envi

fr: Environnement

de: Umwelt

en: Environment

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


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

label: geocat.ch permalink

state: active

type: dataset

de: AGIS Direktdownload

format: SERVICE

de: öV-Haltestellen

format: WMS

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

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


bauinventarobjekte

soci

fr: Population et société

de: Bevölkerung und Gesellschaft

en: Population and society

it: Popolazione e società

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

title_for_slug: bauinventarobjekte

de: Bauinventarobjekte

de: Der vorliegende Datensatz enthält Objekte des Bauinventars. Mit dem Bauinventar (ehemaliges Kurzinventar) wird eine systematische Bestandesaufnahme von kommunal schützenswerten Bauten und Kulturobjekten geführt. Diese werden von der kantonalen Denkmalpflege nach einheitlichen Kriterien gemeindeweise erfasst, dokumentiert und gewürdigt (Verordnung zum Kulturgesetz § 26 Abs. 2).


envi

it: Ambiente

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: 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


de: Archäologische Fundstellen

de: Der Datensatz enthält archäologische Fundstellen als interpretierte Flächen, wobei unterschieden wird zwischen Stätten, die sicher grossflächig sind (A-Fundstellen: z.B. Bauten/Siedlungen, Gräber/Friedhöfe), und Einzelfund-Stellen, die auf grossflächige Fundstellen hinweisen können (B-Fundstellen: z.B. Steinbeil). Die interpretierte Darstellung von archäologischen Fundstellen basiert auf Ausgrabungen und auf aktenkundigen Fundmeldungen, die teilweise bis zu 200 Jahre alt sind, vereinzelt auch auf Flurnamen (z.B. Muracher); sehr alte Fundmeldungen lassen sich manchmal nur schwierig interpretieren und ungenau lokalisieren. Der erste Durchgang der kantonsweiten Erfassung ist Ende 2005 abgeschlossen worden; die Ausarbeitung und Nachführung wird laufend zu Ergänzungen und einzelnen Änderungen führen. Angaben über Datierung, Art, Umfang und Erhaltungsgrad einer Fundstelle können über die Fundstellennummer, die als Attribut auch im GIS-Datensatz enthalten ist, auf Anfrage erschlossen werden.


rote-liste

title_for_slug: rote-liste

media_type: text/html

format: HTML

fr: SPARQL Endpoint avec présélection du graphe

de: SPARQL Endpoint mit Vorauswahl des Graph

en: SPARQL Endpoint with graph preselection

media_type: application/sparql-query

format: SPARQL

fr: Les listes rouges constituent des rapports d’expertise scientifiques qui dressent le bilan du degré de menace pesant sur les espèces indigènes. En Suisse, ces listes sont établies par des spécialistes mandatés par l’OFEV. Il en existe pour 33 groupes d'espèces.

de: Rote Listen sind anerkannte wissenschaftliche Gutachten, in denen der Gefährdungsgrad von Arten dargestellt ist. Sie werden in der Schweiz im Auftrag des BAFU von Fachpersonen erstellt. Für 33 Artengruppen liegen Rote Listen vor.

en: Red lists are scientific expert reports that assess the degree of threat to native species. In Switzerland, these lists are drawn up by specialists commissioned by the FOEN. They exist for 33 species groups.

it: Le Liste rosse si basano su una procedura di valutazione scientificamente riconosciuta, volta alla classificazione delle specie secondo il grado di minaccia. In Svizzera queste Liste sono redatte da specialisti incaricati dall’UFAM ed esistono per 33 gruppi di specie.

de: Umwelt

bundesamt-fur-umwelt-bafu

fr: Office fédéral de l'environnement OFEV

de: Bundesamt für Umwelt BAFU

en: Federal Office for the Environment FOEN

it: Ufficio federale dell'ambiente UFAM

fr: L’autorité compétente pour les questions environnementales. Il est à ce titre chargé de gérer les ressources naturelles selon les principes du développement durable et de protéger l’homme contre les dangers naturels et l’environnement contre les pollutions.

de: Die Fachbehörde für die Umwelt. Es ist zuständig für die nachhaltige Nutzung der natürlichen Ressourcen sowie für den Schutz des Menschen vor Naturgefahren und den Schutz der Umwelt vor übermässigen Belastungen.

en: The federal environmental authority. It is responsible for ensuring that natural resources are used sustainably, that the public is protected against natural hazards, and that the environment is protected from unacceptable adverse impacts.

it: Il servizio specializzato in materia ambientale, è competente per quanto riguarda l'utilizzazione sostenibile delle risorse naturali, la protezione delle persone dai pericoli naturali e dell'ambiente da un inquinamento eccessivo.

political_level: confederation

key: guid

key: uri

value: LINDAS DCAT Harvester BAFU

fr: Listes Rouges

de: Rote Liste

en: Red List

it: Liste rosse


waldflachen

agri

fr: Agriculture, pêche, sylviculture et alimentation

de: Landwirtschaft, Fischerei, Forstwirtschaft und Nahrungsmittel

en: Agriculture, fisheries, forestry and food

it: Agricoltura, pesca, silvicoltura e prodotti alimentari

regi

de: Regionen und Städte

title_for_slug: waldflachen

de: forstwirtschaft nutzungsplanung vegetation wald

protocol: https

de: Datenshop

de: Produktansicht Geodatenshop

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

spatial: Kanton Luzern

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: Waldflächen


soci

de: Bevölkerung und Gesellschaft

educ

de: Bildung, Kultur und Sport

de: eigentumsbeschrankungoreb gebaude-und-anlagen kulturdenkmal kulturguterschutz nutzungsplanung ortsbildschutz zonenplan

de: Im Bauinventar werden schützenswerte und erhaltenswerte Baudenkmäler des Kantons erfasst. Besonders schutzwürdige Kulturobjekte werden ins Kantonale Denkmalverzeichnis eingetragen und unter Schutz gestellt.

