gemeindebeitrag-an-zvv-fr-einw

title_for_slug: gemeindebeitrag-an-zvv-fr-einw

de: oeffentlichefinanzen bezirke gemeinden zvv kantonzuerich oeffentlicherverkehr

de: Gemeindebeitrag an ZVV [Fr./Einw.]

de: Der Beitrag errechnet sich aus dem auf die Gemeinden entfallenden Defizit und wird anteilsmässig nach Angebot (Anzahl Abfahrten, Ant. 80%) und der absoluten, berichtigten Steuerkraft (Ant. 20%) verteilt. Der Gemeindebeitrag wird gemessen an der Einwohnerzahl Ende Vorjahr.

bezirke

display_name: bezirke

oeffentlichefinanzen

display_name: oeffentlichefinanzen

oeffentlicherverkehr

display_name: oeffentlicherverkehr

zvv

display_name: zvv

mobility

de: Mobilität und Verkehr


maintainer: Statistisches Amt des Kantons Zürich, Data Shop

de: bezirke kirchgemeinden gemeinden gemeindefinanzen kantonzuerich steuern gemeindesteuern

state: active

type: dataset

de: Anteil Kirchgemeinden an allg. Gemeindesteuern [%]

media_type: text/csv

format: CSV

mimetype: text/csv

de: Anteil Kirchgemeinden an den allg. Gemeindesteuern (Nettoertrag Rechnungsjahr).

gemeindefinanzen

display_name: gemeindefinanzen

gemeinden

display_name: gemeinden

gemeindesteuern

display_name: gemeindesteuern

kantonzuerich

display_name: kantonzuerich

kirchgemeinden

display_name: kirchgemeinden

steuern

display_name: steuern

national-economy

fr: Economie nationale

de: Volkswirtschaft

en: National economy

it: Economia

statistisches-amt-kanton-zuerich

de: Statistisches Amt Kanton Zürich

de: Das Statistische Amt ist das Kompetenzzentrum für die öffentliche Statistik im Kanton Zürich. Wir versorgen Bevölkerung, Verwaltung, Politik, Gemeinden, Medien, Firmen und sonstige Organisationen mit qualitativ hochstehenden Daten, Informationen und Fakten zum Kanton Zürich. Inhaltlich arbeiten wir unabhängig und objektiv.

political_level: canton

kanton-zuerich

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: ZH-Kt Statistisches Amt - DCAT Harvester


miv-anteil-modal-split

title_for_slug: miv-anteil-modal-split

name: Statistisches Amt des Kantons Zürich, Data Shop

de: bezirke verkehr kantonzuerich verkehrsmittelwahl gemeinden

de: MIV-Anteil (Modal Split) [%]

de: Grundlage für die Berechnung des MIV-Anteils sind die MIV-Wege des Quell-, Ziel- und Binnenverkehrs des jeweiligen Gebiets an einem durchschnittlichen Werktag (DWV). Mit Prognosezustand.

verkehr

display_name: verkehr

verkehrsmittelwahl

display_name: verkehrsmittelwahl

mobility

fr: Mobilité et transports

en: Mobility and Transport

it: Mobilità e trasporti


schul-mittelschule-anz

title_for_slug: schul-mittelschule-anz

de: schuelerinnen mittelschulen kantonzuerich schueler gemeinden schulen bezirke

de: Schül. Mittelschule [Anz.]

de: Anzahl MittelschülerInnen inkl. LangzeitgymnasiastInnen und SchülerInnen privater Mittelschulen (nach Wohnort).

mittelschulen

display_name: mittelschulen

schueler

display_name: schueler

schuelerinnen

display_name: schuelerinnen

schulen

display_name: schulen

education

fr: Education, science

de: Bildung, Wissenschaft

en: Education and science

it: Formazione e scienza


pw-neuzulassungen-anz

title_for_slug: pw-neuzulassungen-anz

de: motorfahrzeuge bezirke gemeinden kantonzuerich fahrzeuge personenwagen motorfahrzeugbestand

de: PW-Neuzulassungen [Anz.]

de: Zwischen dem 1. Oktober des Vorjahres und dem 30. September erstmals in Verkehr gesetzte Personenwagen (PW). Hinweis: Der Wert für den ganzen Kanton umfasst alle Autos mit einem Zürcher Kennzeichen; diese können auch Personen oder Unternehmen gehören, die ihr Domizil nicht im Kanton Zürich haben.

fahrzeuge

display_name: fahrzeuge

motorfahrzeugbestand

display_name: motorfahrzeugbestand

motorfahrzeuge

display_name: motorfahrzeuge

personenwagen

display_name: personenwagen


leerwohnungen-total-anz

title_for_slug: leerwohnungen-total-anz

de: kantonzuerich zimmerzahl bezirke gemeinden wohnungsbestand wohnungen

de: Leerwohnungen Total [Anz.]

de: Leerstehende Wohnungen am 1. Juni des Erhebungsjahres

bezirke

display_name: bezirke

wohnungen

display_name: wohnungen

wohnungsbestand

display_name: wohnungsbestand

zimmerzahl

display_name: zimmerzahl

construction

fr: Construction, logement

de: Bau- und Wohnungswesen

en: Construction and housing

it: Costruzione e abitazione


de: bodenpreise bezirke wohnbauland gemeinden kantonzuerich bauland

de: Baulandpreis (gewichteter Median) [Fr./m2]

de: Gewichteter Medianpreis aller Freihandverkäufe von unbebautem Land der Wohn- und Mischzone inkl. Abbruchliegenschaften. Die letzten drei Jahre sind provisorisch. Keine Angaben bei weniger als 4 Transaktionen. Weitere Infos: www.statistik.zh.ch/bodenpreise

bauland

display_name: bauland

bodenpreise

display_name: bodenpreise

gemeinden

display_name: gemeinden

kantonzuerich

display_name: kantonzuerich

wohnbauland

display_name: wohnbauland

prices

fr: Prix

de: Preise

en: Prices

it: Prezzi


geb-vol-landwirtschaft-zunahme

title_for_slug: geb-vol-landwirtschaft-zunahme

de: gebaeude gemeinden kantonzuerich landwirtschaft bautaetigkeit bezirke

de: Geb.Vol. Landwirtschaft: Zunahme [%]

de: Veränderung des Gebäudevolumens gegenüber Vorjahr (1995: jährliche Veränderung im Zeitraum 1990-2000)

bautaetigkeit

display_name: bautaetigkeit

gebaeude

display_name: gebaeude

landwirtschaft

display_name: landwirtschaft

statistisches-amt-kanton-zuerich

de: Statistisches Amt Kanton Zürich

de: Das Statistische Amt ist das Kompetenzzentrum für die öffentliche Statistik im Kanton Zürich. Wir versorgen Bevölkerung, Verwaltung, Politik, Gemeinden, Medien, Firmen und sonstige Organisationen mit qualitativ hochstehenden Daten, Informationen und Fakten zum Kanton Zürich. Inhaltlich arbeiten wir unabhängig und objektiv.


de: gemeinden motorfahrzeugbestand kantonzuerich motorfahrzeuge personenwagen privatverkehr bezirke fahrzeuge

de: Personenwagen pro 1000 Einwohner [Anz.]

de: Am 30. September eingelöste Personenwagen, geteilt durch die Bevölkerung in Tausend. Der Wert wird auch Motorisierungsgrad genannt. Hinweis: Der Wert für den ganzen Kanton umfasst alle Autos mit einem Zürcher Kennzeichen; diese können auch Personen oder Unternehmen gehören, die ihr Domizil nicht im Kanton Zürich haben.

privatverkehr

display_name: privatverkehr

mobility

de: Mobilität und Verkehr


beschaftigte-im-primarsektor-anz

title_for_slug: beschaftigte-im-primarsektor-anz

de: wirtschaftssektor gemeinden arbeitsmarkt bezirke beschaeftigte landwirtschaft kantonzuerich

de: Beschäftigte im Primärsektor [Anz.]

de: Anzahl Beschäftigte des Primärsektors (Land- und Forstwirtschaft). Aktuellstes Jahr provisorisch.

arbeitsmarkt

display_name: arbeitsmarkt

beschaeftigte

display_name: beschaeftigte

wirtschaftssektor

display_name: wirtschaftssektor

work

fr: Travail, rémunération

de: Arbeit, Erwerb

en: Work and income

it: Lavoro e reddito


neuzulassungen-anderer-antrieb

title_for_slug: neuzulassungen-anderer-antrieb

de: motorfahrzeuge fahrzeuge motorfahrzeugbestand personenwagen kantonzuerich gemeinden bezirke

de: Neuzulassungen anderer Antrieb [%]

de: Anteil der Personenwagen (PW) mit anderem Antrieb an allen Personenwagen, die zwischen dem 1. Oktober des Vorjahrs und dem 30. September erstmals in Verkehr gesetzt wurden. Hinweis: Der Wert für den ganzen Kanton umfasst alle Autos mit einem Zürcher Kennzeichen; diese können auch Personen oder Unternehmen gehören, die ihr Domizil nicht im Kanton Zürich haben.

fahrzeuge

display_name: fahrzeuge

motorfahrzeugbestand

display_name: motorfahrzeugbestand

motorfahrzeuge

display_name: motorfahrzeuge

personenwagen

display_name: personenwagen


de: bautaetigkeit bezirke gemeinden gebaeudeart kantonzuerich landwirtschaft bauinvestitionen

de: Bauinv. Landwirtschaft 5 Jahre [Fr./Einw.]

de: Durchschnittliche Bauinvestitionen der letzten 5 Jahre im Bereich Landwirtschaft (Bauinv. für landwirtschaftliche Ökonomiebauten inkl. Gartenbau), in Franken pro Einwohner.

bauinvestitionen

display_name: bauinvestitionen

bezirke

display_name: bezirke

gebaeudeart

display_name: gebaeudeart

construction

de: Bau- und Wohnungswesen


krw-wahleranteil-fdp

title_for_slug: krw-wahleranteil-fdp

de: parteien wahlen bezirke nationalratswahlen gemeinden kantonzuerich politik

de: KRW Wähleranteil FDP [%]

de: Wähleranteil FDP bei Kantonsratswahlen

gemeinden

display_name: gemeinden

nationalratswahlen

display_name: nationalratswahlen

parteien

display_name: parteien

politik

display_name: politik

wahlen

display_name: wahlen

politics

fr: Politique

de: Politik

en: Politics

it: Politica

statistisches-amt-kanton-zuerich

de: Statistisches Amt Kanton Zürich


de: sekundarschule bezirke schueler schuelerinnen kantonzuerich sekundarstufe gemeinden

de: Schül. Oberstufe [pro 1000 Einw.]

