bildersammlung-annemarie-schwarzenbach

title_for_slug: Bildersammlung Annemarie Schwarzenbach

fr: annemarie-schwarzenbach glam openglam photographie

de: annemarie-schwarzenbach fotografien glam openglam

en: annemarie-schwarzenbach glam openglam photographs

it: fotografia glam openglam

fr: Bildersammlung Annemarie Schwarzenbach

de: Bildersammlung Annemarie Schwarzenbach

en: Bildersammlung Annemarie Schwarzenbach

it: Bildersammlung Annemarie Schwarzenbach

fr: Document numérique (JPG - moyenne résolution)

de: Digitalisate (JPG - mittlere Auflösung)

en: Digital copies (JPG - medium resolution)

it: Digitalizzazione (JPG - media risoluzione)

fr: Document numérique (JPG)en moyenne résolution

en: Digital copies (JPG) in medium resolution.

de: Digitalisate (JPG) in mittlerer Auflösung

it: Digitalizzazione (JPG) a media risoluzione

fr: Métadonnées original

de: Metadaten original

en: Metadata original

it: Metadati originale

fr: Métadonnées version enrichie GLAMhack2017

de: Metadaten angereicherte Version GLAMhack2017

en: Metadata enriched version GLAMhack2017

it: Metadati versione arricchitis GLAMhack2017

fr:
Version des métadonnées enrichies du "3rd Swiss Open Cultural Data Hackathon".

Métadonnées supplémentaire: Land_all, Ort_all, Ort_Wikidata, latlong, URL_Bild_Direkt

Voir: https://make.opendata.ch/wiki/project:annemarie_schwarzenbach_photo_collection

en:
Enriched metadata version from the "3rd Swiss Open Cultural Data Hackathon".

Additional metadata fields: Land_all, Ort_all, Ort_Wikidata, latlong, URL_Bild_Direkt

See: https://make.opendata.ch/wiki/project:annemarie_schwarzenbach_photo_collection

de:
Angereichterte Metadatenversion vom "3rd Swiss Open Cultural Data Hackathon".

Zusätzliche Metadatenfelder: Land_all, Ort_all, Ort_Wikidata, latlong, URL_Bild_Direkt

Siehe: https://make.opendata.ch/wiki/project:annemarie_schwarzenbach_photo_collection

it:
Versione di metadati arricchitis di "3rd Swiss Open Cultural Data Hackathon".

Metadati supplementare: Land_all, Ort_all, Ort_Wikidata, latlong, URL_Bild_Direkt

Vedi: https://make.opendata.ch/wiki/project:annemarie_schwarzenbach_photo_collection

it: Wikimedia Commons (alta risoluzione)

fr:
Annemarie Schwarzenbach, 1908 in Zürich geboren, 1942 in Sils gestorben, gehört zu den herausragenden Schweizer Reporterinnen der 1930er und 1940er Jahre. Sie bezeichnete sich selbst als „unheilbar Reisende“. Die Schriftstellerin, Fotografin und Reisefeuilletonistin war für ihre Zeit provozierend emanzipiert. Sie verkehrte ab 1930 in den Kreisen um die Familie Mann; insbesondere mit den älteren Geschwistern Erika und Klaus pflegte sie eine enge und langjährige Freundschaft. Auf weiten Reisen erkundete sie von 1933 bis zu ihrem Tod 1942 Vorderasien, den Orient, die USA, Spanien und Westafrika. Ihre Reiseberichte und Fotografien fesselten die Schweizer Leserschaft in den 1930er Jahren, und sie faszinieren bis heute. Denn sie sind Zeugnisse einer Zeit, in der das Reisen ein Sturz ins Ungewisse war. Legendär ist ihre Persienreise 1939 mit der Genfer Reiseautorin Ella Maillart. Als erste Frauen reisten die beiden mit einem Ford auf dem Landweg von der Schweiz über den Balkan, Türkei, Persien und Afghanistan bis nach Indien.

Das Foto-Archiv von Annemarie Schwarzenbach umfasst rund 7000 Abzüge. Häufig sind die Abzüge von kurzen Legenden und Kommentaren begleitet. Diese lassen sie die vielen Abenteuer und unplanbaren Ereignisse unterwegs erahnen.

In diesem Datensatz finden Sie über 3000 Bilder, die von Annemarie Schwarzenbach selbst fotografiert wurden.

annemarie-schwarzenbach

display_name: annemarie-schwarzenbach


fluglarm-lmax

regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

envi

fr: Environnement

de: Umwelt

en: Environment

it: Ambiente

title_for_slug: fluglarm-lmax

url: https://geo.ur.ch/?layers=Fluglärm Lmax

label: Web GIS Client des Kanton Uri. Die Daten können direkt via dem Portal in den unterschiedlichsten Datenformaten heruntergeladen werden.

label: geocat.ch permalink

state: active

type: dataset

fr: WMS-Dienst des Kanton Uri

de: WMS-Dienst des Kanton Uri

en: WMS-Dienst des Kanton Uri

it: WMS-Dienst des Kanton Uri

format: WMS

fr: WFS-Dienst des Kanton Uri

de: WFS-Dienst des Kanton Uri

en: WFS-Dienst des Kanton Uri

it: WFS-Dienst des Kanton Uri

format: WFS

fr: Der Fluglärmbelastungskataster bildet den Lärm aufgrund des Betriebs des Heliports in Erstfeld ab. Die Fluglärmberechnungen basieren auf dem Sachplan Infrastruktur der Luftfahrt (SIL) - Entwurf 22.09.2016. Die Lärmbelastungskurven stammen aus Berechnungen, die auf den tatsächlichen bzw. prognostizierten Flugbewegungen und den bekannten Lärmemissionen der verschiedenen Helikoptertypen beruhen. Die zuständige Stelle für die Berechnungen ist das Bundesamt für Zivilluftfahrt (BAZL) in Bern.

spatial: Canton of Uri (UR)

geoinformation-kanton-uri

de: Lisag AG, Geoinformation Kanton Uri

de: Die Lisag AG bewirtschaftet das geografische Informationssystem des Kantons Uri (GIS Uri), ist die verantwortliche Stelle für den ÖREB-Kataster Uri und zudem zuständig für die Durchführung der amtlichen Vermessung auf Urner Kantonsgebiet.

political_level: canton

kanton-uri

capacity: public

type: organization

approval_status: approved

value: Uri-Kt - Geocat Harvester

fr: Fluglärm Lmax

de: Fluglärm Lmax

en: Fluglärm Lmax

it: Fluglärm Lmax


gefahrenkartierung

title_for_slug: gefahrenkartierung

fr: geodonnees risque-naturel evaluation-du-risque risque-naturel

de: geodaten naturrisiko risikobewertung naturgefahr

en: geodata natural-risk risk-assessment natural-hazard

it: geodati rischio-naturale valutazione-del-rischio pericoli-naturali

de: Fliesstiefenkarte Wasser HQ30

de: Fliesstiefenkarte Wasser HQ100

de: Fliesstiefenkarte Wasser HQ300

de: Erhebungsgebiet

de: Naturgefahren Erhebungsgebiet

de: Gefahrenkarte Rutschungen

de: Synoptische Gefahrenkarte

de: Gefahrenkarte Wasser

de: Intensitätskarte Rutschungen permanent

de: Intensitätskarte Rutschungen T30

de: Intensitätskarte Rutschungen T100

de: Intensitätskarte Rutschungen T300

de: Intensitätskarte Wasser HQ30

de: Intensitätskarte Wasser HQ100

de: Intensitätskarte Wasser HQ300

de: Karte der Phänomene Rutschungen

de: Karte der Phänomene Wasser

de: Gefahrenkarte Erhebungsgebiet

de: Gefahrenkarte synoptisch

de: Gefahrenkarte und Nebenprodukte: Darstellung der Gefährdung aufgrund der Prozesse Hochwasser/Überschwemmung und Rutschung/Hangmuren. Nebenprodukte sind Karte der Phänomene, Fliesstiefen- und Intensitätskarte.

kanton-thurgau

de: Der Kanton Thurgau veröffentlicht hier schrittweise OGD-Datensätze der kantonalen Verwaltung.


