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)

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.


maintainer: Bundesamt für Energie BFE

de: ogd103-bfe versorgungssicherheit landesverbrauch-strom endverbrauch-strom

fr: energiedashboard.ch: Consommation en électricité Swissgrid

en: energiedashboard.ch: Electricity consumption Swissgrid

it: energiedashboard.ch: Consumo di elettricità Swissgrid

de: Bundesamt für Energie BFE

state: active

type: dataset

fr: Consommation estimée modélisée

de: Modellbasiert geschätzter Verbrauch

en: Model-based estimated consumption

it: Consumo stimato modellizzato

fr: Les données actuelles ne sont pas disponibles, c'est pourquoi un modèle doit être utilisé pour estimer la consommation sur la base du passé.

en: Real-time data is not available, so a model must be used to estimate consumption based on the past.

de: Zeitnahe Daten sind nicht verfügbar, weshalb mit einem Modell der Verbrauch auf Basis der Vergangenheit geschätzt werden muss.

it: I dati in tempo reale non sono disponibili, quindi è necessario utilizzare un modello per stimare il consumo in base al passato.

format: CSV

fr: Consommation nationale et finale

de: Landesverbrauch und Endverbrauch

en: Nation and final consumtion

it: Consumo nazionale e finale

fr:
La consommation nationale, y compris la consommation des pompes de stockage, est un indicateur important en ce qui concerne l'approvisionnement en électricité. L'évolution est d'abord présentée sur la base des données communiquées à Swissgrid par les gestionnaires de réseau de distribution. Ces données peuvent être incomplètes ou erronées et sont corrigées ultérieurement.

La consommation finale indique la quantité d'électricité consommée par les ménages, le commerce, l'agriculture, les services, l'industrie et les transports. C'est le paramètre le plus important pour les efforts d'économie des Suisses. L'évolution est d'abord présentée sur la base des données communiquées à Swissgrid par les gestionnaires de réseau de distribution. Ces données peuvent être incomplètes ou erronées et sont corrigées.

en:
National consumption, including the consumption of storage pumps, is an important indicator in relation to the electricity supply. The trend is initially presented on the basis of data reported to Swissgrid by distribution grid operators. This data may be incomplete or incorrect and is subsequently corrected.

Final consumption indicates how much electricity is consumed by households, commerce, agriculture, services, industry and transport. It is the decisive factor when it comes to the Swiss' efforts to save energy. The trend is initially presented on the basis of data reported to Swissgrid by distribution grid operators. These data may be incomplete or incorrect and are corrected.

de:
Der Landesverbrauch inklusive des Verbrauchs der Speicherpumpen ist ein wichtiger Indikator in Bezug auf die Stromversorgung. Der Verlauf wird zunächst auf Basis von durch Verteilnetzbetreiber an Swissgrid gemeldeten Daten dargestellt. Diese Daten können unvollständig oder fehlerhaft sein und werden nachkorrigiert.

Der Endverbrauch gibt an, wieviel Strom durch Haushalte, Gewerbe, Landwirtschaft, Dienstleistungen, Industrie und Verkehr verbraucht wird. Wenn es um Sparbemühungen der Schweizer geht ist er die massgebliche Grösse. Der Verlauf wird zunächst auf Basis von durch Verteilnetzbetreiber an Swissgrid gemeldeten Daten dargestellt. Diese Daten können unvollständig oder fehlerhaft sein und werden nachkorrigiert.

it:
Il consumo nazionale, compreso il consumo delle pompe di accumulo, è un indicatore importante in relazione alla fornitura di elettricità. Il trend viene inizialmente presentato sulla base dei dati riferiti a Swissgrid dai gestori delle reti di distribuzione. Questi dati possono essere incompleti o errati e vengono successivamente corretti.

Il consumo finale indica la quantità di elettricità consumata dalle famiglie, dal commercio, dall'agricoltura, dai servizi, dall'industria e dai trasporti. È il fattore decisivo quando si tratta degli sforzi degli svizzeri per risparmiare energia. Il trend viene inizialmente presentato sulla base dei dati riferiti a Swissgrid dai gestori delle reti di distribuzione. Questi dati possono essere incompleti o errati e vengono corretti.

fr:
La consommation nationale de la Suisse, y compris la consommation des pompes d'accumulation des centrales de pompage-turbinage, est un indicateur important en ce qui concerne l'approvisionnement en électricité. Il est important de différencier les chiffres de la consommation. La consommation nationale comprend la consommation totale des ménages, de l'artisanat, de l'agriculture, des services, de l'industrie et des transports (ce que l'on appelle la consommation finale), plus les pertes de transport et de distribution (pertes de réseau). La consommation actuellement affichée comprend, outre la consommation nationale, la consommation d'électricité pour le fonctionnement des pompes de stockage.

La tendance indique l'évolution de la consommation nationale, y compris la consommation des pompes de stockage, par rapport à la consommation moyenne des 10 derniers jours. La valeur prévisionnelle indique la consommation nationale, y compris la consommation des pompes de stockage, estimée sur la base d'un modèle dans 5 jours.

Consommation nationale, y compris la consommation des pompes de stockage, sur les dernières semaines

La consommation d'électricité dépend également de l'utilisation des pompes de stockage. Plus les chiffres sont éloignés dans le temps, plus ils sont robustes. Les données proviennent des gestionnaires de réseau de distribution, qui les communiquent chaque mois à Swissgrid. Ces données peuvent être incomplètes ou erronées et sont corrigées ultérieurement. Ces données de Swissgrid remontent au mieux à 1 mois. Les données de consommation mesurées en temps réel ne sont pas disponibles. Pour déterminer la consommation nationale actuelle, y compris la consommation des pompes de stockage, des modèles sont utilisés. Ces modèles ne tiennent pas compte de l'évolution des températures en Suisse, mais des changements de comportement de la population, par exemple des réactions aux appels à l'économie d'énergie. De tels changements de comportement ne peuvent être détectés que par des données de consommation mesurées.

Consommation finale actuelle sur les dernières semaines

La consommation finale est l'énergie électrique consommée par l'économie, les transports et les ménages privés. Les éventuels efforts d'économie d'électricité peuvent certes être interprétés à partir de ce graphique, mais doivent être considérés comme purement indicatifs en raison de la disponibilité des données. Le graphique se base avant tout sur les données communiquées par le passé à Swissgrid par les gestionnaires de réseau de distribution et corrigées ultérieurement. En raison des incertitudes concernant les données relatives à la consommation finale, l'OFEN a spécialement fait développer un modèle de consommation finale à l'aide des méthodes de science des données les plus récentes, telles que l'apprentissage automatique.

Graphique d'expert comparant les sources de données relatives à la consommation nationale, y compris la consommation des pompes de stockage.

Les analyses montrent qu'il existe différentes différences entre les principales sources de données relatives à la consommation. Ces différences sont illustrées afin de permettre une évaluation des informations qui ont circulé jusqu'à présent. Ces différences s'expliquent par différentes raisons, telles que des approches méthodologiques et des délimitations différentes. Mais les différences sont également dues à des corrections et à des informations complémentaires fournies par le secteur.  Pour la comparaison sur une base mensuelle, les statistiques de l'OFEN sont utilisées comme base et critère de mesure.

