maintainer: Volksschulamt des Kantons Zürich

de: schulen bildung volksschule primarschule schulgemeinden sekundarschule

author: Volksschulamt des Kantons Zürich

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

fr: Informationen für die Volksschule: Kontaktpersonen für die Gemeinden (Karte)

de: Tabelle der Kontaktpersonen des Volksschulamts des Kantons Zürich nach Schulgemeinde und Themen

de: Variabeln: 'Code_P' = Primarstufe, 'Name_P' = Name der Primarschulgemeinde, 'Personal_P' = Name der Kontaktperson für das Thema 'Personal', 'Lohn_P' = Name der Kontaktperson für das Thema 'Lohn', 'Beratung_P' = Name der Kontaktperson für das Thema 'Beratung', 'Sonderpädagogik_P' = Name der Kontaktperson für das Thema 'Sonderpädagogik', 'Code_O' = Oberstufe, 'Name_O' = Name der Oberstufenschulgemeinde, 'Personal_O' = Name der Kontaktperson für das Thema 'Personal', 'Lohn_O' = Name der Kontaktperson für das Thema 'Lohn', 'Beratung_O' = Name der Kontaktperson für das Thema 'Beratung', 'Sonderpädagogik_O' = Name der Kontaktperson für das Thema 'Sonderpädagogik'

de: Für Schulbehörden, Schulleitungen, Schulverwaltungen und Lehrpersonen zusammengestellte Kontaktpersonen des Volksschulamts zu Primar- und Sekundarschulen und den Themen 'Personal', 'Lohn', 'Beratung' und 'Sonderpädagogik'.

bildung

display_name: bildung

primarschule

display_name: primarschule

schulen

display_name: schulen

schulgemeinden

display_name: schulgemeinden

sekundarschule

display_name: sekundarschule

volksschule

display_name: volksschule

volksschulamt-kanton-zuerich

de: Volksschulamt Kanton Zürich

de: Das Volksschulamt ist zuständig für schulorganisatorische Aufgaben und die Verwaltung des Lehrpersonals. Die Abteilungen unterstützen die Schulen in der Personaladministration, in Fragen zu Unterricht, Lehrplan und Lehrmitteln. Sie übernehmen Koordinations-, Aufsichts- und Beratungsaufgaben für den Sonderschulbereich. Sie beraten Behörden, Schulleitungen und Schulverwaltungen.

value: ZH-Kt Volksschulamt - DCAT Harvester


ov-haltekanten

regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

tran

fr: Transports

de: Verkehr

en: Transport

it: Trasporti

title_for_slug: ov-haltekanten

author: Abteilung Verkehr

fr: geocat.ch permalien

de: geocat.ch Permalink

en: geocat.ch permalink

it: geocat.ch link permanente

state: active

type: dataset

de: AGIS Direktdownload

format: SERVICE

de: öV-Haltekanten

format: WMS

de: Dieser Datensatz behinhaltet die Haltekanten des öffentlichen Verkehrs im Kanton Aargau.

de: Abteilung Verkehr

de: AGIS Service Center Kanton Aargau

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

political_level: canton

ag

capacity: public

type: organization

approval_status: approved

value: Aargau-Kt - Geocat Harvester


de: öV-Haltestellen

de: Dieser Datensatz behinhaltet die Haltestellen des öffentlichen Verkehrs im Kanton Aargau.


envi

fr: Environnement

de: Umwelt

en: Environment

it: Ambiente

author: Vermessungsamt

de: Daten der amtlichen Vermessung im Datenmodell Bund (DM.01-AV-CH). Fixpunkte: Die Datenebene enthält die Lagefixpunkte der Kategorie 1 (Punkte der Landesvermessung), der Kategorie 2 (Punkte des Kantons) und der Kategorie 3 (Punkte der amtlichen Vermessung) sowie die Höhenfixpunkte der Kategorie 1 (Punkte des Landesnivellement), der Kategorie 2 (Punkte des kantonalen Nivellement) und der Kategorie 3 (Punkte von kommunalen Nivellementen). Die Punkte stellen den Bezug zum Koordinatensystem her. Die Höhen bzw. Gebrauchshöhen stützen sich auf das offizielle Höhensystem des Landesnivellement von 1902 (LN02) ab. Die Genauigkeits- und Zuverlässigkeitsanforderungen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lage- und Höhengenauigkeit der Fixpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 28). Die äussere Zuverlässigkeit eines jeden Fixpunktes wird durch geeignete statistische Kenngrössen nachgewiesen (TVAV Art. 34). Bodenbedeckung: Die Datenebene enthält die Bodenbedeckungsflächen (Gebäude, Strassen, Gewässer, Wald, etc.), welche flächendeckend als Gebietsaufteilung definiert sind, die Gebäude- bzw. Assekuranznummern und die Objektnamen (z.B. Gebäudebezeichnungen, Gewässernamen, etc.). Die Genauigkeitsanforderungen der Bodenbedeckungsflächen sowie die aufzunehmenden Minimalflächen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit von Einzelpunkten wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 29). Einzelobjekte: Die Datenebene enthält Einzelobjekte (z.B. Mauern, Brunnen, Masten, Brücken, etc.), welche als Punkt-, Linien- oder Flächenelemente dargestellt werden. Die Genauigkeitsanforderungen der Einzelobjekte richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit der Einzelpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 29). Nomenklatur: Die Datenebene enthält die Flur-, Orts- und Geländenamen. Die Flurnamen beziehen sich auf Geländeteile und sind flächendeckend als Gebietsaufteilung abgebildet. Die Ortsnamen beziehen sich auf abgegrenzte Geländeteile und überlagern die Flurnamen. Die Geländenamen stehen für einzelne Geländepunkte, welche nicht Orts- und Flurnamen sind und weisen keine Abgrenzung auf. Liegenschaften: Die Datenebene enthält die Grenzpunkte, die Grundstücke bzw. Parzellen und die selbständigen und dauernden Rechte soweit diese flächenmässig ausgeschieden werden können. Die Genauigkeits- und Zuverlässigkeitsanforderungen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit der Grenzpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 31). Die Äussere Zuverlässigkeit der Grenzpunkte wird durch geeignete Kenngrössen nachgewiesen (TVAV Art. 34). Rohrleitungen: Die Datenebene enthält die Hochdruckleitungen für Gas. Die Genauigkeitsanforderungen der Rohrleitungen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit der Einzelpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 31). Gebäudeadressen: Die Datenebene enthält die Gebäudeadressen bestehend aus Haus- bzw. Polizeinummern sowie Strassen- und Platznamen. Hoheitsgrenzen: Die Datenebene enthält die Hoheitsgrenzpunkte und Hoheitsgrenzen (Gemeinde-, Bezirks-, Kantons- und Landesgrenze). Die Genauigkeits- und Zuverlässigkeitsanforderungen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit der Hoheitsgrenzpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 31). Die Äussere Zuverlässigkeit der Hoheitsgrenzpunkte wird durch geeignete Kenngrössen nachgewiesen (TVAV Art. 34). administrative Einteilungen: Die Datenebene enthält die administrativen Einteilungen einer Gemeinde bzw. eines Operates (Nummerierungsbereiche, Planeinteilungen und Toleranzstufeneinteilung). Datenformat INTERLIS (1 File pro Gemeinde) beinhaltet die folgenden Topics des Datenmodells der amtlichen Vermessung Bund (DM.01-AV-CH), Version 24: FixpunkteKategorie1, FixpunkteKategorie2, FixpunkteKategorie3, Bodenbedeckung, Einzelobjekte, Hoehen, Nomenklatur, Liegenschaften, Rohrleitungen, Nummerierungsbereiche, Gemeindegrenzen, Bezirksgrenzen, Kantonsgrenzen, Landesgrenzen, Planeinteilungen, TSEinteilung, Rutschgebiete, Gebaeudeadressen, PLZOrtschaft und Planrahmen. Datenformat Shape beinhaltet die obig aufgeführten Topics des Datenmodells der amtlichen Vermessung bzw. die folgenden Shapedateien (In Klammer ist der Geometrietyp angegeben): FP_Lagefixpunkte (Punkt), FP_Hoehenfixpunkte (Punkt), HGR_Hoheitsgrenzpunkte (Punkt), HGR_Gemeindegrenzen (Flaeche), HGR_Hoheitsgrenzabschnitte (Linie), GEB_Lokalisationsnamen (Punkt), GEB_Gebaeudeadressen (Punkt), AE_Mehrzweckkataster (Punkt), BB_ProjBoFlaechen (Flaeche), BB_ProjGebaeudenummern (Punkt), BB_ProjObjektnamen (Punkt), BB_BoFlaechen (Flaeche), BB_Gebaeudenummern (Punkt), BB_Objektnamen (Punkt), EO_Flaechenelemente (Flaeche), EO_Linienelemente (Linie), EO_Punktelemente (Punkt), EO_Objektnamen (Punkt), EO_Objektnummern (Punkt), RL_Rohrleitungen (Linie), LS_Grenzpunkte (Punkt), LS_ProjLiegenschaften (Flaeche), LS_ProjSelbstRechte (Flaeche), LS_Liegenschaften (Flaeche), LS_SelbstRechte (Flaeche), NK_Flurnamen (Flaeche) und NK_Nomenklatur (Punkt). Datenformat DXF Geobau beinhaltet die obig aufgeführten Topics des Datenmodells der amtlichen Vermessung in der Layerstruktur gemäss Datenreferenzmodell SN 612020.

