equiden-verteilung-der-geburten

title_for_slug: equiden-verteilung-der-geburten

fr: animaux-de-compagnie animaux-de-rente naissances equides evolution-de-la-population

de: geburten equiden nutztiere heimtiere populationsentwicklung

en: companion-animals births livestock population-trends equids

it: animale-da-reddito evoluzione-della-popolazione animale-da-compagnia nascite equidi

identifier: equids-birthYearAvg

fr: Répartition des naissances des équidés enregistrés.

de: Prozentuale Aufteilung der registrierten Equiden.

en: Distribution of the births of the registered equids.

it: Distribuzione delle nascite di equidi registrati.

animale-da-compagnia

display_name: animale-da-compagnia

animale-da-reddito

display_name: animale-da-reddito

animaux-de-compagnie

display_name: animaux-de-compagnie

animaux-de-rente

display_name: animaux-de-rente

births

display_name: births

companion-animals

display_name: companion-animals

equiden

display_name: equiden

equides

display_name: equides

equidi

display_name: equidi

equids

display_name: equids

geburten

display_name: geburten

heimtiere

display_name: heimtiere

livestock

display_name: livestock

naissances

display_name: naissances

nascite

display_name: nascite

nutztiere

display_name: nutztiere

agriculture

fr: Agriculture, sylviculture

de: Land- und Forstwirtschaft

en: Agriculture, forestry

it: Agricoltura, selvicoltura

identitas

fr: Identitas AG

de: Identitas AG

en: Identitas AG

it: Identitas AG

fr: Équidés - Répartitions des naissances

en: Equids - Distribution of births

de: Equiden - Verteilung der Geburten

it: Equidi - Distribuzione delle nascite


schafe-verteilung-pro-gemeinde

title_for_slug: schafe-verteilung-pro-gemeinde

fr: cartes animaux-de-rente ovins commune

de: gemeinde schafe nutztiere karten

en: sheep maps livestock municipality

it: animale-da-reddito comune mappe ovini

label: Interactive plot

label: Glossary / Documentation

state: active

type: dataset

media_type: CSV

format: CSV

identifier: sheep-map-commune

fr: Représentation géographique de la répartition des moutons enregistrés et vivants par commune et par limites des zones agricoles d'après classification OFAG: total, total par surface en kilomètre carré sans les étendues d'eau, total par 100 habitants, ainsi que les cinq races les plus populaires.

en: Map of the distribution of registered and living sheep by municipality and agricultural zones boundaries according to the FOAG classification: total, total by surface in km2 without waters, total by 100 inhabitants, and the five most popular breeds.

de: Geografische Darstellung der Dichte von lebenden, registrierten Schafen pro Gemeinde und landwirtschaftliche Zonen gemäss Klassifikation BLW: Anzahl absolut, Anzahl pro Landfläche in km2 ohne Gewässer, Anzahl pro 100 Einwohner sowie die fünf beliebtesten Rassen.

it: Rappresentazione geografica della distribuzione delle pecore registrati e vivi per comune e per limiti delle zone agricole secondo la classificazione UFAG: totale, totale per superficie in km2 senza acque, totale per 100 abitanti, e le cinque razze più popolari.

cartes

display_name: cartes

commune

display_name: commune

comune

display_name: comune

gemeinde

display_name: gemeinde

karten

display_name: karten

mappe

display_name: mappe

maps

display_name: maps

municipality

display_name: municipality

ovini

display_name: ovini

ovins

display_name: ovins

schafe

display_name: schafe

sheep

display_name: sheep

identitas

fr: Identitas AG développe et exploite des applications pour l'identification, l'enregistrement et la surveillance des animaux de rente et de compagnie. En tant que prestataire leader de services IT en Suisse pour les données en rapport avec les animaux et la production végétale, nous contribuons à la création de valeur ajoutée pour la santé et le bien-être animale et pour la sécurité des denrées alimentaires. A notre siège à Berne, nous employons environ 110 collaborateurs.

en: Identitas AG develops and operates software for the identitfication, registration and monitoring of livestock and companion animals in Switzerland. As leader provider of IT services in Switzerland for data regarding animals and crops, we contribute to creating added value for animal health/ welfare and safe food. About 110 employees work in our headquarters in Berne.

de: Identitas AG entwickelt und betreibt Applikationen zur Identifizierung, Registrierung und zum Monitoring von Nutz- und Heimtieren. Als führender Schweizer IT-Dienstleister für Daten rund um das Tier und den Pflanzenbau schaffen wir Mehrwert für gesunde Tiere und sichere Lebensmittel. An unserem Sitz in Bern beschäftigen wir rund 110 Mitarbeitende.

it: Identitas AG sviluppa e gestisce applicazioni per l'identificazione, la registrazione e il monitoraggio degli animali da reddito e da compagnia. In qualità di fornitore di servizi informatici leader in Svizzera per i dati relativi agli animali e alla produzione vegetale, contribuiamo alla creazione di valore aggiunto per la salute e il benessere degli animali e la sicurezza alimentare. Nella nostra sede centrale di Berna abbiamo circa 110 dipendenti.

political_level: other

type: organization

approval_status: approved

key: guid

key: uri

value: DCAT Harvester Identitas

fr: Ovins - Répartition par commune

de: Schafe - Verteilung pro Gemeinde

en: Sheep - Distribution by municipality

it: Ovini - Distribuzione per comune


fr: point-dinteret-poi geoservice centre-de-loisirs batiment

de: point-of-interest-poi geodienst freizeitanlage bauwesen-und-gebaute-umwelt

en: point-of-interest-poi geoservice leisure-facility building

it: punto-di-interesse-poi geoservizio centro-per-il-tempo-libero edilizia

label: öffentliche Spielplätze und Sportanlagen in der Gemeinde Emmen

label: geocat.ch permalink

de: Kartendienst mit den Spielplätzen und Sportanlagen in der Zuständigkeit der Gemeinde Emmen

format: WMS

de: WFS Spielplätze und Sportanlagen in der Gemeinde Emmen

format: WFS

fr: Service de carte avec les bâtiments de l'école dans la commune de Emmen

de: Kartendienst mit den öffentlichen Spielplätzen und Sportanlagen in der Gemeinde Emmen

format: SERVICE

de: Gemeinde Emmen

de: Emmen ist mit mehr als 31'000 Einwohnern nach der Stadt Luzern die Gemeinde mit der zweithöchsten Einwohnerzahl im Kanton Luzern. Gemäss kommunaler GIS-Strategie (https://www.emmen.ch/publikationen/344959) stellt Emmen Geodaten öffentlich zur Verfügung, soweit keine Schutzgründe/Datensicherheitsrestriktionen entgegenstehen. Geodaten werden in Form von Kartendiensten auf dem kommunalen Geoportal: https://www.emmen.ch/gemeindeinzahlen/43855 publiziert und als WMS zur öffentlichen Verfügung gestellt. Die zugrunde liegenden Geodaten und Geodienste werden in geocat.ch dokumentiert, die publizierten WMS auf opendata.swiss sind mit geocat.ch synchronisiert.

political_level: commune

value: Gemeinde Emmen - Geocat Harvester

de: Spielplätze und Sportanlagen Gemeinde Emmen (WMS/WFS)


schulanlagen-emmen-wms

title_for_slug: schulanlagen-emmen-wms

fr: point-dinteret-poi construction geoservice batiment

de: point-of-interest-poi bau geodienst bauwesen-und-gebaute-umwelt

en: point-of-interest-poi construction geoservice building

it: punto-di-interesse-poi costruzione geoservizio edilizia

de: Schulhäuser Gemeinde Emmen

de: WFS mit den Schulhäusern der Gemeinde Emmen

de: Kartendienst mit den öffentlichen Schuhäusern in der Gemeinde Emmen

de: Schulanlagen Emmen (WMS/WFS)


parkplatze-emmen-wms

title_for_slug: parkplatze-emmen-wms

fr: reseau-de-transport geoservice point-dinteret-poi batiment

de: verkehrsnetz geodienst point-of-interest-poi bauwesen-und-gebaute-umwelt

en: traffic-network geoservice point-of-interest-poi building

it: rete-di-trasporto geoservizio punto-di-interesse-poi edilizia

label: Webkarte mit den Parkplätzen der Zonen A, B, C sowie Veloparkierungsanlagen in der Zuständigkeit der Gemeinde Emmen

de: DE#Kartendienst mit den öffentlichen Parkplätzen der Zonen A|FR#|EN#|IT#

de: WFS Parkplätze Emmen

de: WFS mit den Parkplätzen und Veloparkierungsanlagen der Gemeinde Emmen

de: Kartendienst mit den öffentlichen Parkplätze der Zonen A, B und C sowie Veloparkierungsanlagen

de: Parkplätze Emmen (WMS/WFS)


