mobility

fr: Mobilité et transports

de: Mobilität und Verkehr

en: Mobility and Transport

it: Mobilità e trasporti

de: geografische-bezeichnungen verkehrsnetze vektor wanderweg geographisches-informationssystem gis-geografisches-informationssystem landschaftsmodell bgdi-bundesgeodaten-infrastruktur

spatial: Schweiz

fr: Map (Preview) Aperçu map.geo.admin.ch

de: Map (Preview) Vorschau map.geo.admin.ch

en: Map (Preview)  map.geo.admin.ch

it: Map (Preview) Previsione map.geo.admin.ch

fr: Aperçu map.geo.admin.ch

de: Vorschau map.geo.admin.ch

en: Preview map.geo.admin.ch

it: Previsione map.geo.admin.ch

format: SERVICE

format: WMS

fr: Download

de: Download

en: Download

it: Download

format: HTML

fr: RESTful API de geo.admin.ch

de: RESTful API von geo.admin.ch

en: RESTful API from geo.admin.ch

it: RESTful API da geo.admin.ch

format: API

fr: swissTLM3D chemins de randonnée pédestre comprend les chemins de randonnée officiels de la Suisse et de la Principauté de Liechtenstein. Le jeu de données est publié en collaboration avec l'Office fédéral des routes OFROU, SuisseMobile, Suisse Rando et les cantons. swissTLM3D chemins de randonnée pédestre fait partie du jeu de données swissTLM3D.

de: swissTLM3D Wanderwege enthält die signalisierten Wanderrouten der Schweiz und des Fürstentums Liechtenstein. Der Datensatz wird in Zusammenarbeit mit dem Bundesamt für Strassen ASTRA, SchweizMobil, Schweizer Wanderwege und den Kantonen publiziert. swissTLM3D Wanderwege bildet einen Teil des Datensatzes swissTLM3D.

en: swissTLM3D hiking trails contains the hiking trails of Switzerland and the Principality of Liechtenstein. This dataset is published in collaboration with the Federal roads office FEDRO, SwitzerlandMobility, Suisse Rando and the cantons. swissTLM3D hiking trails forms a part of the dataset swissTLM3D.

it: swissTLM3D sentieri pedestri comprende i sentieri pedestri ufficiali della Svizzera e del Principato del Liechtenstein. Il set di dati viene pubblicato in collaborazione con l'Ufficio federale delle strade USTRA, SvizzeraMobile, Sentieri Svizzeri e i cantoni. swissTLM3D sentieri pedestri fa parte del set di dati swissTLM3D.

name: Bundesamt für Landestopografie swisstopo

fr: Office fédéral de topographie swisstopo

en: Federal Office of Topography swisstopo

it: Ufficio federale di topografia swisstopo

fr: Le centre de compétence de la Confédération suisse pour les informations géographiques, c'est-à-dire pour la description, la représentation et l’archivage de données à référence spatiale.

de: Das Kompetenzzentrum der Schweizerischen Eidgenossenschaft für Geoinformation, d.h. für die Beschreibung, Darstellung und Archivierung von raumbezogenen Geodaten.

en: The centre of competence for the Swiss Confederation responsible for geographical reference data, for instance the description, representation and archiving of geographic spatial data.

it: Il centro d’eccellenza della Confederazione Elvetica per geoinformazione, cioè per la descrizione, rappresentazione e archiviazione dei dati georeferenziati (geodati).

political_level: confederation

value: Swisstopo - Geocat Harvester

de: swissTLM3D Wanderwege

en: swissTLM3D hiking trails

it: swissTLM3D percorsi escursionistici


geography

fr: Géographie

de: Geographie

en: Geography

it: Geografia

territory

fr: Territoire et environnement

de: Raum und Umwelt

en: Territory and environment

it: Territorio e ambiente

label: geocat.ch permalink

state: active

type: dataset

fr: Shop

de: Shop

en: Shop

it: Shop

fr: swissTLM3D chemin de fer comprend les tracés des chemins de fer à voie normale, à voie étroite et les petits chemins de fer. Toutes les voies sont saisies pour les tronçons à plusieurs voies et dans les zones de gare. swissTLM3D chemin de fer fait partie du jeu de données swissTLM3D. Avec une haute précision et l'intégration de la troisième dimension, swissTLM3D constitue le jeu de données vectorielles en 3D le plus complet de la Suisse.

de: swissTLM3D Eisenbahn enthält die Gleisachsen der Normal- und Schmalspurbahnen sowie der Kleinbahnen. Bei mehrspurigen Anlagen oder in Bahnhofszonen sind immer sämtliche Gleise erfasst. swissTLM3D Eisenbahn ist Bestandteil des Datensatzes swissTLM3D. Mit seiner hohen Genauigkeit und dem Einbezug der dritten Dimension ist swissTLM3D der umfassendste 3D-Vektordatensatz der Schweiz.

en: swissTLM3D Railway contains the path of the railways with narrow-gauge and standard gauge. All tracks are captured in the sections with multiple tracks and in the areas of a station. swissTLM3D Railway is part of the dataset swissTLM3D. With a high accuracy and the incorporation of a third dimension, swissTLM3D is the most extensive 3D vector data set of Switzerland.

it: swissTLM3 linee ferroviarie comprende i tracciati delle linee ferroviarie a scartamento normale, a scartamento ridotto e linee ferroviarie minori. Tutti i binari sono rappresentati singolarmente, anche sui tratti a più binari e in corrispondenza delle stazioni. Fa parte del set di dati swissTLM3D. Il prodotto swissTLM3D offre un'alta precisione e include anche la terza dimensione, si tratta della raccolta dati vettoriali in 3D più completa del territorio svizzero.

de: Bundesamt für Landestopografie swisstopo

type: organization

approval_status: approved

fr: swissTLM3D Chemin de fer

de: swissTLM3D Eisenbahn

en: swissTLM3D Railway

it: swissTLM3D Linee ferroviarie


fr: swissTLM3D Forêt comprend les surfaces boisées de la Suisse et de la Principauté de Liechtenstein. Ils font partie du jeu de données swissTLM3D. Avec une haute précision et l'intégration de la troisième dimension, swissTLM3D constitue le jeu de données vectorielles en 3D le plus complet de la Suisse. Dans swissTLM3D Forêt, les types d'objets "Forêt", "Forêt clairsemée" et "Forêt buissonnante" sont représentés différemment.

de: swissTLM3D Wald enthält die Waldflächen der Schweiz und des Fürstentums Liechtenstein. Sie sind Bestandteil des Datensatzes swissTLM3D. Mit seiner hohen Genauigkeit und dem Einbezug der dritten Dimension ist swissTLM3D der umfassendste 3D-Vektordatensatz der Schweiz. In swissTLM3D Wald sind die Objektarten «Wald», «Wald offen» und «Gebueschwald» unterschiedlich dargestellt.

en: swissTLM3D Forest contains the forested areas of Switzerland and the Principality of Liechtenstein. It is part of the dataset swissTLM3D. With a high accuracy and the incorporation of a third dimension, swissTLM3D is the most extensive 3D vector data set of Switzerland. In swissTLM3D Forest, the object types "Forest", "Sparse forest" and "Bush forest" are represented differently.

it: swissTLM3D Foresta comprende le superfici forestali della Svizzera e del Principato del Liechtenstein e fa parte del set di dati swissTLM3D. Il prodotto swissTLM3D offre un'alta precisione e include anche la terza dimensione, si tratta della raccolta dati vettoriali in 3D più completa del territorio svizzero. In swissTLM3D Foresta i tipi di oggetto "Foresta", "Foresta rada" e "Arbusteto" sono rappresentati in modo diverso.

fr: swissTLM3D Forêt

de: swissTLM3D Wald

en: swissTLM3D Forest

it: swissTLM3D Foresta


mobility

de: Mobilität und Verkehr

fr: swissTLM3D autres transports comprend les différents transports à câble ainsi que d'autres types de transport mécanique. Il fait partie du jeu de données swissTLM3D. Avec une haute précision et l'intégration de la troisième dimension, swissTLM3D constitue le jeu de données vectorielles en 3D le plus complet de la Suisse. Dans le géoportail fédéral, les objets de la couche " autres transports" sont affichés progressivement en fonction des niveaux de zoom.

de: swissTLM3D übrige Bahnen beinhaltet verschiedene Seilbahnarten sowie andere mechanische Transportsysteme. Es ist Bestandteil des Datensatzes swissTLM3D. Mit seiner hohen Genauigkeit und dem Einbezug der dritten Dimension ist swissTLM3D der umfassendste 3D-Vektordatensatz der Schweiz. Im Geoportal des Bundes werden die Objekte der Ebene "übrige Bahnen" entsprechend der ausgewählten Zoomstufe schrittweise dargestellt.

en: swissTLM3D other transportations contains the different types of cable railway and other types of mechanical transport. It is part of the dataset swissTLM3D. With a high accuracy and the incorporation of a third dimension, swissTLM3D is the most extensive 3D vector data set of Switzerland. In the federal geoportal the objects of the layer "other transportations" are displayed gradually according to the different zoomlevels.