de: Vermessungsamt

de: AV: Daten der amtlichen Vermessung, Datenmodell Bund


de: Daten der amtlichen Vermessung im vereinfachten Datenmodell (MOpublic). Fixpunkte: Die Datenebene enthält die Lagefixpunkte der Kategorie 1 (Punkte der Landesvermessung), der Kategorie 2 (Punkte des Kantons) und der Kategorie 3 (Punkte der amtlichen Vermessung) sowie die Höhenfixpunkte der Kategorie 1 (Punkte des Landesnivellement), der Kategorie 2 (Punkte des kantonalen Nivellement) und der Kategorie 3 (Punkte von kommunalen Nivellementen). Die Punkte stellen den Bezug zum Koordinatensystem her. Die Höhen bzw. Gebrauchshöhen stützen sich auf das offizielle Höhensystem des Landesnivellement von 1902 (LN02) ab. Die Genauigkeits- und Zuverlässigkeitsanforderungen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lage- und Höhengenauigkeit der Fixpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 28). Die äussere Zuverlässigkeit eines jeden Fixpunktes wird durch geeignete statistische Kenngrössen nachgewiesen (TVAV Art. 34). Bodenbedeckung: Die Datenebene enthält die Bodenbedeckungsflächen (Gebäude, Strassen, Gewässer, Wald, etc.), welche flächendeckend als Gebietsaufteilung definiert sind, die Gebäude- bzw. Assekuranznummern und die Objektnamen (z.B. Gebäudebezeichnungen, Gewässernamen, etc.). Die Genauigkeitsanforderungen der Bodenbedeckungsflächen sowie die aufzunehmenden Minimalflächen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit von Einzelpunkten wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 29). Einzelobjekte: Die Datenebene enthält Einzelobjekte (z.B. Mauern, Brunnen, Masten, Brücken, etc.), welche als Punkt-, Linien- oder Flächenelemente dargestellt werden. Die Genauigkeitsanforderungen der Einzelobjekte richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit der Einzelpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 29). Nomenklatur: Die Datenebene enthält die Flur-, Orts- und Geländenamen. Die Flurnamen beziehen sich auf Geländeteile und sind flächendeckend als Gebietsaufteilung abgebildet. Die Ortsnamen beziehen sich auf abgegrenzte Geländeteile und überlagern die Flurnamen. Die Geländenamen stehen für einzelne Geländepunkte, welche nicht Orts- und Flurnamen sind und weisen keine Abgrenzung auf. Liegenschaften: Die Datenebene enthält die Grenzpunkte, die Grundstücke bzw. Parzellen und die selbständigen und dauernden Rechte soweit diese flächenmässig ausgeschieden werden können. Die Genauigkeits- und Zuverlässigkeitsanforderungen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit der Grenzpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 31). Die Äussere Zuverlässigkeit der Grenzpunkte wird durch geeignete Kenngrössen nachgewiesen (TVAV Art. 34). Rohrleitungen: Die Datenebene enthält die Hochdruckleitungen für Gas. Die Genauigkeitsanforderungen der Rohrleitungen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit der Einzelpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 31). Gebäudeadressen: Die Datenebene enthält die Gebäudeadressen bestehend aus Haus- bzw. Polizeinummern sowie Strassen- und Platznamen. Hoheitsgrenzen: Die Datenebene enthält die Hoheitsgrenzpunkte und Hoheitsgrenzen (Gemeinde-, Bezirks-, Kantons- und Landesgrenze). Die Genauigkeits- und Zuverlässigkeitsanforderungen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit der Hoheitsgrenzpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 31). Die Äussere Zuverlässigkeit der Hoheitsgrenzpunkte wird durch geeignete Kenngrössen nachgewiesen (TVAV Art. 34). Datenformat INTERLIS (1 File pro Gemeinde) beinhaltet die folgenden Topics des Datenmodells MOpublic: Control_points (Fixpunkte), Land_cover (Bodenbedeckung), Single_objects (Einzelobjekte), Heights (Hoehen), Local_names (Nomenklatur), Ownership (Liegenschaften), Pipelines (Rohrleitungen), Territorial_boundaries (Gemeindegrenzen und übrige Hoheitsgrenzen) und Building_addresses (Gebaeudeadressen). Datenformat Shape beinhaltet die obig aufgeführten Topics des Datenmodells MOpublic bzw. die folgenden Shapedateien (In Klammer ist der Geometrietyp angegeben): FP_LFP (Punkt), BB_BoFlaeche (Flaeche), BB_BBText (Punkt), BB_ProjBoFlaeche (Flaeche), BB_ProjBBText (Punkt), EO_Flaechenelement (Flaeche), EO_FlaechenelementText (Punkt), EO_Linienelement (Linie), EO_LinienelementText (Punkt), EO_Punktelement (Punkt), EO_PunktelementText (Punkt), NK_Namen (Flaeche), NK_NamenPos (Punkt), LS_Grenzpunkt (Punkt), LS_Liegenschaft (Flaeche), LS_LiegenschaftPos (Punkt), LS_SelbstRecht-Bergwerk (Flaeche), LS_SelbstRecht-BergwerkPos (Punkt), LS_ProjLiegenschaft (Flaeche), LS_ProjLiegenschaftPos (Punkt), LS_ProjSelbstRecht-Bergwerk (Flaeche), LS_ProjSelbstRecht-BergwerkPos (Punkt), RL_Linienelement (Linie), RL_LinienelementText (Punkt), GEM_Hoheitsgrenzpunkt (Punkt), GEM_Gemeindegrenze (Flaeche), GEB_Gebaeudeeingang (Punkt), GEB_LokalisationsNamePos (Punkt) und GEB_HausnummerPos (Punkt). Datenformat DXF beinhaltet die obig aufgeführten Topics des Datenmodells MOpublic in der Layerstruktur gemäss der Weisung Datenmodell «MOpublic», Kapitel 9.6 DXF.

