agri

fr: Agriculture, pêche, sylviculture et alimentation

de: baum bauminventar baumkataster stadtbaum

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 Baumkataster

de: Web Map Services WMS

de: WFS Baumkataster

de: Web Feature Services WFS

de: Durch Stadtgrün bewirtschaftete Bäume, Symbole für Laubbaum, Nadelbaum und Obstbaum, mit den Informationen über Gattung, Art, deutscher Name, Pflanzjahr und Standort.

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.

value: Geoinformation Stadt Bern - Geocat Harvester

de: Baumkataster


maintainer: Kantonspolizei des Kantons Zürich, Abteilung Kriminalpolizeiliches Datenmanagement

de: gemeinden gewalt bezirke kantonzuerich cyberkriminalitaet straftaten strafdelikte jugendkriminalitaet strafbarehandlungen einbrueche kriminalitaet

label: Webseite der Polizeilichen Kriminalstatistik beim Bundesamt für Statistik

label: Archiv Jahresberichte Polizeiliche Kriminalstatistik 2009-2020 (im PDF-Format)

label: Erfassungshilfe Polizeiliche Kriminalstatistik (Weiterführende Informationen zu Datenbankstruktur, Zählweise und Erfassung von Straftaten)

label: Vorbereiteter Code in R oder Python für jedes Dataset mit csv-Datei(en)

state: active

type: dataset

de: Anzahl Straftaten nach Tatbestand und Bezirken

media_type: text/csv

de: Anzahl Straftaten nach Tatbestand des StGB, AIG und BetmG seit 2009 nach Bezirken. Tatbestände werden nur in Jahren aufgeführt, in denen sie in Kraft waren. Die Unterscheidung zwischen versuchten und vollendeten Straftaten ist nur im Falle von Straftaten gegen das StGB möglich. Die Spalte Titel (und Reihenfolge_Titel) weist für StGB die Titel (und deren Reihenfolge) des 'Zweiten Buches: Besondere Bestimmungen' gemäss StGB, sowie für BetmG und AIG die Überkategorien (und deren Reihenfolge) gemäss Auswertungen im PKS Jahresbericht auf. Analog weist die Spalte Haupttitel (und Reihenfolge_Haupttitel) eine v.a. für die zahlreichen StGB-Titel generalisierte Kategorie der wichtigsten Titel und deren Reihenfolge auf. Die Bevölkerungszahlen sind der Einwohnerstatistik des Bundes am Ende des Vorjahres entnommen (bis 2010 ESPOP, ab 2011 STATPOP). Damit wird die Häufigkeitszahl berechnet (Anzahl registrierten Straftaten pro 1000 Einwohner).

format: CSV

mimetype: text/csv

de: Anzahl beschuldigte Personen nach Tatbestandsgruppe im ganzen Kanton

de: Anzahl polizeilich beschuldigter Personen nach Tatbestandsgruppe (Haupttitel) des StGB seit 2009. Es wird in juristische und natürliche Personen und letztere in die Alterskategorien Minderjährige (<18) und Erwachsene unterteilt. Jede Person wird im Total StGB bzw. pro Tatbestandsgruppe nur einfach gezählt. Das Zusammenzählen von Personenzahlen über mehrere Tatbestandsgruppen hinweg ist nicht zulässig, da sich eine Person über die verschiedenen Einheiten wiederholen kann. Die Anzahl Beschuldigter lässt sich nicht direkt mit der Anzahl Straftaten vergleichen, da bei gewissen Straftaten mehrere Personen beschuldigt werden können. Die Spalte Haupttitel (und Reihenfolge_Haupttitel) weist eine generalisierte Kategorie der wichtigsten StGB-Titel und deren Reihenfolge auf.

de: Anzahl Gewaltstraftaten nach Schweregrad und Tatbestand ganzer Kanton

de: Unter Gewaltstraftaten werden die Tatbestände des StGB zusammengefasst, welche die vorsätzliche Anwendung oder Androhung von Gewalt gegen Personen beinhalten. Die aufgeführten Tatbestände und ihre Zuordnung zum Schweregrad  richten sich nach den Vorgaben des Bundesamts für Statistik. Das Total aller Gewaltstraftaten pro Jahr bzw. pro Schweregrad (und Jahr) entspricht der Summe der Anzahl der im Datensatz abgebildeten Straftatbestände. Die Bevölkerungszahlen sind der Einwohnerstatistik des Bundes am Ende des Vorjahres entnommen (bis 2010 ESPOP, ab 2011 STATPOP). Damit wird die Häufigkeitszahl berechnet (Anzahl registrierten Straftaten pro 1000 Einwohner).

de: Anzahl Straftaten nach Tatbestandsgruppe für ausgewählte Gemeinden und die Stadtkreise der Stadt Zürich

de: Der Datensatz enthält Daten zu Gemeinden, die zum Zeitpunkt der Erstellung des Datensatzes (Oktober 2022) mehr als 10'000 Einwohner aufwiesen. Die Unterscheidung zwischen versuchten und vollendeten Straftaten ist nur im Falle von Straftaten gegen das StGB möglich. Die Bevölkerungszahlen sind der Einwohnerstatistik des Bundes am Ende des Vorjahres entnommen (bis 2010 ESPOP, ab 2011 STATPOP). Damit wird die Häufigkeitszahl berechnet (Anzahl registrierten Straftaten pro 1000 Einwohner). Die Spalte 'Reihenfolge_Titel weist für StGB die Reihenfolge der Titel des 'Zweiten Buches: Besondere Bestimmungen' gemäss StGB, sowie für BetmG und AIG die Überkategorien (und deren Reihenfolge) gemäss Auswertungen im PKS Jahresbericht auf. Analog weist die Spalte 'Haupttitel' eine v.a. für die zahlreichen StGB-Titel generalisierte Kategorie der wichtigsten Tatbestände aus. Die unter den jeweiligen Titeln zusammengefassten Tatbestände (nicht aber deren Anzahl pro Gemeinde) finden sich z.B. in folgendem Datensatz in der Spalte 'Artikel': https://www.web.statistik.zh.ch/ogd/daten/ressourcen/KTZH_00001202_00002278.csv

de: Anzahl geschädigte Personen nach Tatbestandsgruppe, Alterskategorie und Geschlecht im ganzen Kanton

de: Anzahl geschädigter Personen nach Tatbestandsgruppe (Haupttitel) des StGB seit 2009. Es wird in juristische und natürliche Personen und letztere in die Alterskategorien Minderjährige (<18) und Erwachsene sowie nach Geschlecht unterteilt. Jede Person wird im Total StGB bzw. pro Tatbestandsgruppe nur einfach gezählt. Das Zusammenzählen von Personenzahlen über mehrere Tatbestandsgruppen hinweg ist nicht zulässig, da sich eine Person über die verschiedenen Einheiten wiederholen kann. Die Anzahl Geschädigter lässt sich nicht direkt mit der Anzahl Straftaten vergleichen, da bei gewissen Straftaten mehrere Personen geschädigt sein können. Die Spalte Haupttitel (und Reihenfolge_Haupttitel) weist eine generalisierte Kategorie der wichtigsten StGB-Titel und deren Reihenfolge auf.

de: Anzahl Straftaten nach Tatbestand ganzer Kanton

de: Anzahl Straftaten nach Tatbestand gegen StGB, BetmG und AIG seit 2009 auf Kantonsebene. Tatbestände werden nur in Jahren aufgeführt, in denen sie in Kraft waren. Die Unterscheidung zwischen versuchten und vollendeten Straftaten ist nur im Falle von Straftaten gegen das StGB möglich. Die Spalte Titel (und Reihenfolge_Titel) weist für StGB die Titel (und deren Reihenfolge) des Zweiten Buches: Besondere Bestimmungen gemäss StGB, sowie für BetmG und AIG die Überkategorien (und deren Reihenfolge) gemäss Auswertungen im PKS Jahresbericht auf. Analog weist die Spalte Haupttitel (und Reihenfolge_Haupttitel) eine v.a. für die zahlreichen StGB-Titel generalisierte Kategorie der wichtigsten Titel und deren Reihenfolge auf. Die Bevölkerungszahlen sind der Einwohnerstatistik des Bundes am Ende des Vorjahres entnommen (bis 2010 ESPOP, ab 2011 STATPOP). Damit wird die Häufigkeitszahl berechnet (Anzahl registrierten Straftaten pro 1000 Einwohner).