konzessionen-grundwasserwarmepumpen

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

ener

fr: Énergie

de: Energie

en: Energy

it: Energia

regi

de: Regionen und Städte

envi

de: Umwelt

title_for_slug: konzessionen-grundwasserwarmepumpen

de: WMS-Dienst des Kanton Uri

de: WFS-Dienst des Kanton Uri

fr: Hier sind die vom Kanton Uri verliehenen Konzessionen zur Wärmenutzung mittels Grundwasserwärmepumpen als Punkte aufgeschaltet. Die Wärme aus Grundwasserwärmepumpen wird in den meisten Fällen zu Heizzwecken (mit Wärmepumpen) genutzt. Es gibt aber auch andere Anwendungen wie zum Beispiel zur Kühlung (Klimatisierung, Prozesskühlung). Ergänzende Informationen für die Wärmenutzung aus Boden und Untergrund liefert das Wärmenutzungskonzept Kanton Uri 2017 (auf www.ur.ch) sowie die folgenden Layer auf www.geo.ur.ch: Konzessionen Erdsonden, Zulässigkeit Grundwasserwärmepumpen, Zulässigkeit Erdsonden, Grundwasserleiter und Isohypsen.

geoinformation-kanton-uri

de: Lisag AG, Geoinformation Kanton Uri

de: Die Lisag AG bewirtschaftet das geografische Informationssystem des Kantons Uri (GIS Uri), ist die verantwortliche Stelle für den ÖREB-Kataster Uri und zudem zuständig für die Durchführung der amtlichen Vermessung auf Urner Kantonsgebiet.

fr: Konzessionen Grundwasserwärmepumpen

de: Konzessionen Grundwasserwärmepumpen

en: Konzessionen Grundwasserwärmepumpen

it: Konzessionen Grundwasserwärmepumpen


strassenverkehrslarm-nacht

title_for_slug: strassenverkehrslarm-nacht

fr: Der Strassenlärmkataster wurde mit dem Berechnungsprogramm SLIP erstellt. Grundlagen für die Berechnungen bilden die Verkehrszahlen der Kantons- und Gemeindestrassen aus dem Jahr 2018. Bei den Berechnungen werden die Topographie wie auch Hindernisse (Lärmschutzwände, Gebäude, usw.) mitberücksichtigt.

fr: Strassenverkehrslärm Nacht

de: Strassenverkehrslärm Nacht

en: Strassenverkehrslärm Nacht

it: Strassenverkehrslärm Nacht


nutzungsplanung-ur

title_for_slug: nutzungsplanung-ur

fr: affectation-primaire planification-de-lutilisation-du-sol plan-de-zones espace-reserve-aux-eaux geodonnees-de-base

de: grundnutzung nutzungsplanung zonenplan gewasserraum geobasisdaten

en: primary-use land-use-planning zoning-plan space-provided-for-waters official-geodata

it: utilizzazione-di-base pianificazione-dellutilizzazione piano-delle-zone spazio-riservato-alle-acque geodati-di-base

fr: Der Nutzungsplan (kantonal / regional und kommunal) ordnet die räumlichen Sachverhalte in genereller Weise: die Nutzung des Bodens, die Erschliessung, den Schutz usw., indem er eigentümerverbindlich und parzellenscharf Art und Mass der Nutzung festlegt (Art. 14 RPG).

fr: Nutzungsplanung (UR)

de: Nutzungsplanung (UR)

en: Nutzungsplanung (UR)

it: Nutzungsplanung (UR)


strassenverkehrslarm-tag

title_for_slug: strassenverkehrslarm-tag

de: Der Strassenlärmkataster wurde mit dem Berechnungsprogramm SLIP erstellt. Grundlagen für die Berechnungen bilden die Verkehrszahlen der Kantons- und Gemeindestrassen aus dem Jahr 2018. Bei den Berechnungen werden die Topographie wie auch Hindernisse (Lärmschutzwände, Gebäude, usw.) mitberücksichtigt.

fr: Strassenverkehrslärm Tag

de: Strassenverkehrslärm Tag

en: Strassenverkehrslärm Tag

it: Strassenverkehrslärm Tag


globalstrahlung

regi

fr: Régions et villes

title_for_slug: globalstrahlung

fr: Hier wird die über das Jahr aufsummierte auf eine horizontale Fläche auftreffende solare Strahlungsenergie als Zahlenwert und farblich dargestellt. Die Globalstrahlung setzt sich aus direkter sowie aus diffuser Strahlung der Sonne zusammen. Die Einheit ist kWh/m2. Beträgt der Wert an einem Standort beispielsweise 1000 kWh/m2, trifft an diesem Standort pro Quadratmeter Fläche über das Jahr aufsummiert eine Strahlungsenergie von 1000 kWh auf. Dabei werden die topografischen Verschattungen berücksichtigt. Diese Information hilft bei der Beurteilung, ob ein Standort für eine Nutzung der Sonnenenergie geeignet sein könnte. Für die Eignung von bereits bestehenden Dächern und Fassaden gibt es auf dem Geoportal der Schweiz die Karten Solarenergie: Eignung Dächer sowie Solarenergie: Eignung Fassade, welche auch auf dem Geoportal Uri zu finden sind.

fr: Globalstrahlung

de: Globalstrahlung

en: Globalstrahlung

it: Globalstrahlung


hohen-ur

envi

fr: Environnement

title_for_slug: hohen-ur

fr: ombrage geodonnees-de-base

de: schummerung geobasisdaten

en: hillshade official-geodata

it: ombreggiatura geodati-di-base

fr: WMS-Dienst des Kanton Uri

fr: WFS-Dienst des Kanton Uri

fr: DTMAV (Punkte, Grid, Reliefschattierung). Swissalti3D. Alle 2m ein Koordinatenpunkt mit Höhe.

fr: Höhen (UR)

de: Höhen (UR)

en: Höhen (UR)

it: Höhen (UR)


wasser-leitungen-darstellung-eigentum

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

ener

fr: Énergie

title_for_slug: wasser-leitungen-darstellung-eigentum

url: https://geo.ur.ch/?layers=Wasser Leitungen Darstellung Eigentum

fr: Dieser Datensatz hat den gleichen Inhalt wie der Datensatz "Wasser Leitung öffentlich". Die Wasserleitungen sind gemäss dem Attribut "Eigentum" verschiedenfarbig dargestellt.

fr: Wasser Leitungen Darstellung Eigentum

de: Wasser Leitungen Darstellung Eigentum

en: Wasser Leitungen Darstellung Eigentum

it: Wasser Leitungen Darstellung Eigentum


wasser-leitungen-offentlich

ener

de: Energie

regi

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

title_for_slug: wasser-leitungen-offentlich

fr: In diesem Datensatz werden Leitungen für die Wasserversorgung verwaltet. Im Kanton Uri sind nicht alle Wasserversorgungen digital erfasst. Die Gemeindeverwaltungen oder Wasserversorgungen bestimmen zur Zeit die Verfügbarkeit des öffentlichen oder geschützten Zugangs.

fr: Wasser Leitungen öffentlich

de: Wasser Leitungen öffentlich

en: Wasser Leitungen öffentlich

it: Wasser Leitungen öffentlich


fernwarme-leitungen

envi

de: Umwelt

en: Environment

it: Ambiente

title_for_slug: fernwarme-leitungen

de: WMS-Dienst des Kanton Uri

en: WMS-Dienst des Kanton Uri

it: WMS-Dienst des Kanton Uri

de: WFS-Dienst des Kanton Uri

fr: Fernwärme Trasse und Leitungspunkt.

de: Fernwärme Trasse und Leitungspunkt.

en: Fernwärme Trasse und Leitungspunkt.

it: Fernwärme Trasse und Leitungspunkt.

