envi

fr: Environnement

de: Umwelt

it: Ambiente

it: geocat.ch link permanente

fr: WMS Orthofotos Kanton Zürich in Echtfarben und als Falschfarbeninfrarot

fr: Als Perimeter gilt der ganze Kanton ZH, inkl. einem Buffer von ca. 500m. Die Orthofotos liegen in Echtfarben (RGB) als auch in Falschfarbeninfrarot (CIR) vor. Dieser Geolion-Eintrag bezieht sich auf die CIR-Bilder. Der Geolion-Eintrag zu den RGB-Bildern findet sich hier (GIS-ZH Nr. 394). Die «Kachelung» des Orthofotos stützt sich auf die Rahmenblätter des Uebersichtsplanes. Flugdaten 2015: 14. bis 24. April nördlicher Teil bis Koordinate 1236000 Flugdaten 2016: 6. und 7. Mai südlich Koordinate 1236000 Die Aerotriangulation erfolgte in 2 Blöcken.

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.

fr: Orthofoto Frühjahr Infrarot 2015/16

de: Orthofoto Frühjahr Infrarot 2015/16

en: Orthofoto Frühjahr Infrarot 2015/16

it: Orthofoto Frühjahr Infrarot 2015/16


regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

en: Environment

author: Kanton Zürich

fr: geocat.ch permalien

de: geocat.ch Permalink

en: geocat.ch permalink

state: active

type: dataset

fr: NO2-Immissionen im Kanton Zürich für die Zeitstände 2020 bis aktuell. Es handelt sich dabei um Jahresmittelwerte in 100m x 100m räumlicher Auflösung. Quelle: Luftbelastung: Karten Jahreswerte

format: WMS

fr: NO2-Immissionen im Kanton Zürich für die Zeitstände 2020 bis aktuell. Es handelt sich dabei um Jahresmittelwerte in 100m x 100m räumlicher Auflösung. Quelle: Luftbelastung - Karten Jahreswerte

format: SERVICE

fr: NO2-Immissionen im Kanton Zürich für die Zeitstände 2020 bis aktuell. Die NO2-Immissionen stammen vom Bundesamt für Umwelt (BAFU) und wurden auf das Gebiet des Kantons Zürich geclippt. Die jährliche Datennachführung liegt jeweils nach dem Sommer vor. Es handelt sich dabei um Jahresmittelwerte in 100m x 100m räumlicher Auflösung. Basierend auf Modellrechnungen und den gemessenen Konzentrationen der Schweizer Messstationen von Bund, Kantonen und Städten, werden mit einem Interpolationsverfahren Karten der Luftbelastung berechnet. Der Immissionsgrenzwert für das NO2-Jahresmittel beträgt 30 µg/m³

spatial: Kanton Zürich

fr: Kanton Zürich

de: Kanton Zürich

en: Kanton Zürich

it: Kanton Zürich

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

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.

political_level: canton

kanton-zuerich

capacity: public

type: organization

approval_status: approved

value: ZH - Geocat Harvester

fr: NO2-Immissionen (Karten Jahreswerte)

de: NO2-Immissionen (Karten Jahreswerte)

en: NO2-Immissionen (Karten Jahreswerte)

it: NO2-Immissionen (Karten Jahreswerte)


orthofoto-tosstal

title_for_slug: orthofoto-tosstal

de: orthofoto tosstal

fr: Orthofotos des Kantons Zürich. Vom Kanton ZH als auch vom Bund (SWISSIMAGE) erstellte Bilder.

fr: Orthofoto ARE Tösstal 2007. Aufteilung gemäss Planeinteilung 1:2'500: Kacheln mit einer Fläche von 3.281 km2 (E-W: 2.1875 km, N-S: 1.5 km). Dies entspricht 1/64 der 1:25'000 Kacheln oder einer Übersichtsplan-Kachel.

fr: Orthofoto Tösstal

de: Orthofoto Tösstal

en: Orthofoto Tösstal

it: Orthofoto Tösstal


fr: Ozon-Immissionen im Kanton Zürich für die Zeitstände 2020 bis aktuell. Es handelt sich dabei um Jahresmittelwerte in 100m x 100m räumlicher Auflösung. Quelle: Luftbelastung: Karten Jahreswerte

fr: Ozon, maximales monatliches 98-Perzentil im Kanton Zürich für die Zeitstände 2020 bis aktuell. Die Ozon-Immissionen stammen vom Bundesamt für Umwelt (BAFU) und wurden auf das Gebiet des Kantons Zürich geclippt. Die jährliche Datennachführung liegt jeweils nach dem Sommer vor. Es handelt sich dabei um Jahresmittelwerte in 100m x 100m räumlicher Auflösung. Basierend auf Modellrechnungen und den gemessenen Konzentrationen der Schweizer Messstationen von Bund, Kantonen und Städten, werden mit einem Interpolationsverfahren Karten der Luftbelastung berechnet. Der Immissionsgrenzwert für das maximale monatliche 98-Perzentil Ozon beträgt 100 µg/m3.

fr: Ozon, maximales monatliches 98-Perzentil (Karten Jahreswerte)


envi

fr: Environnement

fr: WMS Orthofotos Kanton Zürich in Echtfarben und als Falschfarbeninfrarot

de: Orthofotos des Kantons Zürich. Vom Kanton ZH als auch vom Bund (SWISSIMAGE) erstellte Bilder.

fr: Als Perimeter gilt der ganze Kanton ZH, inkl. einem Buffer von ca. 500m. Die Orthofotos liegen in Echtfarben (RGB) als auch in Falschfarbeninfrarot (CIR) vor. Dieser Geolion-Eintrag bezieht sich auf die RGB-Bilder. Der Geolion-Eintrag zu den CIR-Bildern findet sich hier (GIS-ZH Nr. 393). Die «Kachelung» des Orthofotos stützt sich auf die Rahmenblätter des Uebersichtsplanes. Flugdaten 2014: 16. bis 18. Juli Sommerflug südlicher Teil bis Koordinate 240.500 inkl. Tösstal Flugdaten 2015: 15., 16., 26. und 28. Juli - Sommerflug nördlich Koordinate 240.500 Die Aerotriangulation erfolgte in 3 Blöcken.

fr: Orthofoto Sommer 2014/15

de: Orthofoto Sommer 2014/15

en: Orthofoto Sommer 2014/15

it: Orthofoto Sommer 2014/15


regi

fr: Régions et villes

fr: geocat.ch permalien

fr: Orthofoto 1981

de: Orthofoto 1981

en: Orthofoto 1981

it: Orthofoto 1981

en: Orthofotos des Kantons Zürich. Vom Kanton ZH als auch vom Bund (SWISSIMAGE) erstellte Bilder.

fr: Orthofoto ZH 1981

de: Orthofoto ZH 1981

en: Orthofoto ZH 1981

it: Orthofoto ZH 1981

spatial: einzelne Gemeinden

geoinformation-kanton-zuerich

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.


de: WMS Orthofotos Kanton Zürich in Echtfarben und als Falschfarbeninfrarot

it: Orthofotos des Kantons Zürich. Vom Kanton ZH als auch vom Bund (SWISSIMAGE) erstellte Bilder.

fr: Als Perimeter gilt der ganze Kanton ZH, inkl. einem Buffer von ca. 500m. Die Orthofotos liegen in Echtfarben (RGB) als auch in Falschfarbeninfrarot (CIR) vor. Dieser Geolion-Eintrag bezieht sich auf die RGB-Bilder. Der Geolion-Eintrag zu den CIR-Bildern findet sich hier (GIS-ZH Nr. 395). Die «Kachelung» des Orthofotos stützt sich auf die Rahmenblätter des Uebersichtsplanes. Flugdaten 2015: 14. bis 24. April nördlicher Teil bis Koordinate 1236000 Flugdaten 2016: 6. und 7. Mai südlich Koordinate 1236000 Die Aerotriangulation erfolgte in 2 Blöcken.

