fr: Annuaire statistique de la Suisse 1919

de: Statistisches Jahrbuch der Schweiz 1919

media_type: application/pdf

format: PDF

mimetype: application/pdf

media_type: text/html

format: HTML

mimetype: text/html

basas-statisticas-e-survistas

display_name: basas-statisticas-e-survistas

bases-statistiques-et-generalites

display_name: bases-statistiques-et-generalites

grundlagen-und-ubersichten

display_name: grundlagen-und-ubersichten

statistical-basis-and-overviews

display_name: statistical-basis-and-overviews

spatial: Schweiz

statistical-basis

fr: Bases statistiques

de: Statistische Grundlagen

en: Statistical basis

it: Basi statistiche

bundesamt-fur-statistik-bfs

fr: Office fédéral de la statistique OFS

de: Bundesamt für Statistik BFS

en: Federal Statistical Office FSO

it: Ufficio federale di statistica UST

fr: L'OFS est le centre de compétence de la statistique publique suisse. Il produit des informations statistiques sur la population, l'économie, la société, la formation et la recherche, le territoire et l'environnement. Ces informations servent à la formation de l’opinion publique. Elles sont un outil de planification et de gouvernance dans tous les domaines de l’action politique et contribuent au bon fonctionnement de notre État moderne et démocratique.

de: Das BFS ist das nationale Kompetenzzentrum der öffentlichen Statistik der Schweiz. Es produziert und publiziert statistische Informationen über den Stand und die Entwicklung von Bevölkerung, Wirtschaft, Gesellschaft, Bildung, Forschung, Raum und Umwelt. Diese Informationen dienen der Meinungsbildung in der Bevölkerung sowie der Planung und Steuerung von zentralen Politikbereichen. Sie leisten einen wichtigen Beitrag für einen modernen, demokratischen Staat.

en: The FSO is Switzerland's national competence centre for official statistics. It produces and publishes statistical information on the status and development of the population, economy, society, education, research, territory and the environment. This information is used for opinion building among the population and for the planning and management of key policy areas. They make an important contribution to a modern, democratic state.

it: L’UST è il centro di competenza nazionale per la statistica pubblica della Svizzera. Produce e pubblica informazioni statistiche sullo stato e sull'evoluzione della popolazione, dell'economia, della società, della formazione, della ricerca, del territorio e dell'ambiente. Queste informazioni servono alla formazione dell'opinione pubblica nonché alla pianificazione e alla gestione in ambiti politici di centrale importanza. Forniscono un importante contributo per uno Stato moderno e democratico.

political_level: confederation

value: BFS – DCAT Harvester


state: active

type: dataset

fr: Annuaire statistique de la Suisse 1907

de: Statistisches Jahrbuch der Schweiz 1907

type: organization

approval_status: approved

key: guid

key: uri


fr: Annuaire statistique de la Suisse 1922

de: Statistisches Jahrbuch der Schweiz 1922


fr: Annuaire statistique de la Suisse 1914

de: Statistisches Jahrbuch der Schweiz 1914


fr: Annuaire statistique de la Suisse 1896

de: Statistisches Jahrbuch der Schweiz 1896


de: Statistisches Jahrbuch der Schweiz 1947

fr: Annuaire statistique de la Suisse 1947


fr: Annuaire statistique de la Suisse 1972

de: Statistisches Jahrbuch der Schweiz 1972


de: Statistisches Jahrbuch der Schweiz 1956

fr: Annuaire statistique de la Suisse 1956


fr: Annuaire statistique de la Suisse 1996

de: Statistisches Jahrbuch der Schweiz 1996


fr: Annuaire statistique de la Suisse 1995

de: Statistisches Jahrbuch der Schweiz 1995


de: Schweizerische Zivilluftfahrtstatistik 2019 - 5. Passagiere

fr: Statistique de l'aviation civile suisse 2019 - 5. Passagers

format: XLS

mobilita-e-trasporti

display_name: mobilita-e-trasporti

mobilitad-e-traffic

display_name: mobilitad-e-traffic

mobilitat-und-verkehr

display_name: mobilitat-und-verkehr

mobilite-et-transports

display_name: mobilite-et-transports

mobility-and-transport

display_name: mobility-and-transport

mobility

fr: Mobilité et transports

de: Mobilität und Verkehr

en: Mobility and Transport

it: Mobilità e trasporti


de: Schweizerische Zivilluftfahrtstatistik 2020 - 5. Passagiere

fr: Statistique de l'aviation civile suisse 2020 - 5. Passagers


de: Die Ausbildungssituation der Kandidatinnen und Kandidaten der höheren Berufsbildung

de: Die höhere Berufsbildung bildet zusammen mit den Hochschulen die Tertiärstufe des schweizerischen Bildungssystems. Sie ermöglicht Berufsleuten mit einem eidgenössischen Fähigkeitszeugnis oder einem gleichwertigen Abschluss auf Sekundarstufe II eine Spezialisierung und eine Vertiefung ihres Fachwissens. Unter der höheren Berufsbildung werden die eidgenössischen Prüfungen (Berufsprüfung, höhere Fachprüfung) und die höheren Fachschulen zusammengefasst. Erstmals wurden im Jahr 2017 Personen, die im Jahr 2016 einen Abschluss der höheren Berufsbildung angestrebt haben, vom BFS zu ihrer Ausbildung befragt. Die vorliegende Publikation enthält die ersten Ergebnisse der Befragung 2019 von Prüfungskandidatinnen und -kandidaten der höheren Berufsbildung von 2018. Dabei stehen die soziodemografische Zusammensetzung, Fragen zur Ausbildung, subjektive Einschätzungen der Kandidatinnen und Kandidaten und zur Finanzierung der Ausbildung im Vordergrund. Um der Bandbreite der Ausbildungen Rechnung zu tragen, werden die Statistiken nach der internationalen Standardklassifikation des Bildungswesens (ISCED) aufgeschlüsselt.

fr: Conditions de formation des candidats aux examens de la formation professionnelle supérieure

fr: La formation professionnelle supérieure constitue, avec les hautes écoles, le degré tertiaire du système éducatif suisse. Elle permet aux professionnels titulaires d’un certificat fédéral de capacité ou d’un diplôme équivalent du degré secondaire II de se spécialiser et d’approfondir leurs connaissances professionnelles. La formation professionnelle supérieure englobe les examens fédéraux (examen professionnel, examen professionnel supé-rieur) et les écoles supérieures. Pour la première fois en 2017, l’OFS a interrogé, à propos de leur formation, des personnes qui étaient candidates à l’obtention d’un diplôme de la formation professionnelle supérieure en 2016. Les premiers résultats de l’enquête 2019 sur les candidats à un examen de la formation professionnelle supérieure de 2018 sont présentés dans cette publication. L’accent est mis sur la composition sociodémographique de la population considérée, sur des questions relatives à la formation, sur la perception subjective des candidats interrogés et sur le financement de la formation. Les statistiques sont ventilées conformément à la classification internationale type de l’éducation (CITE) pour tenir compte de toute la palette des formations.

bildung-und-wissenschaft

display_name: bildung-und-wissenschaft

education-and-science

display_name: education-and-science

education-et-science

display_name: education-et-science

formazione-e-scienza

display_name: formazione-e-scienza

furmaziun-e-scienza

display_name: furmaziun-e-scienza

education

fr: Education, science

de: Bildung, Wissenschaft

en: Education and science

it: Formazione e scienza


gebaude-nach-eigentumertyp-nach-stadt

title_for_slug: gebaude-nach-eigentumertyp-nach-stadt

fr: Bâtiments selon le type de propriétaire, par ville

it: Edifici secondo il tipo de proprietario, per città

de: Gebäude nach Eigentümertyp, nach Stadt

bau--und-wohnungswesen

display_name: bau--und-wohnungswesen

construction-and-housing

display_name: construction-and-housing

construction-et-logement

display_name: construction-et-logement

construcziuns-ed-abitaziuns

display_name: construcziuns-ed-abitaziuns

costruzioni-e-abitazioni

display_name: costruzioni-e-abitazioni

construction

fr: Construction, logement

de: Bau- und Wohnungswesen

en: Construction and housing

it: Costruzione e abitazione

bundesamt-fur-statistik-bfs

fr: Office fédéral de la statistique OFS

de: Bundesamt für Statistik BFS


maintainer: Staatsarchiv des Kantons Zürich

de: verurteilungen gemeinden strafurteile kirchgemeinden

de: Zürcher Stillstandsprotokolle des 17. Jahrhunderts

media_type: application/zip

de: Transkriptionen der handschriftlichen Protokolle des Stillstands (Vorläuferbehörde der Kirchenpflegen) in den Kirchgemeinden des Alten Stadtstaats Zürich. Umgesetzt als XML Dateien nach Vorgaben der TEI. Die Daten wurden im Rahmen eines Projekts des Staatsarchivs und der reformierten Landeskirche des Kantons Zürich erarbeitet.

format: ZIP

mimetype: application/zip

de: Transkriptionen der handschriftlichen Protokolle des Stillstands (Vorläuferbehörde der Kirchenpflegen) in den Kirchgemeinden des Alten Stadtstaats Zürich. Umgesetzt als XML Dateien nach Vorgaben der TEI. Die Daten wurden im Rahmen eines Projekts des Staatsarchivs und der reformierten Landeskirche des Kantons Zürich erarbeitet und stehen unter einer Creative Commons CC-BY-SA 4.0 Lizenz zur Verfügung.