de: Anzahl Straftaten im Bereich Cyber-/Digitale Kriminalität ganzer Kanton

de: Die Cyber-/Digitale Kriminalität wird anhand der Kombination "Straftat - Tatvorgehen (Modus Operandi)" ermittelt. Sie umfasst 33 verschiedene Tatvorgehen und 28 Straftaten des StGB, die in 5 Überkategorien mit  12 Unterkategorien gegliedert werden. In der Auswertung nicht berücksichtigt werden das Betäubungsmittelgesetz (BetmG) oder Bundesnebengesetze wie das Waffengesetz (WG), das Bundesgesetz gegen den unlauteren Wettbewerb (UWG) oder das Markenschutzgesetz (MSchG). Die Bevölkerungszahlen sind der Einwohnerstatistik des Bundes am Ende des Vorjahres entnommen (bis 2010 ESPOP, ab 2011 STATPOP). Damit wird die Häufigkeitszahl berechnet (Anzahl registrierten Straftaten pro 1000 Einwohner).

de: Anzahl Gewaltstraftaten nach Schweregrad und Bezirken

de: Unter Gewaltstraftaten werden die Tatbestände des StGB zusammengefasst, welche die vorsätzliche Anwendung oder Androhung von Gewalt gegen Personen beinhalten. Die aufgeführten Tatbestände und ihre Zuordnung zum Schweregrad  richten sich nach den Vorgaben des Bundesamts für Statistik. Die Summe der Anzahl Straftaten nach Schweregrad entspricht nicht dem Total der Gewaltstraftaten pro Bezirk, da unter Umständen bei mehreren Straftaten pro Fall Subsummierungsregeln zum Zug kommen.  Die Bevölkerungszahlen sind der Einwohnerstatistik des Bundes am Ende des Vorjahres entnommen (bis 2010 ESPOP, ab 2011 STATPOP). Damit wird die Häufigkeitszahl berechnet (Anzahl registrierten Straftaten pro 1000 Einwohner).

de: Opfer von Gewaltstraftaten nach Geschlecht, Altersklasse, Gewaltkategorie und Qualifikation

de:
Die Datei enthält die Anzahl von Opfern (d.h. natürlichen geschädigten Personen) von Gewaltstraftaten pro Jahr unterteilt nach Geschlecht, Altersklasse, Gewaltkategorie und Qualifikation. Die Spalte Gewaltkategorie enthält die Kategorien "ausserhäusliche Gewalt" (abgekürzt: AUSG) und "häusliche Gewalt" (abgekürzt: HG). Ob ein Gewaltdelikt unter die häusliche Gewalt fällt wird über die familiäre Beziehung zwischen Opfer und Täter bestimmt: häusliche Gewalt ist dann erfüllt, wenn es sich beim Täter oder der Täterin um einen aktuellen oder ehemaligen Partner oder eine nahe verwandte Person des Opfers handelt. Bei der ausserhäuslichen Gewalt besteht diese familiäre Vorbeziehung zwischen Oper und Täter nicht.
Pro Jahr und pro Gewaltkategorie wird jedes Opfer nur einmal gezählt, auch wenn es mehrfach pro Jahr zum Opfer wurde. Die Qualifikation beschreibt, ob ein Opfer innerhalb der gesamten Zeitperiode (z.Z. 6 Jahre) auch selbst straffällig als Täter eines Gewaltdeliktes wurde.

de: Die polizeiliche Kriminalstatistik (PKS) des Kantons Zürich wird jährlich publiziert. Sie beinhaltet Zahlen zu den registrierten Straftaten (und zu polizeilich beschuldigten sowie geschädigten Personen) für das Strafgesetzbuch (StGB), das Ausländer- und Integrationsgesetz (AIG) und das Betäubungsmittelgesetz (BetmG). Die polizeiliche Kriminalstatistik verzeichnet nur die polizeilich bekannt gewordene Kriminalität - das sogenannte Hellfeld. Sie ist eine Ausgangsstatistik. D.h. die Straftaten erscheinen nicht zum Tatzeitpunkt in der Statistik. Ausschlaggebend für die Zählung in der PKS ist der Zeitpunkt, an dem die Polizei einen ersten Rapport zur Straftat abgeschlossen hat.

bezirke

display_name: bezirke

cyberkriminalitaet

display_name: cyberkriminalitaet

einbrueche

display_name: einbrueche

gemeinden

display_name: gemeinden

gewalt

display_name: gewalt

jugendkriminalitaet

display_name: jugendkriminalitaet

kantonzuerich

display_name: kantonzuerich

kriminalitaet

display_name: kriminalitaet

strafbarehandlungen

display_name: strafbarehandlungen

strafdelikte

display_name: strafdelikte

straftaten

display_name: straftaten

just

fr: Justice, système juridique et sécurité publique

de: Justiz, Rechtssystem und öffentliche Sicherheit

en: Justice, legal system and public safety

it: Giustizia, sistema giuridico e sicurezza pubblica

kantonspolizei-kanton-zuerich

de: Kantonspolizei Zürich

de: Im Auftrag der Regierung und damit der Bevölkerung übernimmt die Kantonspolizei Zürich Verantwortung für die Sicherheit im Kanton Zürich, indem sie vorausschauend handelt, entschlossen Hilfe leistet und Straftaten konsequent aufklärt. Dabei folgt sie den Grundsätzen der Verhältnismässigkeit und der Gesetzmässigkeit.

political_level: canton

kanton-zuerich

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: ZH-Kt Kantonspolizei - DCAT Harvester

de: Polizeiliche Kriminalstatistik (PKS) des Kantons Zürich


postleitzahl-ur

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: postleitzahl-ur

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

label: geocat.ch permalink

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: Postleitzahl aus TOPIC «PLZOrtschaft» der amtlichen Vermessung.

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.

value: Uri-Kt - Geocat Harvester

fr: Postleitzahl (UR)

de: Postleitzahl (UR)

en: Postleitzahl (UR)

it: Postleitzahl (UR)


egid-und-gebaudenummer

title_for_slug: egid-und-gebaudenummer

fr: EGID und Gebäudenummer. Jedes Gebäude hat eine vortlaufende Nummer die gemeindeweise eindeutig ist.

fr: EGID und Gebäudenummer

de: EGID und Gebäudenummer

en: EGID und Gebäudenummer

it: EGID und Gebäudenummer


grundbuchplan-ur

title_for_slug: grundbuchplan-ur

fr: plan-de-situation geodonnees-de-base

de: situationsplan geobasisdaten

en: situation-plan official-geodata

it: piano-di-situazione geodati-di-base

fr: Diverse Ebenen aus der Amtlichen Vermessung bilden den bekannten Grundbuchplan. Der Katasterplan A4/A3 als PDF können sie ebenfalls hier direkt beziehen.

fr: Grundbuchplan (UR)

de: Grundbuchplan (UR)

en: Grundbuchplan (UR)

it: Grundbuchplan (UR)


bodenbedeckung-ur

title_for_slug: bodenbedeckung-ur

fr: couverture-du-sol geodonnees-de-base

de: bodenbedeckung geobasisdaten

en: land-cover official-geodata

it: copertura-del-suolo geodati-di-base

fr: Die Informationsebene Bodenbedeckung ist eine generalisierte und kartografisch bearbeitete Darstellung realer Objekte mit flächiger Ausdehnung (Gebäude, Strassen, Gewässer, Wald...).

fr: Bodenbedeckung (UR)

de: Bodenbedeckung (UR)

en: Bodenbedeckung (UR)

it: Bodenbedeckung (UR)


fixpunkte-ur

title_for_slug: fixpunkte-ur

fr: Die Fixpunkte bilden die Grundlage aller Daten mit Raumbezug. Darunter fallen beispielsweise die Daten der amtlichen Vermessung, die Landeskartenwerke, Leitungskataster, Daten in geographischen Informationssystemen, Zonenpläne etc. Es werden generell zwei Punktarten unterschieden: die Lagefixpunkte (LFP) und die Höhenfixpunkte (HFP).

fr: Fixpunkte (UR)

de: Fixpunkte (UR)

en: Fixpunkte (UR)

it: Fixpunkte (UR)


einzelobjekte-ur

title_for_slug: einzelobjekte-ur

fr: Die Informationsebene Einzelobjekte umfasst Objekte, die Merkmale der Bodenbedeckung enthalten, welche aufgrund ihrer Eigenschaft oder Ausdehnung aber keine oder unwesentliche flächenmässige Bedeutung haben.

