fr: animaux-de-rente bovins age velage

de: abkalbung rinder nutztiere alter

en: livestock calving cattle age

it: bovini parto eta animale-da-reddito

identifier: cattle-ageLastCalving

fr: Répartition des âges au dernier vêlage (ADV, en mois) par an de l'abattage et par type de race de la vache (races laitières ou races à viande). L'ADV est représenté dans la fourchette 20-300 mois sous la forme d'un boxplot. Le nombre d'observations par catégorie et par année se trouve dans le .csv.

en: Distribution of the age at last calving (ALC, in months) by year of slaughter and by breed type of the cow (dairy breeds or beef breeds). The ALC is shown in the range of 20-300 months in the form of a boxplot. The number of observations per year and by breed type can be found in the .csv.

de: Verteilung des Alters (in Monaten) von Kühen bei der letzten Abkalbung (LKA) nach Jahr der Schlachtung und Rassentyp (Milchrassen oder Fleischrassen). Das LKA wird im Bereich von 20-300 Monaten in Form eines Boxplots wiedergegeben. Die Anzahl Beobachtungen pro Rassentyp und Jahr findet sich im .csv.

it: Ripartizione dell'età al ultimo parto (EUP, in mesi) delle vacche per anno della macellazione e per tipo di razza (razze da latte o razze di carne). L'EUP viene mostrato nell'intervallo di 20-300 mesi sotto forma di boxplot. Il numero di osservazioni per tipo di razza e anno si trova nel file .csv.

abkalbung

display_name: abkalbung

age

display_name: age

alter

display_name: alter

bovini

display_name: bovini

bovins

display_name: bovins

calving

display_name: calving

cattle

display_name: cattle

eta

display_name: eta

parto

display_name: parto

velage

display_name: velage

fr: Bovins - Âge des vaches au dernier vêlage

de: Rinder - Alter von Kühen bei der letzten Abkalbung

en: Cattle - Age of cows at last calving

it: Bovini - Età delle vacche al ultimo parto


rinder-entwicklung-nach-fettgewebe

title_for_slug: rinder-entwicklung-nach-fettgewebe

fr: bovins animaux-de-rente tissus-gras

de: fettgewebe rinder nutztiere

en: livestock fat-tissue cattle

label: Glossary / Documentation

label: Interactive plot

state: active

type: dataset

media_type: CSV

format: CSV

identifier: cattle-evolFatTissue

fr: Nombre de vaches abattues selon les catégories de tissu gras par mois et par année. Les informations sur les catégories de tissu gras ont été mis à disposition par Proviande.

en: Number of cows by fat tissue category per month and per year. The data on the fat tissue category were provided by Proviande.

de: Anzahl geschlachteter Kühe nach Fettgewebeklasse pro Monat und Jahr. Die Angaben zur Fettgewebeklasse wurden von der Proviande zur Verfügung gestellt.

it: Numero di vacche macellate secundo le categorie di tessuto grasso per mese e per anno. Le informazioni sui le categorie di tessuto grasso sono state messe a disposizione da Proviande.

animale-da-reddito

display_name: animale-da-reddito

animaux-de-rente

display_name: animaux-de-rente

fat-tissue

display_name: fat-tissue

fettgewebe

display_name: fettgewebe

livestock

display_name: livestock

nutztiere

display_name: nutztiere

rinder

display_name: rinder

tessuto-grasso

display_name: tessuto-grasso

tissus-gras

display_name: tissus-gras

agriculture

fr: Agriculture, sylviculture

de: Land- und Forstwirtschaft

en: Agriculture, forestry

it: Agricoltura, selvicoltura

identitas

fr: Identitas AG

de: Identitas AG

en: Identitas AG

it: Identitas AG

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

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

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

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

political_level: other

type: organization

approval_status: approved

key: guid

key: uri

value: DCAT Harvester Identitas

fr: Bovins - Évolution du tissu gras

de: Rinder - Entwicklung nach Fettgewebe

en: Cattle - Evolution of fattiness

it: Bovini - Evoluzione del tessuto grasso


equiden-exporte

title_for_slug: equiden-exporte

fr: tracabilite animaux-de-rente equides animaux-de-compagnie exportations

de: heimtiere ruckverfolgbarkeit equiden nutztiere ausfuhren

en: companion-animals livestock exports equids traceability

it: esportazione tracciabilita animale-da-reddito animale-da-compagnia equidi

identifier: equids-exports

fr: Total des exportations notifiées d'équidés. Des informations sur les contingents se trouvent dans le glossaire.

en: Number of notified exports of equids. Information on contingents can be found in the glossary.

de: Anzahl gemeldeter Exporte von Equiden. Informationen zu Kontingenten finden sich im Glossar.

it: Totale delle esportazioni notificate di equidi. Le informazioni sulle contingenti si trovano nel glossario.

animale-da-compagnia

display_name: animale-da-compagnia

animaux-de-compagnie

display_name: animaux-de-compagnie

ausfuhren

display_name: ausfuhren

companion-animals

display_name: companion-animals

equiden

display_name: equiden

equides

display_name: equides

equidi

display_name: equidi

equids

display_name: equids

esportazione

display_name: esportazione

exportations

display_name: exportations

exports

display_name: exports

heimtiere

display_name: heimtiere

ruckverfolgbarkeit

display_name: ruckverfolgbarkeit

tracabilite

display_name: tracabilite

tracciabilita

display_name: tracciabilita

traceability

display_name: traceability

fr: Équidés - Exportations

de: Equiden - Exporte

en: Equids -  Exports

it: Equidi - Esportazioni


fr: bovins abattages animaux-de-rente poids-vif

de: rinder schlachtungen nutztiere lebendgewichte

en: cattle livestock live-weights slaughter

it: peso-vivo macellazione bovini animale-da-reddito

identifier: cattle-liveWeights

fr: Répartition des poids vifs (en kg) des vaches par an de l'abattage et par type de race (races laitières ou races à viande) présentées aux marchés publics pour bétail de boucherie. Il s'agit de vaches de differents âges (voir figure "Âge des vaches à l'abattage") qui ont été abattues le même jour ou dans les quatre jours suivants. Le nombre d'observations par catégorie se trouve dans le .csv. Les informations sur les poids vifs ont été mis à disposition par Proviande.

en: Distribution of the live weights (in kg) of cows by year of slaughter and by breed type (dairy breeds or beef breeds) presented at public markets for fatstock. These are cows of different ages (see figure "Age of cows at slaughter by breed type"), which were slaughtered on the same day, or in the following four days. The number of observations by breed type can be found in the .csv. The data on live weights were provided by Proviande.

de: Verteilung des Lebendgewichts (in kg) von Kühen nach Jahr der Schlachtung und Rassentyp (Milchrassen oder Fleischrassen) aufgeführt an öffentlichen Schlachtviehmärkten. Es handelt sich dabei um Kühe unterschiedlicher Altersklassen (siehe Abbildung «Alter von Kühen bei der Schlachtung»), die gleichentags, oder in den folgenden vier Tagen geschlachtet wurden. Die Anzahl Beobachtungen pro Rassentyp findet sich im .csv. Die Angaben zu den Lebendgewichten wurden von der Proviande zur Verfügung gestellt.

it: Ripartizione di pesi vivi (in kg) delle vacche per anno della macellazione e per tipo di razza (razze da latte o razze di carne). Si tratta di vacche di età differenti (vedi figura "Età delle vacche alla macellazione per tipo di razza") che sono state macellate lo stesso giorno o nei quattro giorni successivi. Il numero di osservazioni per tipo di razza si trova nel file .csv. Le informazioni sui pesi vivi sono state messe a disposizione da Proviande.