geography

fr: Géographie

de: Geographie

en: Geography

it: Geografia

territory

fr: Territoire et environnement

de: Raum und Umwelt

en: Territory and environment

it: Territorio e ambiente

spatial: Schweiz

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

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

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

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

fr: Aperçu map.geo.admin.ch

de: Vorschau map.geo.admin.ch

en: Preview map.geo.admin.ch

it: Previsione map.geo.admin.ch

fr: Serveur de téléchargement de geo.admin.ch

de: Download Server von geo.admin.ch

en: Download server from geo.admin.ch

it: Server di download di geo.admin.ch

format: ZIP

fr: Service WMS de geo.admin.ch

de: WMS Dienst von geo.admin.ch

en: WMS Service from geo.admin.ch

it: Servizio WMS di geo.admin.ch

fr: Télécharger les périmètres NPA6

de: Download PLZ6-Perimeter

it: Prelievo di dati NPA6

format: HTML

fr: WMS pour le géoportail national

de: WMS für das Nationale Geoportal

en: WMS for the national geoportal

fr: RESTful API de geo.admin.ch

de: RESTful API von geo.admin.ch

en: RESTful API from geo.admin.ch

it: RESTful API da geo.admin.ch

format: API

fr: L’article 24 de l’ordonnance sur les noms géographiques (ONGéo) confie à l’Office fédéral de topographie swisstopo la tâche d’établir, de gérer et de publier le nouveau répertoire officiel des localités avec le code postal et le périmètre associés à chacune d’elles. La tenue de ce jeu de données qui correspond à l’exception de la suppression de l’identifiant «IDENT NPA, Chiffres_supplementaires» de la table «NPA»du TOPIC «NPA_Localite» de la mensuration officielle est centralisée par swisstopo. Le jeu de données couvre l'intégralité du territoire suisse et peut être obtenu gratuitement (il est actualisé mensuellement)

de: Mit Artikel 24 der Verordnung über die geografischen Namen (GeoNV) wurde das Bundesamt für Landestopografie swisstopo beauftragt, das neue amtliche Ortschaftenverzeichnis mit Postleitzahl und Perimeter zu erstellen, zu verwalten und zu veröffentlichen. Dieser Datensatz wird zentral bei swisstopo geführt und entspricht mit Ausnahme des entfernten Identifikators «IDENT PLZ, Zusatzziffern» in der Tabelle «PLZ» dem TOPIC «PLZOrtschaft» der amtlichen Vermessung. Der Datensatz ist flächendeckend über die ganze Schweiz und kann kostenlos bezogen werden (Datensatz wird monatlich nachgeführt).

en: In Article 24 of the regulations governing geographic names (GeoNV), the Federal Office of Topography (swisstopo) is commissioned to establish, administer and publish the new official index of cities and towns including postal codes and perimeter. The data are stored centrally at swisstopo and correspond of the TOPIC «PLZOrtschaften» (postal codes and localities) of the official cadastral survey, with the exception that the identifier «IDENT PLZ, Zusatzziffern» of the table «PLZ» has been removed.The data set covers all of Switzerland and is available free of charge.

it: Con l’articolo 24 dell’Ordinanza sui nomi geografici (ONGeo) l’Ufficio federale di topografia è stato incaricato di realizzare, amministrare e pubblicare il nuovo elenco ufficiale delle località con il numero postale d’avviamento e il perimetro. Questo set di dati è gestito a livello centralizzato presso swisstopo e corrisponde ad esclusione del rimosso del identificatore «IDENT CAP, cifre_supplementari» nella tabella «CAP6» al set di dati TOPIC «CAP_localita» della misurazione ufficiale. Il set di dati è capillare per tutto il territorio svizzero e può essere ottenuto gratuitamente (aggiornato con cadenza mensile).

fr: Office fédéral de topographie swisstopo

en: Federal Office of Topography swisstopo

it: Ufficio federale di topografia swisstopo

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

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

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

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

political_level: confederation

value: Swisstopo - Geocat Harvester

fr: Répertoire officiel des localités avec le code postal et le périmètre

de: Amtliches Ortschaftenverzeichnis mit Postleitzahl und Perimeter

en: Official index of cities and towns including postal codes and perimeter

it: Elenco ufficiale delle località con il numero postale d'avviamento e il perimetro


label: Link zu Fachportal

fr: Lien vers la distribution des données

de: Link für Datenbezug

en: Link for download

it: Link per la descrizione dei dettagli

fr: Le service de cartographie de l'inventaire des logements indique par un fond bleu le statut des communes soumises aux dispositions relatives à la construction prévues par la loi sur les résidences secondaires (LRS ; RS 702), qui ne peuvent autoriser la construction de résidences secondaires qu'à des conditions très strictes (art. 7ss LRS). La LRS fixe la limite à 20 pour cent de résidences secondaires. Pour permettre le calcul de la proportion de résidences secondaires, la LRS impose à toutes les communes suisses d'établir chaque année un inventaire des logements. Celui-ci s'appuie sur le Registre fédéral des bâtiments et des logements (RegBL), qui est tenu par les communes et analysé annuellement par l'Office fédéral du développement territorial (ARE), au 31 décembre. L'ARE publie ensuite fin mars l'inventaire des logements et la proportion des résidences secondaires. Les communes dont la proportion de résidences secondaires annoncée en mars a franchi la limite de 20 pour cent, dans un sens ou dans l'autre, suivent une procédure de vérification de cette proportion. Fin octobre, l'état de la procédure (statut) est mis à jour dans la liste des communes. Dans l'inventaire des logements, le pourcentage reste inchangé jusqu'à leur prochaine publication à la fin mars suivante puisque la procédure de vérification qui permet de décider si la part de résidences secondaires est inférieure ou supérieure à 20 pour cent n'implique pas obligatoirement de déterminer dans chaque cas les valeurs exactes. Les communes sont compétentes pour fournir des informations plus détaillées sur la procédure en cours. Si les communes sont tenues d'indiquer dans l'inventaire des logements le nombre total de logements et les résidences principales, elles n'ont aucune obligation de déclarer en tant que telles dans le RegBL les résidences assimilées à des résidences principales ou les résidences secondaires. Les données des inventaires des logements ne peuvent donc pas être comparées d'une commune à une autre en ce qui concerne les résidences secondaires.

de: Der Kartendienst Wohnungsinventar zeigt als Status in blauer Grundierung die Gemeinden, die den baurechtlichen Bestimmungen des Zweitwohnungsgesetzes (ZWG, SR 702) unterstehen und Zweitwohnungen nur unter strengen Auflagen erstellen dürfen (ZWG Art. 7ff.). Das ZWG definiert als Grenze einen Zweitwohnungsanteil von 20 Prozent. Zur Berechnung der Zweitwohnungsanteile verpflichtet das ZWG alle Schweizer Gemeinden jährlich ein Wohnungsinventar zu erstellen. Die Grundlage dafür bildet das eidgenössische Gebäude- und Wohnungsregister (GWR). Das GWR wird von den Gemeinden gepflegt und durch das Bundesamt für Raumentwicklung per 31.12. ausgewertet. Ende März publiziert das ARE die Wohnungsinventare und Zweitwohnungsanteile. Gemeinden, deren Zweitwohnungsanteil im März neu die Grenze von 20 Prozent unter- oder überschreitet, befinden sich in einem Verfahren zur Überprüfung des Zweitwohnungsanteils. Ende Oktober wird in der Liste der Verfahrensstand (Status) aktualisiert. Die Prozentangaben des Wohnungsinventars bleiben bis zur nächsten Publikation der Wohnungsinventare Ende März unverändert, da im Prüfungsverfahren für den Entscheid unter oder über 20 Prozent Zweitwohnungsanteil nicht in jedem Fall die genauen Werte ermittelt werden müssen. Für genauere Informationen zu laufenden Verfahren sind die Gemeinden zuständig. Die Gemeinden müssen im Wohnungsinventar die Gesamtzahl der Wohnungen sowie die Erstwohnungen ausweisen, es gibt für sie jedoch keine Pflicht, Erstwohnungen gleichgestellte Wohnungen oder Zweitwohnungen im GWR als solche zu deklarieren. Deshalb lassen sich die Daten der Wohnungsinventare in Bezug auf die Zweitwohnungen zwischen Gemeinden nicht vergleichen.

en: The apartment inventory map service shows the status in blue primer of the municipalities that are subject to the building regulations of the Second Housing Act (ZWG, SR 702) and are only allowed to set up second homes under strict conditions (ZWG Art. 7ff.). The Second Homes Act defines the limit of a second home proportion at 20 per cent. The Second Homes Act obliges all Swiss municipalities to create an annual apartment inventory to calculate the number of proportion of second apartments. The basis for this is the Swiss Federal Register of Buildings and Dwellings. The Swiss Federal Register of Buildings and Dwellings is maintained by the municipalities and evaluated by the Federal Office for Spatial Development by 31.12. The Federal Office for Spatial Development will publish the apartment inventory and number of second apartments at the end of March. Municipalities whose second apartment share in March was under or over the limit of 20 per cent will be subject to a procedure to check the second apartment share. The list of the state of proceedings (status) will be updated at the end of October. The percentages of the apartment inventory will remain unchanged until the next publication of the apartment inventories at the end of March, as the exact values do not always have to be determined in the examination procedure for the decision below or above 20 percent second home share. For more precise information, the municipalities are responsible for ongoing proceedings. The municipalities have to show the total number of apartments and first apartments in the apartment inventory, however, there is no obligation to declare first apartments, equivalent apartments or second apartments as such in the RBD. Therefore, the data from the apartment inventory regarding second apartments cannot be compared with data from other municipalities.

