fundburo

de:
Standorte der Fundbüros

**Zweck**: Lokalisation der Fundbüros

de: bus buslinien fundburo geodaten geoportal offentlicher-verkehr postauto punktdaten strassenbahn stzh tram tramlinien vbg vbz vektordaten

stadt-zurich

de: Stadt Zürich

name: Newsroom, Verkehrsbetriebe, Departement der Industriellen Betriebe

de: Fundbüro

title_for_slug: fundburo

tran

de: Verkehr

de: Fundbuero.json (GeoJSON-Services)

bus

display_name: bus

buslinien

display_name: buslinien

fundburo

display_name: fundburo

offentlicher-verkehr

display_name: offentlicher-verkehr

postauto

display_name: postauto

punktdaten

display_name: punktdaten

strassenbahn

display_name: strassenbahn

tram

display_name: tram

tramlinien

display_name: tramlinien

vbg

display_name: vbg

vbz

display_name: vbz

vektordaten

display_name: vektordaten


lehrpfade

$oid: OBSOLETE

de:
Lehrpfade für ZüriPlan

**Zweck**: Publikation für Bevölkerung

de: geodaten geoportal lehrpfade punktdaten stzh vektordaten

stadt-zurich

approval_status: approved

fr: Vous trouverez ici les données de la ville de Zürich. En parallèle, Open Data Zürich a son propre portail Open Data où vous pouvez trouver également des manuels et des documents sur le traitement des données. À de rares exceptions près, toutes les données publiées par Open Data Zürich sont sous une licence «Creative Commons Zero».

de: Open Data Zürich stellt hier offene Daten aus der Stadt Zürich zur freien Nutzung bereit. Die Stadt Zürich verfügt parallel über ein eigenes Open Data Portal, auf dem Sie auch Anleitungen und Dokumentationen zum Umgang mit Datensätzen finden. Mit wenigen Ausnahmen sind alle Datensätze der Stadt Zürich mit «Creative Commons Zero» lizenziert.

en: Here you find all publicly available data published by Open Data Zürich. In addition, the city of Zurich runs its own Open Data Portal, where you find manuals and documentation about the handling of datasets. With only a few exceptions, all datasets from Open Data Zürich are licensed under «Creative Commons Zero».

it:
Open Data Zürich propone qui dati pubblici della città di Zurigo, pronti alla libera utilizzazione.
La città di Zurigo dispone parallelamente di un proprio portale Open Data, sul quale sono disponibili anche istruzioni e documentazioni per contestualizzare i datasets.
A parte poche eccezioni tutte le datasets della città di Zurigo hanno una licenza «Creative Commons Zero».

en: City of Zurich

fr: Ville de Zurich

it: Cità di Zurigo

political_level: commune

state: active

type: organization

name: Unternehmensentwicklung, Grün Stadt Zürich, Tiefbau- und Entsorgungsdepartement

de: Lehrpfade

title_for_slug: lehrpfade

type: dataset

key: guid

key: uri

value: ZH - DCAT Harvester

regi

de: Regionen und Städte

en: Regions and cities

fr: Régions et villes

it: Regioni e città

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

format: DXF

format: GPKG

format: JSON

media_type: application/json

de: Lehrpfade.json (GeoJSON-Services)

format: SHP

de: Web Feature Service

format: WFS

de: Web Map Service

format: WMS

de: Web Map Tile Service

format: XML

media_type: application/xml

geodaten

display_name: geodaten

geoportal

display_name: geoportal

lehrpfade

display_name: lehrpfade

stzh

display_name: stzh


de:
Die amtliche Vermessung (AV) liefert geometrische Daten zum Grundeigentum und ist die Grundlage für viele weitere geografische Informationen und Anwendungen in Wirtschaft, Verwaltung und Privatleben.
Achtung: Dies ist der Jahresendstand 2007 der AV-Daten!

**Zweck**: Als amtliche Vermessung im Sinne von Artikel 950 ZGB gelten die zur Anlage und Führung des Grundbuchs vom Kanton genehmigten und vom Bund anerkannten Vermessungen. Die Daten der amtlichen Vermessung sind Georeferenzdaten, die von Behörden des Bundes, der Kantone und der Gemeinden sowie von der Wirtschaft, der Wissenschaft und Dritten zur Gewinnung von Geoinformationen verwendet werden. Auf dem GeoServer sind für die stadtinternen Nutzenden optimierte Tabellenstrukturen aufgeschaltet.

de: adressen archiv av bodenbedeckung geobasidaten geodaten geoportal liegenschaften liniendaten polygondaten punktdaten stzh vektordaten vermessung

name: Kataster, Geomatik + Vermessung, Tiefbau- und Entsorgungsdepartement

de: Amtliche Vermessungsdaten Stadt Zürich Jahresendstand 2007

soci

de: Bevölkerung und Gesellschaft

en: Population and society

fr: Population et société

it: Popolazione e società

gove

de: Regierung und öffentlicher Sektor

en: Government and public sector

fr: Gouvernement et secteur public

it: Governo e settore pubblico

adressen

display_name: adressen

archiv

display_name: archiv

av

display_name: av

bodenbedeckung

display_name: bodenbedeckung

geobasidaten

display_name: geobasidaten

liegenschaften

display_name: liegenschaften

liniendaten

display_name: liniendaten

polygondaten

display_name: polygondaten

vermessung

display_name: vermessung


gesundheitszentrum

de:
Gesundheitszentren für das Alter in der Stadt Zürich

**Zweck**: Lokalisierung der Gesundheitszentren für das Alter

de: alterszentrum geodaten geoportal pflegezentrum punktdaten stzh vektordaten

stadt-zurich

de: Stadt Zürich

name: Geschäftsleitungssekretariat, Gesundheitszentren für das Alter, Gesundheits- und Umweltdepartement

de: Gesundheitszentrum

title_for_slug: gesundheitszentrum

heal

de: Gesundheit

en: Health

fr: Santé

it: Salute

format: CSV

media_type: text/csv

alterszentrum

display_name: alterszentrum

pflegezentrum

display_name: pflegezentrum

punktdaten

display_name: punktdaten


parkplatzverordnung-ppv

de:
Mit Reduktionsgebieten sind jene Gebiete gemeint, die besonders gut an den öffentlichen Verkehr angeschlossen sind. Hier gelten tiefere Parkplatzvorgaben. Die vom Gemeinderat der Stadt Zürich (Legislative) beschlossene Teilrevision der PPV ist 2016 in Kraft gesetzt worden.

**Zweck**: Die Parkplatzverordnung sorgt für die nötigen und möglichen Parkplätze auf Privatgrund für alle Nutzungen.

de: geodaten geoportal parkplatze parkplatzverordnung pflichtparkplatze polygondaten privatgrund reduktionsgebiete stzh vektordaten

stadt-zurich

de: Open Data Zürich stellt hier offene Daten aus der Stadt Zürich zur freien Nutzung bereit. Die Stadt Zürich verfügt parallel über ein eigenes Open Data Portal, auf dem Sie auch Anleitungen und Dokumentationen zum Umgang mit Datensätzen finden. Mit wenigen Ausnahmen sind alle Datensätze der Stadt Zürich mit «Creative Commons Zero» lizenziert.

name: Grundlagen + Strategien, Tiefbauamt, Tiefbau- und Entsorgungsdepartement

de: Parkplatzverordnung PPV

title_for_slug: parkplatzverordnung-ppv

tran

de: Verkehr

en: Transport

fr: Transports

it: Trasporti

parkplatze

display_name: parkplatze

parkplatzverordnung

display_name: parkplatzverordnung

pflichtparkplatze

display_name: pflichtparkplatze

privatgrund

display_name: privatgrund

reduktionsgebiete

display_name: reduktionsgebiete

vektordaten

display_name: vektordaten


arealuberbauungen

de:
Der Datensatz enthält die Perimeter von Arealüberbauungen und altrechtlichen Gesamtüberbauungen. Neue Arealüberbauungen und Änderungen an bestehenden Areal- und Gesamtüberbauungen werden im Rahmen des Baubewilligungsverfahrens von der Bausektion bewilligt und die entsprechenden Perimeter erfasst bzw. bestehende Perimeter geändert.

