title_for_slug: energiedashboard.ch: Stromverbrauch Prognose SDSC

de: strom endverbrauch ogd110-bfe data-science machine-learning prognose

fr: energiedashboard.ch : Prévisions de consommation d'électricité SDSC

en: energiedashboard.ch: Power consumption forecast SDSC

it: energiedashboard.ch: Previsione del consumo di energia SDSC

de: Stromverbrauch Prognose SDSC

fr: Le modèle de consommation d'électricité produit une estimation de la consommation d'électricité à l'échelle nationale suisse. Le modèle est entraîné sur des données provenant des sources suivantes : consommation nationale horaire fournie par Swissgrid (aperçu de l'énergie), données météorologiques de MétéoSuisse (y compris les prévisions météorologiques), et informations calendaires telles que les jours fériés fournis par le paquet de jours fériés python et les jours fériés acquis manuellement. Le modèle appartient à la catégorie des modèles additifs généralisés (GAM) et produit une prédiction sur une base horaire et quotidienne avec un intervalle de confiance inférieur et supérieur de 2,5 et 97,5 respectivement.

en: The electricity consumption model outputs an estimate of electricity consumption at the Swiss national scale. The model is trained on data from the following sources: hourly national consumption provided by Swissgrid (Energy overview), meteorological data from MeteoSwiss (including weather forecast), and calendar information such as public holidays as provided by the python holidays package and manually acquired holidays. The model belongs to the category of Generalized Additive Models (GAMs) and output is a prediction at an hourly and daily basis with a lower and upper confidence interval of 2.5 and 97.5 respectively.

de: Das Stromverbrauchsmodell liefert eine Schätzung des Stromverbrauchs auf nationaler Ebene in der Schweiz. Das Modell wird mit Daten aus den folgenden Quellen trainiert: stündlicher nationaler Verbrauch von Swissgrid (Energieübersicht), meteorologische Daten von MeteoSchweiz (einschliesslich Wettervorhersage) und Kalenderinformationen wie Feiertage, die vom python Holiday Package bereitgestellt werden, sowie manuell erfasste Feiertage. Das Modell gehört zur Kategorie der verallgemeinerten additiven Modelle (GAMs) und liefert eine Vorhersage auf stündlicher und täglicher Basis mit einem unteren und oberen Konfidenzintervall von 2,5 bzw. 97,5.

it: Il modello di consumo di elettricità produce una stima del consumo di elettricità su scala nazionale svizzera. Il modello è addestrato sui dati provenienti dalle seguenti fonti: consumo nazionale orario fornito da Swissgrid (panoramica dell'energia), dati meteorologici di MeteoSvizzera (comprese le previsioni del tempo) e informazioni sul calendario, come i giorni festivi forniti dal pacchetto vacanze di python e i giorni festivi acquisiti manualmente. Il modello appartiene alla categoria dei modelli additivi generalizzati (GAM) e il risultato è una previsione su base oraria e giornaliera con un intervallo di confidenza inferiore e superiore di rispettivamente 2,5 e 97,5 .

data-science

display_name: data-science

endverbrauch

display_name: endverbrauch

machine-learning

display_name: machine-learning

prognose

display_name: prognose

strom

display_name: strom

national-economy

fr: Economie nationale

energy

fr: Energie

politics

en: Politics

it: Politica


maintainer: Bundesamt für Energie BFE

author: Bundesamt für Energie BFE

state: active

type: dataset

media_type: SERVICE

format: SERVICE

mimetype: SERVICE

media_type: CSV

format: CSV

fr: 2020 - Résultats de la mise en oeuvre des prescriptions sur les émisssions de CO2 des voitures de tourisme

en: 2020 - Enforcement results of the CO2 emission regulations for passenger cars

de: 2020 - Vollzugsresultate der CO2-Emissionsvorschriften für Personenwagen

it: 2020 - Risultati dell'attuazione delle prescrizioni sulle emissioni di CO2 per le automobili

fr:
Depuis le 1er juillet 2012, des prescriptions en matière d'émissions de CO2 pour les nouvelles voitures de tourisme sont en vigueur en Suisse, comme dans l'UE. Elles obligent les importateurs suisses de voitures à réduire les émissions des voitures immatriculées pour la première fois en Suisse. Le 1er janvier 2020, la valeur cible de CO2 a été renforcée, passant de 130 g CO2/km à 95 g CO2/km. Cette valeur cible doit être atteinte d'ici fin 2022. Dans le cadre du changement de la méthode de mesure des émissions de CO2, la valeur cible de CO2 a été adaptée de 95 g CO2/km à 118 g CO2/km au 1er janvier 2021. En raison de ce changement, certains attributs subissent une rupture dans la série chronologique (rupture entre les années de référence 2020 et 2021). La comparabilité des attributs suivants n'est donc pas garantie sur l'axe temporel, ou seulement de manière limitée (co2, co2_massgebend, ziel, ziel_abw, sanktion_final, gsc_beansprucht, gsc_reserve). Les dépassements de l'objectif sont sanctionnés par une taxe de sanction.

Rapport succinct sur les résultats d'exécution de l'année précédente, y compris les chiffres clés agrégés. Les données de base sont les données d'immatriculation (IVZ), complétées par des données techniques (base de données de réception par type TARGA et données collectées par l'OFROU) et des liens avec les données des grands importateurs enregistrés.

Base légale: art. 36, al. 3, de l'ordonnance sur le CO2.

en:
Since 1 July 2012, CO2 emission regulations for new passenger cars have been in force in Switzerland - analogous to the EU. They oblige Swiss car importers to reduce the emissions of passenger cars registered for the first time in Switzerland. On 1 January 2020, the CO2 target value was tightened from 130 g CO2/km to 95 g CO2/km. This target value must be achieved by the end of 2022. In the course of the change in the measurement procedure for CO2 emissions, the CO2 target value was adjusted from 95 g CO2/km to 118 g CO2/km as of 1 January 2021. Due to this change, some attributes experience a break in the time series (break between the reference years 2020 and 2021). The comparability of subsequent attributes on the time axis is therefore not or only partially guaranteed (co2, co2_massgebend, ziel, ziel_abw, sanktion_final, gsc_beansprucht, gsc_reserve). Exceeding the target is penalised with a sanction levy.

Short report on the enforcement results of the previous year including aggregated key figures. Data basis is approval data (IVZ), supplemented by technical data (type approval database TARGA and data collected by FEDRO itself) and links with data on registered large importers.

Legal basis CO2 Ordinance, Art. 36. para. 3.

de:
Seit dem 1. Juli 2012 gelten in der Schweiz - analog zur EU - CO2-Emissionsvorschriften für neue Personenwagen. Sie verpflichten die Schweizer Auto-Importeure, die Emissionen der erstmals zum Verkehr in der Schweiz zugelassenen Personenwagen zu senken. Am 1. Januar 2020 wurde der CO2-Zielwert von 130 g CO2/km, auf 95 g CO2/km verschärft. Dieser Zielwert muss bis Ende 2022 erreicht werden. Im Zuge der Umstellung des Messverfahrens der CO2-Emissionen, wurde der CO2-Zielwert per 1. Januar 2021 von 95 g CO2/km, auf 118 g CO2/km angepasst. Aufgrund dieser Umstellung erfahren einige Attribute einen Bruch in der Zeitreihe (Bruch zwischen den Referenzjahren 2020 und 2021). Die Vergleichbarkeit nachfolgender Attribute ist auf der Zeitachse somit nicht oder nur bedingt gewährleistet (co2, co2_massgebend, ziel, ziel_abw, sanktion_final, gsc_beansprucht, gsc_reserve). Überschreitungen der Zielvorgabe werden mit einer Sanktionsabgabe gebüsst.