gemeinden

display_name: gemeinden

kirchgemeinden

display_name: kirchgemeinden

strafurteile

display_name: strafurteile

verurteilungen

display_name: verurteilungen

crime

fr: Criminalité, droit pénal

de: Kriminalität, Strafrecht

en: Crime, criminal justice

it: Criminalità, diritto penale

population

fr: Population

de: Bevölkerung

en: Population

it: Popolazione

staatsarchiv-kanton-zuerich

de: Staatsarchiv Kanton Zürich

de: Das Staatsarchiv ist das Archiv des Kantons Zürich und seiner Rechtsvorgänger. Es bewertet, übernimmt, erschliesst und konserviert Unterlagen aus über tausend Jahren Zürcher Geschichte. Durch die Aufbewahrung dieser Unterlagen macht es staatliches Handeln nachvollziehbar und ermöglicht historische Forschung. Das Staatsarchiv gewährt allen Interessierten Einsicht in diejenigen Bestände, die keinen Beschränkungen mehr unterliegen. Der grösste Teil der Findmittel zu den Archivbeständen kann online abgefragt werden.

political_level: canton

kanton-zuerich

capacity: public

value: ZH-Kt Staatsarchiv - DCAT Harvester


maintainer: Fachstelle Open Government Data des Kantons Zürich

label: Repository: Formale Leitlinien zur Datenpublikation des Kantons Zürich

label: Fachausschuss für offene Behördendaten: Standards

de: Leitlinien des Kantons Zürich für einfach nutzbare Behördendaten (Version 1.0)

de: Version 1.0 in Kraft seit dem 16. Dezember 2021.

de: Handlungsleitende Normen, um offene Behördendaten ('Open Government Data', OGD) bereitzustellen, die einfach nutzbar sind. Der Fachausschuss Open Government Data des Kantons Zürich verwaltet die Normen gemäss der Maxime, den Bedürfnissen der Nutzenden zweckmässig und wirtschaftlich zu entsprechen. Die Fach- und Koordinationsstelle Open Government Data nimmt Feedback (Hinweise, Wünsche und Fragen) zu den Leitlinien entgegen. Entweder als 'Issue' im Repository (verlinkt unter 'Weitere Informationen') oder via E-Mail (verlinkt unter 'Kontakt'). Im Austausch mit den Nutzenden klärt sie deren Bedürfnisse, berät mit den Datenverwaltenden ('Data Custodians') mögliche Lösungen und schlägt diese vierteljährlich dem Fachausschuss zur Inkraftsetzung vor.

national-economy

fr: Economie nationale

de: Volkswirtschaft

en: National economy

it: Economia

fachstelle-ogd-kanton-zuerich

de: Fach- und Koordinationsstelle Open Government Data Kanton Zürich

de: Die Fach- und Koordinationsstelle Open Government Data (OGD) ist das kantonale Kompetenzzentrum für offene Behördendaten im Statistischen Amt. Wir unterstützen die Organisationseinheiten und Organe des Kantons bei der Bereitstellung ihrer nicht schützenswerten Daten als Open Data.

value: ZH-Kt Fachstelle OGD - DCAT Harvester


de: Statistisches Jahrbuch der Schweiz 1928

fr: Annuaire statistique de la Suisse 1928

basas-statisticas-e-survistas

display_name: basas-statisticas-e-survistas

bases-statistiques-et-generalites

display_name: bases-statistiques-et-generalites

grundlagen-und-ubersichten

display_name: grundlagen-und-ubersichten

statistical-basis-and-overviews

display_name: statistical-basis-and-overviews

statistical-basis

fr: Bases statistiques

de: Statistische Grundlagen

bundesamt-fur-statistik-bfs

fr: L'OFS est le centre de compétence de la statistique publique suisse. Il produit des informations statistiques sur la population, l'économie, la société, la formation et la recherche, le territoire et l'environnement. Ces informations servent à la formation de l’opinion publique. Elles sont un outil de planification et de gouvernance dans tous les domaines de l’action politique et contribuent au bon fonctionnement de notre État moderne et démocratique.

de: Das BFS ist das nationale Kompetenzzentrum der öffentlichen Statistik der Schweiz. Es produziert und publiziert statistische Informationen über den Stand und die Entwicklung von Bevölkerung, Wirtschaft, Gesellschaft, Bildung, Forschung, Raum und Umwelt. Diese Informationen dienen der Meinungsbildung in der Bevölkerung sowie der Planung und Steuerung von zentralen Politikbereichen. Sie leisten einen wichtigen Beitrag für einen modernen, demokratischen Staat.


name: Fachstelle Open Government Data des Kantons Zürich

label: DCAT-AP CH – The Swiss Application Profile for Data Portals and Catalogues

label: Datenkatalog

de: API-Endpoint für Datensätze im Metadatenkatalog des Kantons Zürich

media_type: application/json

de: Enthält alle aktuell im Metadatenkatalog des Kantons Zürich enthaltenen Datensätze mit ihren Metadaten: (1) häufig nachgefragte Excel-Tabellen, (2) Indikatoren aus dem Gemeindeporträt, (3) Publikationen des Statistischen Amts sowie (4) offene Behördendaten des Kantons Zürich (ausgenommen offene Geodaten).

format: JSON

mimetype: application/json

de: Enthält alle aktuell im Metadatenkatalog des Kantons Zürich enthaltenen Datensätze mit ihren Metadaten. Folgt dem Metadaten-Standard 'DCAT-AP CH v1' (eCH-0200).

de: Web Service des Metadatenkatalogs des Kantons Zürich


de: Statistisches Jahrbuch der Schweiz 1953

fr: Annuaire statistique de la Suisse 1953

de: Bundesamt für Statistik BFS


maintainer: Alexandre Buttler

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

en: Dataset

en: Zip archive of all datasets detailed in study.

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

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

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

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

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

#References

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

biogeochemisty

display_name: biogeochemisty

climate-change

display_name: climate-change

silvopastoral-systems

display_name: silvopastoral-systems

soil

display_name: soil

transplantation-experiment

display_name: transplantation-experiment

education

de: Bildung, Wissenschaft

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

value: Envidat Harvester

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


wms-emmen

title_for_slug: wms-emmen

fr: reseau-de-transport geoservice point-dinteret-poi velo centre-de-loisirs pieton sig-systeme-dinformation-geographique ouvrage-dart plan-de-quartier plan-de-ville axe-routier batiment dechets energie information transport

de: verkehrsnetz geodienst point-of-interest-poi fahrrad freizeitanlage fussganger gis-geografisches-informationssystem kunstbau quartierplan stadtplan strassenachse bauwesen-und-gebaute-umwelt abfall energie information-ausbildung-kultur verkehr

en: traffic-network geoservice point-of-interest-poi bike leisure-facility pedestrian gis-geographic-information-system art-construction quarter-map city-map road-axis building waste energy information transport

it: rete-di-trasporto geoservizio punto-di-interesse-poi bicicletta centro-per-il-tempo-libero pedone sig-sistema-informativo-geografico opera-di-costruzione piano-di-quartiere carta-di-citta asse-stradale edilizia rifiuti energia informazione trasporti

label: Geoportal Emmen mit Links zum Geoviewer Emmen, WMS Emmen und weiteren Emmen relevanter Geoportale

label: geocat.ch permalink

de: DE#WMS Emmen

de: DE#WMS Emmen mit allen öffentlichen Daten der Gemeinde Emmen

format: WMS

de: DE#DE#WFS Emmen|FR#|EN#|IT#|FR#|EN#|IT#

de: DE#DE#WFS mit allen öffentlichen Geodaten der Gemeinde Emmen|FR#|EN#|IT#|FR#|EN#|IT#

format: WFS

fr: Service de carte avec les bâtiments de l'école dans la commune de Emmen

de: Kartendienst mit allen öffentlichen Daten der Gemeinde Emmen. Enthält Datenlayer zu: Points of Interest (Schulhäuser, Sportanlagen, Spielplätze, Gastrobetriebe Frühförderangebote), Strassendaten (Kunstbauten, Fussgängerstreifen, Verkehrsspiegel, Parkplätze und Veloparkierungsanlagen, Sitzbänke, Abfalleimer, Robidog, Tempo30- und Begegnungszonen, Bushaltestellen), Grünflächen, Quartiervereine, Energieplanung

format: SERVICE

de: Gemeinde Emmen

de: Emmen ist mit mehr als 31'000 Einwohnern nach der Stadt Luzern die Gemeinde mit der zweithöchsten Einwohnerzahl im Kanton Luzern. Gemäss kommunaler GIS-Strategie (https://www.emmen.ch/publikationen/344959) stellt Emmen Geodaten öffentlich zur Verfügung, soweit keine Schutzgründe/Datensicherheitsrestriktionen entgegenstehen. Geodaten werden in Form von Kartendiensten auf dem kommunalen Geoportal: https://www.emmen.ch/gemeindeinzahlen/43855 publiziert und als WMS zur öffentlichen Verfügung gestellt. Die zugrunde liegenden Geodaten und Geodienste werden in geocat.ch dokumentiert, die publizierten WMS auf opendata.swiss sind mit geocat.ch synchronisiert.

political_level: commune

value: Gemeinde Emmen - Geocat Harvester

de: WMS Emmen


maintainer: Varun Sharma

en: mesoscale-modelling antarctica snow-atmosphere-interaction alps blowing-snow

media_type: ZIP

en:
REPRODUCIBILITY_POSTPROCESSING.zip contains data and scripts used to produce all figures from for the associated manuscript submitted for peer review ( see section on Citations below )

The folders stand for the three case studies presented in the manuscript. Two case studies (case Ia and case Ib) are on simulations in Antarctica and one case study
case II is for simulations in the Alps.