Disclaimer Données

Les données relatives à la consommation ne sont pas satisfaisantes. Différentes sources existent, avec des approches méthodologiques différentes. La source la plus fiable est la statistique de l'OFEN, qui ne fournit cependant que des valeurs sur une base mensuelle et qui est actuellement publiée avec un retard d'environ 2 mois.  Il existe également des données de Swissgrid, basées sur les déclarations des gestionnaires de réseau de distribution. Ces données basées sur des valeurs mesurées sont entachées d'incertitudes, car les gestionnaires de réseau de distribution font régulièrement des déclarations erronées, des déclarations complémentaires et des corrections, parfois jusqu'à 6 mois après la publication. Pour que des valeurs actuelles puissent être indiquées, il faut travailler avec des modèles. Ceux-ci tiennent compte des données du passé et notamment des statistiques de l'OFEN. Pour une estimation actuelle de la consommation finale, l'OFEN a fait développer un modèle de science des données utilisant l'apprentissage automatique moderne. Ce modèle prévoit la consommation finale, mais ne peut reproduire que partiellement les changements de comportement importants au sein de la population. L'OFEN travaille actuellement sur de meilleures approches avec le secteur de l'électricité.

en:
The national consumption of Switzerland, including the consumption of the storage pumps of the pumped storage power plants, is an important indicator with regard to the electricity supply. When it comes to consumption, it is essential to differentiate between various figures. National consumption includes the total consumption of households, commerce, agriculture, services, industry and transport (the so-called final consumption) plus transmission and distribution losses (grid losses). In addition to the national consumption, the currently reported consumption also includes the electricity consumption for the operation of the storage pumps.

The trend shows the development of the national consumption including the consumption of the storage pumps compared to the average consumption of the last 10 days. The forecast value indicates the model-based estimated country consumption including the consumption of the storage pumps in 5 days.

Country consumption including consumption of storage pumps over the last few weeks

The electricity consumption also depends on the use of the storage pumps. The further back the figures go, the more reliable they are. The data comes from distribution grid operators, who report the data to Swissgrid on a monthly basis. This data can be incomplete or incorrect and is subsequently corrected. At best, this data from Swissgrid goes back 1 month. Timely measured consumption data are not available. Models are used to determine the current national consumption, including the consumption of the storage pumps. These models do not take into account the temperature development in Switzerland, but they do not take into account changes in the behaviour of the population, for example the reaction to calls to save energy. Such behavioural changes can only be recorded using measured consumption data.

Current final consumption over the last few weeks

The final consumption is the electrical energy consumed by the economy, transport and private households. Any efforts to save electricity can be interpreted from this graph, but must be considered purely indicative due to the data situation. The graph is based primarily on the data reported to Swissgrid in the past by distribution grid operators and subsequently corrected. Due to the uncertainties in the data on final consumption, the SFOE has had a model of final consumption specially developed using the latest data science methods such as machine learning.

Expert graph comparing data sources on national consumption including consumption of storage pumps.

Analyses show that there are various differences between the most important data sources on consumption. These differences are illustrated to enable an assessment of the information that has been circulating so far. The differences have various reasons, such as different methodological approaches and delimitations. However, differences also occur due to corrections and follow-up reports from the industry.  For comparison on a monthly basis, the SFOE statistics are used as a basis and yardstick.

Disclaimer Data

The data situation on consumption is not satisfactory. Different sources exist that follow different methodological approaches. Of these, the most reliable source is the SFOE statistics, which, however, only offer values on a monthly basis and are currently published with a delay of about 2 months.  In addition, there is data from Swissgrid, which is based on reports from the distribution grid operators. These data are based on measured values and are subject to uncertainties, as there are always false reports, subsequent reports and corrections by the distribution grid operators, sometimes up to 6 months after publication. In order to be able to provide current values, models must be used. These take into account past data and, in particular, SFOE statistics. For a current estimate of end consumption, the SFOE has had a data science model developed using modern machine learning. This model forecasts final consumption, but is only able to partially reproduce major changes in the population's behaviour. The SFOE is currently working on better approaches with the electricity industry.

de:
Der Landesverbrauch der Schweiz inklusive des Verbrauchs der Speicherumpen der Pumpspeicherkraftwerke ist ein wichtiger Indikator in Bezug auf die Stromversorgung. Beim Verbrauch gilt es unbedingt zwischen verschiedenen Zahlen zu differenzieren. Der Landesverbrauch enthält den gesamten Verbrauch von Haushalten, Gewerbe, Landwirtschaft, Dienstleistungen, Industrie und Verkehr (der sogenannte Endverbrauch) zuzüglich Übertragungs- und Verteilverluste (Netzverluste). Im aktuell ausgewiesenen Verbrauch ist neben dem Landesverbrauch auch der Stromverbrauch für den Betrieb der Speicherpumpen enthalten.

Der Trend zeigt die Entwicklung des Landesverbrauches inklusive des Verbrauches der Speicherpumpen im Vergleich zum durchschnittlichen Verbrauch der letzten 10 Tage an. Der prognostizierte Wert gibt den modellbasiert geschätzten Landesverbrauch inklusive des Verbrauches der Speicherpumpen in 5 Tagen an.

Landesverbrauch inklusive Verbrauch Speicherpumpen über die letzten Wochen

Der Stromverbrauch hängt auch vom Einsatz der Speicherpumpen ab. Je weiter die Zahlen zurückliegen, desto belastbarer sind sie. Die Daten stammen von Verteilnetzbetreibern, die die Daten monatlich an Swissgrid melden. Diese Daten können unvollständig oder fehlerhaft sein und werden nachkorrigiert. Diese Daten der Swissgrid reichen bestenfalls 1 Monat zurück. Zeitnahe gemessenen Verbrauchsdaten sind nicht verfügbar. Um den aktuellen Landesverbrauch inklusive des Verbrauchs der Speicherpumpen zu ermitteln, werden Modelle herangezogen. Diese Modelle berücksichtigen die Temperaturentwicklung in der Schweiz nicht aber Verhaltensänderungen der Bevölkerung, also beispielsweise Reaktion auf Aufrufe zum Energiesparen. Solche Verhaltensänderungen können nur über gemessene Verbrauchsdaten erfasst werden.

Aktueller Endverbrauch über die letzten Wochen

Der Endverbrauch ist die durch Wirtschaft, Verkehr und Privathaushalte verbrauchte elektrische Energie. Allfällige Anstrengungen zu Stromeinsparungen können aus dieser Grafik zwar interpretiert werden, müssen aber aufgrund der Datenlage als rein indikativ gelten. Die Grafik basiert vor allem auf den in der Vergangenheit durch Verteilnetzbetreiber an Swissgrid gemeldete und nachkorrigierte Daten. Aufgrund der Unsicherheiten in der Datenlage zum Endverbrauch hat das BFE eigens ein Modell des Endverbrauchs mit neuesten Data Science Methoden wie dem maschinellen Lernen entwickeln lassen.

Expertengrafik zum Vergleich der Datenquellen zum Landesverbrauch inklusive Verbrauch der Speicherpumpen.

Analysen zeigen, dass es verschiedene Unterschiede zwischen den wichtigsten Datenquellen zum Thema Verbrauch gibt. Diese Unterschiede werden veranschaulicht, damit eine Einschätzung der bisher kursierenden Information möglich wird. Die Unterschiede haben verschiedene Gründe, wie etwa unterschiedliche methodologische Ansätze und Abgrenzungen. Unterschiede kommen aber auch durch Korrekturen und Nachmeldungen aus der Branche zustande.  Zum Vergleich auf Monatsbasis wird die BFE Statistik als Grundlage und Messlatte herangezogen.