fr: Fernwärme Leitungen

de: Fernwärme Leitungen

en: Fernwärme Leitungen

it: Fernwärme Leitungen


gestaltungsplane-perimeter

regi

fr: Régions et villes

title_for_slug: gestaltungsplane-perimeter

de: eigentumsbeschrankungoreb nutzungsplanung raumplanung sondernutzungsplanung

protocol: https

de: Datenshop

de: Produktansicht Geodatenshop

format: SERVICE

de: Perimeter der Gestaltungspläne

spatial: Kanton Luzern

fr: Géoinformation du canton de Lucerne

de: Geoinformation Kanton Luzern

en: Geoinformation Canton of Lucerne

it: Geoinformazione del Cantone di Lucerna

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

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

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

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

value: LU-Kt - Geocat Harvester

de: Gestaltungspläne: Perimeter


elektro-leitungen

educ

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

ener

en: Energy

it: Energia

envi

fr: Environnement

title_for_slug: elektro-leitungen

en: WFS-Dienst des Kanton Uri

it: WFS-Dienst des Kanton Uri

fr: Elektro Trasse, Bauwerke und Trassepunkte.

geoinformation-kanton-uri

de: Lisag AG, Geoinformation Kanton Uri

de: Die Lisag AG bewirtschaftet das geografische Informationssystem des Kantons Uri (GIS Uri), ist die verantwortliche Stelle für den ÖREB-Kataster Uri und zudem zuständig für die Durchführung der amtlichen Vermessung auf Urner Kantonsgebiet.

fr: Elektro Leitungen

de: Elektro Leitungen

en: Elektro Leitungen

it: Elektro Leitungen


maintainer: Christina Hürzeler

title_for_slug: Ergebnistabellen externe Effekte des Verkehrs 2015 - 2020

author: Bundesamt für Raumentwicklung (ARE)

label: Externe Kosten und Nutzen des Verkehrs in der Schweiz - Strassen-, Schienen-, Luft- und Schiffsverkehr

fr: Table des résultats 2015 effets externes des transports

de: Ergebnistabellen 2015 externe Effekte des Verkehrs

en: Result tables 2015 external effects of transport

it: Tabella dei risultati 2015 effetti esterni dei trasporti

format: XLS

fr: Table des résultats 2016 effets externes des transports

de: Ergebnistabellen 2016 externe Effekte des Verkehrs

en: Result tables 2016 external effects of transport

it: Tabella dei risultati 2016 effetti esterni dei trasporti

fr: Table des résultats 2017 effets externes des transports

de: Ergebnistabellen 2017 externe Effekte des Verkehrs

en: Result tables 2017 external effects of transport

it: Tabella dei risultati 2017 effetti esterni dei trasporti

fr: Table des résultats 2018 effets externes des transports

de: Ergebnistabellen 2018 externe Effekte des Verkehrs

en: Result tables 2018 external effects of transport

it: Tabella dei risultati 2018 effetti esterni dei trasporti

fr: Table des résultats 2019 effets externes des transports

de: Ergebnistabellen 2019 externe Effekte des Verkehrs

en: Result tables 2019 external effects of transport

it: Tabella dei risultati 2019 effetti esterni dei trasporti

fr: Table des résultats 2020 effets externes des transports

de: Ergebnistabellen 2020 externe Effekte des Verkehrs

en: Result tables  2020 external effects of transport

it: Tabella dei risultati 2020 effetti esterni dei trasporti

fr: Transport et mobilité engendrent de nombreux coûts et bénéfices, dont une partie est directement perceptible par les usagers. Exemples : le coût de l’essence ou du billet de train ou l’avantage d’arriver confortablement au travail en train ou en voiture. En payant pour l’essence ou pour le billet de train, les usagers assument une partie des coûts qu’ils occasionnent. Mais il y a des coûts qui, bien qu’étant occasionnés par la mobilité, ne se répercutent pas sur son prix. Ils sont appelés coûts externes et surviennent sous forme de dommages causés dans les domaines suivants : environnement, accidents, santé. Ils sont assumés par des tiers, par la collectivité voire par les générations futures.

en: Transport and mobility result in a range of costs and benefits. Some of these are felt directly by transport users: the costs of petrol or a rail ticket, or the benefit of getting to work conveniently by car or train. By paying for fuel or a ticket, transport users assume a share of the costs that they cause. There are other costs, however. Although these are caused by mobility, they are not reflected in its price. These are known as external costs, and they are environmental and accident and health-related in nature. These costs are borne by third parties, by the general public, or by future generations.

de: Verkehr und Mobilität verursachen eine Reihe von Kosten und Nutzen. Ein Teil davon ist für die Verkehrsteilnehmenden direkt spürbar: als Ausgabe für Benzin und ein Zugbillett oder der Nutzen, mit dem Auto oder dem Zug bequem an den Arbeitsort zu gelangen. Indem Verkehrsteilnehmende für Benzin oder Zugbillette bezahlen, übernehmen sie einen Teil der von ihnen verursachten Kosten. Daneben gibt es Kosten, die zwar durch die Mobilität verursacht werden, sich aber nicht im Preis für die Mobilität niederschlagen. Diese so genannten externen Kosten fallen als Folge von Schäden in der Umwelt, bei Unfällen und bei der Gesundheit an. Getragen werden sie von Dritten, der Allgemeinheit oder zukünftigen Generationen.

it: I trasporti e la mobilità sono all'origine di una serie di costi e benefici, una parte dei quali si ripercuote direttamente sugli utenti. Si tratta, ad esempio, dei costi della benzina o di un biglietto ferroviario oppure dei benefici legati alla comodità di raggiungere il proprio posto di lavoro in automobile o in treno. Pagando la benzina o il biglietto ferroviario, gli utenti della mobilità riprendono una parte dei costi da loro stessi occasionati. Vi sono inoltre costi, anche essi riconducibili alla mobilità, che però non si riflettono sui costi della mobilità: sono quei costi definiti esterni, causati dai danni in relazione all'ambiente, agli incidenti e alla salute, che vengono sopportati da terzi, dalla collettività o dalle future generazioni.

costi-e-benefici-esterni

display_name: costi-e-benefici-esterni

couts-et-benefices-externes

display_name: couts-et-benefices-externes

effets-externes

display_name: effets-externes

effetti-esterni

display_name: effetti-esterni

external-costs-and-benefits

display_name: external-costs-and-benefits

external-effects

display_name: external-effects

externe-effekte

display_name: externe-effekte

externe-kosten-und-nutzen

display_name: externe-kosten-und-nutzen

transport

display_name: transport

trasporti

display_name: trasporti

verkehr

display_name: verkehr

fr: Table des résultats effets externes des transports 2015 - 2020

en: Result tables external effects of transport 2015 - 2020

it: Tabella dei risultati effetti esterni dei trasporti 2015 - 2020

heal

fr: Santé

de: Gesundheit

en: Health

it: Salute

gove

fr: Gouvernement et secteur public

de: Regierung und öffentlicher Sektor

en: Government and public sector

it: Governo e settore pubblico

tran

fr: Transports

de: Verkehr

en: Transport

it: Trasporti

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.

political_level: confederation


maintainer: Bundesamt für Landwirtschaft

title_for_slug: Agrarumweltindikatoren und -kennzahlen auf nationaler Ebene 1990-2015

fr: Données et indicateurs agro-environnementaux au niveau national 1990-2015

it: Indicatori agro-ambientali e dati agroambientali a livello nazionale 1990-2015

author: Bundesamt für Landwirtschaft

de: Entwicklung von Agrarumweltindikatoren und -kennzahlen (Stickstoff, Phosphor, Klima und Energie, Biodiversität, Bodennutzung, Produktionsmittel)

agri

fr: Agriculture, pêche, sylviculture et alimentation

de: Landwirtschaft, Fischerei, Forstwirtschaft und Nahrungsmittel

en: Agriculture, fisheries, forestry and food

it: Agricoltura, pesca, silvicoltura e prodotti alimentari

bundesamt-fur-landwirtschaft-blw

fr: Office fédéral de l'agriculture OFAG

de: Bundesamt für Landwirtschaft BLW

en: Federal Office for Agriculture FOAG

it: Ufficio federale dell'agricoltura UFAG

fr: L’Office fédéral de l’agriculture (OFAG) est le centre de compétence de la Confédération pour les questions relevant du secteur agricole. Il œuvre pour une agriculture multifonctionnelle.

en: The Federal Office for Agriculture (FOAG) is the Confederation's competence center for all core issues relating to the agricultural sector. The FOAG promotes multifunctional agriculture.