The associated data in the form of netcdf files or text files were produced using CRYOWRF v1.0

Apart from reproducibility, the intention of sharing these scripts is also to make it easy for new users to adopt CRYOWRF and be efficient, in particular in processing snowpack outputs.

The scripts were tested on multiple machines (different OSes) with the attached YAML file (wrf_analysis.yml) to setup the conda environment. All scripts were run using spyder as the IDE.

en: REPRODUCIBILITY_SIMULATION.zip contains all the extraneous files required to perform the simulations in associated manuscript (see Citations 3 in README.txt). The purpose of sharing these files apart from purposes of reproducibility is to also serve as templates for users to learn the workflow of CRYOWRF and perform simulations for their own purposes.

en:
This repository contains data required for reproducibility of the results to be published in the associated manuscript.

Apart from reproducibility, the attached datasets also serve as templates for new users to adopt CRYOWRF in their research.

The datasets consist of two folders organized in zip format:

1. REPRODUCIBILITY_SIMULATION:
  Consists of namelists for WPS, WRF and SNOWPACK to reproduce simulations published in the manuscript
  Additional files include datasets (from IMAU-FDM / RACMO, see "credits" below ) as well as helper python scripts to
  produce *.sno files
  which are used as initial conditions for SNOWPACK in CRYOWRF.

2. REPRODUCIBILITY_POSTPROCESSING:
   Includes outputs of CRYOWRF and python scripts used to prepare figures in the manuscript.

Each of the folders have their own readme files for more details.

 Code
citation: Varun Sharma. (2021, July 2). vsharma-next/CRYOWRF: CRYOWRF v1.0 (Version v1.0). Zenodo. http://doi.org/10.5281/zenodo.5060165

location: https://gitlabext.wsl.ch/atmospheric-models/CRYOWRF   (stable releases / institutional repo)
        https://github.com/vsharma-next/CRYOWRF            (dev branches / developer repo)

 Publication
**Introducing CRYOWRF v1.0: Multiscale atmospheric flow simulations with advanced snow cover modelling.**
Varun Sharma, Fraziska Gerber and Michael Lehning, Submitted to Geoscientific Model Development

 Acknowledgements
We thank Peter Kuipers Munneke (P.KuipersMunneke@uu.nl) for preparing and sharing outputs of IMAU-FDM and RACMO used for initial conditions for case Ia. The relevant citations for the methods through which these datasets were generated are:

* Kuipers Munneke, P., S. R. M. Ligtenberg, B. P. Y. Noël, I. M. Howat, J. E. Box, E. Mosley-Thompson, J. R. McConnell, K. Steffen, J. T. Harper, S. B. Das and M. R. van den Broeke. 2015. Elevation change of the Greenland ice sheet due to surface mass balance and firn processes, 1960-2014. The Cryosphere, 9, 2009-2025. doi:10.5194/tc-9-2009-2015

 * Ligtenberg, S. R. M., P. Kuipers Munneke, B. P. Y. Noël, and M. R. van den Broeke. 2018. Brief communication: Improved simulation of the present-day Greenland firn layer (1960-2016). The Cryosphere, 12, doi:10.5194/tc-12-1643-2018

alps

display_name: alps

antarctica

display_name: antarctica

blowing-snow

display_name: blowing-snow

mesoscale-modelling

display_name: mesoscale-modelling

snow-atmosphere-interaction

display_name: snow-atmosphere-interaction

en: Reproducibility Dataset for CRYOWRF v1.0


maintainer: Michael Lehning

en: snow-hydrology alpine-precipitation snow alpine3d-simulations snow-cover snow-distribution alpine3d

en: ADS datasets and simulation inputs

en: The file contains the ADS datasets of snow depth and input data used for simulations.

en:
Dataset (Model input, snow distribution and validation) for the precipitation scaling paper, which should be cited along with the data set citation.

This data is useful for distributed hydrological modelling or other tasks that involve the study of snow distribution and precipitation in the high Alpine.

The format of the data is for Alpine3D (models.slf.ch) model runs but other models could be used, too.

Please cite:

_Vögeli, C., Lehning, M., Wever, N., Bavay M., 2016: Scaling Precipitation Input to Spatially Distributed Hydrological Models by Measured Snow Distribution., Front. Earth Sci. 4: 108. doi: 10.3389/feart.2016.00108._

Dataset is provided as a single zip file.
The archive contains two directories, the valuable distributed snow depth maps for the landscape Davos and the simulation input.
The archive also contains the file: "ReadMeMetadataDataSetPrecipitationScaling" which explains the data structure.

alpine-precipitation

display_name: alpine-precipitation

snow

display_name: snow

snow-cover

display_name: snow-cover

snow-distribution

display_name: snow-distribution

snow-hydrology

display_name: snow-hydrology

education

en: Education and science

en: Precipitation Scaling Data Set (Vögeli et al., Frontiers)


maintainer: EnviDat Support

en: photovoltaic albedo shortwave-radiation snow winter-energy-production

en: Data Forward Scattering

media_type: .zip

en: GROUNDEYE model for multiple forward scattering in snow-covered terrain. Meteorological and topographical input data set for the Meierhofertäli near Davos, Switzerland, September 2017 - August 2018. Output data for September 2017 - August 2018 at hourly resolution and every pixel. Post-processing scripts for plotting and analysis.

en:
 Overview
We present GROUNDEYE, a new model of radiative transfer over mountainous terrain, which considers for the first time the forward scattering properties of snow.
Embedded in the surface process model Alpine3D, the new terrain radiation model GROUNDEYE receives interpolated real weather data with diffuse and direct broadband shortwave radiation for each pixel as well as a spatially variable plane albedo from the module SNOWPACK.

 Format
The GROUNDEYE model is written in c++, as is the entire environment of Alpine3d. The input and output data sets are .xlsx or .txt format, pre- and postprocessing including the generation of all figures is in .R format.

 Structure
In Data_Forward_Scattering.zip you will find all necessary data and model details to
reproduce the results of the JGR publication "How forward-scattering snow and terrain change the Alpine radiation balance with application to solar panels"

 - 	Model Input Data contains the meteorological and topographic input data sets, the BRDF, and preprocessing scripts.
 - Model Code contains the full model Alpine3d including the radiative transfer module GROUNDEYE.
 - Model Output Data contains the results of the simulation of terrain irradiance and irradiance of solar panels; hourly resolution, 1. Sptember 2017 - 31. August 2018.
 - Measurements Solar Testsite contains information and measurements of the solar testsite at the Totalp near Davos, Switzerland.
 - Postprocessing contains all R-Scripts used for the analysis and
plotting of the corresponding data.

In each of these folders you will find detailed information in
the file 'About this Folder.txt'.

albedo

display_name: albedo

photovoltaic

display_name: photovoltaic

shortwave-radiation

display_name: shortwave-radiation

winter-energy-production

display_name: winter-energy-production

en: Data, Code Link and Metadata on Forward Scattering of Snow at Totalp


maintainer: Sven-Eric Hopf

en: fraxinus forest iap ash-dieback monitoring

en: Monitoring of ash trees as part of the Intercantonal Forest Observation Programme

media_type: .pdf

en: Packgroun information on data for the publication: Spread and Severity of Ash Dieback in Switzerland – Tree Characteristics and Landscape Features Explain Varying Mortality Probability, (Klesse et al. 2021, Front. For. Glob. Change 4:645920., doi: 10.3389/ffgc.2021.645920)

en:
In 2013, the Institute for Applied Plant Biology (IAP) started a monitoring programme to study the development and the spatial variation of the ash dieback disease with the aim to find some partially resistant European ash trees (Fraxinus excelsior).
We collaborate as co-authors for the publication: Spread and Severity of Ash Dieback in Switzerland - Tree Characteristics and Landscape Features Explain Varying Mortality Probability (Klesse et al. 2021 in frontiers)

ash-dieback

display_name: ash-dieback

forest

display_name: forest

fraxinus

display_name: fraxinus

iap

display_name: iap

monitoring

display_name: monitoring

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.


vegetation-height-model-nfi

maintainer: Christian Ginzler

title_for_slug: vegetation-height-model-nfi

en: 3d forest photogrammety point-cloud vegetation-height-model

en: 2016 Vegetation Height Model NFI

media_type: geotiff

en:
Data 'Vegetation Height Model NFI 2016' available on request (christian.ginzler@wsl.ch).

A national vegetation height model was calculated for Switzerland using digital aerial images. We used the stereo aerial images acquired by the Federal Office of Topography swisstopo using the ADS80 sensor to first calculate a digital surface model (DSM) with a very high spatial resolution (1 × 1 m). The DSM was then normalized to obtain the actual vegetation heights using the digital terrain model (DTM) SwissAlti3D based on airborne laser data. Buildings are masked out using the NDVI information of the aerial images to produce a vegetation height model, and to produce a vegetation height model (VHM). Such a model will be calculated in the framework of the Swiss National Forest Inventory (NFI) with consistent methods and a very high level of detail. For covering the whole of Switzerland, we used summer aerial images from 2011 to 2016.

format: GeoTIFF

en: 2015 Vegetation Height Model NFI

en:
Data 'Vegetation Height Model NFI 2015' available on request (christian.ginzler@wsl.ch).