abattages

display_name: abattages

animale-da-reddito

display_name: animale-da-reddito

animaux-de-rente

display_name: animaux-de-rente

bovini

display_name: bovini

bovins

display_name: bovins

cattle

display_name: cattle

lebendgewichte

display_name: lebendgewichte

live-weights

display_name: live-weights

livestock

display_name: livestock

macellazione

display_name: macellazione

nutztiere

display_name: nutztiere

peso-vivo

display_name: peso-vivo

poids-vif

display_name: poids-vif

rinder

display_name: rinder

schlachtungen

display_name: schlachtungen

slaughter

display_name: slaughter

fr: Bovins - Poids vifs des vaches présentées aux marchés publics pour bétail de boucherie

de: Rinder - Lebendgewichte von Kühen an öffentlichen Schlachtvieh-märkten

en: Cattle - Live weights of cows presented at public markets for fatstock

it: Bovini - Pesi vivi delle vacche presentati nei mercati pubblici di carne bovina


fr: animaux-de-compagnie animaux-de-rente type-dequide equides evolution-de-la-population type-dutilisation

de: nutzungsarten nutztiere equiden populationsentwicklung heimtiere equidentyp

en: livestock equids companion-animals type-of-use population-trends equid-types

it: equidi evoluzione-della-popolazione animale-da-compagnia tipi-di-equidi animale-da-reddito tipo-di-utilizzo

identifier: equids-companionBreedTypes

fr: Évolution des équidés enregistrés et vivants selon leur type et selon leur utilisation animal de compagnie.

en: Evolution of registered and living equids by equid types and by type of use companion animal.

de: Entwicklung in Prozent und Gesamtbestand der registrierten und lebenden Equiden nach Equidentyp und nach Verwendungszweck Heimtier.

it: Evoluzione degli equidi registrati e vivi secondo il tipo di equidi e il tipo di utilizzo animale da compagnia.

animale-da-compagnia

display_name: animale-da-compagnia

animaux-de-compagnie

display_name: animaux-de-compagnie

companion-animals

display_name: companion-animals

equid-types

display_name: equid-types

equiden

display_name: equiden

equidentyp

display_name: equidentyp

equides

display_name: equides

equidi

display_name: equidi

equids

display_name: equids

evolution-de-la-population

display_name: evolution-de-la-population

evoluzione-della-popolazione

display_name: evoluzione-della-popolazione

heimtiere

display_name: heimtiere

nutzungsarten

display_name: nutzungsarten

population-trends

display_name: population-trends

populationsentwicklung

display_name: populationsentwicklung

tipi-di-equidi

display_name: tipi-di-equidi

tipo-di-utilizzo

display_name: tipo-di-utilizzo

type-dequide

display_name: type-dequide

type-dutilisation

display_name: type-dutilisation

type-of-use

display_name: type-of-use

fr: Équidés - Évolution par type d'utilisation et par type d'équidé

en: Equids - Evolution by type of use and equid types

de: Equiden - Entwicklung nach Verwendungszweck und Equidentyp

it: Equidi - Evoluzione per tipo di utilizzo e per tipo di equidi


schafe-anzahl-tiere-mit-standortwechsel

title_for_slug: schafe-anzahl-tiere-mit-standortwechsel

de: schafe nutztiere standortwechsel

en: relocation sheep livestock

it: trasferimento animale-da-reddito ovini

identifier: sheep-locChangeIndividuals

fr: Total des ovins avec au moins un changement de lieu de détention (soit changement d’emplacement, importation ou exportation) par année, selon le nombre de changements par ovin (Liechtenstein inclus).

de: Anzahl Schafe in der Schweiz und Liechtenstein mit mindestens einem Standortwechsel (Standortwechsel, Einfuhr oder Ausfuhr) pro Jahr, unterteilt nach Anzahl je Schaf.

en: Number of sheep with at least one change of holding (relocation, import or export) by year and by total of relocations each sheep (Liechtenstein included).

it: Numero di ovini in Svizzera e nel Liechtenstein con almeno un trasferimento della azienda detentrici di animali (trasferimento della azienda detentrici di animali, importazione o esportazione) all'anno, suddiviso in base al numero per ovino.

animale-da-reddito

display_name: animale-da-reddito

animaux-de-rente

display_name: animaux-de-rente

changement-demplacement

display_name: changement-demplacement

livestock

display_name: livestock

nutztiere

display_name: nutztiere

ovini

display_name: ovini

ovins

display_name: ovins

relocation

display_name: relocation

schafe

display_name: schafe

sheep

display_name: sheep

standortwechsel

display_name: standortwechsel

trasferimento

display_name: trasferimento

fr: Ovins - Changements d'emplacement par individus

en: Sheep - Animals with relocations

de: Schafe - Anzahl Tiere mit Standortwechsel

it: Ovini - Totale di animali con trasferimento


rinder-geburten-nach-rassen

title_for_slug: rinder-geburten-nach-rassen

fr: races naissances bovins animaux-de-rente evolution-de-la-population

de: populationsentwicklung nutztiere geburten rassen rinder

en: livestock cattle breeds population-trends births

it: bovini evoluzione-della-popolazione animale-da-reddito razze nascite

identifier: cattle-birthBreed

fr: Total des naissances enregistrées de bovins sur le territoire suisse par race. L'évolution des populations les plus importantes est présentée. L'ensemble des autres races est rassemblé sous le terme « races additionnelles ». La liste complète est disponible lors du téléchargement des données. Des informations sur le parcours spécifique de la population des Tachetées rouges et des croisements se trouvent dans le glossaire.

en: Number of notified births of cattle by breed in Switzerland. The evolution of the largest populations is presented. All remaining breeds are grouped under the term "Further breeds". The complete list is available when downloading the data. Information on the particular course of the Red and white cattle population and the crossbreds can be found in the glossary.

de: Anzahl gemeldete Geburten von Rindern in der Schweiz, unterteilt nach Rassen im Zeitverlauf. Die Entwicklung der grössten Populationen wird dargestellt. Alle anderen Rassen sind unter dem Begriff "Weitere Rassen" zusammengefasst. Eine vollständige Liste (.csv) ist beim Herunterladen der Daten verfügbar. Angaben zum besonderen Verlauf der Rotfleckviehpopulation und der Kreuzungen finden sich im Glossar.

it: Totale delle nascite registrate di bovini sul territorio svizzero per razza. Viene mostrato lo sviluppo delle popolazioni più importanti. Tutte le altre razze sono raggruppate sotto il termine "razza addizionale". L'elenco completo è disponibile al momento del download dei dati. Le informazioni sull'andamento specifico della popolazione delli Pezzati rossi e degli incroci si trovano nel glossario.

births

display_name: births

bovini

display_name: bovini

bovins

display_name: bovins

breeds

display_name: breeds

cattle

display_name: cattle

evolution-de-la-population

display_name: evolution-de-la-population

evoluzione-della-popolazione

display_name: evoluzione-della-popolazione

geburten

display_name: geburten

naissances

display_name: naissances

nascite

display_name: nascite

population-trends

display_name: population-trends

populationsentwicklung

display_name: populationsentwicklung

races

display_name: races

rassen

display_name: rassen

razze

display_name: razze

rinder

display_name: rinder

fr: Bovins - Naissances par races

en: Cattle - Births by breed

de: Rinder - Geburten nach Rassen

it: Bovini - Nascite per razza


rinder-entwicklung-nach-kantonen

title_for_slug: rinder-entwicklung-nach-kantonen

fr: bovins evolution-de-la-population canton animaux-de-rente

de: populationsentwicklung rinder nutztiere kanton

en: population-trends canton livestock cattle

it: evoluzione-della-popolazione bovini animale-da-reddito cantone

identifier: cattle-canton

fr: Total des bovins enregistrés et vivants par canton.

de: Bestand der registrierten, lebenden Rinder, unterteilt nach Kanton im Zeitverlauf.

en: Number of registered and living cattle by canton.

it: Totale dei bovini registrati e vivi per cantone.