de: Anzahl OberstufenschülerInnen (exklusiv LangzeitgymnasiastInnen) gemessen an der zivilrechtlichen Bevölkerung Ende Vorjahr

kantonzuerich

display_name: kantonzuerich

schueler

display_name: schueler

schuelerinnen

display_name: schuelerinnen

sekundarschule

display_name: sekundarschule

sekundarstufe

display_name: sekundarstufe

education

de: Bildung, Wissenschaft

statistisches-amt-kanton-zuerich

de: Das Statistische Amt ist das Kompetenzzentrum für die öffentliche Statistik im Kanton Zürich. Wir versorgen Bevölkerung, Verwaltung, Politik, Gemeinden, Medien, Firmen und sonstige Organisationen mit qualitativ hochstehenden Daten, Informationen und Fakten zum Kanton Zürich. Inhaltlich arbeiten wir unabhängig und objektiv.


steuerkraft-pro-kopf-fr

title_for_slug: steuerkraft-pro-kopf-fr

de: gemeindefinanzen steuern bezirke gemeinden steuerkraft kantonzuerich

de: Steuerkraft pro Kopf [Fr.]

de: Steuerkraft pro Einwohner (Nettosteuerertrag zu 100% pro Einwohner; = relative Steuerkraft).

gemeindefinanzen

display_name: gemeindefinanzen

steuerkraft

display_name: steuerkraft

steuern

display_name: steuern

national-economy

de: Volkswirtschaft


bevolkerung-heimat-ausland-pers

title_for_slug: bevolkerung-heimat-ausland-pers

de: nationalitaet gemeinden bezirke heimat auslaender bevoelkerung kantonzuerich

de: Bevölkerung: Heimat Ausland [Pers.]

de: Einwohnerbestand Ende Jahr Heimat Ausland nach zivilrechtlichem Wohnsitz (ab 2010 inkl. vorläufig Aufgenommene, die seit mehr als einem Jahr in der Gemeinde leben, aber ohne Wochenaufenthalter und Asylbewerber).

auslaender

display_name: auslaender

bevoelkerung

display_name: bevoelkerung

heimat

display_name: heimat

nationalitaet

display_name: nationalitaet

population

fr: Population

de: Bevölkerung

en: Population

it: Popolazione


de: gemeinden bezirke schueler schuelerinnen kantonzuerich berufsschule schulen

de: Schül. Berufsschule [pro 1000 Einw.]

de: Anzahl BerufsschülerInnen gemessen an der zivilrechtlichen Bevölkerung Ende Vorjahr

berufsschule

display_name: berufsschule

bezirke

display_name: bezirke

gemeinden

display_name: gemeinden

schulen

display_name: schulen


nrw-wahleranteil-glp

title_for_slug: nrw-wahleranteil-glp

de: kantonzuerich parteien wahlen politik bezirke nationalratswahlen gemeinden

de: NRW Wähleranteil GLP [%]

de: Wähleranteil GLP bei Nationalratswahlen. Hinweis: 2015 werden alle Auslandschweizer zur Stadt Zürich gezählt.

nationalratswahlen

display_name: nationalratswahlen

parteien

display_name: parteien

politik

display_name: politik

wahlen

display_name: wahlen

politics

de: Politik

statistisches-amt-kanton-zuerich

de: Statistisches Amt Kanton Zürich


neuzulassungen-benzinmotor

title_for_slug: neuzulassungen-benzinmotor

de: motorfahrzeugbestand personenwagen fahrzeuge kantonzuerich gemeinden bezirke motorfahrzeuge

de: Neuzulassungen Benzinmotor [%]

de: Anteil der Personenwagen (PW) mit Benzinmotor an allen Personenwagen, die zwischen dem 1. Oktober des Vorjahrs und dem 30. September erstmals in Verkehr gesetzt wurden. Hinweis: Der Wert für den ganzen Kanton umfasst alle Autos mit einem Zürcher Kennzeichen; diese können auch Personen oder Unternehmen gehören, die ihr Domizil nicht im Kanton Zürich haben.

fahrzeuge

display_name: fahrzeuge

kantonzuerich

display_name: kantonzuerich

motorfahrzeugbestand

display_name: motorfahrzeugbestand

motorfahrzeuge

display_name: motorfahrzeuge

personenwagen

display_name: personenwagen

mobility

de: Mobilität und Verkehr

statistisches-amt-kanton-zuerich

de: Das Statistische Amt ist das Kompetenzzentrum für die öffentliche Statistik im Kanton Zürich. Wir versorgen Bevölkerung, Verwaltung, Politik, Gemeinden, Medien, Firmen und sonstige Organisationen mit qualitativ hochstehenden Daten, Informationen und Fakten zum Kanton Zürich. Inhaltlich arbeiten wir unabhängig und objektiv.


de: bezirke religion bevoelkerung gemeinden kantonzuerich konfession

de: Bevölkerung: übrige/andere/ohne Konfession [Pers.]

de: Zivilrechtliche Wohnbevölkerung der Gemeinden nach der Konfession am 31. Dezember. Anzahl Personen übrige anerkannte Religionsgemeinschaften, nicht anerkannte Religionsgemeinschaften und ohne Konfession.

konfession

display_name: konfession

religion

display_name: religion


anteil-jur-pers-an-allg-gemeindesteuern

title_for_slug: anteil-jur-pers-an-allg-gemeindesteuern

de: bezirke gemeindefinanzen gemeindesteuern gemeinden juristischepersonen kantonzuerich steuern

de: Anteil jur. Pers. an allg. Gemeindesteuern [%]

de: Anteil der juristischen Personen an den allg. Gemeindesteuern (Nettoertrag Rechnungsjahr).

gemeindefinanzen

display_name: gemeindefinanzen

gemeindesteuern

display_name: gemeindesteuern

juristischepersonen

display_name: juristischepersonen

steuern

display_name: steuern

national-economy

de: Volkswirtschaft


arbeitsstatten-im-primarsektor-anz

title_for_slug: arbeitsstatten-im-primarsektor-anz

de: landwirtschaftsbetriebe bezirke landwirtschaft arbeitsstaetten kantonzuerich betriebe wirtschaftssektor gemeinden

de: Arbeitsstätten im Primärsektor [Anz.]

de: Anzahl Arbeitsstätten des Primärsektors (Land- und Forstwirtschaft). Aktuellstes Jahr provisorisch.

arbeitsstaetten

display_name: arbeitsstaetten

betriebe

display_name: betriebe

bezirke

display_name: bezirke

gemeinden

display_name: gemeinden

landwirtschaft

display_name: landwirtschaft

landwirtschaftsbetriebe

display_name: landwirtschaftsbetriebe

wirtschaftssektor

display_name: wirtschaftssektor

work

de: Arbeit, Erwerb


maintainer: Statistisches Amt des Kantons Zürich, Wahlen & Abstimmungen

de: kantonsrat eidgenoessischevolksabstimmungen abstimmungen politik regierungsrat kantonalevolksabstimmungen

de: Abstimmungsempfehlungen von Kantonsrat und Regierungsrat des Kantons Zürich

de: Abstimmungsempfehlungen des Zürcher Regierungs- und Kantonsrats zu kantonalen Abstimmungen und des Bundes-, des National- und des Ständerates zu eidgenössischen Abstimmungen. Es besteht kein Anspruch auf Vollständigkeit. Bitte senden Sie Ergänzungen zum Datensatz an die unter Kontakt angegebene E-Mail-Adresse.

abstimmungen

display_name: abstimmungen

eidgenoessischevolksabstimmungen

display_name: eidgenoessischevolksabstimmungen

kantonalevolksabstimmungen

display_name: kantonalevolksabstimmungen

kantonsrat

display_name: kantonsrat

politik

display_name: politik

regierungsrat

display_name: regierungsrat

politics

de: Politik

statistisches-amt-kanton-zuerich

de: Statistisches Amt Kanton Zürich


anteil-ohne-gk

title_for_slug: anteil-ohne-gk

de: Anteil ohne GK [%]

de: Anteil Einwohner ohne ÖV-Güteklasse: Die ÖV-Güteklassen geben lagesscharf Aufschluss über die Erschliessungsqualität einer Gemeinde mit dem ÖV. In einem ersten Schritt werden Haltestellenkategorien definiert, in Abhängigkeit von einem an der Haltestelle verfügbaren ÖV-Verkehrsmittel und dem Kursintervall.  Danach wird in Abhängigkeit von Haltestellenkategorie und Luftliniendistanz die ÖV-Güteklasse ermittelt. Für jede Gemeinde werden die Einwohner pro Güteklasse in Prozent berechnet. (http://www.afv.zh.ch/internet/volkswirtschaftsdirektion/afv/de/verkehrsgrundlagen/verkehrsangebot/erreichbarkeit_und_erschliessung/_jcr_content/contentPar/downloadlist/downloaditems/infoblatt_v_g_teklas.spooler.download.1436273189233.pdf/KTZH_AFV_Broschuere_OeV-Gueteklassen.pdf)

kantonzuerich

display_name: kantonzuerich

oeffentlicherverkehr

display_name: oeffentlicherverkehr

verkehr

display_name: verkehr

mobility

de: Mobilität und Verkehr

de: Das Statistische Amt ist das Kompetenzzentrum für die öffentliche Statistik im Kanton Zürich. Wir versorgen Bevölkerung, Verwaltung, Politik, Gemeinden, Medien, Firmen und sonstige Organisationen mit qualitativ hochstehenden Daten, Informationen und Fakten zum Kanton Zürich. Inhaltlich arbeiten wir unabhängig und objektiv.


de: gemeinden kantonzuerich steuerkraft steuern bezirke

de: Steuerkraft ber. (arith. Mittel 3 Jahre) [Mio.Fr.]

de: Berichtigte Steuerkraft (Steuerkraft nach Finanzausgleich): arithm. Mittel der letzten 3 Jahre.

steuerkraft

display_name: steuerkraft

steuern

display_name: steuern


bauzonen-uberbaut-ha

title_for_slug: bauzonen-uberbaut-ha

de: bezirke gemeinden bodennutzung kantonzuerich zonenart bauzonen bauzonenverbrauch

de: Bauzonen überbaut [ha]

de: Total überbaute Bauzonenfläche

bauzonen

display_name: bauzonen

bauzonenverbrauch

display_name: bauzonenverbrauch

bodennutzung

display_name: bodennutzung

zonenart

display_name: zonenart

construction

de: Bau- und Wohnungswesen


de: bezirke gemeinden steuerbaresvermoegen vermoegen kantonzuerich natuerlichepersonen

de: Steuerb. Vermögen natürl. Pers. 75%-Quantil [Fr.]

de: Steuerbares Vermögen natürlicher Personen des Kantonalen Steueramts. Das 75%-Quantil ist ein Schwellenwert: Drei Viertel der Bevölkerung liegt unter, das andere Viertel über diesem Wert.