de: Kantonales Denkmalverzeichnis und Bauinventar


zonenplane

envi

de: Umwelt

title_for_slug: zonenplane

de: baulinie eigentumsbeschrankungoreb grundeigentum nutzungsplanung raumplanung zonenplan

de: Zonenpläne sind grundeigentümerverbindlich und regeln die zulässige Art der Bodennutzung im Gebiet einer Gemeinde.

de: Zonenpläne


nutzungsplanung-planungszonen

regi

de: Regionen und Städte

title_for_slug: nutzungsplanung-planungszonen

de: bewilligungen eigentumsbeschrankungoreb nutzungsplanung raumplanung zonenplan

de: Die Planungszone bezeichnet ein Gebiet, in dem Nutzungspläne erlassen oder geändert werden müssen.

de: Nutzungsplanung: Planungszonen


trockensubstanzpotential

agri

de: Landwirtschaft, Fischerei, Forstwirtschaft und Nahrungsmittel

title_for_slug: trockensubstanzpotential

de: betriebe boden bodennutzung landwirtschaft landwirtschaftliche-nutzung

de: Datenshop

de: Produktansicht Geodatenshop

de: Bei der Trockensubstanzmethode wird der in Trockensubstanz (TS) umgerechnete Futterbedarf aller auf dem Betrieb gehaltenen Nutztiere - inklusive der geplanten Aufstockung - dem in TS umgerechneten pflanzenbaulichen Produktionspotenzial (TS-Potential) gegenübergestellt.

de: Trockensubstanzpotential


causal-effect-of-lup

maintainer: EnviDat Support

title_for_slug: causal-effect-of-lup

en: land-use-change plan-evaluation psm-did land-use-planning annual-effect

en: causal-effect-of-lup

media_type: application/zip

en:
Data.dbf contains the varibles of 1662 villages in Zhangzhou Cities
PSM_DID.R can re-produce the results

format: ZIP

en:
Title: Does zoning contain built-up land expansion? Causal evidence from Zhangzhou City, China.

Research objective: Built-up land zoning is an imporatant policy measure of land use planning (LUP) to contain built-up land expansion in China. We used a difference-indifference model with propensity score matching to estimate the average and annual effect of built-up land zoning on built-up land expansion in Zhangzhou City, China between 2010 and 2020.

Data: Data.dbf contains the varibles of 1662 villages in Zhangzhou Cities in 1995, 2000, 2005, 2010, 2013, 2015, 2018, and 2020.
XZQDM2 is villages' unique administrative ID;
Area is the land area of village i;
Dis2water is the Euclidean distance from village i to the nearest waterbody;
Dis2coastl is the Euclidean distance from village i to the nearest coastline;
Dis2city is the the Euclidean distance from village i to the city center;
Dis2county is the the Euclidean distance from village i to the nearest county center;
Elevation is the the average elevation within village i;
Dis2road is the the Euclidean distance from village i to the nearest road;
Nei_Built_ is the the area of built-up land (Nei Built.upit) in the neighboring villages of village i in year t;
Treated is a binary variable, Treated = 1 to the villages that were partially or entirely located inside the development-permitted zones, and Treated = 0 to the villages that were entirely located outside the development-permitted zones;
Intensity is the percentage of land that was assigned to the development-permitted zones in village i;
Year represent the year in 1995, 2000, 2005, 2010, 2013, 2015, 2018, and 2020;
BuLE is the dependent variable, representing built-up land expansion in village i in year t;
Town is town' unique administrative ID.

Method: First, we employed propensity score matching to overcome the selection bias and satisfy the parallel trend
assumption. Second, we built four Difference-in-Difference models to estimate the average and annual effect.

annual-effect

display_name: annual-effect

land-use-change

display_name: land-use-change

land-use-planning

display_name: land-use-planning

plan-evaluation

display_name: plan-evaluation

psm-did

display_name: psm-did

educ

de: Bildung, Kultur und 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

value: Envidat Harvester

en: Causal effect of LUP


en: jupyter geospatial buffer density gis roads

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

mimetype: application/zip

en: Plots

en: Random plot locations

en: Road Density Calculation (Python Notebook) online

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

buffer

display_name: buffer

density

display_name: density

geospatial

display_name: geospatial

gis

display_name: gis

jupyter

display_name: jupyter

roads

display_name: roads

en: Dataset for OGRS 2018 publication


maintainer: Benjamin Reuter

en: avalanche-problem-type avalanche-activity snowpack avalanche crocus snow-instability snow-cover-modeling avalanche-danger weissfluhjoch snow

en: 2015-2016 Avalanche problem types from Crocus

en:
Avalanche problem types were derived from a snow cover simulation with the model Crocus for the winter season 2015-2016 at the Weissfluhjoch study plot, Davos, CH.
The data cover the period 01.01.2016-30.04.2016. The binary indicator (0,1) tells whether an avalanche problem type was identified in the snow cover simulation (1) or not (0).
The data are presented in Figure 7 in Reuter et al. (2021).

en: 2015-2016 Avalanche problem types from SNOWPACK

en:
Avalanche problem types were derived from a snow cover simulation with the model SNOWAPCK for the winter season 2015-2016 at the Weissfluhjoch study plot, Davos, CH.
The data cover the period 01.01.2016-30.04.2016. The binary indicator (0,1) tells whether an avalanche problem type was identified in the snow cover simulation (1) or not (0).
The data are presented in Figure 7 in Reuter et al. (2021).

en: 1999-2017 Avalanche problem types from Crocus

en:
Avalanche problem types were derived from snow cover simulations with the model Crocus at the Weissfluhjoch study plot, Davos, CH.
The data cover the winter seasons between 1999-2017 and include the simulated annual frequencies of new snow, wind slab, persistent weak layer, aging-persistent weak layer and wet snow problem types.
The data are presented in Figure 8 in Reuter et al. (2021).

en: 2015-2016 Seismic avalanche acitivity catalogue

en:
Avalanches were indentified in seismic data recorded by two seismic arrays, one deployed at Wannengrat (2,500 m) and another one deployed in the Dischma valley (2,000 m).
The data cover the period from 01.01.2016 - 30.04.2016 and are presented in Figure 7 in Reuter et al. (2021).