de: Das Bundesamt für Landwirtschaft (BLW) ist das Kompetenzzentrum des Bundes für den Agrarsektor. Es setzt sich für eine multifunktionale Landwirtschaft ein.

it: L’Ufficio federale dell’agricoltura (UFAG) è il centro di competenza della Confederazione per le questioni inerenti al settore agricolo. Esso si impegna per un'agricoltura multifunzionale.


unuberbaute-bauzonen-in-kraft-genehmigt

regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

envi

fr: Environnement

de: Umwelt

en: Environment

it: Ambiente

title_for_slug: unuberbaute-bauzonen-in-kraft-genehmigt

de: gebaude-und-anlagen nutzungsplanung raumplanung wirtschaftliche-aktivitaten zonenplan

de: Datenshop

de: Produktansicht Geodatenshop

de: Überbauungsgrad der unüberbauten Bauzonen des genehmigten Zonenplans, laufend aktualisiert

fr: Géoinformation du canton de Lucerne

de: Geoinformation Kanton Luzern

en: Geoinformation Canton of Lucerne

it: Geoinformazione del Cantone di Lucerna

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

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

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

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

de: Unüberbaute Bauzonen, in Kraft (genehmigt)


unuberbaute-areale-in-kraft-genehmigt

title_for_slug: unuberbaute-areale-in-kraft-genehmigt

de: Unüberbaute Areale in Arbeitszonen des genehmigten Zonenplans, laufend aktualisiert

de: Unüberbaute Areale, in Kraft (genehmigt)


abfallanlagen-und-sammelstellen

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

ener

fr: Énergie

de: Energie

en: Energy

it: Energia

title_for_slug: abfallanlagen-und-sammelstellen

fr: Map (Preview)

de: Map (Preview) Stadtplan

en: Map (Preview)

it: Map (Preview)

de: Link zum Thema im Internet-Stadtplan der Stadt Bern

de: WMS Abfallsammelstellen

de: Web Map Services WMS

de: WFS Abfallsammelstellen

de: Web Feature Services WFS

de: Standorte der Entsorgungshöfe und öffentlichen Sammelstellen in der Stadt Bern. Informationen, welche Abfallarten abgegeben werden können. Die Standorte von privaten Unterflursammelstellen für Kehricht und Papier/Karton werden nicht abgebildet.

spatial: Stadt Bern

geoinformation-der-stadt-bern

fr: L’Office du cadastre de la ville de Berne

de: Geoinformation Stadt Bern

en: The Land Surveying Office of the City of Bern

it: L'ufficio del catasto nella Città di Berna

fr: L’Office du cadastre (Geoinformation Stadt Bern) est l’organisme compétent pour la collecte, l’accès et la diffusion d’informations géographiques dans la ville de Berne.

de: Geoinformation Stadt Bern (ehemals Vermessungsamt) ist die Kompetenzstelle für die Beschaffung, den Zugang und die Verbreitung von Geoinformationen in der Stadt Bern.

en: The Surveying Office (Geoinformation Stadt Bern) is the centre of competence in the City of Bern for the procurement of, access to and dissemination of geographic information.

it: L'ufficio del catasto (Geoinformation Stadt Bern) è il centro di competenza per il reperimento, l'accesso e la diffusione di geoinformazioni nella Città di Berna.

political_level: commune

value: Geoinformation Stadt Bern - Geocat Harvester

de: Abfallanlagen und Sammelstellen


zonenplane

regi

de: Regionen und Städte

envi

de: Umwelt

title_for_slug: zonenplane

de: baulinie eigentumsbeschrankungoreb grundeigentum nutzungsplanung raumplanung zonenplan

de: Datenshop

de: Produktansicht Geodatenshop

de: Zonenpläne regeln die zulässigen Nutzungen im Gebiet einer Gemeinde.

de: Geoinformation Kanton Luzern

de: Zonenpläne


okoinfomobil

educ

de: Bildung, Kultur und Sport

ener

de: Energie

title_for_slug: okoinfomobil

de: Map (Preview) Stadtplan

de: Link zum Thema im Internet-Stadtplan der Stadt Bern

de: WMS OekoInfoMobil

de: Haltestellen, an welchen das ÖkoInfoMobil einmal die Woche für 20 Minuten hält und kleine Abfallmengen entgegennimmt. Der Fahrplan ist fix und die Haltezeiten für die jeweiligen Standorte sind angegeben. Die Mitarbeitenden im Mobil beraten kompetent zu Entsorgungsfragen.

de: ÖkoInfoMobil


nutzungsplanung-planungszonen

title_for_slug: nutzungsplanung-planungszonen

de: bewilligungen eigentumsbeschrankungoreb nutzungsplanung raumplanung zonenplan

de: Die Planungszone bezeichnet ein Gebiet, in dem Nutzungspläne erlassen oder geändert werden müssen.

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

de: Nutzungsplanung: Planungszonen


maintainer: Jürg Schweizer

en: avalanche-size snow-avalanche danger-level avalanche-forcasting avalanche-activity

en: Avalanche activity data

media_type: text/csv

en: This data file includes data on the avalanche activity per day aggregated from the avalanche data.

format: CSV

mimetype: text/csv

en: Readme

media_type: application/pdf

en: The Readme file contains the description of the two data files.

format: PDF

mimetype: application/pdf

en: Snow avalanche data

en: This data file includes data on the avalanches mapped in the region of Davos.

en:
These data include all avalanches that were mapped in the region of Davos, Switzerland during the winters 1998-1999 to 2018-2019 (21 years), in total 13,918 avalanches, and the corresponding forecast danger level valid on the day of avalanche occurrence, 3533 days and danger ratings in total.

This avalanche activity data set was analysed and results published by Schweizer et al. (2020). They found that the
number of avalanches per day strongly increased with increasing danger level, but avalanche size was poorly related to avalanche danger level.

The data are provided in two files: the first includes the avalanche data (13,918 records); the second includes the avalanche activity per day (3533 records).
Please refer to the Read-me file for further details on the data.

These data are the basis of the following publication:
Schweizer, J., Mitterer, C., Techel, F., Stoffel, A. and Reuter, B., 2020. On the relation between avalanche occurrence and avalanche danger level. The Cryosphere, 14, 737-750, https://doi.org/10.5194/tc-14-737-2020.

avalanche-activity

display_name: avalanche-activity

avalanche-forcasting

display_name: avalanche-forcasting

avalanche-size

display_name: avalanche-size

danger-level

display_name: danger-level

snow-avalanche

display_name: snow-avalanche

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

key: guid

key: uri

value: Envidat Harvester

en: Snow avalanche data Davos, Switzerland, 1999-2019


maintainer: EnviDat Support

en: terrestiral-laserscanning single-tree lidar tree-volume forest-inventory biomass

media_type: application/zip

format: ZIP

en: Individual tree parameters and volume estimates based on TLS point clouds and allometric models

en:
"ASM" denotes volume estimates calculated by allometric scaling models (as used in Swiss NFI), based on tree height, DBH and D7.
"QSM" denotes volume estimates derived from TLS point clouds by quantitative structure models (QSM).
The remaining variables are geometrical features directly calculated from single tree TLS point clouds.
For more detailed variable descriptions see related publication.

en:
 Dataset
This dataset is based on terrestrial laser scanning (TLS) data acquired during winter 2020/2021 in leaf-off conditions, with a Leica BLK 360 instrument following a tree-centric scanning pattern.
The data was acquired on two sites (47.42°N 8.49°E and 47.504°N, 7.78°E), both of which were managed mixed temperate forest stands.
Individual trees were semi-automatically segmented from the co-registered TLS point clouds.