bezirke

display_name: bezirke

gemeinden

display_name: gemeinden

natuerlichepersonen

display_name: natuerlichepersonen

steuerbaresvermoegen

display_name: steuerbaresvermoegen

vermoegen

display_name: vermoegen

national-economy

de: Volkswirtschaft


de: bevoelkerung bevoelkerungswachstum bezirke gemeinden kantonzuerich

de: Bevölkerungszunahme 1 Jahr [%]

de: Veränderung der Einwohnerzahl (zivilrechtlicher Wohnsitz) gegenüber dem Vorjahr in Prozent. Wert für 1970 aus der Volkszählung (wirtschaftl. Wohnsitz), da keine kant. Erhebung stattgefunden hat.

bevoelkerung

display_name: bevoelkerung

bevoelkerungswachstum

display_name: bevoelkerungswachstum

population

de: Bevölkerung


bevolkerung-durchschnittsalter-jahre

title_for_slug: bevolkerung-durchschnittsalter-jahre

de: bevoelkerungsstatistik bezirke bevoelkerung gemeinden alter altersstruktur kantonzuerich

de: Bevölkerung: Durchschnittsalter [Jahre]

de: Durchschnittsalter Ende Jahr (zivilrechtlicher Wohnsitz; ab 2010 inkl. vorläufig Aufgenommene, die seit mehr als einem Jahr in der Gemeinde leben, aber ohne Wochenaufenthalter und Asylbewerber).

alter

display_name: alter

altersstruktur

display_name: altersstruktur

bevoelkerungsstatistik

display_name: bevoelkerungsstatistik

statistisches-amt-kanton-zuerich

de: Statistisches Amt Kanton Zürich


vza-im-tertiarsektor-anz

title_for_slug: vza-im-tertiarsektor-anz

de: gemeinden beschaeftigte bezirke wirtschaftssektor kantonzuerich dienstleistungssektor arbeitsmarkt

de: VZÄ im Tertiärsektor [Anz.]

de: Beschäftigung des Tertiärsektors (Handel, Instandhaltung/Reparatur, Verkehr/Lagerei, Gastgewerbe/Gastronomie, Information/Kommunikation, Finanz-/Versicherungs-DL, Grundstücks-/Wohnungswesen, weitere DL, öffentliche Verwaltung, Erziehung/Unterrricht, Gesundheit, Kunst) in Vollzeitäquivalenten. Aktuellstes Jahr provisorisch. Ab 2015 Berechnung mit neuem Modell, was in Bezug auf die VZÄ zu einem Bruch in der Zeitreihe zwischen 2014 und 2015 führt. Werte < 4 werden nicht ausgewiesen.

arbeitsmarkt

display_name: arbeitsmarkt

beschaeftigte

display_name: beschaeftigte

dienstleistungssektor

display_name: dienstleistungssektor

kantonzuerich

display_name: kantonzuerich

wirtschaftssektor

display_name: wirtschaftssektor

work

de: Arbeit, Erwerb

statistisches-amt-kanton-zuerich

de: Das Statistische Amt ist das Kompetenzzentrum für die öffentliche Statistik im Kanton Zürich. Wir versorgen Bevölkerung, Verwaltung, Politik, Gemeinden, Medien, Firmen und sonstige Organisationen mit qualitativ hochstehenden Daten, Informationen und Fakten zum Kanton Zürich. Inhaltlich arbeiten wir unabhängig und objektiv.


de: bezirke sars-cov-2 oeffentlicherverkehr gastgewerbe kantonzuerich mobilitaet schulen gewerbe corona

label: Zahlen & Fakten des Kantons Zürich zu COVID-19

de: COVID_19 Massnahmen im Kanton Zürich

de: Geltende Massnahmen auf dem Gebiet des Kantons Zürich sowie Schulferienzeiten im Bezirk Zürich. Spaltenüberschriften: 'Datum' = Beginn der Massnahme; 'Raum' = Gebiet auf das sich die Massnahme resp. Schulferienzeit bezieht; 'Kategorisierung' = Art der Massnahme (bzw. Schulferien)

corona

display_name: corona

gastgewerbe

display_name: gastgewerbe

gewerbe

display_name: gewerbe

mobilitaet

display_name: mobilitaet

oeffentlicherverkehr

display_name: oeffentlicherverkehr

schulen

display_name: schulen

crime

fr: Criminalité, droit pénal

de: Kriminalität, Strafrecht

en: Crime, criminal justice

it: Criminalità, diritto penale

mobility

de: Mobilität und Verkehr

health

fr: Santé

de: Gesundheit

en: Health

it: Sanità


name: Statistisches Amt des Kantons Zürich, Wahlen & Abstimmungen

de: regierungsrat regierungsratswahlen kantonsratswahlen kantonsrat

label: Übersicht über die Testdaten

de: Dokumentation zu kantonalen Echtzeitwahldaten

de: Erklärungen und Definitionen der einzelnen Variablen für die Ressourcen dieses Datensatzes.

format: XLS

de: Wahlsonntag vom 12.02.2023

media_type: application/json

de: Um die Stabilität unserer Server nicht zu gefährden, bitten wir die Konsumentinnen und Konsumenten auf eine Direktverlinkung zu verzichten und stattdessen das File in regelmässigen Abständen auf den eigenen Server herunterzuladen.

format: JSON

mimetype: application/json

de: Echtzeitdaten zu den im Kanton Zürich stattfindenden kantonalen Wahlen. Am Wahlsonntag, ab der Schliessung der letzten Urnen um 12 Uhr, erfolgt die Aktualisierung ca. alle 15 Minuten.

kantonsrat

display_name: kantonsrat

kantonsratswahlen

display_name: kantonsratswahlen

regierungsrat

display_name: regierungsrat

regierungsratswahlen

display_name: regierungsratswahlen

politics

de: Politik

de: Echtzeitdaten am Wahltag des Kantons Zürich - kantonale Wahlen


verkaufe-mfh-anz

title_for_slug: verkaufe-mfh-anz

de: bezirke mehrfamilienhaus handaenderungen kantonzuerich immobilien eigentum gemeinden

de: Verkäufe MFH [Anz.]

de: Anzahl verkaufter Mehrfamilienhäuser (MFH). Die letzten drei Jahre sind provisorisch.

bezirke

display_name: bezirke

eigentum

display_name: eigentum

gemeinden

display_name: gemeinden

handaenderungen

display_name: handaenderungen

immobilien

display_name: immobilien

mehrfamilienhaus

display_name: mehrfamilienhaus

construction

de: Bau- und Wohnungswesen

statistisches-amt-kanton-zuerich

de: Statistisches Amt Kanton Zürich


maintainer: EnviDat Support

en: snow-water-equivalent natural-climate-variability uncertainty cryosphere rain-on-snow climate-change-impact

en: climate realizations

format: SERVICE

en: The dataset contains for three variables (snow water equivalent, surface water input and liquid precipitation) 50 realizations of current and future climate periods for two time horizons (mid end end of century), two emission senarions (RCP 4.5 and 8.5) and 10 climate model chains (all EUR11 chains within CH2018). To quantify natural climate variability for projections of snow conditions and resulting rain-on-snow (ROS) flood events, a weather generator was applied to simulate inherently consistent climate variables for multiple realizations of current and future climates at 100 m spatial and hourly temporal resolution over a 12 x 12 km high-altitude study area in the Swiss Alps. The output of the weather generator was used as input for subsequent simulations with an energy balance snow model. The data was extracted in 2021 from original model output.

climate-change-impact

display_name: climate-change-impact

cryosphere

display_name: cryosphere

natural-climate-variability

display_name: natural-climate-variability

rain-on-snow

display_name: rain-on-snow

snow-water-equivalent

display_name: snow-water-equivalent

uncertainty

display_name: uncertainty

education

de: Bildung, Wissenschaft

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

value: Envidat Harvester

en: Multiple realizations of daily snow water equivalent, surface water input and liquid precipitation projections for mid- and late-century


de: bezirke kantonzuerich gebaeude immobilien gemeinden

de: Gebäudevolumen [1000 m3]

de: Gebäudevolumen gemäss GVZ

gebaeude

display_name: gebaeude

kantonzuerich

display_name: kantonzuerich

statistisches-amt-kanton-zuerich

de: Das Statistische Amt ist das Kompetenzzentrum für die öffentliche Statistik im Kanton Zürich. Wir versorgen Bevölkerung, Verwaltung, Politik, Gemeinden, Medien, Firmen und sonstige Organisationen mit qualitativ hochstehenden Daten, Informationen und Fakten zum Kanton Zürich. Inhaltlich arbeiten wir unabhängig und objektiv.


lwf-tea-bag-sites

maintainer: Marcus Schaub

title_for_slug: lwf-tea-bag-sites

en: decomposition modeling forest roiboos verification green-tea litter carbon tea-bag

en: Site Information

en: Descriptive information on sites

en:
Decomposition of plant litter is a key process for the transfer of carbon and nutrients in ecosystems. Carbon contained in the decaying biomass is released to the atmosphere as respired CO2, and may contribute to global warming. Litterbag studies have been used to improve our knowledge of the drivers of litter decomposition, but they lack comparability because litter quality is plant species-specific. The use of commercial tea bags as a standard substrate was suggested in order to harmonize studies, where green tea and rooibos represent more labile and more recalcitrant C compounds as surrogates of local litter.
The tea bag approach was implemented on eight sites of the Swiss long-term Forest Ecosystem Research (LWF) network (https://www.wsl.ch/LWF). This allowed us to take advantage from the existing infrastructure and data from a previous litterbag study with local litter. In Beatenberg and Schaenis, additional elevation transects were established (1200-1800 m and 540-1150 m, respectively) to examine particularly the effect of temperature on decomposition. In Pfynwald (https://www.wsl.ch/de/ueber-die-wsl/versuchsanlagen-und-labors/flaechen-im-wald/pfynwald.html) and Salgesch, infrastructure of running projects was used to examine the effect of drought and understory removal, respectively. In Novaggio, tea bags were incubated in summer and winter to study the effect of seasonality particularly precipitation. Tea bags are collected after 3, 12, 24, and 36 months; for the two time-shifted experiments additionally after 6 and 9 months.
The study has two primary objectives. Firstly, it contributes to TeaComposition initiative (http://teacomposition.org/) which aims at investigating long-term litter decomposition and its key drivers at present as well as under different future climate scenarios using a common protocol and standard litter (tea) across nine terrestrial biomes. Secondly, the data are used to further develop decomposition models such as Yasso (http://en.ilmatieteenlaitos.fi/yasso) which is used by several countries, including Switzerland to estimate the annual carbon fluxes in dead wood, litter, and soil for reporting in National Greenhouse Gas Inventories under the United Nations Framework Convention on Climate Change and the Kyoto Protocol.