**Zweck**: Die Arealüberbauungsperimeter sind für die Kreisarchitekten des Amts für Baubewilligungen im Rahmen ihrer Beratungstätigkeit sowie für die Erteilung von Bewilligungen wichtig (insbesondere in Bezug auf § 69 ff. PBG und Art. 8 BZO). Der Datensatz wird auch für die Kapazitäts- und Reserveberechnungen des Amts für Städtebau genutzt.

de: arealuberbauungen geodaten geoportal gesamtuberbauungen geschossuberschreitung mehrausnutzung polygondaten stzh vektordaten

name: Kompetenzzentren, Amt für Städtebau, Hochbaudepartement

de: Arealüberbauungen

title_for_slug: arealuberbauungen

regi

de: Regionen und Städte

envi

de: Umwelt

de: Web Feature Service

de: Web Map Service

arealuberbauungen

display_name: arealuberbauungen

geodaten

display_name: geodaten

geoportal

display_name: geoportal

gesamtuberbauungen

display_name: gesamtuberbauungen

geschossuberschreitung

display_name: geschossuberschreitung

mehrausnutzung

display_name: mehrausnutzung

stzh

display_name: stzh


de:
Das digitale 3D-Stadtmodell der Stadt Zürich um 200 ist flächendeckend über das gesamte Gemeindegebiet verfügbar. Es bildet die vergangene Stadt in 3D ab. Zurzeit sind die 3D-Daten lediglich als einzelne OBJ_Wavefront Dateien verfügbar.

**Zweck**: Die Geodatensätzen des historischen 3D-Stadtmodells bilden die Datengrundlage für folgende, interessante Einsatzgebiete: Siedlungsgeschichte, Vermittlung, Lebensbilder, Tourismus, Spielentwicklung, etc.

de: 3d 3d-stadtmodell archaologie geodaten geoportal historische-entwicklung punktdaten rasterdaten stzh

name: GIS-Kompetenzzentrum, Amt für Städtebau, Hochbaudepartement

de: 3D-Stadtmodell Stadt Zürich Jahresendstand 200

de: OBJ (Wavefront OBJ) - in ZIP-File

format: ZIP

media_type: application/zip

de: Web Map Tile Service

format: WMTS

archaologie

display_name: archaologie

historische-entwicklung

display_name: historische-entwicklung

rasterdaten

display_name: rasterdaten


de: Der Online Archivkatalog des Stadtarchivs Zürich ist erreichbar unter https://amsquery.stadt-zuerich.ch/ und bietet eine umfangreiche Sammlung der Archivbestände des Stadtarchiv Zürich. Mit der Search/Retrieve via URL-Schnittstelle (SRU) können Sie gezielt nach Informationen suchen und diese effizient abrufen. Weitere Informationen und eine Anleitung zur Benutzung der Schnittstelle finden sich [hier](https://opendatazurich.github.io/sar-sru-api/).

de: archiv archivdaten geschichte glam openglam stadtrat

stadt-zurich

de: Stadt Zürich

de: Schnittstelle zum Online Archivkatalog des Stadtarchivs Zürich

gove

de: Regierung und öffentlicher Sektor

de: Basis-URL zur SRU Schnittstelle des Stadtarchivs. Query Output als XML

de: Link zum SRU-API-Endpoint

format: API

archiv

display_name: archiv

archivdaten

display_name: archivdaten

geschichte

display_name: geschichte

glam

display_name: glam

openglam

display_name: openglam

stadtrat

display_name: stadtrat


de: Angebote des Nachtlebens in Zürich und Region unterteilt in Afterhour, Hip-Hop / Rap / Reggae, House / Techno / Electro, Jazz / World Music, Latin / Salsa, Live Musik, Partybeats, Rock / Alternative und Casinos.

de: afterhour alternative casino electro hiphop house jazz latin livemusik partybeats reggae rock salsa techno worldmusic zuerichtourismus

stadt-zurich

de: Open Data Zürich stellt hier offene Daten aus der Stadt Zürich zur freien Nutzung bereit. Die Stadt Zürich verfügt parallel über ein eigenes Open Data Portal, auf dem Sie auch Anleitungen und Dokumentationen zum Umgang mit Datensätzen finden. Mit wenigen Ausnahmen sind alle Datensätze der Stadt Zürich mit «Creative Commons Zero» lizenziert.

de: Nachtleben: Clubs und Discos in Zürich (Zürich Tourismus)

econ

de: Wirtschaft und Finanzen

en: Economy and finance

fr: Économie et finances

it: Economia e finanze

de: Query Output als JSON

de: Link zu API-Endpoint: Nachtleben

afterhour

display_name: afterhour

alternative

display_name: alternative

casino

display_name: casino

electro

display_name: electro

hiphop

display_name: hiphop

house

display_name: house

jazz

display_name: jazz

latin

display_name: latin

livemusik

display_name: livemusik

partybeats

display_name: partybeats

reggae

display_name: reggae

rock

display_name: rock

salsa

display_name: salsa

techno

display_name: techno

worldmusic

display_name: worldmusic

zuerichtourismus

display_name: zuerichtourismus


de: Unterkünfte in Zürich und der Region unterteilt in Hotels, Hostels, B&Bs, Camping, Apartment und Ferienwohnungen.

de: apartment camping ferienwohnungen hostel hotel uebernachtung unterkunft zuerichtourismus

de: Unterkünfte in Zürich (Zürich Tourismus)

envi

de: Umwelt

de: Link zu API-Endpoint: Unterkunft

apartment

display_name: apartment

camping

display_name: camping

ferienwohnungen

display_name: ferienwohnungen

hostel

display_name: hostel

hotel

display_name: hotel

uebernachtung

display_name: uebernachtung

unterkunft

display_name: unterkunft


de: Sehenswürdigkeiten in Zürich und der Region unterteilt in Architektur, Kirchen, Denkmäler, Plätze und Strasses, Aussichtspunkte und Kunstobjekte im öffentlichen Raum (KiöR).

de: architektur aussichtspunkt denkmal kior kirche kunstobjekt platz zuerichtourismus

stadt-zurich

de: Stadt Zürich

de: Sehenswürdigkeiten in der Region Zürich (Zürich Tourismus)

de: Link zu API-Endpoint: Sehenswürdigkeiten

architektur

display_name: architektur

aussichtspunkt

display_name: aussichtspunkt

denkmal

display_name: denkmal

kior

display_name: kior

kirche

display_name: kirche

kunstobjekt

display_name: kunstobjekt

platz

display_name: platz


kennzahlen-der-schweizer-pflegeheime

name: Office fédéral de la santé publique, section gestion de données et statistique (DMS)

de: Das Bundesamt für Gesundheit ist beauftragt, Betriebskennzahlen der Schweizer Pflegeheime zu veröffentlichen. Sie basieren auf den Daten der Statistik der sozialmedizinischen Institutionen (SOMED), welche das Bundesamt für Statistik jährlich erhebt. Das Bundesstatistikgesetz vom 9. Oktober 1992 (BStatG; SR 431.01) sowie die Artikel 23 und 59a des Bundesgesetzes vom 18. März 1994 über die Krankenversicherung (KVG; SR 832.10) bilden die rechtliche Grundlage für diese statistische und administrative Erhebung. Der Fokus liegt auf Kennzahlen, die für die Pflege und die Tätigkeiten im Zusammenhang mit der obligatorischen Krankenpflegeversicherung (OKP) relevant sind.

fr: L'Office fédéral de la santé publique est chargé de publier les chiffres-clés relatifs aux établissements médico-sociaux (EMS) suisses. Ces chiffres se basent sur les données de la statistique des institutions médico-sociales (SOMED) que l'Office fédéral de la statistique récolte chaque année. La loi du 9 octobre 1992 sur la statistique fédérale (LFS ; RS 431.01) et les articles 23 et 59a de la loi fédérale du 18 mars 1994 sur l’assurance-maladie (LAMal ; RS 832.10) constituent la base légale de ce relevé statistique et administratif. La priorité est donnée aux chiffres-clés relevant des soins et des activités en lien avec l’assurance obligatoire des soins (AOS).

it: L’Ufficio federale della sanità pubblica ha il compito di pubblicare le cifre chiave delle case di cura in Svizzera. Queste cifre si basano sui dati della statistica degli stabilimenti medico-sociali (SOMED), allestita annualmente dall’Ufficio federale di statistica. Le basi legali di questa rilevazione statistica e amministrativa sono la legge federale sulla statistica del 9 ottobre 1992 (LStat; RS 431.01) e gli articoli 23 e 59a della legge federale sull'assicurazione malattie del 18 marzo 1994 (LAMal; RS 832.10). La priorità è data alle cifre chiave relative alle cure e alle attività legate all'assicurazione obbligatoria delle cure medico-sanitarie (AOMS).

bundesamt-fur-gesundheit-bag

fr: L'Office fédéral de la santé publique (OFSP) fait partie du Département fédéral de l'intérieur. De concert avec les cantons, il assume la responsabilité des domaines touchant à la santé publique ainsi que la mise en œuvre de la politique sanitaire. Autorité à vocation nationale, il représente les intérêts sanitaires de la Suisse dans les organisations internationales et auprès d'autres Etats.