 Background
Accurate estimates of individual tree volume or biomass within forest inventories are essential for calibration and validation of biomass mapping products based on Earth observation data.
Terrestrial laser scanning (TLS) enables detailed and non-destructive volume estimation of individual trees, with existing approaches ranging from simple geometrical features to virtual 3D reconstruction of entire trees. Validating such approaches with weight measurements is a key step before the integration of TLS or other close-range technologies into operational applications such as forest inventories.
In this study, we firstly evaluate individual tree volume estimation approaches based on 3D reconstruction through quantitative structure models (QSM) against destructive reference data of 60 trees and compare them to operational allometric scaling models (ASM).
Secondly, we determine the explanatory power of TLS-derived geometric parameters regarding total tree, stem, coarse wood and fine branch volume.

biomass

display_name: biomass

forest-inventory

display_name: forest-inventory

lidar

display_name: lidar

single-tree

display_name: single-tree

terrestiral-laserscanning

display_name: terrestiral-laserscanning

tree-volume

display_name: tree-volume

en: Individual tree TLS point clouds for tree volume estimation


maintainer: Varun Sharma

en: mesoscale-modelling snow-atmosphere-interaction blowing-snow alps antarctica

en:
REPRODUCIBILITY_POSTPROCESSING.zip contains data and scripts used to produce all figures from for the associated manuscript submitted for peer review ( see section on Citations below )

The folders stand for the three case studies presented in the manuscript. Two case studies (case Ia and case Ib) are on simulations in Antarctica and one case study
case II is for simulations in the Alps.

The associated data in the form of netcdf files or text files were produced using CRYOWRF v1.0

Apart from reproducibility, the intention of sharing these scripts is also to make it easy for new users to adopt CRYOWRF and be efficient, in particular in processing snowpack outputs.

The scripts were tested on multiple machines (different OSes) with the attached YAML file (wrf_analysis.yml) to setup the conda environment. All scripts were run using spyder as the IDE.

en: REPRODUCIBILITY_SIMULATION.zip contains all the extraneous files required to perform the simulations in associated manuscript (see Citations 3 in README.txt). The purpose of sharing these files apart from purposes of reproducibility is to also serve as templates for users to learn the workflow of CRYOWRF and perform simulations for their own purposes.

en:
This repository contains data required for reproducibility of the results to be published in the associated manuscript.

Apart from reproducibility, the attached datasets also serve as templates for new users to adopt CRYOWRF in their research.

The datasets consist of two folders organized in zip format:

1. REPRODUCIBILITY_SIMULATION:
  Consists of namelists for WPS, WRF and SNOWPACK to reproduce simulations published in the manuscript
  Additional files include datasets (from IMAU-FDM / RACMO, see "credits" below ) as well as helper python scripts to
  produce *.sno files
  which are used as initial conditions for SNOWPACK in CRYOWRF.

2. REPRODUCIBILITY_POSTPROCESSING:
   Includes outputs of CRYOWRF and python scripts used to prepare figures in the manuscript.

Each of the folders have their own readme files for more details.

 Code
citation: Varun Sharma. (2021, July 2). vsharma-next/CRYOWRF: CRYOWRF v1.0 (Version v1.0). Zenodo. http://doi.org/10.5281/zenodo.5060165

location: https://gitlabext.wsl.ch/atmospheric-models/CRYOWRF   (stable releases / institutional repo)
        https://github.com/vsharma-next/CRYOWRF            (dev branches / developer repo)

 Publication
**Introducing CRYOWRF v1.0: Multiscale atmospheric flow simulations with advanced snow cover modelling.**
Varun Sharma, Fraziska Gerber and Michael Lehning, Submitted to Geoscientific Model Development

 Acknowledgements
We thank Peter Kuipers Munneke (P.KuipersMunneke@uu.nl) for preparing and sharing outputs of IMAU-FDM and RACMO used for initial conditions for case Ia. The relevant citations for the methods through which these datasets were generated are:

* Kuipers Munneke, P., S. R. M. Ligtenberg, B. P. Y. Noël, I. M. Howat, J. E. Box, E. Mosley-Thompson, J. R. McConnell, K. Steffen, J. T. Harper, S. B. Das and M. R. van den Broeke. 2015. Elevation change of the Greenland ice sheet due to surface mass balance and firn processes, 1960-2014. The Cryosphere, 9, 2009-2025. doi:10.5194/tc-9-2009-2015

 * Ligtenberg, S. R. M., P. Kuipers Munneke, B. P. Y. Noël, and M. R. van den Broeke. 2018. Brief communication: Improved simulation of the present-day Greenland firn layer (1960-2016). The Cryosphere, 12, doi:10.5194/tc-12-1643-2018

alps

display_name: alps

antarctica

display_name: antarctica

blowing-snow

display_name: blowing-snow

mesoscale-modelling

display_name: mesoscale-modelling

snow-atmosphere-interaction

display_name: snow-atmosphere-interaction

educ

en: Education, culture and sport

en: Reproducibility Dataset for CRYOWRF v1.0


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

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

mimetype: application/zip

en: StationData

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

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

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

Publications:

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

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

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

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

complex-terrain

display_name: complex-terrain

cosmo-wrf

display_name: cosmo-wrf

orographic-precipitation

display_name: orographic-precipitation

precipitation-variability

display_name: precipitation-variability

snow-precipitation

display_name: snow-precipitation

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


photogrammetric-drone-data-gruenbodeli

title_for_slug: photogrammetric-drone-data-gruenbodeli

en: uav snow-depth-mapping orthophoto photogrammetry drone-data dsm wingtra

en: Data Access

en:
- ortho (3 cm/25 mm resolution)
- DSM (10 cm resolution)
- HS (10 cm resolution)
- RGB images
- Agisoft Metashape Project Report

All products are also available as a cloud optimized geotiff.

en:
We conducted various drone flights at Grüenbödeli near Davos with the Sony RX1R II mounted on a Wingtra drone during 2020/21/22. The data was processed with the Agisoft Metashape Professional Software.
The following products are available for download:
- DSM 10cm resolution
- Orthomosaic 3cm/25mm resolution
- Snow Raster 10cm resolution
- original RGB images

drone-data

display_name: drone-data

dsm

display_name: dsm

orthophoto

display_name: orthophoto

photogrammetry

display_name: photogrammetry

snow-depth-mapping

display_name: snow-depth-mapping

uav

display_name: uav

wingtra

display_name: wingtra

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: Photogrammetric Drone Data Grüenbödeli


backward-trajectories

maintainer: Michael Sprenger

title_for_slug: backward-trajectories

en: raclets cosmo lagrangian-analysis atmospheric-dynamics complex-topopgraphy alps backward-trajectories

en: Backward Trajectories

en: The trajectories are started from surface up to 5000 m altitude, with an equidistant vertical spacing of 200 m, and calculated 120 h backward in time. As long as the trajectories stay in the COSMO1 domain (essentially the Alpine region), as run operationally at MeteoSwiss, the COSMO analysis wind fields are used. When the trajectories leave this domain, they are extended further backward in time based on ECMWF analysis data. The following fields are traced along the trajectories: longitude, latitude, geopotential height, pressure, specific humidity, temperature and surface topography. Furthermore, it is indicated for each trajectory time instance on which model data, COSMO or ECMWF, the trajectory time is based. Finally, the trajectories are started every hour in the specified time period, and positions and fields are available every 10 min. Each day is saved as a separate file. The last 8 digits of the file names indicate the starting date of the trajectories.

en: Backward trajectories were calculated from two positions: Davos Wolfgang (LON: 9.85361, LAT: 46.83551) and Weissfluhjoch (LON: 9.80646 LAT: 46.83304) for the time period February 2 until March 27 2019 using COSMO or ECMWF, respectively.

alps

display_name: alps

atmospheric-dynamics

display_name: atmospheric-dynamics

backward-trajectories

display_name: backward-trajectories

complex-topopgraphy

display_name: complex-topopgraphy

cosmo

display_name: cosmo

lagrangian-analysis

display_name: lagrangian-analysis

raclets

display_name: raclets

en: Backward  Trajectories


en: orographic-precipitation precipitation-amount climate-change air-temperature cmip5

en: Download Server

en:
Predicting future climatic conditions at high spatial resolution is essential for many applications in science. Here we present data for monthly time series of precipitation and minimum and maximum temperature for four downscaled global circulation models. We used model output statistics in combination with mechanistic downscaling (the CHELSA algorithm) to calculate mean monthly maximum and minimum temperatures, as well as monthly precipitation sums at ~5km spatial resolution globally for the years 1850-2100. We validated the performance of the downscaling algorithm by comparing model output with observed climates for the years 1950-2069.