carbon

display_name: carbon

decomposition

display_name: decomposition

forest

display_name: forest

green-tea

display_name: green-tea

litter

display_name: litter

modeling

display_name: modeling

roiboos

display_name: roiboos

tea-bag

display_name: tea-bag

verification

display_name: verification

en: LWF-Tea bag sites


en: agro-food-trade food-preferences latent-semantic-analysis national-dishes food-taste-similarity

en: National dishes, migration data and data for bilateral food trade flows

media_type: Stata dta file

en:
The data include information on national dishes, migration data and data for bilateral food
trade flows for 5 years as well as importer and exporter GDP, population, distance between
most populated cities, bilateral information on Preferential Trade Agreements, common
official primary langauge, common colonizer post-1945, colonial relationship for overall
171 countries. The final data set contains 145,350 observations (29,070 country pairs for
5 years)

en: dyadic food similarity measures, pairwise similarity and dissimilarity (chord diagram)

media_type: .zip

en:
The figures show the distribution of two dyadic food tastes similarity measures on the base of
the national dishes: (i) the Manhattan distance and the (ii) latent semantic analysis. The chord diagram
shows the results for country pairs with most and less similarity on basis of the LSA-approach.

en: information on ingredients of national dishes

media_type: .csv

en: shows information of ingredients of all national dishes of 171 countries and their names

en: The data described in this article were collected daily over the period 4 June 2018 to 23 August 2018 and contains information of several data sources. The database includes information on national recipes and their ingredients for 171 countries, measures for food taste similarities between all 171 countries as well as bilateral migration and agro-food trade data for 5 years. The database can be used for analyzing e.g., the relation between food preferences and international trade or food preferences and health outcomes (e.g., obesity) across countries.

agro-food-trade

display_name: agro-food-trade

food-preferences

display_name: food-preferences

food-taste-similarity

display_name: food-taste-similarity

latent-semantic-analysis

display_name: latent-semantic-analysis

national-dishes

display_name: national-dishes

education

en: Education and science

en: Data of national dishes, their similarity and trade flows


maintainer: Vanessa Burg

en: biogas bioenergy switzerland manure review

en: Biogas from animal manure in Switzerland: energy potential, technology development and resource mobilization

media_type: PDF

en: Aim of this white paper is to provide decision-makers, administrations and stakeholders with the most current research findings in order to promote the optimal use of bioenergy from manure in the Swiss energy transition. For this purpose, the results of the Swiss competence center for bioenergy research - SCCER BIOSWEET - are summarized and presented in a broader context. If nothing else is mentioned, the results refer to Switzerland and in case of the feedstock to the domestic biomass potentials.

format: PDF

bioenergy

display_name: bioenergy

biogas

display_name: biogas

manure

display_name: manure

review

display_name: review

switzerland

display_name: switzerland

envidat

en: EnviDat: the Environmental Data Portal

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


data-set-on-snow-instability

title_for_slug: data-set-on-snow-instability

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

en: Read-me file on snow instability data

media_type: application/pdf

mimetype: application/pdf

en: 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.

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

en: Data set on snow instability


greenland-shrubs-and-microclimate

maintainer: Nathalie Chardon

title_for_slug: greenland-shrubs-and-microclimate

en: validation sdm arctic climate accessible-shrub-forest field-data

en: GitLab Repository

en: Zip drive of GitLab repository contents (https://gitlab.com/nathaliechardon/gl_microclim).

en: Nuup Kangerlua

en:
 Collected July-August 2011-2013 (PI: Jacob Nabe-Nielsen)

These data collected July-August 2011-2013 (PI: Jacob Nabe-Nielsen). Please contact J. Nabe-Nielsen (jnn_at_ecos.au.dk) to request access to data on all surveyed shrub species. Available here are only data used in Chardon et al. 2022.

 COLUMN ABBREVIATIONS

 Metadata for 90 m spatial grid

year: sampling year

alt: average altitude of grid as recorded during field surveys [m]

long: WGS84 longitude

lat: WGS84 latitude

x_stere: longitude in Polar Stereographic (EPSG 4326)

y_stere: latitude in Polar Stereographic (EPSG 4326)

 Species presence [1] or absence [0] at grid level

pa_bena: _Betula nana_

pa_sagl: _Salix glauca_

 Shrub species cover (calculated from pin hits in 0.7 x 0.7 m frame, based on total of 25 hits) [%]

cov_bena: _Betula nana_ cover

cov_sagl: _Salix glauca_ cover

 Downscaled climate (time series 1984 – 2013)

tempjja: average maximum June-August temperature [ºC]

tempmax: yearly maximum temperature [ºC]

tempmin: yearly minimum temperature [ºC]

tempcont: yearly maximum – minimum temperature [ºC]

precipmam: cumulative March-May precipitation [mm]

precipjja: cumulative June-August precipitation [mm]

insoljja: average June-August insolation [MJ cm-2 yr-1]

 Topography and terrain wetness (calculated from)

twi: SAGA wetness index (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015)

slope: slope angle (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

asp: aspect (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

tcws: Tasseled Cap Wetness component (Sentinal images)

 Community characteristics

rich: shrub species richness averaged across pin-point frames per grid

shan: Shannon diversity (‘vegan’ package; Oksanen et al. 2019)

comp_bena: shrub competition on Betula nana (i.e. summed cover of non-_B. nana_ species) [% cover]

comp_sagl: shrub competition on Salix glauca (i.e. summed cover of non-_S. glauca_ species) [% cover]

en: Kangerluarsunnguaq

en:
 Collected August 2020 (PI: Nathalie Isabelle Chardon)

These data collected August 2020 (PI: Nathalie Isabelle Chardon). Please contact N. I. Chardon (nathalie.chardon_at_gmail.com) for individual quadrat (i.e. 1 m x 1 m) data, which are averaged at the 90 m spatial grid here.

 COLUMN ABBREVIATIONS

 Metadata for 90 m spatial grid

id: sampling zone and unique 90 m grid identifier

long: WGS84 longitude

lat: WGS84 latitude

x_stere: longitude in Polar Stereographic (EPSG 4326)

y_stere: latitude in Polar Stereographic (EPSG 4326)

date: sampling day.month.year

 Species presence [1] or absence [0] at grid level

alcr: _Alnus crispa_

bena: _Betula nana_

dila: _Diapensia lapponica_

emni: _Empetrum nigrum_

hahy: _Harimanella hypoides_

lopr: _Loiseleuria procumbens_

oxpa: _Oxycoccus palustre_

phca: _Phyllodoce caerulea_

rhod: _Rhododendron_ sp.

saar: _Salix arctica_

sagl: _Salix glauca_

sahe: _Salix herbacea_

vaul: _Vaccinium uliginosum_

drin: _Dryas integrifolia_

thpr: _Thymus praecox_

 Habitat characteristics [% cover]

shrub: all shrub species

gram: graminoids

forb: forbs

bryo: bryophytes

lich: lichens

litt: litter

bare: bare ground

stone: stones (> 2 cm)

org: organic crust

 Shrub species height (maximum vegetative height) [mm] and cover (estimated in 1x1 m quadrat) [%]

alcr_c: _Alnus crispa_ cover

alcr_h: _Alnus crispa_ height

bena_c: _Betula nana_ cover

bena_h: _Betula nana_ height

dila_c: _Diapensia lapponica_ cover

dila_h: _Diapensia lapponica_ height

emni_c: _Empetrum nigrum_ cover

emni_h: _Empetrum nigrum_ height

hahy_c: _Harimanella hypoides_ cover

hahy_h: _Harimanella hypoides_ height

lopr_c: _Loiseleuria procumbens_ cover

lopr_h: _Loiseleuria procumbens_ height

oxpa_c: _Oxycoccus palustre_ cover

oxpa_h: _Oxycoccus palustre_ height

phca_c: _Phyllodoce caerulea_ cover

phca_height: _Phyllodoce caerulea_ height

rhod_c: _Rhododendron_ sp. cover

rhod_h: _Rhododendron_ sp. height

saar_c: _Salix arctica_ cover

saar_h: _Salix arctica_ height

sagl_c: _Salix glauca_ cover

sagl_h: _Salix glauca_ height

sahe_c: _Salix herbacea_ cover

sahe_h: _Salix herbacea_ height

vaul_c: _Vaccinium uliginosum_ cover

vaul_h: _Vaccinium uliginosum_ height

drin_c: _Dryas integrifolia_ cover

drin_h: _Dryas integrifolia_ height

 Community characteristics

rich_pa: shrub species richness at grid level

rich: shrub species richness averaged across 3 1x1 m sampling quadrats per grid

shan: Shannon diversity (‘vegan’ package; Oksanen et al. 2019)

comp_bena: shrub competition on _Betula nana_ (i.e. summed cover of non-_B. nana_ species) [% cover]

comp_sagl: shrub competition on _Salix glauca_ (i.e. summed cover of non-_S. glauca_ species) [% cover]

ht_cwm: maximum shrub canopy height (i.e. community weighted mean by abundance of maximum shrub height) [mm]

 Topography and terrain wetness (calculated from)

tcws: Tasseled Cap Wetness component (Sentinal images)

slope: slope angle (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

twi: SAGA wetness index (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015)

asp: aspect (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

 Downscaled climate (time series 1984 – 2013)

insoljja: average June-August insolation [MJ cm-2 yr-1]

precipmam: cumulative March-May precipitation [mm]

tempcont: yearly maximum – minimum temperature [ºC]

tempmin: yearly minimum temperature [ºC]

precipjja: cumulative June-August precipitation [mm]

tempjja: average maximum June-August temperature [ºC]

tempmax: yearly maximum temperature [ºC]

en:
 Study Aim

We collected these data to alternatively train and validate high resolution (~ 90 m) Species Distribution Models (SDMs) and Species Abundance Models (SAMs) for _Betula nana_ L. (dwarf birch, Betulaceae) and _Salix glauca_ L. (grey willow, Salicaceae) in Southwest Greenland to assess how well such models can predict local-scale patterns.

 Data Description

Individual (presence-absence, abundance, maximum vegetative height) and community (species composition, maximum canopy height) shrub data for two fjords near Nuuk, Southwest Greenland. Also provided are corresponding downscaled climate data as well as calculated topographic and terrain wetness indicator variables.

 Nuup Kangerlua (Godthåbsfjord)

_Betula nana_ and _Salix glauca_ presence-absence, abundance, community species richness

 Kangerluarsunnguaq (Kobbefjord)

Shrub presence-absence, abundance, maximum vegetative height, community composition, maximum shrub canopy height

 Methods

 Field survey in Nuup Kangerlua

We conducted a stratified systematic plant survey along the length of Nuup Kangerlua (NK) fjord in Soutwesth Greenland (Fig. 1 in Chardon et al. 2022; following Nabe-Nielsen et al., 2017). At five distinct sites, we sampled along elevational gradients to collect data on presences, absences, abundance, and species composition of all woody species using a 0.7 x 0.7 m pin-point frame (Fig. 1e in Chardon et al. 2022). For model training, we converted these pin-point data to percent cover estimates based on the number of pins dropped (n = 25 per plot) and averaged them across the 119 spatio-climatic grids (see next section) corresponding to the plot locations (for details see Appendix S2 in Chardon et al. 2022).