fr: Einzelobjekte (UR)

de: Einzelobjekte (UR)

en: Einzelobjekte (UR)

it: Einzelobjekte (UR)


nomenklatur-ur

regi

fr: Régions et villes

envi

fr: Environnement

title_for_slug: nomenklatur-ur

fr: Flur-, Orts- und Geländenamen (= geografische Namen der amtlichen Vermessung) von geografischen Gebieten, Landschaftsteilen, Flurstücken, topografischen Objekten usw.

fr: Nomenklatur (UR)

de: Nomenklatur (UR)

en: Nomenklatur (UR)

it: Nomenklatur (UR)


gebaudeadressen-ur

title_for_slug: gebaudeadressen-ur

fr: WMS-Dienst des Kanton Uri

fr: WFS-Dienst des Kanton Uri

fr: Die Gebäudeadresse setzt sich zusammen aus einer Ortschaft mit Postleitzahl, einem Lokalisationsnamen (in der Regel Strassennamen) und einer Hausnummer. Zusätzlich werden die Strassenstücke und benannten Gebiete der amtlichen Vermessung dargestellt.

fr: Gebäudeadressen (UR)

de: Gebäudeadressen (UR)

en: Gebäudeadressen (UR)

it: Gebäudeadressen (UR)


liegenschaften-ur

title_for_slug: liegenschaften-ur

fr: terrain geodonnees-de-base

de: grundstuck geobasisdaten

en: site official-geodata

it: fondo geodati-di-base

fr: Die Informationsebene Liegenschaften enthält die Grundstücke gemäss Art. 655 Abs. 2 ZGB: rechtsgültige und projektierte Liegenschaften (Parzellen), selbständige und dauernde Rechte (z.B. Baurecht). Zudem sind ab dem Massstab 1:1'000 auch die Grenzpunkte und Hoheitsgrenzpunkte sichtbar.

fr: Liegenschaften (UR)

de: Liegenschaften (UR)

en: Liegenschaften (UR)

it: Liegenschaften (UR)


regi

de: Regionen und Städte

envi

de: Umwelt

fr: plan-de-situation geodonnees-de-base

fr: Diverse Ebenen aus der Amtlichen Vermessung bilden den bekannten Grundbuchplan. Den Katasterplan A4/A3 als PDF können sie ebenfalls hier direkt beziehen.

fr: Grundbuchplan Darstellung schwarz-weiss (UR)


planeinteilungen-ur

title_for_slug: planeinteilungen-ur

fr: repartition-du-plan geodonnees-de-base

de: planeinteilung geobasisdaten

en: plan-division official-geodata

it: ripartizione-del-piano geodati-di-base

de: WMS-Dienst des Kanton Uri

de: WFS-Dienst des Kanton Uri

fr: Die Geometrien und Nummern der Planeinteilung des Planes für das Grundbuch.

fr: Planeinteilungen (UR)

de: Planeinteilungen (UR)

en: Planeinteilungen (UR)

it: Planeinteilungen (UR)


administrative-einteilung-ur

regi

fr: Régions et villes

en: Regions and cities

it: Regioni e città

envi

en: Environment

it: Ambiente

title_for_slug: administrative-einteilung-ur

fr: Die Toleranzstufen sind ein Teil des Datenmodells DM01 und geben an, wie genau die Objekte der AV aufgenommen sein müssen. In den entsprechenden Verordnungen ist definiert, welche Standardabweichungen für die einzelnen Aufnahmepunkte sicherzustellen sind.

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: Administrative Einteilung (UR)

de: Administrative Einteilung (UR)

en: Administrative Einteilung (UR)

it: Administrative Einteilung (UR)


hoheitsgrenzen-ur

envi

fr: Environnement

title_for_slug: hoheitsgrenzen-ur

en: WMS-Dienst des Kanton Uri

it: WMS-Dienst des Kanton Uri

en: WFS-Dienst des Kanton Uri

it: WFS-Dienst des Kanton Uri

fr: Da der Kanton Uri keine Bezirke hat, bestehen die Hoheitsgrenzen ausschliesslich aus den Gemeindegrenzen.

fr: Hoheitsgrenzen (UR)

de: Hoheitsgrenzen (UR)

en: Hoheitsgrenzen (UR)

it: Hoheitsgrenzen (UR)


fr: couverture-du-sol geodonnees-de-base

de: bodenbedeckung geobasisdaten

url: https://geo.ur.ch/?layers=Bodenbedeckung Darstellung schwarz weiss

fr: WMS-Dienst des Kanton Uri

fr: WFS-Dienst des Kanton Uri

fr: Die Informationsebene Bodenbedeckung ist eine generalisierte und kartografisch bearbeitete Darstellung realer Objekte mit flächiger Ausdehnung (Gebäude, Strassen, Gewässer, Wald...).

fr: Bodenbedeckung Darstellung schwarz weiss (UR)


waldflachen

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

title_for_slug: waldflachen

de: forstwirtschaft nutzungsplanung vegetation wald

protocol: https

de: Datenshop

de: Produktansicht Geodatenshop

format: SERVICE

de: Wald nach Waldrecht, dieser umfasst unter anderem auch Waldstrassen, Gewässer und Gebäude innerhalb des Waldes.

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: Waldflächen


soci

fr: Population et société

de: Bevölkerung und Gesellschaft

en: Population and society

it: Popolazione e società

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

regi

de: Regionen und Städte

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

de: Im Bauinventar werden schützenswerte und erhaltenswerte Baudenkmäler des Kantons erfasst. Besonders schutzwürdige Kulturobjekte werden ins Kantonale Denkmalverzeichnis eingetragen und unter Schutz gestellt.

de: Kantonales Denkmalverzeichnis und Bauinventar


maintainer: Mountain Hydrology and Mass Movements

en: natural-hazard-fatalities ice-avalanche-fatalities rockfall-fatalities lightning-fatalities earthquake-fatalities landslide-fatalities windstorm-fatalities flood-fatalities debris-flow-fatalities

en: Natural hazard fatalities per year in Swizterland since 1946.

en:
This dataset contains the number of fatalities due to flood, debris flow, landslide, rockfall, windstorm, lightning, ice avalanche, earthquake and other processes like roof avalanche or lacustrine tsunami for each year since 1946.
The following information is contained (by column and column title):

* year
* total number of hazard fatalities
* number of fatalities by flood (German: Hochwasser, Überschwemmung). Flood includes people drowned in flooded or inundated areas or carried away in streams under high-water conditions.
* number of fatalities by debris flow (German: Murgang).
* number of fatalities by landslide (German: Erdrutsch). Landslide includes people killed by landslides and hillslope debris flows (German: Hangmure).
* number of fatalities by rockfall (German: Steinschlag, Fels- und Bergsturz).
* number of fatalities by windstorm (German: Sturm). Windstorm includes people killed by falling objects or trees during very strong wind conditions and people who drowned in lakes because their boat capsized during such conditions.
* number of fatalities by lightning (German: Blitz).
* number of fatalities by ice avalanche (German: Eislawine).
* number of fatalities by earthquake (German: Erdbeben).
* number of fatalities by other processes like roof avalanche, lacustrine tsunami (German: andere Prozesse wie Dachlawine, Tsunami im See).

The data was collected based on newspaper research. For more information please refer to

_Badoux, A., Andres, N., Techel, F., and Hegg, C.: Natural hazard fatalities in Switzerland from 1946 to 2015, Nat. Hazards Earth Syst. Sci., 16, 2747-2768, https://doi.org/10.5194/nhess-16-2747-2016, 2016._

The data collection is financed by the FOEN (with exception of the collection of the avalanche fatalities). The data contains the official statistics of the FOEN on fatalities due to flood, debris flow, landslide, rock fall and avalanche.

Restrictions: The data set is not complete. Only fatalities in or around settlements and on open transportation routes are included. More precisely, fatalities were not collected, when persons exposed themselves to a great danger on purpose. Or fatalities during leisure activities which are connected to a higher risk were not included (this includes e.g. canoeing or river surfing during flood, canyoning, mountaineering, climbing, walking or driving on a closed road).