it: Nella mappa Inventario delle abitazioni sono contrassegnati in blu i Comuni soggetti alle disposizioni edilizie previste dalla legge sulle abitazioni secondarie (LASec; RS 702) e autorizzati a costruire abitazioni secondarie solo nel rispetto di severe condizioni (art. 7 segg. LASec). La LASec fissa un limite del 20 per cento per la quota delle abitazioni secondarie. Per poter calcolare questa quota la LASec obbliga tutti i Comuni svizzeri a stilare ogni anno un inventario delle abitazioni, che si basa sul Registro federale degli edifici e delle abitazioni (REA). Il REA è curato dai Comuni ed esaminato dall'ARE, che prende come data di riferimento il 31 dicembre. A fine marzo l'ARE pubblica gli inventari delle abitazioni e le quote di abitazioni secondarie. Per i Comuni la cui quota di abitazioni secondarie a marzo risulta, diversamente dall'anno precedente, superiore o inferiore al limite del 20 per cento viene avviato un procedimento di verifica della quota di abitazioni secondarie. Alla fine di ottobre viene aggiornato nella lista lo stato della procedura. Le percentuali indicate nell'Inventario delle abitazioni rimangono invariate fino alla pubblicazione successiva di fine marzo, dal momento che per il suddetto procedimento di verifica della quota di abitazioni secondarie non devono sempre essere forniti valori esatti. Informazioni più dettagliate sui procedimenti in corso possono essere richieste ai Comuni. I Comuni devono indicare nell'Inventario delle abitazioni il numero totale di abitazioni e le abitazioni primarie, mentre non hanno l'obbligo di dichiarare nel REA le abitazioni equiparate ad abitazioni primarie né le abitazioni secondarie. Per tale ragione i dati sulle abitazioni secondarie contenuti negli Inventari delle abitazioni non sono comparabili tra un Comune e l'altro.

bundesamt-fur-raumentwicklung-are

fr: Office fédéral du développement territorial ARE

de: Bundesamt für Raumentwicklung ARE

en: Federal Office for Spatial Development ARE

it: Ufficio federale dello sviluppo territoriale ARE

fr: L'ARE est le centre de compétences de la Confédération pour les questions liées au développement territorial, à la politique des transports, au développement durable ainsi qu'à la coopération transnationale en matière de territoire.

de: Das ARE ist die Fachbehörde des Bundes für Fragen der räumlichen Entwicklung, für die Verkehrspolitik, die nachhaltige Entwicklung sowie die transnationale Zusammenarbeit in räumlichen Belangen.

en: The ARE is the federal government's centre of excellence for issues concerning spatial development, transport policy, sustainable development and international cooperation in spatial planning matters.

it: L'ARE è l'autorità federale responsabile per le questioni di sviluppo territoriale, politica dei trasporti, sviluppo sostenibile e collaborazione transnazionale in ambito territoriale.

value: ARE - Geocat Harvester

fr: Inventaire des logements et proportion de résidences secondaires

en: Housing inventory and secondary homes rate

it: Inventario delle abitazioni e quota di abitazioni secondarie


fr: geoservice point-dinteret-poi construction batiment

de: geodienst point-of-interest-poi bau bauwesen-und-gebaute-umwelt

en: geoservice point-of-interest-poi construction building

it: geoservizio punto-di-interesse-poi costruzione edilizia

de: Sitzbänke, Abfalleimer, Grillstellen Emmen

de: Kartendienst mit den Sitzbänken, Abfalleimern und Grillstellen der Gemeinde Emmen

de: WFS Sitzbänke, Abfalleimer und Robidog

fr: Service de carte avec les bâtiments de l'école dans la commune de Emmen

de: Kartendienst mit den Sitzbänken, Abfalleimern (inkl. Möblierung an Bushaltestellen) und Robidogstandorten sowie den öffentlichen Grillstellen in der Zuständigkeit der Gemeinde Emmen

de: Gemeinde Emmen

de: Emmen ist mit mehr als 31'000 Einwohnern nach der Stadt Luzern die Gemeinde mit der zweithöchsten Einwohnerzahl im Kanton Luzern. Gemäss kommunaler GIS-Strategie (https://www.emmen.ch/publikationen/344959) stellt Emmen Geodaten öffentlich zur Verfügung, soweit keine Schutzgründe/Datensicherheitsrestriktionen entgegenstehen. Geodaten werden in Form von Kartendiensten auf dem kommunalen Geoportal: https://www.emmen.ch/gemeindeinzahlen/43855 publiziert und als WMS zur öffentlichen Verfügung gestellt. Die zugrunde liegenden Geodaten und Geodienste werden in geocat.ch dokumentiert, die publizierten WMS auf opendata.swiss sind mit geocat.ch synchronisiert.

de: Sitzbänke, Abfalleimer und Grillstellen Emmen (WMS/WFS)


signalisation-und-markierung-emmen-wms

title_for_slug: signalisation-und-markierung-emmen-wms

de: geodienst verkehrsnetz verkehr

en: geoservice traffic-network transport

it: geoservizio rete-di-trasporto trasporti

de: Strasseninfrastruktur Gemeinde Emmen

de: WFS Strasseninfrastruktur

de: WFS mit der Strasseninfrastruktur in der Gemeinde Emmen

de: Kartendienst mit den Strassen, Tempo 30- und Begegnungszonen (Tempo 20), Fussgängerstreifen, Kunstbauten und Verkehrsspiegel in der Gemeinde Emmen

de: Strasseninfrastruktur Emmen (WMS/WFS)


gastrobetriebe-emmen-wms

title_for_slug: gastrobetriebe-emmen-wms

fr: point-dinteret-poi geoservice construction batiment

de: point-of-interest-poi geodienst bau bauwesen-und-gebaute-umwelt

en: point-of-interest-poi geoservice construction building

it: punto-di-interesse-poi geoservizio costruzione edilizia

de: Gastrobetriebe Emmen

de: Kartendienst mit den Gastro- und Beherbergungsbetrieben in der Zuständigkeit der Gemeinde Emmen

de: WFS Gastrobetriebe Emmen

de: Kartendienst mit den Gastro- und Beherbergungsbetrieben in der Gemeinde Emmen

de: Gastrobetriebe Emmen (WMS/WFS)


quartiervereine-gemeinde-emmen-wms

title_for_slug: quartiervereine-gemeinde-emmen-wms

fr: geoservice plan-de-quartier quartier

de: geodienst quartierplan quartier

en: geoservice quarter-map quarter

de: Quartiervereine Gemeinde Emmen

de: Kartendienst mit den Perimetern der verschiedenen Quartiervereine in der Gemeinde Emmen

de: DE#|FR#|EN#|IT#

de: Kartendienst mit dem Zuständigkeitsgebiet der Quartiervereine in der Gemeinde Emmen

de: Quartiervereine Emmen (WMS/WFS)


energieplan-emmen-wms

title_for_slug: energieplan-emmen-wms

de: Quartiervereine Gemeinde Emmen|

de: Kartendienst mit dem Energieplan der Gemeinde Emmen

de: WFS Energieplan Emmen

de: Geodienst Energieplan Emmen

de: Gemeinde Emmen

de: Emmen ist mit mehr als 31'000 Einwohnern nach der Stadt Luzern die Gemeinde mit der zweithöchsten Einwohnerzahl im Kanton Luzern. Gemäss kommunaler GIS-Strategie (https://www.emmen.ch/publikationen/344959) stellt Emmen Geodaten öffentlich zur Verfügung, soweit keine Schutzgründe/Datensicherheitsrestriktionen entgegenstehen. Geodaten werden in Form von Kartendiensten auf dem kommunalen Geoportal: https://www.emmen.ch/gemeindeinzahlen/43855 publiziert und als WMS zur öffentlichen Verfügung gestellt. Die zugrunde liegenden Geodaten und Geodienste werden in geocat.ch dokumentiert, die publizierten WMS auf opendata.swiss sind mit geocat.ch synchronisiert.

de: Energieplan Emmen WMS


maintainer: EnviDat Support

en: par relative-humidity wind-speed temperature climate-change swiss-forest-lab meteorology precipitation meteo long-term

en: Data plot of the forest station (LEB)

media_type: application/pdf

en: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

format: PDF

mimetype: application/pdf

en: Site photo of the forest station (LEB)

media_type: image/jpeg

en: Site photo of the forest station from below (viewing upwards the slope)

mimetype: image/jpeg

en: Forest station data (LEB)

media_type: text/csv

en: Data file.

mimetype: text/csv

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for one meteorological station in Lens in Switzerland which is located within a natural coniferous forest with Scots pine (_Pinus sylvestris_; 150-170 yrs)) as dominant tree species. The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Lens is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

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.

wsl

capacity: public

value: Envidat Harvester

en: Lens, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards


en: water-front ice-layers snowmicropen snow-temperature snow-stratigraphy weissfluhjoch

en: README

media_type: text/plain

format: TXT

mimetype: text/plain

en: WFJ_ICE_LAYERS dataset

media_type: application/zip

mimetype: application/zip

en: The WFJ_ICE_LAYERS dataset contains multi-instrument snowpack measurements at high temporal resolution, which enable to monitor the formation of deep ice layers due to preferential water flow, at the Weissfluhjoch research site, Davos, Switzerland. It covers the winter 2016/2017, with a focus on the early melting season. This dataset includes traditional snowpack profiles (weekly resolution, 15/11/2016-29/05/2017), SnowMicroPen penetration resistance profiles (daily resolution, 01/02/2017-19/04/2017), snow temperatures measured at different heights in the snowpack (half-hourly resolution, 01/03/2017-15/04/2017) and the water front height derived from an upward-looking ground penetrating radar (3-hour resolution, 04/03/2017-08/04/2017). The measurements are complemented by initialization files for SNOWPACK model simulations with the ice reservoir parameterization at Weissfluhjoch for the winter 2016/2017.

ice-layers

display_name: ice-layers

snow-stratigraphy

display_name: snow-stratigraphy

snow-temperature

display_name: snow-temperature

snowmicropen

display_name: snowmicropen

water-front

display_name: water-front

weissfluhjoch

display_name: weissfluhjoch

en: WFJ_ICE_LAYERS: Multi-instrument data for monitoring deep ice layer formation in an alpine snowpack


maintainer: Ionut Iosifescu Enescu

en: hpc reproducibility open-data open-science open-source research-data-management jupyter-notebooks

en: Open Science Support @ WSL (Poster)

en:
This poster was originally created for the swissuniversities Open Science Action Plan: Kick-Off Forum, and showed to the audience on 17.10.2019. It illustrates how the environmental data portal EnviDat provides the tools for fostering Open Science and Reproducibility of scientific research at WSL.