it: swissTLM3D altri trasporti comprende i differenti tipi di trasporto a fune e altri tipi di trasporto meccanico. Fa parte del set di dati swissTLM3D. Il prodotto swissTLM3D offre un'alta precisione e include anche la terza dimensione, si tratta della raccolta dati vettoriali in 3D più completa del territorio svizzero. All'interno del geoportale federale, gli oggetti del layer "altri trasporti " sono visualizzabili progressivamente, in funzione del livello di zoom selezionato.

fr: swissTLM3D Autres transports

de: swissTLM3D Übrige Bahn

en: swissTLM3D Other transportations

it: swissTLM3D Altri trasporti


fr: swissTLM3D est le modèle topographique du paysage à grande échelle de la Suisse. Il comprend des objets naturels et artificiels ainsi que des données de noms sous forme vectorielle. Avec une haute précision et l'intégration de la troisième dimension, swissTLM3D constitue le jeu de données vectorielles en 3D le plus précis et le plus complet de la Suisse.

de: swissTLM3D ist das grossmassstäbliche Topografische Landschaftsmodell der Schweiz. Es umfasst die natürlichen und künstlichen Objekte wie auch die Namendaten in vektorieller Form. Mit einer hohen Genauigkeit und dem Einbezug der dritten Dimension ist swissTLM3D der genaueste und umfassendste 3D-Vektordatensatz der Schweiz.

en: swissTLM3D is the large-scale topographical landscape model of Switzerland. It includes both natural and artificial landscape features, as well as name data in vector form. With a high accuracy and the incorporation of a third dimension, swissTLM3D is the most extensive and accurate 3D vector data set of Switzerland.

it: swissTLM3D è il Modello topografico dettagliato del paesaggio svizzero. Comprende gli oggetti naturali e artificiali con i dati toponimici in formato vettoriale. Questo prodotto, che offre un'alta precisione e include anche la terza dimensione, rappresenta la raccolta più precisa e completa di dati vettoriali in 3D sul territorio svizzero.


maintainer: OGD-Koordinationsstelle Kanton Thurgau

de: json

media_type: application/json

de: Lotteriefonds Kanton Thurgau: Zugesprochene Beiträge (json)

format: JSON

mimetype: application/json

license: CC BY 4.0

de: csv

media_type: text/csv

de: Lotteriefonds Kanton Thurgau: Zugesprochene Beiträge (csv)

format: CSV

mimetype: text/csv

de: Der Datensatz enthält die zugesprochenen Projektbeiträge aus dem Lotteriefonds des Kantons Thurgau. Zuständig für die Bearbeitung der Gesuche an den Lotteriefonds ist das Kulturamt Kanton Thurgau. Unterstützt werden kulturelle, wissenschaftliche und gemeinnützige Projekte, die einen Bezug haben zum Kanton und von überregionaler Bedeutung sind.Die gesetzlichen Grundlagen und die formalen, inhaltlichen und kulturpolitischen Kriterien für die Beurteilung von Projekten sind online unter www.kulturamt.tg.ch einzusehen.

culture

fr: Culture, médias, société de l'information, sport

de: Kultur, Medien, Informationsgesellschaft, Sport

en: Culture, media, information society, sport

it: Cultura, media, società dell'informazione, sport

kanton-thurgau

fr: Canton de Thurgovie

de: Kanton Thurgau

en: Canton of Thurgau

it: Cantone di Turgovia

de: Der Kanton Thurgau veröffentlicht hier schrittweise OGD-Datensätze der kantonalen Verwaltung.

political_level: canton

key: guid

key: uri

value: TG-Kt DCAT Harvester


geologische-vektordatensatze-geocover

geography

de: Geographie

territory

de: Raum und Umwelt

title_for_slug: geologische-vektordatensatze-geocover

spatial: Landeskarte 1:25'000

fr: Map (Preview) Aperçu map.geo.admin.ch

de: Map (Preview) Vorschau map.geo.admin.ch

en: Map (Preview)  map.geo.admin.ch

it: Map (Preview) Previsione map.geo.admin.ch

fr: Aperçu map.geo.admin.ch

de: Vorschau map.geo.admin.ch

en: Preview map.geo.admin.ch

it: Previsione map.geo.admin.ch

de: RESTful API von geo.admin.ch

en: RESTful API from geo.admin.ch

it: RESTful API da geo.admin.ch

fr: Les jeux de données géologiques GeoCover V2 (standardisés selon le Modèle de données géologiques) se basent sur les cartes existantes de l’Atlas géologique de la Suisse 1:25’000 (GA25) et sur des compilations extraites de cartes géologiques de bonne qualité. La nouvelle version GeoCover V3 (disponible dès le 1er juin 2022), est quant à elle, une version révisée au niveau du contenu. La révision est délimitée géographiquement à la Suisse.

de: Die GeoCover-Datensätze V2 (gemäss Datenmodell Geologie standardisiert) basieren auf den Blättern des Geologischen Atlas der Schweiz 1:25’000 (GA25) und auf Kompilationen der zur Verfügung stehenden, geologischen Karten guter Qualität. Die neue Version GeoCover V3 (verfügbar ab 1. Juni 2022) ist eine inhaltlich überarbeitete Version. Die Überarbeitung ist geografisch auf die Schweiz beschränkt.

en: The GeoCover Datasets V2 (standardized according to the Data Model Geology of Switzerland) is based on the map sheets of the Geological Atlas of Switzerland 1:25 000 and on compilations of the most recent high-quality geological maps that are available. The original versions (datasets not standardized), based solely on the GA25, are available as a complete dataset in the layer «Division geological atlas 25 Vector».

it: I set di dati GeoCover V2 (standardizzati sulla base del Modello di dati geologici) si basano sui fogli dell’Atlante geologico della Svizzera in scala 1:25'000 e sulle compilazioni di carte geologiche di buona qualità attualmente disponibili. La nuova versione GeoCover V3 (disponibile dal 1° giugno 2022) è una versione rivista in termini di contenuto. La revisione è limitata geograficamente alla Svizzera.

de: Das Kompetenzzentrum der Schweizerischen Eidgenossenschaft für Geoinformation, d.h. für die Beschreibung, Darstellung und Archivierung von raumbezogenen Geodaten.

fr: Jeux de données géologiques vectorielles GeoCover

de: Geologische Vektordatensätze GeoCover

en: Geological Vector Datasets GeoCover

it: Set di dati geologici vettoriali GeoCover


geography

en: Geography

it: Geografia

mobility

fr: Mobilité et transports

de: Mobilität und Verkehr

en: Mobility and Transport

it: Mobilità e trasporti

de: Shop

fr: RESTful API de geo.admin.ch

fr: swissTLM3D réseau hydrographique comprend les cours d'eau et les contours de lac de la Suisse et de la Principauté de Liechtenstein. Il fait partie du jeu de données swissTLM3D. Avec une haute précision et l'intégration de la troisième dimension, swissTLM3D constitue le jeu de données vectorielles en 3D le plus complet de la Suisse.

de: swissTLM3D Gewässernetz enthält die Wasserläufe und Seeumrisse der Schweiz und des Fürstentums Liechtenstein. Es ist Bestandteil des Datensatzes swissTLM3D. Mit seiner hohen Genauigkeit und dem Einbezug der dritten Dimension ist swissTLM3D der umfassendste 3D-Vektordatensatz der Schweiz.

en: swissTLM3D hydrographical network contains the watercourses and lake contours of Switzerland and the Principality of Liechtenstein. It is part of the dataset swissTLM3D. With a high accuracy and the incorporation of a third dimension, swissTLM3D is the most extensive 3D vector data set of Switzerland.

it: swissTLM3D rete idrografica che comprende i corsi d'acqua e i contorni dei laghi della Svizzera e del Principato del Liechtenstein. Fa parte del set di dati swissTLM3D. Il prodotto swissTLM3D offre un'alta precisione e include anche la terza dimensione, si tratta della raccolta dati vettoriali in 3D più completa del territorio svizzero.

fr: Office fédéral de topographie swisstopo

de: Bundesamt für Landestopografie swisstopo

en: Federal Office of Topography swisstopo

it: Ufficio federale di topografia swisstopo

fr: Le centre de compétence de la Confédération suisse pour les informations géographiques, c'est-à-dire pour la description, la représentation et l’archivage de données à référence spatiale.

en: The centre of competence for the Swiss Confederation responsible for geographical reference data, for instance the description, representation and archiving of geographic spatial data.

it: Il centro d’eccellenza della Confederazione Elvetica per geoinformazione, cioè per la descrizione, rappresentazione e archiviazione dei dati georeferenziati (geodati).