Fatalities by avalanches are collected at the WSL Institute for Snow and Avalanche Research SLF. You can download the avalanche fatalities per hydrological year [here](https://www.envidat.ch/dataset/avalanche-fatalities-switzerland-1936) and per calendar year [here](https://www.envidat.ch/dataset/avalanche-fatalities-per-calendar-year-since-1936). For a direct comparison with the fatalities presented here, please download the data set with the calendar years and do not consider fatalities in the backcountry (tour) or in terrain close to ski areas (offpiste).

debris-flow-fatalities

display_name: debris-flow-fatalities

earthquake-fatalities

display_name: earthquake-fatalities

flood-fatalities

display_name: flood-fatalities

ice-avalanche-fatalities

display_name: ice-avalanche-fatalities

landslide-fatalities

display_name: landslide-fatalities

lightning-fatalities

display_name: lightning-fatalities

natural-hazard-fatalities

display_name: natural-hazard-fatalities

rockfall-fatalities

display_name: rockfall-fatalities

windstorm-fatalities

display_name: windstorm-fatalities

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

value: Envidat Harvester

en: Number of natural hazard fatalities per year in Switzerland since 1946


en: meteorological-data swiss-forest-lab forest plot lwf meteorological-measurements infrastructure

en: Nationalpark Meteorological Data LWF

en: LWF plot Nationalpark. Photo: Oliver Schramm, 2011

en:
![alt text](https://www.envidat.ch/dataset/4a0ff376-83d2-4ae4-bf2e-c361eb050778/resource/51e98f2d-5524-4ea6-babb-9df0b9aba8f1/download/lwf_nationalpark.jpg "LWF Nationalpark")

LWF Plot Nationalpark

- Community: Zernez / canton GR
- Date of installation: 10 October 1995
- Size of the plot: 2 ha
- Altitude: 1890-1907 m
- Mean slope: 11%
- Geology (in German): Nacheiszeitlicher Schwemmfächer; kalkhaltige Moräne, Dolomite, Kalke, Tonschiefer, Rauhwacken
- Provisional soil types (WSL): Rendzic Leptosol
- Woodland association after EK72: 67: Erico-Pinetum montanae
- Main tree species: Pinus mugo
- Management system: high forest
- Silvicultural system: forest reserve
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 23.7 cm
- Number of trees BHD >= 12 cm (2011): 2450
- Maximum tree age: Pinus mugo 210 yr

More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/nationalpark.html

forest

display_name: forest

infrastructure

display_name: infrastructure

lwf

display_name: lwf

meteorological-data

display_name: meteorological-data

meteorological-measurements

display_name: meteorological-measurements

plot

display_name: plot

swiss-forest-lab

display_name: swiss-forest-lab

en: LWF Nationalpark long-term research site


en: swiss-forest-lab forest-productivity forest-growth-model callibration forest-biomass 3-pg

en: link to 3PG github R package

en: A package source code (development version).

en: r3PG source files v 0.1.2

en: link to 3PG cran R package

en: A package source code (latest release).

en: An R Computing Software package which provides a flexible and easy-to-use interface for the Physiological Processes Predicting Growth (3-PG) model written in Fortran. The r3PG, a new Fortran implementation of 3-PG, serves as a flexible and easy-to-use interface for the 3-PGpjs (monospecific, evenaged and evergreen forests) described in Landsberg & Waring (1997) <doi:10.1016/S0378-1127(97)00026-1> and the 3-PGmix (deciduous, uneven-aged or mixed-species forests) described in Forrester & Tang (2016) <doi:10.1016/j.ecolmodel.2015.07.010>.

callibration

display_name: callibration

forest-biomass

display_name: forest-biomass

forest-growth-model

display_name: forest-growth-model

forest-productivity

display_name: forest-productivity

en: r3PG – An r package for simulating forest growth


en:
Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh removed from the forest between two inventories due to damage that occurred, in this case windthrow, and not due to silvicultural planning. This theme is derived on the level of a sample plot from the cuttings of the sample trees and the salvage cut proportion (according to information from the forester).  The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.

Citation:
 > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

biogeographical-region

display_name: biogeographical-region

economic-region

display_name: economic-region

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

salvage-logging-due-to-wind

display_name: salvage-logging-due-to-wind

educ

en: Education, culture and sport

en: Salvage logging due to wind


en: swiss-forest-lab wdb ozone long-term-forest-ecosystem-research long-term-monitoring

en: Data request

en: In order to quantify effects of ozone on Swiss forest ozone flux calculations are needed. Currently 5 stations are operated to measure the ozone load on forests. These data form the basis for ozone flux calculations. The ozone flux calculation is carried out with the DO3SE model (Büker et al. 2012). This can then be used for epidemiological evaluations for the Swiss forest (Thomas et al. 2005, Braun et al. 2014, Braun et al. 2017, Braun et al. 2020, Braun et al. 2021, Braun et al. 2022).

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

long-term-monitoring

display_name: long-term-monitoring

ozone

display_name: ozone

wdb

display_name: wdb

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: Ozone measurement and analysis in the Intercantonal Forest Observation Program


en: numerical-avalanche-forecasting machine-learning avalanche-danger-level avalanche-forcasting avalanche

en: README

en: This read-me file contains the descriptions of the variables

en: This subset of data includes the meteorological variables (resampled 24-hour averages) and the profile variables extracted from the simulated profiles for each of the weather stations of the IMIS network in Switzerland, and, the quality-controlled danger ratings for dry-snow avalanche conditions forecast in Switzerland.

en: This data set includes the meteorological variables (resampled 24-hour averages) and the profile variables extracted from the simulated profiles for each of the weather stations of the IMIS network in Switzerland, and, the danger ratings for dry-snow conditions assigned in the Swiss avalanche bulletin to the location of the weather station.

en:
Each set includes the meteorological variables (resampled 24-hour averages) and the profile variables extracted from the simulated profiles for each of the weather stations of the IMIS network in Switzerland, and, the danger ratings for dry-snow conditions assigned to the location of the station. The data set of RF 1 contains the danger ratings published in the official Swiss avalanche bulletin, and the data set of RF 2 is a quality-controlled subset of danger ratings.
These data are the basis of the following publication: Pérez-Guillén, C., Techel, F., Hendrick, M., Volpi, M., van Herwijnen, A., Olevski, T., Obozinski, G., Pérez-Cruz, F., and Schweizer, J.: Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland, Nat. Hazards Earth Syst. Sci., 22, 2031–2056, https://doi.org/10.5194/nhess-22-2031-2022, 2022.

avalanche

display_name: avalanche

avalanche-danger-level

display_name: avalanche-danger-level

avalanche-forcasting

display_name: avalanche-forcasting

machine-learning

display_name: machine-learning

numerical-avalanche-forecasting

display_name: numerical-avalanche-forecasting

en: Weather, snowpack and danger ratings data for automated avalanche danger level predictions


en: connectivity planning-principles strategic-planning multifunctionality green-infrastructure

en: Results of the coding on general aspects regarding the strategic plans of urban regions as well as extracts from each plan to justify the coding option – this data was derived from the coding procedure corresponding to items from 5 to 11 of the protocol. The data was discussed qualitatively in the research paper.

en:
The present dataset is part of the published scientific paper Grădinaru, S. R., & Hersperger, A. M. (2019). Green infrastructure in strategic spatial plans: Evidence from European urban regions. Urban forestry & urban greening, 40, 17-28.
The goal of this research was to conduct a comparative analysis of the integration of green infrastructure concept in strategic spatial plans of European Urban regions. Specifically, the paper has the following objectivs: 1) which principles of GI planning are followed in strategic plans of urban regions? 2) can we identify different approaches to GI integration into strategic planning?. The study focues on a sample consisting of 14 case studies spanning 11 countries. We retrieved the strategic plans from the websites of the planning authorities. The list of the reviewed planning documents can be found in Appendix A of the paper. A protocol was developed to perform the content analysis of the strategic plans and gather the data. The detailed list of protocol items can be found in Appendix B of the paper.
The planning documents were read in order to address the protocol items. The answer to the protocol items in each of the first two categories (items 1–11) was documented as text, while the answer for the third category, namely items addressing the planning principles (items 12–36), was coded according to Table 1 of the article. As a result, we provide the folowing outputs:
•	GI_Dataset_1_Items_1-12.xlsx – available on request
o	Results of the coding on general aspects regarding the strategic plans of urban regions as well as extracts from each plan to justify the coding option – this data was derived from the coding procedure coresponding to items from 1 to 12 of the protocol. The data was discussed qualitativly in the research paper.
•	GI_Dataset_2_Items_12-36.csv – freely available
o	Results of the coding on principles of GI planning followed in strategic plans of urban regions– this data was derived from the coding procedure coresponding to items from 12 to 36 of the protocol. The data served as input for the classifications performed through hierarchical cluster analysis. This data is a detailed version of Appendix C in the paper.