animale-da-reddito

display_name: animale-da-reddito

animaux-de-rente

display_name: animaux-de-rente

canton

display_name: canton

cantone

display_name: cantone

kanton

display_name: kanton

livestock

display_name: livestock

nutztiere

display_name: nutztiere

agriculture

fr: Agriculture, sylviculture

de: Land- und Forstwirtschaft

en: Agriculture, forestry

it: Agricoltura, selvicoltura

identitas

fr: Identitas AG

de: Identitas AG

en: Identitas AG

it: Identitas AG

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

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

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

fr: Bovins - Évolution par canton

de: Rinder - Entwicklung nach Kantonen

en: Cattle - Evolution by canton

it: Bovini - Evoluzione per cantone


fr: animaux-de-rente abattages age bovins

de: rinder alter schlachtungen nutztiere

en: slaughter cattle livestock age

it: eta bovini macellazione animale-da-reddito

identifier: cattle-ageSlaughter

fr: Répartition des âges à l'abattage (AA, en mois) par an de l'abattage et par type de race de la vache (races laitières ou races à viande). L'AA est représenté dans la fourchette 20-300 mois sous la forme d'un boxplot. Le nombre d'observations par catégorie et par année se trouve dans le .csv.

de: Verteilung des Alters (in Monaten) von Kühen bei der Schlachtung (AS) nach Jahr der Schlachtung und Rassentyp (Milchrassen oder Fleischrassen). Das AS wird im Bereich von 20-300 Monaten in Form eines Boxplots wiedergegeben. Die Anzahl Beobachtungen pro Rassentyp und Jahr findet sich im .csv.

en: Distribution of the age at slaughter (AS, in months) by year of slaughter and by breed type of the cow (dairy breeds or beef breeds). The AS is shown in the range of 20-300 months in the form of a boxplot. The number of observations per year and by breed type can be found in the .csv.

it: Ripartizione dell'età al momento della macellazione (EM, in mesi) delle vacche per anno della macellazione e per tipo di razza (razze da latte o razze di carne). L'EM viene mostrato nell'intervallo di 20-300 mesi sotto forma di boxplot. Il numero di osservazioni per tipo di razza e anno si trova nel file .csv.

abattages

display_name: abattages

age

display_name: age

alter

display_name: alter

bovini

display_name: bovini

bovins

display_name: bovins

cattle

display_name: cattle

eta

display_name: eta

macellazione

display_name: macellazione

schlachtungen

display_name: schlachtungen

slaughter

display_name: slaughter

identitas

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

fr: Bovins - Âge des vaches à l'abattage

de: Rinder - Alter von Kühen bei der Schlachtung

en: Cattle - Age of cows at slaughter

it: Bovini - Età delle vacche alla macellazione


geography

fr: Géographie

de: Geographie

en: Geography

it: Geografia

territory

fr: Territoire et environnement

de: Raum und Umwelt

en: Territory and environment

it: Territorio e ambiente

de: konzession wasserrecht entnahme ruckgabe kanal wasserrechtskanal stauwehr stauwehrreglement wasserrechtskonzession eheliche-konzession wasserrechtsbezirk wasserrechtsnummer

label: Geometadaten Kanton Zürich: GeoLion

label: geocat.ch permalink

spatial: Kanton Zürich

de: Download via GIS-Browser

de: Die konzessionierten Wasserrechte wurden ab 1997 mit den öffentlichen Oberflächengewässern im GIS erfasst (ohne die ältesten gelöschten WR) und werden seit 1998 als eigener Datensatz in Abstimmung zu den öffentlichen Oberflächengewässern vom AWEL, Abt. Wasserbau nachgeführt. Seit Januar 2022 werden aktive und gelöschte Wasserrechte in getrennten Datensätzen zu Verfügung gestellt. Die Daten sind auf dem SDE Server vom GIS-ZH abgelegt unter: GISZHPUB.WASSERRECHTE_F für die Wasserrechtsweiher (Flächendaten) GISZHPUB.WASSERRECHTE_GELOESCHT_F für die gelöschten Wasserrechtsweiher (Flächendaten) GISZHPUB.WASSERRECHTE_L für die Wasserrechtskanäle und Leitungen (Liniendaten) GISZHPUB.WASSERRECHTE_GELOESCHT_L für die gelöschten Wasserrechtskanäle und Leitungen (Liniendaten) GISZHPUB.WASSERRECHTE_P für die Entnahme- und Rückgabeorte (Punktdaten) GISZHPUB.WASSERRECHTE_GELOESCHT_P für die gelöschten Entnahme- und Rückgabeorte (Punktdaten) Für weitere Infos zu Nutzungszweck usw. klicke auf Mehr:

name:
Amt für Abfall, Wasser, Energie und Luft
             - Abteilung Wasserbau

geoinformation-kanton-zuerich

fr: Département de Géoinformation du Canton de Zurich

de: Geoinformation Kanton Zürich

en: Department of Geoinformation of the Canton of Zurich

it: Dipartimento della geoinformazione del Cantone di Zurigo

fr:
Le département de géomatique est le centre de compétence du system d'information géographique du canton de Zurich.
Nous coordinons l'aquisition des géodonnées d'intérêt commun sur le territoire cantonal et les mettons à disposition des
secteurs public et privé et des particuliers.

en:
The department of geoinformation is the geospatial competency center of the Canton of Zurich.
We coordinate the acquisition of geodata within the cantonal territory and provide public, private, and government sectors with data of outstanding quality.

de: Die Abteilung Geoinformation ist das Kompetenzzentrum für räumliche Informationen im Kanton Zürich. Wir versorgen Gesellschaft, Wirtschaft und öffentliche Verwaltung mit qualitativ hochstehenden Geodaten und Informationen über den Kanton Zürich.

it: La divisione della geoinformazione è il centro d'eccellenza del Cantone di Zurigo per dati georeferenziati. Mettiamo a disposizione dati geografici e informazioni di alta qualità per l'economia, la società e la pubblica amministrazione su tutto il cantone.

political_level: canton

kanton-zuerich

capacity: public

value: ZH - Geocat Harvester

de: Wasserrechte


rinder-anzahl-kuhe-nach-fettgewebe

title_for_slug: rinder-anzahl-kuhe-nach-fettgewebe

fr: tissus-gras animaux-de-rente bovins

de: nutztiere rinder fettgewebe

en: fat-tissue cattle livestock

identifier: cattle-FatTissue

fr: Nombre de vaches abattues selon les catégories de tissu gras par année. Les informations sur les catégories de tissu gras ont été mis à disposition par Proviande.

de: Anzahl geschlachteter Kühe nach Fettgewebeklasse pro Jahr. Die Angaben zur Fettgewebeklasse wurden von der Proviande zur Verfügung gestellt.

en: Number of cows by fat tissue category. The data on the fat tissue category were provided by Proviande.

it: Numero di vacche macellate secundo le categorie di tessuto grasso per anno. Le informazioni sui le categorie di tessuto grasso sono state messe a disposizione da Proviande.

animale-da-reddito

display_name: animale-da-reddito

fat-tissue

display_name: fat-tissue

fettgewebe

display_name: fettgewebe

rinder

display_name: rinder

tessuto-grasso

display_name: tessuto-grasso

tissus-gras

display_name: tissus-gras

fr: Bovins - Nombre des vaches selon tissus gras

de: Rinder - Anzahl Kühe nach Fettgewebe

en: Cattle - Fattiness of cows

it: Bovini - Numero di vacche per tessuto grasso


equiden-verteilung-pro-kanton

title_for_slug: equiden-verteilung-pro-kanton

fr: canton cartes animaux-de-rente animaux-de-compagnie equides

de: equiden heimtiere nutztiere karten kanton

en: livestock equids canton companion-animals maps

it: cantone mappe equidi animale-da-reddito animale-da-compagnia

identifier: equids-map-canton

fr: Représentation géographique de la répartition des équidés enregistrés et vivants par canton : total, total par surface en kilomètre carré sans les étendues d'eau et total par 100 habitants.

en: Map of the distribution of registered and living equids by canton: total, total by surface in km2 without lakes and total by 100 inhabitants.

de: Geografische Darstellung der Dichte von lebenden, registrierten Equiden pro Kanton: Anzahl absolut, Anzahl pro Landfläche in km2 ohne Gewässer, Anzahl pro 100 Einwohner.

it: Rappresentazione geografica della distribuzione degli equidi registrati e vivi per cantone: totale, totale per superficie in km2 senza acque e totale per 100 abitanti.