A national vegetation height model was calculated for Switzerland using digital aerial images. We used the stereo aerial images acquired by the Federal Office of Topography swisstopo using the ADS80 sensor to first calculate a digital surface model (DSM) with a very high spatial resolution (1 × 1 m). The DSM was then normalized to obtain the actual vegetation heights using the digital terrain model (DTM) SwissAlti3D based on airborne laser data. Buildings are masked out using the NDVI information of the aerial images to produce a vegetation height model, and to produce a vegetation height model (VHM). Such a model will be calculated in the framework of the Swiss National Forest Inventory (NFI) with consistent methods and a very high level of detail. For covering the whole of Switzerland, we used summer aerial images from 2010 to 2015.

en: 2019 Vegetation Height Model NFI (current)

en:
Data 'Vegetation Height Model NFI 2019' available on request (christian.ginzler@wsl.ch).

A national vegetation height model was calculated for Switzerland using digital aerial images. We used the stereo aerial images acquired by the Federal Office of Topography swisstopo using the ADS80 sensor to first calculate a digital surface model (DSM) with a very high spatial resolution (1 × 1 m). The DSM was then normalized to obtain the actual vegetation heights using the digital terrain model (DTM) SwissAlti3D based on airborne laser data. Buildings are masked out using the NDVI information of the aerial images to produce a vegetation height model, and to produce a vegetation height model (VHM). Such a model will be calculated in the framework of the Swiss National Forest Inventory (NFI) with consistent methods and a very high level of detail. For covering the whole of Switzerland, we used summer aerial images from 2013 to 2019.

en: 2012 Vegetation Height Model NFI

en:
Data 'Vegetation Height Model NFI 2012' available on request (christian.ginzler@wsl.ch).

A national vegetation height model was calculated for Switzerland using digital aerial images. We used the stereo aerial images acquired by the Federal Office of Topography swisstopo using the ADS80 sensor to first calculate a digital surface model (DSM) with a very high spatial resolution (1 × 1 m). The DSM was then normalized to obtain the actual vegetation heights using the digital terrain model (DTM) SwissAlti3D based on airborne laser data. Buildings are masked out using the NDVI information of the aerial images to produce a vegetation height model, and to produce a vegetation height model (VHM). Such a model will be calculated in the framework of the Swiss National Forest Inventory (NFI) with consistent methods and a very high level of detail. For covering the whole of Switzerland, we used summer aerial images from 2007 to 2012.

en: Countrywide Stereo-Image Matching for Updating Digital Surface Models in the Framework of the Swiss National Forest Inventory

media_type: PDF

en: Thumbnail

media_type: image/jpeg

mimetype: image/jpeg

en:
A national vegetation height model was calculated for Switzerland using digital aerial images.
We used the stereo aerial images acquired by the Federal Office of Topography swisstopo using the ADS80 sensor to first calculate a digital surface model (DSM) with a very high spatial resolution
(1 × 1 m and 0.5 x 0.5 m). The DSM was then normalized to obtain the actual
vegetation heights using a digital terrain model (DTM)
based on laser data with the buildings masked out, and to
produce a vegetation height model (VHM). Such a model
will be calculated in the framework of the Swiss National Forest Inventory (NFI) with consistent methods and a very
high level of detail. For covering the whole of Switzerland,
we use summer aerial images from six years.

Latest version is from 2019.

photogrammety

display_name: photogrammety

point-cloud

display_name: point-cloud

vegetation-height-model

display_name: vegetation-height-model

en: Vegetation Height Model NFI


fr: direct politique votation objets votes cantonal donnees communes weekend initiative resultat temps-reel scrutin dimanche populaire districts communaux canton

de: echtzeitdaten abstimmungen kantonal abstimmungsresultate kantonsresultate abstimmungsvorlagen kantonsergebnisse abstimmungswochenende gemeinderesultate bezirksergebnisse abstimmungssonntag vorlagen politik bezirksresultate echtzeitergebnisse volksinitiative gemeindeergebnisse volksabstimmungen echtzeit abstimmungsergebnisse

it: reale progetto distretti comuni risultati cantonali popolare votazioni politica tempo iniziativa

author: Bundesamt für Statistik BFS, Office fédéral de la statistique OFS, Ufficio federale di statistica UST

fr: Votation populaire du 09.02.2020 – objets cantonaux

de: Volksabstimmung vom 09.02.2020 – Kantonale Vorlagen

it: Votazione popolare del 09.02.2020 – oggetti cantonali

fr: Objets cantonaux dans les cantons: ZH, BE, FR, SO, BS, SH, SG, GR, AG, TG, TI, NE, GE

de: Kantonale Vorlagen in den Kantonen: ZH, BE, FR, SO, BS, SH, SG, GR, AG, TG, TI, NE, GE

it: Oggetti cantonali nei cantoni: ZH, BE, FR, SO, BS, SH, SG, GR, AG, TG, TI, NE, GE

fr: Votation populaire du 24.11.2019 – objets cantonaux

de: Volksabstimmung vom 24.11.2019 – Kantonale Vorlagen

it: Votazione popolare del 24.11.2019 – oggetti cantonali

fr: Objets cantonaux dans les cantons: SZ, NW, ZG, BS, BL, GE

de: Kantonale Vorlagen in den Kantonen: SZ, NW, ZG, BS, BL, GE

it: Oggetti cantonali nei cantoni: SZ, NW, ZG, BS, BL, GE

fr: Votation populaire du 30.06.2019 – objets cantonaux

de: Volksabstimmung vom 30.06.2019 – Kantonale Vorlagen

it: Votazione popolare del 30.06.2019 – oggetti cantonali

fr: Objets cantonaux dans les cantons: FR, SG

de: Kantonale Vorlagen in den Kantonen: FR, SG

it: Oggetti cantonali nei cantoni: FR, SG

fr: Votation populaire du 12.11.2023 – objets cantonaux

de: Volksabstimmung vom 12.11.2023 – Kantonale Vorlagen

it: Votazione popolare del 12.11.2023 – oggetti cantonali

fr: Objets cantonaux dans les cantons: FR

de: Kantonale Vorlagen in den Kantonen: FR

it: Oggetti cantonali nei cantoni: FR

fr: Votation populaire du 13.06.2021 – objets cantonaux

de: Volksabstimmung vom 13.06.2021 – Kantonale Vorlagen

it: Votazione popolare del 13.06.2021 – oggetti cantonali

fr: Objets cantonaux dans les cantons: ZH, UR, FR, SO, BS, BL, SH, SG, GR, TI, GE, JU

de: Kantonale Vorlagen in den Kantonen: ZH, UR, FR, SO, BS, BL, SH, SG, GR, TI, GE, JU

it: Oggetti cantonali nei cantoni: ZH, UR, FR, SO, BS, BL, SH, SG, GR, TI, GE, JU

fr: Votation populaire du 17.11.2019 – objets cantonaux

de: Volksabstimmung vom 17.11.2019 – Kantonale Vorlagen

it: Votazione popolare del 17.11.2019 – oggetti cantonali

fr: Objets cantonaux dans les cantons: SH, SG

de: Kantonale Vorlagen in den Kantonen: SH, SG

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fr: Votation populaire du 22.09.2019 – objets cantonaux

de: Volksabstimmung vom 22.09.2019 – Kantonale Vorlagen

it: Votazione popolare del 22.09.2019 – oggetti cantonali

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fr: Votation populaire du 15.05.2022 – objets cantonaux

de: Volksabstimmung vom 15.05.2022 – Kantonale Vorlagen

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de: Kantonale Vorlagen in den Kantonen: ZH, BE, UR, FR, SO, BL, SH, SG, AG, TI, NE, GE, JU

it: Oggetti cantonali nei cantoni: ZH, BE, UR, FR, SO, BL, SH, SG, AG, TI, NE, GE, JU

fr: Votation populaire du 20.10.2019 – objets cantonaux

de: Volksabstimmung vom 20.10.2019 – Kantonale Vorlagen

it: Votazione popolare del 20.10.2019 – oggetti cantonali

fr: Objets cantonaux dans les cantons: UR, JU

de: Kantonale Vorlagen in den Kantonen: UR, JU

it: Oggetti cantonali nei cantoni: UR, JU

fr: Votation populaire du 10.02.2019 – objets cantonaux

de: Volksabstimmung vom 10.02.2019 – Kantonale Vorlagen

it: Votazione popolare del 10.02.2019 – oggetti cantonali

fr: Objets cantonaux dans les cantons: ZH, BE, UR, BS, BL, SG, TI, GE

de: Kantonale Vorlagen in den Kantonen: ZH, BE, UR, BS, BL, SG, TI, GE

it: Oggetti cantonali nei cantoni: ZH, BE, UR, BS, BL, SG, TI, GE

fr: Votation populaire du 22.10.2023 – objets cantonaux

de: Volksabstimmung vom 22.10.2023 – Kantonale Vorlagen

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de: Kantonale Vorlagen in den Kantonen: UR, SZ, SO

