dataset.

alan

display_name: alan

artificial-lighting

display_name: artificial-lighting

ecosystem-services

display_name: ecosystem-services

outdoor-lighting

display_name: outdoor-lighting

sustainability

display_name: sustainability

educ

en: Education, culture and sport


maintainer: Frank Techel

en: avalanche-forecasting verification avalanche-danger-level avalanche-bulletin avalanche-forecast

en: D_QC for forecasting seasons 2020/2021 - 2022/2023.

en: Data for the 2023/2024 forecasting season. It was added on 26 Aug 2024 by F. Techel.

en: D_QC for forecasting seasons 2001/2002 until 2019/2020.

en:
The data set contains the re-analyzed (or quality-checked) regional avalanche danger levels (D_QC) for Switzerland.

D_QC relates to dry-snow avalanche conditions only. Measuring the avalanche danger level D is not possible; forecast, nowcast, and hindcast assessments of D are judgments by humans interpreting data. However, combining several pieces of information indicating the same D, it can be expected that it is more likely that D_QC represents the avalanche conditions well.

For the **forecasting seasons 2001/2002 until 2019/2020**, the approach to obtain D_QC is described in detail in Appendix A of [Pérez-Guillén et. al. (2022)](https://nhess.copernicus.org/articles/22/2031/2022/nhess-22-2031-2022.html).

For the **forecasting seasons 2020/2021 and later**, D_QC is derived using the following approach:
1. *Combination of forecast (D_forecast) and nowcast (D_nowcast)*: If there was only one assessment available by an observer after a day in the field for a region, and if D_forecast = D_nowcast --> D_QC = D_forecast.
2. *Combination of several nowcast assessments (D_nowcast)*: If two (or more) observers agreed (or majority opinion) in their (independent) assessments of D_nowcast after a day in the field in the same warning region. --> D_QC = D_nowcast.
3. *Hindcast analysis (D_hindcast)*: In Switzerland, avalanche forecasters re-evaluate all situations when D = 4 (high) or D = 5 (very high) were either forecast, should have been forecast, or when forecasters discussed given one of these two levels but had not given them. Generally, two forecasters assess each situation. In these cases, D_QC = D_hindcast. The hindcast analysis, only available since the forecasting season 2020/2021, replaces what was step (2) in Appendix A of [Pérez-Guillén et. al. (2022)](https://nhess.copernicus.org/articles/22/2031/2022/nhess-22-2031-2022.html).

All other cases - ties in case of (1) or (2), no new information from the warning region in question, or if no D_hindcast was available - are not considered quality-checked, and are, thus, not contained in the data set.

In addition to D_QC, the file contains information on the elevation and aspect, where D_QC likely prevails.
- The indicated elevation is the mean of the respective elevations in (1), (2), or (3). At danger level 1 (low), when no elevation is indicated in the Swiss forecast, a value of 1500 m is set.
- For the four cardinal aspects N, E, S, and W, a value of 1 means that there was agreement that D was reached in this aspect and a value of 0 means that there was agreement that D was not reached in this aspect. Intermediate values correspondingly mark disagreements in the assessments.

avalanche-bulletin

display_name: avalanche-bulletin

avalanche-danger-level

display_name: avalanche-danger-level

avalanche-forecast

display_name: avalanche-forecast

avalanche-forecasting

display_name: avalanche-forecasting

verification

display_name: verification

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: Re-analyzed regional avalanche danger levels in Switzerland


en: red-list endemic range-size conservation-priority europe rarity bryophytes

en: This dataset provides data on the occurrence of 672 European bryophyte species in European countries or territories. All species which are considered threatened or near threatened according to the European Red List (Hodgetts et al. 2019) are included (553 species). Further, all species evaluated in the European Red List assessed as LC, DD, NE and EX, which at the same time are either endemic, near endemic or globally rare, are included (119 species). Therefore, the list contains not only all European bryophyte species that are threatened or near threatened, but also all species endemic or near endemic to Europe as well as all globally rare species occurring in Europe. For each of the 553 threatened or near threatened species, we provide a conservation a priority score which was calculated based the Red List status and the proportion of the European population of the global population or the global rarity of the species. For details on the prioritisation approach see Bisang et al. (2025).

bryophytes

display_name: bryophytes

conservation-priority

display_name: conservation-priority

endemic

display_name: endemic

europe

display_name: europe

range-size

display_name: range-size

rarity

display_name: rarity

red-list

display_name: red-list

en: Priority ranking for the conservation of all European bryophyte species - dataset


dataset-on-new-snow-water-equivalent

title_for_slug: dataset-on-new-snow-water-equivalent

en: snowfall precipitation data-assimilation new-snow snow-modelling

en: The .zip file contains all data as described in this repository and associated publication. The .zip file contains a readme file describing its content in detail.

en:
This dataset includes quality-controlled measurements of new snow depth (HN), new snow water equivalent (HNW), snow depth (HS), and snow water equivalent (SWE) from 41 stations located in Switzerland for the period from 2016-09-01 to 2022-08-31.
These data are the basis of the following publication: Magnusson J., Cluzet B., Quéno L., Mott R., Oberrauch M., Mazzotti G., Marty C., Jonas T., 2025, Evaluating methods to estimate the water equivalent of new snow from daily snow depth recordings, Cold Regions Science and Technology, https://doi.org/10.1016/j.coldregions.2025.104435.
Abstract
The water equivalent of new snow (HNW) plays a crucial role in various fields, including hydrological modeling, avalanche forecasting, and assessing snow loads on structures. However, in contrast to snow depth (HS), obtaining HNW measurements is challenging as well as time-consuming and is hence rarely measured. Therefore, we assess the reliability of two semi-empirical methods, HS2SWE and ΔSNOW, for estimating HNW. These methods are designed to simulate continuous water equivalent of the snowpack (SWE) from daily HS only, with changes in SWE yielding daily HNW estimates. We compare both parametric methods against HNW predictions from a physics-based snow model (FSM2oshd) that integrates daily HS recordings using data assimilation. Our findings reveal that all methods exhibit similar performance, with relative biases of less than ~3 % in replicating SWE observations commonly used for model evaluations. However, the ΔSNOW model tends to underestimate daily HNW by ~17 %, whereas HS2SWE and FSM2oshd combined with a particle filter data assimilation scheme provide nearly unbiased estimates, with relative biases below ~5 %. In contrast to the parsimonious parametric methods, we show that the physics-based approach can yield information about unobserved variables, such as total solid precipitation amounts, that may differ from HNW due to concurrent melt. Overall, our results underscore the potential of utilizing commonly available daily HS data in conjunction with appropriate modeling techniques to provide valuable insights into snow accumulation processes. Our study demonstrates that daily SWE observations or supplementary measurements like HNW are important for validating the day-to-day accuracy of simulations and should ideally already be incorporated during the calibration and development of models.
Acknowlegements
These data were recorded by SLF observers and staff members. Their contribution is gratefully acknowledged.