Kurzbericht über die Vollzugsresultate des Vorjahres inkl. aggregierten Kennzahlen. Datengrundlage sind Zulassungsdaten (IVZ), ergänzt um technische Daten (Typengenehmigungsdatenbank TARGA und vom ASTRA eigens erfasst Daten) sowie Verknüpfungen mit Daten zu registrierten Grossimporteuren.

Rechtsgrundlage CO2-Verordnung, Art. 36. Abs. 3.

it:
Dal 1° luglio 2012, in Svizzera sono in vigore le norme sulle emissioni di CO2 per le autovetture nuove, analogamente all'UE. Il regolamento obbliga gli importatori svizzeri di automobili a ridurre le emissioni delle autovetture immatricolate per la prima volta in Svizzera. Il 1° gennaio 2020, il valore obiettivo di CO2 è stato innalzato da 130 g CO2/km a 95 g CO2/km. Questo valore target deve essere raggiunto entro la fine del 2022. In seguito alla modifica della procedura di misurazione delle emissioni di CO2, il valore obiettivo di CO2 è stato adeguato da 95 g CO2/km a 118 g CO2/km a partire dal 1° gennaio 2021. A causa di questa modifica, alcuni attributi subiscono un'interruzione nella serie temporale (interruzione tra gli anni di riferimento 2020 e 2021). La comparabilità degli attributi successivi sull'asse temporale non è quindi garantita o lo è solo parzialmente (co2, co2_massgebend, ziel, ziel_abw, sanktion_final, gsc_beansprucht, gsc_reserve). Il superamento dell'obiettivo viene penalizzato con una sanzione.

Breve relazione sui risultati dell'applicazione della legge dell'anno precedente, compresi i dati chiave aggregati. La base dei dati è costituita dai dati di omologazione (IVZ), integrati da dati tecnici (database di omologazione TARGA e dati raccolti dallo stesso USTRA) e da collegamenti con i dati dei grandi importatori registrati.

Base giuridica Ordinanza CO2, art. 36, comma 3.

energy

de: Energie

en: Energy

it: Energia

mobility

fr: Mobilité et transports

de: Mobilität und Verkehr

en: Mobility and Transport

it: Mobilità e trasporti

bundesamt-fur-energie-bfe

fr: Office fédéral de l'énergie OFEN

de: Bundesamt für Energie BFE

en: Swiss Federal Office of Energy SFOE

it: Ufficio federale dell'energia UFE

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

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

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

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

approval_status: approved

type: organization

political_level: confederation


maintainer: Digital Innovation Office

title_for_slug: energiedashboard.ch: Energieversorgung: Aktuelle Lage (ampelsystem)

de: ogd108-bfe versorgungssicherheit strom gas lage

fr: energiedashboard.ch: Approvisionnement en énergie : Situation actuelle (système de feux)

en: energiedashboard.ch: Energy supply: Current situation (traffic light system)

it: energiedashboard.ch: Disponibilità di energia: Situazione attuale (sistema a semaforo)

label: Wirtschaftlichen Landesversorgung

format: JSON

fr:
La définition de la situation selon le système de feux est basée sur une évaluation globale de la situation. Celle-ci est basée sur des informations statistiques et des estimations des autorités compétentes concernant le développement de la situation d'approvisionnement.

Vous trouverez toutes les informations sur la situation actuelle sur : bwl.admin.ch

en:
The definition of the situation according to the lamp system is based on an overall assessment of the situation. This is based on statistical information and estimates of the competent authorities concerning the development of the supply situation.

All information on the current situation can be found at: bwl.admin.ch

de:
Die Definition der Situation nach dem Ampelsystem basiert auf einer umfassenden Einschätzung der Situation. Diese basiert auf statistischen Informationen und Einschätzungen der zuständigen Behörden über die Entwicklung der Versorgungslage.

Alle Infos zur aktuellen Lage finden sie auf: bwl.admin.ch

it:
La definizione della situazione secondo il sistema a semaforo si basa su una valutazione complessiva della situazione. Ciò si basa sulle informazioni statistiche e sulle stime delle autorità competenti in merito all'evoluzione della situazione dell'offerta.

Tutte le informazioni sulla situazione attuale sono disponibili all'indirizzo: bwl.admin.ch

national-economy

de: Volkswirtschaft

en: National economy

it: Economia

politics

fr: Politique

de: Politik


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

label: swissgrid

label: ENTSO-E

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.

trade

fr: Commerce

de: Handel

en: Trade

it: Commercio


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)

label: ENTSO-E Transparency Platform

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: Fachstelle Open Government Data des Kantons Zürich

label: Repository: Formale Leitlinien zur Datenpublikation des Kantons Zürich

label: Fachausschuss für offene Behördendaten: Standards

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

media_type: text/html

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

format: HTML

mimetype: text/html

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

fachstelle-ogd-kanton-zuerich

de: Fach- und Koordinationsstelle Open Government Data Kanton Zürich

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

political_level: canton

kanton-zuerich

capacity: public

key: guid

key: uri

value: ZH-Kt Fachstelle OGD - DCAT Harvester


name: Fachstelle Open Government Data des Kantons Zürich

label: DCAT-AP CH – The Swiss Application Profile for Data Portals and Catalogues

label: Datenkatalog

de: API-Endpoint für Datensätze im Metadatenkatalog des Kantons Zürich

media_type: application/json

de: Enthält alle aktuell im Metadatenkatalog des Kantons Zürich enthaltenen Datensätze mit ihren Metadaten: (1) häufig nachgefragte Excel-Tabellen, (2) Indikatoren aus dem Gemeindeporträt, (3) Publikationen des Statistischen Amts sowie (4) offene Behördendaten des Kantons Zürich (ausgenommen offene Geodaten).

mimetype: application/json

de: Enthält alle aktuell im Metadatenkatalog des Kantons Zürich enthaltenen Datensätze mit ihren Metadaten. Folgt dem Metadaten-Standard 'DCAT-AP CH v1' (eCH-0200).

de: Web Service des Metadatenkatalogs des Kantons Zürich


title_for_slug: Kennzahlen Neuwagenflotte LNF

fr: Chiffres clés nouveaux véhicules utilitaires légers (VUL)

de: Kennzahlen Neuwagenflotte LNF

en: Key figures for new light commercial vehicles

it: Indicatori parco veicoli commerciali leggeri (VCL)

label: Storymap

fr: Consommation moyenne de carburant

de: Durchschnittlicher Treibstoffverbrauch

en: Average fuel consumption

it: Consumo medio di carburante

fr: Le jeu de données indique la consommation moyenne de carburant des voitures de livraison et tracteurs à sellette légers (véhicules utilitaires légers, VUL) immatriculés pour la première fois, par an et par canton. Les données sont disponibles depuis 2020. La numérotation des cantons est celle de l’OFS.