de: AV: Daten der amtlichen Vermessung, vereinfachtes Datenmodell MOpublic


regi

de: Regionen und Städte

de: Daten der amtlichen Vermessung im Datenmodell Aargau (DM.01-AV-AG). Fixpunkte: Die Datenebene enthält die Lagefixpunkte der Kategorie 1 (Punkte der Landesvermessung), der Kategorie 2 (Punkte des Kantons) und der Kategorie 3 (Punkte der amtlichen Vermessung) sowie die Höhenfixpunkte der Kategorie 1 (Punkte des Landesnivellement), der Kategorie 2 (Punkte des kantonalen Nivellement) und der Kategorie 3 (Punkte von kommunalen Nivellementen). Die Punkte stellen den Bezug zum Koordinatensystem her. Die Höhen bzw. Gebrauchshöhen stützen sich auf das offizielle Höhensystem des Landesnivellement von 1902 (LN02) ab. Die Genauigkeits- und Zuverlässigkeitsanforderungen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lage- und Höhengenauigkeit der Fixpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 28). Die äussere Zuverlässigkeit eines jeden Fixpunktes wird durch geeignete statistische Kenngrössen nachgewiesen (TVAV Art. 34). Bodenbedeckung: Die Datenebene enthält die Bodenbedeckungsflächen (Gebäude, Strassen, Gewässer, Wald, etc.), welche flächendeckend als Gebietsaufteilung definiert sind, die Gebäude- bzw. Assekuranznummern und die Objektnamen (z.B. Gebäudebezeichnungen, Gewässernamen, etc.). Die Genauigkeitsanforderungen der Bodenbedeckungsflächen sowie die aufzunehmenden Minimalflächen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit von Einzelpunkten wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 29). Einzelobjekte: Die Datenebene enthält Einzelobjekte (z.B. Mauern, Brunnen, Masten, Brücken, etc.), welche als Punkt-, Linien- oder Flächenelemente dargestellt werden. Die Genauigkeitsanforderungen der Einzelobjekte richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit der Einzelpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 29). Nomenklatur: Die Datenebene enthält die Flur-, Orts- und Geländenamen. Die Flurnamen beziehen sich auf Geländeteile und sind flächendeckend als Gebietsaufteilung abgebildet. Die Ortsnamen beziehen sich auf abgegrenzte Geländeteile und überlagern die Flurnamen. Die Geländenamen stehen für einzelne Geländepunkte, welche nicht Orts- und Flurnamen sind und weisen keine Abgrenzung auf. Liegenschaften: Die Datenebene enthält die Grenzpunkte, die Grundstücke bzw. Parzellen und die selbständigen und dauernden Rechte soweit diese flächenmässig ausgeschieden werden können. Die Genauigkeits- und Zuverlässigkeitsanforderungen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit der Grenzpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 31). Die Äussere Zuverlässigkeit der Grenzpunkte wird durch geeignete Kenngrössen nachgewiesen (TVAV Art. 34). Rohrleitungen: Die Datenebene enthält die Hochdruckleitungen für Gas. Die Genauigkeitsanforderungen der Rohrleitungen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit der Einzelpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 31). Gebäudeadressen: Die Datenebene enthält die Gebäudeadressen bestehend aus Haus- bzw. Polizeinummern sowie Strassen- und Platznamen. Hoheitsgrenzen: Die Datenebene enthält die Hoheitsgrenzpunkte und Hoheitsgrenzen (Gemeinde-, Bezirks-, Kantons- und Landesgrenze). Die Genauigkeits- und Zuverlässigkeitsanforderungen richten sich nach den Toleranzstufen (TVAV Art. 3). Die Lagegenauigkeit der Hoheitsgrenzpunkte wird als Standardabweichung (mittlere Fehler) definiert (TVAV Art. 31). Die Äussere Zuverlässigkeit der Hoheitsgrenzpunkte wird durch geeignete Kenngrössen nachgewiesen (TVAV Art. 34). administrative Einteilungen: Die Datenebene enthält die administrativen Einteilungen einer Gemeinde bzw. eines Operates (Nummerierungsbereiche, Planeinteilungen und Toleranzstufeneinteilung). Datenformat INTERLIS (1 File pro Gemeinde) beinhaltet die folgenden Topics des Datenmodells der amtlichen Vermessung Aargau (DM.01-AV-AG), Version 25: FixpunkteKategorie1, FixpunkteKategorie2, FixpunkteKategorie3, Bodenbedeckung, Einzelobjekte, Hoehen, Nomenklatur, Liegenschaften, Rohrleitungen, Nummerierungsbereiche, Gemeindegrenzen, Bezirksgrenzen, Kantonsgrenzen, Landesgrenzen, Planeinteilungen, TSEinteilung, Rutschgebiete, Gebaeudeadressen, PLZOrtschaft, Planrahmen, Waldfeststellung (kantonale Option) und Mehrzweckkataster (kommunale Option). Datenformat Shape beinhaltet die obig aufgeführten Topics des Datenmodells der amtlichen Vermessung bzw. die folgenden Shapedateien (In Klammer ist der Geometrietyp angegeben): FP_Lagefixpunkte (Punkt), FP_Hoehenfixpunkte (Punkt), HGR_Hoheitsgrenzpunkte (Punkt), HGR_Gemeindegrenzen (Flaeche), HGR_Hoheitsgrenzabschnitte (Linie), GEB_Lokalisationsnamen (Punkt), GEB_Gebaeudeadressen (Punkt), AE_Mehrzweckkataster (Punkt), BB_ProjBoFlaechen (Flaeche), BB_ProjGebaeudenummern (Punkt), BB_ProjObjektnamen (Punkt), BB_BoFlaechen (Flaeche), BB_Gebaeudenummern (Punkt), BB_Objektnamen (Punkt), EO_Flaechenelemente (Flaeche), EO_Linienelemente (Linie), EO_Punktelemente (Punkt), EO_Objektnamen (Punkt), EO_Objektnummern (Punkt), RL_Rohrleitungen (Linie), LS_Grenzpunkte (Punkt), LS_ProjLiegenschaften (Flaeche), LS_ProjSelbstRechte (Flaeche), LS_Liegenschaften (Flaeche), LS_SelbstRechte (Flaeche), NK_Flurnamen (Flaeche) und NK_Nomenklatur (Punkt). Datenformat DXF Geobau beinhaltet die obig aufgeführten Topics des Datenmodells der amtlichen Vermessung in der Layerstruktur gemäss Datenreferenzmodell SN 612020.

de: AV: Daten der amtlichen Vermessung, Datenmodell Aargau


asiatische-hornisse-ag

agri

fr: Agriculture, pêche, sylviculture et alimentation

de: Landwirtschaft, Fischerei, Forstwirtschaft und Nahrungsmittel

en: Agriculture, fisheries, forestry and food

it: Agricoltura, pesca, silvicoltura e prodotti alimentari

title_for_slug: asiatische-hornisse-ag

author: Landwirtschaft Aargau

de: geocat.ch Permalink

de: AGIS Direktdownload

de: Der Datensatz zeigt eine Übersicht der Funde der invasiven Asiatischen Hornisse im Kanton Aargau. Es sind sowohl Einzelfunde als auch die Standorte der gefundenen Nester aufgeführt.

de: Landwirtschaft Aargau

de: Asiatische Hornisse AG


fr: inventaire-environnement source eau espace-naturel-sensible

de: inventar quelle wasser empfindliche-naturlandschaft

en: inventory spring-hydrology-land water sensitive-natural-area

it: inventario sorgente-idrologia-territorio acqua area-naturale-sensibile

fr: Accès aux données (fr) : téléchargement et services web (Géoportail cantonal)

fr: Datenzugriff (de): Download und Webservices (Kantonales Geoportal)

fr: Inventaire cantonal des sources naturelles

fr: Map (Preview) Visualisation des données

de: Map (Preview) Visualisierung der Daten

en: Map (Preview) Visualization of the data

it: Map (Preview) Visualizzazione dei dati

fr: Visualisation des données

de: Visualisierung der Daten

en: Visualization of the data

it: Visualizzazione dei dati

fr: Les sources naturelles sont des milieux de vie dignes de protection selon la Loi fédérale sur la protection de la nature et du paysage (LPN). Ils abritent une faune et flore très spécialisées et souvent menacées. Les données contiennent l'emplacement de sources naturelles dans le canton, leur importance pour la protection de la nature et leur état, ainsi que des zones tampon autour des sources. L'inventaire n'est pas exhaustif.

spatial: Canton de Fribourg (FR)

fr: Service des forêts et de la nature

de: Service des forêts et de la nature

en: Service des forêts et de la nature

it: Service des forêts et de la nature

fr: Geoinformation Canton Fribourg

de: Geoinformation Kanton Freiburg

en: Geoinformation Canton Fribourg

it: Geoinformazione Canton Friburgo

fr: Les géodonnées publiques du Canton Fribourg sont gérées par le Centre de compétence SIT du Service de la géoinformation SGéo et mises à disposition sous forme de services web et de téléchargement via le géoportail cantonal.

en: The public geodata of the Canton of Fribourg are managed by the GIS Competence Center of the Geoinformation Office GeoA and made available as web services and for download via the cantonal geoportal.

de: Die öffentlichen Geodaten des Kantons Freiburg werden durch das GIS-Kompetenzzentrum des Amtes für Geoinformation GeoA verwaltet und über das kantonale Geoportal als Webservices und zum Download bereitgestellt.

it: I geodati pubblici del Cantone di Friburgo sono gestiti dal Centro di Competenza GIS dell'Ufficio di Geoinformazione GeoA e resi disponibili come servizi web e per il download attraverso il geoportale cantonale.

value: Fribourg-Kt - Geocat Harvester


envi

de: Umwelt

de: abfall altlasten betriebe boden eigentumsbeschrankungoreb risiken umweltbeobachtung umweltschutz wirtschaftliche-aktivitaten

author: Kanton Luzern, Abteilung Geoinformation

protocol: https

de: Datenshop

de: Produktansicht Geodatenshop

de: Altlasten sind belastete Standorte, die wegen der Auswirkungen auf die Umwelt saniert werden müssen.

spatial: Kanton Luzern

de: Kanton Luzern, Abteilung Geoinformation

fr: Géoinformation du canton de Lucerne

de: Geoinformation Kanton Luzern

en: Geoinformation Canton of Lucerne

it: Geoinformazione del Cantone di Lucerna

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

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

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

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

value: LU-Kt - Geocat Harvester

de: Kataster der belasteten Standorte (KbS)