data-assimilation

display_name: data-assimilation

new-snow

display_name: new-snow

precipitation

display_name: precipitation

snow-modelling

display_name: snow-modelling

snowfall

display_name: snowfall

en: Dataset on new snow water equivalent


en: chemistry ice atmosphere laboratory-study snow-atmosphere-interactions

en: Processed data as shown in Figure 4 of publication. X-ray energy in eV in column one, electron count intensity in arb. units in column 2.

en:
The table lists all NEXAFS raw data with the following columns:

* name:						raw file name
* xRayEkinRange_eV: 		Smallest and largest X-ray photon energy in eV
* scientaPassEnergy_eV: 	Pass energy of electron analyzer in eV
* scientaDwellTime_ms: 		Dwell time of electron analyzer in ms
* regionName:				O K-edge for all data
* scientaAcquisitionMode: 	Fixed energy (snapshot) mode
* scientaEkinRange_eV: 		Smallest and largest energy of the kinetic energy window used for detection in this work
* scientaEkinStep_eV		Not defined in fixed energy mode
* scientaLensMode:			Tune table
* scientaRegionIterations: 	Iterations (sweeps)
* scientaSequenceIterations: Iterations (sweeps)
* sampleTemp_dC: 			Temperature as monitored at the sample holder in °C
* cellPressure_mbar: 		Total pressure in measurement cell in mbar
* iceTemp_dC: 				Temperature of ice sample calculated based on vapour pressure (assuming total pressure equals partial pressure of water) in °C
* smplX_mm: 				positon of measurement spot, x dimension in mm
* smplY_mm: 				positon of measurement spot, y dimension in mm
* smplZ_mm: 				positon of measurement spot, z dimension in mm
* sealingTemp:				temperature at base of sample manipulator
* lastModifiedDatestr: 		time and date when data aquisition was finished
* sample: 					sample description

en: Near Ambient Pressure partial Auger-Meitner electron yield X-ray absorption spectra and photon flux data. X-ray energy in eV in column 1, analyzer electron count intensity in arb. units in column 2.

en: Data are compiled that have been used to demonstrate the impact of high water partial pressure on X-ray absorption spectra of ice.

atmosphere

display_name: atmosphere

chemistry

display_name: chemistry

ice

display_name: ice

laboratory-study

display_name: laboratory-study

snow-atmosphere-interactions

display_name: snow-atmosphere-interactions

en: An ice oxygen K-edge NEXAFS spectroscopy data set on gas-phase processing


maintainer: Markus Schlegel

en: spore-dispersal monitoring air-sampling environmental-dna metabarcoding

en: Data files and analysis scripts (Version 1)

en: This ZIP archive contains the data and source code necessary to reproduce the analyses for the manuscript "A case study on the application of spore sampling for the monitoring of fungi". Please refer to README.pdf inside the archive for more information.

en: Data files and analysis scripts (Version 2)

en: This ZIP archive contains the data and source code necessary to reproduce the analyses for the manuscript "A case study on the application of spore sampling for the monitoring of macrofungi". Please refer to README.pdf inside the archive for more information.

en:
Resources associated with the manuscript *A case study on the application of spore sampling for the monitoring of macrofungi* ([Schlegel et al. 2024](https://doi.org/10.1111/1755-0998.13941)).
Airborne environmental DNA (eDNA) monitoring of fungi was evaluated on a species-rich grassland site. Extensive fruiting body surveys yielded 29 waxcap and clavarioid species, 19 of which were also detectable in air eDNA. The analysis indicates that spores of locally occurring fruiting bodies were detected, in addition to a large diversity of common fungal species, while rare and threatened species were under-represented in the air.
*Version 1* of the dataset was submitted during the review phase. *Version 2* contains minor changes and clarifications, and is up to date with the [published supplement](https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1111%2F1755-0998.13941&file=men13941-sup-0001-AppendixS1.pdf) of the study.
The contents of the ZIP archive:
* analysis scripts in [R-Markdown](https://rmarkdown.rstudio.com) format, including the rendered PDF output with figures and tables (*analysis* directory)
* eDNA sample metadata (*amplicon_meta*) and results of the amplicon clustering (*amplicon_data*) and the source code of the pipeline (*amplicon_pipeline*)
* positions of all fruiting body groups found in the surveys (3838 GPS points) (*surveys*)
* positions of the spore traps, taxonomy of studied grassland fungi and red-list species, and many more resources
Please refer to `README.pdf` inside the archive for a complete description of the file structure and information on how to reproduce the analyses.

air-sampling

display_name: air-sampling

environmental-dna

display_name: environmental-dna

metabarcoding

display_name: metabarcoding

monitoring

display_name: monitoring

spore-dispersal

display_name: spore-dispersal

educ

en: Education, culture and sport

en: Supporting information for case study on the application of spore sampling for the monitoring of macrofungi


maintainer: Axel Volkwein

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

en: Block trajectories recorded from local positioning system

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

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

en: Jumps and impacts

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

en: Overview of all tests

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

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

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

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

experiment

display_name: experiment

experiments

display_name: experiments

field-data

display_name: field-data

field-test

display_name: field-test

field-tests

display_name: field-tests

mass-movements

display_name: mass-movements

natural-hazards

display_name: natural-hazards

rockfall

display_name: rockfall

trajectory

display_name: trajectory

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: Repetitive trajectory testing in Tschamut 2014


en: forecast-models avalanche-release-probability machine-learning avalanche-forecast spatial-modelling

en: Human-triggered avalanches 2022/2023

en: The file contains the reported human-triggered avalanches. The variables are as follows: id, release date (date), name, Swiss coordinates (x,y), elevation (ele), remark, reported avalanche size expressed using avalanche activity index (sizeAAI), aspect, event type (type), event (yes = 1, no = 0)

en: Snowline estimates 2023/2024

en: Daily snowline estimates provided by observers for North (aspect = 0) and South (aspect = 180) slopes for the 2023/2024 season. For aspects in between North and South, values are interpolated. Contains observation date (date), coordinates (Swiss coordinates x, y), snowline elevation (snowline), and aspect.

en: Avalanche forecast

en: Data contains forecasts relating to dry-snow avalanche conditions only. Forecast issued at 17.00 local time. Contains variables for the warning region (sector_id), publication date (date), danger level - sub-level combination expressed using numeric values (integer value of danger level -0.33 for sub-level minus, -0 for sub-level neutral, +0.33 for sub-level plus, altitude as indicated in the bulletin, and whether aspect (North = 0, Northeast = 45, ...) was indicated in the bulletin (no: 0, yes: 1).

en: Randomly sampled subset of grid points from 1 km DEM

en: Contains the randomly sampled subset of grid points from 1 km DEM. Three variables: coordinates (x, y) and elevation (elev).

en: Natural avalanches 2022/2023

en: Natural avalanche observation (dry avalanches). 2022-2023 forecasting season.

en: GPS data and human-triggered avalanches

en: Contains (1) the subset of points extracted from GPS tracks recorded during backcountry touring and uploaded to the website skitourenguru.ch and (2) the corresponding subset of human-triggered avalanches. Data (1) was provided by Skitourenguru GmbH and data (2) was collected by SLF. - Variables contained are date, Swiss coordinates (x, y), aspect (N = 0, NE = 45, ...), elevation (ele), and event (event = 1, non-event = 0).