en: This data set shows the average fuel consumption among newly registered vans and light articulated vehicles (collectively: light commercial vehicles, LCVs) per year and per canton. The data series is available from 2020. Numbering of cantons according to FSO.

de: Der Datensatz zeigt den durchschnittlichen Treibstoffverbrauch bei den neuzugelassenen Lieferwagen und leichten Sattelschlepper (Sammelbegriff: leichte Nutzfahrzeuge, LNF) pro Jahr und Kanton. Die Datenreihe ist ab 2020 verfügbar. Nummerierung der Kantone gemäss BFS.

it: Il set di dati mostra il consumo medio di carburante degli autofurgoni e trattori a sella leggeri (iperonimo: veicoli commerciali leggeri, VCL) di nuova immatricolazione per anno e per Cantone. La serie di dati è disponibile dal 2020. Numerazione dei Cantoni secondo UST.

fr: Émissions moyennes de CO2

de: Durchschnittliche CO2-Emissionen

en: Average CO2 emissions

it: Emissioni medie di CO2

fr: Le jeu de données indique les émissions moyennes de CO2 des voitures de livraison et tracteurs à sellette légers (véhicules utilitaires légers, VUL) immatriculés pour la première fois, par an et par canton. Les données sont disponibles depuis 2020. La numérotation des cantons est celle de l’OFS.

en: This data set shows the average CO2 emissions among newly registered vans and light articulated vehicles (collectively: light commercial vehicles, LCVs) per year and per canton. The data series is available from 2020. Numbering of cantons according to FSO.

de: Der Datensatz zeigt die durchschnittlichen CO2-Emissionen bei den neuzugelassenen Lieferwagen und leichten Sattelschlepper (Sammelbegriff: leichte Nutzfahrzeuge, LNF) pro Jahr und Kanton. Die Datenreihe ist ab 2020 verfügbar. Nummerierung der Kantone gemäss BFS.

it: Il set di dati di dati mostra le emissioni medie di CO2 degli autofurgoni e trattori a sella leggeri (iperonimo: veicoli commerciali leggeri, VCL) di nuova immatricolazione per anno e per Cantone. La serie di dati è disponibile dal 2020. Numerazione dei Cantoni secondo UST.

fr: Part de véhicules électriques

de: Anteil Elektrofahrzeuge

en: Share of electric vehicles

it: Percentuale di veicoli elettrici

fr: Le jeu de données indique la part de véhicules électriques dans les voitures de livraison et tracteurs à sellette légers (véhicules utilitaires légers, VUL) immatriculés pour la première fois, par an et par canton. Les données sont disponibles depuis 2020. La numérotation des cantons est celle de l’OFS.

en: This data set shows the share of electric vehicles among newly registered vans and light articulated vehicles (collectively: light commercial vehicles, LCVs) per year and per canton. The data series is available from 2020. Numbering of cantons according to FSO.

de: Der Datensatz zeigt den Anteil der Elektrofahrzeuge bei den neuzugelassenen Lieferwagen und leichten Sattelschlepper (Sammelbegriff: leichte Nutzfahrzeuge, LNF) pro Jahr und Kanton. Die Datenreihe ist ab 2020 verfügbar. Nummerierung der Kantone gemäss BFS.

it: Il set di dati mostra la percentuale di autofurgoni e trattori a sella leggeri (iperonimo: veicoli commerciali leggeri, VCL) elettrici di nuova immatricolazione per anno e per Cantone. La serie di dati è disponibile dal 2020. Numerazione dei Cantoni secondo UST.

fr: Proportion de véhicules 4x4

de: Anteil Allradfahrzeuge

en: Share of all-wheel drive vehicles

it: Percentuale di veicoli a trazione integrale

fr: Le jeu de données indique la proportion de véhicules 4x4 parmi les voitures de livraison et tracteurs à sellette légers (véhicules utilitaires légers, VUL) immatriculés pour la première fois, par an et par canton. Les données sont disponibles depuis 2020. La numérotation des cantons est celle de l’OFS.

en: This data set shows the share of all-wheel drive vehicles among newly registered vans and light articulated vehicles (collectively: light commercial vehicles, LCVs) per year and per canton. The data series is available from 2020. Numbering of cantons according to FSO.

de: Der Datensatz zeigt den Anteil der Allradfahrzeuge bei den neuzugelassenen Lieferwagen und leichten Sattelschlepper (Sammelbegriff: leichte Nutzfahrzeuge, LNF) pro Jahr und Kanton. Die Datenreihe ist ab 2020 verfügbar. Nummerierung der Kantone gemäss BFS.

it: Il set di dati mostra la percentuale di autofurgoni e trattori a sella leggeri (iperonimo: veicoli commerciali leggeri, VCL) a trazione integrale di nuova immatricolazione per anno e per Cantone. La serie di dati è disponibile dal 2020. Numerazione dei Cantoni secondo UST.

fr: Poids à vide moyen

de: Durchschnittliches Leergewicht

en: Average unladen weight

it: Peso a vuoto medio

fr: Le jeu de données indique le poids à vide moyen des voitures de livraison et tracteurs à sellette légers (véhicules utilitaires légers, VUL) immatriculés pour la première fois, par an et par canton. Les données sont disponibles depuis 2020. La numérotation des cantons est celle de l’OFS.

en: This data set shows the average unladen weight among newly registered vans and light articulated vehicles (collectively: light commercial vehicles, LCVs) per year and per canton. The data series is available from 2020. Numbering of cantons according to FSO.

de: Der Datensatz zeigt das durchschnittliche Leergewicht bei den neuzugelassenen Lieferwagen und leichten Sattelschlepper (Sammelbegriff: leichte Nutzfahrzeuge, LNF) pro Jahr und Kanton. Die Datenreihe ist ab 2020 verfügbar. Nummerierung der Kantone gemäss BFS.

it: Il set di dati mostra il peso a vuoto medio degli autofurgoni e trattori a sella leggeri (iperonimo: veicoli commerciali leggeri, VCL) di nuova immatricolazione per anno e per Cantone. La serie di dati è disponibile dal 2020. Numerazione dei Cantoni secondo UST.

fr: Nombre de VUL neufs

de: Anzahl neuer LNF

en: Number of new LCVs

it: Numero di VCL nuovi

fr: Le jeu de données indique le nombre, en chiffres absolus, des voitures de livraison et tracteurs à sellette légers (véhicules utilitaires légers, VUL) immatriculés pour la première fois, par an et par canton. Les données sont disponibles depuis 2020. La numérotation des cantons est celle de l’OFS.

en: This data set shows the absolute number of new registrations for vans and light articulated vehicles (collectively: light commercial vehicles, LCVs) per year and per canton. The data series is available from 2020. Numbering of cantons according to FSO.

de: Der Datensatz zeigt die absolute Anzahl der Neuzulassungen bei den Lieferwagen und leichten Sattelschlepper (Sammelbegriff: leichte Nutzfahrzeuge, LNF) pro Jahr und Kanton. Die Datenreihe ist ab 2020 verfügbar. Nummerierung der Kantone gemäss BFS.

it: Il set di dati mostra il numero assoluto di autofurgoni e trattori a sella leggeri (iperonimo: veicoli commerciali leggeri, VCL) di nuova immatricolazione per anno e Cantone. La serie di dati è disponibile dal 2020. Numerazione dei Cantoni secondo UST.

fr:
Depuis 2020, les principales statistiques sur la flotte de voitures de livraison et de tracteurs à sellette légers neufs (véhicules utilitaires légers, VUL) sont évaluées par année et par canton. Sont indiqués des chiffres-clés par canton, tels que le nombre d’immatriculations, la proportion de véhicules 4x4, la part de véhicules électriques, la consommation moyenne de carburant, le poids à vide moyen et les émissions moyennes de CO2.