Disclaimer Daten

Die Datenlage zum Verbrauch ist nicht zufriedenstellend. Es existieren unterschiedliche Quellen, die unterschiedliche methodologische Ansätze verfolgen. Davon ist die belastbarste Quelle die BFE Statistik, welche jedoch nur Werte auf Monatsbasis bietet und derzeit mit Verzug von etwa 2 Monaten erscheint.  Daneben gibt es Daten der Swissgrid, die auf Meldungen der Verteilnetzbetreiber basieren. Diese Daten basieren auf Messwerten sind mit Unsicherheiten behaftet, da es immer wieder zu Fehlmeldungen, Nachmeldungen und Korrekturen seitens der Verteilnetzbetreiber kommt, teilweise bis zu 6 Monate nach der Veröffentlichung. Damit aktuelle Werte angegeben werden können muss mit Modellen gearbeitet werden. Diese berücksichtigen Daten der Vergangenheit und insbesondere der BFE Statistik. Für eine aktuelle Schätzung des Endverbrauchs hat das BFE ein Data Science Modell unter Nutzung modernen Maschinenlernens entwickeln lassen. Es prognostiziert den Endverbrauch, vermag aber grosse Verhaltensänderungen in der Bevölkerung nur ansatzweise nachzubilden. Das BFE arbeitet derzeit an besseren Ansätzen mit der Strombranche.

it:
Il consumo nazionale della Svizzera, compreso il consumo delle pompe di accumulo delle centrali elettriche con sistema di pompaggio, è un indicatore importante per quanto riguarda l'approvvigionamento elettrico. Quando si parla di consumi, è fondamentale distinguere tra varie cifre. Il consumo nazionale comprende il consumo totale delle famiglie, del commercio, dell'agricoltura, dei servizi, dell'industria e dei trasporti (il cosiddetto consumo finale) più le perdite di trasmissione e distribuzione (perdite di rete). Oltre al consumo nazionale, il consumo attualmente riportato include anche il consumo di elettricità per il funzionamento delle pompe di accumulo.

Il trend mostra l'andamento del consumo nazionale, incluso il consumo delle pompe di accumulo, rispetto al consumo medio degli ultimi 10 giorni. Il valore di previsione indica il consumo stimato del Paese basato sul modello, incluso il consumo delle pompe di accumulo in 5 giorni.

Consumo del Paese incluso il consumo delle pompe di accumulo nelle ultime settimane

Il consumo di elettricità dipende anche dall'uso delle pompe di accumulo. Più le cifre vanno indietro, più sono affidabili. I dati provengono dagli operatori delle reti di distribuzione, che li riportano a Swissgrid su base mensile. Questi dati possono essere incompleti o errati e vengono successivamente corretti. Questi dati di Swissgrid risalgono al massimo a 1 mese fa. Non sono disponibili dati di consumo misurati tempestivamente. I modelli vengono utilizzati per determinare il consumo nazionale attuale, compreso il consumo delle pompe di accumulo. Questi modelli non tengono conto dell'andamento della temperatura in Svizzera, ma non tengono conto dei cambiamenti nel comportamento della popolazione, ad esempio la reazione alle richieste di risparmio energetico. Tali cambiamenti comportamentali possono essere registrati solo utilizzando dati di consumo misurati.

Consumo finale attuale nelle ultime settimane

Il consumo finale è l'energia elettrica consumata dall'economia, dai trasporti e dalle famiglie. Qualsiasi sforzo per risparmiare energia elettrica può essere interpretato da questo grafico, ma deve essere considerato puramente indicativo a causa della situazione dei dati. Il grafico si basa principalmente sui dati riferiti a Swissgrid in passato dai gestori delle reti di distribuzione e successivamente corretti. A causa delle incertezze dei dati sul consumo finale, l'UFE ha fatto sviluppare appositamente un modello di consumo finale utilizzando i più recenti metodi di scienza dei dati come il machine learning.

Grafico di esperti che confronta le fonti di dati sul consumo nazionale, compreso il consumo di pompe di accumulo.

Le analisi mostrano che esistono varie differenze tra le fonti di dati più importanti sul consumo. Queste differenze sono illustrate per consentire una valutazione delle informazioni che sono circolate finora. Le differenze hanno varie ragioni, come approcci metodologici e delimitazioni diverse. Tuttavia, le differenze si verificano anche a causa delle correzioni e dei rapporti di follow-up del settore.  Per il confronto su base mensile, si utilizzano le statistiche dell'UFE come base e metro di paragone.

Dati disclaimer

La situazione dei dati sul consumo non è soddisfacente. Esistono diverse fonti che seguono approcci metodologici differenti. Di queste, la fonte più affidabile è la statistica dell'UFE, che però offre solo valori su base mensile e attualmente viene pubblicata con un ritardo di circa 2 mesi.  Inoltre, ci sono i dati di Swissgrid, che si basano sui rapporti degli operatori della rete di distribuzione. Questi dati si basano su valori misurati e sono soggetti a incertezze, poiché ci sono sempre segnalazioni false, segnalazioni successive e correzioni da parte degli operatori della rete di distribuzione, a volte fino a 6 mesi dopo la pubblicazione. Per poter fornire i valori attuali, è necessario utilizzare dei modelli. Questi tengono conto dei dati passati e, in particolare, delle statistiche dell'OFE. Per una stima attuale del consumo finale, l'UFE ha fatto sviluppare un modello di scienza dei dati utilizzando il moderno apprendimento automatico. Questo modello prevede il consumo finale, ma è in grado di riprodurre solo parzialmente i principali cambiamenti nel comportamento della popolazione. L'UFE sta attualmente lavorando per migliorare gli approcci con l'industria elettrica.

ener

fr: Énergie

de: Energie

en: Energy

it: Energia

gove

fr: Gouvernement et secteur public

de: Regierung und öffentlicher Sektor

en: Government and public sector

it: Governo e settore pubblico

econ

fr: Économie et finances

de: Wirtschaft und Finanzen

en: Economy and finance

it: Economia e finanze

bundesamt-fur-energie-bfe

fr: Office fédéral de l'énergie OFEN

en: Swiss Federal Office of Energy SFOE

it: Ufficio federale dell'energia UFE

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

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

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

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

approval_status: approved

type: organization

political_level: confederation


flussgebiete-einzugsgebiete

regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

envi

fr: Environnement

de: Umwelt

en: Environment

it: Ambiente

title_for_slug: flussgebiete-einzugsgebiete

fr: geocat.ch permalien

de: geocat.ch Permalink

en: geocat.ch permalink

it: geocat.ch link permanente

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

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

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

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

fr: Aperçu map.geo.admin.ch

de: Vorschau map.geo.admin.ch

en: Preview map.geo.admin.ch

it: Previsione map.geo.admin.ch

format: SERVICE

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

de: WMS-BGDI Dienst, Layer "Flussgebiet"

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

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

format: WMS

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

de: WMTS-BGDI Dienst, Layer "Flussgebiet"