Supporting open science is a highly relevant user requirement for EnviDat and for implementing FAIR (Findability, Accessibility, Interoperability and Reusability) principles at dataset level.
EnviDat encourages WSL scientists to complement data publication with a complete description of research methods and the inclusion of the open source software, code or scripts used for processing the dataset or for obtaining the published results. By openly publishing open software (e.g. as Jupyter notebooks) alongside research data sets, researchers can contribute to mitigate reproducibility issues.

EnviDat also promotes and supports, where possible and practical, the publication of software as Jupyter notebooks. Jupyter notebooks provide a solution for improved documentation and interactive execution of open code in a wide range of programming languages (Python, R, Octave/Matlab, Java or Scala). These programming languages are widely used in environmental research at WSL and well supported by the Jupyter-compatible kernels. We have sucessfully interfaced EnviDat-hosted notebooks with the WSL High-Performance Computing (HPC) Linux Cluster through a JupyterHub/JuypterLab beta installation on the HPC cluster implemented in close collaboration with the WSL IT-Services.
For existing software that cannot be easily migrated to Jupyter Notebooks, the Open Science and Reproducibility is assisted by containerisation. We have proven that several Singularity containers can successfully run on WSL's HPC cluster.

Finally, the researchers can upload the data/results complemented by code (e.g. as Jupyter Notebooks, or Singularity containers) and any additional documentation in EnviDat. Consequently, they will receive a DOI for the entire dataset, which they can reference in their science paper in order to publish a more reproducible research.

_License_: This poster 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 this poster in any way you want, for any purposes and without restrictions.

hpc

display_name: hpc

jupyter-notebooks

display_name: jupyter-notebooks

open-data

display_name: open-data

open-science

display_name: open-science

open-source

display_name: open-source

reproducibility

display_name: reproducibility

research-data-management

display_name: research-data-management

en: Open Science Support at the Swiss Federal Research Institute WSL. The EnviDat Concept


en: shortwave-radiation radiation longwave-radiation weisfluhjoch davos

en: Metadata

en: Processing doc

en: Description of processing code that was ported to R by P. Frei (2016).

en: Parameter file

en: Description of ASRB parameters.

en: DATA ACCESS

media_type: SMET

en: ASRB radiation station at the Weissfluhjoch versuchsfeld.

en:
Incoming and outgoing shortwave and longwave 2 min radiation measurements at the Weissfluhjoch research site, Davos, CH. The experimental site at the Weissfluhjoch (WFJ, 46.83 N, 9.81 E) is located at an altitude of 2540 m in the Swiss Alps near Davos. During the winter months, almost all precipitation falls as snow at this altitude. As a consequence, a continuous seasonal snow cover builds up every winter, with a maximum snow height ranging from 153–366 cm over the period 1934–2012. The measurement site is located in an almost flat part of a southeast oriented slope.

 References
1. Marty, C., Philipona, R., Frohlich, C., Ohmura, A.. Altitude dependence of surface radiation fluxes and cloud forcing in the alps: results from the alpine surface radiation budget network. 2002. Theoretical and Applied Climatology. Volume 72. Issue 3-4. 137-155. http://dx.doi.org/10.1007/s007040200019. 10.1007/s007040200019.
2. Christoph Marty. Surface Radiation, Cloud Forcing and Greenhouse Effect in the Alps. 2000. Institute fuer Klimaforschung ETH. Zuercher Klima-Schriften. Volume 79. http://e-collection.library.ethz.ch/eserv/eth:23491/eth-23491-01.pdf.

davos

display_name: davos

longwave-radiation

display_name: longwave-radiation

radiation

display_name: radiation

shortwave-radiation

display_name: shortwave-radiation

weisfluhjoch

display_name: weisfluhjoch

en: ASRB_WFJVF: Shortwave and longwave radiation measurements (2 min) at the Weissfluhjoch research site, Davos


ceilometer-klosters

title_for_slug: ceilometer-klosters

en: klosters ceilometer vertical-visibility raclets cloud-base-height

en: Ceilometer data Klosters

media_type: mat

en: Ceilometer installed at Klosters measuring cloud base height, cloud cover and backscatter profile. The time resolution is 30 seconds, with time given in number of days from January 0, 0000, UTC. More information on the variables in the manufacturer manual.

en: Cloud base height (m) and vertical visibility (m) were measured with the VAISALA Ceilometer CL31 in Klosters (LON: 9.880413, LAT: 46.869019). The CL31 is an instrument with constant reliability for all weather conditions and simultaneous detection of three cloud layers in heights up to 7.6 km.

ceilometer

display_name: ceilometer

cloud-base-height

display_name: cloud-base-height

klosters

display_name: klosters

raclets

display_name: raclets

vertical-visibility

display_name: vertical-visibility

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: Ceilometer Klosters


maintainer: Zhao

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

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

media_type: ZIP

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

austin

display_name: austin

driving-factors

display_name: driving-factors

land-dynamics

display_name: land-dynamics

planning

display_name: planning

urban-land-use

display_name: urban-land-use


radar-wind-profiler-davos-wolfgang

maintainer: Maxim Hervo

title_for_slug: radar-wind-profiler-davos-wolfgang

en: radar wolfgangpass wind-speed wind-direction raclets

en: Radar Wind Profiler Data

en: A Vaisala Lap3000 was installed at Davos Wolfgang to record wind profiles. The time format is number of days fom January 0, 0000 in UTC with a 10 minute resolution.

en: The RADAR wind profiler from Meteoswiss was installed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577) and measured from 2171 m above sea level to 11079 m, with a temporal resolution of 10 minutes.

radar

display_name: radar

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

wolfgangpass

display_name: wolfgangpass

en: RADAR Wind profiler Davos Wolfgang


maintainer: Elisabeth Hafner

en: satellite avalanche natural-hazard snow hazard-mapping

en: SPOT6 Avalanche Outlines 16 January 2019

media_type: SHP

en: Shapefile of the manually mapped avalanche outlines. Please only use this data together with the example key document provided with this dataset and within the limitations of the dataset described in the publications.

format: SHP

en:
Outlines of 6'041 avalanches mapped from SPOT6 satellite data over the Swiss Alps on 16 January 2019. The dataset was acquired following a period with very high avalanche danger. The outlines have different attributes described in the data example key (ExampleKey_AvalMapping16012019.pdf)

The generation of the data is described in:

Bühler, Y., E. D. Hafner, B. Zweifel, M. Zesiger, and H. Heisig (2019), Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps, The Cryosphere, 13(12), 3225-3238, doi:10.5194/tc-13-3225-2019.

The data was comprehensivly validated in a subset area in

Hafner, E.D.; Techel, F.; Leinss, S.; Bühler, Y., 2021: Mapping avalanches with satellites - evaluation of performance and completeness. Cryosphere, 15, 2: 983-1004. doi: 10.5194/tc-15-983-2021

avalanche

display_name: avalanche

hazard-mapping

display_name: hazard-mapping

natural-hazard

display_name: natural-hazard

satellite

display_name: satellite

snow

display_name: snow

en: SPOT6 Avalanche outlines 16 January 2019


maintainer: Tessa Hegetschweiler

en: forest adolescents childhood teenagers recreational-activities

en: Dataset comments

en: The codebook to the dataset

format: XLS

en: Survey at schools 2017

en: The dataset of the questionnaire survey at schools in SPSS-format.

en: Questionnaire school survey

en: The questionnaire used in the survey at 8 schools aimed at teenagers in German.

en: Dataset survey at schools

en: The dataset of the questionnaire survey at schools in excel format.

en: The data results from a questionnaire survey conducted at 8 schools in the cantons Zurich, Aargau and St. Gallen. Respondents aged 13-22 years. The aim of the survey was to gain insight into teenagers' relationship to the forest, reasons for visiting or not visiting the forest and activities in the forest.

adolescents

display_name: adolescents

childhood

display_name: childhood

forest

display_name: forest

recreational-activities

display_name: recreational-activities

teenagers

display_name: teenagers

education

en: Education and science

en: Factors influencing teenagers' forest visit frequency


maintainer: Athanasios Nenes

en: cloud-modeling supersaturation wolfgangpass cloud-droplets raclets sigma-w

en: Predicted cloud droplet numbers (kappa=0.25) Weissfluhjoch

en:
In-situ measurements of aerosol size distributions along with updraft velocities collected from a wind Doppler Lidar are coupled with a state-of-the art droplet activation parameterization (Morales and Nenes, 2014) to determine the cloud droplet number and maximum in-cloud supersaturation that can potentially form over the Davos Wolfgang station between February 24 and March 8 2019. For this, as a sensitivity test, a hygroscopicity parameter of 0.25 is assumed, which is the upper limit determined from the analysis carried out for Weissfluhjoch (https://www.envidat.ch/dataset/ccn-hygroscopicity-predicted-cloud-droplet-numbers-weissfluhjoch). The data file contains:

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

en: Predicted cloud droplet numbers (kappa=0.1) Davos Wolfgang

en:
In-situ measurements of aerosol size distributions along with updraft velocities collected from a wind Doppler Lidar are coupled with a state-of-the art droplet activation parameterization (Morales and Nenes, 2014) to determine the cloud droplet number and maximum in-cloud supersaturation that can potentially form over the Davos Wolfgang station between February 24 and March 8 2019. For this, as a sensitivity test, a hygroscopicity parameter of 0.1 is assumed, which is the lower limit determined from the analysis carried out for Weissfluhjoch (https://www.envidat.ch/dataset/ccn-hygroscopicity-predicted-cloud-droplet-numbers-weissfluhjoch). The data file contains:

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

en: Cloud droplet properties were predicted between February 24 and March 8 2019 for the measurement site Davos Wolfgang (1630 m a.s.l., LON: 9.853594, LAT: 46.835577). Droplet calculations are carried out with the physically based aerosol activation parameterization of Morales and Nenes (2014), employing the “characteristic velocity” approach of Morales and Nenes (2010). Aerosol size distribution observations required to predict the cloud droplet numbers and maximum in-cloud supersaturation are obtained from a Scanning Mobility Particle Size Spectrometer (SMPS) instrument deployed at Davos Wolfgang (https://www.envidat.ch/dataset/aerosol-data-davos-wolfgang). The required vertical velocity measurements are derived from the wind Doppler Lidar (https://www.envidat.ch/dataset/lidar-wind-profiler-data) deployed at the same station and are extracted from the first bin of the instrument, being 200 m above ground level. The hygroscopic properties of the particles measured at Davos Wolfgang could not be estimated, owing to a lack of concurrent CCN measurements. As a sensitivity test, droplet calculations are performed assuming two different values of the aerosol hygroscopicity parameter, 0.1 and 0.25, based on the analysis carried out for Weissfluhjoch. Additional information can be found in Section 2.3 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).

cloud-droplets

display_name: cloud-droplets

cloud-modeling

display_name: cloud-modeling

raclets

display_name: raclets

sigma-w

display_name: sigma-w

supersaturation

display_name: supersaturation

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: Predicted cloud droplet numbers Davos Wolfgang


en: biodiversity biogeography pairwise-stand-comparisons forest-management soil-impacts

en: Baseline data

en: references

media_type: XLS

en: Bibliography of all references cited in data_wohlgemuth_etal_Neobiota_2022.

en: headers

media_type: ascii, tab separated, also as excel table

en: Description of headers for data_wohlgemuth_etal_Neobiota_2022

en: Compiled data on the impacts of seven important NNTs (Acacia dealbata, Ailanthus altissima, Eucalyptus globulus, Prunus serotina, Pseudotsuga menziesii, Quercus rubra, Robinia pseudoacacia) on physical and chemical soil and biodiversity in Europe, and summarise commonalities and differences. A total of 107 publications considered, studies referred to biodiversity attributes and soil properties: 2804 lines and 30 rows.

biodiversity

display_name: biodiversity

biogeography

display_name: biogeography

forest-management

display_name: forest-management

pairwise-stand-comparisons

display_name: pairwise-stand-comparisons

soil-impacts

display_name: soil-impacts

en: Impact of non-native tree species in Europe on soil properties and biodiversity: a review


en: climate ecological-indicator-values taxonomic-diversity grain-size phylogenetic-diversity functional-diversity soil

en: Data access

en:
This dataset comprises a large array of ecological data for the European Alps:

  (1) Current soil and climate predictors at various resolutions.
  (2) GBIF observations of the European Alps Flora (~4,000 species).
  (3) Species habitat suitability maps (1,109 species; based on species observations filtered at 40x40-km)
  at various resolutions used in the study to generate (4); except 'expert'...
  (4) Expert, Taxonomic, phylogenetic and functional diversity of the study region at various resolutions
  (from 100-m to 40-km --> 100-m aggregated & mean to km + non-aggregated/predicted) for CLIM,
  SOIL and CLIM-SOIL models.
  (5) Ecological and altitudinal preferences of the European Alps Flora.
  (6) Data outputs of the related published article.
  (7) All scripts used for analyses.
  (8) Additional files used for analyses.
  (9) Improved set of species habitat suitability maps (~2,600 species; based on species observations
  filtered at 1x1-km) and related taxonomic diversity at 100-m resolution (aggregated to km +
  non-aggregated/predicted) for CLIM, SOIL and CLIM-SOIL models ---> not incorporated in the study.

climate

display_name: climate

ecological-indicator-values

display_name: ecological-indicator-values

functional-diversity

display_name: functional-diversity

grain-size

display_name: grain-size

phylogenetic-diversity

display_name: phylogenetic-diversity

soil

display_name: soil

taxonomic-diversity

display_name: taxonomic-diversity

education

en: Education and science

en: Resolution in species distribution models shapes spatial patterns of plant multifaceted diversity


snow-drift-station-micro-rain-radar

title_for_slug: snow-drift-station-micro-rain-radar

en: mountain rain precipitation radar snow raclets

en: MRR data of period 3

en: The dataset contains measurements between March 8 and March 13, 2019, with time in UTC. The parameters and specifications are given in MRR_parameters.pdf. The zip folder contains hourly data files in netCDF format and a log file, reporting the instrument status.

en: Parameter MRR

en: The PDF contains additional information on parameters which were set for the certain time periods (time in UTC).

en: MRR data of period 4

en: The dataset contains measurements between March 13 and March 22, 2019, with time in UTC. The parameters and specifications are given in MRR_parameters.pdf. The zip folder contains hourly data files in netCDF format and a log file, reporting the instrument status.

en: MMR Data Period 1

en: The dataset contains measurements between March 1 and March 5, 2019, with time in UTC. The parameters and specifications are given in MRR_parameters.pdf. The zip folder contains hourly data files in netCDF format and a log file, reporting the instrument status.

en: MRR data Period 2

en: The dataset contains measurements between March 5 and March 8, 2019, with time in UTC. The parameters and specifications are given in MRR_parameters.pdf. The zip folder contains hourly data files in netCDF format and a log file, reporting the instrument status.

en: The instrument (MRR, Metek) was mounted at Gotschnagrat (LON: 46.859 LAT: 9.849) at a height of 1 m above snow surface (at the beginning of the campaign) with an orientation of 22° with respect to North and a horizontal viewing direction. The sampling time was either 5 s or 10 s, depending on the settings at the specific period. The MRR produces standard outputs like radar reflectivity, doppler velocity, etc., and additional information can be found [here](https://metek.de/de/product/mrr-2/).

mountain

display_name: mountain

precipitation

display_name: precipitation

radar

display_name: radar

rain

display_name: rain

snow

display_name: snow

en: Snow Drift Station - Micro Rain Radar


maintainer: Christoph Marty

en: climate-change-impacts snow water-cycle hydrology snowfall

en: Synthesis report chapter

en: DORA Persistent URL

en:
This report was prepared as one of the synthesis report chapters of the Hydro-CH2018 project of the Federal Office for the Environment (FOEN). An important feature of snow cover is the fact that its volume and duration is subject to large year-to-year fluctuations. As frozen precipitation, snow cover is nothing other than a natural water reservoir that delays precipitation to runoff and is thus of outstanding importance for the seasonal water balance in Switzerland. Over a whole year, approximately 40% (22 km3) of the annual runoff currently comes from snow melting and only 1% from glacier melting. Typically, the snow cover in the Alpine region builds up over the autumn and winter months, reaches its maximum between February and May, depending on the altitude, and dominates the runoff processes during melting in the following spring and summer months.
Due to the great dependence on minus temperatures and precipitation, the snow cover reacts sensitively to temperatures above 0° Celsius and more or less precipitation. Due to climate change and the associated warming, the proportion of precipitation that falls as snow decreases measurably. In addition to this reduction in snowfall, the warmer temperatures also cause the snow cover to melt more quickly. The decline in snowfall has so far mainly affected lower altitudes, where winter temperatures often reach positive levels.
As climate change progresses, this trend is likely to continue and above all affect higher zones. Even at higher altitudes, the snow cover will then start later, melt away earlier and is increasingly no longer permanently present. This development will also have an effect on the water bodies. Today nival regimes, i.e. regimes shaped by snow, are shifting towards pluvial regimes, i.e. regimes dominated by rain. Overall, winter runoff increases, summer runoff decreases. By the end of the century, the proportion of runoff from snowmelt will decrease throughout Switzerland, albeit to a lesser extent than the proportion from glacier melt.

climate-change-impacts

display_name: climate-change-impacts

hydrology

display_name: hydrology

snowfall

display_name: snowfall

water-cycle

display_name: water-cycle

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: Snow and the water cycle in a changing climate


en: aws cryowrf python antarctica consistent

media_type: .zip

en: Scripts to preprocess (and download for some databases) atmospheric weather station data. Furthermore, a list of stations and a map can be created to have an overview.

en:
There are many sources providing atmospheric weather station data for the Antarctic continent. However, variable naming, timestamps and data types are highly variable between the different sources. The published python code intends to make processing of different AWS sources from Antarctica easier. For all datasets that are taken into account variables are renamed in a consistent way. Data from different sources can then be handled in one consistent python dictionary.