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

en:
Since the 2020 reporting year, key figures for new vans and light articulated vehicles (collectively: light commercial vehicles, LCVs) have been evaluated by year and by canton. For each canton, key figures such as the number of registrations, share of all-wheel drive vehicles, share of electric vehicles, average fuel consumption, average unladen weight and average CO2 emissions are shown.

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

de:
Seit dem Berichtsjahr 2020 werden wichtige Kennzahlen zur Neuwagenflotte der Lieferwagen und leichten Sattelschlepper (Sammelbegriff: leichte Nutzfahrzeuge, LNF) des jeweiligen Jahres nach Kanton ausgewertet. Pro Kanton sind Kenngrössen wie die Anzahl Zulassungen, Anteil Allradfahrzeuge, Anteil Elektrofahrzeuge, durchschnittlicher Treibstoffverbrauch, durchschnittliches Leergewicht und durchschnittlicher CO2-Ausstoss ausgewiesen.

Rechtsgrundlage: Energieeffizienzverordnung (EnEV) Art. 11

it:
A partire dall'anno di riferimento 2020, per ciascun Cantone vengono analizzati i principali indicatori relativi al parco veicoli nuovi della categoria autofurgoni e trattori a sella leggeri (iperonimo: veicoli commerciali leggeri, VCL) dell'anno in questione. Per ciascun Cantone sono indicati dati chiave come il numero di immatricolazioni, la percentuale di veicoli a trazione integrale, la percentuale di veicoli elettrico, il consumo medio di carburante, il peso a vuoto medio le emissioni medie di CO2.

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

statistical-basis

fr: Bases statistiques

de: Statistische Grundlagen

en: Statistical basis

it: Basi statistiche

energy

de: Energie

bundesamt-fur-energie-bfe

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).


kennzahlen-neuwagenflotte

title_for_slug: Kennzahlen Neuwagenflotte PW

fr: Statistiques des voitures neuves

de: Kennzahlen Neuwagenflotte PW

en: Key figures for new passenger cars

it: Indicatori parco automobili nuove

fr: Prix moyen d’un véhicule

de: Durchschnittliche Fahrzeugpreise

en: Average vehicle prices

it: Prezzi medi dei veicoli

fr: Le jeu de données indique le prix moyen d’une voiture de tourisme immatriculée pour la première fois, par an et par canton. Les données sont disponibles depuis 2017. La numérotation des cantons est celle de l’OFS.

en: This data set shows the average vehicle prices for newly registered passenger cars per year and per canton. The data series is available from 2017. Numbering of cantons according to FSO.

de: Der Datensatz zeigt die durchschnittlichen Fahrzeugpreise bei den neuzugelassenen Personenwagen pro Jahr und Kanton. Die Datenreihe ist ab 2017 verfügbar. Nummerierung der Kantone gemäss BFS.

it: Il set di dati di dati mostra i prezzi medi delle automobili di nuova immatricolazione per anno e per Cantone. La serie di dati è disponibile dal 2017. Numerazione dei Cantoni secondo UST.

fr: Le jeu de données indique les émissions moyennes de CO2 des voitures de tourisme immatriculées pour la première fois, par an et par canton. Les données sont disponibles depuis 2014. La numérotation des cantons est celle de l’OFS.

en: This data set shows the average CO2 emissions of newly registered passenger cars per year and per canton. The data series is available from 2014. Numbering of cantons according to FSO.

de: Der Datensatz zeigt die durchschnittlichen CO2-Emissionen bei den neuzugelassenen Personenwagen pro Jahr und Kanton. Die Datenreihe ist ab 2014 verfügbar. Nummerierung der Kantone gemäss BFS.

it: Il set di dati di dati mostra le emissioni medie di CO2 delle automobili di nuova immatricolazione per anno e per Cantone. La serie di dati è disponibile dal 2014. Numerazione dei Cantoni secondo UST.

fr: Nombre de voitures de tourisme neuves

de: Anzahl neuer Personenwagen

en: Number of new passenger cars

it: Numero di automobili nuove

fr: Le jeu de données indique les nouvelles immatriculations de voitures de tourisme en chiffres absolus, par an et par canton. Les données sont disponibles depuis 2014. La numérotation des cantons est celle de l’OFS.

en: This data set shows the absolute number of new passenger car registrations per year and per canton. The data series is available from 2014. Numbering of cantons according to FSO.

de: Der Datensatz zeigt die absolute Anzahl von Neuzulassungen bei den Personenwagen pro Jahr und Kanton. Die Datenreihe ist ab 2014 verfügbar. Nummerierung der Kantone gemäss BFS.

it: Il set di dati mostra il numero assoluto di automobili di nuova immatricolazione per anno e per Cantone. La serie di dati è disponibile dal 2014. Numerazione dei Cantoni secondo UST.

fr: Le jeu de données indique le poids à vide moyen des voitures de tourisme immatriculées pour la première fois, par an et par canton. Les données sont disponibles depuis 2014. La numérotation des cantons est celle de l’OFS.

en: This data set shows the average unladen weight for newly registered passenger cars per year and per canton. The data series is available from 2014. Numbering of cantons according to FSO.

de: Der Datensatz zeigt das durchschnittliche Leergewicht bei den neuzugelassenen Personenwagen pro Jahr und Kanton. Die Datenreihe ist ab 2014 verfügbar. Nummerierung der Kantone gemäss BFS.

it: Il set di dati mostra il peso a vuoto medio delle automobili di nuova immatricolazione per anno e per Cantone. La serie di dati è disponibile dal 2014. Numerazione dei Cantoni secondo UST.

fr: Le jeu de données indique la consommation moyenne de carburant des voitures de tourisme immatriculées pour la première fois, par an et par canton. Les données sont disponibles depuis 2014. La numérotation des cantons est celle de l’OFS.

en: This data set shows the average fuel consumption for newly registered passenger cars per year and per canton. The data series is available from 2014. Numbering of cantons according to FSO.

de: Der Datensatz zeigt den durchschnittlichen Treibstoffverbrauch bei den neuzugelassenen Personenwagen pro Jahr und Kanton. Die Datenreihe ist ab 2014 verfügbar. Nummerierung der Kantone gemäss BFS.

it: Il set di dati mostra il consumo medio di carburante delle automobili di nuova immatricolazione per anno e per Cantone. La serie di dati è disponibile dal 2014. Numerazione dei Cantoni secondo UST.

fr: Le jeu de données indique la proportion de véhicules 4x4 dans les nouvelles immatriculations de voitures de tourisme, par an et par canton. Les données sont disponibles depuis 2014. La numérotation des cantons est celle de l’OFS.