en: 1999-2017 Avalanche problem types from SNOWPACK

en:
Avalanche problem types were derived from snow cover simulations with the model SNOWPACK at the Weissfluhjoch study plot, Davos, CH.
The data cover the winter seasons between 1999-2017 and include the simulated annual frequencies of new snow, wind slab, persistent weak layer, aging-persistent weak layer and wet snow problem types.
The data are shown in Figure 8 in Reuter et al. (2021).

en:
Avalanche problem types were derived from snow cover simulations with the models Crocus and SNOWPACK at the Weissfluhjoch study plot, Davos, CH. The data include annual frequencies of avalanche problem types for the seasons 1999-2017 and daily presence of avalanche problem types for the period 01.01.2016 - 30.04.2016.
Avalanche activity was derived from two seismic sensor arrays deployed no further than 15 km from Weissfluhjoch, Davos, CH. The data cover the period 01.01.2016 - 30.04.2016.

avalanche

display_name: avalanche

avalanche-activity

display_name: avalanche-activity

avalanche-danger

display_name: avalanche-danger

avalanche-problem-type

display_name: avalanche-problem-type

crocus

display_name: crocus

snow

display_name: snow

snow-cover-modeling

display_name: snow-cover-modeling

snow-instability

display_name: snow-instability

snowpack

display_name: snowpack

weissfluhjoch

display_name: weissfluhjoch

educ

en: Education, culture and sport

en: Simulated avalanche problem types and seismic avalanche activity around Weissfluhjoch


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

en: Data plot of the forest station (SCB)

media_type: application/pdf

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

format: PDF

en: Forest station data (SCB)

media_type: text/csv

en: Data file.

format: CSV

en: Further details on the data repository

en: Meta data documentation.

en: Data plot of the field station (SCF)

en: Field station data (SCF)

mimetype: text/csv

en: Site photo of the forest station (SCB)

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

en: Site photo of the field station (SCF)

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

en: Site aerial photo (Schänis)

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

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Schänis in Switzerland where one station is located within a natural mixed forest stand (SCB) with European beech (_Fagus sylvatica_; 130-150 yrs), European silver fir (_Abies alba_; 130-150 yrs) and European ash (_Fraxinus excelsior_; 130-150 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, SCF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Schänis is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

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

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


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

educ

en: Education, culture and sport

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


maintainer: Mahdi Jafari

en: convection-in-snow numerical-simulation openfoam vapor-transport snow-density-profile

en: snowpackBuoyantPimpleFoam

en:
This zipped folder includes (1) the source code files, (2) an example case for simulation, and (3) an instruction how to compile and use the new solver.

Note that the most updated version of snowpackBuoyantPimpleFoam can be found in the WSL/SLF GitLab repository: https://gitlabext.wsl.ch/snow-models/snowpackbuoyantpimplefoam

en: snowpackBuoyantPimpleFoam is a two-phase solver implemented to model convection of water vapor with phase change in snowpacks. This new solver is based on the standard solver of buoyantPimpleFoam in the open-source fluid dynamics software, OpenFOAM 5.0 (www.openfoam.org).

convection-in-snow

display_name: convection-in-snow

numerical-simulation

display_name: numerical-simulation

openfoam

display_name: openfoam

snow-density-profile

display_name: snow-density-profile

vapor-transport

display_name: vapor-transport

en: snowpackBuoyantPimpleFoam: an OpenFOAM Eulerian–Eulerian two-phase solver for modelling convection of water vapor in snowpacks


en: geodata-processing digital-planning swiss-geodata cable-yarding digital-elevation-model geoinformation-system open-source qgis-plugin raster-processing seilaplan

en: View video on youtube!

en: DHM-Kacheln zusammenfügen

en:
In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. In this tutorial video, we show how to merge multiple DTM raster tiles into one file, using the QGIS tool ‘Virtual Raster’. This simplifies the digital planning of a cable line using the QGIS plugin ‘Seilaplan’. Please note that the tutorial language is German!

Link to Seilaplan website: https://seilaplan.wsl.ch

***************************

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. In diesem Tutorialvideo zeigen wir, wie man mit dem QGIS-Plugin Virtuelles Raster mehrere DHM-Kacheln zu einem einzigen Rasterfile zusammenfügen und abspeichern kann. Für die Seillinienplanung mit Seilaplan muss nun nur noch eine Datei, mein neues virtuelles Raster, ausgewählt werden.

Link zur Seilaplan-Website: https://seilaplan.wsl.ch

cable-yarding

display_name: cable-yarding

digital-elevation-model

display_name: digital-elevation-model

digital-planning

display_name: digital-planning

geodata-processing

display_name: geodata-processing

geoinformation-system

display_name: geoinformation-system

open-source

display_name: open-source

qgis-plugin

display_name: qgis-plugin

raster-processing

display_name: raster-processing

seilaplan

display_name: seilaplan

swiss-geodata

display_name: swiss-geodata

en: Seilaplan Tutorial: Merge DTM tiles


weissfluhjoch-dataset-for-esm-snowmip

maintainer: Nander Wever

title_for_slug: weissfluhjoch-dataset-for-esm-snowmip

en: weissfluhjoch snow snow-depth snow-models esm-snowmip

en: Dataset Weissfluhjoch for ESM-SnowMIP

en: Conversion script

en:
Use this script to convert WFJ_optimaldataset_v6.smet to wfj.smet, the file that is used for the ESM-SnowMIP project. It also requires the file: WFJ_AirPressure_Data.zip

Use: bash create_wfj_dataset.sh > input/wfj.smet

mimetype: text/x-sh

en: ini file

en: This ini file is required by data-converter from MeteoIO version 1887 (http://models.slf.ch/p/meteoio/)

en: This Weissfluhjoch dataset is a processed version of the Weissfluhjoch dataset version 6 from https://doi.org/10.16904/6. This dataset was specially created for the ESM-SnowMIP project. Here it is documented how this dataset has been created.