animale-da-compagnia

display_name: animale-da-compagnia

animaux-de-compagnie

display_name: animaux-de-compagnie

animaux-de-rente

display_name: animaux-de-rente

canton

display_name: canton

cantone

display_name: cantone

cartes

display_name: cartes

companion-animals

display_name: companion-animals

equiden

display_name: equiden

equides

display_name: equides

equidi

display_name: equidi

equids

display_name: equids

heimtiere

display_name: heimtiere

kanton

display_name: kanton

karten

display_name: karten

livestock

display_name: livestock

mappe

display_name: mappe

maps

display_name: maps

nutztiere

display_name: nutztiere

agriculture

de: Land- und Forstwirtschaft

identitas

de: Identitas AG

fr: Équidés - Répartition par canton

de: Equiden - Verteilung pro Kanton

en: Equids - Distribution by canton

it: Equidi - Distribuzione per cantone


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

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

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

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

label: öffentliche Spielplätze und Sportanlagen in der Gemeinde Emmen

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

format: WMS

de: WFS Spielplätze und Sportanlagen in der Gemeinde Emmen

format: WFS

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

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

format: SERVICE

de: Gemeinde Emmen

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

political_level: commune

value: Gemeinde Emmen - Geocat Harvester

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


parkplatze-emmen-wms

title_for_slug: parkplatze-emmen-wms

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

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

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

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

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

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

de: WFS Parkplätze Emmen

de: WFS mit den Parkplätzen und Veloparkierungsanlagen der Gemeinde Emmen

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

de: Parkplätze Emmen (WMS/WFS)


gastrobetriebe-emmen-wms

title_for_slug: gastrobetriebe-emmen-wms

fr: point-dinteret-poi geoservice construction batiment

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

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

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

de: Gastrobetriebe Emmen

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

de: WFS Gastrobetriebe Emmen

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

de: Gastrobetriebe Emmen (WMS/WFS)


schulanlagen-emmen-wms

title_for_slug: schulanlagen-emmen-wms

fr: point-dinteret-poi construction geoservice batiment

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

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

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

de: Schulhäuser Gemeinde Emmen

de: WFS mit den Schulhäusern der Gemeinde Emmen

de: Kartendienst mit den öffentlichen Schuhäusern in der Gemeinde Emmen

de: Schulanlagen Emmen (WMS/WFS)


fr: geoservice point-dinteret-poi construction batiment

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

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

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

de: Sitzbänke, Abfalleimer, Grillstellen Emmen

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

de: WFS Sitzbänke, Abfalleimer und Grillstellen in Emmen

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

de: Gemeinde Emmen

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

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


signalisation-und-markierung-emmen-wms

title_for_slug: signalisation-und-markierung-emmen-wms

de: geodienst verkehrsnetz verkehr

en: geoservice traffic-network transport

it: geoservizio rete-di-trasporto trasporti

de: Strasseninfrastruktur Gemeinde Emmen

de: WFS Strasseninfrastruktur

de: WFS mit der Strasseninfrastruktur in der Gemeinde Emmen

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

de: Strasseninfrastruktur Emmen (WMS/WFS)


geography

de: Geographie

territory

de: Raum und Umwelt

de: Download via GIS-Browser

de: Mit der Festlegung des Gewässerraums an den oberirdischen Gewässern soll der Raum gesichert werden, welcher nötig ist zur Gewährleistung der natürlichen Funktionen der Gewässer, des Hochwasserschutzes und der Gewässernutzung. Die Gewässerschutzverordnung des Bundes (GSchV) legt dazu konkrete Masse fest, wie gross der Gewässerraum mindestens sein muss (Art. 41a und 41b GSchV). Der Gewässerraum muss dort erhöht werden, wo dies zur Gewährleistung für den Hochwasserschutz, der Revitalisierung, sowie bei überwiegenden Interessen des Natur- und Landschaftsschutzes und der Gewässernutzung erforderlich ist. Im Gewässerraum dürfen nur standortgebundene, im öffentlichen Interesse liegende Bauten und Anlagen erstellt werden. In dicht überbauten Gebieten kann der Gewässerraum den baulichen Gegebenheiten angepasst werden, soweit der Schutz vor Hochwasser gewährleistet ist, bzw. kann die Behörde für zonenkonforme Bauten und Anlagen Ausnahmen bewilligen, sofern keine überwiegenden Interessen entgegenstehen. Rechtmässig erstellte und bestimmungsgemäss nutzbare Bauten und Anlagen im Gewässerraum sind in ihrem Bestand grundsätzlich geschützt. Der Gewässerraum darf nur extensiv gestaltet und bewirtschaftet werden (Art. 41 c GschV) Solange der Gewässerraum nicht festgelegt ist, regeln die Übergangsbestimmungen der GSchV direkt und grundeigentümerverbindlich die Bemessung der von Bauten und Anlagen freizuhaltenden Uferstreifen. Der Gewässerabstand von 5 Meter gemäss § 21 des kantonalen Wasserwirtschaftsgesetzes (WWG), behält bis zu einer allfälligen Anpassung des WWG auch mit der Festlegung des Gewässerraums weiterhin Gültigkeit. Nach der Ersterfassung werden die Daten in den ÖREB-Kataster überführt.

geoinformation-kanton-zuerich

de: Geoinformation Kanton Zürich

de: Die Abteilung Geoinformation ist das Kompetenzzentrum für räumliche Informationen im Kanton Zürich. Wir versorgen Gesellschaft, Wirtschaft und öffentliche Verwaltung mit qualitativ hochstehenden Geodaten und Informationen über den Kanton Zürich.

de: Gewässerraum


quartiervereine-gemeinde-emmen-wms

title_for_slug: quartiervereine-gemeinde-emmen-wms

fr: geoservice plan-de-quartier quartier

de: geodienst quartierplan quartier

en: geoservice quarter-map quarter

de: Quartiervereine Gemeinde Emmen

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

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

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

de: Quartiervereine Emmen (WMS/WFS)


maintainer: EnviDat Support

en: national-dishes agro-food-trade food-taste-similarity latent-semantic-analysis food-preferences

en: National dishes, migration data and data for bilateral food trade flows

media_type: Stata dta file

en:
The data include information on national dishes, migration data and data for bilateral food
trade flows for 5 years as well as importer and exporter GDP, population, distance between
most populated cities, bilateral information on Preferential Trade Agreements, common
official primary langauge, common colonizer post-1945, colonial relationship for overall
171 countries. The final data set contains 145,350 observations (29,070 country pairs for
5 years)

en: information on ingredients of national dishes

media_type: .csv

en: shows information of ingredients of all national dishes of 171 countries and their names

en: dyadic food similarity measures, pairwise similarity and dissimilarity (chord diagram)

media_type: .zip

en:
The figures show the distribution of two dyadic food tastes similarity measures on the base of
the national dishes: (i) the Manhattan distance and the (ii) latent semantic analysis. The chord diagram
shows the results for country pairs with most and less similarity on basis of the LSA-approach.

en: The data described in this article were collected daily over the period 4 June 2018 to 23 August 2018 and contains information of several data sources. The database includes information on national recipes and their ingredients for 171 countries, measures for food taste similarities between all 171 countries as well as bilateral migration and agro-food trade data for 5 years. The database can be used for analyzing e.g., the relation between food preferences and international trade or food preferences and health outcomes (e.g., obesity) across countries.

agro-food-trade

display_name: agro-food-trade

food-preferences

display_name: food-preferences

food-taste-similarity

display_name: food-taste-similarity

latent-semantic-analysis

display_name: latent-semantic-analysis

national-dishes

display_name: national-dishes

education

fr: Education, science

de: Bildung, Wissenschaft

en: Education and science

it: Formazione e scienza

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

value: Envidat Harvester

en: Data of national dishes, their similarity and trade flows


en: soil-ph soil-moisture ecological-maps plants soil-mapping landolt switzerland ecological-indicator-values

en: Description

en: Description of the predictors provided in this dataset

format: XLS

en: Predictors

media_type: application/zip

en: Predictors generated and used in Descombes et al. (2020)

format: ZIP

mimetype: application/zip

en:
Ecologically meaningful predictors are often neglected in plant distribution studies, resulting in incomplete niche quantification and low predictive power of species distribution models (SDMs). Because environmental data are rare and expensive to collect, and because their relationship with local climatic and topographic conditions are complex, mapping them over large geographic extents and at high spatial resolution remains a major challenge. Here, we derived environmental data layers by mapping ecological indicator values (EIVs) in space by using a large set of environmental predictors in Switzerland.