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fr: Votation populaire du 26.09.2021 – objets cantonaux

de: Volksabstimmung vom 26.09.2021 – Kantonale Vorlagen

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de: Kantonale Vorlagen in den Kantonen: BE, LU, UR, SZ, SO, BS, SG, TI, NE

it: Oggetti cantonali nei cantoni: BE, LU, UR, SZ, SO, BS, SG, TI, NE

fr: Votation populaire du 12.03.2023 – objets cantonaux

de: Volksabstimmung vom 12.03.2023 – Kantonale Vorlagen

it: Votazione popolare del 12.03.2023 – oggetti cantonali

fr: Objets cantonaux dans les cantons: BE, UR, SZ, OW, NW, SO, BS, SH, GR, GE

de: Kantonale Vorlagen in den Kantonen: BE, UR, SZ, OW, NW, SO, BS, SH, GR, GE

it: Oggetti cantonali nei cantoni: BE, UR, SZ, OW, NW, SO, BS, SH, GR, GE

fr: Votation populaire du 13.02.2022 – objets cantonaux

de: Volksabstimmung vom 13.02.2022 – Kantonale Vorlagen

it: Votazione popolare del 13.02.2022 – oggetti cantonali

fr: Objets cantonaux dans les cantons: BE, LU, SO, BS, BL, SH, GE, JU

de: Kantonale Vorlagen in den Kantonen: BE, LU, SO, BS, BL, SH, GE, JU

it: Oggetti cantonali nei cantoni: BE, LU, SO, BS, BL, SH, GE, JU

fr: Votation populaire du 30.08.2020 – objets cantonaux

de: Volksabstimmung vom 30.08.2020 – Kantonale Vorlagen

it: Votazione popolare del 30.08.2020 – oggetti cantonali

fr: Objets cantonaux dans les cantons: SH

de: Kantonale Vorlagen in den Kantonen: SH

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de: Volksabstimmung vom 19.11.2023 – Kantonale Vorlagen

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fr: Votation populaire du 10.09.2023 – objets cantonaux

de: Volksabstimmung vom 10.09.2023 – Kantonale Vorlagen

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fr: Objets cantonaux dans les cantons: VS

de: Kantonale Vorlagen in den Kantonen: VS

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fr: Votation populaire du 19.05.2019 – objets cantonaux

de: Volksabstimmung vom 19.05.2019 – Kantonale Vorlagen

it: Votazione popolare del 19.05.2019 – oggetti cantonali

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de: Kantonale Vorlagen in den Kantonen: BE, LU, UR, SZ, OW, ZG, SO, BS, BL, GR, TG, TI, VS, GE

it: Oggetti cantonali nei cantoni: BE, LU, UR, SZ, OW, ZG, SO, BS, BL, GR, TG, TI, VS, GE

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fr: Objets cantonaux dans les cantons: ZH, BE, LU, FR, SO, SH, SG, AG, TG, TI, VD, GE, JU

de: Kantonale Vorlagen in den Kantonen: ZH, BE, LU, FR, SO, SH, SG, AG, TG, TI, VD, GE, JU

it: Oggetti cantonali nei cantoni: ZH, BE, LU, FR, SO, SH, SG, AG, TG, TI, VD, GE, JU

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de: Kantonale Vorlagen in den Kantonen: LU, UR, SZ, FR, SO, BS, BL, SG, AG, TG, GE

it: Oggetti cantonali nei cantoni: LU, UR, SZ, FR, SO, BS, BL, SG, AG, TG, GE

fr: Votation populaire du 27.09.2020 – objets cantonaux

de: Volksabstimmung vom 27.09.2020 – Kantonale Vorlagen

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de: Kantonale Vorlagen in den Kantonen: ZH, LU, UR, SZ, NW, SO, BL, AR, AG, TG, GE

it: Oggetti cantonali nei cantoni: ZH, LU, UR, SZ, NW, SO, BL, AR, AG, TG, GE

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de: Volksabstimmung vom 28.11.2021 – Kantonale Vorlagen

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de: Kantonale Vorlagen in den Kantonen: ZH, LU, UR, OW, BS, BL, SH, VS, NE, GE

it: Oggetti cantonali nei cantoni: ZH, LU, UR, OW, BS, BL, SH, VS, NE, GE

fr: Votation populaire du 30.10.2022 – objets cantonaux

de: Volksabstimmung vom 30.10.2022 – Kantonale Vorlagen

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de: Volksabstimmung vom 25.09.2022 – Kantonale Vorlagen

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fr: Objets cantonaux dans les cantons: ZH, BE, LU, UR, OW, BS, SH, AR, SG, AG, VD, NE

de: Kantonale Vorlagen in den Kantonen: ZH, BE, LU, UR, OW, BS, SH, AR, SG, AG, VD, NE

it: Oggetti cantonali nei cantoni: ZH, BE, LU, UR, OW, BS, SH, AR, SG, AG, VD, NE

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de: Volksabstimmung vom 07.03.2021 – Kantonale Vorlagen

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fr: Objets cantonaux dans les cantons: ZH, BE, LU, ZG, BL, GE

de: Kantonale Vorlagen in den Kantonen: ZH, BE, LU, ZG, BL, GE

it: Oggetti cantonali nei cantoni: ZH, BE, LU, ZG, BL, GE

fr: Votation populaire du 27.11.2022 – objets cantonaux

de: Volksabstimmung vom 27.11.2022 – Kantonale Vorlagen

it: Votazione popolare del 27.11.2022 – oggetti cantonali

fr: Objets cantonaux dans les cantons: ZH, SZ, NW, BS, BL, GR, TG, VS

de: Kantonale Vorlagen in den Kantonen: ZH, SZ, NW, BS, BL, GR, TG, VS

it: Oggetti cantonali nei cantoni: ZH, SZ, NW, BS, BL, GR, TG, VS

fr: Votation populaire du 25.04.2021 – objets cantonaux

de: Volksabstimmung vom 25.04.2021 – Kantonale Vorlagen

it: Votazione popolare del 25.04.2021 – oggetti cantonali

fr:
Un service web pour des résultats en temps réel le dimanche de votations. Les données sont mises à jour en continu sur la base des résultats communaux livrés par les cantons. Les données sont mises à jour à partir de midi, dès la clôture du dernier scrutin.
### Documentation
* [Changelog](https://bfspoku.github.io/ogd/changelog/changelog-ka.html "Changelog")

de:
Webservice für Echtzeit-Ergebnisse am Abstimmungssonntag. Die Daten werden laufend mit den Gemeinderesultaten aus den Kantonen aktualisiert. Die Daten werden nach der Schliessung der letzten Urne um 12 Uhr erstmals aktualisiert.
### Dokumentation
* [Changelog](https://bfspoku.github.io/ogd/changelog/changelog-ka.html "Changelog")

it:
Webservice per i risultati in tempo reale durante la domenica delle votazione. I dati, aggiornati per la prima volta dopo la chiusura dell’ultima urna alle ore 12.00, sono costantemente adeguati ai risultati comunali dei Cantoni.
### Documentazione
* [Changelog](https://bfspoku.github.io/ogd/changelog/changelog-ka.html "Changelog")

fr: Des données en temps réel les jours de votations cantonales

de: Echtzeitdaten am Abstimmungstag zu kantonalen Abstimmungsvorlagen

it: Dati in tempo reale nel giorno delle votazioni per gli oggetti in votazione a livello cantonale

bundesamt-fur-statistik-bfs

de: Das BFS ist das nationale Kompetenzzentrum der öffentlichen Statistik der Schweiz. Es produziert und publiziert statistische Informationen über den Stand und die Entwicklung von Bevölkerung, Wirtschaft, Gesellschaft, Bildung, Forschung, Raum und Umwelt. Diese Informationen dienen der Meinungsbildung in der Bevölkerung sowie der Planung und Steuerung von zentralen Politikbereichen. Sie leisten einen wichtigen Beitrag für einen modernen, demokratischen Staat.

en: The FSO is Switzerland's national competence centre for official statistics. It produces and publishes statistical information on the status and development of the population, economy, society, education, research, territory and the environment. This information is used for opinion building among the population and for the planning and management of key policy areas. They make an important contribution to a modern, democratic state.


en: swe snow remote-sensing snow-depth uas

en: HS map Brämabül, 14. April 2015

media_type: image/tiff

en:
10 cm resolution
15 cm accuracy

format: TIFF

mimetype: image/tiff

en: .tfw file

media_type: tfw

en: coordinates for the .tif raster

en:
This snow depth map was generated 14 January 2015, close to peak of winter accumulation, applying Unmanned Aerial System digital surface models with a spatial resolution of 10 cm. The covered area is 285'000 m2 at the top of Brämabüel, 2490 m a.s.l. covering all expositions. Coordinate system: CH1903LV03.

A detailed description is given here:

Bühler, Y., Adams, M. S., Bösch, R., and Stoffel, A.: Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations, The Cryosphere, 10, 1075-1088, 10.5194/tc-10-1075-2016, 2016.