esm-snowmip

display_name: esm-snowmip

snow

display_name: snow

snow-depth

display_name: snow-depth

snow-models

display_name: snow-models

weissfluhjoch

display_name: weissfluhjoch

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: Weissfluhjoch dataset for ESM-SnowMIP


maintainer: Stephan Zimmermann

en: potassium magnesium fagus-orientalis-lipsky calcium biogeochemical-cycling

en: Concentrations of Ca, Mg, K, and Na in throughfall, litterflow and soil solution of a Hyrcanian forst in Northern Iran

format: XLS

en:
Throughfall, litterflow and soil solution were sampled during one whole year under five Oriental beech trees in a mixed Hyrcanian beech forest. The amounts of Ca2+, Mg2+, K+ and Na+ in these fluxes were calculated based on their concentrations and the sampled volumes, and subsequently compared with the respective fluxes in the rainfall and soil solution of an adjacent forest gap.
In addition six soil profiles, one close to every single tree and one in the forest gap, were analyzed for pH, CaCO3, organic matter and texture.

biogeochemical-cycling

display_name: biogeochemical-cycling

calcium

display_name: calcium

fagus-orientalis-lipsky

display_name: fagus-orientalis-lipsky

magnesium

display_name: magnesium

potassium

display_name: potassium

educ

en: Education, culture and sport

en: Base cation dynamics in an Oriental beech forest


weather-station-klosters

maintainer: Maxime Hervo

title_for_slug: weather-station-klosters

en: weather wind klosters raclets precipitation temperature

en: Weather Station Klosters

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

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

klosters

display_name: klosters

precipitation

display_name: precipitation

raclets

display_name: raclets

temperature

display_name: temperature

weather

display_name: weather

wind

display_name: wind


en: visual-attractiveness adults-and-teenagers perception-of-ecosystem-services online-questionnaire-survey nfi-photos

en: SPSS-Dataset

en: Questionnaire German

mimetype: application/pdf

en: Questionnaire French

en: Questionnaire Italian

en: Codebook to SPSS-file

en: Dataset in csv-format

en: This questionnaire survey was conducted as an online survey and aimed at investigating the relationship between physical forest characteristics, visual attractiveness of forest and the perception of ecological and cultural ecosystem services in urban forests. Each participant was shown 6 photos out of a pool of 50 photos taken from the Swiss National Forest Inventory (NFI) database. Physical forest characteristics were derived from the photos. The study was conducted as part of the "WaMos meets LFI" (WML) project.

adults-and-teenagers

display_name: adults-and-teenagers

nfi-photos

display_name: nfi-photos

online-questionnaire-survey

display_name: online-questionnaire-survey

perception-of-ecosystem-services

display_name: perception-of-ecosystem-services

visual-attractiveness

display_name: visual-attractiveness

en: Relationship between physical forest characteristics, visual attractiveness and perception of ecosystem services in urban forests


maintainer: Virginie Marques

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

en: Code, data and figures

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

en: Metabarcoding data

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

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

 Methods

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

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

 Data content:

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

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

biodiversity

display_name: biodiversity

dna-metabarcoding

display_name: dna-metabarcoding

environmental-dna

display_name: environmental-dna

fish

display_name: fish

fjord

display_name: fjord

marine

display_name: marine

plankton

display_name: plankton

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


en: ecosystem-functions microbial-respiration microbial-biodiversity beta-diversity soil-respiration

en: Bacterial amplicon sequencing

en: Bacterial ASVs and taxonomic tables

en: Fungal amplicon sequencing

en: Fungal ASVs and taxonomic tables

en: Microbial functions and metadata

en: Extracellular enzyme activities, respiration and sites metadata

en: Structural and functional changes of microbial communities in ephemeral stream sediments and adjacent soils between was assessed in an artificial newly created catchment at 3 and 13 years after catchment construction. Bacterial and fungal communities, respiration and extracellular enzyme activities were assessed.

beta-diversity

display_name: beta-diversity

ecosystem-functions

display_name: ecosystem-functions

microbial-biodiversity

display_name: microbial-biodiversity

microbial-respiration

display_name: microbial-respiration

soil-respiration

display_name: soil-respiration

educ

en: Education, culture and sport

en: Chicken Creek 10 years


maintainer: Axel Volkwein

en: impact-testing experiments experiment field-tests rockfall-experiments field-data field-test rockfall mass-movements natural-hazard

en: Raw data (high speed videos, acceleration measurements, laboratory tests)

en:
This file contains

* the high speed videos showing the concrete blocks impacting the gallery roof.
* the raw data of the acceleration measurements
* the data of Tiefbauamt Graubünden containg the tension tests of gallery reinforcment, ME-testing the cushion layer and the tests on concrete samples.

en: Video analysis

en: The zip contains five Excel-files that in turn contain the results of the performed video analyses. The sheet "Daten" contains the pixels of different positions on the blocks that were tracked by the software "Kinovea". The sheet "Auswertung" determines the block kinetics based on the pixel data over time.

en: Acceleration measurements

en: The zip file contains five excel files, one for each test conducted. The first sheet contains the raw data and a data set that went through a low-pass filter. The sheet "Allgemein" contains calibration factors for the sensors and time details of a test. The sheets "DiaDaten" and "DiaDatenGefiltert" show the measurements over time. The block and slab kinetics are determined in the sheets "ZeitintegrationBlock" and "ZeitintegrationPlatte". The sheet "VergleichBeschleunigungen" shows the influence of the low-pass-filter and a running average filter on the block accelerations.

en: Five full-scale field tests were conducted with concrete blocks weighting between 800 and 3200 kg being dropped onto the roof of a gallery structure made from reinforced concrete. The impacts were recorded using high-speed video and acceleration measurements at the falling blocks. The dataset contains the raw data as well as the analyses of the block trajectories, i.e. kinetics and dynamics. Setup of the measurements and the analyses conducted are published in Volkwein, A. "Durchführung und Auswertung von Steinschlagversuchen auf eine Stahlbetongalerie", WSL-Berichte, Heft 68, 2018.