This dataset contains the predictors (raster layers) generated and used in the following publication (Descombes et al. 2020). Only predictors for which we have the rights to share them are provided. Other datasets and predictors can be accessed via the original data provider. Details on the predictors and sources are fully described in the publication. The predictors are provided as GeoTIFF files, at 93 m spatial resolution and Mercator projection ("+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"). The excel file (xlsx) provides a short description of the raster layers.

Paper Citation:

Descombes, P. et al. (2020). Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes. Ecography. (accepted)

ecological-indicator-values

display_name: ecological-indicator-values

ecological-maps

display_name: ecological-maps

landolt

display_name: landolt

plants

display_name: plants

soil-mapping

display_name: soil-mapping

soil-moisture

display_name: soil-moisture

soil-ph

display_name: soil-ph

switzerland

display_name: switzerland


maintainer: Sven-Eric Hopf

en: fraxinus iap ash-dieback forest monitoring

en: Monitoring of ash trees as part of the Intercantonal Forest Observation Programme

media_type: .pdf

en: Packgroun information on data for the publication: Spread and Severity of Ash Dieback in Switzerland – Tree Characteristics and Landscape Features Explain Varying Mortality Probability, (Klesse et al. 2021, Front. For. Glob. Change 4:645920., doi: 10.3389/ffgc.2021.645920)

en:
In 2013, the Institute for Applied Plant Biology (IAP) started a monitoring programme to study the development and the spatial variation of the ash dieback disease with the aim to find some partially resistant European ash trees (Fraxinus excelsior).
We collaborate as co-authors for the publication: Spread and Severity of Ash Dieback in Switzerland - Tree Characteristics and Landscape Features Explain Varying Mortality Probability (Klesse et al. 2021 in frontiers)

ash-dieback

display_name: ash-dieback

forest

display_name: forest

fraxinus

display_name: fraxinus

iap

display_name: iap

monitoring

display_name: monitoring


maintainer: Christian G. Sommer

en: snow kinect wind-packing wind-crust wind-tunnel

en: README

media_type: text/plain

en: Describes the dataset in detail

format: TXT

mimetype: text/plain

en: Processing scripts

en: Contains the Matlab- and R-scripts used to process the Kinect data

en: Kinect data

media_type: Link

en: Links to the dataset

en:
This data sets contains the Microsoft Kinect data from 15 wind tunnel experiments on wind-packing / wind crust formation. These experiments were performed in the winter 2016/17. The Kinect measures distributed snow depth.

The Kinect data is organized per experiment. Each experiment subfolder contains the processed Kinect depth images and some additional files.

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

This dataset is the basis of the following publication:

Sommer C.G., Lehning M. & Fierz C. (2018). Wind Tunnel Experiments: Influence of Erosion and Deposition on Wind-Packing of New Snow. Front. Earth Sci. 6:4. doi: 10.3389/feart.2018.00004

kinect

display_name: kinect

snow

display_name: snow

wind-crust

display_name: wind-crust

wind-packing

display_name: wind-packing

wind-tunnel

display_name: wind-tunnel

en: Wind crust formation: Microsoft Kinect data


maintainer: Matthias Haeni

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

en: Site aerial photo (Alpthal)

media_type: image/jpeg

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

mimetype: image/jpeg

en: Data plot of the forest station (ALB)

media_type: application/pdf

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

format: PDF

mimetype: application/pdf

en: Forest station data (ALB)

media_type: text/csv

mimetype: text/csv

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

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

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


data-set-on-snow-instability

title_for_slug: data-set-on-snow-instability

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

en: Read-me file on snow instability data

en: data set on snow instability

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

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

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

avalanche-forcasting

display_name: avalanche-forcasting

crack-propagation

display_name: crack-propagation

failure-initiation

display_name: failure-initiation

propgatoin-saw-test

display_name: propgatoin-saw-test

rutschblock

display_name: rutschblock

slab

display_name: slab

slab-tensile-support

display_name: slab-tensile-support

snow-avalanche

display_name: snow-avalanche

snow-stability-evaluation

display_name: snow-stability-evaluation

weak-layer

display_name: weak-layer

en: Data set on snow instability


wind-lidar-davos-wolfgang

title_for_slug: wind-lidar-davos-wolfgang

en: raclets lidar profiles wolfgangpass wind

en: Wind LIDAR data Wolfgang

en: A Leosphere WidCube 100S was deployed at Davos Wolfang, measuring wind profiles as well as performing PPI and RHI scans. Time format is in UTC with highest time resolution of 5 seconds.

en: Scanning wind Lidar from Meteoswiss was installed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577) and measured from 200 m above ground to 8100 m. The time resolution is up to 5 seconds. The Lidar was measuring wind profiles but also performed plan position indicator (PPI) and range height indicator (RHI) scans.

lidar

display_name: lidar

profiles

display_name: profiles

raclets

display_name: raclets

wind

display_name: wind

wolfgangpass

display_name: wolfgangpass

en: Wind LIDAR Davos Wolfgang


en: antarctica wrf cryowrf topography landuse

en: WPSinput

en: Input files to run the Weather Research and Forecasting model (WRF) preprocessing system WPS with REMA topography and AntarcticaLC2000 landuse data (see Documentation for details).

en: Documentation

en: Documentation about how the provided dataset was produced including details to reproduce the dataset.

en: Scripts

en: Scripts to run the preprocessing to produce WRF binary tiles from REMA and AntarcticaLC2000 for topography and landuse (see Documentation for procedure).

en:
Reference Elevation Model of Antarctica (REMA) topography and AntarcticaLC2000 landuse data
are now available as static data input for the Weather Research and Forecasting model (WRF). Topography
and landuse are made available at a spatial resolution of 1 km. This documentation describes the methods
applied to convert REMA and AntarcticaLC2000 to WRF readable format and shows how this improves
the representation of the Antarctic topography and landuse categories over coastal Antarctic regions.

antarctica

display_name: antarctica

cryowrf

display_name: cryowrf

landuse

display_name: landuse

topography

display_name: topography

wrf

display_name: wrf

en: REMA topography and AntarcticaLC2000 for WRF


maintainer: Giulia Mazzotti

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

en: Canopy data

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

en: Meteo data to drive FSM2 model

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

en: Snow depth data open sites

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

en: Coordinates data

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

en: Grid point nomenclature

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

en: Snow depth data forest sites

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

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

forest-snow

display_name: forest-snow

forest-structure

display_name: forest-structure

snow-distribution

display_name: snow-distribution

snow-energy-balance

display_name: snow-energy-balance

snow-modelling

display_name: snow-modelling

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

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


maintainer: EnviDat

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

en: NEAD on GitHub

media_type: HTML

format: HTML

en: NEAD syntax v0.1 (EBNF)

en: MeteoIO (future reference implementation)

media_type: text/html

mimetype: text/html

en: PyNEAD (future reference implementation)

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

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

-----------------------

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

License: The NEAD specifications are released to the public domain under a Creative Commons 4.0 CC0 "No Rights Reserved" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions.

csv

display_name: csv

metadata-convention

display_name: metadata-convention

meteorological-data-format

display_name: meteorological-data-format

nead

display_name: nead

research-data-management

display_name: research-data-management

en: Non-Binary Environmental Archive Data (NEAD) format


en: wagital gcos snow meteodat-gmbh snow-water-equivalent

media_type: TXT

en:
Total water reserves of the snow cover [mio m3] for Wägital catchment, Switzerland, for reference date April 1. Data is separated in 2 elevation zones 900m-1500m asl and 1500m-2300m asl. Time period 1943-2022, status 2022-04-20.