Abstract:
Detailed information on the spatial and temporal distribution, and variability of snow depth (HS) is a crucial input for numerous applications in hydrology, climatology, ecology and avalanche research. Nowadays, snow depth distribution is usually estimated by combining point measurements from weather stations or observers in the field with spatial interpolation algorithms. However, even a dense measurement network is not able to capture the large spatial variability of snow depth in alpine terrain.
Remote sensing methods, such as laser scanning or digital photogrammetry, have recently been successfully applied to map snow depth variability at local and regional scales. However, such data acquisition is costly, if manned airplanes are involved. The effectiveness of ground-based measurements on the other hand, is often hindered by occlusions, due to the complex terrain or acute viewing angles. In this paper, we investigate the application of unmanned aerial systems (UAS), in combination with structure-from-motion photogrammetry, to map snow depth distribution. Such systems have the advantage that they are comparatively cost-effective and can be applied very flexibly to cover also otherwise inaccessible terrain. In this study we map snow depth at two different locations: a) a sheltered location at the bottom of the Flüela valley (1900 m a.s.l.) and b) an exposed location (2500 m a.s.l.) on a peak in the ski resort Jakobshorn, both in the vicinity of Davos, Switzerland. At the first test site, we monitor the ablation on three different dates. We validate the photogrammetric snow depth maps using simultaneously acquired manual snow depth measurements. The resulting snow depth values have a root mean square error (RMSE) better than 0.07 to 0.15 m on meadows and rocks and a RMSE better than 0.30 m on sections covered by bushes or tall grass. This new measurement technology opens the door for efficient, flexible, repeatable and cost effective snow depth monitoring for various applications, investigating the worlds cryosphere.

remote-sensing

display_name: remote-sensing

snow-depth

display_name: snow-depth

swe

display_name: swe

uas

display_name: uas

education

fr: Education, science

de: Bildung, Wissenschaft

en: Education and science

it: Formazione e scienza

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

it: EnviDat: il portale dei dati ambientali

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

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.

en: UAS based snow depth maps Brämabüel, Davos, CH


en: roads gis geospatial jupyter density buffer

en: Road data from OSM for the surroundings of Menzberg, Switzerland

media_type: ipynb

en: Road Density Calculation (Python Notebook) online

media_type: url

en: Plots

en: Random plot locations

en:
This dataset contains the road and plot data used for the geospatial analysis example showcased in "Fostering Open Science at WSL with the EnviDat Environmental Data Portal", a contribution to the 5th Open Source Geospatial Research and Education Symposium (OGRS), 2018.
The example uses Jupyter Notebook to calculate road densities in the neighbourhood of sample plot locations with Python. Road data were extracted from OpenStreetMap, while the point data (sample plots) were generated manually.

buffer

display_name: buffer

density

display_name: density

geospatial

display_name: geospatial

gis

display_name: gis

jupyter

display_name: jupyter

roads

display_name: roads

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: Dataset for OGRS 2018 publication


en: spatial-planning large-scale-urban-development-projects land-use-transformations europe strategic-spatial-planning strategic-projects urban-transformations land-use-change

en: QCA R script large-scale urban development projects Europe

media_type: R

en:
For the set up of this R script, we used the “QCA” (Duşa, 2019) and “SetMethods” (Oana and Schneider, 2018) R packages.

The following further resources were also used:
Dusa, A. (2019). QCA with R: A comprehensive resource. Springer: Berlin.
Schneider, C. Q., & Wagemann, C. (2012). Set-theoretic methods for the social sciences. A guide to qualitative comparative analysis. Cambridge: Cambridge University Press.
Thomann, E., Oana, I.-E., & Wittwer, S. (2018). Performing fuzzy- and crisp set QCA with R. A user-oriented beginner's guide. Retrieved from http://www.evathomann.com/links/qca-r-manual

en: QCA data set large-scale urban development projects

media_type: TXT

en: Please refer to the sections: 3. The data set lsUDPs: 3.1 Selection of cases and data collection; 3.2 Data management and operationalization in the Metadata for further details on this resource.

format: TXT

mimetype: text/plain

en:
Table of Content: 1. General context of the data set "lsUDPs" ; 2. Background and aims of the study using the data set lsUDPs; 3. The data set lsUDPs: 3.1 Selection of cases and data collection; 3.2 Data management and operationalisation

1. General context of the data set "lsUDPs"
The data set "lsUDPs" has been generated as part of the CONCUR research project (https://www.wsl.ch/en/projects/concur.html) led by Dr. Anna M. Hersperger and funded by the Swiss National Science Foundation (ERC TBS Consolidator Grant (ID: BSCGIO 157789) for the period 2016-2020. The CONCUR research project is interdisciplinary and aims to develop a scientific basis for adequately integrating spatial policies (in this case, strategic spatial plans) into quantitative land-change modelling approaches at the urban regional level. The first stage (2016-2017) of the CONCUR project focussed on 21 urban regions in Western Europe. The urban regions were selected through a multi-stage strategy for empirical research (see Hersperger, A. M., Grădinaru, S., Oliveira, E., Pagliarin, S., & Palka, G. (2019). Understanding strategic spatial planning to effectively guide development of urban regions. Cities, 94, 96–105. https://doi.org/10.1016/j.cities.2019.05.032 ).

2. Background and aims of the study using the data set lsUDPs
As part of the CONCUR project, a specific task was to examine the relationship between strategic spatial plans and the formulation and implementation (i.e. urban land change) of large-scale urban development projects in Western Europe.
Strategic urban projects are typically large-scale, prominent urban transformations implemented locally with the aim to stimulate urban growth, for instance in the form of urban renewals of deprived neighborhoods, waterfront renewals and transport infrastructures. While strategic urban projects are referred to in the literature with multiple terms, in the CONCOR project we call them large-scale urban development projects (lsUDPs).
Previous studies acknowledged both local and supra-local (or structural) factors impacting the context-specific implementation of lsUDPs. Local governance factors, such as institutional capacity, coordination among public and private actors and political leadership, intertwine with supra-local conditions, such as state re-scaling processes and devolution of state competencies in spatial planning, de-industrialisation and increasing social inequality. Hence, in implementing lsUDPs, multi-scalar factors act in combination.
Because the formulation and implementation of lsUDPs require multi-scalar coordination among coalitions of public and private actors over an extended period of time, they are generally linked to strategic spatial plans (SSPs). Strategic spatial plans convey collective visions and horizons of action negotiated among public and private actors at the local and/or regional level to steer future urban development, and can contain legally binding dispositions, but also indicative guidelines.
The key question remains as to what extent large-scale urban development projects and strategic spatial plans can be regarded as aligned. By alignment, or “concordance”, we mean that strategic projects are formulated and implemented as part of the strategic planning process (“high concordance”), or that the strategic role of projects is reconfirmed in (subsequent) strategic plans (“moderate concordance”). Lack of concordance is found when lsUDPs have been limitedly (or not at all) acknowledged in strategic spatial plans.
We assume that certain local and supra-local factors, characterising the development of the projects, foster (but not strictly “cause”) the degree of alignment between lsUPDs and SSPs. In this study, we empirically examine how, and to what extent, the concordance between 38 European large-scale urban development projects and strategic plans (outcome: CONCOR) has been enabled by five multi-scalar factors (or conditions): (i) the role of the national state (STATE), (ii) the role of (inter)national private actors (PRIVATE), (iii) the occurrence of supra-regional external events (EVENTS), (iv) the degree of transport connectivity (TRANSP), and (v) local resistance from civil society (RESIST). We adopted a (multi-data) case-based qualitative strategy for empirical research and applied the formalised procedure of within- and cross-case comparison offered by fuzzy-set Qualitative Comparative Analysis appropriate for the goal of this study. Based on set theory, QCA formally integrates contextual sensitivity to case specificities (within-case knowledge) with systematic comparative analysis (across-case knowledge).
The research question the data set has been created to reply to is the following: which conditions, and combinations of conditions, enable the concordance between large-scale urban development projects and strategic spatial plans?
The conditions (“independent variables”) considered are. STATE: the set of large-scale urban projects characterized by a high degree of state intervention and support in their formulation and implementation, PRIVATE: the set of large-scale urban projects characterized by a high degree of involvement of (inter)national private actors in their formulation and implementation, EVENTS: the set of large-scale strategic projects whose formulation and implementation have been strongly affected by unforeseen international events and/or global trends, TRANSP: the set of large-scale strategic projects with a high degree of road and/or transit connectivity, and RESIST: set of large-scale strategic projects whose realization has been characterized by resistances that have substantially delayed or modified the project implementation.
The outcome (“dependent variable”) under analysis is CONCOR: the set of large-scale urban projects having a high degree of concordance/alignment/integration with strategic spatial plans

3. The data set lsUDPs

   3.1 Selection of cases and data collection
To generate the current data set on large-scale urban development projects in European urban regions (data set "lsUDPs"), we identified 35 large-scale urban development projects in a sample of the 21 Western urban regions considered in the CONCUR project (see supra, Hersperger et al. 2019): Amsterdam, Barcelona, Copenhagen, Hamburg, Lyon, Manchester, Milan, Stockholm, Stuttgart. The criteria we followed to identify the 35 large-scale urban development projects are: geographical location, size (large-scale), site (located either in the city core or in the larger urban region) and urban function (e.g. housing, transportation infrastructures, service and knowledge economic functions). Employing these criteria facilitated the selection of diverse large-scale urban development projects while still ensuring sufficient comparability.
In 2016, we performed 47 in-depth interviews with experts in urban and regional planning and large-scale strategic projects and infrastructure (i.e. academics and practitioners) about the formulation, implementation and development (1990s–2010s) of each project in each of the 9 selected urban regions. On average, each interviewee answered questions on 3.1 large-scale urban development projects. Three cases were subdivided into two cases because a clear differentiation between specific implementation stages was identified by the interviewees (expansion of the Barcelona airport, cases “bcn_airport80-90” and “bcn_airport00-16”; realisation of Lyon Part-Dieu, cases “lyo_partdieu70-90” and “lyo_partdieu00-16”; MediaCityUK, cases “man_salfordquays80-00” and “man_mediacityuk00-16”). Therefore, from the initial 35 cases, the final number of analysed cases in the lsUDPs dataset is 38.