en: Snowline estimates 2022/2023

en: Daily snowline estimates provided by observers for North (aspect = 0) and South (aspect = 180) slopes for the 2022/2023 season. For aspects in between North and South, values are interpolated. Contains observation date (date), coordinates (Swiss coordinates x, y), snowline elevation (snowline), and aspect.

en: Natural avalanches 2023/2024

en: Natural avalanche observation (dry avalanches). 2023/2024 forecasting season.

en: Model predictions (raw data)

en: Model predictions (raw data) for three models, in nowcast and forecast mode. Not all models are available for both seasons. - These data are the basis for the interpolated predictions, which can be found in the other zip-folder

en: Model predictions (interpolated)

en: The zip file contains csv-files the interpolated model predictions for three models, in forecast and nowcast-mode, for the three different data sets, as described in the publication.

en: Human-triggered avalanches 2023/2024

en:
This data set was used in the analysis by Techel et al. **Can model-based avalanche forecasts match the discriminatory skill of human danger level forecasts? A comparison from Switzerland**. Please note that the title of the preprint was *Forecasting avalanche danger: human-made forecasts vs. fully automated model-driven predictions*, submitted to *Natural Hazards Earth System Sciences* on 20 Aug 2024.
The final manuscript used data from three forecasting seasons (2022/2023, 2023/2024, 2024/2025) , the preprint two forecasting seasons (2022/2023, 2023/2024) .
Currently, the repository contains data from two avalanche forecasting seasons in Switzerland. The third season will be added (to be done, 30 June 2025).
**Interpolated predictions**
- The .zip file contains the interpolated predictions for the three models in nowcast- and forecast- mode. This data is needed to reproduce the figures and tables in the submitted preprint.
The other data are the **raw data** underlying the interpolations:
- Avalanche forecast by WSL Institute for Snow and Avalanche Research SLF, published at 17.00 local time, valid for the following 24 hours and relating to dry snow avalanche conditions.
- Model predictions in *nowcast*- and *forecast*-mode for three models (*danger level*, *instability*, *natural avalanche*), valid for 12.00 local time
- Subset of points extracted from GPS tracks (courtesy of Skitourenguru GmbH)
- Avalanche observations - natural avalanches and human-triggered avalanches
- Estimates of the snowline
- Randomly chosen subset of grid points used for generating reference distributions
For details regarding the data sets refer to the publication.

avalanche-forecast

display_name: avalanche-forecast

avalanche-release-probability

display_name: avalanche-release-probability

forecast-models

display_name: forecast-models

machine-learning

display_name: machine-learning

spatial-modelling

display_name: spatial-modelling

en: Evaluating the predictive performance of human avalanche forecasts and model predictions in Switzerland


present-weather-sensor-klosters

maintainer: Maxime Hervo

title_for_slug: present-weather-sensor-klosters

en: precipitation raclets visibility sensor weather

en: Present Weather Sensor Klosters

en: Present Weather sensor measuring visibility and weather type installed at Klosters.

en: A present weather sensor (Vaisala PWD22) was deployed in Klosters (LON: 9.880413, LAT: 46.869019) for weather observation, combining the functions of a forwardscatter visibility meter and a present weather sensor. Besides measuring ambient light, it detects the intensity as well as the amount of both liquid and solid precipitation. More information can be found in the [User's Manual](ftp://ftp.cmdl.noaa.gov/aerosol/doc/manuals/PWD22_Manual.pdf).

precipitation

display_name: precipitation

raclets

display_name: raclets

sensor

display_name: sensor

visibility

display_name: visibility

weather

display_name: weather

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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


knp-verkehr-festlegungen-flache

regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

envi

fr: Environnement

de: Umwelt

en: Environment

it: Ambiente

tran

fr: Transports

de: Verkehr

en: Transport

it: Trasporti

title_for_slug: knp-verkehr-festlegungen-flache

author: Abteilung Verkehr

fr: geocat.ch permalien

de: geocat.ch Permalink

en: geocat.ch permalink

it: geocat.ch link permanente

de: AGIS Direktdownload

de: Der Datensatz enthält die flächenbezogenen überlagernden rechtskräftigen Festlegungen, die im Verfahren einer kantonalen Nutzungsplanung im Bereich Verkehr genehmigt worden sind: • Zu-/Wegfahrtbeschränkungen • Sondernutzungsvorschriften

de: Abteilung Verkehr

de: AGIS Service Center Kanton Aargau

de: Das AGIS Service Center unterstützt als kantonale Koordinationsstelle die Bereitstellung von Geoinformationen und Metadaten der kantonalen Verwaltung. Die zuständigen kantonalen Fachstellen stellen die öffentlichen Geodaten des Kantons Aargau mittels der Infrastruktur des AGIS Service Center in der Informatik Aargau als Webservices und zum Download bereit.

political_level: canton

value: Aargau-Kt - Geocat Harvester

de: Kantonaler Nutzungsplan Verkehr: Festlegungen (Flächen)


kantonaler-nutzungsplan-verkehr

title_for_slug: kantonaler-nutzungsplan-verkehr

de: Kantonaler Nutzungsplan Verkehr: Perimeter

format: WMS

de: Der Datensatz bildet die Perimeter aller gültigen Kantonalen Nutzungspläne (KNP) im Bereich Verkehr mit rechtskräftigen Festsetzungen ab. Diese Pläne und die zugehörigen Vorschriften sind in der §10 des Baugesetzes, BauG (altrechtlich auch als "Überbauungspläne" bezeichnet) geregelt. Die KNP Verkehr decken die strategischen Freihalteinteressen für Strassenbauvorhaben ab. Ihre Festsetzungen sind öffentlich-rechtliche Eigentumsbeschränkungen (ÖREB); dieser Datensatz gehört zum Bereich Raumnutzung des ÖREB-Katasters. Sie können folgenden Festsetzungen enthalten: • Baulinien: sie gehen anderen Abstandsvorschriften vor. Sie treten an Stelle der generellen Abstandsvorschriften. Baulinien begrenzen die Bebauung und dienen, insbesondere der Sicherung bestehender und geplanter Anlagen und Flächen sowie der baulichen Gestaltung. • Sichtlinien • Zu-/Wegfahrtbeschränkungen • Strassenlinien • Sondernutzungsvorschriften


author: Abteilung Raumentwicklung

de: Nutzungsplanung: Grundnutzung (im Bauzonen- und Kulturlandplan)

de: Nutzungsplanung: linienbezogene Überlagerungen (im Bauzonen- und Kulturlandplan)

de: Nutzungsplanung: objektbezogene Überlagerungen (im Bauzonen- und Kulturlandplan)

de: Nutzungsplanung: flächenbezogene Überlagerungen inkl. Sondernutzungspläne (im Bauzonen- und Kulturlandplan)

de: Die Geobasisdaten richten sich nach dem minimalen Datenmodell des Bundes Nr. 73 Nutzungsplanung. Sie beinhalten Geoinformationen auf kommunaler, kantonaler und Bundes-Ebene.