 Field survey in Kangerluarsunnguaq

We conducted a random stratified plant survey in Kangerluarsunnguaq (K) fjord in Southwest Greenland. We used a preliminary Species Abundance Model trained with summed pin counts of _Betula nana_ in NK fjord (see Fig. S1.3 in Chardon et al. 2022) to stratify the ~ 27 x 17 km fjord landscape into low, medium, and high abundances classes. We randomly selected 90 x 90 m spatio-climatic grids to survey in each class for a total of 200 grids, ensuring that they were accessible by foot or boat (for details see Appendix S2 in Chardon et al. 2022). Within each grid, we sampled within three 1 m2 quadrats arranged in a randomly rotated equilateral triangle centered on the mid-point of the cell. We used a gridded sampling quadrat with 1% delineations (Fig. 1h in Chardon et al. 2022) to record woody species presences, absences, and composition, estimated percent cover, and measured maximum shrub species vegetatitve height. At every plot, we also visually scanned the area in a 20 m radius from the plot and recorded the presence of any additional shrub species to estimate grid-level species richness. As in NK fjord, we averaged these data at the grid level (for details see Appendix S2 in Chardon et al. 2022).

 Biotic variables

We calculated biotic microscale variables from the plant survey data collected in NK and K fjords. We calculated shrub species richness, diversity, and competition (i.e. sum of non-B. nana or non-S. glauca pin hits or percent cover). In K fjord, we also calculated canopy height as the community weighted mean (by abundance) of maximum vegetative shrub height.

 Climate variables

We computed high resolution temperature, precipitation, and insolation for local scale data for the study area by statistically downscaling climate time series (1982 - 2013) from the monthly CHELSA data (Karger et al. 2017). We downscaled these data from 30 arc sec (~ 400 m at the latitude of our study) to our target grid size of ~ 90 m with geographic weighted regression and using the MEaSUREs Greenland Ice Mapping Project (GIMP) Digital Elevation Model (DEM) v. 1 (Howat et al., 2014, 2015). We then calculated 30-year averages of the climate parameters: average summer (June – August) maximum temperature, yearly maximum temperature, yearly minimum temperature, temperature continentality (yearly max. - min. temperatures), cumulative Spring (March – May) precipitation, cumulative summer precipitation, and average summer incident solar radiation (henceforth, insolation) (for calculation details see Appendices S2, S3 in Chardon et al. 2022 and Appendix S2 in von Oppen et al. 2021).

 Topography and terrain wetness indicator variables

We calculated several topographic and terrain wetness indices at a local scale. We derived slope, aspect, and the SAGA wetness index (hereafter TWI; Boehner et al., 2002; Boehner and Selige, 2006) from the GIMP DEM. TWI is a measure of how ‘wet’ an area is, based on water drainage from the surrounding landscape. We also calculated the tasseled cap wetness component (hereafter TCW, Crist and Cicone 1984) from satellite images (for details see Appendices S2, S3 in Chardon et al. 2022) as an alternative measure of wetness.

 Computer code

Attached as zip file and available on GitLab (https://gitlab.com/nathaliechardon/gl_microclim)

 Third-party data

Data used to calculate climate, topography, and terrain wetness indicator variables are publicly available (see Appendix S2 in Chardon et al. 2022 for all data references).

accessible-shrub-forest

display_name: accessible-shrub-forest

arctic

display_name: arctic

climate

display_name: climate

field-data

display_name: field-data

sdm

display_name: sdm

validation

display_name: validation

en: Greenland shrubs and microclimate


en: biogeography pairwise-stand-comparisons biodiversity soil-impacts forest-management

en: headers

media_type: ascii, tab separated, also as excel table

en: Description of headers for data_wohlgemuth_etal_Neobiota_2022

en: references

media_type: XLS

en: Bibliography of all references cited in data_wohlgemuth_etal_Neobiota_2022.

media_type: CSV

en: Baseline data

en: Compiled data on the impacts of seven important NNTs (Acacia dealbata, Ailanthus altissima, Eucalyptus globulus, Prunus serotina, Pseudotsuga menziesii, Quercus rubra, Robinia pseudoacacia) on physical and chemical soil and biodiversity in Europe, and summarise commonalities and differences. A total of 107 publications considered, studies referred to biodiversity attributes and soil properties: 2804 lines and 30 rows.

biodiversity

display_name: biodiversity

biogeography

display_name: biogeography

forest-management

display_name: forest-management

pairwise-stand-comparisons

display_name: pairwise-stand-comparisons

soil-impacts

display_name: soil-impacts

education

en: Education and science

en: Impact of non-native tree species in Europe on soil properties and biodiversity: a review


maintainer: Christoph Marty

en: snowfall snow hydrology climate-change-impacts water-cycle

en: DORA Persistent URL

en: Synthesis report chapter

en:
This report was prepared as one of the synthesis report chapters of the Hydro-CH2018 project of the Federal Office for the Environment (FOEN). An important feature of snow cover is the fact that its volume and duration is subject to large year-to-year fluctuations. As frozen precipitation, snow cover is nothing other than a natural water reservoir that delays precipitation to runoff and is thus of outstanding importance for the seasonal water balance in Switzerland. Over a whole year, approximately 40% (22 km3) of the annual runoff currently comes from snow melting and only 1% from glacier melting. Typically, the snow cover in the Alpine region builds up over the autumn and winter months, reaches its maximum between February and May, depending on the altitude, and dominates the runoff processes during melting in the following spring and summer months.
Due to the great dependence on minus temperatures and precipitation, the snow cover reacts sensitively to temperatures above 0° Celsius and more or less precipitation. Due to climate change and the associated warming, the proportion of precipitation that falls as snow decreases measurably. In addition to this reduction in snowfall, the warmer temperatures also cause the snow cover to melt more quickly. The decline in snowfall has so far mainly affected lower altitudes, where winter temperatures often reach positive levels.
As climate change progresses, this trend is likely to continue and above all affect higher zones. Even at higher altitudes, the snow cover will then start later, melt away earlier and is increasingly no longer permanently present. This development will also have an effect on the water bodies. Today nival regimes, i.e. regimes shaped by snow, are shifting towards pluvial regimes, i.e. regimes dominated by rain. Overall, winter runoff increases, summer runoff decreases. By the end of the century, the proportion of runoff from snowmelt will decrease throughout Switzerland, albeit to a lesser extent than the proportion from glacier melt.

climate-change-impacts

display_name: climate-change-impacts

hydrology

display_name: hydrology

snow

display_name: snow

snowfall

display_name: snowfall

water-cycle

display_name: water-cycle

envidat

en: EnviDat: the Environmental Data Portal

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

en: Snow and the water cycle in a changing climate


root-traits

maintainer: Ivano Brunner

title_for_slug: root-traits

en: drought belowground-biomass ingrowth-cores soil-coring fine-root-traits

en: Root-traits

media_type: application/zip

format: ZIP

mimetype: application/zip

en:
Fine-root traits of Scots pine in response to enhanced soil water availability deriving from long-term irrigation in the Pfynwald

Data_Fig.1.xlsx
Fine-root biomass of the topsoil (0-10 cm) in the dry and irrigated treatment of the Scots pine forest of the years 2003 to 2016 recorded by soil coring

Data_Tab1+2_2005.xlsx
Fine-root traits from roots of ingrowth cores from 2005 after two years of growth in the dry and irrigated treatment of the Scots pine forest

Data_Tab1+2_2016.xlsx
Fine-root traits from roots of ingrowth cores from 2016 after two years of growth, and from roots of the soil-coring sampling from 2016 in the dry and irrigated treatment of the Scots pine forest

belowground-biomass

display_name: belowground-biomass

drought

display_name: drought

fine-root-traits

display_name: fine-root-traits

ingrowth-cores

display_name: ingrowth-cores

soil-coring

display_name: soil-coring


en: forest nitrogen soil picea-abies face co2

en: Collection of soil solution

en: Soil solution collected by suction cups

en: Resin bags

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

en: Soil C and N

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

en: Water analyses

en: Analyses of water, especially soil solution

en: Gross N mineralisation and nitrification

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

en: Collection points

en: Collection points under the trees

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.

face

display_name: face

forest

display_name: forest

nitrogen

display_name: nitrogen

picea-abies

display_name: picea-abies

soil

display_name: soil

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


en: swiss-forest-lab diameter-at-breast-height dbh irrigation tree-species tree scots-pine drought lwf crown-condition forest

en: Long-term Irrigation	Experiment Pfynwald homepage

media_type: text/html

format: HTML

mimetype: text/html

en: Swiss Forest Health Inventory

en: Site photo 3

media_type: JPEG

en: Dataset

media_type: XLSX

en: Site photo 1

en: Site photo 2

en: Shapefile	of measurement location

media_type: URL

en: To study the performance of mature Scots pine (_Pinus sylvestris_ L.) under chronic drought conditions in comparison to their immediate physiological response to drought release, a controlled long-term and large-scale irrigation experiment has been set up in 2003. The experiment is located in a xeric mature Scots pine forest in the Pfynwald (46° 18' N, 7° 36' E, 615 m a.s.l.) in one of the driest inner-Alpine valleys of the European Alps, the Valais (mean annual temperature: 9.2°C, annual precipitation sum: 657 mm, both 1961-1990). Tree age is on average 100 years, the top height is 10.8 m and the stand density is 730 stems ha-1 with a basal area of 27.3 m2 ha-1. The forest is described as _Erico Pinetum sylvestris_ and the soil is a shallow pararendzina characterized by low water retention. The experimental site (1.2 ha; 800 trees) is split up into eight plots of 1'000 m2 each. During April-October, irrigation is applied on four randomly selected plots with sprinklers of 1 m height at night using water from an adjacent water channel. The amount of irrigation corresponds to a supplementary rainfall of 700 mm year-1. Trees in the other four plots grow under naturally dry conditions. Soil moisture has been monitored since the beginning of the project at 3 soil depths (10, 20 and 60 cm). The crown condition of each tree is being assessed each year since 2003. Tree measurement data such as diameter at breast height, tree height, and social status were assessed in 2002, 2009 and 2014. The duration of the irrigation experiment is planned for 20 years.

crown-condition

display_name: crown-condition

dbh

display_name: dbh

diameter-at-breast-height

display_name: diameter-at-breast-height

irrigation

display_name: irrigation

lwf

display_name: lwf

scots-pine

display_name: scots-pine

swiss-forest-lab

display_name: swiss-forest-lab

tree

display_name: tree

tree-species

display_name: tree-species

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

en: Tree measurements 2002-2016 from the long-term irrigation experiment Pfynwald, Switzerland


maintainer: Yohann Chauvier

en: background-data point-process-model environmental-stratification survey-effort plant-species target-group cluster independent-dataset random-stratified-sampling covariate-correction

media_type: ZIP

en: Non-copyright data, scripts, diversity outputs

en:
Aim: While species distribution models (SDMs) are standard tools to predict species distributions, they can suffer from observation and sampling biases, particularly presence-only SDMs that often rely on species observations from non-standardized sampling efforts. To address this issue, sampling background points with a target-group strategy is commonly used, although more robust strategies and refinements could be implemented. Here, we exploited a dataset of plant species from the European Alps to propose and demonstrate efficient ways to correct for observer and sampling bias in presence-only models.