fr: swissTLM3D Réseau hydrographique

de: swissTLM3D Gewässernetz

en: swissTLM3D Hydrographical network

it: swissTLM3D Rete idrografica


de: strassenverkehr verkehrsnachfrage oev verkehrsmittelwahl auto raumplanungsregionen verkehr gemeinden velo miv oeffentlicherverkehr

label: Gesetzliche Grundlage: Gesamtverkehrskonzept 2018 (GVK 2018) (Regierungsratsbeschluss Nr. 25/2018)

label: Gesamtverkehrsmodell Kanton Zürich im GIS-Browser

de: Verkehrsbeziehungen, Verkehrsaufkommen und Verkehrsmittelwahl MIV und ÖV nach Gemeinden

de: Die Daten zeigen die Verkehrsnachfrage im öffentlichen Verkehr (‘oev’) und im motorisierten Individualverkehr (‘miv’) als absolutes Verkehrsaufkommen (Summe aller Personenwege an einem durchschnittlichen Werktag (DWV)) und als Modal Split (prozentuale Anteile der Verkehrsmittelarten an allen Personenwegen). Neben den Gemeinden werden auch die Verkehrsbeziehungen von und zu den Nachbarkantonen berücksichtigt ("_gebietart" = Aussenkorridor). ‘quelle_code’ und ‘ziel_code’ entsprechen den BFS-Nummern der Gemeinden zum angegebenen Gebietsstand (‘gebietsstand_jahr’). Die Verkehrsströme können nach Binnenverkehr und nach Quell-Ziel-Verkehr unterschieden. Beginnen und enden die Verkehrsbeziehungen im gleichen Gebiet (Quelle und Ziel sind identisch), handelt es sich um Binnenverkehr. Beginnen die Verkehrsbeziehungen in einem Betrachtungsgebiet und führen nach ausserhalb und umgekehrt so handelt es sich um Quell-Ziel-Verkehr.

de: Verkehrsbeziehungen, Verkehrsaufkommen und Verkehrsmittelwahl ÖV, MIV und Velo nach Regionen

de: Die Daten zeigen die Verkehrsnachfrage im öffentlichen Verkehr (‘oev’), im motorisierten Individualverkehr (‘miv’) und mit Velo ('vel') als absolutes Verkehrsaufkommen (Summe aller Personenwege an einem durchschnittlichen Werktag (DWV)) und als Bi- und Tri-Modal Split (prozentuale Anteile der Verkehrsmittelarten an allen Personenwegen). Bi-Modal Split umfasst ÖV und MIV, Tri-Modal Split auch Velo. Neben den Regionen werden auch die Verkehrsbeziehungen von und zu den Nachbarkantonen berücksichtigt ("_gebietart" = Aussenkorridor). ‘quelle_code’ und ‘ziel_code’ entsprechen den BFS-Nummern der Regionen zum angegebenen Gebietsstand (‘gebietsstand_jahr’). Die Verkehrsströme können nach Binnenverkehr und nach Quell-Ziel-Verkehr unterschieden werden. Beginnen und enden die Verkehrsbeziehungen im gleichen Gebiet (Quelle und Ziel sind identisch), handelt es sich um Binnenverkehr. Beginnen die Verkehrsbeziehungen in einem Betrachtungsgebiet und führen nach ausserhalb und umgekehrt so handelt es sich um Quell-Ziel-Verkehr.

de: Der Datensatz basiert auf dem Gesamtverkehrsmodell (GVM) des Kantons Zürich. Das GVM bildet die Verkehrsnachfrage auf Basis modellierter Nachfragebeziehungen von A nach B ab im öffentlichen Verkehr (ÖV), im motorisierten Individualverkehr (MIV) und im Fuss- und Veloverkehr. Im Ergebnis liegen die Beziehungsmatrizen in Personenwege für die Verkehrsmittelarten MIV, ÖV, Velo- und Fussverkehr für Gemeinden und Planungsregionen vor. Zum MIV gehören Autos, Motorräder und Mofas. Mit regelmässigen Aktualisierungen werden die laufenden Veränderungen im Verkehrsgeschehen nachgeführt und die Prognosen angepasst. Mehr Informationen zu den Daten in den Beschreibungen der Ressourcen.

auto

display_name: auto

gemeinden

display_name: gemeinden

miv

display_name: miv

oeffentlicherverkehr

display_name: oeffentlicherverkehr

oev

display_name: oev

raumplanungsregionen

display_name: raumplanungsregionen

strassenverkehr

display_name: strassenverkehr

velo

display_name: velo

verkehr

display_name: verkehr

verkehrsmittelwahl

display_name: verkehrsmittelwahl

verkehrsnachfrage

display_name: verkehrsnachfrage

afm-kanton-zuerich

de: Amt für Mobilität des Kantons Zürich

de: Wir gestalten und steuern die Verkehrsentwicklung im Kanton Zürich und legen Wert darauf, dass diese nachhaltig ist. Dazu übernehmen wir die strategische Planung für alle Verkehrsmittel und wahren die Interessen des Kantons gegenüber dem Bund.

kanton-zuerich

capacity: public

value: DCAT Harvester Amt für Mobilität Kanton Zürich

de: Verkehrsnachfrage im ÖV, MIV und im Veloverkehr nach Gemeinden und Regionen im Kanton Zürich


maintainer: Fachstelle Open Government Data des Kantons Zürich

label: Fachausschuss für offene Behördendaten: Standards

label: Repository: Formale Leitlinien zur Datenpublikation des Kantons Zürich

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

media_type: text/html

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

mimetype: text/html

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

national-economy

fr: Economie nationale

de: Volkswirtschaft

en: National economy

it: Economia

fachstelle-ogd-kanton-zuerich

de: Fach- und Koordinationsstelle Open Government Data Kanton Zürich

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

value: ZH-Kt Fachstelle OGD - DCAT Harvester


geography

fr: Géographie

de: Geographie

territory

fr: Territoire et environnement

de: Raum und Umwelt

en: Territory and environment

it: Territorio e ambiente

de: gestein geomorphologie tektonik sedimentation-geologisch geologischer-prozess geowissenschaften geologie geologische-karte bgdi-bundesgeodaten-infrastruktur

de: Map (Preview) Vorschau map.geo.admin.ch

de: Vorschau map.geo.admin.ch

fr: Serveur de téléchargement de geo.admin.ch

de: Download von data.geo.admin.ch

en: Download server from geo.admin.ch

it: Server di download di geo.admin.ch

format: ZIP

fr: Service WMS de geo.admin.ch

de: WMS Dienst von geo.admin.ch

en: WMS Service from geo.admin.ch

it: Servizio WMS di geo.admin.ch

de: RESTful API von geo.admin.ch

fr: Les jeux de données GeoCover sont mis à disposition selon le découpage de la carte nationale au 1:25'000. Le jeu de données « Découpage GeoCover » comprend les métadonnées liées à chaque jeu de données.

de: Die GeoCover-Datensätze stehen gemäss der Blatteinteilung der Landeskarten 1:25'000 zur Verfügung. Der Datensatz "Einteilung GeoCover" beinhaltet die Metadaten zu den einzelnen Datensätzen.

en: The GeoCover datasets are available according to the sheet division of the National Maps 1:25 000. The «Division GeoCover» shows the perimeter, number and name of each map sheet.

it: I set di dati GeoCover sono disponibili conformemente alla divisione delle carte nazionali in scala 1:25'000. Il set di dati “Divisione GeoCover” contiene i metadati relativi a ciascun set di dati.

de: Bundesamt für Landestopografie swisstopo

de: Das Kompetenzzentrum der Schweizerischen Eidgenossenschaft für Geoinformation, d.h. für die Beschreibung, Darstellung und Archivierung von raumbezogenen Geodaten.

fr: Découpage de jeux de données géologiques vectorielles GeoCover

de: Blatteinteilung geologische Vektordatensätze GeoCover

en: Division of Geological Vector Datasets GeoCover

it: Suddivisione dei fogli dei set di dati geologici vettoriali GeoCover


label: Geometadaten Kanton Zürich: GeoLion

spatial: Kanton Zürich

de: Download via GIS-Browser

de: Mit der Festlegung des Gewässerraums an den oberirdischen Gewässern soll der Raum gesichert werden, welcher nötig ist zur Gewährleistung der natürlichen Funktionen der Gewässer, des Hochwasserschutzes und der Gewässernutzung. Die Gewässerschutzverordnung des Bundes (GSchV) legt dazu konkrete Masse fest, wie gross der Gewässerraum mindestens sein muss (Art. 41a und 41b GSchV). Der Gewässerraum muss dort erhöht werden, wo dies zur Gewährleistung für den Hochwasserschutz, der Revitalisierung, sowie bei überwiegenden Interessen des Natur- und Landschaftsschutzes und der Gewässernutzung erforderlich ist. Im Gewässerraum dürfen nur standortgebundene, im öffentlichen Interesse liegende Bauten und Anlagen erstellt werden. In dicht überbauten Gebieten kann der Gewässerraum den baulichen Gegebenheiten angepasst werden, soweit der Schutz vor Hochwasser gewährleistet ist, bzw. kann die Behörde für zonenkonforme Bauten und Anlagen Ausnahmen bewilligen, sofern keine überwiegenden Interessen entgegenstehen. Rechtmässig erstellte und bestimmungsgemäss nutzbare Bauten und Anlagen im Gewässerraum sind in ihrem Bestand grundsätzlich geschützt. Der Gewässerraum darf nur extensiv gestaltet und bewirtschaftet werden (Art. 41 c GschV) Solange der Gewässerraum nicht festgelegt ist, regeln die Übergangsbestimmungen der GSchV direkt und grundeigentümerverbindlich die Bemessung der von Bauten und Anlagen freizuhaltenden Uferstreifen. Der Gewässerabstand von 5 Meter gemäss § 21 des kantonalen Wasserwirtschaftsgesetzes (WWG), behält bis zu einer allfälligen Anpassung des WWG auch mit der Festlegung des Gewässerraums weiterhin Gültigkeit. Nach der Ersterfassung werden die Daten in den ÖREB-Kataster überführt.

