title_for_slug: Kennzahlen alternativ angetriebene PW

de: alternative-antriebe elektromobilitat hybridfahrzeuge co2 ogd92-bfe personenwagen elektrische-fahrzeuge

fr: Chiffres clés des voitures de tourisme neuves à propulsion alternative

de: Kennzahlen alternativ angetriebene PW

en: Key figures for alternatively powered passenger cars

it: Indicatori relativi alle automobili a propulsione alternativa

de: Storymap

fr: Modèles de voitures de tourisme hybrides rechargeables les plus prisés

de: Beliebteste Plug-In PW-Modelle (PHEV)

en: Most popular plug-in models (cars)

it: Automobili plug-in più diffuse (modelli PHEV)

fr: Depuis 2015, l’Office fédéral de l’énergie recense chaque trimestre les modèles de voitures de tourisme hybrides rechargeables (PHEV) les plus prisés; depuis 2021, les données sont disponibles à un rythme mensuel. Tous les modèles hybrides rechargeables disponibles en Suisse sont pris en compte.

en: Since 2015, the Swiss Federal Office of Energy has reported the most popular plug-in models (PHEVs) among passenger cars on a quarterly basis; since 2021, the data has been available on a monthly basis. All plug-in models from Switzerland are counted together.

de: Ab 2015 weist das Bundesamt für Energie quartalsweise die beliebtesten Plug-In Modelle (PHEV) bei den Personenwagen aus, ab 2021 sind die Daten in monatlicher Auflösung verfügbar. Dabei werden alle Plug-In Modelle aus der Schweiz zusammengezählt.

it: Dal 2015 l'Ufficio federale dell'energia indica su base trimestrale i modelli più diffusi di automobili plug-in (PHEV); dal 2021 i dati sono disponibili con risoluzione mensile. Vengono conteggiati tutti i modelli plug-in presenti in Svizzera.

fr: Statistiques des motorisations alternatives des voitures de tourisme

fr:
Sur la base des données anonymisées des nouvelles immatriculations publiées par l’OFROU, l’Office fédéral de l’énergie évalue depuis 2015 les statistiques des motorisations alternatives des voitures de tourisme.
Sont réputés véhicules à propulsion alternative les véhicules dont la propulsion ne se fait pas exclusivement avec un moteur à combustion à essence ou à diesel.
Ce jeu de données montre le nombre de voitures de tourisme à motorisation alternative par type de motorisation et par canton. Les données sont présentées sous forme de totaux annuels (2015-actuel), trimestriels (2015-2020) ou mensuels (2021-actuel).
Légende: t1 = émissions moyennes de CO2, t2 = nombre de BEV, t3 = nombre de PHEV essence, t4 = nombre de PHEV diesel, t5 = nombre de hybride essence, t6 = nombre de hybride diesel, t7 = nombre de gaz naturel, t8 = nombre de nouvelles immatriculations, t10 = nombre de hydrogène

[La numérotation des cantons est celle de l’OFS](https://www.bfs.admin.ch/asset/fr/453856)

Trimestre 1-4 = Q1-Q4, Trimestre 5 = Total annuel
Mois 1-12 = Janvier-Février, Mois 13 = Total annuel

Le total annuel peut être un peu différent des totaux mensuels ou trimestriels cumulés en raison des ajustements rétroactifs dans la source de données utilisée.

en:
On the basis of the anonymised data for new registrations published by FEDRO, the Swiss Federal Office of Energy has evaluated key figures on alternatively powered passenger cars since 2015.
Alternatively powered vehicles are vehicles that are not solely powered by a petrol or diesel internal combustion engine.
This data set shows the number of alternatively powered passenger cars by type of drive per canton. The data is reported as annual totals (2015-current) as well as quarterly totals (2015-2020) and monthly totals (2021-current).

Key: t1 = Average CO2 emissions, t2 = Number of BEVs, t3 = Number of petrol PHEVs, t4 = Number of diesel PHEVs, t5 = Number of hybrid petrol, t6 = Number of hybrid diesel, t7 = Number of natural gas, t8 = Number of new registrations, t10 = Number of hydrogen

[Numbering of cantons according to FSO](https://www.bfs.admin.ch/bfs/en.assetdetail.453856.html)

Quarter 1-4 = Q1-Q4, Quarter 5 = Annual total
Month 1-12 = January-February, Month 13 = Annual total

The annual total may differ slightly from the cumulative monthly or quarterly totals due to retroactive adjustments in the data source used.

de:
Auf Basis der vom ASTRA publizierten, anonymisierten Neuzulassungsdaten wertet das Bundesamt für Energie seit 2015 wichtige Kennzahlen zu den alternativ angetriebenen Personenwagen aus. Als alternativ angetriebenen Fahrzeuge gelten Fahrzeuge, die nicht ausschliesslich über einen Benzin- oder Diesel-Verbrennungsmotor angetrieben werden.
Dieser Datensatz weist die Anzahl alternativ angetriebener Personenwagen nach Antriebsart pro Kanton aus. Die Daten werden als Jahressummen (2015-aktuell) sowie Quartalssummen (2015-2020) bzw. Monatssummen (2021-aktuell) ausgewiesen.

Legende: t1 = Durchschnittliche CO2-Emissionen, t2 = Anzahl BEV, t3 = Anzahl Benzin PHEV, t4 = Anzahl Diesel PHEV, t5 = Anzahl Hybrid Benzin, t6 = Anzahl  Hybrid Diesel, t7 = Anzahl Erdgas, t8 = Anzahl Neuzulassungen, t10 = Anzahl Wasserstoff

[Nummerierung der Kantone gemäss BFS](https://www.bfs.admin.ch/asset/de/453856)

Quartale 1-4 = Q1-Q4, Quartal 5 = Jahressumme
Monate 1-12 = Januar-Dezember, Monat 13 = Jahressumme

Die Jahressumme kann minimal von den kumulierten Monats- bzw. Quartalssummen abweichen. Dies ist auf laufende rückwirkende Anpassungen in der zugrundeliegenden Datenquelle zurückzuführen.

it:
Sulla base dei dati anonimizzati sulle nuove immatricolazioni pubblicati dall'USTRA, dal 2015 l'Ufficio federale dell'energia analizza importanti indicatori relativi alle automobili a propulsione alternativa.
Per veicoli a propulsione alternativa si intendono veicoli che non sono mossi esclusivamente da un motore a combustione a benzina o diesel.
Questo set di dati mostra il numero di auto turistiche con motore alternativo per tipo di motore e per cantone. I dati sono presentati come totali annuali (dal 2015 ad oggi), trimestrali (dal 2015 al 2020) o mensili (dal 2021 ad oggi).

Legenda: t1 = Emissioni medie di CO2, t2 = Numero di BEV, t3 = Numero di PHEV benzina, t4 = Numero di PHEV diesel, t5 = Numero di ibride benzina, t6 = Numero di ibride diesel, t7 = Numero di gas naturale, t8 = Numero nuove immatricolazioni, t10 = Numero di idrogeno

[Numerazione dei Cantoni secondo UST](https://www.bfs.admin.ch/bfs/it.assetdetail.453856.html)

Trimestre 1-4 = Q1-Q4, Trimestre 5 = Totale annuale
Mese 1-12 = Gennaio-Febbraio, Mese 13 = Totale annuale

Il totale annuale può essere leggermente diverso dai totali mensili o trimestrali cumulati a causa di rettifiche retroattive nella fonte dei dati usata.

fr: Modèles de voitures de tourisme entièrement électriques les plus prisés

de: Beliebteste vollelektrische PW-Modelle (BEV)

en: Most popular all-electric models (cars)

it: Modelli più diffusi di automobili completamente elettriche

fr: Depuis 2015, l’Office fédéral de l’énergie recense chaque trimestre les modèles de voitures de tourisme entièrement électriques (BEV) les plus prisés; depuis 2021, les données sont disponibles à un rythme mensuel. Tous les modèles entièrement électriques disponibles en Suisse sont pris en compte.

en: Since 2015, the Swiss Federal Office of Energy has reported the most popular all-electric models (BEVs) among passenger cars on a quarterly basis; since 2021, the data has been available on a monthly basis. All fully electric models from Switzerland are counted together.

de: Ab 2015 weist das Bundesamt für Energie quartalsweise die beliebtesten vollelektrische Modelle (BEV) bei den Personenwagen aus, ab 2021 sind die Daten in monatlicher Auflösung verfügbar. Dabei werden alle vollelektrischen Modelle aus der Schweiz zusammengezählt.

it: Dal 2015 l'Ufficio federale dell'energia indica su base trimestrale i modelli più diffusi di automobili completamente elettriche (BEV); dal 2021 i dati sono disponibili con risoluzione mensile. Vengono conteggiati tutti i modelli completamente elettrici presenti in Svizzera.

fr:
Sur la base des données anonymisées des nouvelles immatriculations publiées par l’OFROU, l’Office fédéral de l’énergie évalue depuis 2015 des chiffres clés importants concernant les voitures de tourisme à propulsion alternative. Sont réputés véhicules à propulsion alternative les véhicules dont la propulsion ne se fait pas exclusivement avec un moteur à combustion à essence ou à diesel. Ce jeu de données comprend, outre les statistiques annuelles agrégées, les chiffres trimestriels et mensuels des parts de marché ainsi que des indications sur les différents cantons. L’Office fédéral de l’énergie évalue par ailleurs les modèles entièrement électriques et hybrides rechargeables les plus prisés.

Base légale: art. 11 de l’ordonnance sur les exigences relatives à l’efficacité énergétique (OEEE)

en:
On the basis of the anonymised data for new registrations published by FEDRO, the Swiss Federal Office of Energy has evaluated key figures for alternatively powered passenger cars since 2015. Alternatively powered vehicles are vehicles that are not solely powered by a petrol or diesel internal combustion engine. In addition to aggregated annual key figures, this data set also contains quarterly and monthly market share figures as well as information regarding the individual cantons. Also, the Swiss Federal Office of Energy ranks the most popular all-electric and plug-in car models.

Legal basis: Energy Efficiency Ordinance (EnEO), Art. 11

de:
Auf Basis der vom ASTRA publizierten, anonymisierten Neuzulassungsdaten wertet das Bundesamt für Energie seit 2015 wichtige Kennzahlen zu den alternativ angetriebenen Personenwagen (PW) aus. Als alternativ angetriebenen Fahrzeuge gelten Fahrzeuge, die nicht ausschliesslich über einen Benzin- oder Diesel-Verbrennungsmotor angetrieben werden. Dieser Datensatz enthält neben aggregierten jährlichen Kennzahlen auch Quartals- und Monatszahlen der Marktanteile sowie Angaben zu den einzelnen Kantonen. Zudem wertet das Bundesamt für Energie bei den PW die beliebtesten vollelektrischen und Plug-In Modellen aus.