connectivity

display_name: connectivity

green-infrastructure

display_name: green-infrastructure

multifunctionality

display_name: multifunctionality

planning-principles

display_name: planning-principles

strategic-planning

display_name: strategic-planning


lwf-isone-long-term-research-site

title_for_slug: lwf-isone-long-term-research-site

en: lwf plot infrastructure swiss-forest-lab meteorological-data forest meteorological-measurements

en: LWF plot Isone. Photo: Anne Thimonier Rickenmann, 2011

en: Isone Meteorological Data LWF

en:
![alt text](https://www.envidat.ch/dataset/f4b44f60-4eed-471f-a09e-749a0f5f0683/resource/cb8e84ea-ad8b-476e-be32-e02a927d2449/download/lwf_isone.jpg "LWF Isone")

LWF Plot Isone

- Community: Isone / canton TI
- Date of installation: 5 September 1995
- Size of the plot: 2 ha
- Altitude (m): 1181-1259
- Mean slope: 58%
- Geology (in German): Untergrund: Südalpin, präpermisches Grundgebirge, Ceneri Zone; schiefriger Biotitplagioklasgneis - Oberfläche: Quartär; Moräne, Hängeschutt-.
- Provisional soil type (WSL) : Humic Cambisol
- Woodland association after EK72: 4: Luzulo niveae-Fagetum dryopteridetosum
- Main tree species: Fagus sylvatica
- Management system: former coppice
- Silvicultural system: unmanaged
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 37.4 cm
- Number of trees BHD >= 12 cm (2011): 1254
- Maximum tree age: Fagus sylvatica uneven-aged - 70-85-100 yr

More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/isone.html

forest

display_name: forest

infrastructure

display_name: infrastructure

lwf

display_name: lwf

meteorological-data

display_name: meteorological-data

meteorological-measurements

display_name: meteorological-measurements

plot

display_name: plot

swiss-forest-lab

display_name: swiss-forest-lab

educ

en: Education, culture and sport

en: LWF Isone long-term research site


en: monitoring air-pollution forest long-term-monitoring long-term-forest-ecosystem-research

en: FORECOMON – Proceedings

media_type: application/pdf

en: Latest version

format: PDF

en: Link to Proceedings in DORA WSL

en: Deprecated version 1.0

en: First version of the proceedings containing errors. This early version it is kept, for traceability purposes, in conformance with DOI rules.

en:
Forest monitoring to assess forest functioning under air pollution and climate change. Proceedings.
FORECOMON 2021 - the 9th forest ecosystem monitoring conference.
7-9 June 2021, WSL, Birmensdorf, Switzerland

The goal of FORECOMON 2021 is to highlight the extensive ICP Forests data series on forest growth, phenology and leaf area index, biodiversity and ground vegetation, foliage and litter fall, ambient air quality, deposition, meteorology, soil and crown condition. We combine novel modeling and assessment approaches and integrate long-term trends to assess air pollution and climate effects on European forests and related ecosystem services. Latest results and conclusions from local scale to European scale studies will be presented and discussed.

Copyright © 2021 by WSL, Birmensdorf
The authors are responsible for the content of their contribution.

air-pollution

display_name: air-pollution

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

long-term-monitoring

display_name: long-term-monitoring

monitoring

display_name: monitoring

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: Forest monitoring to assess forest functioning under air pollution and climate change. Proceedings. FORECOMON 2021 - the 9th forest ecosystem monitoring conference. 7–9 June 2021, Birmensdorf, Switzerland


en: plot-size biodiversity monitoring national-forest-inventory europe species-richness

en: Mega-plot data from Norway, Spain, Slovakia, Switzerland

en:
The mega-plot data consist in a single .RDS file containing 10 columns, where each row represents a mega-plot:
- "Country": refers to either "Spain", "Norway", "Switzerland" or "Slovakia"
- "Single_plot_size": refers to the size of the plots used in the aggregation process to make the mega-plots. The largest value within each country refers to the orignial plot size of each NFI, while the smaller values within each country refers to the downscaled datasets. The analyses were performed independently for each "Country" and "Single_plot_size", so these should not be analysed together.
- "n_plots": refers to the number of plots aggregated
- "A_megaplot": area of the mega-plots (sum of the area of all the aggregated plots). It equals "Single_plot_size" multiplied by "n_plots".
- "SR": Species richness of the mega-plot
- "Hclim": EH variable, corresponds to the climate heterogeneity value of the mega-plot, based on mean annual temperature and annual precipitation
- "Htopo": EH variable, corresponds to the topographic heterogeneity value of the mega-plot, based on slope and Beers aspect
- "Hsoil": EH variable, corresponds to the soil heterogeneity value of the mega-plot, based on soil pH
- "Hba": EH variable, corresponds to the basal area heterogeneity value of the mega-plot
- "Hres": residual environmental heterogeneity

To fit our models, all EH variables have been standardised. In this dataset, EH variables are provided before standardisation (raw).

en:
The data file refers to the data used in Portier et al. "Plot size matters: towards comparable species richness estimates across plot-based inventories" (2022) *Ecology and Evolution*.

This paper describes a methodoligical framework developed to allow meaningful species richness comparisons across plot-based inventories using different plot sizes. To this end, National Forest Inventory data from Switzerland, Slovakia, Norway and Spain were used. NFI plots were aggregated into mega-plots of larger sizes to build rarefaction curves. The data stored here correspond to the mega-plot level data used in the analyses, including for each country the size of the mega-plots in square meters (A), the corresponding species richness (SR) as well as all enrionmental heterogeneity measures described in the corresponding paper. Mega-plots of country-specific downscaled datasets are also provided.

The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). Contact details for data requests from all NFIs can be found in the ENFIN website (http://enfin.info/).

biodiversity

display_name: biodiversity

europe

display_name: europe

national-forest-inventory

display_name: national-forest-inventory

plot-size

display_name: plot-size

species-richness

display_name: species-richness

en: Towards comparable species richness estimates across plot-based inventories - data


maintainer: EnviDat Support

en: taxonomic-diversity functional-diversity species-distribution-models beta-diversity conservation plants zonation migclim land-use-change european-alps phylogenetic-diversity global-change

en: Data Access

format: GeoTIFF

en:
This repository contains extensive data for the European Alps:
  - Observations of ~3,500 plant species
  - Climate (1-km), soil (100-m) and land cover predictors (1km); current and future scenarios (28 CMIP6-GCMs, 2 land cover change and 3 dispersal scenarios i.e., unlimited, no and realistic vegetation dispersal)
  - Flora migration rates (categorical) and ecological preferences (continuous indicator values)
  - Regional maps of barriers to migration and water bodies at 100-m resolution
  - Sampling effort, distance to roads and cities predictors at 100-m resolution
  - Present and future abundances over the study region at 1-km resolution (~2,000 species)
  - Present and future multifaceted and uniqueness of the European Alps' Flora at 1-km resolution
  - Present and future conservation recommendations at 1-km resolution (26 current and future strategies)
  - Phylogenetic data and functional traits of ~2,000 plants (raw data and classification trees)
  - All scripts, data and plots used for the analyses, including a singularity container (mini-linux) to run them

beta-diversity

display_name: beta-diversity

conservation

display_name: conservation

european-alps

display_name: european-alps

functional-diversity

display_name: functional-diversity

global-change

display_name: global-change

land-use-change

display_name: land-use-change

migclim

display_name: migclim

phylogenetic-diversity

display_name: phylogenetic-diversity

plants

display_name: plants

species-distribution-models

display_name: species-distribution-models

taxonomic-diversity

display_name: taxonomic-diversity

zonation

display_name: zonation

educ

en: Education, culture and sport

en: Present and future multifaceted plant diversity, uniqueness and conservation in the European Alps


lwf-lausanne-long-term-research-site

title_for_slug: lwf-lausanne-long-term-research-site

en: forest swiss-forest-lab meteorological-data lwf infrastructure plot meteorological-measurements

en: LWF plot Lausanne. Photo: Anne Thimonier Rickenmann, 2019

en: Lausanne Meteorological Data LWF

en:
![alt text](https://www.envidat.ch/dataset/9f35f697-a226-4719-aefd-b8c96fe5ae7c/resource/d6a48c12-62e6-4c04-9f63-a84d5964bc55/download/lwf_lausanne.jpg "LWF Lausanne")