experiment

display_name: experiment

experiments

display_name: experiments

field-data

display_name: field-data

field-test

display_name: field-test

field-tests

display_name: field-tests

impact-testing

display_name: impact-testing

mass-movements

display_name: mass-movements

natural-hazard

display_name: natural-hazard

rockfall

display_name: rockfall

rockfall-experiments

display_name: rockfall-experiments

en: Rockfall gallery testing Parde 2016


maintainer: Marcia Phillips

en: permafrost permos gemsstock borehole temperature

en: PERMOS DATA POLICY

en: DATASET & PLOTS

en: Data visualiser and download service provided by PERMOS.

en: Map of PERMOS borehole network

en:
Processed ground temperature measurements at the Gemsstock permafrost borehole in canton Uri, Switzerland. The borehole is located at 2940 m asl on a steep (50°) North-West slope (315°). The surface material is bedrock and borehole depth is 40 m. Thermistors used YSI 44008. Year of drilling 2006. This borehole is part of the Swiss Permafrost network, PERMOS (www.permos.ch). Contact phillips@slf.ch for details of processing applied.

Publications

1. A. Haberkorn, M. Phillips, R. Kenner, H. Rhyner, M. Bavay, S.P. Galos, M. Hoelzle. Thermal regime of rock and its relation to snow cover in steep Alpine rock walls: Gemsstock, central Swiss Alps. 2015. Geografiska Annaler: Series A, Physical Geography. Volume 97. Issue 3. 579–597. http://dx.doi.org/10.1111/geoa.12101. 10.1111/geoa.12101.

2. R. Kenner, M. Phillips, C. Danioth, C. Denier, P. Thee, A. Zgraggen. Investigation of rock and ice loss in a recently deglaciated mountain rock wall using terrestrial laser scanning: Gemsstock, Swiss Alps. 2011. Cold Regions Science and Technology. Volume 67. Issue 3. 157–164. http://dx.doi.org/10.1016/j.coldregions.2011.04.006. 10.1016/j.coldregions.2011.04.006.

borehole

display_name: borehole

gemsstock

display_name: gemsstock

permafrost

display_name: permafrost

permos

display_name: permos

temperature

display_name: temperature

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: Processed permafrost borehole data (2940 m asl), Gemsstock, Switzerland


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

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

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

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

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

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

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

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

conservation

display_name: conservation

database

display_name: database

eco-fauna

display_name: eco-fauna

ecosystem-protection

display_name: ecosystem-protection

fauna

display_name: fauna

fungi

display_name: fungi

info-fauna

display_name: info-fauna

lichens

display_name: lichens

red-list

display_name: red-list

soil-biodiversity

display_name: soil-biodiversity

soil-dependency

display_name: soil-dependency

soil-organisms

display_name: soil-organisms

soil-quality

display_name: soil-quality

educ

en: Education, culture and sport

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


maintainer: Michael Lehning

en: Data Source

en: Hourly Gridded Wind Power Time Series

en: Coordinates of Grid Points

en: Description

en: Full Data Description

en:
This dataset consist of simulated hourly power production from an Enercon E82 Turbine at 100 m hub-height. It describes the hourly power output a 1MW turbine would produce in each 0.01° grid cell for the years 2016 and 2017.

100 m wind speed data was taken from the COSMO-1 model (Consortium for Small-scale Modeling 2017), which has a 0.01° horizontal resolution. The domain covered is the whole of Switzerland, with the exclusion of lakes. As such, the number of 0.01◦ pixels within Switzerland amounts to 48657.
Conversion to power output was done based on the power curve of the Enercon E82 Turbine. As power output is lower at altitude due to lower air density, we corrected for this effect as described in (Kruyt et al. 2017).

Please cite the following paper in connection with the dataset:

Paper Citation:
 > _Bert Kruyt, Jérôme Dujardin, and Michael Lehning: Improvement of wind power assessment in complex terrain: The case of COSMO-1 in the Swiss Alps, Front. Energy Res., [doi:10.3389/fenrg.2018.00102] (https://doi.org/10.3389/fenrg.2018.00102)_

mountain-wind-potential

display_name: mountain-wind-potential

topography

display_name: topography

wind-energy

display_name: wind-energy

en: Dataset on Cosmo-1 based Energy Potential in Swiss Alps


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

en: ADS datasets and simulation inputs

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

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

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

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

Please cite:

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

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

alpine-precipitation

display_name: alpine-precipitation

snow

display_name: snow

snow-cover

display_name: snow-cover

snow-distribution

display_name: snow-distribution

snow-hydrology

display_name: snow-hydrology

envidat

en: EnviDat: the Environmental Data Portal

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


maintainer: Matthias Haeni

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

en: Field station data (ISF)

en: Data file.

en: Data plot of the forest station (ISB)

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

en: Site photo of the field station (ISF)

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

mimetype: image/jpeg

en: Forest station data (ISB)

en: Data plot of the field station (ISF)

en: Site aerial photo (Isone)

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

en: Site photo of the forest station (ISB)

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

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Isone in Switzerland where one station is located within a natural broad-leaved forest stand (ISB) with European beech (_Fagus sylvatica_; 70-100 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, ISF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Isone 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: 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: Isone, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards


maintainer: Elisabeth Hafner

en: natural-hazard hazard-mapping snow satellite avalanche

en: SPOT6 Avalanche Outlines 16 January 2019

en: Shapefile of the manually mapped avalanche outlines. Please only use this data together with the example key document provided with this dataset and within the limitations of the dataset described in the publications.

format: SHP

en:
Outlines of 6'041 avalanches mapped from SPOT6 satellite data over the Swiss Alps on 16 January 2019. The dataset was acquired following a period with very high avalanche danger. The outlines have different attributes described in the data example key (ExampleKey_AvalMapping16012019.pdf)

The generation of the data is described in:

Bühler, Y., E. D. Hafner, B. Zweifel, M. Zesiger, and H. Heisig (2019), Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps, The Cryosphere, 13(12), 3225-3238, doi:10.5194/tc-13-3225-2019.