Rechtsgrundlage: Energieeffizienzverordnung (EnEV) Art. 11

it:
Sulla base dei dati anonimizzati sulle nuove immatricolazioni pubblicati dall'Ufficio federale delle strade (USTRA), dal 2015 l'Ufficio federale dell'energia analizza importanti indicatori relativi alle automobili a propulsione alternativa. Per veicoli a propulsione alternativa si intendono veicoli che non sono mossi esclusivamente da un motore a combustione a benzina o diesel. Oltre a indicatori annuali aggregati, questo set di dati contiene anche dati trimestrali e mensili relativi alle quote di mercato e indicazioni sui singoli Cantoni. Inoltre l'Ufficio federale dell'energia analizza i modelli di automobili completamente elettriche e plug-in più diffusi.

Base legale: ordinanza sull’efficienza energetica (OEEne), nello specifico l'articolo 11

alternative-antriebe

display_name: alternative-antriebe

elektrische-fahrzeuge

display_name: elektrische-fahrzeuge

elektromobilitat

display_name: elektromobilitat

hybridfahrzeuge

display_name: hybridfahrzeuge

personenwagen

display_name: personenwagen

tran

fr: Transports

de: Verkehr

en: Transport

it: Trasporti

de: Bundesamt für Energie BFE

bundesamt-fur-energie-bfe

en: Swiss Federal Office of Energy SFOE

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).


state: active

type: dataset

de: Datenstruktur auf Deutsch

en: Data structure in German

media_type: application/json

format: JSON

de: Datenwürfel auf Deutsch

en: Data cube in German

media_type: text/html

format: HTML

de: Datenstruktur auf Englisch

en: Data structure in English

de: Datenwürfel auf Englisch

en: Data cube in English

de: Einzelbasis - Lernende in Liechtenstein bei Jahr, Lehrjahr und Geschlecht

en: Individual basis - Apprentices in Liechtenstein by Year, Apprenticeship year and Sex

banken

display_name: banken

banking-institutions

display_name: banking-institutions

econ

fr: Économie et finances

de: Wirtschaft und Finanzen

en: Economy and finance

it: Economia e finanze

amt-fuer-statistik-fl

de: Amt für Statistik

en: Office of Statistics

en: The Office of Statistics is Liechtenstein's statistical information center. It provides the national and municipal authorities as well as the public with relevant, reliable and coherent statistical information on society, economy and environment in Liechtenstein.

de: Das Amt für Statistik ist das statistische Informationszentrum Liechtensteins. Es liefert den Landes- und Gemeindebehörden sowie der Öffentlichkeit relevante, zuverlässige und kohärente statistische Informationen zu Gesellschaft, Wirtschaft und Umwelt in Liechtenstein.

political_level: other

liechtenstein

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: DCAT Harvester Amt für Statistik FL

de: Einzelbasis - Lernende in Liechtenstein nach Lehrjahr und Geschlecht seit 2017

en: Individual basis - Apprentices in Liechtenstein by apprenticeship year and sex since 2017


de: Wegpendelnde bei Jahr, Arbeitsregion und Wohngemeinde

en: Outward commuters by Year, Region of work and Municipality of residence

erwerbstatigkeit

display_name: erwerbstatigkeit

population-in-gainful-employment

display_name: population-in-gainful-employment

de: Wegpendelnde nach Arbeitsregion und Wohngemeinde seit 2008

en: Outward commuters by region of work and municipality of residence since 2008


maintainer: ERNEUERBARE ENERGIEN

de: Bundesamt für Energie

en: Swiss Federal Office of Energy

fr: Office fédéral de l'énergie

it: Ufficio federale dell'energia

fr: prix de marché trimestriels pour la prime flottante

de: Vierteljahrige Referenz-Marktpreis gleitende Marktprämie

media_type: application/xml

fr: Données trimestrielles  du prix de référence pour la prime de marché flottante

format: XML

fr: prix de marché mensuels pour la prime flottante

de: Monatliche Referenz-Marktpreis gleitende Marktprämie

media_type: text/csv

fr: Données mensuelles du prix de référence pour la prime de marché flottante

format: CSV

de: Vierteljahrliche Referenz-Marktpreis gleitende Marktprämie

fr: Données mensuelle du prix de référence pour la prime de marché flottante

fr:
Les prix du marché de référence sont déterminants pour fixer la prime de marché flottante, soit
la différence entre le taux de rétribution et le prix de marché de référence. Le prix de marché de
référence pour la prime de marché flottante correspond au prix de marché de référence visé à
l’art. 15 OEneR, auquel s’ajoute un prix pour les garanties d’origine.
Le prix des garanties d'origine pour les installations photovoltaïques participant au système de
la prime de marché flottante est calculé sur la base des prix payés en moyenne par les
gestionnaires de réseau durant l'année précédente en Suisse pour des garanties d'origine
concernant des installations photovoltaïques. L'OFEN fixe le prix pour toute l'année en cours.
Le prix des garanties d’origine pour les installations hydroélectriques, pour les installations de
biomasse et pour les installations éoliennes est calculé sur la base d’un pourcentage du prix de
marché de référence visé à l’art. 15 OEneR. Le pourcentage est de:
pour les installations hydroélectriques: 5 %,
pour les installations de biomasse et éoliennes: 10 %.
Les prix s’entendent hors taxe sur la valeur ajoutée.

de:
Die Referenz-Marktpreise sind massgebend für die Festlegung der gleitenden Marktprämie
als Differenz zwischen Vergütungssatz und Referenz-Marktpreis. Der Referenz-Marktpreis für
die gleitende Marktprämie entspricht dem Referenz-Marktpreis nach Artikel 15 EnFV,
zuzüglich eines Preises für die Herkunftsnachweise.
Der Preis für die Herkunftsnachweise für Photovoltaikanlagen in der gleitenden Marktprämie
wird jeweils anhand der Preise, die in der Schweiz im Vorjahr für Herkunftsnachweise für
Photovoltaikanlagen durchschnittlich von den Netzbetreibern bezahlt wurden, berechnet. Das
BFE setzt den Preis für das ganze laufende Jahr fest.
Der Preis für die Herkunftsnachweise für Wasserkraft-, Biomasse- und Windenergieanlagen
wird anhand eines Prozentsatzes des Referenz-Marktpreises nach Artikel 15 EnFV berechnet.
Der Prozentsatz beträgt:
für Wasserkraftanlagen: 5 Prozent,
für Biomasse- und Windenergieanlagen: 10 Prozent.
Die Preise verstehen sich exklusive Mehrwertsteuer.
Publikation: jeweils in der zweiten Woche nach Quartalsende.

gleitende-marktpramie

display_name: gleitende-marktpramie

premio-di-mercato-fluttuante

display_name: premio-di-mercato-fluttuante

prezzo-di-mercato-di-riferimento

display_name: prezzo-di-mercato-di-riferimento

prime-de-marche-flottante

display_name: prime-de-marche-flottante

prix-de-reference

display_name: prix-de-reference

referenz-marktpreis

display_name: referenz-marktpreis

ener

fr: Énergie

de: Energie

en: Energy

it: Energia

fr: Prix de marché de référence pour la prime de marché flottante selon l’art. 30aquinquies OEneR

de: Referenz-Marktpreise für die gleitende Marktprämie gemäss Art. 30aquinquies EnFV

en: Reference market price for the floating market premium

it: Prezzo di mercato di riferimento per il premio di mercato fluttuante ai sensi dell'articolo 30aquinquies OPEn

fr: Office fédéral de l'énergie (OFEN)

en: Swiss Federal Office of Energy (SFOE)

de: Bundesamt für Energie (BFE)

it: Ufficio federale dell'energia (UFE)

bundesamt-fur-energie-bfe

fr: Office fédéral de l'énergie OFEN

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.

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: DCAT I14Y BFE


de: Datenstruktur auf Deutsch

de: Datenstruktur auf Englisch

de: Datenwürfel auf Deutsch

de: Beschäftigte bei Jahr, Voll- /Teilzeit, Wohnsitz, Altersklasse und Geschlecht

en: Persons employed by Year, Part-/full-time, Place of residence, Age group and Sex

beschaftigung

display_name: beschaftigung

employment

display_name: employment

de: Beschäftigte nach Voll-/ Teilzeit, Wohnsitz, Altersklasse und Geschlecht seit 2008

en: Persons employed by part-/ full-time, place of residence, age group and sex since 2008


de: Datenwürfel auf Englisch

en: Data structure in German

de: Bevölkerungsbewegung der ständigen Bevölkerung nach Jahr, Geschlecht und Kennzahl bei Jahr, Geschlecht und Kennzahl

en: Population change of the permantent population by Year, Sex and Key figure by Year, Sex and Key figure

bevolkerungsbewegung

display_name: bevolkerungsbewegung

population-change

display_name: population-change

soci

fr: Population et société

de: Bevölkerung und Gesellschaft

en: Population and society

it: Popolazione e società

amt-fuer-statistik-fl

de: Amt für Statistik

de: Das Amt für Statistik ist das statistische Informationszentrum Liechtensteins. Es liefert den Landes- und Gemeindebehörden sowie der Öffentlichkeit relevante, zuverlässige und kohärente statistische Informationen zu Gesellschaft, Wirtschaft und Umwelt in Liechtenstein.

de: Bevölkerungsbewegung der ständigen Bevölkerung nach Geschlecht und Kennzahl seit 2008

en: Population change of the permanent population by sex and key figure since 2008


en: Data cube in German

en: Data cube in English

en: Data structure in English

de: Einzelbasis - Bilanz (Passiven) bei Jahr, Passiven und Domizil

en: Individual basis - Balance sheet (liabilities) by Year, Liabilities and Domicile

econ

de: Wirtschaft und Finanzen

de: Einzelbasis - Bilanz (Passiven) nach Domizil seit 2017

en: Individual basis - Balance sheet (liabilities) by domicile since 2017


de: Lernende in Liechtenstein bei Jahr, Bildungsfeld, Geschlecht und Wohnland

en: Apprentices in Liechtenstein by Year, Field of education, Sex and Country of residence

duale-ausbildung

display_name: duale-ausbildung

vocational-education-and-training

display_name: vocational-education-and-training

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

amt-fuer-statistik-fl

en: Office of Statistics

en: The Office of Statistics is Liechtenstein's statistical information center. It provides the national and municipal authorities as well as the public with relevant, reliable and coherent statistical information on society, economy and environment in Liechtenstein.

de: Lernende in Liechtenstein nach Bildungsfeld, Wohnland und Geschlecht seit 2014/15

en: Apprentices in Liechtenstein by field of education, country of resindence and sex since 2014/15


maintainer: Digital Innovation Office

de: ogd104-bfe versorgungssicherheit strom kernkraft erneuerbare-energien wasserkraft

fr: energiedashboard.ch: Producion d'électricité Swissgrid

en: energiedashboard.ch: Electricity production Swissgrid

it: energiedashboard.ch: Produzione di elettricità Swissgrid

de: Bundesamt für Energie BFE

fr:
En Suisse, il existe un grand nombre de centrales électriques qui produisent de l'énergie électrique (électricité). En ce qui concerne la production, on distingue les catégories suivantes : centrales nucléaires, centrales thermiques, centrales hydroélectriques fluviales, centrales hydroélectriques à accumulation, centrales éoliennes et centrales photovoltaïques. La production de ces centrales est entraînée par différents facteurs. Alors que les centrales à accumulation et à pompage-turbinage, par exemple, sont fortement guidées par le marché (en fonction des signaux de prix), les centrales nucléaires produisent de l'énergie en bande. En revanche, le photovoltaïque et l'éolien dépendent du soleil et du vent.