    3.2 The data set lsUDPs: Data management and operationalisation
Interviews were fully transcribed and analysed through MAXQDA (version 12.3, VERBI GmbH, Berlin, Germany), and intercoder agreement was evaluated on a sample of nine interviews. We also compiled “synthetic case descriptions” (SCD) for each case (totalling more than 160 SCDs) to spot potential inconsistencies among interviewees’ accounts and to facilitate completion of the “calibration table” for each case (see below). An online expert survey distributed to the interviewees (response rate 78%) helped systematise the information collected during the interviews. We also consulted both academic and gray literature on the case studies to check for possible ambiguity and inconsistencies in the interview data, and to solve discrepancies between our assigned set membership scores and questionnaire values. Site visits were also carried out to retrieve additional information on the selected cases.
For each case (i.e. each of the 38 selected large-scale urban development projects), we operationalised each condition (i.e. STATE, PRIVATE, EVENTS, TRANSP, RESIST) and the outcome (CONCOR) in terms of sets, for subsequent application of Qualitative Comparative Analysis. This process is called “calibration”; we used a number of indicators for each condition to qualitatively assess each large-scale project across the conditions. The case-based qualitative assessment was then transformed into fuzzy-set membership values. Fuzzy-set membership values range from 0 to 1, and should be conceived as “fundamentally interpretative tools” that “operationalize theoretical concepts in a way that enhances the dialogue between ideas and evidence” (Ragin 2000:162, in “Fuzzy-set Social Science”. Chicago: University Press). We employed a four-value fuzzy-set scale (0, 0.33, 0.67, 1) to “quantify” into set membership scores the individual histories of cases retrieved from interview data. Only the condition TRANSP was calibrated as a crisp-set (0, 1).
The translation of qualitative case-based information into numerical fuzzy-set membership values was iteratively performed by populating a calibration table following standard practices recently emerged in QCA when dealing with qualitative (interview) data.

europe

display_name: europe

land-use-change

display_name: land-use-change

land-use-transformations

display_name: land-use-transformations

large-scale-urban-development-projects

display_name: large-scale-urban-development-projects

spatial-planning

display_name: spatial-planning

strategic-projects

display_name: strategic-projects

strategic-spatial-planning

display_name: strategic-spatial-planning

urban-transformations

display_name: urban-transformations

en: lsUDPS Large-scale urban development projects in European urban regions


maintainer: Elisabeth Hafner

en: avalanche satellite validation snow avalanche-hazard-mapping

en: Satellite Avalanche Mapping Validation data and data description

en: 14 shapefiles to be used only together with the data description also provided as a pdf.

en:
Validation points, validation area, ground truth coverage, SPOT 6 avalanche outlines, Sentinel-1 avalanche outlines, Sentinel-2 avalanche outlines, Davos avalanche mapping (DAvalMap) avalanche outlines as shapefiles and a detailed attribute description (DataDescription_EvalSatMappingMethods.pdf).

Coordinate system: CH1903+_LV95

The generation of this dataset is described in detail in:

Hafner, E. D., Techel, F., Leinss, S., and Bühler, Y.: Mapping avalanches with satellites – evaluation of performance and completeness, The Cryosphere, https://doi.org/10.5194/tc-2020-272, 2021.

avalanche

display_name: avalanche

avalanche-hazard-mapping

display_name: avalanche-hazard-mapping

satellite

display_name: satellite

snow

display_name: snow

validation

display_name: validation

education

en: Education and science

en: Satellite avalanche mapping validation data


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

en: WFJ_ICE_LAYERS dataset

en: README

media_type: text/plain

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

ice-layers

display_name: ice-layers

snow-stratigraphy

display_name: snow-stratigraphy

snow-temperature

display_name: snow-temperature

snowmicropen

display_name: snowmicropen

water-front

display_name: water-front

weissfluhjoch

display_name: weissfluhjoch

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


en: mobile-laser-scanning terrestrial-laser-scanning lidar terrestrial-photogrammetry unmanned-aerial-vehicle-laser-scanning forest-inventory 3d-point-cloud structure-from-motion

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

forest-inventory

display_name: forest-inventory

lidar

display_name: lidar

mobile-laser-scanning

display_name: mobile-laser-scanning

structure-from-motion

display_name: structure-from-motion

terrestrial-laser-scanning

display_name: terrestrial-laser-scanning

terrestrial-photogrammetry

display_name: terrestrial-photogrammetry

unmanned-aerial-vehicle-laser-scanning

display_name: unmanned-aerial-vehicle-laser-scanning

envidat

en: EnviDat: the Environmental Data Portal

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

en: Ramerenwald Close Range Remote Sensing Benchmark


en: dna-metabarcoding environmental-dna biodiversity plankton fish fjord marine

en: Code, data and figures

en: Here there are all the data and code to reproduce the figures and analysis presented in the paper, with a README.md file

en: Metabarcoding data

en: Raw metabarcoding data for both primers (teleo and 18S) and corresponding file to perform the demultiplexing and primer trimming

en:
This dataset contains the raw environmental DNA data associated with the publication *Environmental drivers of eukaryotic plankton and fish biodiversity in an Arctic fjord* in the journal Polar Biology (2023).

 Methods

**Sampling**
We sampled the Lilliehöök fjord on the west coast of Spitsbergen (Svalbard, Norway) over 3 days from 3 to 5 of August 2021. Samples were taken from the glacier front up to the fjord mouth of the Krossfjorden system, around 30 km long, after the Lilliehöök fjord merged with the mouth of Möller fjord. The fjord’s maximum depth has been recorded at 373 m (Svendsen et al. 2002) and has no sill at its entrance, thereby facilitating water exchange with the open ocean of the West Spitsbergen Current. We used a research vessel to sample 5 sites for a total of 15 samples, sampling 3 depths per site (3-m, chlorophyll a maximum and 85-m, unless sea floor was shallower). Shallow and intermediate samples between 3-m and 12-m represent ~35-L of water filtered in-situ using long tubing and a peristaltic pump, and all other deeper samples were taken from a total of 3 Niskin bottles (General Oceanics), representing 22-L of water sampled per sample. Water was filtered through a VigiDNA filtration capsule (SPYGEN) with a 0.20-µm pore size using an Athena peristaltic pump (Proactive Environmental Products, Bradenton, Florida) with a flow rate of ~1-L/min. Each sample was handled with single use tubing and gloves.

**Molecular**
To perform the amplification, we used two sets of primers: teleo (forward: ACACCGCCCGTCACTCT, reverse: CTTCCGGTACACTTACCATG; Valentini et al. 2016) and the universal eukaryotic 1389F/1510R primer pair, amplifying the V9-18S rDNA gene (Amaral-Zettler et al. 2009) (forward: TTGTACACACCGCCC, reverse: CCTTCYGCAGGTTCACCTAC).

 Data content:

+ Metabarcoding data: This zip file contains the 2 sequencing libraries filtered to only retain the samples used in the present study.
+ Code, data and figure: This zip file contains all data and code to reproduce the figures and the analysis in the study, with an associated README explaining the content of each folder.

 Additional informations
For more details, please see the Methods in the associated publication: DOI: 10.1007/s00300-023-03187-9.

biodiversity

display_name: biodiversity

dna-metabarcoding

display_name: dna-metabarcoding

environmental-dna

display_name: environmental-dna

fish

display_name: fish

fjord

display_name: fjord

marine

display_name: marine

plankton

display_name: plankton

en: Data: Environmental drivers of eukaryotic plankton and fish biodiversity in an Arctic fjord


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

media_type: TIFF

en: Soil pH modified from SoilGrids

en: Soil organic carbon content modified from SoilGrids

en: Frost change frequency modified from CHELSA

media_type: tif

en: Mean annual temperature modified from CHELSA

en: Soil clay content modified from SoilGrids

en: Precipitation of driest quarter modified from CHELSA

en: Mean diurnal range modified from CHELSA

en: Precipitation seasonality from CHELSA

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

aridity

display_name: aridity

frost-change-velocity

display_name: frost-change-velocity

ground-temperature

display_name: ground-temperature

precipitation

display_name: precipitation

sdms

display_name: sdms

soil

display_name: soil

education

en: Education and science

en: Environmental layers for SDM simulations (GDPlants)


en: ecological-indicator-values plants soil-ph ecological-maps landolt soil-mapping switzerland soil-moisture

en: Description

en: Description of the predictors provided in this dataset

en: Predictors

media_type: application/zip

en: Predictors generated and used in Descombes et al. (2020)

en:
Ecologically meaningful predictors are often neglected in plant distribution studies, resulting in incomplete niche quantification and low predictive power of species distribution models (SDMs). Because environmental data are rare and expensive to collect, and because their relationship with local climatic and topographic conditions are complex, mapping them over large geographic extents and at high spatial resolution remains a major challenge. Here, we derived environmental data layers by mapping ecological indicator values (EIVs) in space by using a large set of environmental predictors in Switzerland.