de: Das Bundesamt für Gesundheit (BAG) ist Teil des Eidgenössischen Departements des Innern. Es ist - zusammen mit den Kantonen - verantwortlich für die Gesundheit der Schweizer Bevölkerung und für die Entwicklung der nationalen Gesundheitspolitik. Zudem vertritt das BAG als nationale Behörde die Schweiz in Gesundheitsbelangen in internationalen Organisationen und gegenüber anderen Staaten.

en: The Federal Office of Public Health (FOPH) is part of the Federal Department of Home Affairs. Along with the cantons it is responsible for public health in Switzerland and for developing national health policy. As the national health authority, the FOPH also represents Switzerland's interests in the field of health in international organisations and with respect to other countries.

it: L'Ufficio federale della sanità pubblica (UFSP) è incorporato nel Dipartimento federale dell'interno. Unitamente ai Cantoni, è responsabile della salute della popolazione svizzera e dello sviluppo della politica nazionale in materia di salute. In qualità di autorità nazionale rappresenta inoltre gli interessi della Svizzera in materia di sanità in seno a organizzazioni internazionali e nei rapporti con altri Stati.

de: Bundesamt für Gesundheit BAG

en: Federal Office of Public Health FOPH

fr: Office fédéral de la santé publique OFSP

it: Ufficio federale della sanità pubblica UFSP

political_level: confederation

de: Bundesamt für Gesundheit

de: Loi fédérale sur l’assurance-maladie

de: Loi sur la statistique fédérale

de: Kennzahlen der Schweizer Pflegeheime

fr: Chiffres-clés des établissements médico-sociaux suisses

it: Cifre chiave delle case di cura svizzere

title_for_slug: Kennzahlen der Schweizer Pflegeheime

heal

de: Gesundheit

gove

de: Regierung und öffentlicher Sektor

fr: Chiffres-clés des EMS suisses. Version allemande.

de: Kennzahlen der Schweizer Pflegeheime. Deutsche Version.

it: Cifre chiave delle case di cura svizzere. Versione tedesca.

de: Aktuell - Kennzahlen der Schweizer Pflegeheime 2023

format: XLS

fr: Chiffres-clés des EMS suisses. Version française.

de: Kennzahlen der Schweizer Pflegeheime. Französische Version.

it: Cifre chiave delle case di cura svizzere. Versione francese.

fr: Actuel - Chiffres-clés des EMS suisses 2023

fr: Chiffres-clés des EMS suisses. Version italienne.

de: Kennzahlen der Schweizer Pflegeheime. Italienische Version.

it: Cifre chiave delle case di cura svizzere. Versione italiana.

it: Attuale - Cifre chiave delle case di cura svizzere 2023

fr: Chiffres-clés des EMS suisses par Canton. Version allemande.

de: Gesamtschweizerische und kantonale Kennzahlen der Schweizer Pflegeheime. Deutsche Version.

it: Cifre chiave delle case di cura svizzere per cantone. Versione tedesca.

de: Aktuell - Kantonale Kennzahlen der Schweizer Pflegeheime 2023

fr: Chiffres-clés des EMS suisses par Canton. Version française.

de: Gesamtschweizerische und kantonale Kennzahlen der Schweizer Pflegeheime. Französische Version.

it: Cifre chiave delle case di cura medicalizzate svizzere per Cantoni. Versione francese.

fr: Actuel - Chiffres-clés des EMS suisses par Canton 2023

fr: Chiffres-clés des EMS suisses par Canton. Version italienne.

de: Gesamtschweizerische und kantonale Kennzahlen der Schweizer Pflegeheime. Italienische Version.

it: Cifre chiave delle case di cura svizzere per cantone. Versione italiana.

it: Attuale - Cifre chiave delle case di cura svizzere per cantone 2023

fr: Dans le document PDF suivant, vous trouverez une définition ainsi que la formule de calcul pour chacun des chiffres-clés pour les établissements médico-sociaux. Version allemande.

de: Im folgenden PDF-Dokument finden Sie eine Definition sowie die Berechnungsformel für jede der Kennzahlen für die Pflegeheime. Deutsche Version.

it: Nel seguente documento PDF troverete una definizione e la formula di calcolo per ciascuna delle cifre chiave per le case di cura. Versione tedesca.

de: Definitionen der Kennzahlen

format: PDF

fr: Dans le document PDF suivant, vous trouverez une définition ainsi que la formule de calcul pour chacun des chiffres-clés pour les établissements médico-sociaux. Version française.

de: Im folgenden PDF-Dokument finden Sie eine Definition sowie die Berechnungsformel für jede der Kennzahlen für die Pflegeheime. Französische Version.

it: Nel seguente documento PDF troverete una definizione e la formula di calcolo per ciascuna delle cifre chiave per le case di cura. Versione francese.

fr: Définitions des chiffres-clés

fr: Dans le document PDF suivant, vous trouverez une définition ainsi que la formule de calcul pour chacun des chiffres-clés pour les établissements médico-sociaux. Version italienne.

de: Im folgenden PDF-Dokument finden Sie eine Definition sowie die Berechnungsformel für jede der Kennzahlen für die Pflegeheime. Italienische Version.

it: Nel seguente documento PDF troverete una definizione e la formula di calcolo per ciascuna delle cifre chiave per le case di cura. Versione italiana.

it: Definizioni delle cifre chiave

fr: Chiffres-clés des EMS suisses 2012. Version allemande, française et italienne.

de: Kennzahlen der Schweizer Pflegeheime 2012. Deutsche, französische und italienische Version.

it: Cifre chiave delle case di cura svizzere 2012. Versione tedesca, francese e italiana.

fr: Archive 2012

de: Archiv 2012

it: Archivio 2012

fr: Chiffres-clés des EMS suisses 2013. Version allemande, française et italienne.

de: Kennzahlen der Schweizer Pflegeheime 2013. Deutsche, französische und italienische Version.

it: Cifre chiave delle case di cura svizzere 2013. Versione tedesca, francese e italiana.

fr: Archive 2013

de: Archiv 2013

it: Archivio 2013

fr: Chiffres-clés des EMS suisses 2014. Version allemande, française et italienne.

de: Kennzahlen der Schweizer Pflegeheime 2014. Deutsche, französische und italienische Version.

it: Cifre chiave delle case di cura svizzere 2014. Versione tedesca, francese e italiana.

fr: Archive 2014

de: Archiv 2014

it: Archivio 2014

fr: Chiffres-clés des EMS suisses 2015. Version allemande, française et italienne.

de: Kennzahlen der Schweizer Pflegeheime 2015. Deutsche, französische und italienische Version.

it: Cifre chiave delle case di cura svizzere 2015. Versione tedesca, francese e italiana.

fr: Archive 2015

de: Archiv 2015

it: Archivio 2015

fr: Chiffres-clés des EMS suisses 2016. Version allemande, française et italienne.

de: Kennzahlen der Schweizer Pflegeheime 2016. Deutsche, französische und italienische Version.

it: Cifre chiave delle case di cura svizzere 2016. Versione tedesca, francese e italiana.

fr: Chiffres-clés des EMS suisses 2017. Version allemande, française et italienne.

de: Kennzahlen der Schweizer Pflegeheime 2017. Deutsche, französische und italienische Version.

it: Cifre chiave delle case di cura svizzere 2017. Versione tedesca, francese e italiana.

fr: Chiffres-clés des EMS suisses 2018. Version allemande, française et italienne.

de: Kennzahlen der Schweizer Pflegeheime 2018. Deutsche, französische und italienische Version.

it: Cifre chiave delle case di cura svizzere 2018. Versione tedesca, francese e italiana.

fr: Chiffres-clés des EMS suisses 2019. Version allemande, française et italienne.

de: Kennzahlen der Schweizer Pflegeheime 2019. Deutsche, französische und italienische Version.

it: Cifre chiave delle case di cura svizzere 2019. Versione tedesca, francese e italiana.

fr: Chiffres-clés des EMS suisses 2020. Version allemande, française et italienne.

de: Kennzahlen der Schweizer Pflegeheime 2020. Deutsche, französische und italienische Version.

it: Cifre chiave delle case di cura svizzere 2020. Versione tedesca, francese e italiana.

fr: Chiffres-clés des EMS suisses 2021. Version allemande, française et italienne.

de: Kennzahlen der Schweizer Pflegeheime 2021. Deutsche, französische und italienische Version.

it: Cifre chiave delle case di cura svizzere 2021. Versione tedesca, francese e italiana.

fr: Chiffres-clés des EMS suisses 2022. Version allemande, française et italienne.

de: Kennzahlen der Schweizer Pflegeheime 2022. Deutsche, französische und italienische Version.