La production en Suisse, ajoutée aux importations et aux exportations, donne la consommation nationale, y compris la consommation des centrales de pompage-turbinage.

La tendance indique l'évolution de la production totale suisse par rapport à la moyenne des 10 derniers jours.

Disclaimer Données

Les données relatives aux mesures de la production suisse ne sont pas satisfaisantes. Alors que des données de mesure avec une résolution horaire sont disponibles pour les moyennes et grandes centrales, de telles données ne sont pas disponibles pour les plus petites installations ou ne concernent que l'injection dans le réseau au lieu de la production. De plus, toutes les données de mesure des installations décentralisées ne sont mises à disposition par les gestionnaires de réseau pour un enregistrement central dans différentes résolutions qu'avec plusieurs semaines de retard, conformément aux exigences légales en matière de promotion ou d'obligation de garantie d'origine. Il faut donc travailler avec des modèles qui prennent en compte les données météorologiques telles que la température, le rayonnement solaire et les précipitations. En outre, le comportement historique des catégories de production d'électricité est pris en compte. La production affichée dans la résolution journalière comprend à la fois des données mesurées et des données modélisées. Il peut y avoir des différences avec les statistiques fédérales de l'électricité en raison des données et des modélisations.

en:
In Switzerland, there are a large number of power plants that produce electrical energy (electricity). In terms of production, a distinction is made between the categories of nuclear power plants, thermal power plants, river hydropower, storage hydropower, wind power and photovoltaics. The production of these power plants is driven by different factors. For example, while storage and pumped storage power plants are strongly market-driven (based on price signals), nuclear power plants produce band energy. Photovoltaics and wind, on the other hand, are dependent on the sun and wind.

The production in Switzerland together with import and export results in the country's consumption including the consumption of pumped storage.

The trend indicates the development of the total Swiss production compared to the average of the last 10 days.

Disclaimer Data

The data situation regarding measurements of Swiss production is not satisfactory. While measurement data in hourly resolution are available for medium and large power plants, such data are not available for the smaller plants or only feed-in to the grid instead of production. In addition, all the measurement data from the decentralised plants are only made available by the grid operators for central recording in different resolutions with a delay of several weeks, in accordance with the legal requirements for subsidies or the guarantee of origin obligation. Thus, models have to be used which take into account meteorological data such as temperature, solar radiation and precipitation. In addition, the historical behaviour of the electricity generation categories is used. The production shown in the daily resolution includes both measured and modelled data. Due to the data situation and the modelling, there may be deviations from the electricity statistics of the federal government.

de:
In der Schweiz gibt es eine Vielzahl an Kraftwerken, die elektrische Energie (Strom) produzieren. Bei der Produktion werden die Kategorien Kernkraftwerke, thermische Kraftwerke, Flusswasserkraft, Speicherwasserkraft, Windkraft und Photovoltaik unterschieden. Die Produktion dieser Kraftwerke ist getrieben durch unterschiedliche Faktoren. Während beispielsweise Speicher- und Pumpspeicherkraftwerke stark marktgetrieben (anhand von Preissignalen) eingesetzt werden, produzieren die Kernkraftwerke Bandenergie. Photovoltaik und Wind sind hingegen abhängig von Sonne und Wind.

Die Produktion in der Schweiz zusammen mit Import und Export ergibt den Landesverbrauch inklusive des Verbrauchs der Pumpspeicher.

Der Trend gibt die Entwicklung der Schweizer Gesamtproduktion im Vergleich zum Durchschnitt der letzten 10 Tage an.

Disclaimer Daten

Die Datenlage in Bezug auf Messungen zur Schweizer Produktion ist nicht zufriedenstellend. Während für mittlere und grosse Kraftwerke Messdaten in stündlicher Auflösung verfügbar sind, sind solche Daten für die kleineren Anlagen nicht verfügbar oder nur die Einspeisung in das Netz anstelle der Produktion. Zudem werden alle Messdaten der dezentralen Anlagen nur mit mehreren Wochen Verzögerung durch die Netzbetreiber für eine zentrale Erfassung in unterschiedlichen Auflösungen bereitgestellt entsprechend den rechtlichen Anforderungen zu Förderung oder zur Herkunftsnachweispflicht. So muss mit Modellen gearbeitet werden, welche meteorologische Daten wie Temperatur, Sonneneinstrahlung, Niederschlag berücksichtigen. Zusätzlich wird das historische Verhalten der Stromerzeugungskategorien herbeigezogen. Die in der Tagesauflösung angezeigte Produktion umfasst sowohl gemessene als auch modellierte Daten. Es kann aufgrund der Datenlagen und den Modellierungen Abweichungen zur Elektrizitätsstatistik des Bundes geben.

it:
In Svizzera, ci sono un gran numero di centrali elettriche che producono energia elettrica (elettricità). In termini di produzione, si distingue tra le categorie di centrali nucleari, centrali termiche, energia idroelettrica fluviale, energia idroelettrica di accumulo, energia eolica e fotovoltaica. La produzione di queste centrali elettriche è guidata da diversi fattori. Per esempio, mentre le centrali di accumulo e di pompaggio sono fortemente orientate al mercato (basandosi sui segnali di prezzo), le centrali nucleari producono energia di banda. Il fotovoltaico e l'eolico, invece, dipendono dal sole e dal vento.

La produzione in Svizzera, insieme all'importazione e all'esportazione, si traduce nel consumo del Paese, compreso il consumo dell'accumulo con pompaggio.

Il trend indica l'andamento della produzione totale svizzera rispetto alla media degli ultimi 10 giorni.

Disclaimer Dati

La situazione dei dati relativi alle misurazioni della produzione svizzera non è soddisfacente. Mentre i dati di misurazione con risoluzione oraria sono disponibili per le centrali elettriche di medie e grandi dimensioni, tali dati non sono disponibili per le centrali più piccole o che si limitano all'immissione in rete invece che alla produzione. Inoltre, tutti i dati di misurazione degli impianti decentralizzati vengono resi disponibili dai gestori di rete solo per la registrazione centrale in diverse risoluzioni con un ritardo di diverse settimane, in conformità con i requisiti legali per le sovvenzioni o l'obbligo di garanzia di origine. Pertanto, è necessario utilizzare modelli che tengano conto dei dati meteorologici come la temperatura, la radiazione solare e le precipitazioni. Inoltre, viene utilizzato il comportamento storico delle categorie di generazione elettrica. La produzione mostrata nella risoluzione giornaliera comprende sia i dati misurati che quelli modellati. A causa della situazione dei dati e della modellazione, potrebbero esserci delle deviazioni rispetto alle statistiche sull'elettricità del governo federale.

ener

de: Energie

en: Energy

gove

fr: Gouvernement et secteur public

de: Regierung und öffentlicher Sektor

en: Government and public sector

it: Governo e settore pubblico

econ

en: Economy and finance

bundesamt-fur-energie-bfe

en: Swiss Federal Office of Energy SFOE

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).


de: Datenstruktur auf Deutsch

en: Data structure in German

de: Datenwürfel auf Deutsch

en: Data cube in German

de: Datenwürfel auf Englisch

de: Datenstruktur auf Englisch

de: Verlust des Landesbürgerrechts durch ausdrücklichen Verzicht bei Jahr, Wohnsitz und Altersklasse

en: Loss of national citizenship through explicit renunciation by Year, Residence and Age group

einburgerungen

display_name: einburgerungen

naturalisations

display_name: naturalisations

soci

en: Population and society

intr

fr: Questions internationales

de: Internationale Themen

en: International issues

it: Tematiche internazionali

de: Verlust des Landesbürgerrechts durch ausdrücklichen Verzicht nach Wohnsitz und Altersklassen seit 2008

en: Loss of national citizenship through explicit renunciation by residence and age group since 2008


en: Data structure in English

en: Data cube in English

de: Einkommensverteilungskonto zu laufenden Preisen seit 2013 bei Jahr, Transaktion und Institutioneller Sektor

en: Allocation of primary income account at current prices since 2013 by Year, Transaction and Institutional sector

national-accounts

display_name: national-accounts

volkswirtschaftliche-gesamtrechnungen

display_name: volkswirtschaftliche-gesamtrechnungen

econ

fr: Économie et finances

de: Wirtschaft und Finanzen

amt-fuer-statistik-fl

de: Amt für Statistik

de: Das Amt für Statistik ist das statistische Informationszentrum Liechtensteins. Es liefert den Landes- und Gemeindebehörden sowie der Öffentlichkeit relevante, zuverlässige und kohärente statistische Informationen zu Gesellschaft, Wirtschaft und Umwelt in Liechtenstein.

de: Einkommensverteilungskonto zu laufenden Preisen seit 2013 nach Sektor

en: Allocation of primary income account at current prices since 2013 by institutional sector


de: Einzelbasis - Bilanz (Passiven) bei Jahr, Passiven, Währungsgebiet und Währung

en: Individual basis - Balance sheet (liabilities) by Year, Liabilities, Currency area and Currency

banken

display_name: banken

banking-institutions

display_name: banking-institutions

en: Office of Statistics

en: The Office of Statistics is Liechtenstein's statistical information center. It provides the national and municipal authorities as well as the public with relevant, reliable and coherent statistical information on society, economy and environment in Liechtenstein.

de: Einzelbasis - Bilanz (Passiven) nach Währungsgebiet und Währung seit 2017

en: Individual basis - Balance sheet (liabilities) by currency area and currency since 2017


title_for_slug: energiedashboard.ch: Tägliche Flüsse in die und aus der Schweiz (Strom)

de: ogd107-bfe stromimport stromexport versorgungssicherheit internationale-vernetzung

fr: energiedashboard.ch: Flux quotidiens d'importation et exportation (Électricité)

en: energiedashboard.ch: Daily import and export flows (Electricity)

it: energiedashboard.ch: Flussi giornalieri di import ed export (Elettricità)

de: Bundesamt für Energie BFE

fr:
Les réseaux électriques, en tant que lien entre la production et la consommation, sont d'une importance capitale pour garantir la sécurité de l'approvisionnement en électricité. Le réseau électrique de la Suisse ne s'arrête pas simplement à la frontière avec les autres pays européens, mais est étroitement relié aux réseaux électriques voisins. Cela est essentiel pour la sécurité de l'approvisionnement : Si la Suisse a besoin d'électricité supplémentaire, elle peut l'importer.