name:
Amt für Abfall, Wasser, Energie und Luft
             - Abteilung Wasserbau

geoinformation-kanton-zuerich

fr: Département de Géoinformation du Canton de Zurich

de: Geoinformation Kanton Zürich

en: Department of Geoinformation of the Canton of Zurich

it: Dipartimento della geoinformazione del Cantone di Zurigo

fr:
Le département de géomatique est le centre de compétence du system d'information géographique du canton de Zurich.
Nous coordinons l'aquisition des géodonnées d'intérêt commun sur le territoire cantonal et les mettons à disposition des
secteurs public et privé et des particuliers.

en:
The department of geoinformation is the geospatial competency center of the Canton of Zurich.
We coordinate the acquisition of geodata within the cantonal territory and provide public, private, and government sectors with data of outstanding quality.

de: Die Abteilung Geoinformation ist das Kompetenzzentrum für räumliche Informationen im Kanton Zürich. Wir versorgen Gesellschaft, Wirtschaft und öffentliche Verwaltung mit qualitativ hochstehenden Geodaten und Informationen über den Kanton Zürich.

it: La divisione della geoinformazione è il centro d'eccellenza del Cantone di Zurigo per dati georeferenziati. Mettiamo a disposizione dati geografici e informazioni di alta qualità per l'economia, la società e la pubblica amministrazione su tutto il cantone.

value: ZH - Geocat Harvester

de: Gewässerraum


geography

de: Geographie

mobility

de: Mobilität und Verkehr

fr: Map (Preview) Aperçu map.geo.admin.ch

en: Map (Preview)  map.geo.admin.ch

it: Map (Preview) Previsione map.geo.admin.ch

fr: Aperçu map.geo.admin.ch

en: Preview map.geo.admin.ch

it: Previsione map.geo.admin.ch

fr: Download

de: Download

en: Download

it: Download

en: RESTful API from geo.admin.ch

it: RESTful API da geo.admin.ch

fr: swissTLM3D routes et chemins comprend le réseau des routes et des chemin de la Suisse et de la Principauté de Liechtenstein. Il fait partie du jeu de données swissTLM3D. Avec une haute précision et l'intégration de la troisième dimension, swissTLM3D constitue le jeu de données vectorielles en 3D le plus complet de la Suisse. Dans le géoportail fédéral, les objets de la couche " routes et chemins" sont affichés progressivement en fonction des niveaux de zoom.

de: swissTLM3D Strassen und Wege enthält das Strassen- und Wegnetz der Schweiz und des Fürstentums Liechtenstein. Es ist Bestandteil des Datensatzes swissTLM3D. Mit seiner hohen Genauigkeit und dem Einbezug der dritten Dimension ist swissTLM3D der umfassendste 3D-Vektordatensatz der Schweiz. Im Geoportal des Bundes werden die Objekte der Ebene "Strassen und Wege" entsprechend der ausgewählten Zoomstufe schrittweise dargestellt.

en: swissTLM3D road and tracks contains the road and path network Switzerland and the Principality of Liechtenstein. It is part of the dataset swissTLM3D. With a high accuracy and the incorporation of a third dimension, swissTLM3D is the most extensive 3D vector data set of Switzerland. In the federal geoportal the objects of the layer "road and tracks" are displayed gradually according to the different zoomlevels.

it: swissTLM3D strade e sentieri comprende la rete stradale e viaria della Svizzera e del Principato del Liechtenstein. Fa parte del set di dati swissTLM3D. Il prodotto swissTLM3D offre un'alta precisione e include anche la terza dimensione, si tratta della raccolta dati vettoriali in 3D più completa del territorio svizzero. All'interno del geoportale federale, gli oggetti del layer "sentieri pedestri " sono visualizzabili progressivamente, in funzione del livello di zoom selezionato.

fr: swissTLM3D Routes et chemins

de: swissTLM3D Strassen und Wege

en: swissTLM3D Roads and Tracks

it: swissTLM3D Strade e sentieri


maintainer: Fachstelle für OGD Basel-Stadt

de: json

de: Wahl eines Mitglieds des Gerichts für fürsorgerische Unterbringungen (json)

license: CC BY 3.0 CH

de: csv

de: Wahl eines Mitglieds des Gerichts für fürsorgerische Unterbringungen (csv)

de: Dieser Datensatz zeigt die Resultate des 1. Wahlgangs der Wahl eines Mitglieds des Gerichts für fürsorgerische Unterbringung vom 9. Mai 2021.

legislation

fr: Législation

de: Gesetzgebung

en: Legislation

it: Legislazione

politics

fr: Politique

de: Politik

en: Politics

it: Politica

kanton-basel-stadt

fr: Canton de Bâle-Ville

de: Kanton Basel-Stadt

en: Canton of Basel-City

it: Cantone di Basilea-Città

de: Der Kanton Basel-Stadt veröffentlicht nach und nach Open Government-Datensätze von kantonalen Ämtern und Dienststellen. Die kantonale Fachstelle für Open Government Data ist als Koordinationsorgan zuständig für die Unterstützung der datenpublizierenden Verwaltungsstellen.

value: BS Kanton Stadt - DCAT Harvester


maintainer: Bundesamt für Energie BFE

fr: Contenu des bassins d'accumulation, dimanche 24:00 heures, bulletin hebdomadaire

it: Grado di riempimento dei bacini di accumulazione, domenica alle ore 24, rapporto settimanale livello di accumulazione

author: Bundesamt für Energie BFE

media_type: CSV

media_type: SERVICE

fr:
Ce relevé, élaboré chaque dimanche à 24h00, recense le contenu (en GWh et en %) des bassins d'accumulation suisses dans 4 régions différentes.
Le taux de remplissage des bassins d'accumulation est relevé dans le cadre de la Statistique de l'électricité, qui fait partie de la statistique publique de la Suisse (base légale: LSF).

de: Der Füllungsgrad der Speicherseen, Sonntag 24h, Wochenbericht Speicherinhalt gibt Auskunft über den Speicherinhalt der Schweizer Speicherseen für vier Regionen in GWh und als Füllgrad in % jeweils am Sonntag 24h auf wöchentlicher Basis. Der Füllungsgrad der Speicherseen wird im Rahmen der Elektrizitätsstatistik erhoben, welche Teil der öffentlichen Statistik der Schweiz ist (gesetzliche Grundlage: BStatG).

statistical-basis

fr: Bases statistiques

de: Statistische Grundlagen

en: Statistical basis

it: Basi statistiche

national-economy

fr: Economie nationale

de: Volkswirtschaft

en: National economy

it: Economia

energy

fr: Energie

de: Energie

en: Energy

it: Energia

territory

de: Raum und Umwelt

bundesamt-fur-energie-bfe

fr: Office fédéral de l'énergie OFEN

de: Bundesamt für Energie BFE

en: Swiss Federal Office of Energy SFOE

it: Ufficio federale dell'energia UFE

fr: L'Office fédéral de l'énergie (OFEN) est le centre de compétences du Département fédéral de l'environnement, des transports, de l'énergie et de la communication (DETEC) pour les questions liées à l'approvisionnement en énergie et à son utilisation.

de: Das Bundesamt für Energie (BFE) ist das Kompetenzzentrum für Fragen der Energieversorgung und der Energienutzung im Eidgenössischen Departement für Umwelt, Verkehr, Energie und Kommunikation (UVEK).

en: The Swiss Federal Office of Energy (SFOE) is the country's competence centre for issues relating to energy supply and energy use at the Federal Department of the Environment, Transport, Energy and Communications (DETEC).

it: L'Ufficio federale dell'energia (UFE) è il centro di competenza in materia di approvvigionamento e di impiego dell'energia in seno al Dipartimento federale dell'ambiente, dei trasporti, dell'energia e delle comunicazioni (DATEC).


geography

fr: Géographie

de: Geographie

en: Geography

it: Geografia

territory

fr: Territoire et environnement

en: Territory and environment

it: Territorio e ambiente

fr: aquifere eaux-souterraines

de: grundwassertrager grundwasser

en: aquifer groundwater

it: acquifero acqua-sotterranea

spatial: Canton de Fribourg (FR)

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

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

fr: Le système d'information des eaux souterraines du canton de Fribourg est utilisé pour la protection et la gestion de la ressource. Ses données sont structurées autour des thèmes suivants: (a) captages d'intérêt public; (b) inventaire des eaux souterraines publiques; (c) sources captées recensées dans l'ancien Atlas de l'approvisionnement en eau; (d) aquifères karstiques; et (e) prélèvements d'eau souterraine à des fins thermiques. On trouvera une brève description des thèmes en suivant les liens ci-dessous, tandis que le détail figure dans le modèle de données accessible depuis la rubrique 'Information sur le contenu'.

de: Auszug des Verzeichnisses der Grundwasservorkommen, mit den öffentlichen Gewässern integriert (Art. 13 ÖSG). Durch Polygone sind die Grundwasservorkommen, mit einer Ergiebigkeit von mehr als 50 l/min wiedergegeben.