it: Cifre chiave delle case di cura svizzere 2022. Versione tedesca, francese e italiana.


snow-depth-mapping

en:
The available datasets are snow depth maps with a spatial resolution of 2m generated from image matching of ADS 80/100 data. Image acquisition took place at peak of winter (time when the thickest snowpack is expected). The snow depth maps are the difference of a summer DSM from the winter DSM of the corresponding date . The summer DSM used is a product of image matching of ADS 80 data from summer 2013. In the available products buildings, vegetation and outliers were masked (set to NoData).
For the elimination of buildings the TLM layer (swisstopo) was used, because this layer might not represent exactly the
state of infrastructure at time of image acquisition, it is possible that mainly in dense settlement some buildings were not successfully masked. For the relevant area above treeline the masking of buildings showed good results. Vegetation got masked for a height above ground > 1m and was detected in a combination of summer and winter data sets. As Outliers were considered unrealistic snow depths caused by a failure of the image matching algorithm. Snow depths > 15m and smaller than < -15m were classified as outliers. Negative snow depth were kept, because of an uncertainty in image orientation accuracy. It is expected that in regions with negative snow depth also positive snow depth are underestimated by the same amount, which means that an estimation of snow volume should be carried out summing up the absolute values of snow depth (also the negative ones). For volume estimation in small regions the user has to take into account, that orientation accuracy of the images is roughly around 1-2 GSD (30cm), which propagates directly to the snow depth product. Areas which are not covered by snow got assigned a value of 0 as snow depth.

The work is published in:

Bühler, Y.; Marty, M.; Egli, L.; Veitinger, J.; Jonas, T.; Thee, P.; Ginzler, C., (2015). Snow depth mapping in high-alpine catchments using digital photogrammetry. Cryosphere, 9 (1), 229-243. doi: 10.5194/tc-9-229-2015

en: aerial-imagery avalanche dsm image-matching snow-depth snow-hydrology snow-water-equivalent

envidat

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.

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

wsl

capacity: public

en: Snow Depth Mapping

title_for_slug: snow-depth-mapping

value: Envidat Harvester

educ

de: Bildung, Kultur und Sport

en: Education, culture and sport

fr: Éducation, culture et sport

it: Istruzione, cultura e sport

en: Snow depth map generated from ADS 80 imagery (CIR). GSD of imagery = 0.25 m

en: Snow depth map 20100416

format: TIFF

media_type: image/tiff

en: Snow depth map 20120320

en: Snow depth map 20130415

en: Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.18 m

en: Snow depth map 20140417

en: Snow depth map 20150415

en:
Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.16 m
Because of very diffuse light conditions at time of image acquisition the quality of the resulting snow depth values are in some regions quite poor.

en: Snow depth map 20160126

en: Snow depth map 20160309

en: Snow depth map 20160420

media_type: application/pdf

aerial-imagery

display_name: aerial-imagery

avalanche

display_name: avalanche

dsm

display_name: dsm

image-matching

display_name: image-matching

snow-depth

display_name: snow-depth

snow-hydrology

display_name: snow-hydrology

snow-water-equivalent

display_name: snow-water-equivalent


en: Data obtained in the free-air CO2 enrichment (FACE) experiment at Hofstetten, NW Switzerland, between 2009 and 2016. This dataset contains analyses of the soil solution throughout the experiment, especially for nitrate, as well as different analyses done at the end of the experiment: ammonium and nitrate captured by ion-exchange resin bags and extracted from soil cores, gross N mineralisation and nitrification measured by isotope dilution.

en: co2 face forest nitrogen picea-abies soil

en: Nitrogen availability under trees exposed to CO2 enrichment (FACE)

en: Collection points under the trees

en: Collection points

en: Soil solution collected by suction cups

en: Collection of soil solution

en: Analyses of water, especially soil solution

en: Water analyses

en: Analyses of C, N and their stable isotopes in the soil, and soil pH

en: Soil C and N

en: Ammonium, nitrate and their 15N in resin bags

en: Resin bags

en: Gross rates of N mineralisation and nitrification (15N dilution)

en: Gross N mineralisation and nitrification

face

display_name: face

forest

display_name: forest

nitrogen

display_name: nitrogen

picea-abies

display_name: picea-abies

soil

display_name: soil


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

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

*References*:

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

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

envidat

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

en: EnviDat: the Environmental Data Portal

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

educ

en: Education, culture and sport

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

en: Shapefile of 307 catchments in Switzerland

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

en: Daily simulated data for the reference period

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

en: Daily simulated data for the future period

climate-change

display_name: climate-change

discharge

display_name: discharge

hydrology

display_name: hydrology

precipitation

display_name: precipitation

snowmelt

display_name: snowmelt

soil-moisture

display_name: soil-moisture


automatic-classification-of-avalanches

en: This dataset contains the classification and localization results obtained during the automatic classification of avalanches during the winter season 2017.

en: avalanche localization machine-learning monitoring seismic

en: Automatic Classification of Avalanches

title_for_slug: automatic-classification-of-avalanches

en:
This tar file contains classification results for two seismic geophone arrays deployed above Davos, Switzerland. The of the first array located at the Dischma Valley was used to automatically identify all seismic events produced by avalanches, whereas the classification perfomed at the second array located at the Wannengrat field site was used to identify airplanes and earthquakes.
Furthermore, this tar file also contains the median back-azimuth of all classified events at the Dischma field site.

format: SERVICE

avalanche

display_name: avalanche

localization

display_name: localization

machine-learning

display_name: machine-learning

monitoring

display_name: monitoring

seismic

display_name: seismic


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 Schänis in Switzerland where one station is located within a natural mixed forest stand (SCB) with European beech (_Fagus sylvatica_; 130-150 yrs), European silver fir (_Abies alba_; 130-150 yrs) and European ash (_Fraxinus excelsior_; 130-150 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, SCF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Schänis is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

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

en: Schänis, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1998 onwards

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

en: Site aerial photo (Schänis)

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

en: Site photo of the forest station (SCB)

en: Site photo of the field station from above (viewing downwards the slope).

en: Site photo of the field station (SCF)

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

en: Data plot of the field station (SCF)

en: Data plot of the forest station (SCB)

en: Meta data documentation.

en: Further details on the data repository

en: Data file.

en: Forest station data (SCB)

en: Field station data (SCF)

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

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

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

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

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

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

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

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

en: accessible-forest forest geoprocessing openstreetmap qgis seilaplan

envidat

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

en: EnviDat: the Environmental Data Portal

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

educ

en: Education, culture and sport

en: Forest vector data (for sample forest range)

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

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

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

en: Digitised sample perimeter

en: Juypter Notebook for Data Preparation

en: Juypter Notebook for Data Preparation (online)

en: Preview

format: PNG

media_type: image/png

accessible-forest

display_name: accessible-forest

forest

display_name: forest

geoprocessing

display_name: geoprocessing

openstreetmap

display_name: openstreetmap

qgis

display_name: qgis

seilaplan

display_name: seilaplan


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

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

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

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

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

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

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

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

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

antartic

display_name: antartic

arctic

display_name: arctic

cryosphere

display_name: cryosphere

laboratory-study

display_name: laboratory-study

polar

display_name: polar

sea-salt-aerosol

display_name: sea-salt-aerosol

snow

display_name: snow


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 Vordemwald in Switzerland where one station is located within a natural mixed forest stand (VOB) with European silver fir (_Abies alba_; 110 yrs) and oak trees (_Quercus sp._; 190-210 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, VOF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Vordemwald is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

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

envidat

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

en: EnviDat: the Environmental Data Portal

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

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

en: Site aerial photo (Vordemwald)

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

en: Site photo of the forest station (VOB)

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

en: Data plot of the forest station (VOB)

en: Data file.

en: Forest station data (VOB)

en: Site photo of the field stationfrom the side.

en: Site photo of the field station (VOF)

en: Data plot of the field station (VOF)

en: Field station data (VOF)

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


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 Bettlachstock in Switzerland where one station is located within a natural mixed forest stand (BTB) with European beech (_Fagus sylvatica_; 170-190 yrs), European silver fir (_Abies alba_; 190 yrs) and Norway spruce (_Picea abies_; 200 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, BTF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Bettlachstock is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

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

educ

en: Education, culture and sport

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

en: Site aerial photo (Bettlachstock)

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

en: Site photo of the field station (BTF)

en: Site photo of the forest station (BTB)

en: Data plot of the field station (BTF)

en: Data plot of the forest station (BTB)

en: Field station data (BTF)

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed


backward-trajectories

en: Backward trajectories were calculated from two positions: Davos Wolfgang (LON: 9.85361, LAT: 46.83551) and Weissfluhjoch (LON: 9.80646 LAT: 46.83304) for the time period February 2 until March 27 2019 using COSMO or ECMWF, respectively.