fr: Kanton Zürich

fr: Département de Géoinformation du Canton de Zurich

fr: Orthofoto Frühjahr 2015/16

de: Orthofoto Frühjahr 2015/16

en: Orthofoto Frühjahr 2015/16

it: Orthofoto Frühjahr 2015/16


fr: PM2.5-Immissionen im Kanton Zürich für die Zeitstände 2020 bis aktuell. Es handelt sich dabei um Jahresmittelwerte in 100m x 100m räumlicher Auflösung. Quelle: Luftbelastung: Karten Jahreswerte

fr: PM2.5-Immissionen im Kanton Zürich für die Zeitstände 2020 bis aktuell. Die PM2.5-Immissionen stammen vom Bundesamt für Umwelt (BAFU) und wurden auf das Gebiet des Kantons Zürich geclippt. Die jährliche Datennachführung liegt jeweils nach dem Sommer vor. Es handelt sich dabei um Jahresmittelwerte in 100m x 100m räumlicher Auflösung. Basierend auf Modellrechnungen und den gemessenen Konzentrationen der Schweizer Messstationen von Bund, Kantonen und Städten, werden mit einem Interpolationsverfahren Karten der Luftbelastung berechnet. Der Immissionsgrenzwert für das PM2.5-Jahresmittel beträgt 10 µg/m3.

fr: PM2.5-Immissionen (Karten Jahreswerte)

de: PM2.5-Immissionen (Karten Jahreswerte)

en: PM2.5-Immissionen (Karten Jahreswerte)

it: PM2.5-Immissionen (Karten Jahreswerte)


elektrizitatsproduktionsanlagen

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

ener

fr: Énergie

de: Energie

en: Energy

it: Energia

envi

de: Umwelt

title_for_slug: elektrizitatsproduktionsanlagen

author: Bundesamt für Energie

protocol: WWW:DOWNLOAD:TEXT, comma separated (CSV)

de: Csv

format: CSV

protocol: WWW:DOWNLOAD:OGC Geopackage (GPKG)

de: Geopackage

format: GPKG

protocol: WWW:DOWNLOAD:INTERLIS 1 (ITF)

de: Interlis

format: INTERLIS

fr: Service WMS-IFDG, couche "Installations production électrique"

de: WMS-BGDI Dienst, Layer "Elektrizitätsproduktionsanlagen"

en: WMS-FSDI service, layer "Electricity production plants"

it: Servizio WMS-IFDG, strato "Impianti produzione elettrica"

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

de: swisstopo REST API

format: 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.

spatial: Schweiz

fr: Office fédéral de l'énergie

de: Bundesamt für Energie

en: Swiss Federal Office of Energy

it: Ufficio federale dell'energia

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).

political_level: confederation

value: BFE - Geocat Harvester

fr: Installations de production d'électricité

de: Elektrizitätsproduktionsanlagen

en: Electricity production plants

it: Impianti di produzione di energia elettrica


regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

envi

fr: Environnement

en: Environment

it: Ambiente

fr: geocat.ch permalien

de: geocat.ch Permalink

en: geocat.ch permalink

it: geocat.ch link permanente

fr: Lien vers la distribution des données

de: Link für Datenbezug

en: Link for data download

it: Link per le fonti dei dati

format: HTML

fr: Service WMS-IFDG, couche "Besoin en charge: véhicules branchés"

de: WMS-BGDI Dienst, Layer "Ladebedarf: Steckerfahrzeuge"

en: WMS-FSDI service, layer "Charging requirements: Plug-in vehicles"

it: Servizio WMS-IFDG, strato "Fabbisogno di ricarica: veicoli plug-in"

fr: Service WMTS-IFDG, couche "Besoin en charge: véhicules branchés"

de: WMTS-BGDI Dienst, Layer "Ladebedarf: Steckerfahrzeuge"

en: WMTS-FSDI service, layer "Charging requirements: Plug-in vehicles"

it: Servizio WMTS-IFDG, strato "Fabbisogno di ricarica: veicoli plug-in"

format: WMTS

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

fr: Map (Preview)

en: Map (Preview)

it: Map (Preview)

fr: Géoportail thématique - map.energie.admin.ch

en: Thematic geoportal - map.energie.admin.ch

it: Geoportale tematico - map.energie.admin.ch

de: Weiterführende Informationen: Fahrzeuge

de: Weiterführende Informationen: Energie

fr: La carte montre, pour chaque commune sélectionnée, la part des véhicules électriques par rapport à toutes les voitures immatriculées (en %). En outre, le nombre de véhicules existants (voitures de tourisme) par type de propulsion (électrique à batterie ou hybride rechargeable) par commune en 2035 est indiqué. Le terme "véhicule branché" désigne à la fois les véhicules électriques à batterie (BEV) et les véhicules hybrides rechargeables (PHEV). L'évolution des nouvelles immatriculations et du parc des véhicules branchés en Suisse par commune se base sur les hypothèses communes à la branche de l'étude de l'OFEN « Conception Infrastructure de recharge 2050 ».

de: Die Karte zeigt je ausgewählte Gemeinde den Anteil der Steckerfahrzeuge bezogen auf alle immatrikulierten Personenwagen (in Prozent). Weiter werden die Anzahl Bestandsfahrzeuge (Personenwagen) pro Antrieb (batterie-elektrisch oder Plug-in-Hybrid) je Gemeinde im Jahr 2035 angegeben. Mit dem Begriff Steckerfahrzeug sind batterie-elektrische Fahrzeuge (BEV) und Plug-in-Hybride (PHEV) zusammen gemeint. Die Entwicklung der Neuzulassungen und des Bestandes der Steckerfahrzeuge in der Schweiz je Gemeinde basieren auf den gemeinsamen Branchenannahmen der BFE-Studie «Verständnis Ladeinfrastruktur 2050».

en: The map shows the proportion of plug-in vehicles in relation to all registered passenger cars (in per cent) for each selected municipality. The number of existing vehicles (passenger cars) per drive system (battery-electric or plug-in hybrid) per municipality in 2035 is also shown. The term plug-in vehicle refers to battery electric vehicles (BEV) and plug-in hybrids (PHEV) together. The development of new registrations and the number of plug-in vehicles in Switzerland per municipality are based on the common industry assumptions of the SFOE study "Understanding charging infrastructure 2050".

it: La mappa mostra la percentuale di veicoli plug-in rispetto a tutte le autovetture immatricolate per ogni comune selezionato. È inoltre indicato, per ogni comune, il numero di veicoli esistenti (autovetture) per sistema di trazione (elettrico a batteria o ibrido plug-in) nel 2035. Il termine veicolo plug-in comprende i veicoli elettrici a batteria (BEV) e gli ibridi plug-in (PHEV). Lo sviluppo delle nuove immatricolazioni e il numero di veicoli plug-in in Svizzera per comune si basano sulle ipotesi di base dello studio dell'UFE «Verständnis Ladeinfrastruktur 2050».

fr: Besoin de recharge : parc de véhicules branchés pour l'année 2035

de: Ladebedarf: Bestand an Steckerfahrzeugen für das Jahr 2035

en: Charging requirements: stock of plug-in vehicles for the year 2035

it: Fabbisogno di ricarica: flotta di veicoli plug-in per l'anno 2035


de: Umwelt

de: hochwasser fliessgewasser hydrologie see naturgefahr gewasser bgdi-bundesgeodaten-infrastruktur

author: Bundesamt für Umwelt / Abteilung Hydrologie

fr: Géoportail thématique

de: Thematisches Geoportal

en: Thematic geoportal

it: Geoportale tematico

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

fr: Service WMS-IFDG, couche "Carte vigilance crues"

de: WMS-BGDI Dienst, Layer "Warnkarte nationale Gewässer"

en: WMS-FSDI service, layer "Flood alert map"

it: Servizio WMS-IFDG, strato "Carta di vigilanza piene"

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

de: Download verschiedener Produkte mit hydrologischem Bezug.

fr: La carte de vigilance crues présente la situation de danger actuelle en matière de crues (niveaux de danger 1 à 5) pour l’ensemble des lacs et des cours d’eau d’intérêt national et fournit des indications régionales concernant les petits et moyens cours d’eau. Les cours d’eau d’intérêt national sont divisés en tronçons, à chacun desquels est attribuée une station de mesure du débit. Les prévisions sont faites à l’aide d’un modèle hydrologique et de plusieurs modèles météorologiques. Les prévisionnistes évaluent et interprètent ces modèles et en déduisent un niveau de danger, en fonction duquel chaque tronçon de cours d’eau et chaque lac se voient attribuer une couleur. Au besoin, les prévisionnistes de l’OFEV demandent à MétéoSuisse ou à l’Institut fédéral pour l’étude de la neige et des avalanches des informations plus précises sur l’évolution météorologique ou l’état du manteau neigeux. L’OFEV informe et alerte également lorsqu’il faut s’attendre localement à une forte hausse des débits de petits et moyens cours d’eau. Dans ces cas, notamment lors d’abondantes précipitations orageuses, il n’est pas possible de dire avec précision quels seront les cours d’eau concernés. C’est pourquoi les régions susceptibles d’être touchées sont marquées sur la carte de vigilance. Pour les petits et moyens cours d’eau, en raison des incertitudes, on distingue trois niveaux de danger seulement : aucun ou faible danger en vert, danger de crues limité en jaune et fort danger de crues en rouge.