maintainer: Frank Techel

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

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

format: XLS

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

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

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

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

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

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

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

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

avalanche-bulletin

display_name: avalanche-bulletin

avalanche-danger-level

display_name: avalanche-danger-level

avalanche-forecast

display_name: avalanche-forecast

avalanche-forecasting

display_name: avalanche-forecasting

verification

display_name: verification

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

key: guid

key: uri

value: Envidat Harvester

en: Re-analyzed regional avalanche danger levels in Switzerland


maintainer: EnviDat Support

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

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

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

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

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

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

atmosphere

display_name: atmosphere

chemistry

display_name: chemistry

ice

display_name: ice

laboratory-study

display_name: laboratory-study

snow-atmosphere-interactions

display_name: snow-atmosphere-interactions

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


maintainer: Irene Cordero

en: heatwave pesticide drought soil soil-biodiversity soil-nutrients land-use

en: All soil data

en: Soil physicochemical characteristics

en:
Soil chemical and biological properties of soils affected by 10 different perturbations related with global change, applied individually or in combination. Greenhouse experiment. Perturbations applied to intact soil cores (15 cm diameter, 15 cm deep) collected in an extensively managed grassland on the WSL grounds in June 2023.
This dataset contains all data on which the publication below was based:
"Fioratti Junod M, Gombeer S, Holmes J, Zimmerman S, Rillig M, Risch AC and Cordero I. Soil functionality declines under multiple superimposed global change perturbations. XXXX "
Please, cite this publication together with the citation of the datafile.
Database includes:
Pot_ID: pot or soil core identifier.
Batch: numeric, 1 or 2.
Enzyme_plate: numeric, from 1 to 6.
Number_of_perturbations: numeric, from 0 to 10.
N_addition: binary. Yes = 1, No = 0.
P_addition: binary. Yes = 1, No = 0.
Defoliation: binary. Yes = 1, No = 0.
Trampling: binary. Yes = 1, No = 0.
Insecticide: binary. Yes = 1, No = 0.
Fungicide: binary. Yes = 1, No = 0.
Herbicide: binary. Yes = 1, No = 0.
Antibiotic: binary. Yes = 1, No = 0.
Drought: binary. Yes = 1, No = 0.
Heat_wave: binary. Yes = 1, No = 0.
Treatment: character. Description of the treatment, 15 levels.
SWC: soil water content. Numeric, unitless.
Green_biomass: green plant biomass. Numeric, g.
Brown_biomass: brown or dead plant biomass. Numeric, g.
Biomass_cut: plant biomass cut during defoliation treatments. Numeric, g.
CO2_flux_light: net CO2 flux under ambient light. Numeric, mg CO2 m-2 h-1.
CO2_flux_dark: ecosystem respiration. Numeric, mg CO2 m-2 h-1.
S: organic matter stabilisation factor (tea bag index). Numeric, unitless.
k: organic matter decomposition rate (tea bag index). Numeric, unitless.
DOC: dissolved organic carbon, mg Kg-1 dry soil.
IC: inorganic carbon, mg Kg-1 dry soil.
Ammonium_KCl: plant available ammonium, mg Kg-1 dry soil.
Nitrate_KCl: plant available nitrate, mg Kg-1 dry soil.
Phosphate: phosphate, mg Kg-1 dry soil.
MBC: microbial biomass carbon, mg Kg-1 dry soil.
MBN: microbial biomass nitrogen, mg Kg-1 dry soil.
PHO: phosphatase activity, nmol h-1 g-1 dry soil.
BG: β-glucosidase, nmol h-1 g-1 dry soil.
XYL: xylosidase, nmol h-1 g-1 dry soil.
CBH: cellobiohydrolase, nmol h-1 g-1 dry soil.
NAG: N-acetylglucosaminidase, nmol h-1 g-1 dry soil.
LAP: leucine aminopeptidase, nmol h-1 g-1 dry soil.
POX: phenoloxidase, nmol h-1 g-1 dry soil.
PER: peroxidase, nmol h-1 g-1 dry soil.
pH: soil pH, unitless
Water_stable_aggregates: water stable aggregates, %
Surface_infiltration_time: water surface infiltration time, s.

drought

display_name: drought

heatwave

display_name: heatwave

land-use

display_name: land-use

pesticide

display_name: pesticide

soil

display_name: soil

soil-biodiversity

display_name: soil-biodiversity

soil-nutrients

display_name: soil-nutrients

en: Escalating effects of multiple perturbations on soil functionality


maintainer: Virginie Marques

en: marine coral-reef fish-composition teleo indian-ocean edna laperouse environmental-dna

en: Data access

en: Please use cyberduck to download for the moment

en:
Fish environmental DNA data set collected in the shallow seamount of La Pérouse located between Madagascar and Réunion Island

In 2019, we sampled the shallow seamount of La Pérouse, located between Madagascar and Réunion Island in the WIO. La Pérouse is an extinct volcano with a summit depth at 60 m below the sea surface. The plateau of the summit has a maximum length of 12 km and a maximum width of 4 km. One side of the seamount might have collapsed in the past, leading to a less common crescent-shaped summit. The abyssal plains that surround this isolated pinnacle are at a depth of 5000 m. Despite its proximity to Réunion Island (160 km northwest), La Pérouse and its surroundings are still poorly studied.
To sample eDNA in this location we used a submersible pump (Subspace, Geneva, Switzerland; nominal flow of ~ 1.0 L/min) to collect samples at various depths during close circuit rebreather dives. The pump has an internal battery and can be activated underwater allowing in situ filtration directly at the targeted habitat and depth. The filtration lasted for 30 min to collect a total water volume of ~ 30 L. During close circuit rebreather dives, water samples from various depths, ranging from 60 to 140 m below the sea surface, were collected using only the Submersible method (eight stations: no replicates). Out of the eight water samples, six were collected during the day, while two were collected at night (IS2_1 at 62 m and IS7_1 at 100–125 m). We applied this filtration protocols to collect and filter water throughout a VigiDNA® 0.2 μM cross-flow filtration capsule (SPYGEN, le Bourget du Lac, France) using disposable sterile tubing for each filtration capsule. After the filtration process, the remaining water in the capsules was emptied. The capsules were then filled with 80 ml of lysis conservation buffer (CL1 buffer SPYGEN, le Bourget du Lac, France) and stored at room temperature in the dark. At La Pérouse, a total of eight water samples were collected during the MONT LA PÉROUSE expedition (https://gombessa-expeditions.com/mont-la-perouse/) on board the ship La Curieuse to the seamount La Pérouse from October 27th to November 5th 2019. We followed a strict contamination control protocol in both field and laboratory stages. Each water sample processing included the use of disposable gloves and single-use filtration equipment to avoid any risk of contamination. Libraries were prepared with ligation using the MetaFast protocol (Fasteris).
Data content:
	* rawdata/: contains the raw reads for each individual sample. One archive contains the paired-end reads specified by the _R1 or _R2 suffix as well as individually tagged PCR replicates (if available) together with an archive containing all extraction and PCR blank samples of the library. Reads have been demultiplexed using cutadapt but not trimmed, individual demultiplexing tags and primers remain present in the sequences.
	* taxadata/: contains the table with all detected taxonomy for each sample after bioinformatic processing (see Polanco et al. 2020 for details; https://doi.org/10.1002/edn3.140) and associated field metadata.
	* metadata/: contains two metadata files, one related to the data collected in the field for each filter, and the second related to the sequencing process in the lab (including the tag sequence, library name, and marker information for each sample)

coral-reef

display_name: coral-reef

edna

display_name: edna

environmental-dna

display_name: environmental-dna

fish-composition

display_name: fish-composition

indian-ocean

display_name: indian-ocean

laperouse

display_name: laperouse

marine

display_name: marine

teleo

display_name: teleo

en: Environmental DNA Marine France LaPerouse 2019


maintainer: Ionuț Iosifescu Enescu

en: Bulk Data Access

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

format: GeoTIFF

en: map preview 0.5MB

mimetype: image/jpeg

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

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

gis

display_name: gis

large-files

display_name: large-files

natural-earth

display_name: natural-earth

qgis

display_name: qgis

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

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


en: avalanche lidar snow wind-drifted-snow low-cost monitoring

media_type: application/zip

format: ZIP

en:
We developed a monitoring system using low-cost lidar (Livox Avia) and optical sensors, to measure snow depth variations in an avalanche release area at a high spatiotemporal scale (centimeter to low decimeter spatial resolution and hourly temporal resolution). A detailed description of the setup and a first data analysis is published in:
[Ruttner-Jansen, P., Voordendag, A., Hartmann, T., Glaus, J., Wieser, A., & Bühler, Y. (2024). Monitoring snow depth variations in an avalanche release area using low cost lidar and optical sensors. EGUsphere, 2024, 1-20.](https://nhess.copernicus.org/articles/25/1315/2025/)
This dataset contains hourly 3D lidar data of the snow surface at the test site Braemabuel in Davos, Switzerland from the winter season 2023-2024 (23.11.2023 - 14.04.2024). Braema1 is installed at 2191 m a.s.l. in the slope, looking up into the avalache release area, and Braema2 is installed at 2255 m a.s.l at the ridge, looking down. Both scans together cover an area of about 15000 m².
The data is in format ".las" and contains the points in XYZ [m], in the scanner own coordinate system
- scalar fields: intensity, return number, timestamp, tag, label
-- tag: binary code from Livox (see [Livox Avia Manual](https://terra-1-g.djicdn.com/65c028cd298f4669a7f0e40e50ba1131/Download/Avia/Livox%20Avia%20User%20Manual%20202204.pdf))
-- label: marking outliers (filtered by distance and SOR to get snow particles in the air)

avalanche

display_name: avalanche

lidar

display_name: lidar

low-cost

display_name: low-cost

monitoring

display_name: monitoring

snow

display_name: snow

wind-drifted-snow

display_name: wind-drifted-snow

en: Snow depth mapping by lidar station Braemabuel


soil-property-maps-for-the-swiss-forest

title_for_slug: soil-property-maps-for-the-swiss-forest

en: soil-properties soil-depth soil-ph forest soil-texture soc dsm switzerland soil

en:
We used 2071 forest soil profiles to model a wide range of soil properties for the forested area of Switzerland. The spatial prediction is based on the principle of «digital soil mapping». This involves linking soil profiles with soil forming factors using statistical or machine learning methods.