en: alps atmospheric-dynamics backward-trajectories complex-topopgraphy cosmo lagrangian-analysis raclets

envidat

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

en: EnviDat: the Environmental Data Portal

en: Backward  Trajectories

title_for_slug: backward-trajectories

en: The trajectories are started from surface up to 5000 m altitude, with an equidistant vertical spacing of 200 m, and calculated 120 h backward in time. As long as the trajectories stay in the COSMO1 domain (essentially the Alpine region), as run operationally at MeteoSwiss, the COSMO analysis wind fields are used. When the trajectories leave this domain, they are extended further backward in time based on ECMWF analysis data. The following fields are traced along the trajectories: longitude, latitude, geopotential height, pressure, specific humidity, temperature and surface topography. Furthermore, it is indicated for each trajectory time instance on which model data, COSMO or ECMWF, the trajectory time is based. Finally, the trajectories are started every hour in the specified time period, and positions and fields are available every 10 min. Each day is saved as a separate file. The last 8 digits of the file names indicate the starting date of the trajectories.

en: Backward Trajectories

alps

display_name: alps

atmospheric-dynamics

display_name: atmospheric-dynamics

backward-trajectories

display_name: backward-trajectories

complex-topopgraphy

display_name: complex-topopgraphy

cosmo

display_name: cosmo

lagrangian-analysis

display_name: lagrangian-analysis

raclets

display_name: raclets


data-set-on-snow-instability

en:
These data on snow instability include three data subsets that were analyzed and the results published by Reuter and Schweizer (2018) who suggest a novel framework on how to describe snow instability by failure initiation, crack propagation and slab tensile support.

Please refer to the Read-me file for further details on the data.

These data are the basis of the following publication:
Reuter, B. and Schweizer, J., 2018. Describing snow instability by failure initiation, crack propagation and slab tensile support. Geophys. Res. Lett., 45, doi: 10.1029/2018GL078069.

en: avalanche-forcasting crack-propagation failure-initiation propgatoin-saw-test rutschblock slab slab-tensile-support snow-avalanche snow-stability-evaluation weak-layer

en: Data set on snow instability

title_for_slug: data-set-on-snow-instability

en: data set on snow instability

en: Read-me file on snow instability data

avalanche-forcasting

display_name: avalanche-forcasting

crack-propagation

display_name: crack-propagation

failure-initiation

display_name: failure-initiation

propgatoin-saw-test

display_name: propgatoin-saw-test

rutschblock

display_name: rutschblock

slab

display_name: slab

slab-tensile-support

display_name: slab-tensile-support

snow-avalanche

display_name: snow-avalanche

snow-stability-evaluation

display_name: snow-stability-evaluation

weak-layer

display_name: weak-layer


present-weather-sensor-klosters

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

en: precipitation raclets sensor visibility weather

en: Present Weather Sensor Klosters

title_for_slug: present-weather-sensor-klosters

educ

en: Education, culture and sport

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

precipitation

display_name: precipitation

sensor

display_name: sensor

visibility

display_name: visibility

weather

display_name: weather


snow-drift-station-flowcapt

en: The FlowCapt is an ultra-robust instrument measuring solid particle acoustic mass - flux intensities (g m‾ ² s‾ ¹) and wind speeds (m s‾ ¹). It was deployed at Gotschnagrat (LON: 46.859 LAT: 9.849). The vertical tube with a length of 1 m monitors snowdrift and snow-blowing; and is mounted at a height between 0.1 an 1.1 m above snow surface. The time within each data set is given in UTC+1.

en: acoustic-mass-flux flowcapt raclets snow-blowing snowdrift wind-speed

en: Snow Drift Station - Flowcapt

title_for_slug: snow-drift-station-flowcapt

en: Average mass flux of 10 min measurement interval. The Cycle duration is 1 min which includes 6 s observation and 54 s stand-by. The time is given in UTC+1.

en: avergaged mass flux

en: The data file contains the maximum mass flux of 10 minute intervalls. The duration of one cycle was 1 min and contained 6s observations and 54 s stand-by. The time is given in UTC+1.

en: maximum mass flux

en: The data file contains the minimum mass flux of 10 minute intervalls. The duration of one cycle was 1 min and contained 6s observations and 54 s stand-by. The time is given in UTC+1.

en: minimum mass flux

en: The data file contains the average wind speed in 10 minute intervalls. The duration of one cycle was 1 min and contained 6s observations and 54 s stand-by. The time is given in UTC+1.

en: average wind speed

en: The data file contains the maximum wind speed in 10 minute intervalls. The duration of one cycle was 1 min and contained 6 s observations and 54 s stand-by. The time is given in UTC+1.

en: maximum wind speed

en: The data file contains the minimum wind speed in 10 minute intervalls. The duration of one cycle was 1 min and contained 6s observations and 54 s stand-by. The time is given in UTC+1.

en: minimum wind spees

acoustic-mass-flux

display_name: acoustic-mass-flux

flowcapt

display_name: flowcapt

snow-blowing

display_name: snow-blowing

snowdrift

display_name: snowdrift

wind-speed

display_name: wind-speed


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

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

envidat

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

en: EnviDat: the Environmental Data Portal

en: Snow Drift Station - 3D Ultrasonic

educ

en: Education, culture and sport

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

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

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

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

en: Sonic wind component u

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

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

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

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

en: Sonic wind component v

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

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

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

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

en: Sonic wind component w

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

en: Sonic temperature

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

en: Sonic time

anemometer

display_name: anemometer

raclets

display_name: raclets

wind

display_name: wind

wind-direction

display_name: wind-direction

wind-velocity

display_name: wind-velocity


radar-wind-profiler-davos-wolfgang

en: The RADAR wind profiler from Meteoswiss was installed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577) and measured from 2171 m above sea level to 11079 m, with a temporal resolution of 10 minutes.

en: raclets radar wind-direction wind-speed wolfgangpass

en: RADAR Wind profiler Davos Wolfgang

title_for_slug: radar-wind-profiler-davos-wolfgang

en: A Vaisala Lap3000 was installed at Davos Wolfgang to record wind profiles. The time format is number of days fom January 0, 0000 in UTC with a 10 minute resolution.

en: Radar Wind Profiler Data

radar

display_name: radar

wolfgangpass

display_name: wolfgangpass


weather-station-klosters

en: A weather station (Lufft WS600) measured meteorological parameters at Klosters (LON: 9.880413, LAT: 46.869019). Detailed information on the specifications can be found [here](https://www.lufft.com/products/compact-weather-sensors-293/ws600-umb-smart-weather-sensor-1832/productAction/outputAsPdf/).

en: klosters precipitation raclets temperature weather wind

envidat

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

en: EnviDat: the Environmental Data Portal

en: Weather Station Klosters

title_for_slug: weather-station-klosters

en: All in one weather station measuring temperature (°C), humidity (%), wind (m s‾ ¹) and precipitation type. The time format is: number of days from January 0, 0000, UTC.

klosters

display_name: klosters

temperature

display_name: temperature


weather-station-davos-wolfgang

en: The dataset contains weather parameters measured at Davos Wolfgang (LON: 9.853594, LAT: 46.835577).

en: pressure raclets relative-humidity temperature weather

en: Weather Station Davos Wolfgang

title_for_slug: weather-station-davos-wolfgang

educ

en: Education, culture and sport

en: The data file contains date and time (YYYY-MM-DD hh:mm:ss) in UTC, temperature (°C), relative humidity (%), pressure (hPa) at 10 m above ground and above sea level. The data was averaged over 10 minutes.

pressure

display_name: pressure

relative-humidity

display_name: relative-humidity


disdrometer-data-davos-wolfgang

en: The dataset contains information on precipitation amount and type for Davos Wolfgang (LON: 9.853594, LAT: 46.835577) from February 8 to March 19 2019. It includes: characteristics of hydrometeors (e.g. diameter, fall velocity, amount per diameter class,...), precipitation rate, radar reflectivity, visibility range, weather codes and instrument performance.

en: davos disdrometer fall-velocity precipitation raclets

en: Disdrometer Data Davos Wolfgang

title_for_slug: disdrometer-data-davos-wolfgang

en:
The zip folder contains one netCDF file per day for Davos Wolfgang from February 8 to March 19 2019 with a temporal resolution of 30 seconds (time in UTC) and includes:

- characteristics of hydrometeors (e.g. diameter, fall velocity, amount per diameter class,...)
- precipitation rate
- radar reflectivity
- visibility range
- weather codes
- instrument performance

davos

display_name: davos

disdrometer

display_name: disdrometer

fall-velocity

display_name: fall-velocity

precipitation

display_name: precipitation


snow-drift-station-snow-and-air-data

en: Snow and air data was monitored at Gotschnagrat (LON: 46.859 LAT: 9.849) by an infrarot radiometer (Campbell SI-111) for snow temperature (°C), a snow height sensor (Lufft SHM-31) for snow height change (cm) and a temperature and humidity sensor (Campbell CS-215) for air temperature (°C) and relative humidity (%). No filter was applied to the sensors and the smapling frequency was 1 Hz.

en: raclets snow snow-depth snow-height snow-temperature temperature

en: Snow Drift Station - Snow and Air Data

title_for_slug: snow-drift-station-snow-and-air-data

en: The relative humidity (%) was measured with a Campbell CS-215 at a height of 2.2 m above ground. The time is given in UTC+1.

en: Relative Humidity

en: The air temperature (°C) was measured with a Campbell CS-215 which was mounted at a height of 2.2 m above ground. The time is given in UTC+1.

en: Air Temperature

en: The snow depth (cm) was measured with a Lufft SHM-31 which was mounted at 1.7 m above ground. The time is given in UTC+1.

en: Snow Depth

en: The snow surface temperature (°C) was measured with a Campbell SI-111 at 0.7 m above ground. The time is given in UTC+1.

en: Snow Surface Temperature

snow

display_name: snow

snow-depth

display_name: snow-depth

snow-height

display_name: snow-height

snow-temperature

display_name: snow-temperature


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

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

envidat

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

en: EnviDat: the Environmental Data Portal

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

educ

en: Education, culture and sport

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

en: Data Set Documentation

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

en: Weekly stable water isotopes

format: TXT

media_type: text/plain

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

en: 7-hourly stable water istopes

catchment

display_name: catchment

chloride

display_name: chloride

deuterium

display_name: deuterium

plynlimon

display_name: plynlimon

stable-water-isotopes

display_name: stable-water-isotopes

streamflow

display_name: streamflow

tracer

display_name: tracer


radiosondes

en: Radiosondes (Windsond, Sparv Embedded AB) were started in Davos Wolfgang (LON: 9.853594, LAT: 46.835577) to report height profiles of pressure, relative humidity and temperature at specific days. In addition to regular launches of radiosondes, sondes were attached to [HoloBalloon](https://www.envidat.ch/group/clouds-in-situ-raclets) to report the ambient conditions of the in-situ measurements. Further profiles of meteorological measures were recorded at [HoloGondel](https://www.envidat.ch/group/clouds-in-situ-raclets) which was installed at the gondola moving between Gotschnaboden and Gotschnagrat at 2285 m a.s.l.

en: holoballoon pressure raclets radiosonde relative-humidity temperature wind wind-direction

en: Radiosondes

title_for_slug: radiosondes

en:
The "template" files contain standard parameters, i.e. ambient pressure (mbar and Pa), temperature (°C), relative humidity (%), wind speed (m s‾ ¹) and wind direction (true deg), as well as additional information, e.g. time (UTC and local time), dew point (°C), mixing ration (g/kg), density (kg m³), altitude (m MSL), height above ground (ft and m AGL), and geographic coordinates (longitude, latitude). If the wind speed was zero, no wind direction is given.
The zip files contain ascents on the following days:
February 10 (morning and afternoon),
February 11 (morning),
February 16 (afternoon),
February 22 (morning),
March 1 (morning),
March 5 (morning),
March 11 (morning),
March 12 (morning),
March 13 (morning),
March 14 (morning),
March 17 (afternoon), and
March 18.
The data was managed with Windsond Receiver Application Demo Version 2.92, (C) Sparv Embedded AB 2016.

en: Radiosond Template Files

en:
The "soundings" radiosond data contains information on ambient pressure (mbar), temperature (C), relative humidity (%), wind speed (m s‾ ¹) and wind direction (true deg), and are ordered with respect to the height above ground (m AGL) with a step size of 20 m. The first line of each file idicates the geographic location and the start time (in UTC) of the ascent. If the wind speed was zero, no wind direction is given.
The zip files contain ascents on the following days:
February 10 (morning and afternoon),
February 11 (morning),
February 16 (afternoon),
February 22 (morning),
March 1 (morning),
March 5 (morning),
March 11 (morning),
March 12 (morning),
March 13 (morning),
March 14 (morning),
March 17 (afternoon), and
March 18.
The data was managed with Windsond Receiver Application Demo Version 2.92, (C) Sparv Embedded AB 2016.

en: Radiosond Sounding Files

holoballoon

display_name: holoballoon

pressure

display_name: pressure

raclets

display_name: raclets

radiosonde

display_name: radiosonde

temperature

display_name: temperature

wind

display_name: wind

wind-direction

display_name: wind-direction


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)

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

envidat

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

en: Description of the predictors provided in this dataset

en: Description

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

en: Predictors

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


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

Paper Citation:

Risch Anita C., Zimmermann, Stefan, Moser, Barbara, Schütz, Martin, Hagedorn, Frank, Firn, Jennifer, Fay, Philip A., Adler, Peter B., Biederman, Lori A., Blair, John M., Borer, Elizabeth T., Broadbent, Arthur A.D., Brown, Cynthia S., Cadotte, Marc W., Caldeira, Maria C., Davies, Kendi F., di Virgilio, Augustina, Eisenhauer, Nico, Eskelinen, Anu, Knops, Johannes M.H., MacDougall, Andrew S., McCulley, Rebecca L., Melbourne, Brett A., Moore, Joslin L., Power, Sally A., Prober, Suzanne M., Seabloom, Eric W., Siebert, Julia, Silveira, Maria L. , Speziale, Karina L., Stevens, Carly J., Tognetti, Pedro M., Virtanen, Risto, Yahdjian, Laura, Ochoa-Hueso, Raul (accepted). Global impacts of fertilization and herbivore removal on soil net nitrogen mineralization are modulated by local climate and soil properties. Global Change Biology

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

We assessed how the removal of mammalian herbivores (Fence) and fertilization with growth-limiting nutrients (N, P, K, plus nine essential macro- and micronutrients; NPK) individually, and in combination (NPK+Fence), affected potential and realized soil net Nmin across 22 natural and semi-natural grasslands on five continents. Our sites spanned a comprehensive range of climatic and edaphic conditions found across the grassland biome. We focused on grasslands, because they cover 40-50% of the ice-free land surface and provide vital ecosystem functions and services. They are particularly important for forage production and C sequestration. Worldwide, grasslands store approximately 20-30% of the Earth’s terrestrial C, most of it in the soil (Schimel, 1995; White et al., 2000).

en: ammonification climate fertilization global-change grassland herbivore mineralization nitrification nitrogen nutrient-network soil

envidat

en: EnviDat: the Environmental Data Portal

en: Anthropogenic change and soil net N mineralization

educ

en: Education, culture and sport

en:
we assessed how the removal of mammalian herbivores (Fence) and fertilization with growth-limiting nutrients (N, P, K, plus nine essential macro- and micronutrients; NPK) individually, and in combination (NPK+Fence), affected potential and realized soil net Nmin across 22 natural and semi-natural grasslands on five continents. Our sites spanned a comprehensive range of climatic and edaphic conditions found across the grassland biome. We focused on grasslands, because they cover 40-50% of the ice-free land surface and provide vital ecosystem functions and services. They are particularly important for forage production and C sequestration. Worldwide, grasslands store approximately 20-30% of the Earth’s terrestrial C, most of it in the soil (Schimel, 1995; White et al., 2000).