Innovation: Recent methods correct for observer bias by using covariates related to accessibility in model calibrations (classic bias covariate correction, Classic-BCC). However, depending on how species are sampled, accessibility covariates may not sufficiently capture observer bias. Here, we introduced BCCs more directly related to sampling effort, as well as a novel corrective method based on stratified resampling of the observational dataset before model calibration (environmental bias correction, EBC). We compared, individually and jointly, the effect of EBC and different BCC strategies, when modelling the distributions of 1’900 plant species. We evaluated model performance with spatial block split-sampling and independent test data, and assessed the accuracy of plant diversity predictions across the European Alps.

Main conclusions: Implementing EBC with BCC showed best results for every evaluation method. Particularly, adding the observation density of a target group as bias covariate (Target-BCC) displayed most realistic modelled species distributions, with a clear positive correlation (r≃0.5) found between predicted and expert-based species richness. Although EBC must be carefully implemented in a species-specific manner, such limitations may be addressed via automated diagnostics included in a provided R function. Implementing EBC and bias covariate correction together may allow future studies to address efficiently observer bias in presence-only models, and overcome the standard need of an independent test dataset for model evaluation.

background-data

display_name: background-data

cluster

display_name: cluster

covariate-correction

display_name: covariate-correction

environmental-stratification

display_name: environmental-stratification

independent-dataset

display_name: independent-dataset

plant-species

display_name: plant-species

point-process-model

display_name: point-process-model

random-stratified-sampling

display_name: random-stratified-sampling

survey-effort

display_name: survey-effort

target-group

display_name: target-group

en: Novel methods to correct for observer and sampling bias in presence-only species distribution models


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

media_type: TXT

format: TXT

en:
Total water reserves of the snow cover [mio m3] for Wägital catchment, Switzerland, for reference date April 1. Data is separated in 2 elevation zones 900m-1500m asl and 1500m-2300m asl. Time period 1943-2022, status 2022-04-20.

Funded currently or in the past by:

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

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

gcos

display_name: gcos

meteodat-gmbh

display_name: meteodat-gmbh

snow

display_name: snow

snow-water-equivalent

display_name: snow-water-equivalent

wagital

display_name: wagital

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


maintainer: James W. Kirchner

en: streamflow ecohydrology diurnal-cycle diel-cycle evapotranspiration sap-flow snowmelt groundwater

en: MODIS EVI2 script

media_type: text/plain

en: Google Earth Engine script for computing MODIS two-band enhanced vegetation index (EVI2) values and averaging them over subcatchments.

mimetype: text/plain

en: Metadata

en: Column descriptions for both data files, and map coordinates for all measurement locations.

en: MODIS NDSI script

en: Google Earth Engine script for computing MODIS normalized difference snow index (NDSI) values and averaging them over subcatchment areas.

en: 30-minute time series

en: 30-minute hydrometeorological and ecohydrological time series.

en: Daily time series

en: Daily hydrometeorological and ecohydrological time series

en: Subcatchment polygons

en: Subcatchment polygons for use in the Google Earth Engine scripts.

en:
Hydrometerological and ecohydrological time series from Sagehen Creek and Independence Creek, Sierra Nevada, USA, illustrating hydrological responses to daily cycles in snowmelt and evapotranspiration forcing.

Data include 30-minute time series of

- weather variables,

- sap flow fluxes,

- groundwater levels (in two riparian transects of shallow groundwater wells),

- and stream stages (at 12 sites spanning a 500-meter elevation gradient),

and daily time series of

- temperature, precipitation, and snow water equivalent at three nearby snow telemetry stations

- diel cycle index values for groundwater levels and stream stages,

- and MODIS normalized difference snow index (NDSI) and enhanced vegetation index (EVI2) values averaged over selected subcatchments.

Google Earth Engine scripts for extracting the MODIS data are also provided.

diel-cycle

display_name: diel-cycle

diurnal-cycle

display_name: diurnal-cycle

ecohydrology

display_name: ecohydrology

evapotranspiration

display_name: evapotranspiration

groundwater

display_name: groundwater

sap-flow

display_name: sap-flow

snowmelt

display_name: snowmelt

streamflow

display_name: streamflow

en: Daily cycles in solar flux, snowmelt, transpiration, groundwater, and streamflow at Sagehen and Independence Creeks, Sierra Nevada, USA


maintainer: Frank Techel

en: observations avalanches alps shapefile snow

en: Selected wet snow avalanche activity data Davos, Switzerland (2011-2014)

en: Zip archive with shapefiles for the selected wet snow avalanche polygons, including metadata.

en:
Polygons of wet snow avalanches in the Davos area, as documented by the Swiss avalanche warning service. The georeferenced outlines of the avalanches contain both the release as well as the deposit area, but without separating between both.

The dataset is a subset of the total record of 1615 avalanches classified as wet snow avalanches from October 2011 - September 2014, containing those 255 avalanches exceeding 0.0125 km^2. Every polygon comes with meta data, including the date of occurrence.

This dataset is the underlying dataset to:
Wever, N., Vera Valero, C. and Techel, F. (2018) _Coupled snow cover and avalanche dynamics simulations to evaluate wet snow avalanche activity_. Submitted to J. Geophys. Res., in review.

alps

display_name: alps

avalanches

display_name: avalanches

observations

display_name: observations

shapefile

display_name: shapefile

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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


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

en: Site aerial photo (Neunkirch)

media_type: image/jpeg

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

mimetype: image/jpeg

en: Field station data (NEF)

media_type: text/csv

en: Data file.

en: Site photo of the field station (NEF)

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

en: Forest station data (NEB)

en: Data plot of the field station (NEF)

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

en: Site photo of the forest station (NEB)

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

en: Data plot of the forest station (NEB)

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

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

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


maintainer: Marcia Phillips

en: permafrost borehole permos meteo-station ritigraben

en: DATA ACCESS

media_type: SMET

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

borehole

display_name: borehole

meteo-station

display_name: meteo-station

permafrost

display_name: permafrost

permos

display_name: permos

ritigraben

display_name: ritigraben

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

en: RIT2: Meteorological station at Ritigraben borehole site


maintainer: Andrin Caviezel

en: rockfall rockfall-experiments stonenodedata induced-rockfall natural-hazards dem rockfall-runout stonenode sensor-stream deposition-points

en: Induced Rockfall Data, Tschamut, GR

en:
 Induced Rockfall Experimental Data, Tschamut, GR, Switzerland

The archive contains site specific geographical data as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 30–80 kg of mass. Additionally availabe are the StoneNode v1.0 sensor streams for the equipped rocks.

The _OverviewFigures_ file serves as quick representation of the included data. It also contains the overview table for the rock masses in order to set the dimensions accordingly in RAMMS::ROCKFALL.

The archive contains the following folders:
<\ul>
* DEM: ArcGIS compatible DEM Tschamut_DEM_2m (grid resolution of 2 m) as well as the .asc grid used in RAMMS::ROCKFALL simulations
* _DepositionPoints_: containing two folders _RF Tschamut Frozen_ and _RF Tschamut WET_ containing each a shapefile with the complete deposition data of each series as well as deposition shapfiles for each rock shape.
* _DigitizedRocks_: Digitized point clouds of the used rocks as used for RAMMS::ROCKFALL simulations (.pts files) (dimensions have to be checked).
* _Orthofoto_: Orthophoto of the experimental site obtained via UAV with 5 cm resolution.

en: StoneNode v1.0 Data for equipped experimental Runs.

en: Corresponding StoneNode v1.0 data streams belonging to the deposition points.

en:
 Dataset of an experimental campaign of induced rockfall in Tschamut, Grisons, Switzerland.

The data archive contains site specific geographical data such as DEM and orthophoto as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 30–80 kg of mass. Additionally available are all the StoneNode data streams for rocks equipped with a sensor. The data set consists of

* Deposition points from two series (wet (27/10/2016) and frozen (08/12/2016) ground)
* Digital Elevation Model (grid resolution 2 m) obtained via UAV
* Orthophoto (5 cm resolution) obtained via UAV
* Digitized rock point clouds (.pts input files for RAMMS::ROCKFALL)
* StoneNode v1.0 raw data stream for equipped rocks.

Further information is found in

* A. Caviezel et al., _Design and Evaluation of a Low-Power Sensor Device for Induced Rockfall Experiments_, IEEE Transactions on Instrumentation and Measurement, 2018, 67, 767-779, http://ieeexplore.ieee.org/document/8122020/
* P. Niklaus et al., _StoneNode: A low-power sensor device for induced rockfall experiments_, 2017 IEEE Sensors Applications Symposium (SAS), 2017, 1-6, http://ieeexplore.ieee.org/document/7894081/

dem

display_name: dem

deposition-points

display_name: deposition-points

induced-rockfall

display_name: induced-rockfall

natural-hazards

display_name: natural-hazards

rockfall

display_name: rockfall

rockfall-experiments

display_name: rockfall-experiments

rockfall-runout

display_name: rockfall-runout

sensor-stream

display_name: sensor-stream

stonenode

display_name: stonenode

stonenodedata

display_name: stonenodedata

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: Induced Rockfall Dataset (Small Rock Experimental Campaign), Tschamut, Grisons, Switzerland


maintainer: Yves Bühler

en: release hazard-mapping snow avalanche hazard-indication-mapping ramms

en: RAMMS simulations NHESS

en: Simulated maximum pressures for the scenarios frequent and extreme taking the forest layer into account

en: Potential Release  Area  NHESS

en: This folder contains the output of the different investigated algorithms plus the reference dataset, used for training and validation.

en:
This dataset contains the output and reference data published in the paper "Automated snow avalanche release area delineation - validation of existing algorithms and proposition of a new object-based approach for large scale hazard indication mapping" Yves Bühler, Daniel von Rickenbach, Andreas Stoffel, Stefan Margreth, Lukas Stoffel, Marc Christen (2018) Natural Hazards And Earth System Sciences.

Abstract:
Snow avalanche hazard is threatening people and infrastructure in all alpine regions with seasonal or permanent snow cover around the globe. Coping with this hazard is a big challenge and during the past centuries, different strategies were developed. Today, in Switzerland, experienced avalanche engineers produce hazard maps with a very high reliability based on avalanche cadastre information, terrain analysis, climatological datasets and numerical modelling of the flow dynamics for selected avalanche tracks that might affect settlements. However, for regions outside the considered settlement areas such area-wide hazard maps are not available mainly because of the too high cost, in Switzerland and in most mountain regions around the world. Therefore, hazard indication maps, even though they are less reliable and less detailed, are often the only spatial planning tool available. To produce meaningful and cost-effective avalanche hazard indication maps over large regions (regional to national scale), automated release area delineation has to be combined with volume estimations and state-of-the-art numerical avalanche simulations.