A quantile regression forest (QRF) approach was applied to predict the following soil properties at six depth ranges: clay, gravel, sand, fine earth density, SOC. The depth ranges correspond to the standard depths of the [GlobalSoilMap.Net](https://www.isric.org/) specification: 0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm. In addition, the total soil depth down to a non-root-permeable layer or solid rock soil thick was predicted. To quantify the uncertainty for each predicted pixel, the upper and lower limit of the 90% prediction interval derived from QRF was calculated. More details on the methods and results are described in Baltensweiler et al. 2021 and Baltensweiler et al 2022.

The soil property maps, and the uncertainty maps are provided as a GeoTIFF files at 25 m resolution. The excel file (xlsx) provides a short description of the raster layers.

**The soil and the uncertainty maps can be viewed in a simple web-GIS application available at:**
[www.wsl.ch/soilmaps](https://www.wsl.ch/soilmaps).

dsm

display_name: dsm

forest

display_name: forest

soc

display_name: soc

soil

display_name: soil

soil-depth

display_name: soil-depth

soil-ph

display_name: soil-ph

soil-properties

display_name: soil-properties

soil-texture

display_name: soil-texture

switzerland

display_name: switzerland

en: Soil property maps for the Swiss forest


maintainer: Elisabeth Hafner

en: validation satellite snow avalanche-hazard-mapping avalanche

en: Satellite Avalanche Mapping Validation data and data description

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

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

Coordinate system: CH1903+_LV95

The generation of this dataset is described in detail in:

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

avalanche-hazard-mapping

display_name: avalanche-hazard-mapping

satellite

display_name: satellite

validation

display_name: validation

en: Satellite avalanche mapping validation data


en: saltation-parameterizations aerodynamic-entrainment large-eddy-simulation drifting-snow snow-saltation splash

en: Code and input files to reproduce the simulations in Melo et al. (2024), as well as the post-processing scripts used in the analysis.

en:
This dataset includes the necessary files to reproduce the results presented in Melo et al. (2024):
- code version used in the simulations (also available at the institutional [GitLab repository](https://gitlabext.wsl.ch/atmospheric-models/les-lsm) - tag 'r2024')
- input files to run the simulations
- post-processing scripts (MATLAB and FORTRAN)
- selected raw output files
- post-processed results (MATLAB data files).
 	**Note:** Direct model outputs are not provided because of their size, but they can be shared upon request. They can be reproduced by running the code. The post-processed results allow you to reproduce all figures in Melo et al. (2024).

aerodynamic-entrainment

display_name: aerodynamic-entrainment

drifting-snow

display_name: drifting-snow

large-eddy-simulation

display_name: large-eddy-simulation

saltation-parameterizations

display_name: saltation-parameterizations

snow-saltation

display_name: snow-saltation

splash

display_name: splash

en: Understanding snow saltation parameterizations - Reproducibility


maintainer: Lyu

en: species-occurrence species-richness species-range-map pinales species-distribution-model fagales

en: Species range maps of species in Fagales and Pinales (with enough occurrences)

en: species_map.zip includes species 1690 range maps generated based on the integrated method described in Lyu et al., (2022). File names with (polygon) indicate that this map is only generated by polygon mapping, as there are not enough occurrences for SDMs.

en: Occurrence maps for species of Fagales and Pinales with few records

en: Point_buffer.zip includes 267 occurrence maps of species that do not have enough occurrence data for polygon mapping.

en:
This database contains 1957 distribution maps of species from Fagales and Pinales constructed based on a method integrating polygon mapping and SDMs (Lyu et al., 2022).
To construct the maps, we first collected occurrence data from 48 different sources. According to the number of occurrences after data cleaning, three kinds of maps are constructed: (1) For species with more than 20 occurrences, we performed SDM and polygon mapping described in Lyu et al. (2022) and select the integration of the two layers as the distribution range; (2) For species with more than 4 but less than 20 occurrences, we only use polygon mapping to draw the distribution range; (3) For species with less than 4 occurrences, a 20-km buffer was generated around the occurrences as the distribution range. The maps were manually checked and evaluated (see Note S3 and Table S9 in Lyu et al., 2022 for details).

fagales

display_name: fagales

pinales

display_name: pinales

species-distribution-model

display_name: species-distribution-model

species-occurrence

display_name: species-occurrence

species-range-map

display_name: species-range-map

species-richness

display_name: species-richness

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Species distribution maps of Fagales and Pinales (GDPlants)


aerosol-data-weissfluhjoch

title_for_slug: aerosol-data-weissfluhjoch

en: aerosol raclets particles ice-nucleation inp linda

en: APS Dataset Weissfluhjoch

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

mimetype: application/x-netcdf

en: OPC-GT526S Dataset Weissfluhjoch

media_type: text/csv

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

format: CSV

mimetype: text/csv

en: OPC-N3 Dataset Weissfluhjoch

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

en: LINDA Dataset Weissfluhjoch

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

en: SMPS Dataset Weissfluhjoch

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

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

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

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

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

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

aerosol

display_name: aerosol

ice-nucleation

display_name: ice-nucleation

inp

display_name: inp

linda

display_name: linda

particles

display_name: particles

raclets

display_name: raclets

en: Aerosol Data Weissfluhjoch


en: european-summer-drought forests growth-reaction-to-drought-stress drought-2018 fagus-sylvatica leaf-senescence

en: Drought and Beech: 1000 beeches data set

en: The data set comprises data from the Drought and Beech 1000 beeches project. The actual data file with monitoring data of 963 beech trees in northern Switzerland over four years (Drought_and_Beech_1000beeches_Data_v1.0.csv), a metadata file with column descriptions (Drought_and_Beech_1000beeches_Legend_v1.0.csv), and a file defining the different survey periods (Drought_and_Beech_1000beeches_Survey_periods_v1.0.csv).

en: This study investigated multi-year drought impacts on beech forests through a unique large-scale monitoring of 963 individual beech trees, which showed either premature leaf discoloration during the drought in summer 2018 or no visible damage. We conducted the study in two highly drought-affected regions in northern Switzerland and one less drought-affected region located further south. We quantified the development of crown dieback and tree mortality as well as secondary drought damage, i.e. the presence of bleeding cankers and bark beetle infestations, in these trees for three consecutive years. We also determined the impact of several potential climate- and stand-related (predisposing) factors on mortality and drought legacy processes.

european-summer-drought

display_name: european-summer-drought

fagus-sylvatica

display_name: fagus-sylvatica

forests

display_name: forests

growth-reaction-to-drought-stress

display_name: growth-reaction-to-drought-stress

leaf-senescence

display_name: leaf-senescence

educ

en: Education, culture and sport

en: Data on multi-year drought impacts on European beech in northern Switzerland


maintainer: Marco Moretti

en: remote-sensing sustainability pollination urbanization urban-biodiversity honeybees

en: Dataset 2

en: Dataset 2: Processed data for modelling balance urban greenspace

en: Not used:

en: Not used for the analyses and not link to the paper.

en: Dataset 1

en: Dataset 1: Processed data for urban beekeeping 2012 & 2018

en: Dataset 3

en: Dataset on the the number of beehives and apiaries on the years 2012, 2013, 2014, 2015, 2016, 2017, 2018 in several Swiss cities.

en: Data on: (1) (Dataset 1) spatial distribution of urban beekeeping (number of hives and number of beekeeping locations) in 14 Swiss cities (Geneva, Lausanne, Biel, Neuchatel, Basel, Zurich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) for the period 2012-2018; (2) (Dataset 2) aggregated data to model the sustainability of urban beekeeping.

honeybees

display_name: honeybees

pollination

display_name: pollination

remote-sensing

display_name: remote-sensing

sustainability

display_name: sustainability

urban-biodiversity

display_name: urban-biodiversity

urbanization

display_name: urbanization

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: Challenging the sustainability of urban beekeeping: evidence from Swiss cities


maintainer: Matthias Haeni

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

en: Forest station data (ALB)

en: Data plot of the forest station (ALB)

media_type: application/pdf

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

format: PDF

mimetype: application/pdf

en: Site aerial photo (Alpthal)

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

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for one meteorological station in Alpthal in Switzerland which is located within a natural coniferous forest (ALB) with Norway spruce (_Picea abies_; 180-230 yrs) as dominant tree species. The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Alpthal is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

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


photogrammetric-drone-data-gruenbodeli

title_for_slug: photogrammetric-drone-data-gruenbodeli

en: dsm orthophoto wingtra drone-data snow-depth-mapping uav photogrammetry

en: Data Access

en:
- ortho (3 cm/25 mm resolution)
- DSM (10 cm resolution)
- HS (10 cm resolution)
- RGB images
- Agisoft Metashape Project Report

All products are also available as a cloud optimized geotiff.

en:
We conducted various drone flights at Grüenbödeli near Davos with the Sony RX1R II mounted on a Wingtra drone during 2020/21/22. The data was processed with the Agisoft Metashape Professional Software.
The following products are available for download:
- DSM 10cm resolution
- Orthomosaic 3cm/25mm resolution
- Snow Raster 10cm resolution
- original RGB images

drone-data

display_name: drone-data

dsm

display_name: dsm

orthophoto

display_name: orthophoto

photogrammetry

display_name: photogrammetry

snow-depth-mapping

display_name: snow-depth-mapping

uav

display_name: uav

wingtra

display_name: wingtra

educ

en: Education, culture and sport

en: Photogrammetric Drone Data Grüenbödeli


maintainer: Varun Sharma

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

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

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

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

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

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

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

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

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

The datasets consist of two folders organized in zip format:

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

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

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

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

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

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

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

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

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

alps

display_name: alps

antarctica

display_name: antarctica

blowing-snow

display_name: blowing-snow

mesoscale-modelling

display_name: mesoscale-modelling

snow-atmosphere-interaction

display_name: snow-atmosphere-interaction

envidat

en: EnviDat: the Environmental Data Portal

en: Reproducibility Dataset for CRYOWRF v1.0


maintainer: Anita C. Risch

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

en:
site.code: Site code

Continent: Continent

sampleID: Sample ID

nn.latitude: Lattitude

nn.longitude: Longitude

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

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

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

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

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

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

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

ch.pH: pH

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

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

ch.porespace: Pore space (vol%)

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

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

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

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

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

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

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

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

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

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

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

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

ch.CtoN: C to N ratio

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

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

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

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

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

Paper Citation:

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

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

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

global

display_name: global

grassland

display_name: grassland

mineralization

display_name: mineralization

nutrient-network

display_name: nutrient-network

soil

display_name: soil

soil-biology

display_name: soil-biology

soil-chemistry

display_name: soil-chemistry

worldwide

display_name: worldwide

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

en: Site photo of the field station (CIF)

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

en: Data plot of the field station (CIF)

en: Data file.

en: Data plot of the forest station (CIB)

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

en: Site photo of the forest station (CIB)

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

en: Site aerial photo (Chironico)

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

en: Forest station data (CIB)

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 Chironico in Switzerland where one station is located within a natural coniferous forest stand (CIB) with Norway spruce (_Picea abies_; 160-180 yrs) and European silver fir (_Abies alba_; 140-160 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, CIF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Chironico 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: Chironico, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 2000 onwards


tree-species-map-of-switzerland

maintainer: Tiziana Li Koch

title_for_slug: tree-species-map-of-switzerland

en: map forest tree-species remote-sensing switzerland

media_type: image/png

en: Image of the tree species map

format: PNG

en:
Dominant tree species map of Switzerland
We created a tree species map of Switzerland for the dominant tree species in the forested areas. The spatial resolution of the map is 10 m and the coordinate system is ETRS89-extended / LAEA Europe (EPSG 3035). The map comprises Sentinel-2 index time series from the year 2020, a digital elevation model and species reference data from the Swiss National Forest Inventory. The map is available as raster (.tif) or vector dataset (.gpkg).
**Access will be granted upon request.**
In total, the following 15 species were mapped:
*Abies alba*, *Acer pseudoplatanus*, *Alnus glutinosa*, *Alnus incana*, *Betula pendula*, *Castanea sativa*, *Fagus sylvatica*, *Fraxinus excelsior*, *Picea abies*, *Pinus cembra*, *Pinus mugo arborea*, *Pinus sylvestris*, *Quercus petraea*, *Quercus robur*, *Sorbus aucuparia*.

 Approach

 Data
- Swiss National Forest Inventory Data (stand species with > 60 % dominance in upper canopy; on at least more than 9 plots dominant)
- Sentinel-2 time series (2020, Indices: CCI, CIRE, NDMI, EVI, NDVI)
- Digital elevation model (DEM) (swissalti3d, 5 m)
- Biogeographical regions (Federal Office for the Environment FOEN)
- Forest mask 2017 (Approach: Waser et al., 2015)

 Modeling approach
We identified the most meaningful variables that led to separation of the respective groups by using random forest models with a forward feature selection (Meyer et al., 2018; Ververidis & Kotropoulos, 2005). In this approach, the final random forest model is solely built from the selected meaningful variables. By identifying meaningful variables, we can determine which variables might influence the grouping. Further, to avoid overfitting and overly optimistic results, we applied 10-fold spatial cross-validation and put all pixels from a plot in the same spatial fold.
The modeling was realized using the CAST package in R (Meyer et al., 2022), based on the well-known caret package (Kuhn, 2022). We used the ranger package in R (Wright & Ziegler, 2017) to implement the random forest models, due to its short computation time.

 Training data for modeling
- 295 Sentinel-2, DEM & Biogeographical variables
- 10525 tree species pixels

 Selected variables for final model
1. EVI of 2020.05.16
2. NDMI of 2020.03.12
3. CIRE of 2020.04.16
4. NDMI of 2020.07.05
5. CCI of 2020.05.11
6. dem
7. CCI of 2020.08.14
8. NDMI of 2020.08.24
9. CCI of 2020.12.22
10. NDMI of 2020.04.21
11. NDMI of 2020.11.17
12. NDMI of 2020.08.09
13. CIRE of 2020.03.22
14. CIRE of 2020.08.09
14. CCI of 2020.11.02
15. CIRE of 2020.06.10

 Overall Accuracy of final model
- 0.759

 Nationwide prediction
- Predicted throughout forest mask 2017 (Approach: Waser et al., 2015)
- Not applied on incomplete Sentinel-2 time series (own category in final map: incomplete_ts)
- Applied the Area of Applicability (Meyer 2022) to sort out pixels outside of the feature space; basically where the model had not the same values for pixels as in the available training data

 *Be aware that the map is only validated with the training data itself, an independent validation with other data sources remains missing*

 References
- Kuhn, M. (2022). Classification and Regression Training. 6.0-93.
- Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., & Nauss, T. (2018). *Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation*. Environmental Modelling and Software, 101, 1-9. https://doi.org/10.1016/j.envsoft.2017.12.001
- Meyer, H., Milà, C., & Ludwig, M. (2022). *CAST: 'caret' Applications for Spatial-Temporal Models*. 0.7.0.
- Ververidis, D., & Kotropoulos, C. (2005). *Sequential forward feature selection with low computational cost*. 2005 13th European Signal Processing Conference.
- Waser, L., Fischer, C.,Wang, Z., & Ginzler, C. (2015). *Wall-to-Wall Forest Mapping Based on Digital Surface Models from Image-Based Point Clouds and a NFI Forest Definition*. Forests, 6, 12, 4510–4528.
- Wright, M. N., & Ziegler, A. (2017). *ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R*. Journal of Statistical Software, 77(1), 1-17. https://doi.org/doi:10.18637/jss.v077.i01

forest

display_name: forest

map

display_name: map

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

tree-species

display_name: tree-species

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Tree species map of Switzerland


maintainer: Michael Lehning

en: snowpack terrestrial-laser-scanning snow-conservation snow-farming snow winter-tourism

en: Martell .smet

en: Meteodata used as input for SNOWAPCK simulations, orginating from Station Hintermartell.

en: HS Martell autumn 2015

media_type: text/plain

en:
Snow depth/ height of snow heap in autumn 2015 obtained by terrestrial laserscanning
cell size: 10 cm
spatial reference: European Datum 1950 UTM
Nodata: -9999

format: TXT

mimetype: text/plain

en: HS Fluela spring 2015

en:
Snow depth/ height of snow heap in spring 2015 obtained by terrrestrial laserscanning
cell size: 10 cm
spatial reference: CH1903

en: HS Fluela autumn 2015

en:
Snow depth/ height of snow heap in autumn 2015 obtained by terrestrial laserscanning
cell size: 10 cm
spatial reference: CH1903
Nodata: -9999

en: HS Martell spring 2015

en:
Snow depth/ height of snow heap in spring 2015 obtained by terrestrial laserscanning
cell size: 10 cm
spatial reference: European Datum 1950 UTM
Nodata: -9999

en: Martell .sno

en: Input snow profile (.sno file) used for SNOWPACK simulations of the Martell heap

en: Fluela .smet

en: Meteodata used as input for SNOWAPCK simulations, orginating from Station Windtunnel and DAV.

en: Fluela .sno

en: Input snow profile (.sno file) used for SNOWPACK simulations of the Flüela heap

en:
Two data sets obtained for snow farming projects (Fluela, Davos, CH and Martell, IT) in 2015.