fr: Geoinformation Canton Fribourg

de: Geoinformation Kanton Freiburg

en: Geoinformation Canton Fribourg

it: Geoinformazione Canton Friburgo

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

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

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

it: I geodati pubblici del Canton Friburgo sono gestiti dal Centro di competenza GIS dell'Ufficio della Misurazione e della Geomatica (SCG) e messi a disposizione tramite il geoportale cantonale come servizi web e per il download.

value: Fribourg-Kt - Geocat Harvester

fr: Système d'information des eaux souterraines


elektrizitatsproduktionsanlagen

culture

fr: Culture, médias, société de l'information, sport

de: Kultur, Medien, Informationsgesellschaft, Sport

en: Culture, media, information society, sport

it: Cultura, media, società dell'informazione, sport

title_for_slug: elektrizitatsproduktionsanlagen

protocol: WWW:DOWNLOAD:Text (.csv), comma separated

de: Csv

media_type: Text (.csv), comma separated

protocol: WWW:DOWNLOAD:Geopackage (ogc)

de: Geopackage

media_type: Geopackage (ogc)

format: GPKG

de: Interlis

media_type: INTERLIS

format: INTERLIS

fr: Map (Preview) Géoportail thématique - map.energie.admin.ch

de: Map (Preview) Fachportal - map.energie.admin.ch

en: Map (Preview) Thematic geoportal - map.energie.admin.ch

it: Map (Preview) Geoportale tematico - map.energie.admin.ch

de: Fachportal - map.energie.admin.ch

fr: Web Map Services WMS

de: Web Map Services WMS

en: Web Map Services WMS

it: Web Map Services WMS

de: swisstopo REST API

fr: Une installation de production d’électricité est un dispositif technique destiné à produire de l’électricité. Il est dans l’intérêt de tous de disposer d’un aperçu géographique de l’ensemble des installations de production d’électricité de la Suisse. Le développement des installations produisant de l’électricité avec des énergies renouvelables doit notamment être présenté de manière transparente. Il importe aussi de montrer quelle est la proportion de ces installations par rapport à celles produisant de l’électricité avec des énergies non renouvelables. Cet aperçu doit permettre de faire la distinction entre l‘hydraulique, le photovoltaïque, l’éolien, la biomasse, la géothermie, l’énergie nucléaire, le pétrole, le gaz naturel et le charbon. Cette base de données comprend uniquement les installations de production d’électricité enregistrées dans le système suisse des garanties d’origine. Sont donc concernées d’une part les installations d’une puissance supérieure à 30 kilovoltampères (kVA) et les petites installations (à partir de 2 kilowatts (kW)) qui se sont enregistrées sur une base volontaire et d’autre part, les installations encouragées par une rétribution de l’injection, une rétribution unique, un financement des frais supplémentaires ou une contribution d’investissement (art. 19 et 24, LEne). Seules les installations de production d’électricité en service sont prises en compte.

de: Eine Elektrizitätsproduktionsanlage ist eine technische Einrichtung zur Erzeugung von Strom.Es ist von allgemeinem Interesse, eine räumliche Übersicht über sämtliche Elektrizitätsproduktionsanlagen der Schweiz zu haben. Insbesondere soll der Zubau an Produktionsanlagen, die Elektrizität aus erneuerbaren Energien produzieren, transparent dargestellt werden. Zudem kann das Verhältnis der Anlagen, die Elektrizität aus erneuerbaren Energien produzieren, zu den Anlagen, die Elektrizität aus nicht erneuerbaren Energien produzieren, sichtbar gemacht werden. Man unterscheidet zwischen Wasserkraft, Photovoltaik, Windenergie, Biomasse, Geothermie, Kernenergie, Erdöl, Erdgas und Kohle. Dieser Datenbestand enthält alle Elektrizitätsproduktionsanlagen, welche im Schweizerischen Herkunftsnachweissystem registriert sind. Darunter fallen zum einen alle Anlagen mit einer Leistung grösser 30 Kilovoltampere (kVA), sowie Kleinanlagen (grösser als 2 Kilowatt), welche freiwillig für die Ausstellung von Herkunftsnachweisen (HKN) registriert worden sind. Zum anderen enthält der Datenbestand Anlagen, die durch eine Einspeisevergütung, Einmalvergütung, Mehrkostenfinanzierung oder einen Investitionsbeitrag geförderten werden (gemäss Art. 19 und 24; EnG). Es sind nur Elektrizitätsproduktionsanlagen enthalten, welche in Betrieb sind.

en: An electricity production plant is a technical facility for generating electricity. It is in the public interest to have a spatial overview of all electricity production plants in Switzerland. In particular, the expansion of production plants that produce electricity from renewable energy should be presented transparently. The ratio of plants producing electricity from renewable energy to plants producing electricity from non-renewable energy can also be shown. A distinction is made between hydropower, photovoltaics, wind energy, biomass, geothermal energy, nuclear energy, oil, natural gas and coal. This database contains all electricity production plants that are registered in the Swiss proof of origin system and includes all plants with an output exceeding 30 kilovolt-amperes (kVA), as well as small plants (more than 2 kilowatts) that have been voluntarily registered to display proof of origin. The database also includes plants that are subsidised by a feed-in rate, a one-off payment, additional cost financing or an investment contribution (pursuant to Art. 19 and 24; EnA). Only electricity production plants that are in operation are included.

it: Un impianto di produzione di energia elettrica è un dispositivo tecnico per la produzione di energia elettrica. È di interesse generale avere una panoramica spaziale di tutti gli impianti di produzione di energia elettrica presenti in Svizzera. In particolare, l'espansione degli impianti di produzione che generano elettricità da fonti energetiche rinnovabili dovrebbe essere presentata in modo trasparente. Inoltre, è possibile rendere visibile il rapporto tra gli impianti che producono elettricità da fonti energetiche rinnovabili e quelli che producono elettricità da fonti energetiche non rinnovabili. Si distingue tra energia idroelettrica, fotovoltaica, eolica, biomassa, geotermica, nucleare, petrolio, gas naturale e carbone. Questo set di dati comprende tutti gli impianti di produzione di energia elettrica registrati nel sistema svizzero delle garanzie di origine. Da un lato, questo include tutti gli impianti con una capacità superiore a 30 kilovolt-ampere (kVA), così come i piccoli impianti (superiori a 2 kilowatt) che sono stati registrati a titolo volontario per il rilascio di garanzie di origine. Dall'altro lato, il set di dati include impianti che sono sovvenzionati tramite una rimunerazione per l'immissione di elettricità, un versamento unico, un finanziamento dei costi aggiuntivi o un contributo d'investimento (secondo gli art. 19 e 24; LEne). Sono inclusi solo gli impianti di produzione di elettricità che sono in funzione.

value: BFE - Geocat Harvester

fr: Installations de production d'électricité

de: Elektrizitätsproduktionsanlagen

en: Electricity production plants

it: Impianti di produzione di energia elettrica


present-weather-sensor-klosters

maintainer: Maxime Hervo

title_for_slug: present-weather-sensor-klosters

en: raclets precipitation weather visibility sensor

en: Present Weather Sensor Klosters

media_type: mat

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

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

precipitation

display_name: precipitation

raclets

display_name: raclets

sensor

display_name: sensor

visibility

display_name: visibility

weather

display_name: weather

education

fr: Education, science

de: Bildung, Wissenschaft

en: Education and science

it: Formazione e scienza

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

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


maintainer: Frank Techel

en: shapefile alps observations avalanches snow

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

media_type: application/zip

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

mimetype: application/zip

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

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

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

alps

display_name: alps

avalanches

display_name: avalanches

observations

display_name: observations

shapefile

display_name: shapefile

snow

display_name: snow


maintainer: Océane Hames

en: openfoam snow-transport mosaic arctic-sea-ice snow-deposition

en: Paper Draft

media_type: PDF

en: This is the initial draft of the paper where the snowBedFoam 1.0. results are tested against Terrestrial Laser Scan (TLS) measurements of snow dynamics on Arctic sea ice (MOSAiC expedition). It highlights the numerical limitations of our model as well as the measurement uncertainties, which may occur in such extreme environments.

format: PDF

en: snowBedFoam 1.0.

media_type: .zip

en:
OpenFOAM-2.3.0. scripts containing the implemented Eulerian-Lagrangian snow transport model.

The code file provided here is not updated. The most recent version of the script can be found on the WSL/SLF GitLab repository: https://gitlabext.wsl.ch/atmospheric-models/snowbedfoam-v1-0.git

en: MOSAiC Met City preliminary (Level 2) wind data

media_type: zip-compressed folder of csv

en:
Extract from the preliminary MOSAiC atmospheric data (Level 2) for the time period 6th to 13th of November, 2019.
This data was used to set up the fluid forcing parameters in the snowBedFoam simulations on MOSAiC sea ice. It is being published in this repository with the kind permission from Matthew Shupe and his team.
The final processed and quality controlled version of this dataset (Level 3) has not been published yet. A complete reference will be added in this description once it is released.

en:
snowBedFoam 1.0. is a snow transport solver implemented in the Computational Fluid Dynamics (CFD) software OpenFOAM. It is adapted from the standard multi-phase flow solver DPMFoam for application in snow-influenced environments.