de: Die Hochwasserwarnkarte zeigt die aktuelle Hochwassergefahr (Stufe 1 bis 5) für alle Fliessgewässer und Seen von gesamtschweizerischem Interesse sowie für die Regionen in Bezug auf kleine und mittlere Fliessgewässer. Die Fliessgewässer von gesamtschweizerischem Interesse sind in einzelne Flussabschnitte unterteilt. Zu jedem einzelnen Flussabschnitt ist eine Abflussmessstation definiert. Die Vorhersage beruht auf einem hydrologischen Modell und verschiedenen Wettermodellen. Abhängig von der Einschätzung und Interpretation der verschiedenen Modelle durch die Prognostikerinnen und Prognostiker werden die einzelnen Flussabschnitte und Seen entsprechend der Gefahrenstufe farblich gekennzeichnet. Bei Bedarf fordern die Prognostikerinnen und Prognostiker des BAFU bei MeteoSchweiz und/oder dem WSL-Institut für Schnee- und Lawinenforschung SLF genauere Auskünfte über die Wetterentwicklung und den Zustand der Schneedecke an. Das BAFU warnt und informiert auch dann, wenn lokal mit einem starken Anstieg der Abflüsse an kleinen und mittelgrossen Fliessgewässern zu rechnen ist. In solchen Fällen - insbesondere bei Gewittern mit Starkregen - kann nicht präzise vorausgesagt werden, welche Gewässer betroffen sein werden. Deshalb werden die voraussichtlich betroffenen Regionen auf der Hochwasserwarnkarte gekennzeichnet. Für kleine und mittelgrosse Fliessgewässer werden wegen den grösseren Unsicherheiten nur drei Gefahrenstufen unterschieden: keine oder geringe Gefahr (grün), mässige Hochwassergefahr (gelb) und grosse Hochwassergefahr (rot).

en: The flood alert map shows the current flood danger level (1 to 5) for all and for the regions in relation to small and medium-sized rivers. The rivers of national interest are divided into individual stretches of river, each of which has a defined discharge gauging station. The forecast is based on a hydrological model and various weather models. Depending on how the forecasters assess and interpret the models, individual stretches of river and lakes are colour-coded by danger level. If necessary, the FOEN forecasters may request more detailed information on weather developments and the status of snow cover from MeteoSwiss and/or the WSL Institute for Snow and Avalanche Research (SLF). The FOEN also issues alerts and information when a strong increase in discharge is expected locally on small and medium-sized rivers. In such cases – especially during thunderstorms with heavy rainfall – it is not possible to predict precisely which water bodies will be affected. Therefore, the regions likely to be affected are marked on the flood alert map. There are only three danger levels for small and medium-sized rivers due to the greater uncertainties involved: no or minor danger (green), moderate flood danger (yellow) and high flood danger (red).

it: La carta di vigilanza di piena mostra il pericolo attuale di piena (gradi da 1 a 5) per tutti i corsi d’acqua e i laghi di interesse nazionale e per le regioni relativamente ai corsi d’acqua di piccole e medie dimensioni. I corsi d’acqua d’interesse nazionale sono suddivisi in tratti fluviali. A ogni tratto è attribuita una stazione di misurazione della portata. Le previsioni si basano su un modello idrologico e diversi modelli meteorologici. La determinazione del grado di pericolo dei singoli tratti fluviali o dei laghi e la relativa indicazione sulla carta mediante colori avviene in base alla valutazione e all’interpretazione dei risultati dei diversi modelli idrologici e meteorologici da parte degli addetti alle previsioni. Se necessario, gli addetti alle previsioni dell’UFAM possono richiedere precisazioni a MeteoSvizzera e/o all’Istituto per lo studio della neve e delle valanghe (SLF) del WSL in merito all’evoluzione meteorologica e alle condizioni della copertura nevosa. L’UFAM allerta e fornisce informazioni anche quando localmente è previsto un aumento del livello di corsi d’acqua di piccole e medie dimensioni. In questi casi, ma in particolare in caso di temporali con forti precipitazioni, non è possibile stabilire con precisione quali saranno i corsi d’acqua interessati. Le regioni presumibilmente interessate vengono quindi segnalate sulla carta di allerta piene. Per i corsi d’acqua di piccole e medie dimensioni, a causa dell’elevato grado di incertezza, si distinguono solo tre gradi di pericolo: pericolo nullo o debole (verde), pericolo moderato di piena (giallo) e pericolo forte di piena (rosso).

fr: Office fédéral de l'environnement /Division Hydrologie

en: Federal Office for the Environment / Hydrology Division

it: Ufficio federale dell'ambiente / Divisione Idrologia

bundesamt-fur-umwelt-bafu

fr: Office fédéral de l'environnement OFEV

de: Bundesamt für Umwelt BAFU

en: Federal Office for the Environment FOEN

it: Ufficio federale dell'ambiente UFAM

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

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

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

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

value: BAFU - Geocat Harvester

fr: Carte de vigilance crues

de: Hochwasserwarnkarte

en: Flood alert map

it: Carta di vigilanza delle piene


media_type: application/json

format: JSON

de: Resultate der Landsgemeinde vom 07. Mai 2023, strukturiert als JSON

landsgemeinde

display_name: landsgemeinde

gove

fr: Gouvernement et secteur public

de: Regierung und öffentlicher Sektor

en: Government and public sector

it: Governo e settore pubblico

staatskanzlei-glarus

de: Staatskanzlei Glarus

de: Die Staatskanzlei steht dem Landrat und dem Regierungsrat des Kantons Glarus, deren Geschäftsverkehr sie organisiert und koordiniert, als Stabsstelle zur Verfügung. Sie unterstützt Landammann und Regierungsrat, Landratspräsident, Landrat und landrätliche Kommissionen bei der Führung, Planung, Koordination, Information und Dokumentation.

key: guid

key: uri

value: DCAT Harvester Kt. GL

de: Landsgemeinde vom 07. Mai 2023


de: Resultate der Landsgemeinde vom 06. Mai 2007, strukturiert als JSON

de: Landsgemeinde vom 06. Mai 2007


de: Resultate der Landsgemeinde vom 06. September 2020, strukturiert als JSON

de: Landsgemeinde vom 06. September 2020


maintainer: Ufficio di statistica del Canton Ticino (Ustat)

media_type: application/zip

it: Questo archivio zip contiene un cubo di dati in formato PC Axis px.

it: Questo archivio zip contiene un file csv con i risultati in forma lunga (con una sola colonna numerica) e un file txt con la descrizione del contenuto (metadati)

it: Questo archivio zip contiene un file xlsx con tre fogli: uno con i risultati statistici (Tabella), uno con l'elenco e la descrizione delle variabili (Definizioni) e uno con il titolo, la fonte e la data dell'ultima modifica (Informazioni).

it:
Titoli conseguiti presso le scuole professionali di base ticinesi, secondo il tipo, l'ambito di formazione, la modalità e il sesso, per distretto di residenza, dal 2012 al 2023

[Fonte: Statistica della formazione professionale di base (SBG-SFPI), Ufficio federale di statistica, Neuchâtel](https://www3.ti.ch/DFE/DR/USTAT/index.php?fuseaction=definizioni.fonti&tema=36&id=1081&proID=151)

[Elaborazione: Ufficio di statistica (Ustat), Giubiasco](https://www.ti.ch/ustat)

Ultima modifica: 17.03.2025

Variabili presenti nel cubo di dati:
Anno: l'anno di ottenimento del titolo
Tipo: il tipo di formazione svolta
Ambito_formazione: l'ambito professionale in cui lo studente ha conseguito il titolo
Modalita: il tipo di svolgimento della formazione (scuola a tempo pieno o a tempo parziale)
Sesso: il sesso

education-et-science

display_name: education-et-science

formazione-e-scienza

display_name: formazione-e-scienza

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

ustat

it:
L'Ufficio di statistica del Cantone Ticino (Ustat) è il servizio centrale della statistica cantonale e il suo coordinatore ai sensi della [Legge sulla statistica cantonale](https://www4.ti.ch/dfe/dr/ustat/chi-siamo/basi-legali/leggi-cantonali) del 22 settembre 2009.