LWF Plot Lausanne

- Community: Lausanne / canton VD
- Date of installation: 5 September 1994
- Size of the plot: 2 ha
- Altitude: 800-814 m
- Mean slope: 7%
- Geology (in German): Untergrund: Tertiär, Miozän, Burdigalien, obere Meeresmolasse; Sandstein - Oberfläche: Quartär, Würm; würmeiszeitliche Moräne
- Soil types (WSL) : Distric Cambisols
- Woodland association after EK72: 8: Milio-Fagetum
- Main tree species: Fagus sylvatica
- Management system: high forest
- Silvicultural system: group selection
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 59.9 cm
- Number of trees BHD >= 12 cm (2011): 650
- Maximum tree age: Abies alba 160-170 yr - Picea abies 160-170 yr - Fagus sylvatica 160-170 yr

More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/lausanne.html

forest

display_name: forest

infrastructure

display_name: infrastructure

lwf

display_name: lwf

meteorological-data

display_name: meteorological-data

meteorological-measurements

display_name: meteorological-measurements

plot

display_name: plot

swiss-forest-lab

display_name: swiss-forest-lab

en: LWF Lausanne long-term research site


en: icp-forests dendrochronology swiss-forest-lab stand-history long-term-forest-ecosystem-research dominant-trees tree-ring-width

en: Data Request

en:
Two stem core samples of 12 to 20 trees outside the plot of each main species in the plot were taken at breast height (1.3 m above ground) with a SUUNTO corer. Tree ring width and density were determined with the instruments CATRAS and TSAP, the Densitometer DENDRO 2003 and a stereo-microscope. The selected trees included at least 12 pre-dominant or dominant trees and if possible also 4 subdominant or surpressed trees. NOTE: The samplings were carried out between 1996 and 1999 for most plots and in 2003 for the plot Lantsch. The cores cover a time span depending on the age of the oldest trees on a plot. On one plot the oldest sampled tree ring grew in the year 1646.

 Purpose:

Reconstruction of stand history and tree growth

 Manual Citation:

 * Paolo Cherubini, Matthias Dobbertin, 1997: Bestandesgeschichte (Dendrochronologie). In: Peter Brang (ed.), Aufnahmeanleitung LWF. Langfristige Waldökosystem-Forschung LWF, Eidg. Forschungsanstalt WSL, Birmensdorf, 3 S.

 * Cherubini, P.; Dobbertin, M., 1998: The Swiss long-term forest ecosystem research: methods for reconstructing forest history. In: Borghetti, M. (ed): Società Italiana di Selvicoltura ed Ecologia Forestale (SISEF), Atti I: 19-22.

 Paper Citation:

 * Cherubini, P., Fontana, G., Rigling, D., Dobbertin, M., Brang, P., Innes, J.L., 2002. Tree-life history prior to death: two fungal root pathogens affect tree-ring growth differently. J. Ecol. 90, 839-850.

dendrochronology

display_name: dendrochronology

dominant-trees

display_name: dominant-trees

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

stand-history

display_name: stand-history

tree-ring-width

display_name: tree-ring-width

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: Dendrochronological analyses of tree core samples (dominant trees) LWF


maintainer: Joan Casanelles Abella

en: tartu floral-resources poznan floral-inventories angiospermae antwerp zurich paris plants urban

en: List of plant species sampled in the five cities (Antwerp, Paris, Poznan, Tartu and Zurich) during the different sampling perionds (four for Zurich, three for the other cities) with their frequency (oc1-oc16) within a 100 m radius

en: Trait values for the sampled plants

en: Floral units, mean number of flowers per floral unit and floral abundance of the different plants counted in quadrats in the study sites in Zurich during the four sampling periods

en: Data of a survey of flowering plants in 80 sites in five European cities and urban agglomerations (Antwerp, Belgium; greater Paris, France; Poznan, Poland; Tartu, Estonia; and Zurich, Switzerland) sampled between April and July 2018.

angiospermae

display_name: angiospermae

antwerp

display_name: antwerp

floral-inventories

display_name: floral-inventories

floral-resources

display_name: floral-resources

paris

display_name: paris

plants

display_name: plants

poznan

display_name: poznan

tartu

display_name: tartu

urban

display_name: urban

zurich

display_name: zurich

en: Flowering Plants (Angiospermae) in Urban Green Areas in five European Cities


increment

title_for_slug: increment

en:
Increase in the volume of stemwood with bark of the trees and shrubs starting at 12 cm dbh that have survived between two inventories and of the losses (modelled for the half period), plus the volume of the gains. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees.

Citation:
 > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_

altitudinal-vegetation-zone

display_name: altitudinal-vegetation-zone

avalanches-silvaprotect

display_name: avalanches-silvaprotect

biogeographical-region

display_name: biogeographical-region

conifers-broadleaves

display_name: conifers-broadleaves

economic-region

display_name: economic-region

increment

display_name: increment

landslide-mudflow-silvaprotect

display_name: landslide-mudflow-silvaprotect

lower-higher-altitudinal-zones

display_name: lower-higher-altitudinal-zones

main-tree-species

display_name: main-tree-species

ownership

display_name: ownership

potential-cost-for-timber-harvesting

display_name: potential-cost-for-timber-harvesting

processes-in-channels-silvaprotect

display_name: processes-in-channels-silvaprotect

production-region

display_name: production-region

protection-forest-region

display_name: protection-forest-region

rockfall-silvaprotect

display_name: rockfall-silvaprotect

site-quality

display_name: site-quality

stage-of-stand-development

display_name: stage-of-stand-development

tree-species-woody-species

display_name: tree-species-woody-species

educ

en: Education, culture and sport

en: Increment


en: spei drought-stress grapevine-yellows dendroecology southern-switzerland

en: Dataset 1

en: Sample identification, symptomatology (symptomatic vs. asymptomatic), coordinates (CH1903), phloem disarrangement class, drought-sensitivity, slope sector.

en: Dataset 2. Ring width.

en:
Ring width measured with cellSens from slides.
Measures expressed as [μm] and arranged per ID (see dataset 1) and year.

en:
Dataset collected from dendroecological study on trunks of grapevines ('Chardonnay' cv.) infected by the "Flavescence dorée" phytoplasma (FDp) in Origlio (southern Switzerland) in 2019-2020. Ring widths were measured with cellSens (Olympus Corporation). Calculations and analysis were conducted within R.
The Flavescence dorée phytoplasma (FDp) causes a severe grapevine (Vitis vinifera) disease. Anatomical modification due to FDp infections are known to occur but research so far focused on stems and leaf tissues and, in particular, on their phloem structure. In this paper, we applied dendrochronological techniques on wood rings and analysed the anatomical structures of the trunk of the susceptible grapevine cultivar ‘Chardonnay’ in order to verify their response to FDp infections.
In this study, we tested the impact of FDp and drought stress on xylem ring width and also described phloem anomalies inside the trunk of grapevines. We concluded that drought and FDp infection both have a significant effect on ring width reductions and that FDp supersedes the effect of drought conditions (calculated after the SPEI index) in infected specimens.

dendroecology

display_name: dendroecology

drought-stress

display_name: drought-stress

grapevine-yellows

display_name: grapevine-yellows

southern-switzerland

display_name: southern-switzerland

spei

display_name: spei

en: Impact of the “Flavescence dorée” phytoplasma on xylem growth and phloem anomalies in trunks of ‘Chardonnay’ grapevines (Vitis vinifera)


en: snow-melt snow-cover-fraction snow-water-equivalent operational-snow-hydrological-service snow-climatology

en: Data package

en: This dataset comprises the climatology on gridded data of snow water equivalent and snow melt runoff spanning 1998-2022, with a spatial resolution of 1 km and daily temporal resolution. This data was produced with the conceptual OSHD model (Temperature Index Model).

operational-snow-hydrological-service

display_name: operational-snow-hydrological-service

snow-climatology

display_name: snow-climatology

snow-cover-fraction

display_name: snow-cover-fraction

snow-melt

display_name: snow-melt

snow-water-equivalent

display_name: snow-water-equivalent

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: Climatological snow data since 1998, OSHD


maintainer: Athanasios Nenes

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

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: 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: 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:
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

weissfluhjoch

display_name: weissfluhjoch

en: CCN, hygroscopicity, predicted cloud droplet numbers Weissfluhjoch


en: snow-depth climate homogenisation break-detection snow-measurements

en: ACMANT HSavg

media_type: application/zip

format: ZIP

en: Climatol dHS1

en: HOMER dHS1

en: Input files for HOMER

en: Climatol HSavg

en: HOMER HSavg

en: Station list

en: List of stations used in this study. This file also serves as the station input file for HOMER

en: Station relocations

en: Metadata compiled from various sources

en: Breakpoints (R data file)

en: Break points detected by all three methods and draft metadata table

en: ACMANT dHS1

en:
Data set consists of monthly mean values for snow depth and days with snow on the ground intended for the use of break detection with ACMANT, Climatol and HOMER. List and coordinates of stations used as well as metadata and break detection results from all three methods is included.