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

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

format: WMTS

fr: RESTful API de geo.admin.ch

de: RESTful API von geo.admin.ch

en: RESTful API from geo.admin.ch

it: RESTful API da geo.admin.ch

format: API

fr: Download

de: Download

en: Download

it: Download

format: HTML

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

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

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

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

spatial: Schweiz

fr: Office fédéral de l'environnement /Division Hydrologie

de: Bundesamt für Umwelt / Abteilung Hydrologie

en: Federal Office for the Environment / Hydrology Division

it: Ufficio federale dell'ambiente / Divisione Idrologia

bundesamt-fur-umwelt-bafu

fr: Office fédéral de l'environnement OFEV

de: Bundesamt für Umwelt BAFU

en: Federal Office for the Environment FOEN

it: Ufficio federale dell'ambiente UFAM

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

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

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

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

value: BAFU - Geocat Harvester

fr: Bassin fluvial

de: Flussgebiete (Einzugsgebiete)

en: River basins (catchment areas)

it: Bacini fluviali (bacini imbriferi)


soci

fr: Population et société

de: Bevölkerung und Gesellschaft

en: Population and society

it: Popolazione e società

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

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

author: Kanton Luzern, Abteilung Geoinformation

protocol: https

de: Datenshop

de: Produktansicht Geodatenshop

de: Im Bauinventar werden schützens- und erhaltenswerte Baudenkmäler erfasst. Besonders schutzwürdige Kulturobjekte werden ins kantonale Denkmalverzeichnis eingetragen und unter Schutz gestellt.

spatial: Kanton Luzern

de: Kanton Luzern, Abteilung Geoinformation

fr: Géoinformation du canton de Lucerne

de: Geoinformation Kanton Luzern

en: Geoinformation Canton of Lucerne

it: Geoinformazione del Cantone di Lucerna

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

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

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

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

political_level: canton

capacity: public

value: LU-Kt - Geocat Harvester

de: Kantonales Denkmalverzeichnis und Bauinventar


name: Statistisches Amt des Kantons Zürich, Analysen & Studien

de: wohnbautaetigkeit gemeinden

fr: GWR-Vorlage: Gebäude- und Wohnungsbestand, Bautätigkeit

de: Wohnbautätigkeit in den Gemeinden des Kantons Zürich nach Zimmerzahl und Gebäudekategorie

media_type: text/csv

de:
Der Stichtag der Datengrundlage ist jeweils der 31. Dezember des letzten ausgewiesenen Jahres.
Wohnungen in neuen/abgebrochenen Gebäuden. Aufstockungen bzw. Umbauten wurden nicht berücksichtigt.
Aufgrund von Registernachführungen können sich die Zahlen insbsondere für Zeiträume nahe beim Stichtag bei zukünftigen Auswertungen noch ändern.
Die Datenaufbereitung basiert auf der Schablone hier: https://www.web.statistik.zh.ch/ogd/daten/ressourcen/KTZH_00002162_00004263.html

de: Anzahl der Neubau- und Abbruchwohnungen sowie des Wohnbausaldos pro Gemeinde sowie beim CSV zusätzlich nach Zimmerzahl und Gebäudekategorie. Weitere Erläuterungen zur Auswertung (inkl. GWR-Stichtag) finden sich in den Excel-Datei oder beim CSV in den Metadaten. Die Rohdaten zur Bezifferung der Wohnbautätigkeit sind OGD (siehe 'Verwandte Datensätze') . Eine Auswertungsvorlage ist unten verlinkt (siehe 'Weitere Informationen').

gemeinden

display_name: gemeinden

wohnbautaetigkeit

display_name: wohnbautaetigkeit

statistisches-amt-kanton-zuerich

de: Statistisches Amt Kanton Zürich

de: Das Statistische Amt ist das Kompetenzzentrum für die öffentliche Statistik im Kanton Zürich. Wir versorgen Bevölkerung, Verwaltung, Politik, Gemeinden, Medien, Firmen und sonstige Organisationen mit qualitativ hochstehenden Daten, Informationen und Fakten zum Kanton Zürich. Inhaltlich arbeiten wir unabhängig und objektiv.

key: guid

key: uri

value: ZH-Kt Statistisches Amt - DCAT Harvester


maintainer: EnviDat Support

en: switzerland wetlands environmental--policy environmental---history historical-carthography ecology

en: Wetlands 1850 - 2010 (shapefiles)

media_type: application/zip

en:
 History of wetlands - GIS-Layers
 Location of wetlands based on signatures in topographic maps
* Basis 1850: original measuring table sheets Dufour created between the years 1834 and 1863.
* Basis 1900/ Basis 1950: The Siegfried maps were updated and published at regular intervals between 1870 and 1949. This long period of time made it possible to reconstruct wetlands for two periods based on the Siegfried Maps: the first edition (hereinafter referred to as "1900") and the last edition ("1950").
* Basis 2010: Map sheets from the National Map from 2006 to 2011 were used for the most recent time period of the reconstruction.
 Location of wetlands due to reconstructions
The reconstruction **must not** be interpreted in the sense of a surface-accurate separation, but shows without claim to location accuracy the approximate extent of the areas that would have corresponded to the mapping criteria for wetlands used in today's topographic maps around 1850, 1900 and 1950. The basics can be found in Stuber & Bürgi 2019, chapter 5.4.

format: ZIP

mimetype: application/zip

en:
Naturally, large parts of the Swiss Plateau are characterised by wetlands
and meandering rivers. That this is no longer the case today is the result
of centuries of efforts to obtain dry land. But how did this process take
place? What were the relevant actors and what were their motivations? And
what can be said about the ecological consequences of this development?

In a research project on the history of wetlands in Switzerland since 1700,
we conducted (a) a historical analysis of the development of land use in
wetlands and the actors involved, (b) a historical-cartographic
reconstruction of wetland extent since 1850 and (c) an evaluation of
ecological effects of changes in wetlands on various organisms groups.

The series of GIS layers on wetland history stem from the second part of
the project. The area reconstruction is based on digitized and homogenized
signatures from national map series, as they have been available since
about 1850. Details about the digitalization process and the homogenization
procedures applied ("Rekonstruktionen") are included in Stuber & Bürgi
2019.

Book Citation:
 > Stuber M, Bürgi M (2019) Vom «eroberten Land» zum Renaturierungsprojekt. Geschichte der Feuchtgebiete in der Schweiz seit 1700. "Bristol Schriftenreihe", Band 59. Haupt Verlag, Bern, Stuttgart, Wien. 262 Seiten.

ecology

display_name: ecology

environmental---history

display_name: environmental---history

environmental--policy

display_name: environmental--policy

historical-carthography

display_name: historical-carthography

switzerland

display_name: switzerland

wetlands

display_name: wetlands

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

value: Envidat Harvester

en: History of wetlands in Switzerland since 1850


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

en: Snowdepth maps

format: GeoTIFF

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

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

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

The work is published in:

avalanches

display_name: avalanches

davos

display_name: davos

dischma

display_name: dischma

photogrammetry

display_name: photogrammetry

snow-depth-distribution

display_name: snow-depth-distribution

snow-hydrology

display_name: snow-hydrology

educ

de: Bildung, Kultur und Sport

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


en: sediment-transport snow mars erosion model

media_type: text/plain

en: Datasets of Figure 4b

format: TXT

mimetype: text/plain

en: Datasets of Figure 3a

en: Datasets of Figure 2

en: Datasets of Figure 4a

en: Datasets of Figure 3b

en:
The files contain the datasets used to produce Figures 2, 3, and 4 of the manuscript ([doi: 10.1002/2016GL071822](http://dx.doi.org/10.1002/2016GL071822)).