Le flux physique d'électricité ou échange d'interconnexion désigne la quantité d'énergie en gigawattheures par heure (GWh/h) qui circule sur les lignes électriques transfrontalières entre l'étranger et la Suisse.

Le chiffre relatif à l'importation / exportation est la somme des quantités d'électricité importées et exportées pendant une journée. Une valeur négative signifie que la Suisse exporte en somme de l'électricité.  La flèche de tendance indique la tendance des importations & exportations de la Suisse. Il faut distinguer 4 cas : 1. Si la Suisse importe plus d'électricité que la moyenne des 10 derniers jours, la flèche est orientée à la hausse. 2. si la Suisse importe moins que la moyenne des 10 derniers jours, elle est orientée à la baisse. 3) Dans le cas des exportations, si la Suisse exporte plus que la moyenne des 10 derniers jours, la flèche pointe vers le bas. 4. si la Suisse exporte moins que la moyenne des 10 derniers jours, la flèche pointe vers le haut.

Disclaimer Données

Les données proviennent de l'ENTSO-E. Elles sont disponibles par heure, mais pour des raisons de simplicité, elles sont totalisées sur la journée pour le tableau de bord de l'OFEN et mises à jour quotidiennement.

en:
Electricity grids are of central importance as a link between production and consumption to ensure the security of electricity supply. Switzerland's electricity grid does not simply stop at the border with the rest of Europe, but is closely connected to neighbouring electricity grids. This is central to security of supply: If Switzerland needs additional electricity, it can be imported.

The physical flow of electricity or interconnection exchange is the amount of energy in gigawatt hours per hour (GWh/h) that flows between foreign countries and Switzerland via the cross-border power lines.

The import/export figure is the sum of the imported and exported electricity quantities during one day. A negative value means that Switzerland is exporting electricity in total.  The trend arrow indicates the trend of Switzerland's import & export. A distinction is made between 4 cases: 1. if Switzerland imports more electricity overall than on average over the last 10 days, the arrow points upwards. 2. if Switzerland imports less electricity than on average over the last 10 days, the arrow points downwards. 2. if Switzerland imports less than the average over the last 10 days, the arrow points downwards. 3. in the case of exports, the arrow points downwards if Switzerland exports more than the average over the last 10 days. 4. if Switzerland exports less than average in the last 10 days, the arrow points upwards.

Disclaimer data

The data come from ENTSO-E. They are available per hour, but for simplicity they are summed up over the day for the BFE Dashboard and updated daily.

de:
Stromnetze sind als Bindeglied zwischen Produktion und Verbrauch von zentraler Bedeutung, um die Stromversorgungssicherheit zu gewährleisten. Das Stromnetz der Schweiz hört nicht einfach an der Grenze zum europäischen Ausland auf, sondern ist eng mit den benachbarten Stromnetzen verbunden. Dies ist für die Versorgungssicherheit zentral: Benötigt die Schweiz zusätzlichen Strom, kann dieser importiert werden.

Als physikalischer Stromfluss oder Verbundaustausch wird die Energiemenge in Gigawattstunden pro Stunde (GWh/h) bezeichnet, die über die grenzüberschreitenden Stromleitungen zwischen dem Ausland und der Schweiz fliesst.

Die Zahl zum Import / Export ist die Summe der importierten und exportierten Strommengen während eines Tages. Ein negativer Wert bedeutet, dass die Schweiz in der Summe Strom exportiert.  Der Trendpfeil gibt die Tendenz von Import & Export der Schweiz an. Dabei sind 4 Fälle zu unterscheiden: 1. Importiert die Schweiz insgesamt mehr Strom als durchschnittlich in den letzten 10 Tagen zeigt er nach oben. 2. Importiert die Schweiz weniger als durchschnittlich in den letzten 10 Tagen, zeigt er nach unten. 3. Für den Fall des Exportes gilt, dass der Pfeil nach unten zeigt, wenn die Schweiz mehr exportiert als durchschnittlich in den letzten 10 Tagen. 4. Wenn die Schweiz weniger exportiert als durchschnittlich in den letzten 10 Tagen, dann zeigt der Pfeil nach oben.

Disclaimer Daten

Die Daten stammen von der ENTSO-E. Sie sind pro Stunde verfügbar, werden aber der Einfachheit halber für das BFE Dashboard über den Tag aufsummiert und täglich aktualisiert.

it:
Le reti elettriche sono di importanza centrale come collegamento tra produzione e consumo, per garantire la sicurezza dell'approvvigionamento elettrico. La rete elettrica della Svizzera non si ferma semplicemente al confine con il resto dell'Europa, ma è strettamente collegata alle reti elettriche vicine. Questo è fondamentale per la sicurezza dell'approvvigionamento: Se la Svizzera ha bisogno di energia elettrica aggiuntiva, può importarla.

Il flusso fisico di energia elettrica o scambio di interconnessione è la quantità di energia in gigawattora all'ora (GWh/h) che scorre tra l'estero e la Svizzera attraverso le linee elettriche transfrontaliere.

La cifra di import/export è la somma delle quantità di energia elettrica importate ed esportate durante un giorno. Un valore negativo significa che la Svizzera sta esportando elettricità in totale.  La freccia di tendenza indica l'andamento dell'import-export della Svizzera. Si distingue tra 4 casi: 1. se la Svizzera importa complessivamente più elettricità rispetto alla media degli ultimi 10 giorni, la freccia punta verso l'alto. 2. se la Svizzera importa meno elettricità rispetto alla media degli ultimi 10 giorni, la freccia punta verso il basso. 2. Se la Svizzera importa meno della media degli ultimi 10 giorni, la freccia punta verso il basso. 3. Nel caso delle esportazioni, la freccia punta verso il basso se la Svizzera esporta più della media degli ultimi 10 giorni. 4. Se la Svizzera esporta meno della media negli ultimi 10 giorni, la freccia punta verso l'alto.

Disclaimer Dati

I dati provengono da ENTSO-E. Sono disponibili per ora, ma per semplicità vengono sommati nell'arco della giornata per il cruscotto BFE e aggiornati giornalmente.

ener

de: Energie

en: Energy

gove

en: Government and public sector

econ

en: Economy and finance


maintainer: Amt für Statistik

de: Datenstruktur auf Deutsch

en: Data structure in German

de: Datenwürfel auf Deutsch

en: Data cube in German

de: Rindviehhalter und Rindviehbestand bei Jahr, Kennzahl, Tierkategorie und Zone | Region | Gemeinde

en: 361.001 Cattle owners and cattle livestock by Year, Key figure, Animal category and Zone | region | municipality

agriculture

display_name: agriculture

landwirtschaft

display_name: landwirtschaft

agri

fr: Agriculture, pêche, sylviculture et alimentation

de: Landwirtschaft, Fischerei, Forstwirtschaft und Nahrungsmittel

en: Agriculture, fisheries, forestry and food

it: Agricoltura, pesca, silvicoltura e prodotti alimentari

de: Rindviehhalter und Rindviehbestand nach Tierkategorie (detailliert), Zone, Region und Gemeinde seit 2010

en: 361.002 Cattle owners and cattle livestock by animal category, zone, region and municipality since 2005


de: Datenwürfel auf Englisch

en: Data cube in English

de: Datenstruktur auf Englisch

en: Data structure in English

de: Arbeitslose zuzüglich Personen mit Zwischenverdienst bei Jahr, Geschlecht, Dauer der Arbeitslosigkeit und Monat

en: Unemployed persons and persons with temporary earnings by Year, Sex, Duration of unemployment and Month

arbeitslosigkeit

display_name: arbeitslosigkeit

unemployment

display_name: unemployment

de: Arbeitslose zuzüglich Personen mit Zwischenverdienst nach Dauer der Arbeitslosigkeit, Geschlecht und Monat seit 2006

en: Unemployed persons and persons with temporary earnings by duration of unemployment, sex and month since 2006


author: Amt für Statistik

de: Kennzahlen der Vorsteherwahlen nach Jahr, Kennzahl und Gemeinde

en: Key figures on mayorial elections by Year, Key figure and Municipality by Year, Key figure and Municipality

elections-referendums

display_name: elections-referendums

wahlen-abstimmungen

display_name: wahlen-abstimmungen

gove

de: Regierung und öffentlicher Sektor

amt-fuer-statistik-fl

de: Amt für Statistik

en: Office of Statistics

de: Das Amt für Statistik ist das statistische Informationszentrum Liechtensteins. Es liefert den Landes- und Gemeindebehörden sowie der Öffentlichkeit relevante, zuverlässige und kohärente statistische Informationen zu Gesellschaft, Wirtschaft und Umwelt in Liechtenstein.

de: Kennzahlen der Vorsteherwahlen nach Gemeinde seit 1975

en: Key figures on mayoral elections by municipality and political party since 1975


de: Arbeitslose bei Jahr, Altersklasse, Geschlecht und Monat

en: Unemployed persons by Year, Age group, Sex and Month

econ

de: Wirtschaft und Finanzen

en: The Office of Statistics is Liechtenstein's statistical information center. It provides the national and municipal authorities as well as the public with relevant, reliable and coherent statistical information on society, economy and environment in Liechtenstein.