Study sites and experimental design
The 22 sites contributing to this project are part of the Nutrient Network Global Research Cooperative (NutNet, https://nutnet.umn.edu/). Mean annual temperature across our 22 sites ranged from -4 to 22°C, mean annual precipitation from 252 to 1,592 mm, and elevations from 6 to 4,261 m above sea level (Fig. 1, Supplementary Table S1). Soil organic C varied from 0.8 to 7.8%, soil total N from 0.1 to 0.6%, and the soil C:N ratio from 9.1 to 21.5. Soil clay content spanned from 3.0 to 35%, and soil pH from 3.4 to 7.6 (Supplementary Table S2). Thus, the sites covered a wide range of environments in which grasslands occur (Fig. 1, Supplementary Table S1 & S2).
At each site, the effects of nutrient addition and herbivore removal were tested via a randomized-block design (Borer et al., 2014; Supplementary Fig. S1a). Three replicate blocks with 10 treatment plots each were established at each site, with the exception of the site at bldr.us, where only two blocks were established (Supplementary Fig. S1a). The 10 plots were randomly assigned to a nutrient or fencing treatment, but only a subset of four plots was used in the current study, each with a different treatment (see below; Supplementary Fig. S1a). All plots were 5 x 5 m and divided into four 2.5 x 2.5 m subplots (Supplementary Fig. S1b). Each subplot was further divided into four 1 x 1 m square sampling plots, one of which was set aside for soil sampling (Borer et al., 2014; Supplementary Fig. S1b). Plots were separated by at least 1 m wide walkways.
In this study, we collected data from the following four treatments: (i) untreated control plots (Control), (ii) herbivore removal plots (Fence), (iii) plots fertilized with N, P, K, plus nine essential macro and micronutrients (NPK), and (iv) plots with simultaneous fertilizer addition and herbivore removal (NPK+Fence; Supplementary Fig. S1a). The number of years of treatment differed among sites (2 – 9 years since start of treatment; Supplementary Table S1). For the nutrient additions, all sites applied 10 g N m-2 yr-1 as time-release urea; 10 g P m-2 yr-1 as triple-super phosphate; 10 g K m-2 yr-1 as potassium sulfate. A micro-nutrient mix (Fe, S, Mg, Mn, Cu, Zn, B, Mo, Ca) was applied at 100 g m-2 together with K in the first year of treatments but not thereafter.
The vertebrate herbivore removal treatment (Fence) was established by fencing two plots, one control and one NPK plot, within each of the blocks (Supplementary Fig. S1a). We designed the fences so that they would effectively exclude aboveground mammalian herbivores with a body mass of over 50 g (Borer et al., 2014). At the majority of sites, the height of the fences was 180 cm, and the fence design included wire mesh (1 cm holes) on the first 90 cm along with a 30 cm outward-facing flange stapled to the ground to exclude burrowing animals; climbing and subterranean animals may potentially still access these plots (Borer et al., 2014). For slight modifications in fence design at a few sites see Supplementary Table S3. While most sites only had native herbivores, a few sites (4) were also grazed by domestic animals (Supplementary Table S1).
Potential and realized soil net N mineralization, ammonification, nitrification and other soil properties
Each site participating in the study received a package containing identical material from the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) to be used for sampling and on-site N incubations. For the field incubation, we followed the protocol by Risch et al. (2015, 2019). Briefly, we drove a 5 x 15 cm (diameter x depth) steel cylinder 13.5 cm deep into the soil after clipping the vegetation at randomized locations in each plot. The top 1.5 cm of the cylinder remained empty to capture incoming N from run-off or deposition with a polyester mesh bag (mesh-size 250 µm) filled with 13.2 ± 0.9 g of acidic and alkaline exchanger resin (1:1 mixture; ion-exchanger I KA/ion-exchanger III AA, Merck AG, Darmstadt). The bag was fixed in place with a metal Seeger ring (Bruetsch-Rüegger Holding, Urdorf, Switzerland). We then removed 1.5 cm soil at the bottom of the cylinder and placed another resin bag to capture N leached from the soil column. The exchange resin was saturated with H+ and Cl- prior to filling the bags by stirring the mixture in 1.2M HCl for 1 h and then rinsing it with demineralized water until the electrical conductivity of the water reached 5 μS/cm. The cylinders were then re-inserted into the cored hole, level with the soil surface, and incubated for an average of 42 days (range 40 to 57days). The individual site coordinators chose the timing of incubation to start approximately six weeks prior to peak plant biomass production. All incubations were completed between February 2015 and January 2016 accounting for differences in growing season between northern and southern hemispheres. At the end of the incubation, the cylinders were re-collected and immediately shipped to WSL in an insulated box together with cold packs to halt further mineralization. Gloves were worn at all times to avoid contamination of the samples. Upon arrival at WSL, we extracted the resin bags and a 20 g subsample of sieved soil (4 mm) separately in a 100 ml PE-bottle with 80 ml 1 M KCl for 1.5 h on an end-over-end shaker and filtered through ashless folded filter paper (DF 5895 150, ALBET LabScience). We measured NO3- (colorimetrically; Norman & Stucki, 1981) and NH4+concentrations (flow injection analysis; FIAS 300, Perkin Elmer) on these filtrates.
At the start of the field incubation, we additionally collected two soil cores of 5 x 12 cm (diameter x depth) in each sampling plot and composited them to measure potential soil net Nmin, soil chemical and biological properties (see below). We also collected an additional sample (5 x 12 cm) to assess soil physical properties, which remained within the steel cylinder. Both ends were tightly closed with plastic caps. Cores were carefully packed to avoid further disturbance, and together with the composited soil samples, were shipped to the laboratory at WSL within a few days after collection.
From the composited samples, we extracted an equivalent of 20 g dry soil with KCl, as described above, and measured NO3- and NH4+concentrations. Realized soil net Nmin was then calculated as the difference between the inorganic N content of samples collected at the end of the incubation (plus N extracted from the bottom resin bag) and the N content at the beginning of the incubation; values were scaled to represent daily mineralization rates (mg N kg-1soil d-1; Risch et al., 2015). Realized soil net Nmin values represent an average period of 42 days prior to peak biomass, typically the highest period of biological activity, and not the entire year (Risch et al., 2019).
A second subsample of the composited sample was used to determine potential soil net Nmin in the laboratory (Risch et al., 2019). Briefly, we weighed duplicate samples (8 g dry soil) into 50-ml Falcon tubes. Soil moisture was brought to 60% of the field capacity of each plot, the Falcon tubes tightly closed and then incubated at 20°C for 42 days in a dark room. Every week the Falcon tubes were opened and ventilated. At the end of the incubation, the soil samples were extracted the same way as described above and NO3- and NH4+ was determined. Potential soil net Nmin was calculated as the difference between the N content before and after the incubation and scaled to represent daily values (mg N kg-1soil d-1). Using our NO3- and NH4+ measures we also calculated potential and realized soil net nitrification and soil net ammonification to be able to better understand the drivers of fertilization and herbivore removal effects on potential and realized soil net Nmin.
A third subsample of the composite soil sample was sieved (2 mm mesh) and microbial biomass (μg Cmic g-1 soil dry weight) was estimated by measuring the maximal respiratory response to the addition of glucose solution (4 mg glucose per g soil dry weight dissolved in distilled water; substrate-induced respiration method) on approximately 5.5 g of soil (Anderson & Domsch, 1978). The rest of the composited sample was dried at 65°C for 48 h, ground and sieved (2 mm mesh) to assess a series of soil chemical properties (Risch et al., 2019). We measured the percentage of clay as an indicator of soil texture (Gee & Bauder, 1986; Risch et al., 2019).
Statistical analyses
Potential and realized soil net Nmin were square root transformed to account for a highly skewed data distribution (yt = sign(y)*sqrt|y|; negative values in the data set impeded log transformation). To assess treatment effects on potential and realized soil net Nmin, we used linear mixed effects models (LMMs) fitted by maximum likelihood using the lme function from the nlme package (version 3.131.1; Pinheiro et al., 2016), R version 3.6.1; R Foundation for Statistical Computing. Treatment (Control, Fence, NPK, NPK+Fence) was a fixed factor, with site and block as random factors, where block was nested within site. We also tested for effects of time since start of treatments in preliminary analyses by adding total treatment years as an additional fixed factor. We did not find a significant effect of years of treatment, and thus dropped this variable from the models. The LMMs were corrected using varIdent if the homogeneity of variance criterion was not met (Pinheiro et al., 2016). To visualize our results, we calculated treatment effects using Cohens’ d statistic (Cohen, 1977; Koricheva et al., 2013). Note that calculating response ratios (or log response ratios) was not possible with our data, as we have both negative and positive values. We also fitted LMMs for potential and realized soil net ammonification and nitrification to gain more insight into how global change affects the processes underpinning potential and realized soil net Nmin. We also sqrt-transformed (see above) these dependent variables. Treatment was included as a fixed factor with random factors as described above. In addition, we assessed how potential and realized soil net Nmin were related to potential and realized soil net ammonification and nitrification, respectively. For this, we calculated site averages for each treatment separately. We then ran LMMs, with potential and realized soil net Nmin as the dependent variable, potential and realized soil net ammonification/nitrification as the independent ones. Site was included as a random factor.
Based on our previous work (Risch et al., 2019) and the existing literature (Schimel & Bennett, 2004; Liu et al., 2017), we developed a priori causal conceptual models of relationships among treatments, environmental drivers, and potential and realized soil net Nmin (Supplementary Fig. S2) to test with structural equation modelling (SEM) using a d-sep approach (Shipley, 2009; Lefcheck, 2016). The variables included in the model were long-term climatic conditions, specifically, site-level mean annual precipitation (MAP) and temperature of the wettest quarter (T.q.wet), plot-level soil texture (clay content) and soil microbial biomass. Mean annual precipitation and T.q.wet were obtained from WorldClim (Hijmans et al., 2005) (http://www.worldclim.org/) and together with the experimental treatments were predicted to directly affect soil properties and soil net Nmin (Supplementary Fig. S2). Soil clay content was, in turn, predicted to affect microbial biomass and soil net Nmin. Because we determined microbial biomass prior to incubating the samples in the laboratory or field, we assumed that the abundance of these microbes would be responsible for N process rates and not vice versa (Supplementary Fig. S2). We tested our conceptual model (Supplementary Fig. S2) using the piecewiseSEM package (version 2.0.2; Lefcheck, 2016) in R 3.4.0, in which a structured set of linear models are fitted individually. This approach allowed us to account for the nested experimental design, and overcome some of the limitations of standard structural equation models, such as small sample sizes (Shipley, 2009; Lefcheck, 2016). We used the lme function of the nlme package to model response variables, including site as a random factor. Good fit of the SEM was assumed when Fisher’s C values were non-significant (p > 0.05). For all significant interactions between covariates and experimental treatments detected in the SEMs, we calculated treatment effect sizes, i.e. the differences in potential or realized soil net Nmin between Control and treatments (Fence, NPK, NPK+Fence) and plotted these values against the climate or soil covariates. Finally, we fitted LMMs for the soil variables included in our SEMs, with treatment as the fixed factor, and with site and block as random factors, where block was nested within site.