CHELSA_cmip5_ts is licensed under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.

air-temperature

display_name: air-temperature

climate-change

display_name: climate-change

orographic-precipitation

display_name: orographic-precipitation

precipitation-amount

display_name: precipitation-amount

educ

en: Education, culture and sport

en: High resolution monthly precipitation and temperature timeseries for the period 2006-2100


maintainer: Franziska Gerber

en: topography cryowrf wrf cosmo-wrf landuse static-data

en: Static data for WRF - Soiltype bottom

en: Static data for WRF - Soiltype top

en: Example namelist.wps to use with the 1s static data

en: Static data for WRF - Topography

en: Documentation

en: Documentation of the dataset

en:
Example GEOGRID.TBL to use with the 1s static data;
Note about naming:
* 121 -> 1-2-1 topography smoothing applied to dataset
* 1 -> one smoothing cycle applied to the data (needs to be changed depending on the chosen resolution to avoid too steep slope angles)

en: Static data for WRF - Landuse

en: Static input data (topography, landuse and soiltype) for the WRF preprocessing system WPS is provided for Switzerland and its neighboring countries between 45-49 N and 4-12 E. The data is provided at a resolution of 1 s. Topography is based on the Aster dataset, while landuse is extracted from the Corine landuse dataset. Soil type is set to silty clay loam for the entire domain. This static input data is valid for WRF and CRYOWRF.

cosmo-wrf

display_name: cosmo-wrf

cryowrf

display_name: cryowrf

landuse

display_name: landuse

static-data

display_name: static-data

topography

display_name: topography

wrf

display_name: wrf

en: High resolution static data for WRF over Switzerland


maintainer: Marco Moretti

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

en: Wild bee trait data

en: Text file containing the scores of the 8 used traits. The data originates from the European trait database (compiler: Stuart Roberts; pollinator loss module of the EU- FP6 ALARM project).

en: Protected areas

en: Protected areas considered, divided in three groups and rasters: protected areas sensu stricto, protected areas sensu lato and all protected areas

en: Variable importance, model evaluation, partial dependence plots

en: The compress file containes a set of txt files with the variable importance, the model evaluation metrics and the partial dependence plots of each of the responses used (alpha and beta taxonomic and functional diversity metrics; community weighted means of 8 functional traits)

en: Spatial environmental predictors and responses

en:
The .zip file contains the rasters of the environmental predictors and the predicted responses:
-  **Environmental predictors.**:

  -  The raster on the climate contains the PCA axes on the CHELSA variables.

  -  The raster on resource availability contains the PCA axes on plant communities

  -  The raster on land-use contains the land-use composition at 2500 m.

  -  The raster on beekeeping intensity contains thedensity of hives within 2500 m

-  **Responses**:
- The predicted responses without masking the waterbodies (Diversity_stack_revised_Selected_descriptors.tif).
- The predicted responses masking the waterbodies (masked.stack.all.diversity.tiff).

en: Taxonomic and functional metrics in the community plots

en:
Response data used for the modeling.
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: Elevation and waterbodies

en: Zip file containing two rasters: the digital elevation model of Switzerland and the waterbodies of Switzerland.

en: Deprecated-Predictors

en:
The compress file includes the rster files of the predictors used in the analyses, including the climate, resource availability, land-use and beekeeping intensity metrics.

The raster on the climate contains the PCA axes on the CHELSA variables.
The raster on resource availability contains the PCA axes on plant communities
The raster on land-use contains the land-use composition at 2500 m.
The raster on beekeeping intensity contains thedensity of hives within 2500 m

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 3343 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).

The dataset also includes the following:
- The used predictors to model the spatial distribution of the community metrics (climate PCA, vegetation PCA, land-use metrics, beekeeping intensity).
-The three types of protected areas, defined according to the protective measures.
- The model evaluation, variable importance and partial dependece data.

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

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: Data on wild bee taxonomic and functional diversity in Switzerland


maintainer: Océane Hames

en: mosaic arctic-sea-ice snow-transport snow-deposition openfoam

en: Paper Draft

en: This is the initial draft of the paper where the snowBedFoam 1.0. results are tested against Terrestrial Laser Scan (TLS) measurements of snow dynamics on Arctic sea ice (MOSAiC expedition). It highlights the numerical limitations of our model as well as the measurement uncertainties, which may occur in such extreme environments.

en: MOSAiC Met City preliminary (Level 2) wind data

en:
Extract from the preliminary MOSAiC atmospheric data (Level 2) for the time period 6th to 13th of November, 2019.
This data was used to set up the fluid forcing parameters in the snowBedFoam simulations on MOSAiC sea ice. It is being published in this repository with the kind permission from Matthew Shupe and his team.
The final processed and quality controlled version of this dataset (Level 3) has not been published yet. A complete reference will be added in this description once it is released.

en: snowBedFoam 1.0.

en:
OpenFOAM-2.3.0. scripts containing the implemented Eulerian-Lagrangian snow transport model.

The code file provided here is not updated. The most recent version of the script can be found on the WSL/SLF GitLab repository: https://gitlabext.wsl.ch/atmospheric-models/snowbedfoam-v1-0.git

en:
snowBedFoam 1.0. is a snow transport solver implemented in the Computational Fluid Dynamics (CFD) software OpenFOAM. It is adapted from the standard multi-phase flow solver DPMFoam for application in snow-influenced environments.

To simulate aeolian snow transport, snowBedFoam 1.0. handles coupled Eulerian–Lagrangian phases, which involve a finite number of particles (snow) spread in a continuous phase (air). The snow erosion and deposition are modelled through physically-based equations such as the ones employed in the well-established LES-Lagrangian Stochastic Model (Comola and Lehning, 2017 ; Sharma et al., 2018). This modelling approach is computationally intensive and thus adapted to simulate snow movement and distribution on small scale terrain.

snowBedFoam 1.0. was tested against topographical data collected on Arctic sea ice during the MOSAiC expedition (Clemens-Sewall, 2021) . Together with atmospheric data from the MOSAiC Met City (Shupe et al., 2021) used for the fluid forcing, the model was able to accurately simulate the zones of erosion and deposition of snow along a complex ice ridge structure.

The core code of snowBedFoam 1.0. is also accessible on the WSL/SLF GitLab repository (more details in the Resources section).

arctic-sea-ice

display_name: arctic-sea-ice

mosaic

display_name: mosaic

openfoam

display_name: openfoam

snow-deposition

display_name: snow-deposition

snow-transport

display_name: snow-transport

educ

en: Education, culture and sport

en: snowBedFoam: an OpenFOAM Eulerian-Lagrangian solver for modelling snow transport


en: photovoltaic reflection irradiance albedo mountain snow energy

en: PV Data

en: Windows users use the 7-zip app to extract the zip-file.

en:
 Overview
The SUNWELL Modelling Environment is a combination of data and code that models electricity production from satellite-derived irradiance data and other spatial data sets for all of Switzerland. This ensemble accompanies the publication "The bright side of PV production in snow-covered mountains", published in the Proceedings of the National Academy of Science and reproduces all results and figures of. Code and resources are in their original form (with documentation). A new version with a more generalized application to PV modelling and with more flexibility in terms of input and output formats will be released in the coming months.

 Format
All code is written and has to be executed in Matlab. The input and output data sets are also in the Matlab-specific .mat format. Whenever publicly available, the original data is provided as geotif, .xlsx or other common format.
This is the case for:

- Digital Elevation Model (InputsFromMatlab/MSG/OriginalData/ASTERDEM),
- Landsurface cover type (InputsFromMatlab/MSG/OriginalData/CORINE),
- Population Density (InputsFromMatlab/MSG/OriginalData/popdensRaster,
- Electricity production from three of our validation sites (/Validation/WSL),
- Measured irradiance for two validation sites (/Validation/ASRB)
The ‘Metadata’ documents in the respective folders provide further information about the data sources and processing. Figures are produced either in .pdf or .png format.

 Structure
The central level of the SUNWELL environment holds the 5 Mains, which run the different modelling aspects of the paper; each code is documented separately. Additional code is located in the ‘DataProcessing’ and the ‘functions’ folder. Functions are called in the different Mains.