The following data sources are taken into account:

* AAD: Australian Antarctic Division (https://data.aad.gov.au/aws)
* ACECRC: Antarctic Climate and Ecosystems Cooperative Research Centre by the Australian Antarctic Division
* AMRC: Antarctic Meteorological Research Center (ftp://amrc.ssec.wisc.edu/pub/aws/q1h/)
* BAS: British Antarctic Survey (ftp://ftp.bas.ac.uk/src/ANTARCTIC_METEOROLOGICAL_DATA/AWS/; https://legacy.bas.ac.uk/met/READER/ANTARCTIC_METEOROLOGICAL_DATA/)
* CLIMANTARTIDE: Antarctic Meteo-Climatological Observatory by the italian National Programme of Antarctic Research (https://www.climantartide.it/dataaccess/index.php?lang=en)
* IMAU: Institute for Marine and Atmospheric research Utrecht (Lazzara et al., 2012), https://www.projects.science.uu.nl/iceclimate/aws/antarctica.ph
* JMA: Japan Meteorological Agency (https://www.data.jma.go.jp/antarctic/datareport/index-e.html)
* NOAA: National Oceanic and Atmospheric Administration (https://gml.noaa.gov/aftp/data/meteorology/in-situ/spo/)
* Other/AWS_PE: Princess Elisabeth (PE), KU Leuven, Prof. N. van Lipzig, personal communication
* Other/DDU_transect: Stations D-17 and D-47 (in transect between Dumont d’Urville and Dome C, Amory, 2020)
* PANGAEA: World Data Center (e.g. König-Langlo, 2012)

Important notes

* Information about data sources is available. Some downloading scripts are included in the provided code. However, users should make sure to comply with the data providers terms and conditions.
* Given changing download options of the differnent institutions the above links may not permanently work and data has to be retrieved by the user of this dataset.
* No quality control is applied in the provided preprocessing software - quality control is up to the user of the datasets. Some dataset are quality controlled by the owner.

Acknowledgements
--------------------------
We thank all the data providers for making the data publicly available or providing them upon request. Full acknowledgements can be found in Gerber et al., submitted.

References
---------------
Amory, C. (2020). “Drifting-snow statistics from multiple-year autonomous measurements in Adélie Land, East
Antarctica”. The Cryosphere, 1713–1725. doi: 10.5194/tc-14-1713-2020

Gerber, F., Sharma, V. and Lehning, M.: CRYOWRF - a validation and the effect of blowing snow on the Antarctic SMB, JGR - Atmospheres, submitted.

König-Langlo, G. (2012). “Continuous meteorological observations at Neumayer station (2011-01)”. Alfred Wegener
Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, PANGAEA, doi: 10.1594/PANGAEA.
775173

antarctica

display_name: antarctica

aws

display_name: aws

consistent

display_name: consistent

cryowrf

display_name: cryowrf

python

display_name: python

education

en: Education and science

en: Preprocessing Antarctic Weather Station (AWS) data in python


en: permafrost borehole ground-temperature ritigraben permos

en: PERMOS DATA POLICY

media_type: PDF

en: Map of PERMOS borehole network

media_type: URL

en: DATA ACCESS

en:
_ENVIDAT NOTE: Data currently unvailable and measures are being taken to recover and restore the data files._

Processed ground temperature measurements at the Ritigraben permafrost borehole (RIT_0102) in canton Valais, Switzerland. The borehole is located at 2690 m asl on a flat site. The surface material is coarse blocks and borehole depth is 30 m. Thermistors used YSI 44006. Year of drilling 2002. This borehole is part of the Swiss Permafrost network, PERMOS (www.permos.ch). Contact phillips@slf.ch for details of processing applied.

borehole

display_name: borehole

ground-temperature

display_name: ground-temperature

permafrost

display_name: permafrost

permos

display_name: permos

ritigraben

display_name: ritigraben

en: RIT1: Processed permafrost borehole data (2690  m asl), Ritigraben, Switzerland


snow-depth-mapping

maintainer: Mauro Marty

title_for_slug: snow-depth-mapping

en: snow-hydrology dsm avalanche aerial-imagery image-matching snow-water-equivalent snow-depth

en: Snow depth map 20160420

media_type: image/tiff

format: TIFF

mimetype: image/tiff

en: Snow depth map 20160309

en: Snow depth map 20130415

en: Snow depth map generated from ADS 80 imagery (CIR). GSD of imagery = 0.25 m

en: Snow depth map 20100416

en: Snow depth map 20160126

en:
Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.16 m
Because of very diffuse light conditions at time of image acquisition the quality of the resulting snow depth values are in some regions quite poor.

en: Snow depth map 20120320

en: Snow depth map 20150415

en: Snow depth map 20140417

en: Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.18 m

en:
The available datasets are snow depth maps with a spatial resolution of 2m generated from image matching of ADS 80/100 data. Image acquisition took place at peak of winter (time when the thickest snowpack is expected). The snow depth maps are the difference of a summer DSM from the winter DSM of the corresponding date . The summer DSM used is a product of image matching of ADS 80 data from summer 2013. In the available products buildings, vegetation and outliers were masked (set to NoData).
For the elimination of buildings the TLM layer (swisstopo) was used, because this layer might not represent exactly the
state of infrastructure at time of image acquisition, it is possible that mainly in dense settlement some buildings were not successfully masked. For the relevant area above treeline the masking of buildings showed good results. Vegetation got masked for a height above ground > 1m and was detected in a combination of summer and winter data sets. As Outliers were considered unrealistic snow depths caused by a failure of the image matching algorithm. Snow depths > 15m and smaller than < -15m were classified as outliers. Negative snow depth were kept, because of an uncertainty in image orientation accuracy. It is expected that in regions with negative snow depth also positive snow depth are underestimated by the same amount, which means that an estimation of snow volume should be carried out summing up the absolute values of snow depth (also the negative ones). For volume estimation in small regions the user has to take into account, that orientation accuracy of the images is roughly around 1-2 GSD (30cm), which propagates directly to the snow depth product. Areas which are not covered by snow got assigned a value of 0 as snow depth.

The work is published in:

Bühler, Y.; Marty, M.; Egli, L.; Veitinger, J.; Jonas, T.; Thee, P.; Ginzler, C., (2015). Snow depth mapping in high-alpine catchments using digital photogrammetry. Cryosphere, 9 (1), 229-243. doi: 10.5194/tc-9-229-2015

aerial-imagery

display_name: aerial-imagery

avalanche

display_name: avalanche

dsm

display_name: dsm

image-matching

display_name: image-matching

snow-depth

display_name: snow-depth

snow-hydrology

display_name: snow-hydrology

snow-water-equivalent

display_name: snow-water-equivalent


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

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

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

en: Observed snow profiles

media_type: .csv

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

en: ReadMe

en: ReadMe file explaining data structure and content

en: Simulated snow profiles

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

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

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

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

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

failure-layer

display_name: failure-layer

rutschblock

display_name: rutschblock

snow-instability

display_name: snow-instability

snow-profiles

display_name: snow-profiles

snow-stratigraphy

display_name: snow-stratigraphy

snowpack

display_name: snowpack

snowpack-simulation

display_name: snowpack-simulation

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: Observed and simulated snow profile data


data-set-on-snow-instability

title_for_slug: data-set-on-snow-instability

en: failure-initiation crack-propagation slab snow-avalanche slab-tensile-support weak-layer snow-stability-evaluation rutschblock propgatoin-saw-test avalanche-forcasting

en: data set on snow instability

en: Read-me file on snow instability data

en:
These data on snow instability include three data subsets that were analyzed and the results published by Reuter and Schweizer (2018) who suggest a novel framework on how to describe snow instability by failure initiation, crack propagation and slab tensile support.

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

These data are the basis of the following publication:
Reuter, B. and Schweizer, J., 2018. Describing snow instability by failure initiation, crack propagation and slab tensile support. Geophys. Res. Lett., 45, doi: 10.1029/2018GL078069.

avalanche-forcasting

display_name: avalanche-forcasting

crack-propagation

display_name: crack-propagation

failure-initiation

display_name: failure-initiation

propgatoin-saw-test

display_name: propgatoin-saw-test

slab

display_name: slab

slab-tensile-support

display_name: slab-tensile-support

snow-avalanche

display_name: snow-avalanche

snow-stability-evaluation

display_name: snow-stability-evaluation

weak-layer

display_name: weak-layer

en: Data set on snow instability


maintainer: Matthias Haeni

en: precipitation swiss-forest-lab long-term par wind-speed meteo meteorology climate-change relative-humidity temperature

en: Forest station data (ALB)

en: Site aerial photo (Alpthal)

en: Site aerial photo from the Alpthal forest station. Source: Google. 2016. Site aerial photo from the Alpthal forest station. Retrieved November 11, 2016, from https://www.google.ch/maps

en: Data plot of the forest station (ALB)

en: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for one meteorological station in Alpthal in Switzerland which is located within a natural coniferous forest (ALB) with Norway spruce (_Picea abies_; 180-230 yrs) as dominant tree species. The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Alpthal is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

en: Alpthal, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards


en: elevation-gradient biodiversity community-indices functional-diversity protected-areas wild-bee climate resource-availability beta-diversity hymenoptera predictive-modelling switzerland anthophila urbanization

media_type: .txt

en:
Lat: latitude community plot
Lon: longitude community plot
Richness: species richness
Shannon: Shannon diversity index
FDis: functional dispersion
TOP: Trait Onion Peeling (functional richness)
TED: Trait Even Distribution (functional evenness)
LCBD_taxo: local community contributions to taxonomic beta diversity
LCBD_fun: local community contributions to functional beta diversity
ITD: community weighted means inter-tegula distance
feeding_specialization: community weighted means feeding specialization
tong_length: community weighted means tongue length
phenostart: community weighted means start of the phenology
phenoduration: community weighted means duration of the phenoloy
cleptoparasite: community weighted means cleptoparasites
belowgound: community weighted means proportion belowground
solitary: community weighted means proportion solitary

en:
Raw data supporting the paper "Countrywide wild bee taxonomic and functional diversity reveal a spatial mismatch between alpha and beta-diversity facets across multiple ecological gradients". It contains taxonomic and functional metrics in 4591 community-plots distributed across Switzerland. The calculated metrics are:
- Alpha taxonomic community metrics: species richness and Shannon diversity
- Alpha functional community metrics: Functional richness (using the Trait Onion Peeling index, TOP), functional eveness (using the Trait Even Distribution index, TED) and the functional dispersion.
- Community weighted means of 8 functional traits
- The local community contributions on the functional and taxonomic beta diversity (LCBD).