Nella prima veste l'Ustat produce e diffonde per la collettività e per lo Stato: dati, elaborazioni, contributi analitici di vario genere e livello di approfondimento, consulenze statistiche e tematiche, su praticamente tutti i settori della statistica pubblica.
Alla base di questi risultati vi è un'attività che copre tutte le fasi del processo statistico: dalla definizione del fabbisogno alla rilevazione, trattamento e validazione dei dati, alla loro archiviazione e documentazione, all'elaborazione, analisi ed interpretazione e alla diffusione dei risultati. Progressivamente, le attività di analisi e di ricerca tematica e la diffusione stanno progressivamente assumendo maggior peso.

Nella veste di coordinatore della statistica cantonale l'Ustat opera in prima misura attraverso la preparazione, l'aggiornamento annuale e la rendicontazione del [Programma pluriennale](https://www4.ti.ch/dfe/dr/ustat/chi-siamo/programma-dattivita/programma-pluriennale), in collaborazione con la [Commissione scientifica](https://www4.ti.ch/dfe/dr/ustat/chi-siamo/la-commissione-scientifica) e gli altri attori della [statistica cantonale](https://www4.ti.ch/dfe/dr/ustat/chi-siamo/statistica-cantonale). L'Ustat assicura pure il coordinamento con l'esterno, in primis con il programma e l'attività della [statistica nazionale](https://www4.ti.ch/dfe/dr/ustat/chi-siamo/il-sistema-statistico), ma pure con gli altri servizi statistici e di ricerca regionali, nazionali e transfrontalieri.

value: DCAT Harvester Kt. TI


name: Ufficio di statistica del Canton Ticino (Ustat)

it:
Diplomi conseguiti presso le scuole specializzate superiori ticinesi, secondo il tipo, la formazione e il sesso, per distretto di residenza, dal 2012 al 2023

[Fonte: Statistica dei diplomi (SBA), Ufficio federale di statistica, Neuchâtel](https://www3.ti.ch/DFE/DR/USTAT/index.php?fuseaction=definizioni.fonti&tema=36&id=1101&proID=465)

[Elaborazione: Ufficio di statistica (Ustat), Giubiasco](https://www.ti.ch/ustat)

Ultima modifica: 17.03.2025

Variabili presenti nel cubo di dati:
Anno: l'anno di ottenimento del titolo
Tipo: il tipo di diploma conseguito
Formazione: l'ambito in cui lo studente ha conseguito il diploma
Sesso: il sesso
Distretto_residenza: il distretto di residenza dello studente


it:
Maturità conseguite presso le scuole ticinesi, secondo il tipo e il sesso degli studenti, per distretto di residenza, dal 2012 al 2023

[Fonte: Statistica dei diplomi (SBA), Ufficio federale di statistica, Neuchâtel](https://www3.ti.ch/DFE/DR/USTAT/index.php?fuseaction=definizioni.fonti&tema=36&id=1101&proID=465)

[Elaborazione: Ufficio di statistica (Ustat), Giubiasco](https://www.ti.ch/ustat)

Ultima modifica: 17.03.2025

Variabili presenti nel cubo di dati:
Anno: l'anno di ottenimento del titolo
Tipo: il tipo di maturità conseguita
Sesso: il sesso
Distretto_residenza: il distretto di residenza dello studente

it: Ufficio di statistica del Canton Ticino (Ustat)


flussgebiete-einzugsgebiete

regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

envi

fr: Environnement

de: Umwelt

en: Environment

it: Ambiente

title_for_slug: flussgebiete-einzugsgebiete

fr: geocat.ch permalien

de: geocat.ch Permalink

en: geocat.ch permalink

it: geocat.ch link permanente

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

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

de: Vorschau map.geo.admin.ch

it: Previsione map.geo.admin.ch

fr: Service WMS-IFDG, couche "Bassin fluvial"

de: WMS-BGDI Dienst, Layer "Flussgebiet"

en: WMS-FSDI service, layer "River basins"

it: Servizio WMS-IFDG, strato "Bacino fluviale"

fr: Service WMTS-IFDG, couche , Layer "Bassin fluvial"

de: WMTS-BGDI Dienst, Layer "Flussgebiet"

en: WMTS-FSDI service, layer "River basins"

it: Servizio WMTS-IFDG, strato "Bacino fluviale"

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

fr: Download

de: Download

en: Download

it: Download

fr: Les bassins fluviaux constituent le niveau d’agrégation supérieur du jeu de géodonnées de base « Bassins versants topographiques des eaux suisses ». Les grands bassins versants avec une surface de plusieurs milliers de km2 sont drainés par de grands cours d’eau : Rhin, Aar, Reuss, Limmat, Thur, Rhône, Ticino, Inn, Adda et Adige.

de: Die Flussgebiete bilden die oberste Aggregationsebene des Geobasisdatensatzes «Topographische Einzugsgebiete der Schweizer Gewässer». Die grossen Gebiete mit Flächen von meist mehreren 1000 km² werden durch grosse Fliessgewässer entwässert: Rhein, Aare, Reuss, Limmat, Thur, Rhone, Ticino, Inn, Adda und Adige.

en: The river basins form the uppermost aggregation level for the "Topographical catchment areas of Swiss waterbodies" geodata set. The large areas with a surface area of usually more than 1000km² are drained by large watercourses: the Rhine, Aar, Reuss, Limmat, Thur, Rhône, Ticino, Inn, Adda and Adige.

it: I bacini fluviali costituiscono il livello di aggregazione superiore del record di geodati di base «Bacini imbriferi topografici dei corsi d'acqua svizzeri». I grandi bacini con superfici che si estendono per lo più per diverse migliaia di km² sono drenati da grandi corsi d'acqua: Reno, Aare, Reuss, Limmat, Thur, Rodano, Ticino, Inn, Adda e Adige.

de: Bundesamt für Umwelt / Abteilung Hydrologie

fr: Bassin fluvial

de: Flussgebiete (Einzugsgebiete)

en: River basins (catchment areas)

it: Bacini fluviali (bacini imbriferi)


maintainer: EnviDat Support

en: snow-depth-distribution snow-hydrology photogrammetry davos dischma avalanches

en: Snowdepth maps

format: GeoTIFF

en:
The available datasets are snow depth maps with a spatial resolution of 0.5 m derived from images of the survey camera Vexcel Ultracam mounted on a piloted airplane. Image acquisition was carried out during the approximately peak of winter (time when the thickest snowpack is expected) in spring. The snow depth maps are calculated by the subtraction of a summer-DTM from the processed winter- DSM of the corresponding date. The summer-DTM used was derived from a point cloud of an airborne laser scanner from 2020.

Due to the occurrence of inaccuracies of the calculated snow depth values caused by the photogrammetric method, we applied different masks to significantly increase the reliability of the snow depth maps. We masked out settled areas, high-frequented streets and technical constructions, pixels with high vegetation (height > 0.5 m) , outliers and unrealistic snow depth values. In addition, we modified the snow depth values of snow-free pixels to 0.
The information on buildings and infrastructure comes from the exactly classified ALS point cloud and the TLM dataset from Swisstopo (https://www.swisstopo.admin.ch/de/geodata/landscape/tlm3d.html#links). High vegetation is also derived from the classification and the calculated object height from the point cloud.
Outliers and unrealistic snow depth values are defined as negative snow depth values and snow depths exceeding 10 m. The classification of each pixel of the corresponding orthophoto into snow-covered or snow-free is based on the application of a threshold of the NDSI or manually determined ratios of the RGB values.

An extensive accuracy assessment proves the high accuracy of the snow depth maps with a root mean square error of 0.25 m for the year 2017 and 0.15 m for the following snow depth maps.