en: This data set shows the share of all-wheel drive vehicles among newly registered passenger cars per year and per canton. The data series is available from 2014. Numbering of cantons according to FSO.

de: Der Datensatz zeigt den Anteil von Allradfahrzeugen bei den neuzugelassenen Personenwagen pro Jahr und Kanton. Die Datenreihe ist ab 2014 verfügbar. Nummerierung der Kantone gemäss BFS.

it: Il set di dati mostra la percentuale di automobili a trazione integrale di nuova immatricolazione per anno e per Cantone. La serie di dati è disponibile dal 2014. Numerazione dei Cantoni secondo UST.

fr: Le jeu de données indique la part de véhicules électriques dans les nouvelles immatriculations de voitures de tourisme, par an et par canton. Les données sont disponibles depuis 2014. La numérotation des cantons est celle de l’OFS.

en: This data set shows the share of electric vehicles among newly registered passenger cars per year and per canton. The data series is available from 2014. Numbering of cantons according to FSO.

de: Der Datensatz zeigt den Anteil Elektrofahrzeuge bei den neuzugelassenen Personenwagen pro Jahr und Kanton. Die Datenreihe ist ab 2014 verfügbar. Nummerierung der Kantone gemäss BFS.

it: Il set di dati mostra la percentuale di automobili elettriche di nuova immatricolazione per anno e per Cantone. La serie di dati è disponibile dal 2014. Numerazione dei Cantoni secondo UST.

fr:
Depuis 2014, les principales statistiques sur la flotte de voitures de tourisme neuves sont évaluées par année et par canton. Les statistiques portent sur le nombre d’immatriculations, la proportion de véhicules 4x4, la part de véhicules électriques, la consommation moyenne de carburant, le poids à vide moyen, les émissions moyennes de CO2 et, depuis 2017, le prix moyen d’un véhicule.

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

en:
Since the 2014 reporting year, key figures for new passenger car registrations have been evaluated by year and by canton. For each canton, key figures such as the number of registrations, share of all-wheel drive vehicles, share of electric vehicles, average fuel consumption, average unladen weight, average CO2 emissions and also, from 2017, average vehicle prices are shown.

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

de:
Seit dem Berichtsjahr 2014 werden wichtige Kennzahlen zur Neuwagenflotte der Personenwagen (PW) des jeweiligen Jahres nach Kanton ausgewertet. Pro Kanton sind Kenngrössen wie die Anzahl Zulassungen, Anteil Allradfahrzeuge, Anteil Elektrofahrzeuge, durchschnittlicher Treibstoffverbrauch, durchschnittliches Leergewicht, durchschnittlicher CO2-Ausstoss und ab 2017 auch die durchschnittlichen Fahrzeugpreise ausgewiesen.

Rechtsgrundlage: Energieeffizienzverordnung (EnEV) Art. 11

it:
A partire dall'anno di riferimento 2014, per ciascun Cantone vengono analizzati i principali indicatori relativi al parco automobili nuove dell'anno in questione. Per ciascun Cantone sono indicati dati chiave come il numero di immatricolazioni, la percentuale di veicoli a trazione integrale, la percentuale di veicoli elettrici, il consumo medio di carburante, il peso a vuoto medio, le emissioni medie di CO2 e, dal 2017, anche i prezzi medi dei veicoli.

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

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.

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).


maintainer: EnviDat Support

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

media_type: application/zip

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

format: ZIP

mimetype: application/zip

en: StationData

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

education

fr: Education, science

de: Bildung, Wissenschaft

en: Education and science

it: Formazione e scienza

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

value: Envidat Harvester

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 B_Transition_Analysis.R
Further Analysis of transition numbers

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

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

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

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

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

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

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

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

 05_Service_Data
Any service calculations will be saved in here

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

en:
Data and scripts of publication:

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

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

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

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

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

land-use

display_name: land-use

land-use-change

display_name: land-use-change

landscape-services

display_name: landscape-services

modeling

display_name: modeling

statistical-model

display_name: statistical-model

switzerland

display_name: switzerland

urbanization

display_name: urbanization

en: Land use projections and services for Switzerland


en: soil-water-balance growing-degree-days temperature growing-season-length precipitation relative-humidity solar-radiation

en: CHELSA-BIOCLIM+ Technical Documentation

media_type: PDF

en: Technical documentation document including the variable names, units, scaling, offset, and background.

format: PDF

en: CHELSA-BIOCLIM+

media_type: geotiff

en: Global extented set of bioclimatic variables at different temporal (e.g. monthly, annual, long term normals) resolution in Geotiff format.

format: GeoTIFF

en: A multitude of physical and biological processes on which ecosystems and human societies depend are governed by climatic conditions. Understanding how these processes are altered by climate change is central to mitigation efforts. Based on mechanistically downscaled climate data, we developed a set of climate-related variables at yet unprecedented spatiotemporal detail as a basis for environmental and ecological analyses. We created gridded data for near-surface relative humidity (hurs), cloud area fraction (clt), near-surface wind speed (sfcWind), vapour pressure deficit (vpd), surface downwelling shortwave radiation (rsds), potential evapotranspiration (pet), climate moisture index (cmi), and site water balance (swb), at a monthly temporal and 30 arcsec spatial resolution globally starting 1980 until 2018. At the same spatial resolution, we further estimated climatological normals of frost change frequency (fcf), snow cover days (scd), potential net primary productivity (npp), growing degree days (gdd), and growing season characteristics for the periods 1981-2010, 2011-2040, 2041-2070, and 2071-2100, considering three shared socioeconomic pathways (SSP126, SSP370, SSP585) and five Earth system models. Time-series variables showed high accuracy when validated against observations from meteorological stations. Climatological normals were also highly correlated to observations although some variables showed notable biases, e.g., snow cover days (scd). Together, the data sets presented here allow improving our understanding of patterns and processes that are governed by climate, including the impact of recent and future climate changes on the world’s ecosystems and associated services to societies.

growing-degree-days

display_name: growing-degree-days

growing-season-length

display_name: growing-season-length

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

soil-water-balance

display_name: soil-water-balance

solar-radiation

display_name: solar-radiation

temperature

display_name: temperature

en: CHELSA-BIOCLIM+ A novel set of global climate-related predictors at kilometre-resolution


dma-mediathek

maintainer: Digitale Medien der Armee DMA, Kundendienst Medienagentur

title_for_slug: dma-mediathek

fr: ddps 360 video photo militaire groupes-thematiques mediatheque

de: video militar vbs foto mediathek 360 themes

en: media-library military ddps video photograph theme-world 360

it: ambiti-tematici 360 militare mediateca foto video ddps

fr: MNA - Métadonnées

de: DMA - Metadaten

en: AFDM - Metadata

it: MDE - Metadati

fr: Les métadonnées montrent les clients et ressources de la médiathèque.

de: Die Metadaten zeigen, welche Mandanten und Assets erfasst sind.

en: The metadata show the clients and assets of the media library.

it: I metadati mostrano i clienti e asset della mediateca.

fr: MNA - Ressources

de: DMA - Assets

en: AFDM - Assets

it: MDE - Asset

fr: Cette liste contient les ressources saisies par MNA.

de: Diese Liste enthält die von DMA erfassten Assets.

en: This list contains the assets of AFDM.

it: Questa lista contiene i asset dello MDE.