 Manuscript Abstract:
Despite being the main sediment entrainment mechanism in aeolian transport, granular splash is still poorly understood. We provide a deeper insight into the dynamics of sand and snow ejection with a stochastic model derived from the energy and momentum conservation laws. Our analysis highlights that the ejection regime of uniform sand is inherently different from that of heterogeneous sand. Moreover, we show that cohesive snow presents a mixed ejection regime, statistically controlled either by energy or momentum conservation depending on the impact velocity. The proposed formulation can provide a solid base for granular splash simulations in saltation models, leading to more reliable assessments of aeolian transport on Earth and Mars.

erosion

display_name: erosion

mars

display_name: mars

model

display_name: model

sediment-transport

display_name: sediment-transport

snow

display_name: snow

en: Energy- and momentum-conserving model of splash entrainment in sand and snow saltation


en: radiation atmospheric-conditions wind temperature-profiles precipitation

en: Site photo 1

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

en: DATA ACCESS

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

en: STB2 CSV

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

mimetype: text/csv

en: Site photo 2

en: Site photo 3

en: Latest data from Stillberg climate station

en: Overview plot

media_type: image/png

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

format: PNG

en: README

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

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

atmospheric-conditions

display_name: atmospheric-conditions

precipitation

display_name: precipitation

radiation

display_name: radiation

temperature-profiles

display_name: temperature-profiles

wind

display_name: wind

educ

en: Education, culture and sport

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


en: traits dispersal reef-fish ecology biodiversity

en: Main text figures

en: R Script used for figures in main text in: Donati et al. 2019: "A process-based-model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages" accepted in Ecography, August 2019.

en: Input for spatial congruence test overlap between proportions of low and high dispersal and observed low- and high- dispersal-related traits in assemblages (30-50% threshold). see Table 2.

en: Coral reef grid cells (shape, dbf, etc. files)

en:
Grid cells of 5 × 5°, corresponding to circa 555 × 555 km at the Equator (see Parravicini et al. 2013, Leprieur et al. 2016)

Parravicini, V. et al. 2013. Global patterns and predictors of tropi- cal reef sh species richness. – Ecography 36: 1254–1262.

Leprieur, F. et al. 2016. Plate tectonics drive tropical reef biodiver- sity dynamics. – Nat. Commun. 7: 11461.

en: Donati et al. main script

en:
Main script used in : Donati et al. 2019 "A process-based-model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages"
accepted in Ecography, August 2019

en: Functions used in R scripts in: Donati et al. 2019 "A process-based-model supports an association between  # dispersal and the prevalence of species traits in tropical reef fish assemblages" accepted in Ecography, August 2019

en: Functions for Figures illustrated in Donati et al. 2019 "A process-based-model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages" accepted in Ecography, August 2019.

en: Supplementary material Appendix 1 Figures illustrated in Donati et al. 2019 "A process-based-model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages". accepted in Ecography, August 2019

en: Tropical reef fish trait data

en:
Species trait (i.e. adult body size, home range mobility behaviour, circadian activity and schooling) data gathered from the General Approach to Species Abundance Relationships (GASPAR) database (Kulbicki et al. 2013, Parravicini et al. 2013).

Kulbicki, M. et al. 2013. Global biogeography of reef shes: a hier- archical quantitative delineation of regions. – PLoS One 8: e81847.

Parravicini, V. et al. 2013. Global patterns and predictors of tropi- cal reef sh species richness. – Ecography 36: 1254–1262.

To complement the trait data with Pelagic Larval Duration information refer to the following publication:

Luiz, Osmar J., et al. "Adult and larval traits as determinants of geographic range size among tropical reef fishes." Proceedings of the National Academy of Sciences 110.41 (2013): 16498-16502.

In Donati et al. "A process-based model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages" accepted in Ecography in August 2019, we used the PLD gathered from Luiz et al. 2013 as described in the methods - section "Species trait data".

en: Global coast files from https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html used in the Figures

en: Global simulated distribution of species under a range of dispersal parameter values

en:
Global distribution of simulated species richness under a range of dispersal parameter values d
d ∈ [1.75°: 5.45°].

en: Dispersal parameter value d per simulation starting with a minimum dispersal value, where each species is able to colonise each adjacent cell, and ending with the value of d for which all the extant habitats, including the most remote Pacific islands, have been colonised (d ∈ [1.75°: 5.45°]).

en: Simulated rates of speciation and extinction under a range of dispersal parameters. Net diversification rates are calculated from the difference between speciation and extinction rates. This data allows the creation of Figure A4 in Supporting information, Appendix 1.

en: Global species distribution of tropical reef fish species

en: Each species corresponds to a "Gaspar_code" gathered from the General Approach to Species Abundance Relationships (GASPAR) database (Kulbicki et al. 2013, Parravicini et al. 2013) and is linked to a specific Fishned ID corresponding to a specific Latitude and Longitude.

en: Input for the Global distribution maps for proportions of species richness for (a) simulated dispersal parameter value (b) observed dispersal-related traits

en: Geographic distribution data (species richness and proportions of high/low dispersal-related traits) on predominantly low dispersing and high dispersing species expected based on the simulations, and those where low- and high- dispersal related traits values are observed for the five investigated traits.

en:
This dataset contains all data and R codes (R Development Core Team, https://www.R-project.org) used in the following publication: Donati GFA, Parravicini V, Leprieur F, Hagen O, Gaboriau T, Heine C, Kulbicki M, Rolland J, Salamin N, Albouy C, Pellissier L. "A process-based model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages" accepted by Ecography in August 2019. When using this data and R scripts the above publication should be cited.

The interaction of habitat dynamics with species dispersal abilities could generate gradients
in species diversity and prevalence of life-history and ecological traits, when the latter are associated with dispersal potential. In this dataset, we use a spatial mechanistic model of speciation, extinction and dispersal, constrained by a dispersal parameter. This model allows to simulate the interplay between reef habitat dynamics over the past 140 million years and dispersal, shaping lineage diversification history and global assemblage composition of over 6000 tropical reef fish species. Global trait distribution data of tropical reef fish are used to evaluate the congruence between simulations and observations.

biodiversity

display_name: biodiversity

dispersal

display_name: dispersal

ecology

display_name: ecology

reef-fish

display_name: reef-fish

traits

display_name: traits

envidat

en: EnviDat: the Environmental Data Portal

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

en: Simulated and observed prevalence of dispersal-related traits in tropical reef fish assemblages worldwide


maintainer: Yohann Chauvier

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

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

en: gbif.range R package version 1.1 (current)

en: GitHub URL

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

en: Test version, not to be used.

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

accepted-name

display_name: accepted-name

conservation

display_name: conservation

ecoregions

display_name: ecoregions

filtering

display_name: filtering

flags

display_name: flags

gbif

display_name: gbif

hard-limit

display_name: hard-limit

landsca

display_name: landsca

large-scale

display_name: large-scale

occurrence-records

display_name: occurrence-records

r-package

display_name: r-package

range-maps

display_name: range-maps

species-distribution

display_name: species-distribution

species-observations

display_name: species-observations

synonyms

display_name: synonyms

taxonomy

display_name: taxonomy

educ

en: Education, culture and sport

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


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

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.

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: SNOWPACK settings

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

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


aerosol-data-davos-wolfgang

title_for_slug: aerosol-data-davos-wolfgang

en: aerosol inp drincz particles ice-nucleation raclets

en: SMPS Dataset Davos Wolfgang

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

mimetype: application/x-netcdf

en: OPC GT-526S Dataset Davos Wolfgang

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

en: DRINCZ Dataset Davos Wolfgang

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

en: OPC-N3 Dataset Davos Wolfgang

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

en: APS Dataset Davos Wolfgang

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

en:
Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs) and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room.