 Columns
Monthly means for each hydrological year: Nov, Dec, Jan, Feb, Mar, Apr with May to Oct set to zero

break-detection

display_name: break-detection

climate

display_name: climate

homogenisation

display_name: homogenisation

snow-depth

display_name: snow-depth

snow-measurements

display_name: snow-measurements

educ

en: Education, culture and sport

en: Input data for break point detection of Swiss snow depth time series


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 B_Transition_Analysis.R
Further Analysis of transition numbers

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

mimetype: application/zip

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

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

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

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

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

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

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

 05_Service_Data
Any service calculations will be saved in here

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

en:
Data and scripts of publication:

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

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

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

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

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

land-use

display_name: land-use

land-use-change

display_name: land-use-change

landscape-services

display_name: landscape-services

modeling

display_name: modeling

statistical-model

display_name: statistical-model

switzerland

display_name: switzerland

urbanization

display_name: urbanization

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: Land use projections and services for Switzerland


radar-wind-profiler-davos-wolfgang

maintainer: Maxim Hervo

title_for_slug: radar-wind-profiler-davos-wolfgang

en: wolfgangpass wind-direction radar raclets wind-speed

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


en: dust hill turbulence snow atmosphere particles deposition

en: preferential deposition patterns of all simulations performed

en: The data consist of matlab files. Each files contains a 2D array with the values of deposition at the surface nodes. Each array has Nx x Ny values. Please refer to the original paper for additional information on the spatial resolution and grid size.

en:
Preferential deposition of snow and dust over complex terrain is responsible for a wide range of environmental processes, and accounts for a significant source of uncertainty in surface mass balances of cold and arid regions. Despite the growing body of literature on the subject, previous studies reported contradictory results on the location and magnitude of deposition maxima and minima. This study aims at unraveling the governing processes of preferential deposition in neutrally stable atmosphere and to reconcile seemingly inconsistent results of previous works. For this purpose, a comprehensive modeling approach is developed, based on large eddy simulations of the turbulent airflow, Lagrangian stochastic model of particle trajectories, and immersed-boundary method to represent the underlying topography. The model performance is tested against wind tunnel measurements of dust deposition around isolated and sequential hills. A scale analysis is then performed to investigate the dependence of snowfall deposition on the particle Froude and Stokes numbers, which fully account for the governing processes of inertia, flow advection, and gravity. Additional simulations are performed, to test whether the often used assumption of inertialess particles yields accurate deposition patterns. We finally show that our scale analysis provides qualitatively similar results for hills with different aspect ratios.

This dataset contains the results of the LES-LSM model. Each Matlab file contains a 2D array of deposition values (in kg/m2) in each surface node (ix, iy) of the Cartesian grid. The file names are consistent with the simulation numbers listed in the original paper. For additional information, please refer to "Preferential deposition of snow and dust over hills: governing processes and relevant scales" by F. Comola, M. G. Giometto, S. T. Salesky, M. B. Parlange, and M. Lehning, Journal of Geophysical Research: Atmospheres, 2019.

atmosphere

display_name: atmosphere

deposition

display_name: deposition

dust

display_name: dust

hill

display_name: hill

particles

display_name: particles

snow

display_name: snow

turbulence

display_name: turbulence


en: long-term-forest-ecosystem-research stem-diameter swiss-forest-lab tree-growth icp-forests

en: Data Request

en:
Continuous stem radius measurements to investigate carbon balance and water relations of trees

 Purpose:

Assessment of growth and water related stem changes

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

stem-diameter

display_name: stem-diameter

swiss-forest-lab

display_name: swiss-forest-lab

tree-growth

display_name: tree-growth

educ

en: Education, culture and sport

en: Automated point dendrometer measurements at LWF sites


forclim

title_for_slug: forclim

en: model swiss-forest-lab forest-dynamics vegetation-height-model tree-growth gap-model

en: ForClim repository and documentation

en: Register for access to current and past releases of ForClim source code, installers, input data, user guides, and technical documentation.

en: ForClim structure

en: Structure of ForClim (adapted from Rasche et al. 2011).

en:
ForClim is a cohort-based model that was developed to analyze successional pathways of various forest types in Central Europe. Following the standard approach of gap models ForClim simulates the establishment; growth and mortality of trees on multiple independent patches (typically n = 200) in annual time steps to derive regional-scale stand dynamics. ForClim is currently parameterized for ca. 180 tree species dominant of temperate forests worldwide. The model has been tested comprehensively for the representation of natural forest dynamics of temperate forests of the Northern Hemisphere, with an emphasis on European forests. ForClim may be freely used under the terms of the "GNU GENERAL PUBLIC LICENSE v3" license.

![alt text](https://www.envidat.ch/dataset/a049e6ad-caac-492a-9771-90856c48ed03/resource/e1c9f03a-2e55-444b-afee-fa1f7f50dee0/download/forclim_4submodels.jpg "ForClim structure")

forest-dynamics

display_name: forest-dynamics

gap-model

display_name: gap-model

model

display_name: model

vegetation-height-model

display_name: vegetation-height-model

envidat

en: EnviDat: the Environmental Data Portal

en: ForClim


en: precipitation temperature-profiles wind radiation atmospheric-conditions

en: DATA ACCESS

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

en: Site photo 3

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

en: README

media_type: text/plain

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

format: TXT

en: Site photo 2

en: Site photo 1

en: Latest data from Stillberg climate station

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

en: STB2 CSV

en: Overview plot

media_type: image/png

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

format: PNG

en:
 Important

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

atmospheric-conditions

display_name: atmospheric-conditions

precipitation

display_name: precipitation

radiation

display_name: radiation

temperature-profiles

display_name: temperature-profiles

wind

display_name: wind

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


snow-drift-station-flowcapt

maintainer: Benjamin Walter

title_for_slug: snow-drift-station-flowcapt

en: acoustic-mass-flux snow-blowing snowdrift raclets wind-speed flowcapt

en: avergaged mass flux

en: Average mass flux of 10 min measurement interval. The Cycle duration is 1 min which includes 6 s observation and 54 s stand-by. The time is given in UTC+1.

format: XLS

en: maximum mass flux

en: The data file contains the maximum mass flux of 10 minute intervalls. The duration of one cycle was 1 min and contained 6s observations and 54 s stand-by. The time is given in UTC+1.

en: minimum mass flux

en: The data file contains the minimum mass flux of 10 minute intervalls. The duration of one cycle was 1 min and contained 6s observations and 54 s stand-by. The time is given in UTC+1.

en: maximum wind speed

en: The data file contains the maximum wind speed in 10 minute intervalls. The duration of one cycle was 1 min and contained 6 s observations and 54 s stand-by. The time is given in UTC+1.

en: minimum wind spees

en: The data file contains the minimum wind speed in 10 minute intervalls. The duration of one cycle was 1 min and contained 6s observations and 54 s stand-by. The time is given in UTC+1.

en: average wind speed

en: The data file contains the average wind speed in 10 minute intervalls. The duration of one cycle was 1 min and contained 6s observations and 54 s stand-by. The time is given in UTC+1.

en: The FlowCapt is an ultra-robust instrument measuring solid particle acoustic mass - flux intensities (g m‾ ² s‾ ¹) and wind speeds (m s‾ ¹). It was deployed at Gotschnagrat (LON: 46.859 LAT: 9.849). The vertical tube with a length of 1 m monitors snowdrift and snow-blowing; and is mounted at a height between 0.1 an 1.1 m above snow surface. The time within each data set is given in UTC+1.