de: Abteilung Raumentwicklung

de: 73C-CH Nutzungsplanung (kommunal)


agri

fr: Agriculture, pêche, sylviculture et alimentation

de: Landwirtschaft, Fischerei, Forstwirtschaft und Nahrungsmittel

en: Agriculture, fisheries, forestry and food

it: Agricoltura, pesca, silvicoltura e prodotti alimentari

regi

de: Regionen und Städte

author: Landwirtschaft Aargau

de: Die Polygone zeigen die 1 km-Umkreise um die Fundorte der invasiven Asiatischen Hornisse im Kanton Aargau.

de: Landwirtschaft Aargau

de: Asiatische Hornisse 1km Puffer


knp-verkehr-festlegungen-linien

envi

de: Umwelt

tran

de: Verkehr

title_for_slug: knp-verkehr-festlegungen-linien

de: geocat.ch Permalink

de: AGIS Direktdownload

de: Der Datensatz enthält die linienbezogenen überlagernden rechtskräftigen Festlegungen, die im Verfahren einer kantonalen Nutzungsplanung im Bereich Verkehr genehmigt worden sind: • Baulinien: sie gehen anderen Abstandsvorschriften vor. Sie treten an Stelle der generellen Abstandsvorschriften. Baulinien begrenzen die Bebauung und dienen, insbesondere der Sicherung bestehender und geplanter Anlagen und Flächen sowie der baulichen Gestaltung. • Sichtlinien • Zu-/Wegfahrtbeschränkungen • Strassenlinien

de: Abteilung Verkehr

de: AGIS Service Center Kanton Aargau

de: Kantonaler Nutzungsplan Verkehr: Festlegungen (Linien)


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: Planungszonen kommunal

de: Planungszonen kommunal

en: Planungszonen kommunal

it: Planungszonen kommunal

fr: Planungszonen kantonal

de: Planungszonen kantonal

en: Planungszonen kantonal

it: Planungszonen kantonal

fr: Planungszonen andere

de: Planungszonen andere

en: Planungszonen andere

it: Planungszonen andere

fr: ÖREB: Planungszonen kommunal

de: ÖREB: Planungszonen kommunal

en: ÖREB: Planungszonen kommunal

it: ÖREB: Planungszonen kommunal

fr: ÖREB: Planungszonen kantonal

de: ÖREB: Planungszonen kantonal

en: ÖREB: Planungszonen kantonal

it: ÖREB: Planungszonen kantonal

fr: ÖREB: Planungszonen andere

de: ÖREB: Planungszonen andere

en: ÖREB: Planungszonen andere

it: ÖREB: Planungszonen andere

fr: MGDM CH Datenmodell, geodienste.ch (INTERLIS, GeoPackage, Shapefile)

fr: MGDM CH Datenmodell, geodienste.ch

de: MGDM CH Datenmodell, geodienste.ch

en: MGDM CH Datenmodell, geodienste.ch

it: MGDM CH Datenmodell, geodienste.ch

fr: Planungszonen sind Gebiete, in denen ein vorsorgliche Bauverbot gilt. Müssen Nutzungspläne angepasst werden oder liegen keine vor, so kann die zuständige Behörde für genau bezeichnete Gebiete Planungszonen bestimmen. Innerhalb der Planungszonen darf nichts unternommen werden, was die Nutzungsplanung erschweren könnte. Planungszonen sind auf maximal 5 Jahre befristet. Der Datensatz ist auch Bestandteil des Katasters der öffentlich-rechtlichen Eigentumsbeschränkungen (ÖREB).

amt-geoinformation-sz

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


verkehrszahlung

regi

de: Regionen und Städte

title_for_slug: verkehrszahlung

fr: Verkehrszählung

de: Verkehrszählung

en: Verkehrszählung

it: Verkehrszählung

fr: Strassenverkehrszählung: Messstelle

de: Strassenverkehrszählung: Messstelle

en: Strassenverkehrszählung: Messstelle

it: Strassenverkehrszählung: Messstelle

fr: Das Tiefbauamt (TBA) betreibt auf dem Hauptstrassennetz des Kantons Schwyz Verkehrsmessstellen. Die erhobenen Daten dienen als wichtige Grundlage und Information für verkehrstechnische Aufgaben und Strassenbauprojekte. Weiter finden sie Anwendung im Verkehrsmodell und bei der Berechnung des Lärmbelastungskatasters und für Verkehrsauskünfte innerhalb der kantonalen Verwaltung als auch an Dritte. Die Kennwerte der permanenten Verkehrserhebung geben zudem Aufschluss über die Verkehrsentwicklung.

spatial: Kanton Schwyz (SZ)

fr: SZ Tiefbauamt

de: SZ Tiefbauamt

en: SZ Tiefbauamt

it: SZ Tiefbauamt

de: Amt für Geoinformation Kanton Schwyz

value: SZ-Kt Geoinformation - Geocat Harvester


maintainer: Bundesamt für Energie BFE

title_for_slug: Schweizerische Elektrizitätsstatistik: Schweizerische Elektrizitätsbilanz - Monatswerte

fr: ogd35-bfe electricite production importation exportation consommation

de: ogd35-bfe elektrizitat produktion import export verbrauch

en: ogd35-bfe electricity production import export

it: ogd35-bfe elettricita produzione importazione esportazione consumo

author: Bundesamt für Energie BFE

fr: Cette statistique recense sur une base mensuelle la production, l'importation, l'exportation et la consommation d'électricité en Suisse. Elle présente la production des centrales hydrauliques, des centrales nucléaires, des centrales thermiques classiques et renouvelables.A partir de l’année  2020, la production des centrales thermiques, des éoliennes et des installations photovoltaïques est indiquée séparément. Elle indique également la consommation des installations de pompage d'accumulation et les pertes en ligne. Elle est établie dans le cadre de la Statistique de l'électricité, qui fait partie de la statistique publique de la Suisse (base légale: LSF).

de: Die Schweizerische Elektrizitätsbilanz - Monatswerte gibt Auskunft über Produktion, Import / Export und Verbrauch von Elektrizität in der Schweiz auf monatlicher Basis. Bei der Produktion werden Wasserkraftwerke, Kernkraftwerke, konventionell-thermische und erneuerbare Kraftwerke unterschieden.Die Produktion der thermischen Kraftwerke, der Windkraft- und Photovoltaikanlagen wird seit dem Jahr 2020 neu separat ausgewiesen.  Der Verbrauch der Speicherpumpen und die Leitungsverluste werden ebenfalls ausgewiesen. Die monatliche Elektrizitätsbilanz wird im Rahmen der Elektrizitätsstatistik erstellt, welche Teil der öffentlichen Statistik der Schweiz ist (gesetzliche Grundlage: BStatG).