Funded currently or in the past by:

- Federal Office of Meteorology and Climatology MeteoSwiss in the context of GCOS Switzerland
- Meteodat GmbH
- Institute of Geography, University of Zurich
- WSL Institute for Snow and Avalanche Research SLF
- Institute of Geography, ETH Zurich (IAC ETH Zurich)
- AG Kraftwerk Wägital (AXPO and EWZ)

See also https://www.meteodat.ch/waegital.html

gcos

display_name: gcos

meteodat-gmbh

display_name: meteodat-gmbh

snow

display_name: snow

snow-water-equivalent

display_name: snow-water-equivalent

wagital

display_name: wagital

en: Snow water equivalent for reference date April 1 for Wägital catchment, starting 1943


en: gis jupyter density buffer roads geospatial

en: Plots

en: Random plot locations

en: Road data from OSM for the surroundings of Menzberg, Switzerland

en: Road Density Calculation (Python Notebook) online

media_type: url

media_type: ipynb

en:
This dataset contains the road and plot data used for the geospatial analysis example showcased in "Fostering Open Science at WSL with the EnviDat Environmental Data Portal", a contribution to the 5th Open Source Geospatial Research and Education Symposium (OGRS), 2018.
The example uses Jupyter Notebook to calculate road densities in the neighbourhood of sample plot locations with Python. Road data were extracted from OpenStreetMap, while the point data (sample plots) were generated manually.

buffer

display_name: buffer

density

display_name: density

geospatial

display_name: geospatial

gis

display_name: gis

jupyter

display_name: jupyter

roads

display_name: roads

en: Dataset for OGRS 2018 publication


maintainer: Anita C. Risch

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

en:
site.code: Site code

Continent: Continent

sampleID: Sample ID

nn.latitude: Lattitude

nn.longitude: Longitude

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

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

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

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

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

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

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

ch.pH: pH

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

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

ch.porespace: Pore space (vol%)

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

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

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

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

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

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

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

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

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

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

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

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

ch.CtoN: C to N ratio

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

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

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

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

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

Paper Citation:

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

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

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

global

display_name: global

grassland

display_name: grassland

mineralization

display_name: mineralization

nutrient-network

display_name: nutrient-network

soil

display_name: soil

soil-biology

display_name: soil-biology

soil-chemistry

display_name: soil-chemistry

worldwide

display_name: worldwide

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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


maintainer: Ionuț Iosifescu Enescu

en: large map 10GB

media_type: image/tiff

format: TIFF

mimetype: image/tiff

en: huge map 100GB

en: map 0.2GB

en: map 1GB

en: map preview 0.5MB

en: map 2GB

en: map 0.5GB

en: Bulk Data Access

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

en:
The attached data are some large GIS raster files (GeoTIFFs) made with Natural Earth data. Natural Earth is a free vector and raster map data @ naturalearthdata.com. The data used for creating these large files was the "Cross Blended Hypso with Shaded Relief and Water". Data was concatenated to achieve larger and larger files. Internal pyramids were created, in order that the files can be opened easily in a GIS software such as QGIS or by a (future) GIS data visualisation module integrated in EnviDat.

Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com

large-files

display_name: large-files

natural-earth

display_name: natural-earth

qgis

display_name: qgis

en: Large GIS raster data derived from Natural Earth Data (Cross Blended Hypso with Shaded Relief and Water)


en: species-richness species-distribution-model species-range-map pinales fagales species-occurrence

en: Species range maps of species in Fagales and Pinales (with enough occurrences)

media_type: ZIP

en: species_map.zip includes species 1690 range maps generated based on the integrated method described in Lyu et al., (2022). File names with (polygon) indicate that this map is only generated by polygon mapping, as there are not enough occurrences for SDMs.

en: Occurrence maps for species of Fagales and Pinales with few records

en: Point_buffer.zip includes 267 occurrence maps of species that do not have enough occurrence data for polygon mapping.

en:
This database contains 1957 distribution maps of species from Fagales and Pinales constructed based on a method integrating polygon mapping and SDMs (Lyu et al., 2022).
To construct the maps, we first collected occurrence data from 48 different sources. According to the number of occurrences after data cleaning, three kinds of maps are constructed: (1) For species with more than 20 occurrences, we performed SDM and polygon mapping described in Lyu et al. (2022) and select the integration of the two layers as the distribution range; (2) For species with more than 4 but less than 20 occurrences, we only use polygon mapping to draw the distribution range; (3) For species with less than 4 occurrences, a 20-km buffer was generated around the occurrences as the distribution range. The maps were manually checked and evaluated (see Note S3 and Table S9 in Lyu et al., 2022 for details).

fagales

display_name: fagales

pinales

display_name: pinales

species-distribution-model

display_name: species-distribution-model

species-occurrence

display_name: species-occurrence

species-range-map

display_name: species-range-map

species-richness

display_name: species-richness

en: Species distribution maps of Fagales and Pinales (GDPlants)


maintainer: James W. Kirchner

en: groundwater sap-flow snowmelt diel-cycle evapotranspiration diurnal-cycle streamflow ecohydrology

en: MODIS EVI2 script

en: Google Earth Engine script for computing MODIS two-band enhanced vegetation index (EVI2) values and averaging them over subcatchments.

en: 30-minute time series

en: 30-minute hydrometeorological and ecohydrological time series.

en: Subcatchment polygons

en: Subcatchment polygons for use in the Google Earth Engine scripts.

en: Daily time series

en: Daily hydrometeorological and ecohydrological time series

en: MODIS NDSI script

en: Google Earth Engine script for computing MODIS normalized difference snow index (NDSI) values and averaging them over subcatchment areas.

en: Metadata

en: Column descriptions for both data files, and map coordinates for all measurement locations.

en:
Hydrometerological and ecohydrological time series from Sagehen Creek and Independence Creek, Sierra Nevada, USA, illustrating hydrological responses to daily cycles in snowmelt and evapotranspiration forcing.

Data include 30-minute time series of

- weather variables,

- sap flow fluxes,

- groundwater levels (in two riparian transects of shallow groundwater wells),

- and stream stages (at 12 sites spanning a 500-meter elevation gradient),

and daily time series of

- temperature, precipitation, and snow water equivalent at three nearby snow telemetry stations

- diel cycle index values for groundwater levels and stream stages,

- and MODIS normalized difference snow index (NDSI) and enhanced vegetation index (EVI2) values averaged over selected subcatchments.

Google Earth Engine scripts for extracting the MODIS data are also provided.

diel-cycle

display_name: diel-cycle

diurnal-cycle

display_name: diurnal-cycle

ecohydrology

display_name: ecohydrology

evapotranspiration

display_name: evapotranspiration

groundwater

display_name: groundwater

sap-flow

display_name: sap-flow

snowmelt

display_name: snowmelt

streamflow

display_name: streamflow

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

en: Daily cycles in solar flux, snowmelt, transpiration, groundwater, and streamflow at Sagehen and Independence Creeks, Sierra Nevada, USA


en: plynlimon streamflow tracer catchment stable-water-isotopes deuterium chloride precipitation

en: 7-hourly stable water istopes

en: Data set of 7-hourly stable water isotope data measured at Carreg Wen (precipitation) and Upper Hafren (streamflow).

en: Data Set Documentation

en: This file contains a documentation of the data sets, including references to other helpful documentations.

en: Weekly stable water isotopes

en: Data set of weekly stable water isotope data collected at Carreg Wen (precipitation) and Lower Hafren and Tanllwyth (streamflow).

en: The data base contains timeseries of stable water isotopes in precipitation and streamflow at Plynlimon, Wales, UK. One data set contains weekly stable water isotope data from the Lower Hafren and Tanllwyth catchments, and the other data set contains 7-hourly stable water isotope data from Upper Hafren. Both data sets also include chloride concentrations in precipitation and streamflow.