fr: MNA - Médiathèque

de: DMA - Mediathek

en: AFDM - Media library

it: MDE - Mediateca

media_type: html

fr: Compétences en photos et en vidéos.

en: Expertise for photographs and videos.

de: Kompetenz in Fotos und Videos.

it: Competenza sotto forma di foto e video.

mimetype: html

fr: MNA - Médiathèque historique

de: DMA - Historische Mediathek

en: AFDM - Historic Media Library

it: MDE - Mediateca storica

fr: Photos et films historiques.

de: Historische Momentaufnahmen in Fotos und in Filmen.

en: Historical snapshots and video footage.

it: Istantanee storiche in forma di foto e filmati.

fr: Sis à Berne, le MNA est le centre de compétences pour les produits et services dans les domaines de la formation et de la communication.

de: Das DMA mit Standort in Bern ist das Kompetenzzentrum für Produkte und Dienstleistungen in den Bereichen der Ausbildung und der Kommunikation.

en: The AFDM, located in Bern, is the competence center for products and services in the fields of education and communication.

it: Il MDE, con sede a Berna, è il centro di competenza per i prodotti e le prestazioni nei settori dell'istruzione e della comunicazione.

ambiti-tematici

display_name: ambiti-tematici

ddps

display_name: ddps

foto

display_name: foto

groupes-thematiques

display_name: groupes-thematiques

media-library

display_name: media-library

mediateca

display_name: mediateca

mediathek

display_name: mediathek

mediatheque

display_name: mediatheque

militaire

display_name: militaire

militar

display_name: militar

militare

display_name: militare

military

display_name: military

culture

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

de: Kultur, Medien, Informationsgesellschaft, Sport

en: Culture, media, information society, sport

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

name: Digitale Medien der Armee (DMA), Departement VBS

fr: Sis à Berne, le Centre des médias numériques de l’armée MNA est le centre de compétences pour les produits et services dans les domaines de la formation et de la communication.

en: The Armed Forces Digital Media Centre AFDM, located in Bern, is the competence center for products and services in the fields of education and communication.

de: Das Zentrum digitale Medien der Armee DMA mit Standort in Bern ist das Kompetenzzentrum für Produkte und Dienstleistungen in den Bereichen der Ausbildung und der Kommunikation.

it: Il Centro dei media digitali dell’esercito MDE, con sede a Berna, è il centro di competenza per i prodotti e le prestazioni nei settori dell'istruzione e della comunicazione.


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

en: Daily simulated data for the future period

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

en: Daily simulated data for the reference period

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

en: Shapefile of 307 catchments in Switzerland

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

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

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

*References*:

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

climate-change

display_name: climate-change

discharge

display_name: discharge

hydrology

display_name: hydrology

precipitation

display_name: precipitation

snowmelt

display_name: snowmelt

soil-moisture

display_name: soil-moisture

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

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


maintainer: Anita C. Risch

en: soil-biology global nutrient-network soil-chemistry worldwide grassland mineralization soil

en:
site.code: Site code

Continent: Continent

sampleID: Sample ID

nn.latitude: Lattitude

nn.longitude: Longitude

bioclim.T.ann: Mean annual temperature (°C)

bioclim.T.var: Temperature variability (°C)

bioclim.T.q.wet: Temperature of the wettest quarter (°C)

bioclim.T.q.dry: Temperature of the driest quarter (°C)

bioclim.T.q.warm: Temperature of the warmest quarter (°C)

bioclim.P.ann: Mean annual preciptiation (mm)

bioclim.P.q.wet: Precipitation of the wettest quarter (mm)

ch.pH: pH

ch.WSK.vol: Water holding capacity (vol%)

ch.bulkdensity.gcm3: Bulk density (g cm-3)

ch.porespace: Pore space (vol%)

ch.sand.perc: Soil texture: sand (%)

ch.silt.perc: Soil texture: silt (%)

ch.clay.perc: Soil texture: clay (%)

ch.NO3start.mgg: NO3 at the beginning of the incubation (mgkg)

ch.Ntot.perc: Total soil N (%)

ch.Ctot.perc: Total soil C (%)

ch.Corg.perc: Soil organic C (%)

ch.netNminfield.mgkgd: Realised soil net N mineralisation (mg kg-1 d-1)

ch.netNminlab.mgkgd: Potential soil ne N mineralisation (mg kg-1 d-1)

archaea.cpg.log: Ammonia oxidising archaea (archaeal amoA gene copies g-1 soil)

bacteria.cpg.log: Ammonia oxidising bacteria (bacterial amoA gene copies g-1 soil)

micr.biom: Microbial biomass (ug g-1 soil)

ch.CtoN: C to N ratio

ch.netNnitriffield.mgkgd.sqrt: Realised soil net nitrification, transformed (mg kg-1 d-1)

ch.netNammoniffield.mgkgd.sqrt: Realised soil net ammonification, transformed (mg kg-1 d-1)

ch.netNnitriflab.mgkgd.sqrt: Potential soil net nitrification, transformed (mg kg-1 d-1)

ch.netNammoniflab.mgkgd.sqrt: Potential soil net ammonifiation, transformed (mg kg-1 d-1)

format: XLS

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

Paper Citation:

Risch, A. C.; Zimmermann, S.; Ochoa-Hueso, R.; Schütz, M.; Frey, B.; Firn, J. L.; Fay, P. A.; Hagedorn, F.; Borer, E. T.; Seabloom, E. W.; et al. Soil net nitrogen mineralisation across global grasslands. Nat. Commun. 2019, 10 (1), 4981 (10 pp.). doi.org/10.1038/s41467-019-12948-2

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

We conducted coordinated measurements of realised and potential soil net Nmin, and assessed water holding capacity, bulk density, C and N content, texture, pH, pore space, microbial biomass, and archaeal (AOA) and bacterial (AOB) ammonia oxidiser abundance using identical materials and methods across 30 grasslands on six continents. The sites covered a globally relevant range of climatic and edaphic conditions. Climate data was obtained from worldclim - Global climate data https://www.worldclim.org/

global

display_name: global

grassland

display_name: grassland

mineralization

display_name: mineralization

nutrient-network

display_name: nutrient-network

soil

display_name: soil

soil-biology

display_name: soil-biology

soil-chemistry

display_name: soil-chemistry

worldwide

display_name: worldwide


maintainer: Christoph Marty

en: Graubunden input

media_type: application/x-tar

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

mimetype: application/x-tar

en: Aare input

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

en: Dataset

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

en: CH2014 REPORT

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

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

en:
 Overview

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

 Experimental Setup

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

 Data

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

snow-depth

display_name: snow-depth

snow-water-equivalent

display_name: snow-water-equivalent

en: Alpine3D simulations of future climate scenarios CH2014


en: tls preferential-deposition alpine3d lidar lee-side-flow-field

media_type: ZIP

en:
The data presented here corresponds to the publication "A Close-Ridge Small-Scale Atmospheric Flow Field and its Influence on Snow Accumulation" (Gerber et al., 2017), which investigates an eddy-like structure in the vicinity of the Sattelhorn in the Dischma valley (Davos Switzerland) and its influence on snow accumulation. The dataset contains:

* Alpine3D: Alpine3D snow depth grids (25 m resolution) for two simulations with and without snow redistribution.
* ARPS: 10 ARPS simulations (25 m horizontal resolution) with different model setups (wind direction, wind speed, stability).
* LiDAR: Processed LiDAR PPI (D2_PPI_1h) and RHI (D2_cross_1h) across the valley with a hourly resolution for the period 27 October 2015 01:00 - 29 October 2015c 21:00 (spatial resolution: 25 m).
* meteostations-dischma: Meteorological station data of two meteorological stations in the Dischma valley with 10 minute resolution for the period 28 October 2015 - 30 October 2015.
* TLS: Snow depth change data (m) between 28 October 2015 and 30 October 2015 based on terrestrial laser scans.