anthophila

display_name: anthophila

beta-diversity

display_name: beta-diversity

biodiversity

display_name: biodiversity

climate

display_name: climate

community-indices

display_name: community-indices

elevation-gradient

display_name: elevation-gradient

functional-diversity

display_name: functional-diversity

hymenoptera

display_name: hymenoptera

predictive-modelling

display_name: predictive-modelling

protected-areas

display_name: protected-areas

resource-availability

display_name: resource-availability

switzerland

display_name: switzerland

urbanization

display_name: urbanization

wild-bee

display_name: wild-bee

education

en: Education and science

en: Data on wild bee taxonomic and functional diversity in Switzerland


wind-crust-formation-snowmicropen-data

title_for_slug: wind-crust-formation-snowmicropen-data

en: smp snowmicropen wind-packing wind-crust snow

en: README

en: Describes the dataset in detail

en: Processing scripts

en: Contains the R-scripts used to process the SMPs

en: SMP data

media_type: Link

en: Links to the dataset

en:
This dataset contains the SnowMicroPen (SMP) data from 38 wind tunnel experiments on wind-packing / wind crust formation. These experiments were performed in the winters 2015/16 and 2016/17 and include more than 1000 SMP measurements.

The SMPs are organized per experiment. Each experiment subfolder contains the processed SMP profiles and some additional files.

Please refer to the README for more details on the data. The processing scripts are available for download as well. The scripts are mainly provided as documentation and would need to be adjusted to be used.

This dataset is the basis of the following publication:

Sommer C.G., Lehning M., & Fierz C. (2017). Wind tunnel experiments: Saltation is necessary for wind-packing. Journal of Glaciology, 63(242), 950-958. doi:10.1017/jog.2017.53

smp

display_name: smp

snow

display_name: snow

snowmicropen

display_name: snowmicropen

wind-crust

display_name: wind-crust

wind-packing

display_name: wind-packing

envidat

en: EnviDat: the Environmental Data Portal

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

en: Wind crust formation: SnowMicroPen data


en: orthoimage dem gletscher drone glacier uav griesgletscher sankt-annafirn alps findelengletscher dsm orthophoto

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Sankt Annafirn on the 28th of September 2015.

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Findelengletscher on the 19th of April 2016.

en: This folder contains all UAV flights paths that we created for the Sankt Annafirn, the Findelengletscher and Griesgletscher. They have been created with the software eMotion2, that was developped for the eBee platform from Sensefly.

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Griesgletscher on the 26th of September 2015.

media_type: application/pdf

en: This .pdf contains an orthoimage for each dataset as well as a Table which displays the number of flights made, image acquired, number of GCP set and the total area covered.

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Sankt Annafirn on the 15th of April 2015.

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Findelengletscher on the 30th of August 2016.

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Griesgletscher on the 13th of September 2016.

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Findelengletscher on the 21th of July 2015.

en: Bulk Data Access

en: Link to the S3 bucket where all the data resources can be downloaded in bulk

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Findelengletscher on the 2nd of July 2016.

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Findelengletscher on the 15th of September 2015.

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Findelengletscher on the 09th of April 2015.

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Sankt Annafirn on the 15th of July 2015.

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Griesgletscher on the 3rd of August 2015.

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Findelengletscher on the 22th of September 2016.

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Griesgletscher on the 15th of August 2015.

en:
 UAV-derived DSMs and orthoimages

Unmanned Aerial Vehicle (UAV) surveys were conducted between 2015 and 2016 on the Sankt Annafirn, Findelen- and Griesgletscher, situated in the Swiss Alps. Three surveys at the Sankt Annafirn allowed for a full glacier coverage, four surveys at Griesgletscher allowed an almost full glacier coverage and seven surveys at Findelengletscher allowed for a partial coverage of the glacier tongue (see individual datasets for exact extent). For each survey, a high resolution orthoimage and a Digital Surface Model (DSM) was created.

 UAV surveys:

Prior flight, Ground Control Points (GCPs) were deployed on the glacier surface and measured with a differential GPS (Trimble R7 or Leica GPS 1200). They allowed precise georeferencing of the UAV-derived datasets. UAV flight plans were planned with the software *eMotion 2* and a SenseFly eBee was used as surveying platform. The images were then processed with the software Agisoft Photoscan Pro 1.1.6 .

The location and dates of each survey can be found in the table together with the number of flights performed (Nflights), the number of acquired images (Nimages), the number of GCPs set (NGCPs) and the surveyed area.

A folder for each dataset is available (see folder name in table), which contains:

- An orthoimage *glacier_date_photoscan_oi_CH1903+_LV95_0.1m.tif*

- A Digital Surface Model *glacier_date_photoscan_dsm_CH1903+_LV95_0.1m.tif*

- The Agisoft Photoscan automatic processing report  *glacier_date_photoscan_report.pdf*

where:

- *glacier* is the name of the surveyed glacier

- *date* is the date of the UAV image acquisition

- *photoscan* is the name of the photogrammetric software

- *oi* or *dsm* the type of dataset

- *CH1903+_LV95* is the coordinate system and datum of the dataset

- *0.1m* is the resolution of the dataset in meter

- *.tif* is the extention of the dataset

Details about the UAV surveys, the image processing and the accuracy of the UAV-derived products can be found in this publication below.

Paper Citation:
 > _Gindraux et al. 2017. Accuracy Assessment of Digital Surface Models from Unmanned Aerial Vehicles’Imagery on Glaciers, Remote Sensing, 9, 186, 1-15, [doi: 10.3390/rs9020186](https://doi.org/10.3390/rs9020186)._

The folder UAV_flight_paths.zip contains all UAV flights performed on the Sankt Annafirn, Findelengletscher and Griesgletscher. The flights were planned with the software eMotion2 and have the .afp extention.

alps

display_name: alps

dem

display_name: dem

drone

display_name: drone

dsm

display_name: dsm

findelengletscher

display_name: findelengletscher

glacier

display_name: glacier

gletscher

display_name: gletscher

griesgletscher

display_name: griesgletscher

orthoimage

display_name: orthoimage

orthophoto

display_name: orthophoto

sankt-annafirn

display_name: sankt-annafirn

uav

display_name: uav

en: UAV-derived Digital Surface Models and orthoimages for three alpine glaciers


maintainer: Achille Capelli

en: snow-water-equivalent swe gnss liquid-water-content snow lwc gps

media_type: text/csv

en: Snow and meteo data from automatic sensors from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020.

en: GNSS derived SWE, LWC and HS from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-20

en: Snow profiles data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en: Snow profiles data from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020.

en: Webcam images

en: Webcam images showing some typical differences in the snow conditions at the four sites (Küblis, Klosters, Laret and Weissfluhjoch) for two time periods (14-18.3.2019 and 27.1-11.2.2020). Both time periods included a precipitation event with partial rain-on–snow occurring at the lower locations.

en: Snow profiles data from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020.

en: Daily data from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en: GNSS derived SWE, HS, and LWC for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en: Snow and meteo data from automatic sensors from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020.

en: Snow and meteo data from automatic sensors for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en: Daily observer data from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020.

en: Daily data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en: Daily data from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en: Daily observer data from automatic sensors from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020.

en: Snow and meteo data from automatic sensors for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

en: GNSS derived SWE, LWC and HS from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-20

en: Daily observer data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en: Manual profiles

en: Data of the traditional manual profiles for the locations Weissfluhjoch, Klosters and Küblis. For the location of Küblis there are no traditional manual profile data. We provided the data as both PDF and .CAAML format (See https://niviz.org/ or http://caaml.org/Schemas/V5.0/Profiles/SnowProfileIACS/CAAMLv5_SnowProfileIACS.pdf). Moreover, we added LWC and density profiles when available (all locations).

en: GNSS derived data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

en: Daily data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en: Profile data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

en: This database contains GNSS derived snow water equivalent (SWE), liquid water content (LWC), and snow height (HS) and reference data collected during the two winter 2018-2020 at 4 sites Weissfluhjoch (2540 m asl, 46°49’47’’ N, 9°48’34’’E), Laret (1515 m asl, . 46°50’2’’N, 9°52’17’’E), Klosters (1200 m asl, 46°51’49’’N, 9°53’17’’E), and Küblis (815 m asl, 46°54’48’’N, 9°46’54’’E).

gnss

display_name: gnss

gps

display_name: gps

liquid-water-content

display_name: liquid-water-content

lwc

display_name: lwc

envidat

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: Snow water equivalent measurements with low-cost GNSS receivers along a steep elevation gradient in the East-ern Swiss Alps


en: longwave-radiation snow-height meteo-station gemstock shortwave-radiation

en: DATA ACCESS

en:
Meteo data at Gemsstock [Level 2 dataset](http://models.slf.ch/p/dataset-processing/).