‘InputsFromMatlab’ contains the radiation and albedo input data sets in separate subfolders (SIS/SISDIR/ALB). The original data is not publicly available, but can be requested for research purposes free of charge. We provide a processed subset of the data set that was used to run the SUNWELL simulations. The MSG subfolder contains additional spatial input data sets.

‘Outputs’ contains the output files from the different mains (matching names, Main_CHallpixels.m  Prod_CHallpixels)

‘Publication_figures’ contains all individual figures from the PNAS publication, as well as the generating code (/code_plot) and the power point figures (/ppts) that provide the combined final figures.

‘Validation’ contains the data sets used in the model validation:

- Electricity production from three of our validation sites (/WSL),
- Measured irradiance for two validation sites (/ASRB)

Electricity production from a validation site at Lac des Toules in Wallis (/LDT), this data set was provided under an NDA and cannot be made publicly available.

Paper Citation:
 > _Annelen Kahl; Jérôme Dujardin; Michael Lehning (2018). Dataset on PV Production in Snow Covered Mountains. PNAS - Proceedings of the National Academy of Sciences. (in press)_

albedo

display_name: albedo

energy

display_name: energy

irradiance

display_name: irradiance

mountain

display_name: mountain

photovoltaic

display_name: photovoltaic

reflection

display_name: reflection

snow

display_name: snow


maintainer: Giulia Francesca Azzurra Donati

en: urban-spatial-planning human-dominated-landscapes biodiversity integrated-modelling circuit-theory ecological-corridors

en: Binarized "green" and "grey" landcover data (Cantons of Aargau and Zürich)

en: "Green" landcover refers to lawns and vegetated urban surfaces such as, for example private gardens, public parks, cemeteries, green stripes along roadsides, etc. While "grey" landcover refers to impervious grounds such as roads, troittoirs, other impervious. Binarized maps, 30x30m resolution.

en: Amphibian breeding sites of national importance - cantons of Aargau and Zürich

en: Amphibian breeding sites of national importance (focal nodes) in the cantons of Aargau and Zürich, 30x30 m resolution, binarised ascii format.

en: Amphibian species specific landscape resistance maps.

en: For each amphibian species we used the weighted average Ensemble model suitability map and converted it to a resistance map using an exponential decay function following (Duflot et al., 2018) using the species-specific mpa threshold previously computed (Preau et al., 2020). The function assigns a resistance value of 1,000 when habitat suitability equals zero, and a value of one when habitat suitability is greater than or equal to the binarization threshold. The exponential shape strengthens the barrier effect of less favorable areas.

en: Amphibian monitoring data

en: Processed amphibian occurrence data (2017-2019) in the cantons of Aargau and Zürich. The dataset includes information for the following species: _Alytes obstetricans_,_Bombina variegata_, _Bufo bufo_, _Hyla arborea_, _Ichtyosaura alpestris_, _Lissotriton helveticus_, _Lissotriton vulgaris_, _Rana temporaria_, _Salamandra salamandra_, and _Triturus cristatus_.

en: Amphibian whole life cycle predictors - 10m resolution

en:
Amphibian whole life cycle predictors include 6 variable types:
(1)	Topographic – slope [°]: derived from the Swiss Topographical Landscape model (swissTLM 3D 2015) at 2 m resolution (elevation uncertainty ± 0.5m) and, subsequently, aggregated to a median value;
(2)	Hydrologic: two predictors including nearest distance to water [m] (all water-related objects and rasterized lotic, lentic and reedbeds from the regional cadastral maps – AV: Daten der Amtliche Vermessung) and runoff coefficient [C] (between 0 and 1 relating amount of runoff to amount of precipitation, mapped AV: Daten der Amtliche Vermessung data against literature values for different land covers);
(3)	Edaphic – soil moisture variability (not available): can gathered from the spatial modelling of Ecological Indicator Values in Switzerland (Descombes et al., 2020) and resampled from 100m to 10m;
(4)	Vegetation: we calculated the median and standard deviation of the Normalized Difference Vegetation Index (NDVI) from all available cloud free Sentinel-2 pixels available between April and October for the years 2016 to 2019 (Drusch et al., 2012) and the median vegetation height [m] (not available) can gathered from Ginzler and Hobi (2015);
(5)	Land-use derived: includes three predictors, grassland density [grassland/cell] (not available) - can be gathered from Pazur et al. (2021), total grasslands area evaluated using a spatial weight matrix and a 250m neighborhood radius around each cell; urbanization [buildings/cell] – average building density for a 250m neighborhood around each cell based on the AV: Daten der Amtliche Vermessung ; and traffic intensity [vehicles/day] – average daily vehicle count (Nationales Personenverkehrsmodells);
(6)	Movement-ecology related: includes three predictors of proximity – nearest forest distance [m]; nearest rock-gravel and sandy area (RGS) distance [m] and nearest road distance [m] (considering any road type) – all calculated based on the AV: Daten der Amtliche Vermessung maps.

en: The data includes (1) amphibian occurrence data (2017-2019) for ten species across the cantons of Aargau and Zürich gathered from the Coordination Center for the Protection of Amphibians and Reptiles of Switzerland (http://www.karch.ch), (2) amphibian whole-life cycle environmental predictors (i.e. topographic, hydrologic, edaphic, vegetation, land-use derived, movement-ecology related), and (3) local urban "green" and "grey" landcover data which can be used to identify opportunities for Blue-Green Infrastructure (through green or grey transitions) in support of regional landscape connectivity.

biodiversity

display_name: biodiversity

circuit-theory

display_name: circuit-theory

ecological-corridors

display_name: ecological-corridors

human-dominated-landscapes

display_name: human-dominated-landscapes

integrated-modelling

display_name: integrated-modelling

urban-spatial-planning

display_name: urban-spatial-planning

envidat

en: EnviDat: the Environmental Data Portal

en: Amphibian and urban-rural landscape data Swiss Lowlands


maintainer: Anita C. Risch

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

en:
site.code: Site code

Continent: Continent

sampleID: Sample ID

nn.latitude: Lattitude

nn.longitude: Longitude

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

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

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

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

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

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

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

ch.pH: pH

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

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

ch.porespace: Pore space (vol%)

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

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

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

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

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

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

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

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

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

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

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

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

ch.CtoN: C to N ratio

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

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

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

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

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

Paper Citation:

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

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

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

global

display_name: global

grassland

display_name: grassland

mineralization

display_name: mineralization

nutrient-network

display_name: nutrient-network

soil

display_name: soil

soil-biology

display_name: soil-biology

soil-chemistry

display_name: soil-chemistry

worldwide

display_name: worldwide

educ

en: Education, culture and sport

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: clouds ice-nucleation backward-trajectories raclets aerosol

en: This dataset contains number concentrations of ice-nucleating particles active at -15 °C observed at Weissfluhjoch during February and March 2019, as well as complementary data (measured aerosol number concentrations and modelled total precipitation along air mass trajectories). This data formed the basis of our paper with the title “Towards parameterising atmospheric concentrations of ice-nucleating particles active at moderate supercooling”.

aerosol

display_name: aerosol

backward-trajectories

display_name: backward-trajectories

clouds

display_name: clouds

ice-nucleation

display_name: ice-nucleation

raclets

display_name: raclets

en: Ice nucleating particle concentrations active at -15 °C at Weissfluhjoch


maintainer: Marcia Phillips

en: meteo-station permafrost borehole ritigraben permos

en: DATA ACCESS

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

borehole

display_name: borehole

meteo-station

display_name: meteo-station

permafrost

display_name: permafrost

permos

display_name: permos

ritigraben

display_name: ritigraben

en: RIT2: Meteorological station at Ritigraben borehole site


weissfluhjoch-dataset-for-esm-snowmip

maintainer: Nander Wever

title_for_slug: weissfluhjoch-dataset-for-esm-snowmip

en: weissfluhjoch snow esm-snowmip snow-models snow-depth

en: Dataset Weissfluhjoch for ESM-SnowMIP

en: Conversion script

en:
Use this script to convert WFJ_optimaldataset_v6.smet to wfj.smet, the file that is used for the ESM-SnowMIP project. It also requires the file: WFJ_AirPressure_Data.zip

Use: bash create_wfj_dataset.sh > input/wfj.smet

mimetype: text/x-sh

en: ini file

en: This ini file is required by data-converter from MeteoIO version 1887 (http://models.slf.ch/p/meteoio/)

en: This Weissfluhjoch dataset is a processed version of the Weissfluhjoch dataset version 6 from https://doi.org/10.16904/6. This dataset was specially created for the ESM-SnowMIP project. Here it is documented how this dataset has been created.

esm-snowmip

display_name: esm-snowmip

snow

display_name: snow

snow-depth

display_name: snow-depth

snow-models

display_name: snow-models

weissfluhjoch

display_name: weissfluhjoch

en: Weissfluhjoch dataset for ESM-SnowMIP


maintainer: Matthias Haeni

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

en: Data plot of the field station (JUF)

en: Data file.