To simulate aeolian snow transport, snowBedFoam 1.0. handles coupled Eulerian–Lagrangian phases, which involve a finite number of particles (snow) spread in a continuous phase (air). The snow erosion and deposition are modelled through physically-based equations such as the ones employed in the well-established LES-Lagrangian Stochastic Model (Comola and Lehning, 2017 ; Sharma et al., 2018). This modelling approach is computationally intensive and thus adapted to simulate snow movement and distribution on small scale terrain.

snowBedFoam 1.0. was tested against topographical data collected on Arctic sea ice during the MOSAiC expedition (Clemens-Sewall, 2021) . Together with atmospheric data from the MOSAiC Met City (Shupe et al., 2021) used for the fluid forcing, the model was able to accurately simulate the zones of erosion and deposition of snow along a complex ice ridge structure.

The core code of snowBedFoam 1.0. is also accessible on the WSL/SLF GitLab repository (more details in the Resources section).

arctic-sea-ice

display_name: arctic-sea-ice

mosaic

display_name: mosaic

openfoam

display_name: openfoam

snow-deposition

display_name: snow-deposition

snow-transport

display_name: snow-transport

en: snowBedFoam: an OpenFOAM Eulerian-Lagrangian solver for modelling snow transport


maintainer: Esther R. Frei

en: treeline swiss-alps climate-change g-tree forest

en: GTREE Website

media_type: text/plain

en: climate data G-TREE experiment, Davos, Switzerland

format: TXT

mimetype: text/plain

en:
G-TREE is a global research network composed of 40 arctic and alpine researchers from around the world. This summer, our field experiment will be established at numerous sites across Canada, France, Norway, Spain, the United States, and Venezuela. Other sites are being developed in Russia, Scotland, and Sweden.

The Stillberg research area is located in the Eastern Swiss Alps near Davos, Switzerland. The site has been used for several long-term monitoring as well as experimental studies for the last four decades. Our G-TREE experiment consists of a lowest site located in a subalpine Larch-Spruce forest (Larici-Picetum) dominated by Larix decidua and Picea abies (1930 m a.s.l.), a transition zone site dominated by alpine shrubs (2100 m a.s.l.), and an uppermost site in an alpine meadow with some dwarf shrubs (2390 m a.s.l.).

climate-change

display_name: climate-change

forest

display_name: forest

g-tree

display_name: g-tree

swiss-alps

display_name: swiss-alps

treeline

display_name: treeline

en: G-TREE: Global Treeline Range Expansion Experiment Davos, Switzerland


maintainer: EnviDat Support

en: blowing-snow large-eddy-simulations drifting-snow meteorological-data mass-flux antarctica eddy-covariance-measurements turbulence micro-rain-radar

en: Measurement data from snow particle counter at MRR site

en: Near-surface observations of drifting snow from a Snow Particle Counter (SPC) installed at the location of the Micro Rain Radar.

en: Simulation data for a situation with significant snow transport

en: Data from two LES-LSM simulations called Drift_1 and Drift_2, which reproduce the conditions observed at the S17 site on 11th January 2019, 00:40 to 00:50 UTC.

en: Simulation data for a situation with negligible snow transport

en: Results from two LES-LSM simulations called NoDrift_1 and NoDrift_2, which reproduce the conditions observed at the S17 site on 12th January 2019, 05:40 to 05:50 UTC.

en: Measurement data from Micro Rain Radar (MRR)

media_type: NetCDF

en: Remote sensing data of blowing snow from a Micro Rain Radar (MRR).

en: Measurement data from micro-meteorological station

media_type: zip-compressed folder of ASCII files

en: Automatic in-situ measurements of micro-meteorology and snow transport

media_type: zip-compressed folder of text files (f90)

en: Source code of the large-eddy simulations coupled with a Lagrangian stochastic model (LES-LSM), available on GitLab.

en: Readme

media_type: TXT

en: Meta data

en:
This dataset contains measurement and simulation data. The measurements characterize the standard meteorology, turbulence, and snow transport at the S17 site near Syowa Station in East Antarctica during an expedition in austral summer 2018/2019. Large-eddy simulations with sublimating particles provide additional insight into the latent and sensible heat exchange between snow and air in two example situations observed at the S17 site.

A part of the measurement data was recorded by an automatic measurement station from 10th January 2019 to 26th January 2019. This measurement station was equipped with standard meteorological sensors, a three-dimensional ultrasonic anemometer, an open-path infrared gas analyzer, a snow particle counter, an infrared radiometer for measurements of the surface temperature, and a sonic ranging sensor measuring changes in snow surface elevation.

At a horizontal distance of approximately 500 m, a Micro Rain Radar (MRR) was installed in a tilted configuration with an elevation angle of 7° for remote sensing of blowing snow between 25th December 2018 and 24th January 2019. In addition, near-surface in-situ measurements of snow transport were performed at the location of the MRR by deploying a snow particle counter from 27th December 2018 to 24th January 2019.

The simulations cover a 18 x 18 x 6 m³ domain and reproduce the steady-state conditions during a 10-min interval with significant snow transport and another 10-min interval with negligible snow transport. We provide the model source code and the post-processed simulation data, i.e., horizontally averaged quantities as a function of height and time.

antarctica

display_name: antarctica

blowing-snow

display_name: blowing-snow

drifting-snow

display_name: drifting-snow

eddy-covariance-measurements

display_name: eddy-covariance-measurements

large-eddy-simulations

display_name: large-eddy-simulations

mass-flux

display_name: mass-flux

meteorological-data

display_name: meteorological-data

micro-rain-radar

display_name: micro-rain-radar

turbulence

display_name: turbulence

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

en: Meteorology and snow transport at S17 near Syowa, Antarctica, in austral summer 2018/2019


lidar-davos-wolfgang

maintainer: Patric Seifert

title_for_slug: lidar-davos-wolfgang

en: lidar clouds polly raclets tropos

en: Raman Lidar Davos

en: A portable Raman lidar system (Polly) from Leibnitz Institute for Tropospheric Research (Tropos) was deployed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Please use this [link](http://polly.tropos.de/?p=lidarzeit&Ort=39), to be directly forwarded to the Davos location and select the date of interest from the calendar (bold numbers). The data can be requested directly at the Polly team.

clouds

display_name: clouds

lidar

display_name: lidar

polly

display_name: polly

raclets

display_name: raclets

tropos

display_name: tropos

en: LIDAR Davos Wolfgang


maintainer: Maria Elvira Murazzi

en: forest-trees nanoplastic polystyrene 13c-labelling uptake

en: Nanoplastic in forests experiment

media_type: XLS

en:
Data embargoed until August 2022, and it will be provided upon request

-  excel file containing: two working sheets (leaf supply and root supply experiment) with a table with the values 13C-nanopolystyrene in the different tissues of the two forest tree species; graph showing the differences between controls and treatments of 13C-nanopolystyrene enrichment for each tissue of each forest tree species; ANOVA table showing significant values after having exposed the tree species to 13C-nanopolystyrene

format: XLS

en:
The fate of plastic in the environment is of global concern, because its production recently has
increased strongly and it accumulates in terrestrial and aquatic ecosystems. Although some knowledge on
its role in aquatic and terrestrial ecosystems was gained in the recent decade, hitherto very little is known
about the impact of micro and nanoplastics in forest ecosystems.

The aim of this pioneering project was to explore if nanoplastics are taken up by forest trees species
through leaves or roots. In greenhouse experiments, we exposed leaves or roots of seedling of two forest
trees species to solutions with highly 13C-labelled polystyrene nanoparticles (13C-nPS, 99 atom%) and
examined if they were incorporated in different above- and belowground tissues.

Treated part of the trees for both species showed significant 13C-enrichment, indicating that trees take up nanoparticles. However, the overall 13C signal strength in tissues that were not exposed to the 13C label remained low (Δδ13C<1‰) and was confined to a few seedlings, leaving it ambiguous whether nanoplastic transport occurs or not.