In this paper we validate existing potential release area (PRA) delineation algorithms, published in peer-reviewed journals, that are based on digital terrain models and their derivatives such as slope angle, aspect, roughness and curvature. For validation, we apply avalanche cadastre data from three different ski resorts in the vicinity of Davos, Switzerland, where experienced ski-patrol staff mapped most avalanches in detail since many years. After calculating the best fit input parameters for every tested algorithm, we compare their performance based on the reference datasets. Because all tested algorithms do not provide meaningful delineation between individual potential release areas (PRA), we propose a new algorithm based on object-based image analysis (OBIA). In combination with an automatic procedure to estimate the average release depth (d0), defining the avalanche release volume, this algorithm enables the numerical simulation of thousands of avalanches over large regions applying the well-established avalanche dynamics model RAMMS. We demonstrate this for the region of Davos for two hazard scenarios, frequent (10 – 30 years return period) and extreme (100 – 300 years return period). This approach opens the door for large scale avalanche hazard indication mapping in all regions where high quality and resolution digital terrain models and snow data are available.

avalanche

display_name: avalanche

hazard-indication-mapping

display_name: hazard-indication-mapping

hazard-mapping

display_name: hazard-mapping

ramms

display_name: ramms

release

display_name: release

snow

display_name: snow

en: Automated Avalanche Release Area (PRA) Delineation Davos


en: landslide-testsite landslides pore-water pressure-perturbations tramm cces

en: TRAMM Ruedlingen data

en: TRAMM project data from the Ruedlingen site (exp 1 + 2). Measured parameters include soil suction, water level, soil volumetric water content, rain intensity, and soil temperature.

en:
A landslide testsite dataset related to pore water pressure perturbations on the stability of unsaturated silty sand slopes leading to the initiation and propagation of the shear deformations and eventual rapid mass movements. This project was initiated and led by the Institute of Geotechnical Engineering (IGT) of the Swiss Federal Institute of Technology (ETH Zurich) and was incorporated in a Swiss national (TRAMM) and a European Union (SafeLand) multidisciplinary research project. Field site: The experimental slope is 7.5 m wide by 35 m long, located in the Swiss lowlands on an east facing slope over-looking the river Rhine, at an altitude of ~ 350 masl. Originally there were forestry covertures of circa 80%, heights of 5-20 m. Shrubs up to 1-5 m high and a free herb layer covered ~ 50% of the surface. The average gradient was determined to be from 38° to 43° with a slightly concave surface. The underlying rock consists mainly of Molasse, which is formed by alternate layers of sea deposits under the Tethys Sea (Seawater Molasse) and land deposits (Freshwater Molasse). Several augured samples, as well as an outcrop of the bedrock about 20 m above the selected field, revealed horizontal layering of fine grained sand- and marlstone at the test site. The sandstone was later proven to be highly permeable and fissured. Grain-size distributions were determined and the soil was classified as medium-low plasticity silty sand. Site instrumentation:Measurements of soil suction, groundwater level, soil volumetric water content, rain intensity and soil temperature were taken and combined with geophysical monitoring using Electrical Resistance Tomography (ERT) and investigations into subsurface flow by means of tracer experiments. Deformations were monitored during the experiment, both on the surface via photogrammetrical methods and within the soil mass, using a flexible probe equipped with strain gauges at different points and two axis inclinometers on the top and acoustic sensors. Instruments were installed mainly in three clusters at depths of 15, 30, 60, 90, 120, and 150 cm below the ground surface over the slope, including jet-fill tensiometers, TDRs, Decagon TDRs, piezometers, soil temperature sensors, deformation probes, earth pressure cells, acoustic sensors and rain gauges. A ring-net barrier (provided by Geobrugg AG) was set up at the foot of the slope to protect the road. Experiments: A sprinkling experiment was carried out in September 2008 to investigate the hydrological and mechanical response of the slope (Experiment 1), followed by a second one to trigger a landslide in March 2009 (Experiment 2).

Publications

1. Lehmann, P., F. Gambazzi, B. Suski, L. Baron, A. Askarinejad, S. M. Springman, K. Holliger, and D. Or (2013), Evolution of soil wetting patterns preceding a hydrologically induced landslide inferred from electrical resistivity survey and point measurements of volumetric water content and pore water pressure, Water Resour. Res., 49, 7992–8004, doi:[10.1002/2013WR014560](http://dx.doi.org/10.1002/2013WR014560).

2. Springman, S. M., Kienzler, P., Casini, F., & Askarinejad, A. (2009). Landslide triggering experiment in a steep forested slope in Switzerland. In 17th International Conference of Soil Mechanics and Geotechnical Engineering, Alexandria, Egypt (pp. 1698-1701). doi: [10.3233/978-1-60750-031-5-1698](http://dx.doi.org/10.3233/978-1-60750-031-5-1698)

cces

display_name: cces

landslide-testsite

display_name: landslide-testsite

landslides

display_name: landslides

pore-water

display_name: pore-water

pressure-perturbations

display_name: pressure-perturbations

tramm

display_name: tramm

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

en: TRAMM project Ruedlingen experimental landslide dataset, Switzerland


en: digital-zoning-plans land-use-simulations land-use-change spatial-planning land-use-transformations

media_type: ascii x, y, z

en: Legend simulated maps

en:
Table of Content: 1. General context of the data set "LUSzoning”; 2. Background and aims of the study using the data set LUSzoning; 3. The data set LUSzoning.
1. General context of the data set "LUSzoning".
The data set "LUSzoning" stands for Land-use simulations integrating zoning regulations in Spanish functional urban areas. The data set has been generated as part of the CONCUR research project (https://www.wsl.ch/en/projects/concur.html) led by Dr. Anna M. Hersperger and funded by the Swiss National Science Foundation (ERC TBS Consolidator Grant (ID: BSCGIO 157789) for the period 2016-2021. The CONCUR research project is interdisciplinary and aims to develop a scientific basis for adequately integrating spatial policies (in this case, digital zoning plans) into quantitative land-change modelling approaches at the urban regional level.
2.	Background and aims of the study using the data set “LUSzoning”.
As part of the CONCUR project, a specific task was to integrate planning spatial policies in land-change modelling. Planning can be implemented in modelling using either hard or gradual restrictions. Different studies have addressed the inclusion of spatial planning policies in land-use change modelling. However, the integration of zoning constraints is generally established as hard or Boolean-based restrictions (e.g., whether urban development is allowed or not), while not accounting for the spatial heterogeneity or gradual characteristics within planning zones (e.g., whether planning regulations allow low, medium or high urban density), though these could improve real patterns simulations in urban areas. We assume Spanish General Zoning plans were suitable to explore the integration of planning into land-change modelling as soft constrains because they define land-use intensities in the buildable zoning areas. In light of the above considerations, the overall aim of the study was to model urban land-use changes using a multi-scenario approach that integrates digitized zoning plans for the Functional Urban Areas (FUAs) of Madrid, Barcelona, Valencia, and Zaragoza. The following specific objectives were addressed: i) to analyse the role of planning by defining three future scenarios that integrate digitized zoning plans and one scenario that assumes almost no planning intervention; ii) to introduce zoning constraints that reflect different degrees of urban densities; iii) to generate a transferable spatially-explicit modelling framework to integrate planning into land-use change simulations.
Four future land-use demands scenarios were defined for the FUAs. Storylines were created considering probable development scenarios related to zoning plans, current Spanish legislation and sustainability goals defined along two axes: a high market-oriented vs. high planning-intervention axis, and an axis of short-term economic growth vs. long-term sustainable growth. The sustainable development scenario (S1) is characterized by low gross floor area (GFA) growth that is limited to areas that are currently under development according to zoning plans. The business-as-usual scenario (S2) is characterized by medium GFA growth in the range of on-going trends. The strong development scenario (S3) is characterized by high GFA growth rates. Growth is restricted to buildable areas without urbanization project designated in zoning plans. The unrestricted development scenario (S4) prioritizes a high degree of market liberalization characterized by high GFA growth that surpasses population demands. S4 follows a rapid economic growth pattern with almost no planning intervention.
3.	 The data set “LUSzoning”.
The dataset includes 16 .asc raster layers providing the simulated land-uses under four defined scenarios for Barcelona, Madrid, Valencia and Zaragoza Functional Urban Areas (FUAs) for 2030. The simulated raster layers were created using CLUMondo simulation framework and have a spatial resolution of 30m. The .asc layers name include the name of the FUA and scenario number. For example, the output from simulating the urban growth for the city of Zaragoza under Scenario 2 is named “Zaragoza_S2.tif”. Furthermore, a .txt file named “Legend.txt” includes the numeric value of the land-use and the category of land-use that represents to interpret the .asc raster layers. The name of the land-use classes is a reclassification of the Urban Atlas 2012 land-use classes within the four Spanish FUAs analyzed.

digital-zoning-plans

display_name: digital-zoning-plans

land-use-change

display_name: land-use-change

land-use-simulations

display_name: land-use-simulations

land-use-transformations

display_name: land-use-transformations

spatial-planning

display_name: spatial-planning

en: LUSzoning: Land-use simulations integrating zoning regulations in Spanish functional urban areas


disdrometer-data-davos-wolfgang

title_for_slug: disdrometer-data-davos-wolfgang

en: davos precipitation disdrometer fall-velocity raclets

en: 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

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.

davos

display_name: davos

disdrometer

display_name: disdrometer

fall-velocity

display_name: fall-velocity

precipitation

display_name: precipitation

raclets

display_name: 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.


maintainer: Athanasios Nenes

en: dmt-ccn-chamber raclets hygroscopicity weissfluhjoch supersaturation cloud-droplets sigma-w cloud-modeling ccn

en: CCN data Weissfluhjoch

en:
In-situ CCN measurements were conducted between February 24 and March 8 2019 at Weissfluhjoch. The data file contains:

* Date and time (DD/MM/YYYY hh:mm:ss) in UTC
* Filtered CCN number concentrations (in cmˉ³) measured at 6 discrete supersaturations (SS) ranging from 0.09% to 0.74%. The name of each CCN column is indicative of the SS developed within the instrument.
* Instrument characteristics such as sample temperature (in °C) and pressure (in mbar)
* A code that refers to the reason of filtering.