de: Arbeitslose nach Altersklasse, Geschlecht und Monat seit 2006

en: Unemployed persons by age group, sex and month since 2006


de: Datenstruktur auf Deutsch

de: Bauprojekte bei Bewilligungsquartal, Art der Arbeiten, Art der Bauwerke und Gemeinde

en: Construction projects by Year of approval, Type of work, Type of buildings and Municipality

bautatigkeit

display_name: bautatigkeit

construction-activity

display_name: construction-activity

de: Bauprojekte nach Art der Arbeiten, Art der Bauwerke und Gemeinde seit 2015

en: Construction projects by type of work, type of buildings and municipality since 2015


en: Data structure in German

de: Datenwürfel auf Deutsch

de: Schulpersonal (in VZÄ) an Schulen bei Schuljahr, Schultyp, Schulstufe, Personalkategorie und Geschlecht

en: School staff (in FTE) in schools by School year, Type of school, Level of education, Category of staff and Sex

schools

display_name: schools

schulen

display_name: schulen

educ

de: Bildung, Kultur und Sport

en: Education, culture and sport

de: Schulpersonal (in VZÄ) an Schulen nach Schuljahr, Schultyp, Schulstufe, Personalkategorie und Geschlecht seit 2010/2011

en: School staff (in FTE) by School year, Type of school, Level of education, Category of staff and Sex since 2010/2011


en: Data cube in German

de: Datenstruktur auf Englisch

de: Datenwürfel auf Englisch

de: Beschäftigte aus dem Ausland bei Jahr, Wohnregion und Arbeitsgemeinde

en: Persons employed from abroad by Year, Region of residence and Municipality of work

beschaftigung

display_name: beschaftigung

employment

display_name: employment

econ

en: Economy and finance

de: Beschäftigte aus dem Ausland nach Wohnregion und Arbeitsgemeinde seit 2008

en: Persons employed from abroad by region of residence and municipality of work since 2008


en: Data structure in English

en: Data cube in English

de: Vollzeitäquivalente bei Jahr, Wirtschaftszweig, Arbeitsgemeinde und Geschlecht

en: Full-time equivalents by Year, Economic activity, Municipality of work and Sex

amt-fuer-statistik-fl

de: Amt für Statistik

de: Vollzeitäquivalente nach Wirtschaftszweig, Arbeitsgemeinde und Geschlecht seit 2008

en: Full-time equivalents by economic activity, municipality of work and sex since 2008


weather-station-davos-wolfgang

maintainer: Jörg Wieder

title_for_slug: weather-station-davos-wolfgang

en: weather raclets temperature relative-humidity pressure

en: Weather Station Davos Wolfgang

en: The data file contains date and time (YYYY-MM-DD hh:mm:ss) in UTC, temperature (°C), relative humidity (%), pressure (hPa) at 10 m above ground and above sea level. The data was averaged over 10 minutes.

mimetype: text/csv

en: The dataset contains weather parameters measured at Davos Wolfgang (LON: 9.853594, LAT: 46.835577).

pressure

display_name: pressure

raclets

display_name: raclets

relative-humidity

display_name: relative-humidity

temperature

display_name: temperature

weather

display_name: weather

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.

value: Envidat Harvester


maintainer: EnviDat Support

en: wind-direction anemometer wind wind-speed raclets wind-velocity

en: Sonic wind component u

en:
The data contains values for u direction of the wind in a sampling frequency of 20 Hz.
Erroneour spikes were filterd in two steps.

1. Define moving window uₓ = u(i-3:i+3)
2. If u(i)-mean(uₓ) > 2*std(uₓ):u(i) = NaN

Frozen detectors resulted in continuous zero values (not filtered).
The wind speed and direction were calculated as follows:

wind speed = sqrt(u² + v²)
wind direction = 180° + (180° / PI)* arctan2 (-u, -v)

format: SERVICE

en: Sonic wind component v

en:
The data contains values for v direction of the wind in a sampling frequency of 20 Hz.
Erroneour spikes were filterd in two steps.

1. Define moving window uₓ = u(i-3:i+3)
2. If u(i)-mean(uₓ) > 2*std(uₓ):u(i) = NaN

Frozen detectors resulted in continuous zero values (not filtered).
The wind speed and direction were calculated as follows:

wind speed = sqrt(u² + v²)
wind direction = 180° + (180° / PI)* arctan2 (-u, -v)

en: Sonic temperature

en: The data set contains values of temperature (°C) measured with the anemometer at a sampling frequency of 20 Hz.

en: Sonic time

en: The data set contains the time in YYYY-mm-dd HH:MM:SS.sss for the anemometer data. The time is given in UTC+1.

en: Sonic wind component w

en:
The data contains values for w direction of the wind in a sampling frequency of 20 Hz.
Erroneour spikes were filterd in two steps.

1. Define moving window uₓ = u(i-3:i+3)
2. If u(i)-mean(uₓ) > 2*std(uₓ):u(i) = NaN

Frozen detectors resulted in continuous zero values (not filtered).
The wind speed and direction were calculated as follows:

wind speed = sqrt(u² + v²)
wind direction = 180° + (180° / PI)* arctan2 (-u, -v)

en: A Young 81000 sonic anemomenter was deployed at Gotschnagrat (LON: 46.859 LAT: 9.849) to record three components of the wind velocity (u, v, w in [m s‾ ¹]) and air temperature (Ts in [°C]). The anemomenter was mounted in direction North at a height of 1.5 m above snow surface at the beginning. The time within each data set is given in UTC+1. Instrument specifications can be found [here](http://www.youngusa.com/Manuals/81000-90(I).pdf) .

anemometer

display_name: anemometer

wind

display_name: wind

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

wind-velocity

display_name: wind-velocity

en: Snow Drift Station - 3D Ultrasonic


en: antarctica landuse topography wrf cryowrf

en: Documentation

media_type: application/pdf

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

format: PDF

mimetype: application/pdf

en: WPSinput

media_type: application/zip

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

format: ZIP

mimetype: application/zip

en: Scripts

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

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

antarctica

display_name: antarctica

cryowrf

display_name: cryowrf

landuse

display_name: landuse

topography

display_name: topography

wrf

display_name: wrf

educ

en: Education, culture and sport

en: REMA topography and AntarcticaLC2000 for WRF


maintainer: Michael Eisenring

en: herbivory phytochemistry climate-change drought-legacy fagus-sylvatica

format: XLS

en:
What are the research data files about:
Raw data on various beech (Fagus sylvatica) leaf traits. Beech leaf chemistry (primary and specialized metabolites), leaf damage measurements from various herbivore feeding guilds and crown health assessment

Which methods were used:
Field surveys on trees that showed either prematured defoliation due to drought-stress or showed not pre-mature defoliation. .

When and where was the data collected/analyzed:
Field data was collected in 2019, 2020
Data was analyzed in 2021-2023

climate-change

display_name: climate-change

drought-legacy

display_name: drought-legacy

fagus-sylvatica

display_name: fagus-sylvatica

herbivory

display_name: herbivory

phytochemistry

display_name: phytochemistry

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: Legacy effects of premature defoliation in response to an extreme drought event modulate phytochemical profiles with subtle consequences for leaf herbivory in European beech


en: curacao marine fish caribbean-region coral-reefs edna teleo environmental-dna fish-composition

en: Data access

en:
Fish taxonomic, functional and phylogenetic composition variations recovered from eDNA metabarcoding along the coast of Curacao

In February 2020, we collected a total of 20 water samples, from 10 stations, with two filtration replicates per station, in the two investigated coastal areas. Each station consisted of a transect of 2 km at an overall constant distance from the coast. We recorded the GPS coordinates at the start and end of the transect, which we used to map the transect positions. We conducted eDNA sampling by using a filtration device composed of an Athena® peristaltic pump (Proactive Environmental Products LLC, Bradenton, Florida, USA; nominal flow of 1.0 L/min), a VigiDNA® 0.20 μM cross-flow filtration capsule (SPYGEN, le Bourget du Lac, France) and disposable sterile tubing for each filtration capsule. We performed two filtration replicates in parallel on each side of a boat, at each station, for 30 min corresponding to a volume of ~30 L of water filtered by each capsule. At the end of each filtration, the water inside the capsules was emptied, and we filled the capsules with 80 ml of CL1 Conservation buffer (SPYGEN, le Bourget du Lac, France) and stored at room temperature. We followed a strict contamination control protocol in both field and laboratory stages (Valentini et al., 2016). Each water sample processing included the use of disposable gloves and single-use filtration equipment to avoid any risk of contamination. Libraries were prepared with ligation using the MetaFast protocol (Fasteris).

caribbean-region

display_name: caribbean-region

coral-reefs

display_name: coral-reefs

curacao

display_name: curacao

edna

display_name: edna

environmental-dna

display_name: environmental-dna

fish

display_name: fish

fish-composition

display_name: fish-composition

marine

display_name: marine

teleo

display_name: teleo

en: Environmental DNA Marine Curacao 2020


en: senegal teleo metabarcoding fish edna

en: Please use cyberduck to download for the moment

en:
Monitoring fish diversity through eDNA metabarcoding along the Casamance river

In 2018, water samples were collected in 13 locations along the Casamance in Senegal. Samples were taken either from a boat or by filtering from the side of the river. At each station, two filtration replicates were performed using a peristaltic pump to conduct environmental DNA (eDNA) sampling. Each filtration targeted a maximum duration of 30 min, during which approximately 30 liters of water were filtered through each filtration capsule. After filtration, the water inside the capsules was removed, and the capsules were filled with 50 ml of conservation buffer for preservation at room temperature. We followed strict contamination control protocols throughout both the fieldwork and laboratory processes, adhering to the guidelines of Valentini et al. (2016). To prevent contamination, each sample was processed using disposable gloves and single-use filtration equipment. We use primers for eukaryote and fish. The MiSeq Reagent Kit v3 (2x75 bp) (Illumina, San Diego, CA, USA) was used for paired-end sequencing at a theoretical sequencing depth of 200,000 reads per sample.

Data content:
	* rawdata/: contains the raw reads for each individual sample. One archive contains the paired-end reads specified by the _R1 or _R2 suffix as well as individually tagged PCR replicates (if available) together with an archive containing all extraction and PCR blank samples of the library. Reads have been demultiplexed using cutadapt but not trimmed, individual demultiplexing tags and primers remain present in the sequences.
	* taxadata/: contains the table with all detected taxonomy for each sample after bioinformatic processing (see Polanco et al. 2020 for details; https://doi.org/10.1002/edn3.140) and associated field metadata.
	* metadata/: contains two metadata files, one related to the data collected in the field for each filter, and the second related to the sequencing process in the lab (including the tag sequence, library name, and marker information for each sample)

metabarcoding

display_name: metabarcoding

senegal

display_name: senegal

en: Environmental DNA Freshwater Senegal Casamance 2018


en: meteoswiss complex-terrain numerical-simulation cosmo-wrf high-resolution

en: MeteorologicalData

en: Scripts to process meteorological data (see Section 2 in COSMOWRFdocumentation.pdf).

en: WPSSetup

en: Scripts that need to be changed to compile WPS (see COSMOWRFdocumentation.pdf, Section 4)

en: StaticData

en:
Scripts to process geographical/static data (see Section 3 in COSMOWRFdocumentation.pdf).
- ascii_to_bin_landuse.f90
- ascii_to_bin_soiltype.f90
- ascii_to_bin_topo_wgs84_ws2sf1.f90
- see_slopes.ncl
- soiltype_writing.py
- toposmooth_wgs84_cosmobound.ncl
- write_geogrid.o

en: WPSrun

en: Scripts to run the WRF pre-processing (WPS) for COSMO-WRF simulation (see Section 5 in COSMOWRFdocumentation.pdf)

en: WRFrun

en: Scripts to run COSMO-WRF simulation (see Section 6 in COSMOWRFdocumentation.pdf)

en: Postprocessing

en: Postprocessing scripts to map WRF output on swiss coordinates (see Section 7 in COSMOWRFdocumentation.pdf)

en: COSMO-WRF documentation v1.0

en:
Gerber, Franziska; Sharma, Varun (2018). Running COSMO-WRF on very-high resolution over complex terrain. EnviDat. doi:10.16904/envidat.35