fr: Statistique suisse de l'électricité: bilan mensuel suisse de l'électricité

it: Statistica svizzera dell'elettricità: bilancio svizzero dell'elettricità - valori mensili

ener

fr: Énergie

de: Energie

en: Energy

it: Energia

gove

fr: Gouvernement et secteur public

de: Regierung und öffentlicher Sektor

en: Government and public sector

it: Governo e settore pubblico

econ

fr: Économie et finances

de: Wirtschaft und Finanzen

en: Economy and finance

it: Economia e finanze

de: Bundesamt für Energie BFE

bundesamt-fur-energie-bfe

fr: Office fédéral de l'énergie OFEN

en: Swiss Federal Office of Energy SFOE

it: Ufficio federale dell'energia UFE

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

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

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

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

political_level: confederation


title_for_slug: Gebäudeprogramm: Jahresbericht und Jahresstatistik

fr: ogd18-bfe batiments renovation energie-renouvelable chauffage isolation proprietaires subventions

de: ogd18-bfe gebaude sanierung erneuerbare-energie heizung dammung hauseigentumer fordermittel

fr: Versements des subventions selon domaine de mesures par canton et année de référence

fr: Versements des subventions selon domaine de mesures (isolation thermique, installations techniques, rénovation du système, nouvelle construction, approvisionnement en chaleur centralisé, mesures indirectes) par canton et année de référence

de: Auszahlungen nach Massnahmenbereich (Haustechnik, Wärmedämmung, Systemsanierung, Neubau, Zentrale Wärmeversorgung, Indirekte Massnahmen) pro Kanton und Berichtsjahr

de: Vordefinierte SPARQL Query auf LINDAS für Auszahlungen nach Massnahmenbereich pro Kanton und Berichtsjahr

format: SPARQL

fr: Nombre de demandes soutenues (toutes les mesures directes)

de: Anzahl unterstützter Gesuche (alle direkten Massnahmen)

fr: Aperçu du nombre de demandes et bâtiments pour les années de référence (2017 à 2022) (projets agrégés par année de versement)

de: Übersicht zur Anzahl Gesuche und Gebäude für Berichtsjahre (2017 bis 2022) (Projekte zusammengefasst nach Auszahlungsjahr)

de: Vordefinierte SPARQL Query auf LINDAS für Anzahl unterstützter Gesuche

de: Energiewirkungen je Massnahmenbereich

fr: Effets énergétiques induits par domaine de mesures (isolation thermique, installations techniques, rénovation du système, nouvelle construction, approvisionnement en chaleur centralisé, mesures indirectes), depuis le début du Programme Bâtiments (calculés sur la base du ModEnHa 2015)

de: Anhaltende Energiewirkungen je Massnahmenbereich (Haustechnik, Wärmedämmung, Systemsanierung, Neubau, Zentrale Wärmeversorgung, Indirekte Massnahmen), seit Start des Gebäudeprogramms (berechnet auf Basis des HFM 2015)

de: Vordefinierte SPARQL Query auf LINDAS für Energiewirkungen je Massnahmenbereich

fr: Effets sur les émissions de CO2 induits

de: CO2-Wirkungen je Massnahmenbereich

fr: Effets sur les émissions de CO2 induits par domaine de mesures (isolation thermique, installations techniques, rénovation du système, nouvelle construction, approvisionnement en chaleur centralisé, mesures indirectes), depuis le début du Programme Bâtiments (calculés sur la base du ModEnHa 2015)

de: Anhaltende CO2-Wirkungen je Massnahmenbereich (Haustechnik, Wärmedämmung, Systemsanierung, Neubau, Zentrale Wärmeversorgung, Indirekte Massnahmen), seit Start des Gebäudeprogramms (berechnet auf Basis des HFM 2015)

de: Vordefinierte SPARQL Query auf LINDAS für CO2-Wirkungen je Massnahmenbereich

fr: Le Programme Bâtiments a été lancé en 2010 pour inciter les propriétaires à assainir leurs biens sur le plan énergétique et à apporter par là une contribution concrète à l’amélioration de l’efficacité énergétique et à la protection du climat. Jusqu’à fin 2016, le Programme Bâtiments comprenait un volet A commun à tout le pays pour l’assainissement de l’enveloppe du bâtiment et un volet B variable selon les cantons pour les énergies renouvelables, la récupération de chaleur et l’amélioration des installations techniques. Jusqu’à présent, Le Programme Bâtiments a versé environ 4,1 milliards de francs de subventions. Cela a permis d’économiser un total d’environ 27 millions de tonnes de CO2 ou environ 104 milliards de kilowattheures d’énergie sur la durée de vie des décisions subventionnées. Le parc immobilier suisse a ainsi pu réduire sa consommation annuelle d’énergie de 4.4 milliards de kilowattheures (kWh) et ses émissions annuelles de CO2 de 1'251’000 tonnes (t). Le Programme Bâtiments est donc un élément central de la politique énergétique et climatique suisse. Vous trouverez des informations sur les résultats et les effets du Programme Bâtiments dans les rapports annuels respectifs et les statistiques correspondantes.

de: Seit 2010 motiviert Das Gebäudeprogramm Eigentümer/-innen, mit der energetischen Sanierung ihrer Liegenschaft einen konkreten Beitrag zur Energieeffizienz und zum Klimaschutz zu leisten. Bis Ende 2016 bestand das Programm aus einem schweizweit einheitlichen Teil A (energetische Sanierung der Gebäudehülle) und einem kantonal unterschiedlichen Teil B (Einsatz erneuerbarer Energien, Abwärmenutzung und Optimierung der Gebäudetechnik). Bisher wurden durch Das Gebäudeprogramm rund 4,1 Milliarden Franken (Mia. Fr.) an Fördermitteln ausbezahlt. Damit konnten über die Lebensdauer der geförderten Massnahmen insgesamt rund 27 Millionen Tonnen (Mio. t) CO2 vermieden bzw. rund 104 Milliarden Kilowattstunden (Mia. kWh) Energie eingespart werden. Dadurch verbraucht der Schweizer Gebäudepark heute 4,4 Mia. Kilowattstunden (kWh) weniger Energie und stösst 1'251’000 Tonnen (t) weniger CO2 aus. Das Gebäudeprogramm ist damit ein wichtiger Pfeiler der Schweizer Energie- und Klimapolitik. Informationen zu den Resultaten und Wirkungen des Gebäudeprogramms können Sie den jeweiligen Jahresberichten und den dazugehörigen Statistiken entnehmen.

it:
Dal 2010, il Programma Edifici spinge i proprietari a dare un contributo concreto all'efficienza energetica e alla protezione del clima ristrutturando i loro immobili. Fino alla fine del 2016, il programma era composto da una parte A, uguale in tutta la Svizzera (ristrutturazione energetica dell'involucro dell'edificio), e da una parte B, diversa a seconda del Cantone (uso di energie rinnovabili, sfruttamento del calore residuo e ottimizzazione della tecnica degli edifici). Finora, il Programma Edifici ha versato circa 4,1 miliardi di franchi (miliardi di CHF) di sovvenzioni. Ciò ha permesso di evitare circa 27 milioni di tonnellate (milioni di t) di CO2 e di risparmiare circa 104 miliardi di chilowattora (miliardi di kWh) di energia durante il ciclo di vita delle misure sovvenzionate. Di conseguenza, il parco immobiliare svizzero consuma oggi 4,4 miliardi di Il Programma Edifici è quindi un pilastro importante della politica energetica e climatica svizzera. Per saperne di più sui risultati e gli effetti del Programma Edifici, dai un'occhiata ai rapporti annuali e alle statistiche.
(Traduzione fatta da DeepL pro)