The data was comprehensivly validated in a subset area in

Hafner, E.D.; Techel, F.; Leinss, S.; Bühler, Y., 2021: Mapping avalanches with satellites - evaluation of performance and completeness. Cryosphere, 15, 2: 983-1004. doi: 10.5194/tc-15-983-2021

avalanche

display_name: avalanche

hazard-mapping

display_name: hazard-mapping

natural-hazard

display_name: natural-hazard

satellite

display_name: satellite

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: SPOT6 Avalanche outlines 16 January 2019


en: minimum-temperature timeseries maximum-temperature climate-layers free-climate-data precipitation climate air-temperature century

en: CHELSA Technical Documentation

en: As the CHELSA project is continuously expanded, certain new parameters, time scales etc. are not included anymore in the original publication (Karger et al. 2017). We are therefore provide this document as a guideline of the current climatic parameters available and will expand it as new parameters or time periods will become available. Please refer to the version history for changes made in the document.

en: Data Access

format: GeoTIFF

en:
CHELSAcruts is a delta change monthly climate dataset for the years 1901-2016 for mean monthly maximum temperatures, mean monthly minimum temperatures, and monthly precipitation sum. Here we use the delta change method by B-spline interpolation of anomalies (deltas) of the CRU TS 4.01 dataset. Anomalies were interpolated between all CRU TS grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2 (Karger et al. 2017, Scientific Data). This method has the assumption that climate only varies on the scale of the coarser (CRU TS) dataset, and the spatial pattern (from CHELSA) is consistent over time. This is certainly a rather crude assumption, and for time periods for which more accurate data is available CHELSAcruts should be avoided if possible (e.g. use CHELSA V1.2 for 1979-2015). Different to CHELSA V1.2, CHELSAcruts does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages.

CHELSAcruts is licensed under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.

air-temperature

display_name: air-temperature

century

display_name: century

climate

display_name: climate

climate-layers

display_name: climate-layers

free-climate-data

display_name: free-climate-data

maximum-temperature

display_name: maximum-temperature

minimum-temperature

display_name: minimum-temperature

timeseries

display_name: timeseries

en: CHELSAcruts -  High resolution temperature and precipitation timeseries for the 20th century and beyond


maintainer: Franziska Gerber

en: antarctica cryowrf reproducibility blowing-snow surface-mass-balance

en: Processed CRYOWRF output used for the analysis and the figures in the paper.

en: Python scripts to reproduces figures in Gerber et al., 2022, submitted.

en: Processed WRF output (run with Noah-MP land surface model) used for the analysis and the figures in the paper.

en: Python scripts to reproduces figures in Gerber et al., 2022, revised

mimetype: application/x-zip-compressed

en: Namelist and input files and information to run the WRF and CRYOWRF simulations used in the paper.

en: Static input data from the WRF simulations (used in the python scripts in Scripts_Reproducibility).

en:
This dataset contains data and scripts for "CRYOWRF - a validation and the effect of blowing snow on the Antarctic SMB" (Gerber et al., submitted).

* Simulation_setup: Namelists and input information to run the simulation. Some input files need to be downloaded from Sharma et a., 2021.
* Static_input: Static topography input file of WRF (geo_em.d01).
* WRF_27km_NoahMP: Preprocessed WRF output of the simulation run with the WRF using the surface parameterization Noah-MP to reproduce the figures and results in the paper.
* WRF_27km_CRYOWRF: Preprocessed WRF output of the simulation run with CRYOWRF to reproduce the figures and results in the paper.
* Scripts_Reproducibility: Python scripts to reproduce the figures and results in the paper.

Note:

* To run some of the scripts Atmospheric Weather station data needs to be prepared using Gerber and Lehning, 2022.
* AWS data is not provided and needs to be downloaded from the corresponding databases. Please make sure to comply with the respective terms and conditions.

antarctica

display_name: antarctica

blowing-snow

display_name: blowing-snow

cryowrf

display_name: cryowrf

reproducibility

display_name: reproducibility

surface-mass-balance

display_name: surface-mass-balance

en: Reproducibility dataset for CRYOWRF validation


aerosol-data-weissfluhjoch

title_for_slug: aerosol-data-weissfluhjoch

en: linda inp particles raclets ice-nucleation aerosol

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: LINDA Dataset Weissfluhjoch

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

en: OPC-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: OPC-GT526S Dataset Weissfluhjoch

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

en: 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

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: Aerosol Data Weissfluhjoch


en: beech forest-reserves swiss-plateau forest-in temperate-forests

en: Lookup

en: The file lookup.csv contains description for the values shown in the 5 result tables.

en: Results TreMs

en: The file results_trems.csv contains results for the density of TreMs per ha.

en: Results habitat trees

en: The file results_habitat_trees.csv contains results for the number of habitat trees.

en: Data description

en: This document briefly describes the datasets.

en: Results trees

en:
The file results_trees.csv contains results of living, standing and lying and dead standing
trees as well as for stumps (trees found on the plot but broken / cut below 1.3 m).

en: Results lying deadwood

en: The file results_lying_deadwood.csv contains results of the lying deadwood survey.

en: Results regeneration

en: The file results_regeneration.csv contains results of the regeneration survey.

en:
The Jorat is one of the largest continously forested areas in the Swiss Plateau. On a forested area of 778 ha, the Parc Naturel du Jorat (PNJ), a periurban parc, has been established in 2022. To document the initial state of the forest within the perimeter of the PNJ, a sample plot inventory (SPI) was carried out on 132 sample plots (SP) in winter 2021/22.
This dataset contains results from this sample plot inventory. It consists of the following files:
- results_trees.csv: Results for or living and dead trees.
- results_regeneration.csv: Results for trees with DBH < 7.0 cm, assessed in three height classes.
- results_lying_deadwood.csv: Results for lying deadwood, assessed no three line transects
- results_trems.csv: Results for occurence of tree related microhabitats (TreMs)
- results_habitat_trees.csv: Results for occurence / densities of trees carrying at least one TreM or with a DBH >= 80 for living trees or >= 36 cm for dead trees respectively.
- lookup.csv: Contains lookup tables which describe the respective results in-depth.
- data_description.pdf: Briefly describes the datasets mentioned above.