acoustic-mass-flux

display_name: acoustic-mass-flux

flowcapt

display_name: flowcapt

raclets

display_name: raclets

snow-blowing

display_name: snow-blowing

snowdrift

display_name: snowdrift

wind-speed

display_name: wind-speed

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

en: Snow Drift Station - Flowcapt


disdrometer-data-laret

maintainer: Daniel Wolfensberger

title_for_slug: disdrometer-data-laret

en: raclets disdrometer visibility precipitation hydrometeors

en: Disdrometer Data Laret

en: The zip folder contains one netCDF file per day for Laret from February 7 to March 29 2019 with a temporal resolution of 30 seconds (time in UTC). The naming convention for the files is Laret_YYYYMMDD.nc and the files include the following: characteristics of hydrometeors (e.g. diameter, fall velocity, amount per diameter class,...), precipitation rate, radar reflectivity, visibility range, weather codes and instrument performance.

en:
A laser optical disdrometer (Parsivel² , OTT Hydromet) was used to measure hydrometeors by extinction when passing a laser beam. The instrument can classify eight different kinds of precipitation, including rain, hail, snow, drizzle, and hybrid forms.
The dataset contains information on precipitation amount and type for the period of February 7 to March 29 2019 in Laret.

disdrometer

display_name: disdrometer

hydrometeors

display_name: hydrometeors

visibility

display_name: visibility

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: biogas switzerland manure review 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


lwf-tea-bag-sites

maintainer: Marcus Schaub

title_for_slug: lwf-tea-bag-sites

en: tea-bag carbon forest litter decomposition green-tea roiboos verification modeling

en: Site Information

en: Descriptive information on sites

en:
Decomposition of plant litter is a key process for the transfer of carbon and nutrients in ecosystems. Carbon contained in the decaying biomass is released to the atmosphere as respired CO2, and may contribute to global warming. Litterbag studies have been used to improve our knowledge of the drivers of litter decomposition, but they lack comparability because litter quality is plant species-specific. The use of commercial tea bags as a standard substrate was suggested in order to harmonize studies, where green tea and rooibos represent more labile and more recalcitrant C compounds as surrogates of local litter.
The tea bag approach was implemented on eight sites of the Swiss long-term Forest Ecosystem Research (LWF) network (https://www.wsl.ch/LWF). This allowed us to take advantage from the existing infrastructure and data from a previous litterbag study with local litter. In Beatenberg and Schaenis, additional elevation transects were established (1200-1800 m and 540-1150 m, respectively) to examine particularly the effect of temperature on decomposition. In Pfynwald (https://www.wsl.ch/de/ueber-die-wsl/versuchsanlagen-und-labors/flaechen-im-wald/pfynwald.html) and Salgesch, infrastructure of running projects was used to examine the effect of drought and understory removal, respectively. In Novaggio, tea bags were incubated in summer and winter to study the effect of seasonality particularly precipitation. Tea bags are collected after 3, 12, 24, and 36 months; for the two time-shifted experiments additionally after 6 and 9 months.
The study has two primary objectives. Firstly, it contributes to TeaComposition initiative (http://teacomposition.org/) which aims at investigating long-term litter decomposition and its key drivers at present as well as under different future climate scenarios using a common protocol and standard litter (tea) across nine terrestrial biomes. Secondly, the data are used to further develop decomposition models such as Yasso (http://en.ilmatieteenlaitos.fi/yasso) which is used by several countries, including Switzerland to estimate the annual carbon fluxes in dead wood, litter, and soil for reporting in National Greenhouse Gas Inventories under the United Nations Framework Convention on Climate Change and the Kyoto Protocol.

carbon

display_name: carbon

decomposition

display_name: decomposition

forest

display_name: forest

green-tea

display_name: green-tea

litter

display_name: litter

modeling

display_name: modeling

roiboos

display_name: roiboos

tea-bag

display_name: tea-bag

verification

display_name: verification

en: LWF-Tea bag sites


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

en: Sonic wind component v

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

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

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

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

en: Sonic time

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

en: Sonic wind component w

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

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

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

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

en: Sonic temperature

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

en: Sonic wind component u

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

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

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

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

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

anemometer

display_name: anemometer

raclets

display_name: raclets

wind

display_name: wind

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

wind-velocity

display_name: wind-velocity

en: Snow Drift Station - 3D Ultrasonic


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

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

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


en: COST European Snow Booklet (ESB)

en: Please find here the web version of the COST European Snow Booklet (ESB).

mimetype: application/pdf

en: European Snow Booklet public metadata

en:
The core of the European Snow Booklet (ESB), the country reports of 38 European countries, are based on information and metadata on snow measurements collected through a comprehensive survey, the ESB Questionnaire, between August 2017 and March 2018. The station metadata (for example location, elevation, measurement start date) from each country was provided by one or several institutions responsible for snow observations. By answering the questionnaire the contacts agreed that the information and metadata provided could be presented in the ESB. For cases where institutions indicated that information on station locations could be made publicly available, the metadata for the variables snow depth (HS), depth of snowfall (HN) and water equivalent of the snow cover (SWE), is additionally provided here for download (METADATA.xlsx).
Please note that not all institutions gave permission to publish their station metadata. This metadata has to be requested from the non-personal institution contacts provided in the METADATA.xlsx file. In some cases, however, no institutional contact is available and thus the contact of the country representative who filled in the questionnaire is used.
For current, updated station metadata we refer to the (non-personal) institutional contacts provided in METADATA.xlsx and/or the institution webpages. Note, however, that we cannot guarantee for completeness of the dataset for all countries because operational networks are prone to changes.

en: List of authors and list of the editorial board of the European Snow Booklet

en:
Here you can find the list of all lead authors of the different chapters of the European Snow Booklet (ESB).
In addition a list of the editorial board that reviewed the ESB is given.

en: European Snow Booklet unpublished metadata

en: The entire station metadata of all European institutions that contributed data is stored in METADATA_unpublished.xlsx. This file, however, cannot be accessed, since not all institutions gave permission to publish their station metadata. If you wish to access the metadata, please send a request to marty@slf.ch stating clearly why you want to access these metadata. NOTE: in this file ONLY station metadata (Lat/Lon/Altitude) is given, NO measured snow depth data.

en: COST European Snow Booklet (ESB) - DORA Link

media_type: text/html

en: DORA Link of the COST European Snow Booklet (ESB).

format: HTML

en:
The European Snow Booklet (ESB) is a book of reference for snow measurements that has been produced through collaboration with many European snow practitioners and snow scientists in the framework of the European Cooperation in Science and Technology (COST) Action ES1404 “A European network for a harmonised monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction (HarmoSnow)”. The ESB provides a unique collection of information about operational snow observations in the European countries and the methods used to perform basic measurements of snow on the ground: snow depth (HS), depth of snowfall (HN), water equivalent of the snow cover (SWE) and presence of snow on the ground (PSG). Information and station metadata (for example location, elevation) for these basic snow variables were collected through a comprehensive survey, the ESB questionnaire between August 2017 and March 2018. Numerous institutions of 38 European countries provided detailed information describing the status of the operational snow observations and the methods used at the time of the survey. Based on the information provided, a country report was written for each European country. Similarities and differences among the countries, that is, the choice of snow variables to be measured, the measurement principles applied, the number of stations, or the spatial and elevational station distribution are pointed out. Thus the collection of country reports demonstrates the relevance of snow measurements for each country. Thus, the intention of the ESB is to foster better knowledge transfer regarding snow measurements between the snow science and operational communities and to improve the communication of information to the general public.

For detailed information on the European countries, we refer to the ESB, which can be downloaded here (envidat.ch). Please note that the ESB is not a living document and information and station metadata are from August 2017 till March 2018, except for Latvia (metadata updated in December 2018).

depth-of-snowfall

display_name: depth-of-snowfall

harmonisation-of-snow-measurements

display_name: harmonisation-of-snow-measurements

snow-depth

display_name: snow-depth

snow-measurement-methods

display_name: snow-measurement-methods

water-equivalent-of-the-snow-cover

display_name: water-equivalent-of-the-snow-cover

en: European Snow Booklet – an Inventory of Snow Measurements in Europe