For more details about the simulation and observation data, see Gerber et al., 2017.

Publication: Gerber et al., 2017: A Close-Ridge Small-Scale Atmospheric Flow Field and its Influence on Snow Accumulation, submitted to JGR - Atmospheres.

lee-side-flow-field

display_name: lee-side-flow-field

lidar

display_name: lidar

preferential-deposition

display_name: preferential-deposition

tls

display_name: tls

education

en: Education and science

en: DISCHMEX - Observations and simulations of the close-ridge small-scale atmospheric flow field and snow accumulation at Sattelhorn, Dischma valley, Davos, Switzerland.


maintainer: Thorsten Bartels-Rausch

en: laboratory-study sea-salt-aerosol cryosphere snow antartic polar arctic

en: Experimental results of uptake coefficient vs. temperature and ozone concentration

media_type: text/csv

en:
Data to Figure 3: Measured uptake coefficients as a function of temperature at 5E12 molecules per cm3 ozone concentration.

Data to Figure 6: Measured uptake coefficients at different temperatures as a function of gas- phase ozone concentration.

mimetype: text/csv

en: Experimental Results: Uptake coefficients vs. temperature and ozone concentration

en:
* Final Data to Figure 3: Uptake coefficients as a function of temperature at 5E12 molecules per cm3 ozone concentration.
* Final Data to Figure 6: Uptake coefficients at different temperatures as a function of gas- phase ozone concentration.

The average ozone uptake coefficients of the three cycles between the bypass and the flow tube per repeat is taken as the mean uptake coefficient for each experiment. Gamma was corrected for gas phase diffusion limitation following the Cooney–Kim– Davis (CKD) method52 (see supporting material of publication for details).

en: The reaction of ozone with bromide in polar regions results in the formation of reactive bromide species with impacts on ozone budget and the oxidative capacity of the lower atmosphere. Here, we present a data investigating the temperature dependence of bromide oxidation by ozone using a coated wall flow tube reactor coated with an aqueous mixture of citric acid and sodium bromide, a proxy for sea salt aerosol in snow or the free troposphere. Thus study shows the effect of of organic species at relatively mild temperatures between the freezing point and eutectic temperature as typical for Earth's cryosphere.

antartic

display_name: antartic

arctic

display_name: arctic

cryosphere

display_name: cryosphere

laboratory-study

display_name: laboratory-study

polar

display_name: polar

sea-salt-aerosol

display_name: sea-salt-aerosol

snow

display_name: snow

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: Kinetic experiments on the oxidation of bromide by ozone from 289-245 K.


digitizing-historical-plague

maintainer: Christian Ginzler

title_for_slug: digitizing-historical-plague

en: palaeo-epidemiology black-death bubonic-plague plague-inventory historical-plague plague-outbreaks plague-mapping yersinia-pestis

en: We present newly digitized data on 6,929 plague outbreaks that occurred between 1347 and 1900 AD across Europe. The data base on an inventory initially published 1976. For georeferencing the information of Tele Atlas 2009 was used. The coordinates are in the reference systems ETRS89 and WGS84.

black-death

display_name: black-death

bubonic-plague

display_name: bubonic-plague

historical-plague

display_name: historical-plague

palaeo-epidemiology

display_name: palaeo-epidemiology

plague-inventory

display_name: plague-inventory

plague-mapping

display_name: plague-mapping

plague-outbreaks

display_name: plague-outbreaks

yersinia-pestis

display_name: yersinia-pestis

en: Digitizing historical plague


maintainer: Lisa Hülsmann

en: forest-inventory forest-reserves mortality-models tree-mortality primeval-forests fagus-sylvatica

en: German data

en:
READ ME for

Hülsmann L, Bugmann H, Commarmot B, Meyer P, Zimmermann S, Brang P
Data from: Does one model fit all? Patterns of beech mortality in natural forests of three European regions.
Ecological Applications http://dx.doi.org/10.5061/dryad.h4s6t

Forest inventory data of beech for the development of individual-tree mortality models from strict forests reserves in Switzerland, Germany and Ukraine.

The data package provides the files
CH_reduced: inventory data of the reduced dataset from Switzerland that was used to assess the influence of additional environmental and stand characteristics on beech mortality
CH: inventory data from strict forest reserves in Switzerland
DE: inventory data from strict forest reserves in Germany
UA: inventory data from a primeval beech forest in Ukraine

A set of three consecutive inventories was used to generate records for the calibration of mortality models based on trees that were alive in the first and second inventory and either dead or alive in the third inventory.
Diameter at breast height: dbh (mm)
Annual relative basal area increment: relBAI (), calculated based on the first and the second dbh measurement as the compound annual growth rate of the trees basal area (Bigler and Bugmann 2004)
Status of the tree: y, 0 = alive, 1 = dead
Length of the mortality period: dt (years)

Additional environmental and stand characteristics were derived for the reduced Swiss dataset.
Note the varying seasonal level of the covariates considering the entire year, the growing season from April to September (G) and the winter month January to March (W).
All climatic covariates were calculated for both the ‘growth’ (2) and the ‘mortality’ period (3). The stand covariates were derived for the second inventory of each record (2).

Mean diameter at breast height: dbh.mean.2 (mm)
Interquartile range of diameter at breast height: dbh.IQR.2 (mm)
Median diameter at breast height: dbh.median.2 (mm)
Basal area per hectare: BA.2 (m2 ha-1)
Number of trees per hectare: N.2 (ha-1)
pH-value: pH ()
Available water capacity in the soil: AWC (mm)
Mean annual precipitation sum: P.2, P.3 (mm)
Mean precipitation sum during growing season: PG.2, PG.3 (mm)
Mean annual air temperature: mT.2, mT.3 (°C)
Mean air temperature during growing season: mTG.2, mTG.3 (°C)
Mean air temperature during winter months: mTW.2, mTW.3 (°C)
Mean growing degree-days: GDD.2, GDD.3 ()
Mean number of months during growing season with water deficit: mDEFdurG.2, mDEFdurG.3 ()
Mean annual maximum water deficit: mDEFmax.2, mDEFmax.3 (mm)
Overall maximum of the annual maximum water deficit: maxDEFmax.2, maxDEFmax.3 (mm)
Mean of cumulative water deficit during growing season: mDEFcumG.2, mDEFcumG.3 (mm)
Overall maximum of cumulative water deficitduring growing season: maxDEFcumG.2, maxDEFcumG.3 (mm)
Percentage of years with at least one month water deficit: percDEF.2, percDEF.3 (%)

en: Swiss data full

en: Ukrainian data

en: Swiss data reduced

en:
The datasets comprise nearly 19’000 trees of European beech (_Fagus sylvatica_ L.) from unmanaged forests in Switzerland, Germany / Lower Saxony and Ukraine. Tree death was modelled as a function of size and growth, i.e., stem diameter (DBH) and relative basal area increment (relBAI). To explain the spatial and temporal variability in mortality patterns, we considered a large set of environmental and stand characteristics.