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

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

Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3788 , TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³).

Coriolis μ and DRINCZ: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l minˉ¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the DRoplet Ice Nuclei Counter Zurich (DRINCZ, ETH Zurich) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles.

aerosol

display_name: aerosol

drincz

display_name: drincz

ice-nucleation

display_name: ice-nucleation

inp

display_name: inp

particles

display_name: particles

raclets

display_name: raclets

envidat

en: EnviDat: the Environmental Data Portal

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

en: Aerosol Data Davos Wolfgang


maintainer: Nander Wever

en: antarctica snow-density snowmicropen microstructure snow-density-profile

en: Jupyter notebook for processing the SnowMicroPen files.

en: Manual snow pits and other snow density measurements

en: List of SnowMicroPen measurements

en: List of all SnowMicroPen measurements, including date and location, and processing status. This list has been generated by the python code (see other resources in this dataset).

en: Documentation SnowMicroPen measurements

en: This *.zip archive contains all available documentation of the SnowMicroPen measurements, including the mapping of the SMP measurements in the terrestrial laser scan field at PEA (see documentation for 20161220) and at the Hammarryggen Ice Rise (see documentation for 20190104 and 20190111).

en: Python code exported from the Jupyter notebook for processing the SnowMicroPen files

en: SnowMicroPen pts files and processed txt files

en: Zip archive containing the acquired *.pts files (binary files recorded by the SnowMicroPen), and the converted *.txt files using the SMP.ipynb jupyter notebook (see the python code in the other resources in this dataset)

en: SnowMicroPen (SMP) measurements and manual snowpits from Dronning Maud Land, East Antarctica. Measurements were taken in the vicinity of the Belgium Princess Elisabeth Station (PEA), in a transect towards the coast, and on the Lokeryggen and Hammarryggen Ice Rises near the coast. Measurements were taken during 3 individual campaigns in the 2016-2017, 2018-2019 and 2019-2020 field seasons.

antarctica

display_name: antarctica

microstructure

display_name: microstructure

snow-density

display_name: snow-density

snow-density-profile

display_name: snow-density-profile

snowmicropen

display_name: snowmicropen

educ

en: Education, culture and sport

en: SnowMicroPen measurements and manual snowpits from Dronning Maud Land, East Antarctica


maintainer: Elisabeth D. Hafner

en: avalanche-activity aerial-imagery remote-sensing avalanche avalanche-mapping

en: 1999 avalanche data

en:
**for English see below**
Dieser Datensatz enthält die **Umrisse der 11'120 Lawinen** die aus schwarzweiss Luftbildern, welche zwischen dem 25.2.1999 und dem 1.3.1999 aufgenommen wurden, kartiert wurden. Die Lawinenumrisse haben verschiedene Attribute welche im **beigelegten Beispielschlüssel** beschrieben sind (Beispielschluessel_1999_d.pdf).
Es gibt drei Shapefiles:
* **avalanches1999_endversion1.shp**: die kartierten Lawinen (für Attribute siehe Beispielschlüssel!)
* **area_images_1999.shp**: Fläche die durch die Luftbilder abgedeckt wurde
* **clouds_1999.shp**: grobe Umrisse der Wolken in den Bildern
#
 *******************************************************************************
This dataset contains the **outlines of 11'120 avalanches** mapped from panchromatic aerial imagery taken between February 25, 1999 and March 1, 1999. The avalanche outlines have various attributes which are described in the **attached example key** (ExampleKey_AvalMapping_1999_e.pdf).
There are three shapefiles:
* **avalanches1999_endversion1.shp**: the mapped avalanches (for attributes see example key!)
**area_images_1999.shp**: Area covered by the aerial images
**clouds_1999.shp**: rough outlines of the clouds in the images

en:
**for English see below**
*******************************************************************************

 Datenbeschrieb

Dieser Datensatz enthält die **Umrisse der 11'120 Lawinen** die aus schwarzweiss Luftbildern, welche zwischen dem 25.2.1999 und dem 1.3.1999 aufgenommen wurden, kartiert wurden. Die Lawinenumrisse haben verschiedene Attribute welche im **beigelegten Beispielschlüssel** beschrieben sind (Beispielschluessel_1999_d.pdf).

Es gibt drei Shapefiles:
* **avalanches1999_endversion1.shp**: die kartierten Lawinen (für Attribute siehe Beispielschlüssel!)
* **area_images_1999.shp**: Fläche die durch die Luftbilder abgedeckt wurde
* **clouds_1999.shp**: grobe Umrisse der Wolken in den Bildern

 Bildverfügbarkeit:

Die entzerrten Luftbilder können von der [Swisstopo](https://s.geo.admin.ch/rpq3hufnb588) einzeln über den Downloadlink heruntergeladen werden (herzlichen Dank an Holger Heisig für die Prozessierung und ans BAFU für die Finanzierung der Prozessierung!!). Über "Erweiterte Werkzeuge"/ "Datei importieren" können die Bilder nach hereinziehen des Orthofotolinks direkt im map.geo.admin.ch angezeigt werden.

 Weiterführende Litertaur:
Details zu den Luftbildern und zur Kartierung, sowie eine kleine Analyse und Empfehlungen zur Verwendung der Daten finden sich unter

- **FAN Publikation** (Info folgt)

- Dal, F. J., Hafner, E. D., Peters, T., Narnhofer, D., Caye Daudt, R., Heisig, H., & Bühler, Y. (2024). Automated snow avalanche mapping with deep learning in aerial imagery from the extreme avalanche winter of 1999. In K. Gisnås, P. Gauer, H. Dahle, M. Eckerstorfer, A. Mannberg, & K. Müller (Eds.), Proceedings of the international Snow Science Workshop 2024 (pp. 1264-1271). Norwegian Geotechnical Institute.

- Hafner, E. D., Techel, F., Heisig, H., Dal, J. F., & Bühler, Y. (2024). Remotely sensed avalanche activity during three extreme avalanche periods in Switzerland. In K. Gisnås, P. Gauer, H. Dahle, M. Eckerstorfer, A. Mannberg, & K. Müller (Eds.), Proceedings of the international Snow Science Workshop 2024 (pp. 1222-1229). Norwegian Geotechnical Institute.

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

 Data description

This dataset contains the **outlines of 11'120 avalanches** mapped from panchromatic aerial imagery taken between February 25, 1999 and March 1, 1999. The avalanche outlines have various attributes which are described in the **attached example key** (ExampleKey_AvalMapping_1999_e.pdf).

There are three shapefiles:
* **avalanches1999_endversion1.shp**: the mapped avalanches (for attributes see example key!)
**area_images_1999.shp**: Area covered by the aerial images
**clouds_1999.shp**: rough outlines of the clouds in the images

 Image availability:
The rectified aerial images can be downloaded individually from [Swisstopo](https://s.geo.admin.ch/bg6beetinfdc) via the download link (many thanks to Holger Heisig for processing and to BAFU for financing the processing!!). The images can be displayed directly in map.geo.admin.ch via “Advanced tools”/“Import file” after dragging in the orthophoto link.