Documentation about the procedure to run COSMO-WRF

en: This is a technical documentation of the procedure to run the Weather Research and Forecasting (WRF) model over complex alpine terrain using Consortium for Small-Scale Modeling (COSMO) reanalysis by the Federal Office of Meteorology and Climatology (MeteoSwiss) as initial and boundary conditions (COMSO-WRF). The setup is adapted for very high resolution simulations based on COSMO-2 (2.2 km resolution) reanalysis. This document gives an overview over steps to setup COSMO-WRF and adaptations needed to run COSMO-WRF. Additionally, the calculation of precipitation rate at a horizontal plane and remapping COSMO-WRF output on Swiss coordinates are documented.

complex-terrain

display_name: complex-terrain

cosmo-wrf

display_name: cosmo-wrf

high-resolution

display_name: high-resolution

meteoswiss

display_name: meteoswiss

numerical-simulation

display_name: numerical-simulation


maintainer: Andrin Caviezel

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

en: Induced Rockfall Data, Tschamut, GR

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

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

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

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

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

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

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

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

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

Further information is found in

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

dem

display_name: dem

deposition-points

display_name: deposition-points

induced-rockfall

display_name: induced-rockfall

natural-hazards

display_name: natural-hazards

rockfall

display_name: rockfall

rockfall-experiments

display_name: rockfall-experiments

rockfall-runout

display_name: rockfall-runout

sensor-stream

display_name: sensor-stream

stonenode

display_name: stonenode

stonenodedata

display_name: stonenodedata

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


maintainer: Daniel Scherrer

en: climate-change forest-management forest ground-vegetation fagus-sylvatica air-pollution

en: Site Information

en: This table contains information about the 254 forest sites used in the publication of Scherrer et al. 2024.

en: Taxa Data

en: This table contains information about the individual taxa found in the historic and current vegetation relevés in Scherrer et al. 2024.

en: This dataset comprised 254 historical (1937 – 1948) and resurveyed (2019 – 2022) vegetation relevés of beech-dominated forests sites in the Swiss Jura mountains. The data contains species lists as well as site information.

air-pollution

display_name: air-pollution

climate-change

display_name: climate-change

fagus-sylvatica

display_name: fagus-sylvatica

forest

display_name: forest

forest-management

display_name: forest-management

ground-vegetation

display_name: ground-vegetation

en: Resurveyed vegetation releves of beech forests in the Swiss Jura Mountains


maintainer: Marco Moretti

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

en: Dataset 2

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

en: Dataset 3

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

en: Not used:

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

en: Dataset 1

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

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

honeybees

display_name: honeybees

pollination

display_name: pollination

remote-sensing

display_name: remote-sensing

sustainability

display_name: sustainability

urban-biodiversity

display_name: urban-biodiversity

urbanization

display_name: urbanization

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


en: yellowstone biogeochemical-cycling bacteria fungi detritus soil-biodiversity wolf decomposition carrion carcass

en:
Site	ID for different carcasses across the Yellowstone Landscape
Location	Sampling location, either under carcass or control plot
Species	Indicating whether carcass was from bison or elk
Gender	Gener of carcass; F = female, M = male, ? = unknown
Lat	Latitude
Long	Longitude
DSK	Days since kill; in days
Soil.N	Soil N concentration (%)
Soil.C	Soil C concentration (%)
Veg.C	Vegetation C concentration (%); dominant grasses sampled
Veg.N	Vegetation N concentration (%); dominant grasses sampled
Veg.CtoN	Vegetation C to N ratio; dominant grasses sampled
Soil.resp	Soil respiration (g m-2 h-1)
soil.temp	Soil temperature (°C)
Soil.moist	Soil moisture (volumentric; %)
CRR	Cotton rotting rate
bacteria.richness	Bacteria richness
bacteria.diversity	Bacteria diverstiy
fungi.richness	Fungi richness
fungi.diversity	Fungi diversity
bacteria.abundance	Bacteria abundance
fungi.abundance	Fungi abundance

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

Paper Citation:

Risch, A.C., Frossard, A., Schütz, M., Frey, B., Morris, A.W., Bump, J.K. (accepted) Effects of elk and bison carcasses on soil microbial communities and ecosystem functions in Yellowstone, USA. (accepted). Functional Ecology
doi: ...

Methods
Study area and study sites
This study was conducted in YNP’s Northern Range (NR), located in north-western Wyoming and south-western Montana, USA (~44.9163° N, 110.4169° W). The NR expands over ~1000 km2 and features long cold winters and short dry summers. Grasslands and shrublands dominate the NR that is the home of large migratory herds of bison (winter counts 2017: ~3919 individuals; Geremia, Wallen, & White, 2017) and elk (~5349 individuals) as well as their main predators, approximately five packs of wolves with a total of 33 individuals (Smith et al., 2017). As part of a long-term research program within YNP, wolf predation has been studied since their reintroduction in 1995.
For our study, we received ground-truthed coordinates of bison and elk carcasses from winter 2016/17 (November 2016 through April 2017) from the YNP Wolf Project. Between June 20 and July 1, 2017, we visited 24 carcasses in total. At five sites, we could not sample as the carcasses were no longer found. In total we located remains (hairmats, rumen content, bones, teeth) of 19 adult male and female carcasses (7 bison, 12 elk; Supplementary Table 1). Live body weights of adult bison and elk are approximately 730 kg (male bison), 450 kg (female bison), 330 kg (male elk), and 235 kg (female elk, Meagher, 1973; Quimby & Johnson, 1951).
The kills and subsequent consumption happened between 34 and 173 days prior to our sampling (hereafter “days since kill”, DSK), for which we accounted in our statistics. Note that wolves and other scavengers consumed the soft tissue of the carcasses quickly, hence, there is close to no soft tissue left for decomposition as compared to an intact body left on the soil surface. The 19 carcass sites covered the extent of YNP’s NR, with both bison and elk carcasses showing similar distributions; elevation ranged from 1703 to 2884 m a.s.l. (Supplementary Fig 1 & Supplementary Table 1). The carcasses were all located in grassland or sage-brush shrubland, with or without sparsely scattered trees, and both bison and elk carcasses showed the same distribution of DSK. At each study site, we selected a reference plot (hereafter “control”) that was of comparable size, slope aspect and vegetation to the carcass location (hereafter “carcass”). The control was at least 10 m away (Danell, Berteaux, & Brathen, 2002; Melis et al., 2007) from the carcass itself to ensure the absence of potential direct and indirect carcass effects (paired design; (Bump, Webster, et al., 2009; Bump, Peterson, et al., 2009).

Ecosystem functions and soil properties
We randomly collected 50 g of mineral soil from three locations on both control and carcass plots to a depth of 5 cm with sterile techniques and gently mixed the material to obtain a composite sample. Half the soil sample was immediately bagged in plastic bags (whirl packs), stored in a cooler with ice packs (~5 ºC), sieved (2-mm) and frozen within 4-6 hours of collection to assess soil microbial communities. For this purpose, we extracted total genomic DNA from 0.5 g soil using the PowerSoil DNA Isolation Kit (Qiagen, Hilden, Germany). DNA concentrations were measured using PicoGreen (Molecular Probes, Eugene, OR, USA). PCR amplifications of partial bacterial small-subunit ribosomal RNA genes (region V3–V4 of 16S rRNA) and fungal ribosomal internal transcribed spacers (region ITS2) were performed as described previously (Frey et al., 2016). Each sample consisting of 40 ng DNA was amplified in triplicate and pooled before purification with Agencourt AMPure XP beads (Beckman Colter, Berea, CA, USA) and quantified with the Qubit 2.0 fluorometric system (Life Technologies, Paisley, UK). Amplicons were sent to the Genome Quebec Innovation Center (Montreal, Canada) for barcoding using the Fluidigm Access Array technology and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, CA, USA).
Quality control of bacterial and fungal reads was performed using a customized pipeline (Supplementary Table 2; Frey et al., 2016). Paired-ends reads were matched with USEARCH (Edgar & Flyvbjerg, 2015), substitution errors were corrected using Bayeshammer (Nikolenko, Korobeynikov, & Alekseyev, 2013) and PCR primers were trimmed (allowing for 1 mismatch, read length >300 bp for 16S and >200 bp for ITS primers) using Cutadapt (M. Martin, 2011). Sequences were dereplicated and singleton reads removed prior to clustering into operational taxonomic units (OTUs) at 97% identity using USEARCH (Edgar, 2013). The remaining centroid sequences were tested for the presence of ribosomal signatures using Metaxa2 (Bengtsson-Palme et al., 2015) or ITSx (Bengtsson-Palme et al., 2013). Taxonomic assignments of the OTUs were obtained using Bayesian classifier (Wang, Garrity, Tiedje, & Cole, 2007) with a minimum bootstrap support of 60% implemented in mothur (Schloss et al., 2009) by querying the bacterial and fungal reads against the SILVA Release 128 (Quast et al., 2013) and UNITE 8.0 (Abarenkov et al., 2010) reference databases for 16S and ITS OTUs, respectively.
Abundances of the bacterial 16S rRNA gene and fungal ITS amplicon were determined by quantitative real-time PCR (qPCR) on an ABI7500 Fast Real-Time PCR system (Applied Biosystems, Foster City, CA, USA) as described previously (Frossard et al., 2018). The same primers (without barcodes) and cycling conditions as for the sequencing approach were used for the 16S and ITS qPCR. Three standard curves per target region were obtained using tenfold serial dilutions of plasmids generated from cloned targets (Frey, Niklaus, Kremer, Lüscher, & Zimmermann, 2011). Data were converted to represent mean copy number of targets per gram of soil (dry weight).
The other half of the soil sample was bagged in paper, dried to constant weight at 60°C, passed through a 2 mm sieve and analyzed for total C and N concentration with a CE Instruments NC 2100 soil analyzer (CE Elantech Inc., Lakewood NJ, USA). We also collected 20 mature and undamaged leaves of the dominant grass species growing on control and carcass sites, but taxa were not recorded. The plant material was dried at 60°C, finely ground till homogenized and also analyzed to obtain total C and N concentrations. Soil temperature (10 cm depth) was measured with a waterproof digital thermometer (Barnstead International, Dubuque IA, USA) at three locations each at the control and carcass site. Soil moisture (0 – 10 cm depth) was measured with time domain reflectometry (Field-Scout TDR-100; Spectrum Technologies, Plainfield IL, USA) at five randomly chosen points on control and carcass sites. We measured soil respiration at five randomly chosen points at both control and carcass sites with a PP-Systems SRC-1 soil respiration chamber (closed circuit) attached to a PP-Systems EGM-4 infrared gas analyzer (PP-Systems, Amesbury, MA, USA). For each measurement the soil chamber (15 cm high; 10 cm diameter) was tightly placed on the soil surface, after clipping plants to avoid measuring plant respiration or photosynthesis. Measurements were conducted over 120 s.
In addition, we assessed the decomposition rates of standardized OM using the cotton strip assay (Latter & Howson, 1977; Latter & Walton, 1988). Cotton cloth tensile strength loss (CTSL) is a measure of decomposition, and an index to express the combined effect of soil microclimatic, physical, chemical and biological properties on decomposition while accounting for OM quality (Latter & Walton, 1988; Risch, Jurgensen, & Frank, 2007; Withington & Sanford Jr., 2007). We placed five 20 cm wide x 13 cm long sheets of 100% unbleached cotton cloth (American Type SM 1/18’’, Warp: 34/1, Weft: 20/1, Weave plain, 29.5 picks/cm warp, 22 picks/cm weft, 237 g/m2; Daniel Jenny & Co., Switzerland;) at each carcass and control site vertically into the soil by making slits with a flat spade to a depth of 12 cm. We inserted each cloth with the spade, and then pushed the slit closed to assure tight contact with the soil. The cloths were retrieved after 18 to 27 days. After retrieval, the cloths were air-dried, remaining soil gently removed by hand, and 1.5 cm wide strips were cut at the 3.5-5.0 cm (top) and the 9-10.5 cm (bottom) soil depth. The strips were equilibrated at 50 % relative humidity and 20°C for 48 hours (climate chamber) prior to strength testing (Scanpro Awetron TH-1 tensile strength tester; AB Lorentzen and Wettre, Kista, Sweden). Cotton rotting rate (CRR) = (CTScontrol - CTSfinal/CTSfinal)1/3 * (365/t), where CTScontrol is the cotton tensile strength of a control cloth and CTSfinal the cotton tensile strength of the incubated sample, t is the incubation period in days. Control cloths were inserted into the ground and immediately retrieved to account for tensile strength loss associated with cloth insertion. We averaged the CRR of top and bottom strips for further analyses as no difference was found between the two. All sampling and cloth insertion took place between June 20 and July 1, 2017, cloths were retrieved between July 17 and 20, 2017. Soil respiration, average CRR, vegetation N concentration and vegetation C:N ratio are defined as ecosystem functions, soil C and N concentration, soil temperature and moisture as soil abiotic properties, and bacterial and fungal richness (number of taxa), diversity (Shannon) and abundance as soil biotic properties.