fr: Programme Bâtiments: rapports annuels et statistique annuelles

it: Programma Edifici: rapporti e statistiche annuali

soci

fr: Population et société

de: Bevölkerung und Gesellschaft

en: Population and society

it: Popolazione e società


einschrankungen-binnenschifffahrt

regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

tran

fr: Transports

de: Verkehr

en: Transport

it: Trasporti

title_for_slug: einschrankungen-binnenschifffahrt

fr: geodonnees navigation-fluviale

de: geodaten binnenschiffahrt

en: geodata inland-navigation

it: geodati navigazione-interna

author: Kanton Thurgau, Amt für Geoinformation

fr: geocat.ch permalien

de: geocat.ch Permalink

en: geocat.ch permalink

it: geocat.ch link permanente

de: Einschränkungen Binnenschifffahrt

de: ThurGIS Shop

de: Bei Schifffahrtsbeeinflussungen handelt es sich um lokalisierbare Bereiche auf Gewässern, wo die Schifffahrt rechtswirksam oder informativ beeinflusst wird. Eine Schifffahrtsbeeinflussung kann mit Hilfe von Schifffahrtszeichen materialisiert sein, das ist jedoch nicht zwingend. Bei Schifffahrtszeichen handelt es sich um Tafeln mit Sichtzeichen, verschieden ausgeprägte Schwimmkörper, Lichter, Radarreflektoren und Kardinalzeichen die eine Beeinflussung im Gewässer signalisieren. Mehrere Schifffahrtszeichen können zusammen eine Schifffahrtsbeeinflussung signalisieren. Umgekehrt ist zu beachten, dass nicht alle Schifffahrtsbeeinflussungen signalisiert werden müssen, wie etwa Uferzonen oder generelle Fahrverbote auf dem gesamten Gewässer.

spatial: Kanton Thurgau (TG)

fr: Kanton Thurgau, Amt für Geoinformation

de: Kanton Thurgau, Amt für Geoinformation

en: Kanton Thurgau, Amt für Geoinformation

kanton-thurgau

fr: Canton de Thurgovie

de: Kanton Thurgau

en: Canton of Thurgau

it: Cantone di Turgovia

de: Der Kanton Thurgau veröffentlicht hier schrittweise OGD-Datensätze der kantonalen Verwaltung.

value: TG-Kt - Geocat Harvester


agri

fr: Agriculture, pêche, sylviculture et alimentation

de: Landwirtschaft, Fischerei, Forstwirtschaft und Nahrungsmittel

en: Agriculture, fisheries, forestry and food

it: Agricoltura, pesca, silvicoltura e prodotti alimentari

envi

fr: Environnement

de: Umwelt

en: Environment

it: Ambiente

de: baum bauminventar baumkataster stadtbaum

author: Geoinformation Stadt Bern

fr: Map (Preview)

de: Map (Preview) Stadtplan

en: Map (Preview)

it: Map (Preview)

de: Link zum Thema im Internet-Stadtplan der Stadt Bern

de: Baumkataster

de: Web Map Services WMS

de: WFS Baumkataster

de: Web Feature Services WFS

de: Durch Stadtgrün bewirtschaftete Bäume, Symbole für Laubbaum, Nadelbaum und Obstbaum, mit den Informationen über Gattung, Art, deutscher Name, Pflanzjahr und Standort.

spatial: Stadt Bern

de: Geoinformation Stadt Bern

geoinformation-der-stadt-bern

fr: L’Office du cadastre de la ville de Berne

en: The Land Surveying Office of the City of Bern

it: L'ufficio del catasto nella Città di Berna

fr: L’Office du cadastre (Geoinformation Stadt Bern) est l’organisme compétent pour la collecte, l’accès et la diffusion d’informations géographiques dans la ville de Berne.

de: Geoinformation Stadt Bern (ehemals Vermessungsamt) ist die Kompetenzstelle für die Beschaffung, den Zugang und die Verbreitung von Geoinformationen in der Stadt Bern.

en: The Surveying Office (Geoinformation Stadt Bern) is the centre of competence in the City of Bern for the procurement of, access to and dissemination of geographic information.

it: L'ufficio del catasto (Geoinformation Stadt Bern) è il centro di competenza per il reperimento, l'accesso e la diffusione di geoinformazioni nella Città di Berna.

political_level: commune

value: Geoinformation Stadt Bern - Geocat Harvester


grundwasserschutzareale-differenz-ur

regi

de: Regionen und Städte

title_for_slug: grundwasserschutzareale-differenz-ur

de: grundwasserschutzareal geobasisdaten

de: geocat.ch Permalink

fr: WMS-Dienst des Kanton Uri

de: WMS-Dienst des Kanton Uri

en: WMS-Dienst des Kanton Uri

it: WMS-Dienst des Kanton Uri

fr: WFS-Dienst des Kanton Uri

de: WFS-Dienst des Kanton Uri

en: WFS-Dienst des Kanton Uri

it: WFS-Dienst des Kanton Uri

fr: Grundwasserschutzareale sind speziell ausgeschiedene Gebiete, in welchen der Schutz des unterirdischen Gewässers im Hinblick auf eine künftige Grundwasserbewirtschaftung (Nutzung oder Anreicherung) vorsorglich sichergestellt werden soll. In diesen Arealen dürfen keine Bauten und Anlagen erstellt werden, welche die künftige Bewirtschaftung beeinträchtigen können.

spatial: Canton of Uri (UR)

fr: Amt für Umwelt Kt. Uri

de: Amt für Umwelt Kt. Uri

en: Amt für Umwelt Kt. Uri

it: Amt für Umwelt Kt. Uri

geoinformation-kanton-uri

de: Lisag AG, Geoinformation Kanton Uri

de: Die Lisag AG bewirtschaftet das geografische Informationssystem des Kantons Uri (GIS Uri), ist die verantwortliche Stelle für den ÖREB-Kataster Uri und zudem zuständig für die Durchführung der amtlichen Vermessung auf Urner Kantonsgebiet.

value: Uri-Kt - Geocat Harvester

fr: Grundwasserschutzareale Differenz (UR)

de: Grundwasserschutzareale Differenz (UR)

en: Grundwasserschutzareale Differenz (UR)

it: Grundwasserschutzareale Differenz (UR)


grundwasserschutzzonen-differenz-ur

envi

de: Umwelt

title_for_slug: grundwasserschutzzonen-differenz-ur

author: Amt für Umwelt Kt. Uri

fr: Grundwasserschutzzonen dienen dazu, Trinkwassergewinnungsanlagen und das Grundwasser unmittelbar vor seiner Nutzung als Trinkwasser vor Beeinträchtigungen zu schützen. Sie sind um die im öffentlichen Interesse liegenden Grundwasserfassungen auszuscheiden, d.h. um alle Fassungen, deren Wasser den Anforderungen der Lebensmittelgesetzgebung entsprechen muss, sowie um Grundwasser-Anreicherungsanlagen. Die Grundwasserschutzzonen sind das wichtigste Instrument des nutzungsorientierten, planerischen Grundwasserschutzes. Grundwasserschutzzonen werden gegliedert in: Zone S1 (Fassungsbereich), Zone S2 (Engere Schutzzone), Zone S3 (Weitere Schutzzone)

fr: Grundwasserschutzzonen Differenz (UR)

de: Grundwasserschutzzonen Differenz (UR)

en: Grundwasserschutzzonen Differenz (UR)

it: Grundwasserschutzzonen Differenz (UR)


japankafer-befallsherd-und-pufferzone

agri

de: Landwirtschaft, Fischerei, Forstwirtschaft und Nahrungsmittel

regi

fr: Régions et villes

title_for_slug: japankafer-befallsherd-und-pufferzone

de: betriebe landwirtschaft landwirtschaftliche-nutzung wirtschaftliche-aktivitaten

protocol: https

de: Datenshop

de: Produktansicht Geodatenshop

de: Der Japankäfer (Popillia japonica) zählt zu den Quarantäneorganismen, von denen die grössten Schäden zu erwarten sind und deren Bekämpfung am dringendsten ist. Dazu werden Befallsherde sowie dazugehörige Pufferzonen ausgewiesen.