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

en: Forest station data (JUB)

en: Data plot of the forest station (JUB)

en: Site aerial photo (Jussy)

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

mimetype: image/jpeg

en: Site photo of the field station (JUF)

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

en: Site photo of the forest station (JUB)

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

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 Jussy in Switzerland where one station is located within a natural broad-leaved forest stand (JUB) with sessile oak (_Quercus petrea_; 90 yrs), aspen (_Populus tremula_; 60 yrs) and European hornbeam (_Carpinus betulus_; 60 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, JUF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Jussy 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

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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


maintainer: Athanasios Nenes

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

en: Predicted cloud droplet number concentrations Weissfluhjoch

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

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

en: Hygroscopicity parameter Weissfluhjoch

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

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

Missing values are indicated by "-999".

en: CCN data Weissfluhjoch

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

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

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

_Error_code_:

0000: Measurements without error

1000: Period of transient operation after a supersaturation change

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

3000: Irregular values in the room temperature housing the instrument

9999: Missing data

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

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

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

ccn

display_name: ccn

cloud-droplets

display_name: cloud-droplets

cloud-modeling

display_name: cloud-modeling

dmt-ccn-chamber

display_name: dmt-ccn-chamber

hygroscopicity

display_name: hygroscopicity

raclets

display_name: raclets

sigma-w

display_name: sigma-w

supersaturation

display_name: supersaturation

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: CCN, hygroscopicity, predicted cloud droplet numbers Weissfluhjoch


modeling-snow-failure-with-dem

title_for_slug: modeling-snow-failure-with-dem

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

en: Python file

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

mimetype: text/x-python

en: Dataset

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

en: Read-me

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

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

discrete-element-modelling

display_name: discrete-element-modelling

snow

display_name: snow

snow-avalanche

display_name: snow-avalanche

snow-failure

display_name: snow-failure

snow-micro-mechanics

display_name: snow-micro-mechanics

en: Modeling snow failure with DEM


maintainer: Frank Techel

en: snow avalanche-forecast european-alps danger-level warning-regions

en: shape file warning regions Alps

en: Forecast avalanche danger level

en: contains the highest forecast danger level (dangerLevelMax) for each day and warning region

en:
This dataset contains the data used in the publication by Techel et al., 2018 _Spatial consistency and bias in avalanche forecasts - a case study in the European Alps_ (Nat Haz Earth Syst Sci).
For details on the data please refer to this publication.
The dataset contains the following:
- shape files for the warning regions in the Alps
- highest forecast danger level for each warning region and day

avalanche-forecast

display_name: avalanche-forecast

danger-level

display_name: danger-level

european-alps

display_name: european-alps

warning-regions

display_name: warning-regions

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Forecast avalanche danger level European Alps 2011 - 2015


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

en: Data plot of the field station (CIF)

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

en: Data file.

en: Site photo of the forest station (CIB)

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

en: Data plot of the forest station (CIB)

en: Forest station data (CIB)

en: Site photo of the field station (CIF)

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

en: Site aerial photo (Chironico)

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

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 Chironico in Switzerland where one station is located within a natural coniferous forest stand (CIB) with Norway spruce (_Picea abies_; 160-180 yrs) and European silver fir (_Abies alba_; 140-160 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, CIF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Chironico 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: 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: Chironico, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 2000 onwards


en: review manure biogas switzerland bioenergy

en: Production de biogaz à partir d’engrais de ferme en Suisse

en: L'objectif de ce livre blanc est de fournir aux décideurs, aux administrations et aux parties prenantes les résultats de recherche les plus récents afin de promouvoir l'utilisation optimale de la bioénergie issue des engrais de ferme dans la transition énergétique suisse. A cette fin, les résultats du centre de compétence suisse pour la recherche en bioénergie - SCCER BIOSWEET - sont résumés et présentés dans un contexte plus large. Si rien d'autre n'est mentionné, les résultats se réfèrent à la Suisse et, dans le cas de la matière première, aux potentiels nationaux de biomasse.

bioenergy

display_name: bioenergy

biogas

display_name: biogas

manure

display_name: manure

review

display_name: review

switzerland

display_name: switzerland

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal


maintainer: Yohann Chauvier

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

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

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


maintainer: Giulia Mazzotti

en: snow-modelling forest-structure snow-distribution snow-energy-balance forest-snow

en: Canopy data

en: Includes all canopy structure metrics computed at all surveyed locations

en: Grid point nomenclature

en: The grid structure of each forest site and the respective nomenclature applied to label individual points within each grid are outlined here.  Each point marks the location of snow depth measurements and co-located canopy structure parameters.

en: Snow depth data forest sites

en: Contains all snow depth data acquired at all forest sites during winters 2012/13, 2013714 and 2014/15.

en: Meteo data to drive FSM2 model

en: Includes meteorological driving data used to run the forest snow model FSM2, processed and parsed in text files that match the format required by FSM2. Separate files are available for each field area (Laret, Drusatscha and Ischlag) and season (2012/13, 2013/14, 2014/15). Each file includes data for the period from October 1st to May 31st at hourly resolution.

en: Coordinates data

en: Includes coordinate information of each surveyed point (7 x 276 = 1932 forest points, 2 open areas)

en: Snow depth data open sites

en: Contains all snow detph data accquired at open sites nearby the forest stands during the same period.

en:
This dataset contains all snow, canopy and meteorological data presented and used in the publication: Mazzotti, G., Essery, R., Moeser, D. & Jonas T. (2020) 'Resolving spatial variability of forest snow using an energy-balance model with a 1-layer canopy'. Water Resources Research, https://doi.org/10.1029/2019WR026129.
This publication must be cited when using this dataset.

forest-snow

display_name: forest-snow

forest-structure

display_name: forest-structure

snow-distribution

display_name: snow-distribution

snow-energy-balance

display_name: snow-energy-balance

snow-modelling

display_name: snow-modelling

en: Snow depth, canopy structure and meterorological datasets from the Davos area, Switzerland, Winters 2012/13-2014/15, used for high-resolution forest snow modelling


en: seilaplan open-source geoinformation-system geodata-processing digital-planning qgis-plugin raster-processing swiss-geodata digital-elevation-model cable-yarding

en: DHM-Kacheln zusammenfügen

en: View video on youtube!

en:
In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. In this tutorial video, we show how to merge multiple DTM raster tiles into one file, using the QGIS tool ‘Virtual Raster’. This simplifies the digital planning of a cable line using the QGIS plugin ‘Seilaplan’. Please note that the tutorial language is German!

Link to Seilaplan website: https://seilaplan.wsl.ch

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

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. In diesem Tutorialvideo zeigen wir, wie man mit dem QGIS-Plugin Virtuelles Raster mehrere DHM-Kacheln zu einem einzigen Rasterfile zusammenfügen und abspeichern kann. Für die Seillinienplanung mit Seilaplan muss nun nur noch eine Datei, mein neues virtuelles Raster, ausgewählt werden.

Link zur Seilaplan-Website: https://seilaplan.wsl.ch

cable-yarding

display_name: cable-yarding

digital-elevation-model

display_name: digital-elevation-model

digital-planning

display_name: digital-planning

geodata-processing

display_name: geodata-processing

geoinformation-system

display_name: geoinformation-system

open-source

display_name: open-source

qgis-plugin

display_name: qgis-plugin

raster-processing

display_name: raster-processing

seilaplan

display_name: seilaplan

swiss-geodata

display_name: swiss-geodata

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Seilaplan Tutorial: Merge DTM tiles