 Further Reading:
Details on the aerial images and mapping, as well as a brief analysis and tips on using the data can be found at

- **FAN Publication** (info to follow)

- Dal, F. J., Hafner, E. D., Peters, T., Narnhofer, D., Caye Daudt, R., Heisig, H., & Bühler, Y. (2024). Automated snow avalanche mapping with deep learning in aerial imagery from the extreme avalanche winter of 1999. In K. Gisnås, P. Gauer, H. Dahle, M. Eckerstorfer, A. Mannberg, & K. Müller (Eds.), Proceedings of the international Snow Science Workshop 2024 (pp. 1264-1271). Norwegian Geotechnical Institute.

- Hafner, E. D., Techel, F., Heisig, H., Dal, J. F., & Bühler, Y. (2024). Remotely sensed avalanche activity during three extreme avalanche periods in Switzerland. In K. Gisnås, P. Gauer, H. Dahle, M. Eckerstorfer, A. Mannberg, & K. Müller (Eds.), Proceedings of the international Snow Science Workshop 2024 (pp. 1222-1229). Norwegian Geotechnical Institute.

aerial-imagery

display_name: aerial-imagery

avalanche

display_name: avalanche

avalanche-activity

display_name: avalanche-activity

avalanche-mapping

display_name: avalanche-mapping

remote-sensing

display_name: remote-sensing

en: Avalanche outlines February and March 1999 from aerial imagery


automatic-classification-of-avalanches

maintainer: Alec van Herwijnen

title_for_slug: automatic-classification-of-avalanches

en: localization avalanche seismic monitoring machine-learning

en:
This tar file contains classification results for two seismic geophone arrays deployed above Davos, Switzerland. The of the first array located at the Dischma Valley was used to automatically identify all seismic events produced by avalanches, whereas the classification perfomed at the second array located at the Wannengrat field site was used to identify airplanes and earthquakes.
Furthermore, this tar file also contains the median back-azimuth of all classified events at the Dischma field site.

mimetype: application/x-tar

en: This dataset contains the classification and localization results obtained during the automatic classification of avalanches during the winter season 2017.

localization

display_name: localization

machine-learning

display_name: machine-learning

monitoring

display_name: monitoring

seismic

display_name: seismic

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: Automatic Classification of Avalanches


en: bumblebees bombus-pasquorum nutrient-intake dietary-patterns lad-use-chages rural basel zurich bombus-lapidarius bern urban macronutrients foraging-behaviour pollen-metabarcoding bees

en:
Processed data. The dataset contains the phylogeneitc diversity metrics based on the plant composition from the leg-baskets per bumblebee individual. It contains:
- SampleID: Combining bumblebee ID, location, landscape and replica
- Location: canton, levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: landscape type, levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) within location and landscape. Levels: A-E.
- LocationLandscape: combining location and landscape
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- PVar: phylogenetic variance
- PRic: Phylogenetic richness
- PEve: Phylogenetic evenness
- PClu: Phylogenetic clustering

en:
The dataset contains the amino acid content and ratios for the two bumblebees. Measures are taken only from pollen from the leg-baskets. SampleID is not the same as in the metabarcoding, several samples from different bumblebees were merged within a Location ,Landscape and Replica. Not for all the samples there is information on both amino acids and fatty acids. The columns are:
- SampleID_chemical: The ID for chemical analyses
- Location: the three Swiss cantons studied. Levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: the two typea of human-dominated ecosystems, nested to the Swiss canton. Levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) nested with the location and the landscape. Levels: A-E.
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- LocationLandscape: Combining location and landscape
- Asp, Thr, ..., Arg: Content of individual amino acids. Amino acids are written as abbreviated names. FUll names be checked here: https://en.wikipedia.org/wiki/Amino_acid
- sum: total sum of amino acid content (mg)
- sumessential: total sum essential amino acids (mg)
-sumnon-essential: total sum non-essential amino acids (mg)
- proportionnonessentialessential: proportion between essential and non-essential amino acids.

en:
The dataset contains the fatty acid content and ratios for the two bumblebees. Measures are taken only from pollen from the leg-baskets. SampleID is not the same as in the metabarcoding, several samples from different bumblebees were merged within a Location ,Landscape and Replica. Not for all the samples there is information on both amino acids and fatty acids. The columns are:
- SampleID_chemical: The ID for chemical analyses
- Location: the three Swiss cantons studied. Levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: the two typea of human-dominated ecosystems, nested to the Swiss canton. Levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) nested with the location and the landscape. Levels: A-E.
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- LocationLandscape: combination between location and landscape
- Caprylic, Pelargonic, ..., Lacceroic: Content of individual fatty acids acids.
- sum: total sum of fatty acid content (mg)
- unsaturatedsum: total sum unsaturated fatty acids (mg)
- saturatedsum: total sum saturated fatty acids (mg)
- omega3sum: total sum Omega 3 fatty acids (mg)
- omega6sum: total sum Omega 6 fatty acids (mg)
- omega9sum: total sum Omega 9 fatty acids (mg)
- omega6to3ratio: ratio Omega 6 to Omega3 fatty acids
-saturatedtounsaturatedration: ratio between saturated and unsaturated fatty acids

en:
Processed data. The dataset contains the functional diversity metrics based on the plant composition from the leg-baskets per bumblebee individual. It contains:
- SampleID: Combining bumblebee ID, location, landscape and replica
- Location: canton, levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: landscape type, levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) within location and landscape. Levels: A-E.
- LocationLandscape: combining location and landscape
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- Fric.w: Weighted functional richness
-Fric: functional richness
-FEve.w: Weighted functional evenness
- FEve: functional evenness
- FDiv.w: weighted functional divergence
- FDiv: functional divergence

en:
Processed data. The dataset contains the plant species richness in the leg-baskets per bumblebee individual. It contains:
- SampleID: Combining bumblebee ID, location, landscape and replica
- Location: canton, levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: landscape type, levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) within location and landscape. Levels: A-E.
- LocationLandscape: combining location and landscape
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- Plant species richness: the number of plant species found in the pollen of the leg-baskets.

en:
The dataset contains the information on the 16 study sites:
- Location: the three Swiss cantons studied. Levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: the two typea of human-dominated ecosystems, nested to the Swiss canton. Levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) nested with the location and the landscape. Levels: A-E.
- Site_ID: combination of location, landscape and replicate
- X: X coordinate, WGS84
- Y: Y coordinate WGS84
- Plant S in landscape: Plant species richness within the 800 m calculated using both information from the Global Biodiversity Information. Facility (GBIF) and InfoFlora (www.infoflora.ch).
- % Buildings: the proportion of buildings and other impervious surfaces within 800 m radius.
- % Agricultural: the proportion of agricultural land and other impervious surfaces within 800 m radius.
- % Water: the proportion of water and other impervious surfaces within 800 m radius.
- % Meadows & grasslands: the proportion of meadows and grasslands and other impervious surfaces within 800 m radius.
The raw metabarcoding data is archived in NCBI project PRJNA1137320.