beech

display_name: beech

forest-in

display_name: forest-in

forest-reserves

display_name: forest-reserves

swiss-plateau

display_name: swiss-plateau

temperate-forests

display_name: temperate-forests

en: Sample plot inventory data form the Parc Naturel du Jorat


en: time-series studibach groundwater alptal switzerland

media_type: text/plain

en:
Groundwater time series of the monitoring network in the Studibach (C7) catchment, Alptal, Switzerland published in
Rinderer M., van Meerveld I, McGlynn B. (2019): From points to patterns – Assessing runoff source area dynamics and hydrological connectivity using time series clustering. Water Resources Research, doi: 2018WR023886R

format: TXT

mimetype: text/plain

en: Groundwater time series between 2010 and 2014 of the distributed monitoring system in the Studibach (C7), Alptal, Switzerland. Data published in Rinderer M., van Meerveld I, McGlynn B. (2019): From points to patterns – Assessing runoff source area dynamics and hydrological connectivity using time series clustering. Water Resources Research, doi: 2018WR023886R

alptal

display_name: alptal

groundwater

display_name: groundwater

studibach

display_name: studibach

switzerland

display_name: switzerland

time-series

display_name: time-series

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: Groundwater time series Studibach (Rinderer et al., 2019, WRR)


global-cryosphere-watch-data-survey

maintainer: Mathias Bavay

title_for_slug: global-cryosphere-watch-data-survey

en: data-portal automatic-publication data-usage gcw data-exchange

en: Data interoperability survey

en: Third party data usage

en: Two surveys on the topic of data usage where conducted for the Global Cryosphere Watch data portal. The first one focused on the data provider point of view while the second one focused on the data user point of view. 37 data providers (ie institutions) worldwide provided their answers for the first survey (from fall 2017 until summer 2018) while 54 users (contacted through various mailing list such as the Cryolist) answered the questions on their third party data usage (fall 2019 until January 2020).

automatic-publication

display_name: automatic-publication

data-exchange

display_name: data-exchange

data-portal

display_name: data-portal

data-usage

display_name: data-usage

gcw

display_name: gcw

en: Global Cryosphere Watch data survey


en: wms-data digital-elevation-model cable-yarding swiss-geodata digital-planning qgis-plugin seilaplan open-source geoinformation-system

en: View video on youtube!

en: WMS-Layer als Hintergrundkarten laden

en:
In order to digitally plan a cable line using the QGIS plugin ‘Seilaplan’, maps with various background information are helpful. In this tutorial we show you how to obtain maps that are helpful for cable line planning, for example a national map of Switzerland at different scales, the NFI vegetation height model or the NFI forest mix rate. For this we explain what WMS datasets are and how to integrate them into QGIS. No download of large data is needed for this, only a good internet connection. Please note that the tutorial language is German!

Link for the integration of WMS data: https://wms.geo.admin.ch/

Link to the description on the Swisstopo website:
https://www.geo.admin.ch/en/geo-services/geo-services/portrayal-services-web-mapping/web-map-services-wms.html

Link to the Seilaplan website: https://seilaplan.wsl.ch

**************************

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung sind verschiedene Hintergrundkarten hilfreich. In diesem Tutorialvideo zeigen wir, was WMS Daten sind und wie man diese in QGIS einbinden kann. Dafür müssen die Daten nicht heruntergeladen werden. Es braucht lediglich eine gute Internetverbindung. Für die Seillinienplanung hilfreiche Karten sind bspw. die Landeskarte der Schweiz in verschiedenen Massstäben, das Vegetationshöhenmodell LFI oder der Waldmischungsgrad LFI.

Link zur Einbindung der WMS Daten: https://wms.geo.admin.ch/

Link zur Beschreibung auf der Swisstopo Webseite: https://www.geo.admin.ch/de/geo-dienstleistungen/geodienste/darstellungsdienste-webmapping-webgis-anwendungen/web-map-services-wms.html

Link zur Seilaplan-Website: https://seilaplan.wsl.ch

cable-yarding

display_name: cable-yarding

digital-elevation-model

display_name: digital-elevation-model

digital-planning

display_name: digital-planning

geoinformation-system

display_name: geoinformation-system

open-source

display_name: open-source

qgis-plugin

display_name: qgis-plugin

seilaplan

display_name: seilaplan

swiss-geodata

display_name: swiss-geodata

wms-data

display_name: wms-data

en: Seilaplan Tutorial: Load WMS layers as background maps


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

en: Field station data (BTF)

en: Data file.

en: Data plot of the forest station (BTB)

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

en: Data plot of the field station (BTF)

en: Site photo of the field station (BTF)

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

en: Site photo of the forest station (BTB)

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

en: Site aerial photo (Bettlachstock)

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

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Bettlachstock in Switzerland where one station is located within a natural mixed forest stand (BTB) with European beech (_Fagus sylvatica_; 170-190 yrs), European silver fir (_Abies alba_; 190 yrs) and Norway spruce (_Picea abies_; 200 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, BTF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Bettlachstock is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

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


en: shading fractional-snow shortwave-radiation longwave-radiation forest

en: Radiation Data 18.04.2019

en:
Spatiotemporal incoming/outgoing shortwave and longwave radiation as well as sunlit and shaded-snow view fractions below an evergreen forest stand in the Swiss alps. Data was collected on 18th of April 2018 along a 48m long, heterogeneous forest stand under full snow cover conditions.

The presented data is used and further described in the following paper: Malle et al. (2019). Shading by Trees and Fractional Snow Cover control the Subcanopy Radiation Budget. Journal of Geophysical Research Atmospheres. DOI:10.1029/2018JD029908.

en: Seasonal sub-canopy radiation data

en:
Seasonal, spatially aggregated incoming and outgoing shortwave radiation below an evergreen forest stand collected with a cable car system.