The data set contains for each site:

* 10 cm GIS raster of snow depth calculated from terrestrial laserscanning surveys (TLS) in the end of winter season (April/May)

* 10 cm GIS raster of snow depth calculated from TLS in the end of summer season (October)

Input files for SNOWPACK model:

* .sno: snow profile at the end of winter

* .smet: meteorological data measured by weather stations in the area

For more details see Grünewald, T., Lehning, M., and Wolfsperger, F.: Snow farming: Conserving snow over the summer season, The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-93, in review, 2017.

snow

display_name: snow

snow-conservation

display_name: snow-conservation

snow-farming

display_name: snow-farming

snowpack

display_name: snowpack

terrestrial-laser-scanning

display_name: terrestrial-laser-scanning

winter-tourism

display_name: winter-tourism

envidat

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

en: Snowfarming data set Davos and Martell 2015


en: meteorological-data snowpack rain-on-snow catchment-data snow-hydrology

en: The file contains all data needed to run simulations with the snow cover model SNOWPACK.

mimetype: application/zip

en: Meteorological data used to run SNOWPACK for 58 catchments in the Swiss Alps. The data consists of a 2 km grid of "virtual meteorological stations" for each catchment. It was used to simulate snow cover processes during rain-on-snow events, therefore meteorological data of each catchment contains at least one rain-on-snow event. Further information can be found in the attached readme.txt and in Würzer & Jonas et al. (2017), currently under review in Hydrological Processes.

catchment-data

display_name: catchment-data

meteorological-data

display_name: meteorological-data

rain-on-snow

display_name: rain-on-snow

snow-hydrology

display_name: snow-hydrology

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

en: Meteorological data for investigation of rain-on-snow events in 58 catchments in Switzerland


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

en: Data Forward Scattering

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

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

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

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

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

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

albedo

display_name: albedo

photovoltaic

display_name: photovoltaic

shortwave-radiation

display_name: shortwave-radiation

winter-energy-production

display_name: winter-energy-production

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


en: functional-diversity spatial-factors metacommunity beta-diversity environmental-factors water-filled-tree-holes community-assembly taxonomic-diversity

en: This dataset contains trait information used to characterize the macroinvertebrates in our study, for the Indian forest plot. The dataset includes seven trait groups: aquatic developmental stage, maximum body size, locomotion, dispersal mode, respiration mode, feeding group, and cohort production interval. These trait groups are further divided into 32 distinct categories.

en: This dataset includes physical and environmental characteristics of water-filled tree holes (WTHs), such as water depth, height, opening size, hole origin and orientation, circumference at breast height, WTH type, and water volume. It also contains physico-chemical parameters, nutrient concentrations (ammonium, nitrogen, phosphate, sulfate, chloride), CDOM, Chlorophyll-A, turbidity, and dry organic matter (FPOM, CPOM, total).

en: Code to do the analysis and figures of the paper.

format: OCTET

en: This dataset describes the animal community composition sampled from 35 water-filled tree holes (WTHs) per site. It includes abundance and taxonomic identification (to morphospecies level) of aquatic macroinvertebrates and Chordata.

en: This dataset provides the geographic coordinates (latitude and longitude) of each sampled water-filled tree hole (WTH) for the Brazilian forest plot.

en: This dataset includes results from variation partitioning analysis used to assess the relative contributions of environmental and spatial factors to taxonomic and functional β-diversity and their components across biogeographic regions. It contains site-level values for pure and shared effects of predictors. Used to generate Fig_2.

en: This dataset provides the geographic coordinates (latitude and longitude) of each sampled water-filled tree hole (WTH) for the Indian forest plot.

en: This dataset provides the geographic coordinates (latitude and longitude) of each sampled water-filled tree hole (WTH) for the French forest plot.

en: This dataset contains trait information used to characterize the macroinvertebrates in our study, for the Brazilian forest plot. The dataset includes seven trait groups: aquatic developmental stage, maximum body size, locomotion, dispersal mode, respiration mode, feeding group, and cohort production interval. These trait groups are further divided into 32 distinct categories.

en: This Excel file contains metadata that provides detailed information about all other files included here. It describes the ten accompanying datasets, outlining the type of data each contains, the variables used, their descriptions, and the units of measurement (where applicable).

en: This dataset contains trait information used to characterize the macroinvertebrates in our study, for the French forest plot. The dataset includes seven trait groups: aquatic developmental stage, maximum body size, locomotion, dispersal mode, respiration mode, feeding group, and cohort production interval. These trait groups are further divided into 32 distinct categories.

en: This dataset contains values of taxonomic and functional β-diversity for each study site, along with their respective components: species richness difference vs. turnover (taxonomic), and functional richness difference vs. functional turnover (functional). Used to generate Fig. 1.

en: These datasets and code support research on the community assemblage of aquatic macroinvertebrates in ephemeral microhabitats, specifically water-filled tree holes (WTH), in three biogeographically and climatically distinct old-growth forests: Temperate-Mediterranean (France), Neotropical (Brazil) and Palaeotropical (India). By integrating standardised biological sampling, detailed environmental measurements and spatial analysis, the dataset allows for an in-depth examination of how local habitat conditions and spatial configuration influence taxonomic and functional beta-diversity patterns. The data capture the variation in species composition and ecological traits in 35 WTH per forest site and are particularly important for understanding how environmental filtering and spatial processes shape beta-diversity in natural forest ecosystems.

beta-diversity

display_name: beta-diversity

community-assembly

display_name: community-assembly

environmental-factors

display_name: environmental-factors

functional-diversity

display_name: functional-diversity

metacommunity

display_name: metacommunity

spatial-factors

display_name: spatial-factors

taxonomic-diversity

display_name: taxonomic-diversity

water-filled-tree-holes

display_name: water-filled-tree-holes

en: Spatial factors shape taxonomic and functional beta-diversity in water-filled tree holes in different biogeographical regions


climarctic-seasons

maintainer: Aline Frossard

title_for_slug: climarctic-seasons

en: microbial-functions greenhouse-gases soil arctic microbial-biodiversity

en: Climarctic seasons dataset Envidat.xlsx

en:
Samples description
•	sample number: sample list number (S1 to S126)
•	season: season during which the samples were collected
•	location: one of the two location where the samples were collected in the vicinity of Ny Alesund on Svalbard
•	slope site: sites where the samples were collected along the toposequence (upslope = top of the slope, midslope = middle of the slope, downslope = bottom of the slope)
•	soil layer: depth layer where the sample were collected (biocrust = 0-2 cm and mineral soil = 5-10 cm)
•	replicate: replicated sampled (3 replicates per site)
Environmental variables
•	pH = pH value of the soil
•	sand = proportion of sand of the soil [%]
•	silt = proportion of silt of the soil [%]
•	clay = proportion of clay of the soil [%]
•	GWC = Gravimetric Water content in the soil [%]
•	OM = Organic Matter content in the soil [g]
•	N = Total Nitrogen content in soil [mg/g]
•	C = Total Carbon content in soil [mg/g]
•	C/N = ration of C content over N content [ratio]
•	NO3 = Nitrate content in soil [mg/kg]
•	NO2 = Nitrite content in soil [mg/kg]
•	NH4 = Ammonium content in soil [mg/kg]
•	Ptot = Total Phosphorus content in soil [mg/kg]
•	Pavail = Available Phosphorus content in soil [mg/kg]
•	PO4 = Phsphate content in soil [mg/kg]
•	CO2 = CO2 fluxes from soil [mmol CO2 m-2 d-1]
•	CH4 = CH4 fluxes from soil [umol CH4 m-2 d-1]
•	N2O = N2O fluxes from soil [umol CH4 m-2 d-1]
•	GPP = Gross Primary Production [mmol CO2 m-2 d-1]
•	ER = Ecosystem respiration [mmol CO2 m-2 d-1]
•	NEE = Net Ecosystem Exchange [mmol CO2 m-2 d-1]
•	glucoOM = glucosidase enzyme activity [mmol h-1 gOM-1]
•	23 xyloOM = xylosidase enzyme activity [mmol h-1 gOM-1]
•	chitOM = N-acetyl-glucosaminidase (chitinase) enzyme activity [mmol h-1 gOM-1]
•	phosOM = phosphatase enzyme activity [mmol h-1 gOM-1]
•	leuOM = leucine-aminopeptidase enzyme activity [mmol h-1 gOM-1]
•	PPOM = Polyphenol Peroxidase enzyme activity [mmol h-1 gOM-1]
•	POOM = Polyphenol Oxidase enzyme activity [mmol h-1 gOM-1]
•	30. Bac16SOM = Bacterial 16S gene abundance [copies gOM-1]
•	31. BacamoAOM = Bacterial amoA gene abundance [copies gOM-1]
•	32. ArchamoAOM = Archaeal 16S gene abundance [copies gOM-1]
•	33. nifHOM = nifH gene abundance [copies gOM-1]
•	34. nirSOM = nirS gene abundance [copies gOM-1]
•	35. nosZOM = nosZ gene abundance [copies gOM-1]
•	36. nxrBOM = nxrB gene abundance [copies gOM-1]
•	37. mcrAOM = mcrA gene abundance [copies gOM-1]
•	38. pmoAOM = pmoA gene abundance [copies gOM-1]

en: This dataset includes measurements of potential microbial functions (potential extracellular enzyme activities and microbial functional genes abundance) and soil physico-chemical properties (pH, nutrients, texture, etc) in biocrusts and underlying mineral soils in High-Arctic tundra near Ny-Ålesund (Svalbard) during each season along a toposequence.

arctic

display_name: arctic

greenhouse-gases

display_name: greenhouse-gases

microbial-biodiversity

display_name: microbial-biodiversity

microbial-functions

display_name: microbial-functions

soil

display_name: soil

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Climarctic seasons


maintainer: Tobias Wechsler

en: wasserkraft kleinstwasserkraftwerke datensatz speed2zero schweiz

en: .XLSX-Datei von CH-Kleinstwasserkraftwerke: 1'682 Kleinstwasserkraftwerke (≤ 300 kW) in der Schweiz.

en: Shapefile der Einzugsgebiete (2 km² ), in denen sich die jeweiligen Kleinstwasserkraftwerke befinden (erstellt von Yasser Haddad. ETH).