en:
The dataset contains the filtered plant composition1 (removing species with too low relative abundances) and the relative abundance obtained from DNA metabarcoding on pollen samples for both the leg-baskets and the body. The dataset contains the following columns:
- SampleID: Combining bumblebee ID, location, landscape and replica
- Abundance: numeric, relative abundance
- BumblebeeID: numerical ID for each bumblebee individual within a location, landscape and replicate sampling place.
- Location: canton, levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: landscape type, levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) within location and landscape. Levels: A-E.
- LocationLandscape:
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- Transportation_structure: bumblebee pollen transportation structure, levels: B = Body; L = leg-baskets (i.e. corbicula)
- Kingddom: taxonomic
- Phylum: taxonomic
- Order: taxonomic
- Family: taxonomic
- Genus: taxonomic
- Species: plant sicentific name
-Binomial_abundance: Logic, indicating if the plant species is included (1).
The raw metabarcoding data is archived in NCBI project PRJNA1137320

en:
The dataset contains the plant trait values obtined from published sources. It contains:
- Kingddom: taxonomic
- Phylum: taxonomic
- Order: taxonomic
- Family: taxonomic
- Genus: taxonomic
- Species: species name
- plant.species: full scientific name
- native_exotic: origin status. As presented in https://www.doi.org/10.16904/envidat.210
- pollination mode: vector of pollination. As presented in https://www.doi.org/10.16904/envidat.210
- Flowering_months_duration: Number of flowering months. As presented in https://www.doi.org/10.16904/envidat.210
- start_flowering: month of the starting of the flowering. As presented in https://www.doi.org/10.16904/envidat.210
- growth_form_category: plant growth form. As presented in https://www.doi.org/10.16904/envidat.210
- plant-height: plant heght in meters. As presented in https://www.doi.org/10.16904/envidat.210
- inflorescence: Logic, plant with single flowers (0) or inflorescences (1). As presented in https://www.doi.org/10.16904/envidat.210
- structural_blossom_class: depicting the accessibility of the flowers. As presented in https://www.doi.org/10.16904/envidat.210
- symmetry: floral symmetry. As presented in https://www.doi.org/10.16904/envidat.210
- nectar: logic, nectar present. As presented in https://www.doi.org/10.16904/envidat.210
- pollen: logic, pollen rewards present. As presented in https://www.doi.org/10.16904/envidat.210
- oil: logic. oil present. As presented in https://www.doi.org/10.16904/envidat.210
- sugar.concentration: the amount of sugar present in the nectar, in mg.
Additional information can be found in https://doi.org/10.1016/j.dib.2021.107243

en:
Data on the diet of urban and rural bumblebees individuals, Bombus lapidarius and Bombus pascuorum, collected in Switzerland (cantons Bern, Basel and Zürich) obtained using metabarconding and chemical analyses from two pollen transportation structures, that is, the body and the leg-baskets (i.e., corbicula). The bumblebees were collected by Egenberger et al., 2019 (https://doi.org/10.5061/dryad.qt8mr4v)
The data contains:
- The composition and relartive abundance of plant species in the body and the leg-baskets, separately.
- The content and ratios of two groups of macronutrients, that is, amino acids and fatty acids.
The dataset contains the data used in the paper "Urban bumblebees diversify their foraging strategy to maintain nutrient intake" as well as unpublished data.

basel

display_name: basel

bees

display_name: bees

bern

display_name: bern

bombus-lapidarius

display_name: bombus-lapidarius

bombus-pasquorum

display_name: bombus-pasquorum

bumblebees

display_name: bumblebees

dietary-patterns

display_name: dietary-patterns

foraging-behaviour

display_name: foraging-behaviour

lad-use-chages

display_name: lad-use-chages

macronutrients

display_name: macronutrients

nutrient-intake

display_name: nutrient-intake

pollen-metabarcoding

display_name: pollen-metabarcoding

rural

display_name: rural

urban

display_name: urban

zurich

display_name: zurich


en: cosmo-wrf snow-precipitation complex-terrain orographic-precipitation precipitation-variability

en: StationData

en: Snow depth maps over Dischma valley for 26 January 2016 and 9 March 2016 and snow depth differences between the two dates.

en:
The data presented here corresponds to the publication "Spatial variability in snow precipitation and accumulation in COSMO-WRF simulations and radar estimations over complex terrain" (Gerber et al., 2018a), which investigates the precipitation variability of snow precipitation in the central northern part of the Grisons (CH) and the publication "The importance of near-surface winter precipitation processes in complex alpine terrain" (Gerber et al., 2018b). The dataset contains:

* WRFsimulations: WRF simulation output for simulations with 4x (14x) terrain smoothing with an output timestep of 30 min/5 min and horizontal grid spacings of 1350 m, 450 m, 150 m and 50 m (currently: data available upon request).
* StationData: Meteorological station data of 18 meteorological stations in the central northern part of the Grisons with 30 minute resolution for the period 1 January 2016 till 1 May 2016.
* ADS80data: Photogrammetrically determined snow depth distribution data over the Dischma valley for the 26 January 2016 and 9 March 2016. Snow heights are corrected for buildings, vegetation (> 1m), outliers, and pixles, which are obivously snow-free on the pictures (Bühler et al., 2015). In addition the snow depth differences (snow depth on 9 March 2016 minus snow depth on 26 January 2016) are provided.

For more details about the simulation and observation data, see Gerber et al., 2018 and Gerber and Sharma (2018).

Publications:

Bühler, Y., Marty, M., Egli, L., Veitinger, J., Jonas, T., Thee, P., and Ginzler, C.: Snow depth mapping in high-alpine catchments using digital photogrammetry. Cryosphere, 9, 229–243, doi:10.5194/tc-9-229-2015, 2015.

Gerber, F., Besic, N., Sharma, V., Mott, R., Daniels, M., Gabella, M., Berne, A., Germann, U., and Lehning, M.: Spatial variability in snow precipitation and accumulation in COSMO-WRF simulations and radar estimations over complex terrain, The Cryosphere, 12, 3137–3160, doi:10.5194/tc-12-3137-2018, 2018.

Gerber, F., Mott, R. and Lehning, M.: The importance of near-surface winter precipitation processes in complex alpine terrain, Journal of Hydrometeorology, accepted, 2018.

Gerber, F., and Sharma, V.: Running COSMO-WRF on very-high resolution over complex terrain. Laboratory of Cryospheric Sciences CRYOS, École Polytechnique Fédérale de Lausanne EPFL, Lausanne, Switzerland. doi:10.16904/envidat.35, 2018.

complex-terrain

display_name: complex-terrain

cosmo-wrf

display_name: cosmo-wrf

orographic-precipitation

display_name: orographic-precipitation

precipitation-variability

display_name: precipitation-variability

snow-precipitation

display_name: snow-precipitation

educ

en: Education, culture and sport

en: DISCHMEX - High-resolution WRF simulations in complex alpine terrain and station measurements


maintainer: EnviDat

en: meteorological-data-format csv research-data-management nead metadata-convention

en: PyNEAD (future reference implementation)

en: NEAD syntax v0.1 (EBNF)

en: MeteoIO (future reference implementation)

mimetype: text/html

en: NEAD on GitHub

media_type: text/html

en:
Acknowledgement: The NEAD format includes NetCDF metadata and is proudly inspired by both SMET and NetCDF formats. NEAD is designed as a long-term data preservation and exchange format.

The NEAD specifications were presented at the "WMO Data Conference 2020 - Earth System Data Exchange in the 21st Century" (Virtual Conference).

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Summary:
The Non-Binary Environmental Data Archive (NEAD) format is being developed as a generic and intuitive format that combines the self-documenting features of NetCDF with human readable and writeable features of CSV. It is designed for exchange and preservation of time series data in environmental data repositories.

License: The NEAD specifications are released to the public domain under a Creative Commons CC0 "No Rights Reserved" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions.

csv

display_name: csv

metadata-convention

display_name: metadata-convention

meteorological-data-format

display_name: meteorological-data-format

nead

display_name: nead

research-data-management

display_name: research-data-management

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: Non-Binary Environmental Archive Data (NEAD) format