Statistical analyses
Univariate analyses for ecosystem functions, soil biotic and abiotic properties
We tested whether individual ecosystem functions, soil biotic and abiotic properties differed between carcass and control (“Location”), bison and elk (“Species”) and days since kill (“DSK”). For this purpose, we used linear mixed effect models (LMM, “nlme” package v 3.1 – 131.1 in R v 3.4.4; Pinheiro, Bates, DebRoy, & Sarkar, 2018; R Core Team, 2019) with Location, Species, Location x Species and DSK as fixed effects. Site was included as random effect to account for the paired design. We developed a separate model for all dependent variables. All but bacterial richness, fungal richness, fungal diversity and vegetation N concentration were natural-log transformed to meet model assumptions. For each LMM, we calculated contrasts to assess the specific comparisons we were interested in with the “lsmeans” package v 2.27-62 (Lenth & Love, 2018): 1) carcass vs control, 2) carcass bison vs control bison, and 3) carcass elk vs control elk. We also tested whether we had differences between bison and elk carcasses or the sites where bison and elk were killed and included contrasts 4) carcass bison vs carcass elk and 5) control bison vs control elk.
We calculated the log response ratio (LRR = ln[carcass/control]) to obtain carcass effects for all variables for both species separately. LRR < 0 indicates higher value at control compared to carcass, LRR > 0 indicates higher values at carcass compared control. We used LRRs for visualization and to assess spatial patterns in carcass effects across YNP. For this purpose we calculated the Moran’s I statistic for each ecosystem function, soil biotic and abiotic property based on a latitude-longitude matrix with the “moran.test” function in the “spdep” package version 1.1-3 (Bivand et al., 2019).
Multivariate analyses
Rare OTUs, defined as OTUs with a low abundance of reads, were retained in multivariate methods because they only marginally influence these analyses (Gobet, Quince, & Ramette, 2010). Bray–Curtis dissimilarity matrices were generated based on square-root-transformed matrices. We used Principal Coordinate Analyses (PCoA) to assess how soil bacterial and fungal communities differed between control and carcass of bison and elk (“vegan” package v 2.5-4, Oksanen et al., 2019). We then extracted PCoA axes scores 1 and 2 and used LMM (“nlme” package) with Location, Species, Location x Species and DSK as fixed effects. Site was, again, included as random effect. We again calculated the contrasts as described above using the “lsmeans” package. We also assessed how ecosystem functions, and soil abiotic and biotic properties were related to the soil bacteria and fungi community structure associated with bison and elk control and carcasses using the “envfit” function in the “vegan” package (Oksanen et al., 2019).
Indicator species analyses were performed using the multipatt function implemented in the “indicspecies” package version 1.7.6 with 100000 permutations (De Caceres & Jansen, 2016). This step allowed to identify OTUs that led to changes in multivariate patterns between control and carcass of both bison and elk separately (De Cáceres, Legendre, & Moretti, 2010). The multipatt function uses a point biserial correlation coefficient statistical test. Indicator OTUs were defined as bacterial and fungal OTUs with more than 50 sequences, i.e., removing rare taxa and taxa with low abundances containing little indicator information (Rime et al., 2015) and that were significantly correlated with Location (p < 0.05, correlation coefficient > 0.3). A heatmap of these OTUs were generated with the vegan and ggplot2 packages. The indicator analyses were performed in R version 3.3.3 (R Core Team, 2017).

References
Abarenkov, K., Henrik Nilsson, R., Larsson, K.-H., Alexander, I. J., Eberhardt, U., Erland, S., … Kõljalg, U. (2010). The UNITE database for molecular identification of fungi – recent updates and future perspectives. New Phytologist, 186(2), 281–285. doi:10.1111/j.1469-8137.2009.03160.x
Bengtsson-Palme, J., Hartmann, M., Eriksson, K. M., Pal, C., Thorell, K., Larsson, D. G. J., & Nilsson, R. H. (2015). metaxa2: improved identification and taxonomic classification of small and large subunit rRNA in metagenomic data. Molecular Ecology Resources, 15(6), 1403–1414. doi:10.1111/1755-0998.12399
Bengtsson-Palme, J., Ryberg, M., Hartmann, M., Branco, S., Wang, Z., Godhe, A., … Nilsson, R. H. (2013). Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods in Ecology and Evolution, 4(10), 914–919. doi:10.1111/2041-210X.12073
Bivand, R., Altman, M., Anselin, L., Assuncao, R., Berke, O., Blanchet, G., … Yu, D. (2019). spdep: Spatial dependence, weighthing schemes, statistics. R package version 1.1-3.
Bump, J. K., Peterson, R. O., & Vucetich, J. A. (2009). Wolves modulate soil nutrient heterogeneity and foliar nitrogen by configuring the distribution of ungulate carcasses. Ecology, 90(11), 3159–3167.
Bump, J. K., Webster, C. R., Vucetich, J. A., Peterson, R. O., Shields, J. M., & Powers, M. D. (2009). Ungulate carcasses perforate ecological filters and create biogeochemical hotspots in forest herbaceous layers allowing trees a competitive advantage. Ecosystems, 12(6), 996–1007. doi:10.1007/s10021-009-9274-0
Danell, K., Berteaux, D., & Brathen, K. A. (2002). Effect of muskox carcasses on nitrogen concentration in tundra vegetation. Arctic, 55(4), 389392.
De Caceres, M., & Jansen, F. (2016). indicspecies: relationship between species and groups of species. R package version 1.7.6.
De Cáceres, M., Legendre, P., & Moretti, M. (2010). Improving indicator species analysis by combining groups of sites. Oikos, 119(10), 1674–1684. doi:10.1111/j.1600-0706.2010.18334.x
Edgar, R. C. (2013). UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods, 10, 996.
Edgar, R. C., & Flyvbjerg, H. (2015). Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics, 31(21), 3476–3482. doi:10.1093/bioinformatics/btv401
Frey, B., Niklaus, P. A., Kremer, J., Lüscher, P., & Zimmermann, S. (2011). Heavy-machinery traffic impacts methane emissions as well as methanogen abundance and community structure in oxic forest soils. Applied and Environmental Microbiology, 77(17), 6060–6068. doi:10.1128/AEM.05206-11
Frey, B., Rime, T., Phillips, M., Stierli, B., Hajdas, I., Widmer, F., & Hartmann, M. (2016). Microbial diversity in European alpine permafrost and active layers. FEMS Microbial Ecology, 92(3), fiw018.
Frossard, A., Donhauser, J., Mestrot, A., Gygax, S., Bååth, E., & Frey, B. (2018). Long- and short-term effects of mercury pollution on the soil microbiome. Soil Biology and Biochemistry, 120, 191–199. doi:https://doi.org/10.1016/j.soilbio.2018.01.028
Geremia, C., Wallen, R., & White, P. J. (2017). Status report of the Yellowstone bison population, September 2017. Yellowstone National Park, Mammoth, WY, USA: National Park Service, Yellowstone Center for Resources.
Gobet, A., Quince, C., & Ramette, A. (2010). Multivariate cutoff level analysis (MultiCoLA) of large community data sets. Nucleic Acids Research, 38(15), e155–e155. doi:10.1093/nar/gkq545
Latter, P., & Howson, G. (1977). The use of cotton strips to indicate cellulose decomposition in the field. Pedobiologia, (17), 145–155.
Latter, P., & Walton, D. (1988). The cotton strip assay for cellulose decomposition studies in soil: history of the assay and development. In Cotton strip assay: an index for decomposition in soils (pp. 7–9). ITE Symposium, Institute of Terrestrial Ecology, Natural Environment Research Council, UK.
Lenth, R., & Love, J. (2018). lsmeans: least-squares means. R package version 2.27-62.
Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.Journal, 17(1), 10–12.
Meagher, M. M. (1973). The bison of Yellowstone National Park. NPS Scientific Monograph (Vol. 1). National Park Service, Yellowstone Center for Resources.
Melis, C., Selva, N., Teurlings, I., Skarpe, C., Linnell, J. D. C., & Andersen, R. (2007). Soil and vegetation nutrient response to bison carcasses in Białowieża Primeval Forest, Poland. Ecological Research, 22(5), 807–813. doi:10.1007/s11284-006-0321-4
Nikolenko, S. I., Korobeynikov, A. I., & Alekseyev, M. A. (2013). BayesHammer: Bayesian clustering for error correction in single-cell sequencing. BMC Genomics, 14(1), S7. doi:10.1186/1471-2164-14-S1-S7
Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., … Wagner, H. H. (2019). vegan: community ecology package. R package version 2.5-4.
Pinheiro, J., Bates, D., DebRoy, S., & Sarkar, D. (2018). nlme: Linear and nonlinear mixed effect models. R package version 3.1-131.1.
Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., … Glöckner, F. O. (2013). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research, 41(Database issue), D590–D596. doi:10.1093/nar/gks1219
Quimby, D. C., & Johnson, D. E. (1951). Weights and measurements of Rocky Mountain elk. Journal of Wildlife Management, 15, 57–62.
R Core Team. (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Zurich, Switzerland.
R Core Team. (2019). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
Rime, T., Hartmann, M., Brunner, I., Widmer, F., Zeyer, J., & Frey, B. (2015). Vertical distribution of the soil microbiota along a successional gradient in a glacier forefield. Molecular Ecology, 24(5), 1091–1108. doi:10.1111/mec.13051
Risch, A. C., Jurgensen, M. F., & Frank, D. A. (2007). Effects of grazing and soil micro-climate on decomposition rates in a spatio-temporally heterogeneous grassland. Plant and Soil, 298(1–2), 191–201. doi:10.1007/s11104-007-9354-x
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., … Weber, C. F. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology, 75(23), 7537–7541. doi:10.1128/AEM.01541-09
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Please cite this paper together with the citation for the datafile.