The work is published in:

avalanches

display_name: avalanches

davos

display_name: davos

dischma

display_name: dischma

photogrammetry

display_name: photogrammetry

snow-depth-distribution

display_name: snow-depth-distribution

snow-hydrology

display_name: snow-hydrology

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

value: Envidat Harvester

en: Snow depth mapping by airplane photogrammetry (2017 -  ongoing)


en: radiation atmospheric-conditions wind temperature-profiles precipitation

en: Site photo 1

en: This site photo is outdated and has been replaced with an updated version (see description).

en: DATA ACCESS

en: This data access is outdated and has been replaced with an updated version (see description).

en: STB2 CSV

media_type: text/csv

en: This dataset is outdated and has been replaced with an updated version (see description).

mimetype: text/csv

en: Site photo 2

en: Site photo 3

en: Latest data from Stillberg climate station

en: Overview plot

media_type: image/png

en: This overview plot is outdated and has been replaced with an updated version (see description).

format: PNG

en: README

media_type: text/plain

en: This description is outdated and has been replaced with an updated version (see description).

format: TXT

en:
 Important
This EnviDat entry is outdated. The most recent, usable version of the data can be found under the new EnviDat entry "Long-term meteorological station Stillberg, Davos, Switzerland at 2090 m a.s.l.." The entry can be found under this link <https://www.envidat.ch/#/metadata/meteo-stillberg> and with this DOI <https://doi.org/10.16904/envidat.396>.

atmospheric-conditions

display_name: atmospheric-conditions

precipitation

display_name: precipitation

radiation

display_name: radiation

temperature-profiles

display_name: temperature-profiles

wind

display_name: wind

educ

it: Istruzione, cultura e sport

en: Long-term meteorological and snow station at 2090 m a.s.l., Stillberg, Davos, Switzerland (1975 - present)


maintainer: Frank Techel

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

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

format: XLS

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

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

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

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

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

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

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

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

avalanche-bulletin

display_name: avalanche-bulletin

avalanche-danger-level

display_name: avalanche-danger-level

avalanche-forecast

display_name: avalanche-forecast

avalanche-forecasting

display_name: avalanche-forecasting

verification

display_name: verification

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Re-analyzed regional avalanche danger levels in Switzerland


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

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

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

en: information on ingredients of national dishes

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

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

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

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

agro-food-trade

display_name: agro-food-trade

food-preferences

display_name: food-preferences

food-taste-similarity

display_name: food-taste-similarity

latent-semantic-analysis

display_name: latent-semantic-analysis

national-dishes

display_name: national-dishes

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


maintainer: Franziska Koch

en: snow-depth liquid-water-content gps snow-cover snow

en: Dataset with GPS-derived snow cover properties and validation data

en: This file includes the data as described in the Read-me file.

en: Read-me

media_type: application/pdf

en: The read-me file contains the description of the data provided.

format: PDF

mimetype: application/pdf

en:
This data set includes GPS-derived snow water equivalent (SWE), snow depth (HS) and liquid water content (LWC) data for three entire snow-covered seasons (2015-2016, 2016-2017, 2017-2018) at the study plot Weissfluhjoch 2540 m a.s.l. (Davos, Switzerland). The procedure to derive these snow properties is described in Koch et al. (2019). The novel approach is based on a combination of GPS signal attenuation and time delay.

The dataset also includes corresponding validation data for SWE and HS measured at Weissfluhjoch, and some additional meteorological data used for interpretation of the snow cover evolution.

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

These data are the basis of the following publication:
> Koch, F., Henkel, P., Appel, F., Schmid, L., Bach, H., Lamm, M., Prasch, M., Schweizer, J., and Mauser, W., 2019. Retrieval of snow water equivalent, liquid water content and snow height of dry and wet snow by combining GPS signal attenuation and time delay. Water Resources Research, 55(5), 4465-4487. https://doi.org/10.1029/2018WR024431

gps

display_name: gps

liquid-water-content

display_name: liquid-water-content

snow

display_name: snow

snow-cover

display_name: snow-cover

snow-depth

display_name: snow-depth

en: GPS-derived data of SWE, HS and LWC and corresponding validation data


maintainer: Camille Albouy

en: teleo-primers 12s actinopterygii atlantic-ocean barcoding environmental-dna mitochondrial-dna

en: This file contains the Teleo Barcode for the Atlantic fishes collected during a French scientific survey (Evhoe) for stock assessment.

en:
Mitochondrial tree based on the 12s sequences obtained for altantic fish.
This data represent the maximum likelihood phylogenetic tree allowing to assess 1) the clustering of the different sequences generated in
this study and their closest sequences recovered through a MegaBLAST on GenBank, 2) the robustness of the clades and 3) the
phylogenetic dissimilarities through the branch length of the tree among the different sequences. This tree was built with IQtree2
using the best substitution model found by modelFinder. The robustness of the node was evaluated using 1000 ultra-fast bootstrap
(node information indicated on the right of the /) and SH-aLRT procedures (node information indicated on the left of the /), using a hillclimbing nearest neighbor interchange (NNI) search to reduce the risk of overestimating branch supports with ultra-fast Bootstrap
caused by too severe model violations.

en: This file contains the metadata associated with each scientific survey

en: This file contains the 12s mitochondrial sequences for the Atlantic fishes collected on a French scientific survey (Evhoe) for stock assessment.

en: This file contains the metadata associated with each 12 s mitochondrial sequence.

en:
The global biodiversity crisis driven by anthropogenic pressures significantly threatens marine ecosystems functioning. The rate of climate change and the impacts of anthropogenic pressures outpacing the capabilities of our observation tools, stresses the need to develop new methods to assess these rapid modifications. Environmental DNA (eDNA; DNA traces released by organisms) metabarcoding has emerged as a non-invasive method that has been widely developed over the last decade. Thanks to a large spatio-temporal coverage, high detection of rare species and its time and cost effectiveness, eDNA metabarcoding represents a promising biomonitoring tool. However, capturing fish diversity using eDNA requires a high-quality genetic reference database, which we are currently still lacking. For the South European Atlantic shelf area, we estimated that only 41% of the fish species present were recorded in the available eDNA reference databases. Improving reference databases can notably rely on opportunistic sampling enabling the reporting of sequences for new species. Therefore, the data provided here consists of barcoding 95 species of ray-finned and cartilaginous fishes over the 12S mitochondrial DNA gene. We generated 168 12S barcodes from fishes that were sampled in the Bay of Biscay (Northeast Atlantic, France) between 2017 and 2019. We also provided the “Teleo” barcode associated with a specific 12S region for each individual. In addition to the sequences, we provided for each individual the taxonomy, the details associated with the barcode (Genbank accession number, chromatograms), a photograph, as well as 5 ecomorphological measures and 11 life-history traits. These traits document several functions such as dispersion, diet, habitat use, and position in the food web. Furthermore, we provided the metadata of each sampling site (date, station, sampling hour, gear, latitude, longitude, depth) and environmental variables measured in situ (conductivity, salinity, water temperature, water density, air temperature).
This data set is highly valuable to improve the Northeast Atlantic eDNA genetic database, thus helping to better understand the effects of environmental forcing in the Bay of Biscay, a transition zone housing mixed assemblages of boreal, temperate and subtropical fish species susceptible to display variability in functional traits to adapt to changing conditions.

actinopterygii

display_name: actinopterygii

atlantic-ocean

display_name: atlantic-ocean

barcoding

display_name: barcoding

environmental-dna

display_name: environmental-dna

mitochondrial-dna

display_name: mitochondrial-dna

teleo-primers

display_name: teleo-primers

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: ATLFISHREF A 12S mitochondrial reference dataset for metabarcoding Atlantic Fishes frequently caught during scientific surveys in the Bay of Biscay


irkis-soil-moisture-measurements-davos

maintainer: Nander Wever

title_for_slug: irkis-soil-moisture-measurements-davos

en: soil-moisture meteo-station dischma-catchment davos meteorological-measurements

en: IRKIS soil moisture data (zip file)

en: Compressed archive (zip) containing soil moisture measurements, as well as meteorological measurements and interpolated meteorological time series.

en:
Meteorological and soil moisture measurements from soil moisture stations installed from October 2010 - October 2013 in the area surrounding Davos, in particular in the Dischma catchment.

There are in total 7 stations: 1202, 1203, 1204, 1205, 222, 333 and SLF2. For each of the stations, there is a:

* vwc_[stn].smet: containing the soil moisture measurements

* station_[stn].smet: in-situ measured meteorlogical parameters. Note, the quality of these measurements for stations 1202, 1203, 1204 and 1205 is very low, with data gaps. Use this data with care. For stations 222, 333 and SLF2, data quality is high and only the default cautiousness should be applied.

* interpolatedmeteo_[stn].smet contains per stations a dataset derived by interpolating from several stations in the Davos area to the stations location. This dataset was generated from the output of the Alpine3D model, of which simulations are presented in the Wever et al. (2017) manuscript.