We acknowledge that the new method developed might be not sensitive enough to unequivocally detect
mechanisms of nanoplastic uptake and transport at environmentally realistic concentrations.

forest-trees

display_name: forest-trees

nanoplastic

display_name: nanoplastic

polystyrene

display_name: polystyrene

uptake

display_name: uptake

en: Nanoplastics in forests: Exploring the effects of nanoplastics on forest soils and tree physiology (NanoPlast)


en: snowpack-simulation rutschblock snow-instability snowpack snow-stratigraphy snow-profiles failure-layer

en: Observed snow profiles

media_type: .csv

en: Observed snow stratigraphy in form of one *.csv-file per snow profile from the DAV and SWISS data sets (in respective subfolders). Each file is named after the profile ID which allows identifying the corresponding simulated profile in the "simulated_profiles" folder. One row corresponds to one observed snow layer. The content of the columns is described in Table 2 of the ReadMe file.

en: ReadMe

en: ReadMe file explaining data structure and content

en: SWISS data set: Properties of observed snow profiles and simulated failure layers

en: For each profile pair from the SWISS data set, this file contains one row with information on the observed snow stability and all extracted features describing the simulated failure layers. Please note that only those profile pairs which fulfilled the similarity criteria (column similarity_obs_sim = 1, 121 profiles) were used to validate the RF model. The content of the columns is explained in Table 1 of the ReadMe file.

en: DAV data set: Properties of observed snow profiles and simulated failure layers

en:
For each profile pair from the DAV data set, this file contains one row with information on the observed snow stability and all extracted features describing the simulated failure layers. The content of the columns is explained in Table 1 of the ReadMe file. Only those profile pairs which fulfilled predefined similarity criteria (column
"similarity_obs_sim" = 1) and which belonged to the stability classes "stable" and "unstable" (column "stability_class"
= 0 or 1) were used to train the RF model. Please note that for each of the two snow profiles with RB score 7 (profile
IDs 7228 and 9526), three additional layers were included in this data set to obtain a balanced set of training data (73
unstable and 73 stable profiles).

en: Simulated snow profiles

en: Simulated snow stratigraphy in form of one *.csv-file per snow profile from the DAV and SWISS data sets (in respective subfolders). Each file is named after the profile ID which allows identifying the corresponding observed profile in the "observed_profiles" folder. One row corresponds to one simulated snow layer. The content of the columns 1-7 is described in Table 3 of the ReadMe file. The remaining columns (8-31) contain all features that were used in the development of the RF model and are described in Table 1 of the ReadMe file.

en:
This data set includes information on all observed and simulated snow profiles that were used to train and validate the
random forest model described in Mayer et al. (2022). The RF model was trained to assess snow instability from simulated snow stratigraphy. The data set contains observed snow profiles from the region of Davos (DAV subset, 512 profiles) and from all over Switzerland (SWISS subset, 230 profiles). For each observed snow profile, there is a corresponding simulated profile which was obtained using meteorological input data for the numerical snow cover model SNOWPACK. The information on the observed snow profile contains a Rutschblock test result including the depth of the failure interface. As part of the study described in Mayer et al. (2022), each observed snow profile was manually compared to its simulated counterpart and the simulated layer corresponding to the Rutschblock failure layer was identified.

The data are provided in the following form: one file each per observed and simulated snow profile (2x512 files DAV, 2x230 files SWISS), two files (1 file DAV, 1 file SWISS) containing the observed information on snow instability, the allocation between observed and simulated failure layer, and all features extracted from the simulated weak layers that were used to develop the RF model.

failure-layer

display_name: failure-layer

rutschblock

display_name: rutschblock

snow-instability

display_name: snow-instability

snow-profiles

display_name: snow-profiles

snow-stratigraphy

display_name: snow-stratigraphy

snowpack

display_name: snowpack

snowpack-simulation

display_name: snowpack-simulation

en: Observed and simulated snow profile data


en: snow-cover vertical-wind-motion boundary-layer-winds snow-depth saltation

en: Data Compilation

media_type: ZIP

en: Compressed TXT files

en:
The data correspond to the experiments presented and discussed in a paper regarding the interaction between turbulent wind fluctuations and snow saltation mass-fluxes (Paterna, 2016). Each of the nine data files corresponds to a different experiment presented in the paper and conducted in the winter 2014/2015 in the WSL/SLF cold wind tunnel in Davos. For each file the five columns indicate the time from the beginning of the experiment, the streamwise (u’) and the vertical (w’) wind velocity fluctuations, the streamwise (qx) and the vertical (qz) snow mass-flux components. From these time-series the scales of the snow saltation and of the turbulent flow are obtained with respect to the eddy-cycles and snow saltation cycles. From spectral analysis of the time-series a decoupling of the snow saltation from the turbulence forcing reveals two regimes of interaction: a turbulence-dependent regime occurring with weak saltation, and a turbulence-independent regime with strong saltation. Further details can be found at the link below.

Publication http://onlinelibrary.wiley.com/doi/10.1002/2016GL068171/abstract

boundary-layer-winds

display_name: boundary-layer-winds

saltation

display_name: saltation

snow-cover

display_name: snow-cover

snow-depth

display_name: snow-depth

vertical-wind-motion

display_name: vertical-wind-motion

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

en: Wind tunnel measurement data of drifting snow and turbulent wind fluctuations


maintainer: Anita C. Risch

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

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

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

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

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

Paper Citation:

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

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

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

ammonification

display_name: ammonification

climate

display_name: climate

fertilization

display_name: fertilization

global-change

display_name: global-change

grassland

display_name: grassland

herbivore

display_name: herbivore

mineralization

display_name: mineralization

nitrification

display_name: nitrification

nitrogen

display_name: nitrogen

nutrient-network

display_name: nutrient-network

soil

display_name: soil

en: Anthropogenic change and soil net N mineralization


en: forest picea-abies soil face co2 nitrogen

en: Soil C and N

media_type: text/csv

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

en: Gross N mineralisation and nitrification

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

en: Water analyses

en: Analyses of water, especially soil solution

en: Resin bags

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

en: Collection points

en: Collection points under the trees

en: Collection of soil solution

en: Soil solution collected by suction cups

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

face

display_name: face

forest

display_name: forest

picea-abies

display_name: picea-abies

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


maintainer: Christoph Marty

en: CH2014 REPORT

en:
CH2014-Impacts: Toward Quantitative Scenarios of Climate Change Impacts in Switzerland

The CH2014-Impacts report presents the results of a concerted effort of 15 Swiss research institutions to describe impacts of climate change in Switzerland quantitatively. This dataset contributes to the Crosphere/Snowcover section.

en: Dataset

en: Datasets are grouped under experiment "Aare" and "Graubunden".Data is also made available in netCDF format. A range of tools are available to [read netCDF](http://www.unidata.ucar.edu/software/netcdf/software.html).

en: Graubunden input

media_type: application/x-tar

en: Grid input data for the 'Graubunden' CH2014 simulation.

mimetype: application/x-tar

en: Aare input

en: Grid input data for the 'Aare' CH2014 simulation.

en:
 Overview

The CH2014-Impacts initiative is a concerted national effort to describe impacts of climate change in Switzerland quantitatively, drawing on the scientific resources available in Switzerland today. The initiative links the recently developed Swiss Climate Change Scenarios CH2011 with an evolving base of quantitative impact models. The use of a common climate data set across disciplines and research groups sets a high standard of consistency and comparability of results. Impact studies explore the wide range of climatic changes in temperature and precipitation projected in CH2011 for the 21st century, which vary with the assumed global level of greenhouse gases, the time horizon, the underlying climate model, and the geographical region within Switzerland. The differences among climate projections are considered using three greenhouse gas scenarios, three future time periods in the 21st century, and three climate uncertainty levels (Figure 1). Impacts are shown with respect to the reference period 1980-2009 of CH2011, and add to any impacts that have already emerged as a result of earlier climate change.

 Experimental Setup

Future snow cover changes are simulated with the physics-based model Alpine3D (Lehning et al., 2006). It is applied to two regions: The canton of Graubünden and the Aare catchment. These domains are modeled with a Digital Elevation Model (DEM) with a resolution of 200 m × 200 m. This defines the simulation grid that has to be filled with land cover data and downscaled meteorological input data for each cell for the time period of interest at hourly resolution. The reference data set consists of automatic weather station data. All meteorological input parameters are spatially interpolated to the simulation grid. The reference period comprises only thirteen years (1999–2012), because the number of available high elevation weather stations for earlier times is not sufficient to achieve unbiased distribution of the observations with elevation. The model uses projected temperature and precipitation changes for all greenhouse gas scenarios (A1B, A2, and RCP3PD) and CH2011 time periods (2035, 2060, and 2085).

 Data

Snow cover changes are projected to be relatively small in the near term (2035) (Figure 5.1 top), in particular at higher elevations above 2000 m asl. As shown by Bavay et al. (2013) the spread in projected snow cover for this period is greater between different climate model chains (Chapter 3) than between the reference period and the model chain exhibiting the most moderate change. In the 2085 period much larger changes with the potential to fundamentally transform the snow dominated alpine area become apparent (Figure 5.1 bottom). These changes include a shortening of the snow season by 5–9 weeks for the A1B scenario. This is roughly equivalent to an elevation shift of 400–800 m. The slight increase of winter precipitation and therefore snow fall projected in the CH2011 scenarios (with high associated uncertainty) can no longer compensate for the effect of increasing winter temperatures even at high elevations. In terms of Snow Water Equivalents (SWE), the projected reduction is up to two thirds toward the end of the century (2085). A continuous snow cover will be restricted to a shorter time period and/or to regions at increasingly high elevation. In Bern, for example, the number of days per year with at least 5 cm snow depth will decrease by 90% from now 20 days to only 2 days on average.

climate-change

display_name: climate-change

snow-water-equivalent

display_name: snow-water-equivalent

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

en: Alpine3D simulations of future climate scenarios CH2014


en: avalanche-forecasting avalanche-danger avalanche snow-instability snow danger-level

en: Field observations of snow instabilities

media_type: CSV

en: Read-me file

en: This read-me file contains the descriptions of the variables.

en:
This data set includes 589 snow profile observations including a rutschblock test, observations of signs of instability and an assessment of the local avalanche danger level, mainly recorded in the region of Davos (eastern Swiss Alps) during the winter seasons 2001-2002 to 2018-2019.

These data were analyzed and results published by Schweizer et al. (2021). They characterized the avalanche danger levels based on signs of instability (whumpfs, shooting cracks, recent avalanches), snow stability class and new snow height.