bacteria

display_name: bacteria

biogeochemical-cycling

display_name: biogeochemical-cycling

carcass

display_name: carcass

carrion

display_name: carrion

decomposition

display_name: decomposition

detritus

display_name: detritus

fungi

display_name: fungi

soil-biodiversity

display_name: soil-biodiversity

wolf

display_name: wolf

yellowstone

display_name: yellowstone

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: Elk and bison carcasses in Yellowstone, USA


modeling-snow-failure-with-dem

title_for_slug: modeling-snow-failure-with-dem

en: snow-avalanche snow-failure snow snow-micro-mechanics discrete-element-modelling

en: Dataset

en: The zip file contains the data underlying the figures presented in Bobillier et al. (2019). The figures can be reproduced with the python file, which is also provided.

en: Python file

en: Python file to reproduce the figures in Bobillier et al. (2019)

mimetype: text/x-python

en: Read-me

en: The read-me file contains a description of all the data provided in the zip file.

en: This data set includes the modeling results described in the research article by Bobiller et al. (2020). All the figures in the article can be reproduced with the data provided.

discrete-element-modelling

display_name: discrete-element-modelling

snow

display_name: snow

snow-avalanche

display_name: snow-avalanche

snow-failure

display_name: snow-failure

snow-micro-mechanics

display_name: snow-micro-mechanics

en: Modeling snow failure with DEM


maintainer: Robert Pazúr

en: vegetation-indices cropland switzerland remote-sensing grassland habitat sentinel-2 agriculture machine-learning phenology

en: (current version) Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.7 (v1.5)

format: GeoTIFF

en: (current version) Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.6 (v1.5)

en: Assuming that sealed surfaces have little or no phenological response we calculated a mask of unsealed surfaces based on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.6.

en: (current version) Cropland, grassland, and shrubland map of Switzerland (v.1.5)

en: Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.6  (v1.0)

media_type: image/tiff

en:
!!! Please check out the newer version in the repository !!!
Mask of unsealed surfaces of Switzerland based on 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.6

format: TIFF

en: Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.7 (v1.0)

en: !!! Please check out the newer version in this repository !!! Assuming that sealed surfaces have little or no phenological response we calculated a mask of unsealed surfaces based on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.7.

en: Cropland, grassland, and shrubland map of Switzerland (v1.0)

en: !!! Please check out the newer version in this repository !!! (1) cropland; (2) grassland; (3) shrubland; other LC classes are masked out from the map

en: (current version) Cropland and grassland map of Switzerland (v.1.5)

en: GEE (Google Earth Engine) script (JavaScript) to derived the output map  (v1.5)

en: The code used in Google Earth Engine to derive the cropland, grassland and shrubland map of Switzerland. Compare to the previous version the newer version includes the shadow masking procedure which might be important in the mountainous areas.

en: GEE (Google Earth Engine) script (JavaScript) to derived the output map  (v1.0)

en: !!! Please check out the newer version in the repository !!! The code used in Google Earth Engine to derive the cropland, grassland and shrubland map of Switzerland

en: Cropland and grassland map of Switzerland  (v.1.0)

en: !!! Please check out the newer version in this repository !!! (1) cropland; (2) grassland; other LC classes are masked out from the map

en: We developed a map of cropland and grassland allocation for Switzerland based on several indices dominantly derived from Sentinel-2 satellite imagery captured over multiple growing seasons. The classification model was trained based on parcel-based data derived from landholder reporting. The mapping was conducted on Google Earth Engine platform using random forest classifier. Areas of high vegetation, shrubland, sealed surface and non-vegetated areas were masked out from the country-wide map. The resulting map has high accuracy in lowlands as well as mountainous areas.

agriculture

display_name: agriculture

cropland

display_name: cropland

grassland

display_name: grassland

habitat

display_name: habitat

machine-learning

display_name: machine-learning

phenology

display_name: phenology

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

vegetation-indices

display_name: vegetation-indices

en: Cropland and grassland map of Switzerland based on Sentinel-2 data


maintainer: Michael Lehning

en: sublimation stable-water-isotopes paleoclimate snow post-depositional-processes weissfluhjoch latent-heat natural-tracer

en: Details about the different folders

en: Stable water isotopes in vapor

en:
Stable water isotopes in vapor measurements. Processed (calibrated) and raw data for February - June 2017. Raw data for January and February 2020.
For the time in between July 2017 and April 2020, raw vapor isotopic measurements are available, but no calibrations were performed during that time. For data delivery, please contact one of the authors.
This folder also contains the post-processing scripts for stable water isotopes in vapor measurements.

en:
Details on the data collected during the different field campaigns and available in this dataset.
Information on the sampling procedure of the snow isotopes samples.

en: Weather

en: Meteorological data, snow height measurement, and SNOWPACK simulations covering the different field campaigns.

en: Stable water isotopes in snow

en: Stable water isotopes in snow measurements

en:
Notice: Changes to the dataset are still possible. Please do not use this dataset until the final publication with a DOI. Contact the authors if you have questions about this.

This dataset contains measurements of stable water isotopes in snow and vapor on the Weissfluhjoch from different field campaigns (Winter 2017 (Trachsel, 2019), January 2020, December 2020, and March 2021 (Sadowski et al., 2022).
Snow profiles and surface samples are available at different frequencies for each campaign. Please see "Data_description.pdf" for details.

Scripts and SNOWPACK simulations used in (Trachsel, 2019) and (Sadowski et al., 2022) are also provided.

latent-heat

display_name: latent-heat

natural-tracer

display_name: natural-tracer

paleoclimate

display_name: paleoclimate

post-depositional-processes

display_name: post-depositional-processes

stable-water-isotopes

display_name: stable-water-isotopes

sublimation

display_name: sublimation

weissfluhjoch

display_name: weissfluhjoch

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 snow and vapor on the Weissfluhjoch


en: dry-snow-avalanche climate-projections avalanche wet-snow-avalanche snow-instability snow snow-stratigraphy climate-change

en: SNOWPACK settings

en: contains .ini and .sno file indicating the settings used to drive SNOWPACK with the climate scenarios

en: Projections of avalanche activity

en: contains one file per IMIS station that indicates the daily probability for the occurrence of dry- or wet-snow avalanches in the vicinity of the respective station for each day during the time period 1.12.1981-31.05.2099 (months December to May) and all climate model chains and RCP scenarios.

media_type: text/plain

format: TXT

en: Climate scenarios .smet

en: contains one .smet file per combination of station, climate model chain and RCP scenario. Each .smet file contains the downscaled meteorological data (time span 1.9.1981-31.8.2099) that was used to drive the SNOWPACK simulations.

en:
This data set includes 21st century projections of dry- and wet-snow avalanche activity for seven sites in the Swiss Alps as well as the downscaled climate scenarios and SNOWPACK configuration files used to produce these projections. The methods used to produce these data are described in the research article:
Mayer, S., Hendrick, M., Michel, A., Richter, B., Schweizer, J., Wernli, H., and van Herwijnen, A.: Changes in snow avalanche activity in response to climate warming in the Swiss Alps, EGUsphere, 2024, 1-32, https://doi.org/10.5194/egusphere-2024-1026, 2024.
We used downscaled climate projections to drive the snow cover model SNOWPACK and evaluated future dry- and wet-snow avalanche occurrences in the vicinity of seven automatic weather stations from the IMIS network using machine learning models trained with observed records of avalanche activity. Projections are available for three different emission scenarios (Representative Concentration Pathways, RCP2.6, RCP4.5 and RCP8.5) and eight climate model chains from the CH2018 ensemble. The output of the model chain is given by a daily probability for dry- or wet-snow avalanche occurrences. These probabilities can then be used to classify a day as a dry- and wet-snow avalanche day (AvD) or non-avalanche day.

avalanche

display_name: avalanche

climate-change

display_name: climate-change

climate-projections

display_name: climate-projections

dry-snow-avalanche

display_name: dry-snow-avalanche

snow

display_name: snow

snow-instability

display_name: snow-instability

snow-stratigraphy

display_name: snow-stratigraphy

wet-snow-avalanche

display_name: wet-snow-avalanche


en: snow-avalanche climate-change ramms climada avalanche risk

en: By using hazard scenarios based on CH2018 climate projections and the RAMMS avalanche model, we generated large-scale hazard indication maps for future avalanche hazards. By employing the open-source probabilistic risk assessment platform CLIMADA, along with building data and vulnerability functions, we estimated risks for the present time and two future time frames: mid-century (2060) and the end of the century (2085). An uncertainty and sensitivity analysis complemented the study to account for potential fluctuations in model assumptions. The data contains the hazard information and data used for the Climada risk analysis.

climada

display_name: climada

ramms

display_name: ramms

risk

display_name: risk

snow-avalanche

display_name: snow-avalanche

en: Assessing the impacts of climate change on snow avalanche-induced risk in alpine regions