fields      = timestamp TSS HS VW DW VW_MAX OSWR ISWR ILWR RECORD AUX_VOLTAGE LOGGER_VOLTAGE LW_SENSOR_TEMP VENTILATION_FLAG OLWR

en: Meteorological station at Gemstock (3021 m asl) in Canton Uri. The station includes in/out LW/SW and a snow height sensor.

gemstock

display_name: gemstock

longwave-radiation

display_name: longwave-radiation

meteo-station

display_name: meteo-station

shortwave-radiation

display_name: shortwave-radiation

snow-height

display_name: snow-height

education

en: Education and science

en: EnviDat: the Environmental Data Portal

en: GEM2: Meteorological and snow station at Gemsstock (3021 m asl), Canton Uri, Switzerland


maintainer: Frank Techel

en: observations alps shapefile snow avalanches

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

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

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

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

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

alps

display_name: alps

avalanches

display_name: avalanches

observations

display_name: observations

shapefile

display_name: shapefile

snow

display_name: snow


automatic-detection-of-avalanches

maintainer: Matthias Heck

title_for_slug: automatic-detection-of-avalanches

en: hidden-markov-model snow avalanche-activity monitoring seismic

en: Results and Scripts

en: This zip package contains the classification results, coherence results, reference data set, python scripts used for the classification and the start and end times for the pre-processed continuous seismic data. A further readme file is also include for more detailed information.

en: This dataset contains the results obtained by an automatic classification using hidden Markov models of a continuous seismic dataset. To avoid long computational times, we reduced the seismic data using pre-processing step. The start and end times of the windows used for the classification are also included in this dataset. Furthermore, an avalanche reference data set is included and the python scripts used to perform the processing steps and the classification.

avalanche-activity

display_name: avalanche-activity

hidden-markov-model

display_name: hidden-markov-model

monitoring

display_name: monitoring

seismic

display_name: seismic

en: Automatic detection of avalanches


en: snow shortwave-radiation albedo winter-energy-production photovoltaic

en: Data Forward Scattering

en: GROUNDEYE model for multiple forward scattering in snow-covered terrain. Meteorological and topographical input data set for the Meierhofertäli near Davos, Switzerland, September 2017 - August 2018. Output data for September 2017 - August 2018 at hourly resolution and every pixel. Post-processing scripts for plotting and analysis.

en:
 Overview
We present GROUNDEYE, a new model of radiative transfer over mountainous terrain, which considers for the first time the forward scattering properties of snow.
Embedded in the surface process model Alpine3D, the new terrain radiation model GROUNDEYE receives interpolated real weather data with diffuse and direct broadband shortwave radiation for each pixel as well as a spatially variable plane albedo from the module SNOWPACK.

 Format
The GROUNDEYE model is written in c++, as is the entire environment of Alpine3d. The input and output data sets are .xlsx or .txt format, pre- and postprocessing including the generation of all figures is in .R format.

 Structure
In Data_Forward_Scattering.zip you will find all necessary data and model details to
reproduce the results of the JGR publication "How forward-scattering snow and terrain change the Alpine radiation balance with application to solar panels"

 - 	Model Input Data contains the meteorological and topographic input data sets, the BRDF, and preprocessing scripts.
 - Model Code contains the full model Alpine3d including the radiative transfer module GROUNDEYE.
 - Model Output Data contains the results of the simulation of terrain irradiance and irradiance of solar panels; hourly resolution, 1. Sptember 2017 - 31. August 2018.
 - Measurements Solar Testsite contains information and measurements of the solar testsite at the Totalp near Davos, Switzerland.
 - Postprocessing contains all R-Scripts used for the analysis and
plotting of the corresponding data.

In each of these folders you will find detailed information in
the file 'About this Folder.txt'.

albedo

display_name: albedo

photovoltaic

display_name: photovoltaic

winter-energy-production

display_name: winter-energy-production

envidat

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: Data, Code Link and Metadata on Forward Scattering of Snow at Totalp


en: satellite hazard-mapping snow natural-hazard avalanche

en: SPOT6 Avalanche outlines 24 January 2018

en: Shapefile of the manually mapped avalanche outlines. Please only use this data together with the example key document provided with this dataset.

en:
Outlines of 18'737 avalanches mapped from SPOT6 satellite data over the Swiss Alps on 24 January 2018. The outlines have different attributes described in the data example key (ExampleKey_AvalMapping.pdf)

The generation of the data is described in:

Bühler, Y., E. D. Hafner, B. Zweifel, M. Zesiger, and H. Heisig (2019), Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps, The Cryosphere, 13(12), 3225-3238, doi:10.5194/tc-13-3225-2019.

Abstract.
Accurate and timely information on avalanche occurrence are key for avalanche warning, crisis management and avalanche documentation. Today such information is mainly available at isolated locations provided by observers in the field. The achieved reliability considering accuracy, completeness and reliability of the reported avalanche events is limited. In this study we present the spatial continuous mapping of a large avalanche period in January 2018 covering the majority of the Swiss Alps (12’500 km2).

We tested different satellite sensors available for rapid mapping during a first avalanche period. Based on these experiences, we tasked SPOT6/7 data for data acquisition to cover the second, much larger avalanche period. We manually mapped the outlines of 18’737 individual avalanche events, applying image enhancement techniques to analyze regions in cast shadow as well as brightly illuminated ones. The resulting dataset of mapped avalanche outlines, having a unique completeness and reliability, is evaluated to produce maps of avalanche occurrence and avalanche size. We validated the mapping of the avalanche outlines using photographs acquired from helicopters just after the avalanche period.

This study demonstrates the applicability of optical, very high spatial resolution satellite data to map an exceptional avalanche period with very high completeness, accuracy and reliability over a large region. The generated avalanche data is of great value to validate avalanche bulletins, complete existing avalanche databases and for research applications by enabling meaningful statistics on important avalanche parameters.

Koordinate System: CH1903+ LV95 LN02

avalanche

display_name: avalanche

hazard-mapping

display_name: hazard-mapping

natural-hazard

display_name: natural-hazard

satellite

display_name: satellite

education

en: Education and science

en: EnviDat: the Environmental Data Portal


en: wind-speed meteo climate-change meteorology precipitation long-term relative-humidity temperature swiss-forest-lab par

en: Site aerial photo (Bettlachstock)

media_type: image/jpeg

en: Site aerial photo from both Bettlachstock stations. Source: Google. 2016. Site aerial photo from both Bettlachstock stations. Retrieved November 11, 2016, from https://www.google.ch/maps

en: Site photo of the field station (BTF)

en: Site photo of the field station from the side.

en: Data plot of the forest station (BTB)

en: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

en: Site photo of the forest station (BTB)

en: Site photo of the forest station from the side.

en: Field station data (BTF)

en: Data file.

en: Data plot of the field station (BTF)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Bettlachstock in Switzerland where one station is located within a natural mixed forest stand (BTB) with European beech (_Fagus sylvatica_; 170-190 yrs), European silver fir (_Abies alba_; 190 yrs) and Norway spruce (_Picea abies_; 200 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, BTF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Bettlachstock is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

en: Bettlachstock, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1997 onwards


maintainer: Peter Bebi

en: soil-temperature experiment climate-change carbon-dioxide vegetation

en: Project description

en: FACE experimental design

media_type: PNG

format: PNG

en: FACE DATASETS

media_type: application/zip

en:
 Overview
High elevation ecosystems are important in research about environmental change because shifts in climate associated with anthropogenic greenhouse gas emissions are predicted to be more pronounced in these areas than in most other regions of the world. This project involves a Free Air CO2 Enrichment (FACE) and soil warming experiment located in a natural treeline environment near Davos, Switzerland (Stillberg, 2200 m a.s.l.). Elevated atmospheric CO2 concentrations (+200 ppm) were applied from 2001 to 2009, and a soil warming treatment (+4 K) was added in 2007 (ongoing). The combined CO2 enrichment and warming treatment reflects conditions expected to occur in this region in approximately 2050. A broad range of ecological and biogeochemical research is carried out as part of this environmental change project.

#Experimental setup
The experiment consists of 40 hexagonal 1.1 m2 plots, 20 with a Pinus mugo ssp. uncinata (mountain pine, evergreen) individual in the centre and 20 with a Larix decidua (European larch, deciduous) individual in the centre. A dense cover of understorey vegetation surrounds the tree in each plot, including the dominant dwarf shrub species Vaccinium myrtillus (bilberry), Vaccinium gaultherioides (group V. uliginosum agg., northern bilberry) and Empetrum nigrum ssp. hermaphroditum (crowberry) plus several herbaceous and non-vascular species.

At the beginning of the experimental period, the 40 plots were assigned to 10 groups of four neighbouring plots (two larch and two pine trees per group) in order to facilitate the logistics of CO2 distribution and regulation. Half of these groups were randomly assigned to an elevated CO2 treatment while the remaining groups served as controls and received no additional CO2. In spring 2007, one plot of each tree species identity was randomly selected from each of the 10 CO2 treatment groups and assigned a soil warming treatment, yielding a balanced design with a replication of five individual plots for each combination of CO2 level, warming treatment and tree species.

#Data
Soil and air conditions have been monitored closely throughout the study period, with most measurements made during the combined CO2 x warming experiment (2007-2009).

carbon-dioxide

display_name: carbon-dioxide

experiment

display_name: experiment

soil-temperature

display_name: soil-temperature

vegetation

display_name: vegetation

envidat

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: FACE: Stillberg CO2 enrichment and soil warming study