en: Risch et al. 2020 - Raw data for publication "Global impacts of fertilization and herbivore removal on soil net nitrogen mineralization are modulated by local climate and soil properties"

ammonification

display_name: ammonification

climate

display_name: climate

fertilization

display_name: fertilization

global-change

display_name: global-change

grassland

display_name: grassland

herbivore

display_name: herbivore

mineralization

display_name: mineralization

nitrification

display_name: nitrification

nitrogen

display_name: nitrogen

nutrient-network

display_name: nutrient-network

soil

display_name: soil


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

Paper Citation:

Risch, A. C.; Zimmermann, S.; Ochoa-Hueso, R.; Schütz, M.; Frey, B.; Firn, J. L.; Fay, P. A.; Hagedorn, F.; Borer, E. T.; Seabloom, E. W.; et al. Soil net nitrogen mineralisation across global grasslands. Nat. Commun. 2019, 10 (1), 4981 (10 pp.). doi.org/10.1038/s41467-019-12948-2

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

We conducted coordinated measurements of realised and potential soil net Nmin, and assessed water holding capacity, bulk density, C and N content, texture, pH, pore space, microbial biomass, and archaeal (AOA) and bacterial (AOB) ammonia oxidiser abundance using identical materials and methods across 30 grasslands on six continents. The sites covered a globally relevant range of climatic and edaphic conditions. Climate data was obtained from worldclim - Global climate data https://www.worldclim.org/

en: global grassland mineralization nutrient-network soil soil-biology soil-chemistry worldwide

en:
site.code: Site code

Continent: Continent

sampleID: Sample ID

nn.latitude: Lattitude

nn.longitude: Longitude

bioclim.T.ann: Mean annual temperature (°C)

bioclim.T.var: Temperature variability (°C)

bioclim.T.q.wet: Temperature of the wettest quarter (°C)

bioclim.T.q.dry: Temperature of the driest quarter (°C)

bioclim.T.q.warm: Temperature of the warmest quarter (°C)

bioclim.P.ann: Mean annual preciptiation (mm)

bioclim.P.q.wet: Precipitation of the wettest quarter (mm)

ch.pH: pH

ch.WSK.vol: Water holding capacity (vol%)

ch.bulkdensity.gcm3: Bulk density (g cm-3)

ch.porespace: Pore space (vol%)

ch.sand.perc: Soil texture: sand (%)

ch.silt.perc: Soil texture: silt (%)

ch.clay.perc: Soil texture: clay (%)

ch.NO3start.mgg: NO3 at the beginning of the incubation (mgkg)

ch.Ntot.perc: Total soil N (%)

ch.Ctot.perc: Total soil C (%)

ch.Corg.perc: Soil organic C (%)

ch.netNminfield.mgkgd: Realised soil net N mineralisation (mg kg-1 d-1)

ch.netNminlab.mgkgd: Potential soil ne N mineralisation (mg kg-1 d-1)

archaea.cpg.log: Ammonia oxidising archaea (archaeal amoA gene copies g-1 soil)

bacteria.cpg.log: Ammonia oxidising bacteria (bacterial amoA gene copies g-1 soil)

micr.biom: Microbial biomass (ug g-1 soil)

ch.CtoN: C to N ratio

ch.netNnitriffield.mgkgd.sqrt: Realised soil net nitrification, transformed (mg kg-1 d-1)

ch.netNammoniffield.mgkgd.sqrt: Realised soil net ammonification, transformed (mg kg-1 d-1)

ch.netNnitriflab.mgkgd.sqrt: Potential soil net nitrification, transformed (mg kg-1 d-1)

ch.netNammoniflab.mgkgd.sqrt: Potential soil net ammonifiation, transformed (mg kg-1 d-1)

global

display_name: global

soil-biology

display_name: soil-biology

soil-chemistry

display_name: soil-chemistry

worldwide

display_name: worldwide


digitizing-historical-plague

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.

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

en: Digitizing historical plague

title_for_slug: digitizing-historical-plague

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: 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 one meteorological station in Alpthal in Switzerland which is located within a natural coniferous forest (ALB) with Norway spruce (_Picea abies_; 180-230 yrs) as dominant tree species. The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Alpthal is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

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

envidat

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

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

en: Site aerial photo from the Alpthal forest station. Source: Google. 2016. Site aerial photo from the Alpthal forest station. Retrieved November 11, 2016, from https://www.google.ch/maps

en: Site aerial photo (Alpthal)

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

en: Data plot of the forest station (ALB)

en: Forest station data (ALB)

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


sensitivity-of-modeled-snow-instability

en: We investigated the sensitivity of modeled snow instability to meteorological input data using SNOWPACK. We therefore used input data from the automatic weather station at the Weissfluhjoch field site for the year 2016-2017. We investigated three scenarios and performed 14'000 simulations for each scenario. The dataset contains extracted output data from modeled SNOWPACK simulations, including setup files to reproduce the simulations. For further information read the README file.

en: global-sensitivity-analysis numerical-avalanche-forecasting snow-cover-modeling snow-instability snowpack

envidat

en: EnviDat: the Environmental Data Portal

en: Sensitivity of modeled snow instability

title_for_slug: sensitivity-of-modeled-snow-instability

educ

en: Education, culture and sport

en: Reference run for the sensitivity analysis at Weissfluhjoch field site for winter season 2016-2017. Meteorological data from the automatic weather stations are used. No biases were introduced to the input data.

en: Reference run

en: Manually observed snow profiles at Weissfluhjoch field site for winter season 2016-2017, including layer depth, hand hardness, grain size and grain shape for each layer.

en: Manually observed snow profiles

en: Extracted output data for case ALL. Here biases were introduced to meteorological input data during the whole simulation period. Data include quasi random biases, which were introduced, extracted output of simulated snow profiles and sobol indices for each output variable.

en: Case ALL

en: Extracted output data for case SL. Here biases were introduced to meteorological input data during the period of slab formation. Data include quasi random biases, which were introduced, extracted output of simulated snow profiles and sobol indices for each output variable.

en: Case SL

en: Extracted output data for case ALL. Here biases were introduced to meteorological input data during the period of weak layer formation. Data include quasi random biases, which were introduced, extracted output of simulated snow profiles and sobol indices for each output variable.

en: Case WL

global-sensitivity-analysis

display_name: global-sensitivity-analysis

numerical-avalanche-forecasting

display_name: numerical-avalanche-forecasting

snow-cover-modeling

display_name: snow-cover-modeling

snow-instability

display_name: snow-instability

snowpack

display_name: snowpack


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

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

envidat

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

en: Stability tests, avalanche observations, Switzerland, Norway

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

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

en: ECT data (Norway, Switzerland)

en: Rutschblock data (Switzerland)

avalanche-danger

display_name: avalanche-danger

avalanche-observations

display_name: avalanche-observations

extended-column-test

display_name: extended-column-test

norway

display_name: norway

rutschblock-test

display_name: rutschblock-test

switzerland

display_name: switzerland


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

en: antarctica cryowrf landuse topography wrf

envidat

en: EnviDat: the Environmental Data Portal

en: REMA topography and AntarcticaLC2000 for WRF

educ

en: Education, culture and sport

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

en: Documentation

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

en: Scripts

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

en: WPSinput

antarctica

display_name: antarctica

cryowrf

display_name: cryowrf

landuse

display_name: landuse

topography

display_name: topography

wrf

display_name: wrf