The data are provided in a csv file (589 records); the variables are described in the corresponding read-me file.

These data are the basis of the following publication: Schweizer, J., Mitterer, C., Reuter, B., and Techel, F.: Avalanche danger level characteristics from field observations of snow instability, Cryosphere, 15, in review, 2021.

 Acknowlegements
Many of the data were recorded by SLF observers and staff members, among those Roland Meister, Stephan Harvey, Lukas Dürr, Marcia Phillips, Christine Pielmeier and Thomas Stucki. Their contribution is gratefully acknowledged.

avalanche

display_name: avalanche

avalanche-danger

display_name: avalanche-danger

avalanche-forecasting

display_name: avalanche-forecasting

danger-level

display_name: danger-level

snow

display_name: snow

snow-instability

display_name: snow-instability


maintainer: Zhao

en: planning land-dynamics urban-land-use driving-factors austin

en: Potential driving factors of urban transformations of Austin over 25 years

en: In this study, the Austin metropolitan area, Texas, U.S., one of the fastest urban transformations and transformations regions, is selected to test the hypothesis that spatial planning and policies are important factors of urban transformations. Despite ample previous work in understanding the interactions between human and urban form transformation at specific areas, the actual interventions and outcomes of planning and policies (e.g., ‘smart growth’) on urban forms have been poorly measured. In this study, the potential influencing factors of urban transformations of Austin over 25 years were selected and collected.

austin

display_name: austin

driving-factors

display_name: driving-factors

land-dynamics

display_name: land-dynamics

planning

display_name: planning

urban-land-use

display_name: urban-land-use


en: species-observations gbif hard-limit species-distribution occurrence-records large-scale conservation range-maps r-package accepted-name filtering synonyms ecoregions landsca taxonomy flags

en: GitHub URL

media_type: R

en: wsl.gbif R package version 0.1 (deprecated)

en: Test version, not to be used.

en: gbif.range R package version 0.2 (current)

en: Although species range may be obtained using expert maps or modeling methods, expert data is often species-limited and statistical models need more technical expertise as well as many species observations. When unavailable, such information may be extracted from the Global Biodiversity Information facility (GBIF), the largest public data repository inventorying georeferenced species observations worldwide. However, retrieving GBIF records at large scale may be tedious if users are unaware of specific tools and functions that need to be employed. Here we present *gbif.range*, a R library that contains automated methods to generate species range maps from scratch using in-house ecoregions shapefiles and an easy-to-use GBIF download wrapper. Finally, this library also offers a set of additional very useful parameters and functions for large GBIF datasets (generate doi, extract GBIF taxonomy, records filtering...). [gbif.range R project](https://github.com/8Ginette8/gbif.range)

accepted-name

display_name: accepted-name

conservation

display_name: conservation

ecoregions

display_name: ecoregions

filtering

display_name: filtering

flags

display_name: flags

gbif

display_name: gbif

hard-limit

display_name: hard-limit

landsca

display_name: landsca

large-scale

display_name: large-scale

occurrence-records

display_name: occurrence-records

r-package

display_name: r-package

range-maps

display_name: range-maps

species-distribution

display_name: species-distribution

species-observations

display_name: species-observations

synonyms

display_name: synonyms

taxonomy

display_name: taxonomy

en: gbif.range - An R package to generate species range maps based on ecoregions and a user-friendly GBIF wrapper


maintainer: Rebecca Mott

en: patchy-snow-covers dischmex snow-patch eddy-covariance-measurements turbulent-sensible-heat-fluxes

en: Eddy-covariance measurement with an ultrasonic anemometer (R.M. Young Company) in 0.7 m.

en: Eddy-covariance measurement with an ultrasonic anemometer (R.M. Young Company) in 3.3 m.

en: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 2.2 m.

en: Eddy-covariance measurement with an ultrasonic anemometer (DA-600, Kaijo-Denki) in 0.3 m on a daily basis.

en: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 2.6 m.

en: Gletschboden setup

media_type: image/png

en: Background map based on data © 2014/2016 swisstopo (JD100042)

format: PNG

mimetype: image/png

en: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific Inc.) in 3.2 m on a daily basis.

en: Eddy-covariance measurement with an ultrasonic anemometer (CSAT3, Campbell Scientific Inc.) in 1.7 m on a daily basis.

en: Output explanation

en: Eddy-covariance measurement with an ultrasonic anemometer (R.M. Young Company) in 3.5 m.

en:
This dataset contains eddy-covariance measurements in the ablation period of 2014-2016. Measurements were taken from two turbulence towers over a long-lasting snow patch, which are 5 m apart from each other (2014 and 2015). The turbulence towers were equipped with two YOUNG ultrasonic anemometers mounted 0.7 m (in 2014) and 3.3 m (in 2015) above snow-free ground, two ultrasonic anemometers (CSAT3, Campbell Scientific, Inc.) mounted at 2.6 m (in 2014) and 2.2 m (in 2015) above snow-free ground and one anemometer (DA-600, Kaijo Denki) mounted at 0.3 m above snow surface.

The measurement setup changed in 2016 and includes a measurement above the snow-free ground in upwind direction (Swiss coordinates: 790191/176689). The measurement tower is equipped with one ultrasonic anemometer (CSAT3, Campbell Scientific, Inc.) in 3.3 m above the snow-free ground. Additionally, one measurement tower is installed above the long-Lasting snow patch and equipped with the same setup as 2015.

Turbulence data were sampled at a frequency of 20 Hz. The processing of the data to quality controlled fluxes has been done with the Biomicrometeorology flux software (Thomas et al., 2009). The program applies plausibility tests and a despiking test after Vickers and Mahrt (1997) on the measured data. The routine further applies a time-lag correction and considers the deployment (e.g. the sonic azimuth). A frequency response correction (Moore, 1986) is done and a three-dimensional rotation is performed. Finally, quality assurance/quality control (QA/QC) flags after Foken et al., (2004) are issued and fast Fourier transform power and co-spectra are calculated. The change in snow height is considered in the post-processing for every measurement day. The turbulence data were averaged to 30 minute intervals.

dischmex

display_name: dischmex

eddy-covariance-measurements

display_name: eddy-covariance-measurements

patchy-snow-covers

display_name: patchy-snow-covers

snow-patch

display_name: snow-patch

turbulent-sensible-heat-fluxes

display_name: turbulent-sensible-heat-fluxes

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

en: DISCHMEX - Impact of extreme land-surface heterogeneity on micrometeorology over spring snow-cover


en: antarctica landuse topography wrf cryowrf

en: Documentation

media_type: application/pdf

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

mimetype: application/pdf

en: Scripts

media_type: application/zip

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

en: WPSinput

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

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

antarctica

display_name: antarctica

cryowrf

display_name: cryowrf

landuse

display_name: landuse

topography

display_name: topography

wrf

display_name: wrf

en: REMA topography and AntarcticaLC2000 for WRF


en: antarctica terrestrial-laser-scans depth-of-snowfall gridded-data terrestiral-laserscanning

en: Scan from 27 December 2018, 10 cm resolution

media_type: ascii x, y, z

en: Scan from 2 January 2019, 10 cm resolution

en: Scan from 2 January 2019, 1 mm resolution

en: Scan from 11 January 2019, 10 cm resolution

en: Scan from 21 December 2018, 1 mm resolution

en: Scan from 27 December 2018, 1 mm resolution

en: Scan from 4 January 2019, 10 cm resolution

en: Scan from 21 December 2018, 10 cm resolution

en: Scan from 11 January 2019, 1 mm resolution

en: Scan from 4 January 2019, 1 mm resolution

en:
Surface topography maps (spatial extent: 400 m x 400 m) obtained at approximately 300 m from the top of the Hammarryggen Ice Rise in Dronning Maud Land, East Antarctica, using a Riegl VZ-6000 Terrestrial Laser Scanner (TLS). Scans were obtained on 5 days in the 2018-2019 Austral summer: on December 21 and 27, and January 2, 4 and 11. By using reflectors installed on bamboo poles, scans were registered with respect to the reflectors, such that the difference between two successive scans reveals the spatial patterns of erosion and deposition of snow. On each scan day, we used multiple scan positions to create one combined point cloud. After applying an octree filter on the point cloud, a 3D surface was obtained. For each day, the dataset contains a 1 mm and a 10 cm octree filter resolution file, only including points in a 400 m x 400 m area centered around the scan positions.

Notes:

* All files in the dataset are in the same coordinate system. However, this coordinate system is arbitrary (i.e., not related to any global coordinate system).

* From the installed reflectors, 4 reflectors could be used over the full period. The scan accuracy is generally higher within the area enclosed by the reflectors.

* The scans from January 2 were found to have exhibited small tilt during the scan and are of lesser accuracy.

* By walking along fixed corridors, disturbance of the snow was limited.

depth-of-snowfall

display_name: depth-of-snowfall

gridded-data

display_name: gridded-data

terrestiral-laserscanning

display_name: terrestiral-laserscanning

terrestrial-laser-scans

display_name: terrestrial-laser-scans

en: Terrestrial laser scans on Hammarryggen Ice Rise, Dronning Maud Land, East Antarctica