At the soil moisture measurement sites, Decagon 10HS sensors were installed, at 10, 30, 50, 80 and 120 cm depth. Per depth 2 sensors were installed, labelled A and B in the datafiles. Note that at stations 1203, 1204 and 1205, sensors were only installed at 10, 30 and 50 cm depth.

The files follow the SMET format: https://models.slf.ch/docserver/meteoio/SMET_specifications.pdf and metadata for the stations can be found in the header of the smet files.

Please cite the Wever et al. (2017) reference when using this data in publications.

For a more detailed description, please refer to: Wever, N., Comola, F., Bavay, M., and Lehning, M.: Simulating the influence of snow surface processes on soil moisture dynamics and streamflow generation in an alpine catchment, Hydrol. Earth Syst. Sci., 21, 4053-4071, https://doi.org/10.5194/hess-21-4053-2017, 2017.

davos

display_name: davos

dischma-catchment

display_name: dischma-catchment

meteo-station

display_name: meteo-station

meteorological-measurements

display_name: meteorological-measurements

soil-moisture

display_name: soil-moisture

en: IRKIS Soil moisture measurements Davos


maintainer: Lisa Hülsmann

en: empirical-mortality-models forest-reserves inventory-data tree-growth tree-mortality

en: Calibration data

en:
A set of three consecutive inventories was used to generate records for the calibration of mortality models based on trees that were alive in the first and second inventory and either dead or alive in the third inventory.

 Meta data
Reserve: Name of the forest reserve

Plot: Permanent plot number

 Tree data
Species: Latin species name (or species group, cf. _Quercus_ spp.)

y: Status of the tree with 0 = alive and 1 = dead

dt: Length of the mortality period (years)

DBH: Diameter at breast height (mm) of the second inventory

relBAI: Annual relative basal area increment (-) calculated based on the first and the second DBH measurement as the compound annual growth rate of the trees basal area

 Stand data
_All stand variables were calculated for the second inventory._

mDBH: Mean diameter at breast height (mm)

iqrDBH: Interquartile range of diameter at breast height (mm)

BA: Basal area per hectare (m2 ha-1)

N: Number of trees per hectare (ha-1)

 Climate data
_Both climate variables were calculated for the period between the second and the third inventory._

P: Mean annual precipitation sum (mm)

mT: Mean annual air temperature (°C)

en:
The dataset comprises > 90 000 records from inventories in 54 strict forest reserves in [Switzerland](https://www.wsl.ch/de/wald/biodiversitaet-naturschutz-urwald/naturwaldreservate.html) and [Lower Saxony / Germany](http://naturwaelder.de/) along a considerable environmental gradient. It was used to develop parsimonious, species-specific mortality models for 18 European tree species based on tree size and growth as well as additional covariates on stand structure and climate.

 Inventory data
Measurements had been conducted repeatedly on up to 14 permanent plots per reserve for up to 60 years with re-measurement intervals of 4 - 27 years. The permanent plots vary in size between 0.03 and 3.47 ha. The inventories provide diameter measurements at breast height (DBH) and information on the species and status (alive or dead) of trees with DBH ≥ 4 cm for Switzerland and ≥ 7 cm for Germany.

 Data selection
We excluded three permanent plots where at least 80 % of the trees died during an interval of 10 years, and mortality could be clearly assigned to a disturbance agent. Mortality in the remaining stands was rather low, with a mean annual mortality rate of 1.5 % and strong variation between plots from 0 to 6.5 % (assessed for trees of all species with DBH ≥ 7 cm).
We only used data from permanent plots with at least 20 trees per species to obtain reliable plot-level mortality rates even for species with low mortality rates (about 5 % during 10 years), and selected tree species occurring on at least 10 plots to cover sufficient ecological gradients. This led to a dataset of 197 permanent plots and 18 tree or shrub species: _Abies alba_ Mill., _Acer campestre_ L., _Acer pseudoplatanus_ L., _Alnus incana_ Moench., _Betula pendula_ Roth, _Carpinus betulus_ L., _Cornus mas_ L., _Corylus avellana_ L., _Fagus sylvatica_ L., _Fraxinus excelsior_ L., _Picea abies_ (L.) Karst, _Pinus mugo_ Turra, _Pinus sylvestris_ L., _Quercus pubescens_ Willd., _Quercus_ spp. (_Q. petraea_ Liebl. and _Q. robur_ L.; not properly differentiated in the Swiss inventories), _Sorbus aria_ Crantz, _Tilia cordata_ Mill. and _Ulmus glabra_ Huds..

 Predictors of tree mortality
We considered tree size and growth as key indicators for mortality risk. Radial stem growth between the first and second inventory and DBH at the second inventory were used to predict tree status (alive or dead) at the third inventory. To this end, the annual relative basal area increment (relBAI) was calculated as the compound annual growth rate of the trees basal area. Additional covariates on stand structure and climate comprise mean annual precipitation sum (P), mean annual air temperature (mT), the mean and the interquartile range of DBH (mDBH, iqrDBH), basal area (BA) and the number of trees (N) per hectare.

 Further information
For further information, refer to Hülsmann _et al_. (in press) How to kill a tree – Empirical mortality models for eighteen species and their performance in a dynamic forest model. _Ecological Applications_.

empirical-mortality-models

display_name: empirical-mortality-models

forest-reserves

display_name: forest-reserves

inventory-data

display_name: inventory-data

tree-growth

display_name: tree-growth

tree-mortality

display_name: tree-mortality

en: Calibration data for empirical mortality models of 18 European tree species


maintainer: Paraskevi Georgakaki

en: blowing-snow ice-crystal-number-concentration wrf liquid-water-content jungfraujoch swiss-alps ice-water-content secondary-ice-production

en: Observational data

en:
The .zip file contains 6 .csv files including all microphysical measurements taken at the Jungfraujoch station during the two periods of interest:

* 25-28 January 2014 : northwesterly-winds (NW)
* 29 January - 1 February 2014 : southeasterly-winds (SE)

The total Ice Crystal Number Concentration (ICNC; reported in L⁻¹) is measured by the two-dimensional stereo hydrometeor spectrometer (2D-S; Lawson et al., 2006) over the size range 10-1280 μm. The total ice water content (IWC; reported in gm⁻³) is also derived by the 2D-S data using the Brown and Francis (1995) mass-diameter relationship. Finally, the quantification of the liquid water content (LWC; reported in gm⁻³) is based on the liquid droplet size distribution data derived from a DMT cloud droplet probe (CDP; Lance et al., 2010) over the size range 2-50 μm. The frequency of the measurements in the .csv files is 30 min, to match the output frequency of the model. Datetime is also reported in the files (DD/MM/YYYY h:mm) in UTC.

Acknowledgment: Collection and pre-processing of the microphysical measurements has been performed by Gary Lloyd (gary.lloyd@manchester.ac.uk). More details about the instrumentation during the CLACE2014 campaign can be found in Lloyd et al. (2015).

en: WRF outputs

en:
The .zip file contains all the NetCDF outputs produced by the mesoscale Weather Research and Forecasting (WRF) model (version: 4.0.1). Two case studies were simulated in this study, between 25-28 January 2014 (00:00 UTC) and 29 January - 1 February 2014 (00:00 UTC), respectively. The former is denoted as "NW" and the latter as "SE", owing to the prevailing wind conditions at the Jungfraujoch (JFJ) station.

The outputs come from the innermost domain of the simulations with a spatial resolution of 1 km and are extracted from the nearest to JFJ model grid point (46.55°N 7.98°E). The dimensions of each model variable is [145,100], which corresponds to time and height, respectively. The frequency of the outputs is 30 min. Note that the first 24 hours of each simulation (i.e., the first 48 outputs) were considered spin up time.