This dataset contains the predictors (raster layers) generated and used in the following publication (Descombes et al. 2020). Only predictors for which we have the rights to share them are provided. Other datasets and predictors can be accessed via the original data provider. Details on the predictors and sources are fully described in the publication. The predictors are provided as GeoTIFF files, at 93 m spatial resolution and Mercator projection ("+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"). The excel file (xlsx) provides a short description of the raster layers.

Paper Citation:

Descombes, P. et al. (2020). Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes. Ecography. (accepted)

ecological-indicator-values

display_name: ecological-indicator-values

ecological-maps

display_name: ecological-maps

landolt

display_name: landolt

plants

display_name: plants

soil-mapping

display_name: soil-mapping

soil-moisture

display_name: soil-moisture

soil-ph

display_name: soil-ph

switzerland

display_name: switzerland

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.


maintainer: Mountain Hydrology and Mass Movements

en: debris-flow-fatalities rockfall-fatalities windstorm-fatalities ice-avalanche-fatalities lightning-fatalities earthquake-fatalities natural-hazard-fatalities landslide-fatalities flood-fatalities

media_type: CSV

en: Natural hazard fatalities per year in Swizterland since 1946.

format: CSV

mimetype: text/csv

en:
This dataset contains the number of fatalities due to flood, debris flow, landslide, rockfall, windstorm, lightning, ice avalanche, earthquake and other processes like roof avalanche or lacustrine tsunami for each year since 1946.
The following information is contained (by column and column title):

* year
* total number of hazard fatalities
* number of fatalities by flood (German: Hochwasser, Überschwemmung). Flood includes people drowned in flooded or inundated areas or carried away in streams under high-water conditions.
* number of fatalities by debris flow (German: Murgang).
* number of fatalities by landslide (German: Erdrutsch). Landslide includes people killed by landslides and hillslope debris flows (German: Hangmure).
* number of fatalities by rockfall (German: Steinschlag, Fels- und Bergsturz).
* number of fatalities by windstorm (German: Sturm). Windstorm includes people killed by falling objects or trees during very strong wind conditions and people who drowned in lakes because their boat capsized during such conditions.
* number of fatalities by lightning (German: Blitz).
* number of fatalities by ice avalanche (German: Eislawine).
* number of fatalities by earthquake (German: Erdbeben).
* number of fatalities by other processes like roof avalanche, lacustrine tsunami (German: andere Prozesse wie Dachlawine, Tsunami im See).

The data was collected based on newspaper research. For more information please refer to

_Badoux, A., Andres, N., Techel, F., and Hegg, C.: Natural hazard fatalities in Switzerland from 1946 to 2015, Nat. Hazards Earth Syst. Sci., 16, 2747-2768, https://doi.org/10.5194/nhess-16-2747-2016, 2016._

The data collection is financed by the FOEN (with exception of the collection of the avalanche fatalities). The data contains the official statistics of the FOEN on fatalities due to flood, debris flow, landslide, rock fall and avalanche.

Restrictions: The data set is not complete. Only fatalities in or around settlements and on open transportation routes are included. More precisely, fatalities were not collected, when persons exposed themselves to a great danger on purpose. Or fatalities during leisure activities which are connected to a higher risk were not included (this includes e.g. canoeing or river surfing during flood, canyoning, mountaineering, climbing, walking or driving on a closed road).

Fatalities by avalanches are collected at the WSL Institute for Snow and Avalanche Research SLF. You can download the avalanche fatalities per hydrological year [here](https://www.envidat.ch/dataset/avalanche-fatalities-switzerland-1936) and per calendar year [here](https://www.envidat.ch/dataset/avalanche-fatalities-per-calendar-year-since-1936). For a direct comparison with the fatalities presented here, please download the data set with the calendar years and do not consider fatalities in the backcountry (tour) or in terrain close to ski areas (offpiste).

debris-flow-fatalities

display_name: debris-flow-fatalities

earthquake-fatalities

display_name: earthquake-fatalities

flood-fatalities

display_name: flood-fatalities

ice-avalanche-fatalities

display_name: ice-avalanche-fatalities

landslide-fatalities

display_name: landslide-fatalities

lightning-fatalities

display_name: lightning-fatalities

natural-hazard-fatalities

display_name: natural-hazard-fatalities

rockfall-fatalities

display_name: rockfall-fatalities

windstorm-fatalities

display_name: windstorm-fatalities

en: Number of natural hazard fatalities per year in Switzerland since 1946


maintainer: Matthias Haeni

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

en: Site photo of the field station (VSF)

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

en: Data plot of the forest station (VSB)

media_type: application/pdf

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

en: Field station data (VSF)

media_type: text/csv

en: Data file.

en: Data plot of the field station (VSF)

en: Forest station data (VSB)

en: Site aerial photo (Visp)

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

en: Site photo of the forest station (VSB)

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

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Visp in Switzerland where one station is located within a natural mixed forest stand (VSB) with Scots pine (_Pinus sylvestris_; 40-80 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, VSF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Visp is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

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


aerosol-data-weissfluhjoch

title_for_slug: aerosol-data-weissfluhjoch

en: ice-nucleation aerosol raclets inp particles linda

en: APS Dataset Weissfluhjoch

media_type: application/x-netcdf

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

mimetype: application/x-netcdf

en: SMPS Dataset Weissfluhjoch

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

en: LINDA Dataset Weissfluhjoch

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

en: OPC-GT526S Dataset Weissfluhjoch

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

en: OPC-N3 Dataset Weissfluhjoch

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

en:
Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room.

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

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

Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³).

Coriolis μ and LINDA: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l min‾¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles.

aerosol

display_name: aerosol

ice-nucleation

display_name: ice-nucleation

inp

display_name: inp

linda

display_name: linda

particles

display_name: particles

raclets

display_name: raclets

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

en: Aerosol Data Weissfluhjoch


wind-crust-formation-snowmicropen-data

title_for_slug: wind-crust-formation-snowmicropen-data

en: snowmicropen snow wind-crust wind-packing smp

en: README

en: Describes the dataset in detail

en: Processing scripts

en: Contains the R-scripts used to process the SMPs

en: SMP data

media_type: Link

en: Links to the dataset

en:
This dataset contains the SnowMicroPen (SMP) data from 38 wind tunnel experiments on wind-packing / wind crust formation. These experiments were performed in the winters 2015/16 and 2016/17 and include more than 1000 SMP measurements.

The SMPs are organized per experiment. Each experiment subfolder contains the processed SMP profiles and some additional files.

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

This dataset is the basis of the following publication:

Sommer C.G., Lehning M., & Fierz C. (2017). Wind tunnel experiments: Saltation is necessary for wind-packing. Journal of Glaciology, 63(242), 950-958. doi:10.1017/jog.2017.53

smp

display_name: smp

snow

display_name: snow

snowmicropen

display_name: snowmicropen

wind-crust

display_name: wind-crust

wind-packing

display_name: wind-packing

en: Wind crust formation: SnowMicroPen data


en: wagital snow-water-equivalent gcos meteodat-gmbh snow

en:
Total water reserves of the snow cover [mio m3] for Wägital catchment, Switzerland, for reference date April 1. Data is separated in 2 elevation zones 900m-1500m asl and 1500m-2300m asl. Time period 1943-2023, status 2023-04-17.

Funded currently or in the past by:

- Federal Office of Meteorology and Climatology MeteoSwiss in the context of GCOS Switzerland
- Meteodat GmbH
- Institute of Geography, University of Zurich
- WSL Institute for Snow and Avalanche Research SLF
- Institute of Geography, ETH Zurich (IAC ETH Zurich)
- AG Kraftwerk Wägital (AXPO and EWZ)

See also https://www.meteodat.ch/waegital.html

gcos

display_name: gcos

meteodat-gmbh

display_name: meteodat-gmbh

snow-water-equivalent

display_name: snow-water-equivalent

wagital

display_name: wagital

en: Snow water equivalent for reference date April 1 for Wägital catchment, starting 1943


digitizing-historical-plague

title_for_slug: digitizing-historical-plague

en: palaeo-epidemiology bubonic-plague plague-outbreaks historical-plague black-death plague-mapping plague-inventory yersinia-pestis

en: We present newly digitized data on 6,929 plague outbreaks that occurred between 1347 and 1900 AD across Europe. The data base on an inventory initially published 1976. For georeferencing the information of Tele Atlas 2009 was used. The coordinates are in the reference systems ETRS89 and WGS84.

black-death

display_name: black-death

bubonic-plague

display_name: bubonic-plague

historical-plague

display_name: historical-plague

palaeo-epidemiology

display_name: palaeo-epidemiology

plague-inventory

display_name: plague-inventory

plague-mapping

display_name: plague-mapping

plague-outbreaks

display_name: plague-outbreaks

yersinia-pestis

display_name: yersinia-pestis

en: Digitizing historical plague


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

en: Site aerial photo (Jussy)

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

en: Site photo of the forest station (JUB)

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

en: Site photo of the field station (JUF)

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

en: Data plot of the field station (JUF)

en: Data file.

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

en: Forest station data (JUB)

en: Data plot of the forest station (JUB)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Jussy in Switzerland where one station is located within a natural broad-leaved forest stand (JUB) with sessile oak (_Quercus petrea_; 90 yrs), aspen (_Populus tremula_; 60 yrs) and European hornbeam (_Carpinus betulus_; 60 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, JUF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Jussy is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

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