en: Shapefile der 2’388 registrierten Wasserkraftanlagen in der Schweiz aus den beiden Datensätzen WASTA (> 300 kW) und CH-Kleinstwasserkraftwerke (≤ 300 kW).

en: Shapefile von CH-Kleinstwasserkraftwerke: 1'682 Kleinstwasserkraftwerke (≤ 300 kW) in der Schweiz.

en: .XLSX-Datei der 2’388 registrierten Wasserkraftanlagen in der Schweiz aus den beiden Datensätzen WASTA (> 300 kW) und CH-Kleinstwasserkraftwerke (≤ 300 kW).

en: Die 1'682 erfassten Kleinstwasserkraftwerke im Datensatz CH-Kleinstwasserkraftwerke

en:
In der Schweiz werden Wasserkraftanlagen (WKA) mit einer Leistung von mehr als 300 kW systematisch erfasst und in der nationalen Wasserkraftstatistik (WASTA) zusammengetragen. Die WASTA bildet eine umfangreiche Datengrundlage für Analysen bezüglich Wasserkraft, auch im Kontext von Klimawandel, Biodiversitätsverlust und Energiewende. Nicht erfasst auf nationaler Ebene waren bis anhin die sogenannten Kleinstwasserkraftwerke, WKA mit einer Leistung von weniger als 300 kW. Kleinstwasserkraftwerke tragen zwar nicht wesentlich zur Schweizer Elektrizitätsversorgung bei, haben jedoch aufgrund ihrer hohen Anzahl Auswirkungen auf die Gewässerökologie, die mit den Klimawandel noch stärker unter Druck gerät.
Der neu erstellte Datensatz CH-Kleinstwasserkraftwerke umfasst 1'682 WKA mit einer Leistung von weniger als 300 kW. In den zwei Datensätzen WASTA (> 300 kW) und CH-Kleinstwasserkraftwerke (≤ 300 kW) sind zurzeit 2’388 WKA erfasst. Im Austausch mit den 26 Kantonen wurde jedoch klar, dass es sich nicht um eine vollständige Sammlung von Kleinstwasserkraftwerken handelt. Die 1'682 Kleinstwasserkraftwerke machen rund 70% aller erfassten WKA in der Schweiz aus, tragen jedoch mit weniger als 600 GWh/Jahr lediglich rund 1.5% zur schweizerischen Wasserkraftproduktion bei. In einem optimistischen Szenario, in dem die jährliche Produktionserwartung tendenziell überschätzt wird, liegt dieser Anteil um 0.3% höher. Von den erfassten 2’388 WKA erzeugen die 10% der grössten Anlagen über 90% der schweizerischen Wasserkraftproduktion. Der Datensatz CH-Kleinstwasserkraftwerke ist im Rahmen der ETH-Initiative SPEED2ZERO entstanden, die Klimadienstleistungen in den Bereichen Klimawandel, Biodiversitätsverlust und Energiewende erbringt (https://speed2zero.ethz.ch).
Hier verfügbare Datensätze:
- CH-Kleinstwasserkraftwerke - der hier vorgestellte Datensatz mit 1'682 Kleinstwasserkraftwerken
- EZG-Kleinstwasserkraftwerke - die Einzugsgebiete (2 km² ), in denen sich die 1'682 Kleinstwasserkraftwerke befinden (erstellt von Yasser Haddad. ETH)
- Metadaten - sämtliche Metadaten zum Datensatz CH-Kleinstwasserkraftwerke
- CH-WKA - eine Zusammenstellung sämtlicher 2'388 erfassten Wasserkraftanlagen in der Schweiz aus den beiden Datensätzen WASTA (> 300 kW) und CH-Kleinstwasserkraftwerke (≤ 300 kW)

datensatz

display_name: datensatz

kleinstwasserkraftwerke

display_name: kleinstwasserkraftwerke

schweiz

display_name: schweiz

wasserkraft

display_name: wasserkraft

en: CH-Kleinstwasserkraftwerke - ein schweizweiter Datensatz zu Kleinstwasserkraftwerken


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

en: Site photo of the field station (NOF)

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

en: Data plot of the field station (NOF)

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

en: Forest station data (NOB)

en: Data file.

en: Site photo of the forest station (NOB)

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

en: Site aerial photo (Novaggio)

en: Site aerial photo from both Novaggio stations. Source: Google. 2016. Site aerial photo from both Novaggio stations. Retrieved November 15, 2016, from https://www.google.ch/maps

en: Field station data (NOF)

en: Data plot of the forest station (NOB)

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 Novaggio in Switzerland where one station is located within a natural deciduous forest stand (NOB) with Turkey oak (_Quercus cerris_; 70 yrs), sweet chestnut (_Castanea sativa_; 90 yrs) and silver birch (_Betula pendula_; 70 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, NOF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Novaggio 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: Novaggio, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1996 onwards


maintainer: Amelie Fees

en: avalanche-activity avalanche glide-snow-avalanche time-lapse-photography snowpack-simulation

en: Glide distances for opening glide-cracks

en: This folder contains all glide-cracks where glide-distances were extracted from time-lapse photographs. Glide-distances were calculated in x-direction (width) and y-direction (downslope, length) by georeferencing the images.

en: README

en: README.txt explaining the dataset and columns of tables in more detail.

en: SNOWPACK simulations

en: SNOWPACK simulations (.pro and .smet) covering season 2008/09 to 2021/22 at 10 virtual locations on Dorfberg. The locations correspond to the most common glide-snow avalanche paths.

en: Glide-snow avalanche activity

en: Table that contains all glide-snow avalanche events that were detected in time-lapse photographs (season 2008/09 to 2021/22). The columns are explained in the README.txt

en:
This dataset includes the processed data of the glide-snow avalanche activity and dynamics on Dorfberg (Davos, Switzerland) covering seasons 2008/09 to 2021/22. This dataset was described in the research article:

Fees, A., van Herwijnen A., Altenbach, M., Lombardo, M., Schweizer, J.: Glide-snow avalanche characteristics at different time-scales extracted from time-lapse photography, Annals of Glaciology, 91

We extracted the dynamics of opening glide-cracks and the glide-snow avalanche activity from time-lapse photographs. Glide-snow avalanches were separated into surface and interface events using the liquid water content which was simulated with SNOWPACK at 10 virtual stations on Dorfberg.

avalanche

display_name: avalanche

avalanche-activity

display_name: avalanche-activity

glide-snow-avalanche

display_name: glide-snow-avalanche

snowpack-simulation

display_name: snowpack-simulation

time-lapse-photography

display_name: time-lapse-photography

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Glide-snow avalanche activity on Dorfberg, Davos, Switzerland


en: occurrence flora-change uetliberg zurich plant-species

en:
The list gives the clearly identifiable plant species from Schinz (1775). Die Reise auf den Uetliberg. Verlag des Waysenhauses, Zürich). Several species given in Schinz (1775) are not identifiable to the species level and are not included in the list. Similarly, Vicia pisiformis, Erbsenartige Wicke, given by Schinz (1775) is probably a misidentification and also not included in the list.

Latin plant species name Schinz 1775: Latin plant name (if) given by Schinz (1775); na: not available
German plant species name Schinz 1775: German plant name given by Schinz (1775)
Latin plant species name Info Flora 2024: Latin plant name according to www.infoflora.ch in 2024
German plant species name Info Flora 2024: German plant name according to www.infoflora.ch in 2024
Plant species still occurring in 2020: Plant species given in Schinz (1775) still occurring at the Uetliberg in 2020 (Zürcherische Botanische Gesellschaft. 2020. Flora des Kanton Zürich. Haupt, Bern); occurrence of Thesium alpinum, Alpen-Bergflachs, according to floristic knowledge of Rolf Holderegger and Michèle Büttner; 1: still occurring; 0: no longer occurring / extinct.

en:
The list gives the clearly identifiable plant species from Schinz (1775). Die Reise auf den Uetliberg. Verlag des Waysenhauses, Zürich). Several species given in Schinz (1775) are not identifiable to the species level and are not included in the list. Similarly, *Vicia pisiformis*, Erbsenartige Wicke, given by Schinz (1775) is probably a misidentification and also not included in the list.
Latin plant species name Schinz 1775: Latin plant name (if) given by Schinz (1775); na: not available
German plant species name Schinz 1775: German plant name given by Schinz (1775)
Latin plant species name Info Flora 2024: Latin plant name according to www.infoflora.ch in 2024
German plant species name Info Flora 2024: German plant name according to www.infoflora.ch in 2024
Plant species still occurring in 2020: Plant species given in Schinz (1775) still occurring at the Uetliberg in 2020 (Zürcherische Botanische Gesellschaft. 2020. Flora des Kanton Zürich. Haupt, Bern); occurrence of *Thesium alpinum*, Alpen-Bergflachs, according to floristic knowledge of Rolf Holderegger and Michèle Büttner; 1: still occurring; 0: no longer occurring / extinct.

flora-change

display_name: flora-change

occurrence

display_name: occurrence

plant-species

display_name: plant-species

uetliberg

display_name: uetliberg

zurich

display_name: zurich

en: Plant species list from 1775 and 2020 for Uetliberg Zürich