spatial: Kanton Luzern

de: Kanton Luzern, Abteilung Geoinformation

fr: Géoinformation du canton de Lucerne

de: Geoinformation Kanton Luzern

en: Geoinformation Canton of Lucerne

it: Geoinformazione del Cantone di Lucerna

fr: La géoinformation du canton de Lucerne est responsable de la collecte, du traitement et de la gestion des données à référence spatiale. Elle dirige, coordonne et surveille les travaux de la géoinformation et de la mensuration officielle.

en: Geoinformation of the Canton of Lucerne is responsible for the collection, processing and management of spatial data. It manages, coordinates and monitors the work of geoinformation and of the cadastral surveying.

de: Die Geoinformation des Kantons Luzern ist für die Erhebung, Verarbeitung und Verwaltung von raumbezogenen Daten zuständig. Sie leitet, koordiniert und überwacht die Arbeiten der Geoinformation und der amtlichen Vermessung.

it: La Geoinformation del Cantone di Lucerna è responsabile della raccolta, dell'elaborazione e della gestione dei dati territoriali. Gestisce, coordina e monitora il lavoro della geoinformazione e della misurazione ufficiale.

value: LU-Kt - Geocat Harvester

de: Japankäfer: Befallsherd und Pufferzone


waldflachen

regi

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

title_for_slug: waldflachen

de: forstwirtschaft nutzungsplanung vegetation wald

author: Kanton Luzern, Abteilung Geoinformation

fr: geocat.ch permalien

de: geocat.ch Permalink

de: Wald nach Waldrecht, dieser umfasst unter anderem auch Waldstrassen, Gewässer und Gebäude innerhalb des Waldes.

de: Waldflächen


bader-aktualisierung-der-webseite

title_for_slug: bader-aktualisierung-der-webseite

de: wassertemperatur pegelstand

de: xls

de: Bäder Aktualisierung der Webseite (xls)

de: turtle

media_type: text/turtle

de: Bäder Aktualisierung der Webseite (turtle)

format: RDF Turtle

de: Bäder Aktualisierung der Webseite (n3)

de: rdfxml

media_type: application/rdf+xml

de: Bäder Aktualisierung der Webseite (rdfxml)

format: RDF XML

de: json

media_type: application/json

de: Bäder Aktualisierung der Webseite (json)

format: JSON

de: parquet

de: Bäder Aktualisierung der Webseite (parquet)

format: PARQUET

de: csv

de: Bäder Aktualisierung der Webseite (csv)

de: jsonl

de: Bäder Aktualisierung der Webseite (jsonl)

de: jsonld

de: Bäder Aktualisierung der Webseite (jsonld)

format: JSON-LD

de: Bäder Aktualisierung der Webseite

pegelstand

display_name: pegelstand

wassertemperatur

display_name: wassertemperatur

de: stadt-st-gallen

stadt-st-gallen

fr: Ville de Saint-Gall

de: Stadt St.Gallen

en: City of St.Gallen

it: Città di San Gallo

fr: Avec une population de 80 000 habitants, Saint-Gall, huitième ville de Suisse, est la métropole économique, politique, sociale et culturelle de la Suisse orientale. Ses 1400 ans d'histoire se mêlent à la créativité urbaine, à une économie innovante et à un paysage préservé.

en: With a population of 80,000, St.Gallen, Switzerland’s eighth largest city, is the economic, political, social and cultural metropolis of eastern Switzerland. Its 1400 years of history blend with urban creativity, an innovative economy and an unspoilt landscape.

de: St.Gallen, die achtgrösste Stadt der Schweiz, ist mit 80'000 Einwohnern die wirtschaftliche, politische, soziale und kulturelle Metropole der Ostschweiz. Ihre 1400-jährige Geschichte verbindet sich mit urbaner Kreativität, einer innovativen Wirtschaft und einer intakten Landschaft.

it: Con una popolazione di 80.000 abitanti, San Gallo, l'ottava città più grande della Svizzera, è la metropoli economica, politica, sociale e culturale della Svizzera orientale. I suoi 1400 anni di storia si fondono con la creatività urbana, un'economia innovativa e un paesaggio incontaminato.

value: DCAT Harvester Stadt St. Gallen


maintainer: OGD-Koordinationsstelle St.Gallen

de: elektrizitat smart-meter solar strom real-time pv

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (parquet)

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (ov2)

de: shp

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (shp)

de: kml

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (kml)

format: KML

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (jsonld)

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (json)

de: geojson

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (geojson)

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (csv)

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (rdfxml)

de: turtle

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (turtle)

de: gpx

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (gpx)

format: GPX

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (n3)

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (jsonl)

de: xls

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (xls)

format: XLS

de: fgb

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (fgb)

de: Der Datensatz zeigt die Echtzeitdaten der Solaranlagen der St.Galler Stadtwerke (sgsw), welche im Rahmen des Pilotprojekts "Kraftwerk St.Gallen" mit Stromzähler ausgerüstet wurden. Nur ein Teil der Solaranlagen, vorwiegend die Kleineren, wurden mit Smart-Me Zählern ausgerüstet. Der Datensatz wird alle 5 Minuten aktualisiert.Alle Solaranlagen der St.Galler Stadtwerke finden Sie in folgendem Datensatz:[https://daten.stadt.sg.ch/explore/dataset/solaranlagen-der-stgaller-stadtwerke](https://daten.stadt.sg.ch/explore/dataset/solaranlagen-der-stgaller-stadtwerke)

elektrizitat

display_name: elektrizitat

pv

display_name: pv

real-time

display_name: real-time

smart-meter

display_name: smart-meter

solar

display_name: solar

strom

display_name: strom

ener

fr: Énergie

de: Energie

en: Energy

it: Energia


regi

de: Regionen und Städte

envi

fr: Environnement

de: Umwelt

fr: schutzzone ver--und-entsorgung wasserbewirtschaftung eigentumsbeschrankungoreb grundwasserschutz trinkwasser hydrologie ressourcennutzung grundwasser ressourcen gewasserschutz gewasserschutzrecht