The presented data is used and further described in the following paper: Malle et al. (2019). Shading by Trees and Fractional Snow Cover Control the Subcanopy Radiation Budget. Journal of Geophysical Research Atmospheres. DOI:10.1029/2018JD029908.

en: Radiation Data 25.4.2018

en:
Spatiotemporal incoming/outgoing shortwave and longwave radiation as well as sunlit and shaded-snow view fractions below an evergreen forest stand in the Swiss alps. Data was collected on 25th of April 2018 along a 48m long, heterogeneous forest stand under partial snow cover conditions.

The presented data is used and further described in the following paper: Malle et al. (2019). Shading by Trees and Fractional Snow Cover control the Subcanopy Radiation Budget. Journal of Geophysical Research Atmospheres. DOI:10.1029/2018JD029908.

en: Sky-view fraction along the cablecar transect

en: Data is used and described in the following paper: Malle et al. (2019). Shading by Trees and Fractional Snow Cover control the Subcanopy Radiation Budget. Journal of Geophysical Research. DOI:10.1029/2018JD029908.

en: This data set consists of incoming and outgoing short- and longwave radiation as well as sunlit-snow-view-fraction as described in the JGR-Atmospheres paper "Shading by trees and fractional snow cover control the sub-canopy radiation budget", by Malle et al. (2019). Data was collected along a 48m long, heterogeneous forest transect between January and June 2018 close to Davos, Switzerland.

forest

display_name: forest

fractional-snow

display_name: fractional-snow

longwave-radiation

display_name: longwave-radiation

shading

display_name: shading

shortwave-radiation

display_name: shortwave-radiation

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.


data-broedlin-cnp

title_for_slug: data-broedlin-cnp

en: drying-rewetting carbon soil stoichiometry forest phosphorus nitrogen dissolved-organic-matter mineralization

en: Data Broedlin-CNP

en: Data Broedlin-CNP-Release

en: Raw data of C-N-P mobilization experiment from Oi, Oe/Oa, and A horizons of beech forest soils along a nutrient availability gradient.

en: Mircocosm experiment to identify the individual patterns and controls of C, N, and P mobilization in soils under beech forests. Organic and mineral horizons sampled along a nutrient availability gradient in Germany were exposed to either permanent moist conditions or to dry spells in microcosms and quantified the release of inorganic and organic C, N, and P.

carbon

display_name: carbon

dissolved-organic-matter

display_name: dissolved-organic-matter

drying-rewetting

display_name: drying-rewetting

mineralization

display_name: mineralization

nitrogen

display_name: nitrogen

phosphorus

display_name: phosphorus

soil

display_name: soil

stoichiometry

display_name: stoichiometry

en: Data Broedlin CNP


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

en: Data plot of the field station (CLF)

en: Data file.

en: Data plot of the forest station (CLB)

en: Site aerial photo (Celerina)

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

en: Site photo of the field station (CLF)

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

en: Site photo of the forest station (CLB)

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

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

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 Celerina in Switzerland where one station is located within a natural coniferous forest stand (CLB) with Swiss pine (_Pinus cembra_; 210-250 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, CLF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Celerina 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

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


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

en: Shapefile of 307 catchments in Switzerland

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

en: Daily simulated data for the future period

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

en: Daily simulated data for the reference period

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

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

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

*References*:

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

discharge

display_name: discharge

hydrology

display_name: hydrology

snowmelt

display_name: snowmelt

soil-moisture

display_name: soil-moisture

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


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

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

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

snowpack

display_name: snowpack

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


maintainer: Daniela Brito Melo

en: mixed-grain-transport saltation drifting-snow snow-cohesion large-eddy-simulations

en: Data in Figures

en: Simulation results presented in Figures 1 to 11 of the research article "Modeling snow saltattion: the effect of grain size and interparticle cohesion" in csv format.

en: LES-LSM code

en: Source code used to perform the analysis presented at the research article "Modeling snow saltation: the effect of grain size and interparticle cohesion". The source code is also available at the WSL/SLF GitLab Repository (https://gitlabext.wsl.ch/atmospheric-models/les-lsm).

en:
This dataset includes the parallel application and the main results supporting the research article "Modeling snow saltation: the effect of grain size and interparticle cohesion" published at the Journal of Geophysical Research: Atmospheres.
The code is a flow solver based on the Large Eddy Simulation (LES) technique coupled with a Lagrangian Stochastic Model (LSM). The interaction of snow particles with the bed is modeled with statistical and physically-based models for aerodynamic entrainment, rebound and splash, following the works of Groot Zwaaftink et al. (2014), Comola and Lehning (2017) and Sharma et al. (2018). This algorithm was also used by Sigmund et al. (2021) to model snow sublimation.

drifting-snow

display_name: drifting-snow

large-eddy-simulations

display_name: large-eddy-simulations

mixed-grain-transport

display_name: mixed-grain-transport

saltation

display_name: saltation

snow-cohesion

display_name: snow-cohesion


en: urbanization urban-ecology spiders species-abundance-distributions snails sdms diversity species-richness-models insects arthropods birds

en: contains the raw data to perform the species richness models of all species

en: abundance and occurrence data of the 1446 species studied, used to investigate species-abundance distributions and species-occurrence distributions

en: contains the raw data to perform species distribution models, only for common species

en: contains the raw data to perform the species richness models of the rare species

en: contains the raw data to perform the species richness models of the hypercommon species

en:
Richness, site occurrence and abundance data of bees, beetles, birds, hoverflies, net-wingeds, true bugs, snails, spiders, milipides, wasps collected in the city of Zurich using different sampling techniques, and the environmental variables for each sampling site.
Data are provided on request to contact person against bilateral agreement.

arthropods

display_name: arthropods

birds

display_name: birds

diversity

display_name: diversity

insects

display_name: insects

sdms

display_name: sdms

snails

display_name: snails

species-abundance-distributions

display_name: species-abundance-distributions

species-richness-models

display_name: species-richness-models

spiders

display_name: spiders

urban-ecology

display_name: urban-ecology

urbanization

display_name: urbanization

en: Ecological properties of urban ecosystems. Biodiversity dataset of Zurich