catchment

display_name: catchment

chloride

display_name: chloride

deuterium

display_name: deuterium

plynlimon

display_name: plynlimon

precipitation

display_name: precipitation

stable-water-isotopes

display_name: stable-water-isotopes

tracer

display_name: tracer

en: Stable water isotopes in precipitation and streamflow at Plynlimon, Wales, UK


maintainer: Robert Pazúr

en: sentinel-2 switzerland machine-learning phenology cropland vegetation-indices grassland habitat remote-sensing agriculture

en: GEE (Google Earth Engine) script (JavaScript) to derived the output map  (v1.5)

media_type: JS

en: The code used in Google Earth Engine to derive the cropland, grassland and shrubland map of Switzerland. Compare to the previous version the newer version includes the shadow masking procedure which might be important in the mountainous areas.

en: Cropland and grassland map of Switzerland  (v.1.0)

media_type: TIFF

en: !!! Please check out the newer version in this repository !!! (1) cropland; (2) grassland; other LC classes are masked out from the map

en: (current version) Cropland, grassland, and shrubland map of Switzerland (v.1.5)

media_type: geotiff

format: GeoTIFF

en: (current version) Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.6 (v1.5)

en: Assuming that sealed surfaces have little or no phenological response we calculated a mask of unsealed surfaces based on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.6.

en: (current version) Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.7 (v1.5)

en: Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.6  (v1.0)

en:
!!! Please check out the newer version in the repository !!!
Mask of unsealed surfaces of Switzerland based on 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.6

en: Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.7 (v1.0)

en: !!! Please check out the newer version in this repository !!! Assuming that sealed surfaces have little or no phenological response we calculated a mask of unsealed surfaces based on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.7.

en: (current version) Cropland and grassland map of Switzerland (v.1.5)

en: Cropland, grassland, and shrubland map of Switzerland (v1.0)

en: !!! Please check out the newer version in this repository !!! (1) cropland; (2) grassland; (3) shrubland; other LC classes are masked out from the map

en: GEE (Google Earth Engine) script (JavaScript) to derived the output map  (v1.0)

en: !!! Please check out the newer version in the repository !!! The code used in Google Earth Engine to derive the cropland, grassland and shrubland map of Switzerland

en: We developed a map of cropland and grassland allocation for Switzerland based on several indices dominantly derived from Sentinel-2 satellite imagery captured over multiple growing seasons. The classification model was trained based on parcel-based data derived from landholder reporting. The mapping was conducted on Google Earth Engine platform using random forest classifier. Areas of high vegetation, shrubland, sealed surface and non-vegetated areas were masked out from the country-wide map. The resulting map has high accuracy in lowlands as well as mountainous areas.

agriculture

display_name: agriculture

cropland

display_name: cropland

grassland

display_name: grassland

habitat

display_name: habitat

machine-learning

display_name: machine-learning

phenology

display_name: phenology

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

vegetation-indices

display_name: vegetation-indices

en: Cropland and grassland map of Switzerland based on Sentinel-2 data


mountain-permafrost-hydrology

maintainer: Jeannette Noetzli

title_for_slug: mountain-permafrost-hydrology

en: ice-rich-permafrost monitoring-techniques mass-movements climate-change-impacts permafrost-degradation mountain-permafrost-hydrology

en: DORA Persistent URL

media_type: PDF

en: Synthesis report chapter of the Hydro-CH2018 project of the Federal Office for the Environment (FOEN)

en: This report was prepared as one of the synthesis report chapters of the Hydro-CH2018 project of the Federal Office for the Environment (FOEN). In earlier reports such as the CH2014-IMPACTS report (CH-Impacts 2014), the topic of mountain permafrost hydrology was not addressed. Here, we provide a baseline of the available knowledge of mountain permafrost in the Swiss Alps for future reference. We compile an overview of the current understanding of mountain permafrost in the Swiss Alps, its distribution and characteristics, observed and projected changes, and expected impacts on slope stability, infrastructure and hydrological aspects. We also briefly describe the measurement techniques and modelling approaches applied. The chapter closes with a summary of the most important open research questions. The literature cited mainly includes studies on mountain permafrost published in scientific journals and assessments of long-term observation data. We focus on permafrost hydrology interactions wherever information is available. However, systematic studies on permafrost hydrology in mountain areas are still limited.

climate-change-impacts

display_name: climate-change-impacts

ice-rich-permafrost

display_name: ice-rich-permafrost

mass-movements

display_name: mass-movements

monitoring-techniques

display_name: monitoring-techniques

mountain-permafrost-hydrology

display_name: mountain-permafrost-hydrology

permafrost-degradation

display_name: permafrost-degradation

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

en: Mountain Permafrost Hydrology


en: deposition atmosphere dust snow particles hill turbulence

en: preferential deposition patterns of all simulations performed

media_type: application/zip

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


backward-trajectories

maintainer: Michael Sprenger

title_for_slug: backward-trajectories

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

en: Backward Trajectories

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

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

alps

display_name: alps

atmospheric-dynamics

display_name: atmospheric-dynamics

backward-trajectories

display_name: backward-trajectories

complex-topopgraphy

display_name: complex-topopgraphy

cosmo

display_name: cosmo

lagrangian-analysis

display_name: lagrangian-analysis

raclets

display_name: raclets

en: Backward  Trajectories


en: viscous-deformation sintering snow-failure snow-avalanche fiber-bundle-model snow-mechanics snow acoustic-emissions

en: fiber-bundle-model-for-snow-failure

en: Data underlying Figures in publication "Modelling Snow Failure Behavior and Concurrent Acoustic Emissions Signatures with a Fiber Bundle Model" submitted to "Geophysical Research Letters"

en:
This dataset contains modeled and experimental results for laboratory snow failure experiments and the concurrent acoustic emissions signatures for different loading rates. For modelling the snow failure we used a fiber bundle model that includes sintering and viscous deformation.

The data underlay the figures in the publication "Modelling Snow Failure Behavior and Concurrent Acoustic Emissions Signatures with a Fiber Bundle Model" submitted for publication to "Geophysical Research Letters".

acoustic-emissions

display_name: acoustic-emissions

fiber-bundle-model

display_name: fiber-bundle-model

sintering

display_name: sintering

snow-avalanche

display_name: snow-avalanche

snow-failure

display_name: snow-failure

snow-mechanics

display_name: snow-mechanics

viscous-deformation

display_name: viscous-deformation

en: Fiber Bundle Model for snow failure and concurrent Acoustic Emissions


en: alptal switzerland time-series groundwater studibach

media_type: text/plain

en:
Groundwater time series of the monitoring network in the Studibach (C7) catchment, Alptal, Switzerland published in
Rinderer M., van Meerveld I, McGlynn B. (2019): From points to patterns – Assessing runoff source area dynamics and hydrological connectivity using time series clustering. Water Resources Research, doi: 2018WR023886R

en: Groundwater time series between 2010 and 2014 of the distributed monitoring system in the Studibach (C7), Alptal, Switzerland. Data published in Rinderer M., van Meerveld I, McGlynn B. (2019): From points to patterns – Assessing runoff source area dynamics and hydrological connectivity using time series clustering. Water Resources Research, doi: 2018WR023886R

alptal

display_name: alptal

groundwater

display_name: groundwater

studibach

display_name: studibach

switzerland

display_name: switzerland

time-series

display_name: time-series

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

en: Groundwater time series Studibach (Rinderer et al., 2019, WRR)


en: cosmo-wrf high-resolution meteoswiss complex-terrain numerical-simulation

en: COSMO-WRF documentation

en: Documentation about the procedure to run COSMO-WRF

en: WPSSetup

en: Scripts that need to be changed to compile WPS (see COSMOWRFdocumentation.pdf, Section 4)

en: Postprocessing

en: Postprocessing scripts to map WRF output on swiss coordinates (see Section 7 in COSMOWRFdocumentation.pdf)

en: StaticData

en:
Scripts to process geographical/static data (see Section 3 in COSMOWRFdocumentation.pdf).
- ascii_to_bin_landuse.f90
- ascii_to_bin_soiltype.f90
- ascii_to_bin_topo_wgs84_ws2sf1.f90
- see_slopes.ncl
- soiltype_writing.py
- toposmooth_wgs84_cosmobound.ncl
- write_geogrid.o

en: WRFrun

en: Scripts to run COSMO-WRF simulation (see Section 6 in COSMOWRFdocumentation.pdf)

en: WPSrun

en: Scripts to run the WRF pre-processing (WPS) for COSMO-WRF simulation (see Section 5 in COSMOWRFdocumentation.pdf)

en: MeteorologicalData

en: Scripts to process meteorological data (see Section 2 in COSMOWRFdocumentation.pdf).