de: geocat.ch Permalink

en: geocat.ch permalink

it: geocat.ch link permanente

fr: WFS-Dienst des Kantons Schwyz

de: WFS-Dienst des Kantons Schwyz

en: WFS-Dienst des Kantons Schwyz

it: WFS-Dienst des Kantons Schwyz

fr: ÖREB: Grundwasserschutzzonen

de: ÖREB: Grundwasserschutzzonen

en: ÖREB: Grundwasserschutzzonen

it: ÖREB: Grundwasserschutzzonen

fr: MGDM CH Datenmodell, geodienste.ch (INTERLIS, GeoPackage, Shapefile)

fr: MGDM CH Datenmodell, geodienste.ch

de: MGDM CH Datenmodell, geodienste.ch

en: MGDM CH Datenmodell, geodienste.ch

it: MGDM CH Datenmodell, geodienste.ch

fr: Interlisfile nach Modell SZ_planerischerGewaesserschutz_V3_Erfassung

format: INTERLIS

fr: Grundwasserschutzzonen provisorisch

de: Grundwasserschutzzonen provisorisch

en: Grundwasserschutzzonen provisorisch

it: Grundwasserschutzzonen provisorisch

fr: Grundwasserschutzzonen in Kraft

de: Grundwasserschutzzonen in Kraft

en: Grundwasserschutzzonen in Kraft

it: Grundwasserschutzzonen in Kraft

fr: Grundwasserschutzzonen sind Teil des planerischen Gewässerschutzes. Sie dienen dem Schutz vor Beeinträchtigungen des Grundwassers, welches der Nutzung als Trinkwasser dient. Grundwasserschutzzonen sind somit das wichtigste Instrument des nutzungsorientierten Grundwasserschutzes. Der Datensatz ist auch Bestandteil des Katasters der öffentlich-rechtlichen Eigentumsbeschränkungen (ÖREB)

fr: SZ Amt für Umwelt und Energie

de: SZ Amt für Umwelt und Energie

en: SZ Amt für Umwelt und Energie

it: SZ Amt für Umwelt und Energie

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.

fr: Grundwasserschutzzonen

de: Grundwasserschutzzonen


educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

regi

fr: Régions et villes

envi

en: Environment

it: Ambiente

fr: ver--und-entsorgung wasserbewirtschaftung grundwasserschutz trinkwasser hydrologie ressourcennutzung grundwasser ressourcen gewasserschutz

fr: Der Gewässerschutzbereich Au umfasst die nutzbaren unterirdischen Gewässer und die Randgebiete, die zu ihrem Schutz notwendig sind. Sie sind Teil des planerischen Gewässerschutzes.

fr: Gewässerschutzbereich Au

de: Gewässerschutzbereich Au

en: Gewässerschutzbereich Au

it: Gewässerschutzbereich Au


fr: Environnement

fr: wasserbewirtschaftung grundwasserschutzareal schutzzone eigentumsbeschrankungoreb gewasserschutzbereich hydrologie ressourcennutzung gewasser trinkwasser grundwasserschutz grundwasser gewasserschutz ressourcen gewasserschutzrecht flachennutzungsplanung

fr: geocat.ch permalien

fr: Die Gewässerschutzkarte ist das entscheidende Instrument des planerischen Gewässerschutzes und dient der nachhaltigen Bewirtschaftung der natürlichen Wasserressourcen. Das Kartenwerk setzt sich aus Daten zu den besonders gefährdeten Gewässerschutzbereichen, den Grundwasserschutzzonen und -arealen sowie zu Quellen und Grundwasserfasusngen zusammen.

fr: Gewässerschutzkarte

de: Gewässerschutzkarte

en: Gewässerschutzkarte

it: Gewässerschutzkarte


fr: eigentumsbeschrankungoreb ver--und-entsorgung wasserbewirtschaftung hydrologie trinkwasser ressourcennutzung grundwasser ressourcen gewasserschutzrecht

fr: WFS-Dienst des Kantons Schwyz

fr: ÖREB: Grundwasserschutzzareale

de: ÖREB: Grundwasserschutzzareale

en: ÖREB: Grundwasserschutzzareale

it: ÖREB: Grundwasserschutzzareale

fr: MGDM CH Datenmodell, geodienste.ch (INTERLIS, GeoPackage, Shapefile)

fr: MGDM CH Datenmodell, geodienste.ch

fr: Grundwasserschutzareal

de: Grundwasserschutzareal

en: Grundwasserschutzareal

it: Grundwasserschutzareal

fr: Grundwasserschutzareale sind Teil des planerischen Gewässerschutzes. Sie dienen der Sicherstellung des Grundwassers bezüglich einer künftigen Grundwasserbewirtschaftung (Anreicherung oder Nutzung). Der Datensatz ist auch Bestandteil des Katasters der öffentlich-rechtlichen Eigentumsbeschränkungen (ÖREB)

fr: Grundwasserschutzareale

de: Grundwasserschutzareale

en: Grundwasserschutzareale

it: Grundwasserschutzareale


archaologische-zonenplane

soci

fr: Population et société

de: Bevölkerung und Gesellschaft

en: Population and society

it: Popolazione e società

educ

fr: Éducation, culture et sport

regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

de: Umwelt

title_for_slug: archaologische-zonenplane

de: archaologie archaologische-zonen

author: Kanton Zürich

fr: Die archäologischen Zonenpläne stellen einen Verdachtsflächen-Kataster dar.

fr: WFS für Open-Government-Data des Kantons Zürich (OGD ZH WFS)

fr: Archäologische Zonen und Denkmalschutzobjekte

fr: Bauliche Bodeneingriffe innerhalb der Verdachtsflächen sind der Kantonsarchäologie vorgängig zu melden.

spatial: Kanton Zürich

fr: Kanton Zürich

de: Kanton Zürich

en: Kanton Zürich

it: Kanton Zürich

geoinformation-kanton-zuerich

fr: Département de Géoinformation du Canton de Zurich

de: Geoinformation Kanton Zürich

en: Department of Geoinformation of the Canton of Zurich

it: Dipartimento della geoinformazione del Cantone di Zurigo

fr:
Le département de géomatique est le centre de compétence du system d'information géographique du canton de Zurich.
Nous coordinons l'aquisition des géodonnées d'intérêt commun sur le territoire cantonal et les mettons à disposition des
secteurs public et privé et des particuliers.

en:
The department of geoinformation is the geospatial competency center of the Canton of Zurich.
We coordinate the acquisition of geodata within the cantonal territory and provide public, private, and government sectors with data of outstanding quality.

de: Die Abteilung Geoinformation ist das Kompetenzzentrum für räumliche Informationen im Kanton Zürich. Wir versorgen Gesellschaft, Wirtschaft und öffentliche Verwaltung mit qualitativ hochstehenden Geodaten und Informationen über den Kanton Zürich.

it: La divisione della geoinformazione è il centro d'eccellenza del Cantone di Zurigo per dati georeferenziati. Mettiamo a disposizione dati geografici e informazioni di alta qualità per l'economia, la società e la pubblica amministrazione su tutto il cantone.

value: ZH - Geocat Harvester

fr: Archäologische Zonenpläne

de: Archäologische Zonenpläne

en: Archäologische Zonenpläne

it: Archäologische Zonenpläne