Missing or filtered CCN values are indicated by "-999", while a declaration of each "Error_code" can be found in the following list:

_Error_code_:

0000: Measurements without error

1000: Period of transient operation after a supersaturation change

2000: Sharp change (decrease) in the room temperature housing the instrument, which induces a reset in column temperature

3000: Irregular values in the room temperature housing the instrument

9999: Missing data

en: Predicted cloud droplet number concentrations Weissfluhjoch

en:
In-situ measurements of aerosol size distributions along with updraft velocities collected from a wind Doppler Lidar are coupled with a state-of-the art droplet activation parameterization (Morales and Nenes, 2014) to determine the cloud droplet number and maximum in-cloud supersaturation that can potentially form over the Weissfluhjoch station between February 24 and March 8 2019. For this, a hygroscopicity parameter of 0.25 is assumed, according to the CCN-derived values. The data file contains:

* Date and time (DD/MM/YYYY hh:mm:ss) in UTC
* The wind-lidar derived standard deviation of updraft velocities (sigma-w) in m/s, used to apply the parameterization employing a single characteristic velocity (Morales and Nenes, 2010)
* The maximum predicted cloud parcel supersaturation (Smax%)
* The total in-situ aerosol number concentration (Naer) in cmˉ³, measured by the SMPS
* The predicted cloud droplet numbers (Nd) in cmˉ³
* The activation fraction (%)

en: Hygroscopicity parameter Weissfluhjoch

en:
The aerosol hygroscopicity parameter is derived combining the CCN and SMPS size distribution data collected between February 24 and March 8 2019 at Weissfluhjoch. The data file contains:

* Date and time (DD/MM/YYYY hh:mm:ss) in UTC
* The instrument supersaturation (SS%)
* The calculated hygroscopicity parameter (kappa)
* The calculated critical dry diameter of the particles (Dcr) in nm.

Missing values are indicated by "-999".

en:
Cloud Condensation Nuclei (CCN) data: A Droplet Measurement Technologies (DMT) single-column continuous-flow streamwise thermal gradient chamber (CFSTGC; Roberts and Nenes, 2005) was deployed at the measurement site Weissfluhjoch (2700 m a.s.l., LON: 9.806475, LAT: 46.832964) to record the in-situ CCN number concentrations between February 24 and March 8 2019 for different supersaturations (SS). To account for the difference between the ambient (~735 mbar) and the calibration pressure (~800 mbar), the SS reported by the instrument is adjusted by a factor of 0.92. The CFSTGC was cycled between 6 discrete SS values ranging from 0.09% to 0.74%, producing a full CCN spectrum every hour. The raw CCN measurements are filtered to discount periods of transient operation and whenever the room temperature housing the instrument changed sufficiently to induce a reset in column temperature. Additional information can be found in Section 2.1.2 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).

Hygroscopicity data: The CCN number concentration measurements were directly related to the size distribution and total aerosol concentration data measured by the Scanning Mobility Particle Size Spectrometer (SMPS) instrument at the same station (https://www.envidat.ch/dataset/aerosol-data-weissfluhjoch) to infer the particles hygroscopicity parameter (kappa). For each SMPS scan, the particles critical dry diameter (Dcr) is estimated by integrating backward the SMPS size distribution, until the aerosol number matches the CCN concentration observed for the same time period as the SMPS scan. Assuming the particle chemical composition is internally mixed, the kappa is determined from Dcr and SS, applying Köhler theory. Additional information can be found in Section 2.2 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).

Predicted cloud droplet numbers: Droplet calculations are carried out with the physically based aerosol activation parameterization of Morales and Nenes (2014), employing the “characteristic velocity” approach of Morales and Nenes (2010). Aerosol size distribution observations required to predict the cloud droplet numbers and maximum in-cloud supersaturation are obtained from the SMPS instrument deployed at Weissfluhjoch. The required vertical velocity measurements are derived from the wind Doppler Lidar (https://www.envidat.ch/dataset/lidar-wind-profiler-data) deployed at Davos Wolfgang and are extracted for the altitude of interest, being 1100 m above ground level for Weissfluhjoch. Additional information can be found in Section 2.3 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).

ccn

display_name: ccn

cloud-droplets

display_name: cloud-droplets

cloud-modeling

display_name: cloud-modeling

dmt-ccn-chamber

display_name: dmt-ccn-chamber

hygroscopicity

display_name: hygroscopicity

sigma-w

display_name: sigma-w

supersaturation

display_name: supersaturation

weissfluhjoch

display_name: weissfluhjoch

en: CCN, hygroscopicity, predicted cloud droplet numbers Weissfluhjoch


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

media_type: TIFF

en: Mean diurnal range modified from CHELSA

format: TIFF

media_type: tif

en: Mean annual temperature modified from CHELSA

en: Soil organic carbon content modified from SoilGrids

en: Precipitation of driest quarter modified from CHELSA

en: Precipitation seasonality from CHELSA

en: Frost change frequency modified from CHELSA

en: Soil clay content modified from SoilGrids

en: Soil pH modified from SoilGrids

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

aridity

display_name: aridity

frost-change-velocity

display_name: frost-change-velocity

ground-temperature

display_name: ground-temperature

sdms

display_name: sdms

soil

display_name: soil

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

en: Environmental layers for SDM simulations (GDPlants)


en: terrestrial-laser-scans tls polar sea-ice-elevation sea-ice

en: Link to post-processed TLS’s of Antarctic sea ice

en: Data processing documentation

media_type: DOC

en: Post Processed TLS's

en: Compressed dat files

en:
This dataset comprises of a post-processed set of terrestrial laser scans (TLS’s) of Antarctic sea ice obtained during the Sea Ice Physics and Ecosystem Experiment-2 (SIPEX-2, http://seaice.acecrc.org.au/sipex2012/) in September-November 2012. The post-processing steps include the registration of the individual scans into a single 3-dimensional point cloud, the removal of unwanted noise caused by particles in the air (i.e., snow crystals), and the final generation of surface grids based on the cleaned individual point returns. The final product includes the ‘xyz’ coordinates of the individual point measurements, and gridded surfaces covering study areas of 100m x 100 m, and at resolutions of 0.01 m, 0.1 m, 0.25 m, 0.5 m and 1 m for each of the survey dates. Additionally, subgrid statistics that include the mean elevation, standard deviation, minimum and maximum elevations, range, and number of point returns in each gridcell are generated. The final product is provided in space-delimited text files, with the surface grids provided in Digital Terrain Model (DTM) format ready for visualization in any GIS software.

How to cite:
Please also cite the original publication when using this data set.: Trujillo, E., K. Leonard, T. Maksym, and M. Lehning (2016), Changes in snow distribution and surface topography following a snowstorm on Antarctic sea ice, J. Geophys. Res. Earth Surf., 121, doi:[10.1002/2016JF003893](https://dx.doi.org/10.1002/2016JF003893).

polar

display_name: polar

sea-ice

display_name: sea-ice

sea-ice-elevation

display_name: sea-ice-elevation

terrestrial-laser-scans

display_name: terrestrial-laser-scans

tls

display_name: tls

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: High resolution sea ice surface topography from the SIPEX-2 expedition, East Antarctica, 2012


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

en: shape file warning regions Alps

en: Forecast avalanche danger level

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

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

avalanche-forecast

display_name: avalanche-forecast

danger-level

display_name: danger-level

european-alps

display_name: european-alps

snow

display_name: snow

warning-regions

display_name: warning-regions

en: Forecast avalanche danger level European Alps 2011 - 2015


en: antarctica snow sea-salt-aerosol arctic ice

en: Fig 1B_2

en: Individual data to mean shown in Figure 1B

en: Fig 1D

en: Data to Figure 1D

en: Fib 1B_3

en: Fig 1F_2

en: Individual data to mean shown in Figure 1F

en: Fig 1B

en: Data in Figure 1B

en: Code

media_type: m

en: Matlab code to read and plot the data

en: Fig 1F

en: Data to Figure 1F

en: Fig 1F_1

en: Fig 1B_1

en: Read Me

media_type: application/pdf

en: Description of the data tables

en: Fig 1A

en: Data Figure 1A

en: Fig 1C

en: Data to Figure 1C

en: Fig 1E

en: Data to Figure 1E

en: This table lists all NEXAFS data files in chronological order with PE carbon to oxygen signal intensity ratios -from which the atomic ratio were calculated in the paper - before ("preCtoO") and after ("postCtoO") each NEXAFS measurement. Also listed is the change in Na ("lossSodi"), O ("lossOxy"), Cl ("lossChlor"), and C ("lossCarb") signal intensity after the NEXAFS measurement compared to prior to the NEXAFS measurement.

en: This table lists all data files in chronological order. "SampleTemp" refers to the reading of the PT-1000 at the side of the sample holder. "cellPressure" is the water vapour pressure during the measurements. From this, in presence of ice, the temperature of the ice sample can be calculated which is given in the column "iceTemp". "dT" is the difference, or temperature offset, between sensor's reading and the ice samples temperature. The offset is due to the sensor being mounted away from the cooled spot of the sample were it experiences heating from warmer parts of the sample holder. The off-set is used to calibrate the temperature sensor and the resulting temperature during the experiments is given in the column "temperature". "relHum" is the relative humidity above (supercooled) liquid and "concBrine" is the concentration of a NaCl solution in mol/l in equilibrium with ice at the experimental temperature. "Area" is the integrated PE signal.

en: Laboratory experiments are presented on the phase change at the surface of sodium chloride – water mixtures at temperatures between 259 K and 240 K. High selectivity to the upper few nanometres of the frozen solution – air interface is achieved by using electron yield near-edge X-ray absorption fine structure (NEXAFS) spectroscopy. We present the NEXAFS spectrum of the hydrohalite.

en: Data set on interfacial supercooling and the precipitation of hydrohalite in frozen NaCl solutions by X-ray absorption spectroscopy


maintainer: Rebecca Mott

en: riegl-vz6000 patchy-snow-covers terrestrial-laser-scanning tls snow-ablation-rates

media_type: application/zip

en: Digital Elevation Modell 2016

en: Snowmask 2015

en: Snowmask 2016

en: Digital Elevation Modell 2015

en: Digital Elevation Modell 2014

en: Snowmask 2014

en: Gletschboden test site

en: We recorded snow ablation maps with a terrestrial laser scanner (TLS, Riegl-VZ6000) at the Gletschboden area. The TLS position is located approximately 30 vertical meters above the Gletschboden area at a northerly exposed slope. In total 44 TLS measurement sets have been conducted in three consecutive years 2014-2016 (2014: 13 measurements; 2015: 17 measurements; 2016: 14 measurements). The TLS system has a single-point measurement frequency of 300 kHz and a beam divergence of 0.007°. This set-up allows a horizontal resolution of approximately 0.01 m in 100 m distance to the TLS position. One scan of the Gletschboden area lasts approximately 15 minutes. The travel time from the laser scanner towards the surface is recorded and afterwards converted into a point cloud of distances. 5 reflectors located at the Gletschboden area and in the closer surroundings were additionally scanned during each measurement to transform the point cloud from the scanner own coordinate system into Swiss coordinates. Additionally, orthophotos have been created by using pictures recorded from the TLS in order to provide snow mask maps. Snow and bare ground can be distinguished by the RGB color information of the orthophoto. Cells with blue band information greater than 175 were categorized as snow and all cells with values smaller or equal 175 were categorized as bare ground.

patchy-snow-covers

display_name: patchy-snow-covers

snow-ablation-rates

display_name: snow-ablation-rates

terrestrial-laser-scanning

display_name: terrestrial-laser-scanning

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

en: DISCHMEX - High-resolution daily snow ablation rates in an Alpine environment