 Inventory data
The strict forest reserves in Switzerland and Germany had been established in the period of 1961-1975 and 1971-1974, respectively. Every reserve included up to 10 permanent plots ranging from 0.09 to 1.8 ha in size, with slightly irregular re-measurement intervals. Permanent plots with pure or mixed beech stands were selected from the reserves of both networks. Reserves with considerable wind disturbance during the monitored intervals were excluded from the analysis. In addition to data from the Swiss and German reserves, data from a 10 ha plot in the primeval beech forest Uholka in Western Ukraine including three remeasurements were used. The inventory data provide diameter measurements at breast height (dbh) for revisited trees with a diameter of more than 4, 7 and 6 cm for Switzerland, Germany and Ukraine, respectively.

 Mortality predictors
A set of three consecutive inventories was used to generate records for the calibration of mortality models based on trees that were alive in the first and second inventory and either dead or alive in the third inventory. As an explanatory variable, the annual relative basal area increment (relBAI) was calculated based on the first and the second dbh measurement as the compound annual growth rate of the trees basal area. Tree dbh in the second inventory was used in addition to relBAI to model tree status (alive or dead) of the third inventory.

To increase the generality of the mortality models, we selected environmental variables that are known to have a considerable influence on growth and mortality of beech. We emphasized the effects of water availability using a large set of drought characteristics that were calculated based on the local site water balance. We also related beech mortality to soil pH, temperature, precipitation and growing degree-days. Additionally, we considered stand characteristics that reflect the development stage, competition and structure of the forests.

 Further information
For further information, refer to Hülsmann _et al_. (2016) Does one model fit all? patterns of beech mortality in natural forests of three European regions. _Ecological Applications_.

fagus-sylvatica

display_name: fagus-sylvatica

forest-inventory

display_name: forest-inventory

forest-reserves

display_name: forest-reserves

mortality-models

display_name: mortality-models

primeval-forests

display_name: primeval-forests

tree-mortality

display_name: tree-mortality

en: Data from: Does one model fit all? patterns of beech mortality in natural forests of three European regions


maintainer: Avalanche Warning Service SLF

en: Avalanche fatalities per year in Switzerland since 1936/1937

en:
This dataset contains the statistics on the number of avalanche fatalities per hydrological year in Switzerland. The data set commences with the beginning of the hydrological year 1936/37 on 01/10/1936. After the completion of a hydrological year, the new data is appended to the existing dataset.

The following information is contained (by column and column title):
 - hydrological year
 - number of fatalities in the backcountry (=tour)
 - number of fatalities in terrain close to ski areas (=offpiste)
 - number of fatalities on transportation corridors including ski runs, roads, railway lines (=transportation.corridors)
 - number of fatalities in or around buildings or in settlements (= buildings)
 - sum (of all four categories)

The definition for these four categories as described in the guidelines to the avalanche accident database:

**tour**:
activities include back-country ski, snowboard or snow-shoe touring

**offpiste**:
access from ski area, generally from the top of a skilift with short hiking distances

**transportation.corridors** ([Techel et al., 2016](http://www.geogr-helv.net/71/147/2016/ )):
people travelling or recreating on open or temporarily closed
transportation corridors (e.g. a road user or a skier on a ski run) and people working on open or closed transportation corridors (e.g. maintenance crews on roads, professional rescue teams)

**buildings** ([Techel et al., 2016](http://www.geogr-helv.net/71/147/2016/ )):
people inside or just outside buildings, and workers on high alpine building
sites

avalanche-accident-statistics

display_name: avalanche-accident-statistics

avalanche-accidents

display_name: avalanche-accidents

avalanche-fatalities

display_name: avalanche-fatalities

fatalities-in-or-around-buildings

display_name: fatalities-in-or-around-buildings

fatalities-in-settlements

display_name: fatalities-in-settlements

fatalities-in-the-backcountry

display_name: fatalities-in-the-backcountry

fatalities-on-transportation-corridors

display_name: fatalities-on-transportation-corridors

education

en: Education and science

en: Number of avalanche fatalities per hydrological year in Switzerland since 1936-1937


maintainer: Henning Löwe

en: unconfined-compression-experiments spherical-model-snow microstructure discrete-element-modelling 3d-ct-images snow-mechanics

en: Data Structure (Python Notebook - view online)

media_type: ipynb

media_type: text/plain

format: TXT

mimetype: text/plain

en: Data Access

media_type: HTTPS

en: Data Structure (Python Notebook)

en:
For the investigation of microstructural and mechanical properties of snow unconfined compression experiments and 3D computed tomography (CT) imaging were performed on sintered rounded grain snow and spherical model snow. The spherical model snow was generated to create geometrically simplified, well-defined microstructures for calibration of numerical models, such as discrete element models (DEM) in which the microstructure is represented by spherical particles.
In the experiments, microstructural variation was created by varying the sintering time (contact size) and the density of the ice sphere samples (number of contacts). The 3D CT images allow for a complete reconstruction of the entire experimental sample (cylindrical sample dimension: diameter = 33.6 mm; height = 14 mm).

discrete-element-modelling

display_name: discrete-element-modelling

microstructure

display_name: microstructure

snow-mechanics

display_name: snow-mechanics

spherical-model-snow

display_name: spherical-model-snow

unconfined-compression-experiments

display_name: unconfined-compression-experiments

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: Unconfined compression experiments and 3D CT images of spherical model snow and RG snow samples


envidat-supports-open-science

maintainer: Ionut Iosifescu Enescu

title_for_slug: envidat-supports-open-science

en: research-data-management juypter-notebooks envidat open-science wsl

en: Article as PDF

media_type: application/pdf

mimetype: application/pdf

en:
The article "EnviDat Supports Open Science" originally appeared in WSLintern No. 3 (2020), page 14-15 and it is republished here with permission from the WSLintern editorial team. It contains guidelines for WSL scientists about the main issues behind Open Science and how to pragmatically approach the complexities of doing Open Science with EnviDat’s support.

License: This article is released by WSL and the EnviDat team to the public domain under a Creative Commons 4.0 CC0 "No Rights Reserved" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions.

display_name: envidat

juypter-notebooks

display_name: juypter-notebooks

open-science

display_name: open-science

research-data-management

display_name: research-data-management

wsl

display_name: wsl

en: EnviDat Supports Open Science


maintainer: Marcia Phillips

en: borehole permos permafrost meteo-station ritigraben

en: DATA ACCESS

media_type: SMET

en: Meterological station at the [Ritigraben permafrost borehole](http://www.envidat.ch/dataset/rit1) (RIT_0102) in canton Valais, Switzerland. The station is located at 2690 m asl on a flat site.

borehole

display_name: borehole

meteo-station

display_name: meteo-station

permafrost

display_name: permafrost

permos

display_name: permos

ritigraben

display_name: ritigraben

en: RIT2: Meteorological station at Ritigraben borehole site