The following sets of sensitivity simulations were presented in this study:

* CNTRL_NW.nc, CNTRL_SE.nc : Sensitivity simulations performed with the default version of WRF
* DS_PHILL_NW.nc, DS_PHILL_SE.nc : Sensitivity simulations including a description of the droplet shattering process following Phillips et al. (2018)
* DS_SULL_NW.nc, DS_SULL_SE.nc : Sensitivity simulations including a description of the droplet shattering process following Sullivan et al. (2018)
* PHILL_NW.nc, PHILL_SE.nc : Sensitivity simulations including a description of the ice-ice collisional break-up process following Phillips et al. (2017)
* TAKAH_NW.nc, TAKAH_SE.nc : Sensitivity simulations including a description of the ice-ice collisional break-up process following Sullivan et al. (2018) based on Takahashi et al. (1995)
* TAKAHrim_NW.nc, TAKAHrim_SE.nc : Same set-up as before but applied only to collisions between rimed ice particles
* BIPS_Xk0_DY_NW.nc, BIPS_Xk0_DY_SE.nc : Sensitivity simulations including a source of Xk0 (either 10.0 or 100.0 L⁻¹) ice crystals with sizes of DY (either 10 or 100 μm) in the first atmospheric level of the model
* BIPS100_PHILL_NW.nc, BIPS100_PHILL_SE.nc : Sensitivity simulations where the combined effect of blowing snow and ice-ice collisional break-up is examined

en:
This repository contains all WRF model outputs and observational data sets used for the paper:
Georgakaki, P., Sotiropoulou, G., Vignon, É., Billault-Roux, A.-C., Berne, A., and Nenes, A.: Secondary ice production processes in wintertime alpine mixed-phase clouds, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-760, in review, 2021.

blowing-snow

display_name: blowing-snow

ice-crystal-number-concentration

display_name: ice-crystal-number-concentration

ice-water-content

display_name: ice-water-content

jungfraujoch

display_name: jungfraujoch

liquid-water-content

display_name: liquid-water-content

secondary-ice-production

display_name: secondary-ice-production

swiss-alps

display_name: swiss-alps

wrf

display_name: wrf

en: Data for the publication "Secondary ice production processes in wintertime alpine mixed-phase clouds"


maintainer: James W. Kirchner

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

en: Data Set Documentation

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

en: Weekly stable water isotopes

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

mimetype: text/plain

en: 7-hourly stable water istopes

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

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

catchment

display_name: catchment

chloride

display_name: chloride

deuterium

display_name: deuterium

plynlimon

display_name: plynlimon

precipitation

display_name: precipitation

stable-water-isotopes

display_name: stable-water-isotopes

streamflow

display_name: streamflow

tracer

display_name: tracer

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

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


maintainer: Athanasios Nenes

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

en: Predicted cloud droplet number concentrations Weissfluhjoch

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

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

en: Hygroscopicity parameter Weissfluhjoch

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

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

Missing values are indicated by "-999".

en: CCN data Weissfluhjoch

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

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

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

_Error_code_:

0000: Measurements without error

1000: Period of transient operation after a supersaturation change

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

3000: Irregular values in the room temperature housing the instrument

9999: Missing data

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

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

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

ccn

display_name: ccn

cloud-droplets

display_name: cloud-droplets

cloud-modeling

display_name: cloud-modeling

dmt-ccn-chamber

display_name: dmt-ccn-chamber

hygroscopicity

display_name: hygroscopicity

raclets

display_name: raclets

sigma-w

display_name: sigma-w

supersaturation

display_name: supersaturation

weissfluhjoch

display_name: weissfluhjoch

en: CCN, hygroscopicity, predicted cloud droplet numbers Weissfluhjoch


en: urbanization switzerland land-use landscape-services land-use-change modeling statistical-model

en: Data for Assessing potential landscape service trade-offs driven by urbanization in Switzerland

en:
The provided folder structure follows the set up of the scripts.
Data is only provided when it has been fully produced by us, otherwise dummies are given.
For data that will be produced by one of the scripts, no dummies are given.
The folders may contain sub-folders, which would be filled if the scripts were all run

 00_Analysis_Data
Data produced throughout the process of the calibration and projection that is later needed for analysis

 01_Raw_Data
Primary data from external sources that were used to compile the rasters and numbers used during the calibration and projection of landscape transitions as well as service calculation

 02_Prepared_Data
Data that will be prepared for further use by scripts are put in here
Additionally it contains the parameter sheets for the transition predictors that we compiled

 03_Model_Data
 Model outputs
Folder were the projection models statistics will be saved
 Models
Folder into which the actual transition models will be saved
 Residuals
Folder into which data on models residuals will be saved for future use

 04_Projected_Data
The output of the projections will be saved into this folder

 05_Service_Data
Any service calculations will be saved in here

 06_Results_Analysis
Any Analysis plots and tables as well as intermediate data will be saved here in the respective sub-folder

mimetype: application/zip

en: Scripts for Assessing potential landscape service trade-offs driven by urbanization in Switzerland

en:
Scripts ordered by their main purpose within sequential folders in this ReadMe marked with

Within the folders the capital letters refer to the parallel or sequential use of the scripts; scripts here in ReadMe are marked with

 00_Function Scripts
 Functions.r
Script containing several functions for analyzing statistical models: cohen.kappa, meva.table, D2, max-Tss, plot-TSS

 Service_functions.R
Script containing functions for calculating all services and returning sums (and median for bd and rec) as well as rasters

 01_Data Preparation
 A_AS_raster_preparation.R
Preparation of the land use rasters for the years 1985, 1997, and 2009 based on the original Arealstatistik (AS/ASCH); Assigning our categories and assining new values to all "construction sites" for calibrating the models
For model projection starting point (2009) and for service calculations (all ASCH years) the "other" categories were assigned to the land use categories based on dominant neighbor, so that there were no empty cells
Creation of water & mountainous area raster that stayed stable throughout the projections
Creation of MFH raster (2009), to have as base for all new projection years, as this category never becames any other

 B_Factors_preparation.R
Preparation of the factor rasters that were used to calculate service values per cell (see Table 2): LWfactor, Transportation, Vol, Rivers, Lakes, slope, TWW, Hazard_risk

 B_Predictors_preparation.R
Preparation of the raster of the predictors used for calibrating (ASCH years) and projecting (stable) the transition models that do not depend on the land use categories: Temperature (ASCH years), solar radiation (stable), Public transportation access (stable), Elevation (stable), Workers density (ASCH years), Population change (ASCH transitions)

 B_Regions_preparation.R
Preparation of bioregions as raster to be used to mask asxxxx later to calculated services and land use area just for one region

 C_Pred_proj.R
Projecting the dynamic predictors into the future for projecting the land use transitions with the model: Linear extrapolations for workers densities and population; more detailed extrapolation of temperature (based on data for all stations that matched between the temp table we used for the calibration period and the projections from CS2M, and then making a spatial interpolation for all years based on the interpolation of temperature for all years per station)

 02_Model calibration
 A_Bioreg_pred_corr.R
Calculating correlation between the predictors for each bioregion and land use category, Output are those with too high correlation so that one predictor can be removed from transitions were both predictors occur

 A_trans_numbers_bioreg.R
Calculate transition numbers for each bioregion to decide which transitions to primarily consider

 B_Bioreg_ext_eval.R
Calculating TSS based on the external evaluation for the bioregion transitions that happened often enough to try to model them (calibration data: 85-97, evaluation data: 97-09)

 B_Bioreg_int_eval.R
Calculating TSS based on the internal evaluation for the bioregion transitions that happened often enough to try to model them (calibration data: 70%, evaluation data: 30%)

 C_Bioreg_TSS_analysis.R
Extracting TSS numbers from internal and external evaluation, calculating mean and median for each transition for each region for each evaluation method and saving as csv per region to be able to select only the transitions with high enough model performance

 D_Bioreg_Models_for_prediction.R
Calibration of the models that were then used to project land use transitions into the future; calibrated separately for each bioregion

 E_Residuals_trans_models.R
Calculation of Moran's I statistics to analyze the residuals of the land use transition models

 03_Projections
 A_Bioreg_Projections_run.R
Master script for: Extrapolaitng into the future with the regional models and then assembling for each time step to calculate new AS predictors; Several Outputs: txt file to test for NAs in Predictors, txt file with random seed number for sampling, txt file with times for calculation steps (check progress of script while running), csv sheet with projected transition frequencies, tif-rasters for every region and time step, pdf with raster plot for every region and time step

 04_Service calculation
 A_Bioreg_as_services.R
Calculate services per bioregion for years 1985, 1997, 2009

 A_Service_calculation_bioregions.R
Extracting service numbers(sums) per bioregions for all projection runs and storing them in a list of matrixes to be available for further plotting; can be run for swiss wide projections or regional ones

 05_Analysis
 A_Bioreg_ASCH_development.R
Extracting the occurrence of each category per region for all runs (first part), then for ASCH years, and then plotting them nicely; only works for regional projections

 A_Bioreg_Predictors_importanceD2.R
Calculating D2 for each transition and each considered predictor from bivariate models to get value of importance

 B_Bioreg_Pred_imp_analysis.R
Using calculated D2 to sort Predictors according to their importance for each transition

 A_Bioreg_Spatial_results_analysis.R
Plotting randomly sampled runs for ASCH and services to analyses their spatial development

 A_Bioreg_Transition_numbers_check_v1.R
Analyzing the frequency for all transitions for all regions and producing the point plots for each transition separately

 A_Bioreg_Transition_numbers_check_v2.R
Analyzing percentage and numbers of all transitions but plotting them per region and originating ASCH category

 B_Transition_Analysis.R
Further Analysis of transition numbers

 A_Service_analysis.R
Plotting of the service development for bioregions in several ways for analyzing

en:
Data and scripts of publication:

Madleina Gerecke, Oskar Hagen, Janine Bolliger, Anna M. Hersperger, Felix Kienast, Bronwyn Price, Loïc Pellissier (2019) Assessing potential landscape service trade-offs driven by urbanization in Switzerland. Palgrave communications.

Contains land use projections for Switzerland and scripts and data for these projections as well as the calculation of landscape services.

Data Folder:
Contains sub-folder with the data necessary for this study (provided were no copyright issues, otherwise placeholders with descriptions), and folders where produced data may be stored

Scripts Folder:
Contains scripts organized into subfolders depending on their purpose

Note: Some abbreviations within the scripts and data are derived from German words and not English.

land-use

display_name: land-use

land-use-change

display_name: land-use-change

landscape-services

display_name: landscape-services

modeling

display_name: modeling

statistical-model

display_name: statistical-model

switzerland

display_name: switzerland

urbanization

display_name: urbanization

en: Land use projections and services for Switzerland


maintainer: Tobias Wechsler

en: wasserkraft speed2zero schweiz datensatz kleinstwasserkraftwerke

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

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

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

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

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

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

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

datensatz

display_name: datensatz

kleinstwasserkraftwerke

display_name: kleinstwasserkraftwerke

schweiz

display_name: schweiz

wasserkraft

display_name: wasserkraft

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: CH-Kleinstwasserkraftwerke - ein schweizweiter Datensatz zu Kleinstwasserkraftwerken


en: birds biodiversity insects fungal-pathogen fungi urban-ecology urban urban-biodiversity

en: Data tables including individual bird data with biometry, prey species from faecal samples, and fungi species from swab samples.

en:
We captured birds in urban parks and peri-urban forests in the cities of Lugano and Zurich. The urban sites were located in an urban forest (Lugano) and in a cemetery (Zurich), and the bird assemblages were comparable with those of the surrounding forests.
We captured individual birds during sessions lasting multiple days using mist-nets, concentrating the activities within 3-4 hours after sunrise and before sunset. We extracted birds from nets using single-use nitrile gloves and gently wiped their body parts with a dry foam swab MW940 (Medical Wire & Equipment), which was then placed in a screw-capped tube (2 ml), which was previously filled with 1 ml ethanol puriss. (Sigma-Aldrich) under sterile conditions.Then, we marked each bird with an alphanumeric leg band, identified the species, determined the sex and age whenever possible, measured for relevant biometrics (e.g. wing chord), the amount of body fat and pectoral muscle (i.e. body condition), and weighed.
Afterwards, we placed each bird in a disposable paper bag within a cotton bag that ensured respiration and left it for few minutes in a safe and calm place to allow for defecation and the collection of faecal samples. The faecal sample was collected from insectivorous and omnivorous birds, and few granivorous birds that are known to occasionally eat invertebrates, such as the house sparrow Passer domesticus.
After releasing the bird, we stored the faecal sample in a screw-capped tube (2 ml), which was previously filled with 1 ml ethanol puriss. (Sigma-Aldrich) under sterile conditions. We wore single-use nitrile gloves and used sterile toothpicks to collect solid samples. The tubes were initially stored in the refrigerator during the field campaign and no later than seven days after collection at -20°C until prepared for DNA extraction.
DNA extraction and library preparation was performed at WSL Phytopathology facilites, Sequencing was performed at Lausanne Genomic Technologies Facility (University of Lausanne; [https://wp.unil.ch/gtf/](https://wp.unil.ch/gtf/)) on an AVITI benchtop sequencing system (Element Biosciences). Bioinformatics was performed at the Genetic Diversity Center (ETH Zurich) using a custom pipeline.

biodiversity

display_name: biodiversity

birds

display_name: birds

fungal-pathogen

display_name: fungal-pathogen

fungi

display_name: fungi

insects

display_name: insects

urban

display_name: urban

urban-biodiversity

display_name: urban-biodiversity

urban-ecology

display_name: urban-ecology

en: Urban bird capture data with prey species and fungal community


climarctic-seasons

maintainer: Aline Frossard

title_for_slug: climarctic-seasons

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

en: Climarctic seasons dataset Envidat.xlsx

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

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

arctic

display_name: arctic

greenhouse-gases

display_name: greenhouse-gases

microbial-biodiversity

display_name: microbial-biodiversity

microbial-functions

display_name: microbial-functions

soil

display_name: soil

en: Climarctic seasons


en: pmcamx-uf nucleation cloud-droplets total-number modeling

en: data_JRL_New particle formation events can reduce cloud droplets in boundary layer clouds at the continental scale.Paper_2023GL106182.zip

en: This repository contains PMCMx-UF model outputs used for the paper: D. Patoulias K. Florou S. N. Pandis and A. Nenes: New particle formation events can reduce cloud droplets in boundary layer clouds at the continental scale, Geophysical Research Letters, in review, 2023.

cloud-droplets

display_name: cloud-droplets

modeling

display_name: modeling

nucleation

display_name: nucleation

pmcamx-uf

display_name: pmcamx-uf

total-number

display_name: total-number

en: Dataset for the publication New particle formation events can reduce cloud droplets in boundary layer clouds at the continental scale


maintainer: Jokin Idoate-Lacasia

en: carbon-storage environmental-gradients biomass-dynamics forest-reserves forest-inventory

en: R markdown document used to download the data files, reproduce the analysis and plot generation.

format: OCTET

en: Climatic space covered by the Swiss National Forest Inventory (NFI) that will be used in Figure 5.

en:
This dataset documents the analyses and the plot generation for the manuscript **Long-term biomass dynamics in temperate forests in Europe after cessation of management**.
We analyse biomass development in an extensive network of 224 plots in 37 natural forest reserves (NFR) covering a wide environmental gradient with mean annual temperatures ranging from 1 to 10.4 °C and mean annual precipitation ranging from 901 to 2317 mm. Forest inventories have been conducted approximately every 10 years during the last 60 years.
It consists of the following files:
+ **Long_term_biomass_script.Rmd**: R markdown document to reproduce the analysis and the plot generation.
+ **dat_biomass.RDS**: Biomass values for each permanent plot at each inventory. This dataset is used for data exploration and for the Biomass model, and to create Figure 2 and Figure 3.
+ **dat_biomass_change.RDS**: Biomass change values between inventories for each permanent plot. It includes the explanatory variables as well. This dataset is used for the Biomass change model and to create Figure 4 and Figure 5.
+ **NFI_climspace_netCDF.RDS**: Climatic space covered by the Swiss National Forest Inventory (NFI) that is used in Figure 5.

biomass-dynamics

display_name: biomass-dynamics

carbon-storage

display_name: carbon-storage

environmental-gradients

display_name: environmental-gradients

forest-inventory

display_name: forest-inventory

forest-reserves

display_name: forest-reserves

educ

en: Education, culture and sport

en: Long-term biomass dynamics of temperate forests in Europe after cessation of management


maintainer: Martin Schneebeli

en: snow-cast depth-hoar snow weak-layer rounded-snow new-snow 3d-model 3d-print

en: Snow models in STL-format

en: The zipped file contains all data described in the file 3d_snow_models_metadata. The stl-files should be ready for additive manufacturing.

en: Description of snowtypes, filenames and other metadata

en: This file contains information about the stl-files of the different snow models. Snowtype (Schneeart), International Snow Classification, preview picture, notes about the origin of the snow, filename of the stl-data, and physical size of the model are given.

en: The dataset contains several snow models in the Standard Tesselated Geometry File Format (stl) for 3D visualization, printing and additive manufacturing. Different snow types are available (new snow, rounded snow, depth hoar, buried surface hoar, graupel).

depth-hoar

display_name: depth-hoar

new-snow

display_name: new-snow

rounded-snow

display_name: rounded-snow

snow

display_name: snow

snow-cast

display_name: snow-cast

weak-layer

display_name: weak-layer

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.