en: This is a technical documentation of the procedure to run the Weather Research and Forecasting (WRF) model over complex alpine terrain using Consortium for Small-Scale Modeling (COSMO) reanalysis by the Federal Office of Meteorology and Climatology (MeteoSwiss) as initial and boundary conditions (COMSO-WRF). The setup is adapted for very high resolution simulations based on COSMO-2 (2.2 km resolution) reanalysis. This document gives an overview over steps to setup COSMO-WRF and adaptations needed to run COSMO-WRF. Additionally, the calculation of precipitation rate at a horizontal plane and remapping COSMO-WRF output on Swiss coordinates are documented.

complex-terrain

display_name: complex-terrain

cosmo-wrf

display_name: cosmo-wrf

high-resolution

display_name: high-resolution

meteoswiss

display_name: meteoswiss

numerical-simulation

display_name: numerical-simulation

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: Running COSMO-WRF on very-high resolution over complex terrain


maintainer: Thorsten Bartels-Rausch

en: reaction brine snow interface chemistry solution alpine ozone heterogeneous arctic metamorphism laboratory-study antarctica

en: Matlab Script to view and plot data

media_type: .m

en: Acetone data

media_type: dat

en:
intensity of the acetone-water-cluster and water-cluster (as derived by a chemical ionisation mass spectrometer) with time in the carrier gas downstream of packed bed flow tube holding the snow sample or the by-pass, respectively. Time "0" denotes the time when the mass spectrometer data acquisition was started. When the carrier
gas with acetone was passed via the packed bed flow tube, that is switched from by-pass to pass the snow, acetone concentration in the carrier gas rapidly dropped due to surface adsorption and slowly recovered as the adsorption sites saturate. From this, the surface area can be obtained.

en: Data Figure 1

en: Data of published Figure

en: Ozone data

en: Trend of ozone mixing ratio ( _O3mixingRatio_ppb_ with time ( _time_s_ ) as measured downstream of the snow sample during the experiments. Time "0" denotes the time when data acquisition with ozone analyser was started. And, ozone (_nOzone_molec_per_s_ ) loss for each experiment with duration of ozone exposure ( _duration_s_ ) of ozone exposure, as derived from the ozone trends shown above. Time "0" denotes the time when the carrier gas with ozone was passed via the snow sample.

en: data list

en: Lists file names, snow sample properties, and flow settings for each data-set.

en: Matlab Data

media_type: mat

en: All data as Matlab mat file

en: Readme file

en: This file shows data, code, and plots.

en: Earth’s snow cover is very dynamic on diurnal time scales. The changes to the snow structure during this metamorphism have wide ranging impacts such as on avalanche formation and on the capacity of surface snow to exchange trace gases with the atmosphere. Here, we investigate the influence of dry metamorphism, which involves fluxes of water vapor, on the chemical reactivity of bromide in the snow. For this, the heterogeneous reactive loss of ozone at a concentration of 5-6E12 molecules cm-3 is investigated in artificial, shock-frozen snow samples doped with 6.2 uM sodium bromide and with varying metamorphism history. The oxidation of bromide in snow is one reaction initiating polar bromine releases and ozone depletions.

alpine

display_name: alpine

antarctica

display_name: antarctica

arctic

display_name: arctic

brine

display_name: brine

chemistry

display_name: chemistry

heterogeneous

display_name: heterogeneous

interface

display_name: interface

laboratory-study

display_name: laboratory-study

metamorphism

display_name: metamorphism

ozone

display_name: ozone

reaction

display_name: reaction

snow

display_name: snow

solution

display_name: solution

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

en: Data set on bromide oxidation by ozone in snow during metamorphism from laboratory study


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

en: CHELSA-BIOCLIM+ Technical Documentation

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

en: CHELSA-BIOCLIM+

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

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

growing-degree-days

display_name: growing-degree-days

growing-season-length

display_name: growing-season-length

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

soil-water-balance

display_name: soil-water-balance

solar-radiation

display_name: solar-radiation

temperature

display_name: temperature

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


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

en: StationData

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

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

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

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

Publications:

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

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

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

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

complex-terrain

display_name: complex-terrain

cosmo-wrf

display_name: cosmo-wrf

orographic-precipitation

display_name: orographic-precipitation

precipitation-variability

display_name: precipitation-variability

snow-precipitation

display_name: snow-precipitation

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


en: amphibians wetlands conservation landscape occupancy-models

en: Covariate data 1

media_type: CSV

en: This file contains covariate data that does not change over time for 856 ponds: the region the pond is in, whether the pond is old or new (constructed since 1991), elevation, water surface area, fluctuations of the water table (yes, no), the percent forest cover in 100m radius around the pond, the area or large (>6m wide) roads in 1km radius around the pond.

en: Readme

en: Description of methods and datasets.

en: Original raw data

media_type: XLSX

en: This file contains the original raw monitoring data as provided by the canton of Aargau. Access is restricted and only considered upon request and in consultation with the canton of Aargau.

en: Observation data

en: This file contains detection/non-detection data for the 12 amphibian species between 1999 and 2019 for 3 annual visits. Detectable denotes whether or not a visit j was made to pond i in year t. ObservedID gives a unique ID for the observer collecting the data during visit j to pond i in year t.

en: Covariate data 2

en: This file contains covariate data that changes over time for 856 ponds in 21 years (long format): whether or not a pond exists in year _t_, the age of constructed ponds in year _t_, structural connectivity of the pond in year _t_.

en: Model code

media_type: Rmd

en: This R markdown file contains all code and descriptions necessary to load and format the provided data and to fit the dynamic occupancy models described in the associated publication, including JAGS code for the fitted models.

en: Model code, description

en: This is the output of the R markdown file that provides JAGS model code and describes how to format the provided data and fit the dynamic occupancy models described in the associated publication, for a quick overview.

en:
In the canton of Aargau, hundreds of new ponds have been constructed since the 1990s to benefit declining amphibian populations. This dataset consists of monitoring data for all 12 pond-breeding amphibian species in the canton of Aargau from 1999 to 2019 in 856 ponds, and environmental variables that describe the ponds and the landscape surrounding the ponds. Species observation data is detection/non-detection data from repeat visits during survey years, during which all potentially suitable ponds in an area were surveyed. Environmental variables describing the ponds are whether the pond has been newly constructed since 1991 or not, pond age (if constructed), elevation a.s.l., the water surface area, and whether the water table fluctuates or not. Environmental variables describing the surroundings of the ponds are the percent area of forest within a circular buffer of radius 100m around the pond, the area of large (width ≥6m) roads within a circular buffer of radius 1km around the pond, and structural connectivity.

The canton of Aargau is the owner of the monitoring data; the original datafile is only disclosed upon request and in consultation with the canton of Aargau.

The edited dataset contains cleaned observation data for the 12 amphibian species, as well as compiled and edited covariate data and code to fit dynamic occupancy models.

amphibians

display_name: amphibians

conservation

display_name: conservation

landscape

display_name: landscape

occupancy-models

display_name: occupancy-models

wetlands

display_name: wetlands

en: Amphibian observation and pond data (Aargau, Switzerland)


en: review bioenergy switzerland biogas manure

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

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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


en: snow compression-experiments microstructure snow-mechanics x-ray-tomography

en: README

media_type: TXT

en: README file explaining the organization of the data

en: Snowmicroquakes Data

en: Compression experiments, X-ray images and simulation data with supplemented README file

en:
When snow is compressed with a certain speed, micro-snowquakes are triggered in the porous structure of bonded crystals. The present dataset covers uniaxial compression experiments of snow at different strain rates and concurrent X-ray tomography imaging documenting this feature.

The experiments were conducted in a micro-compression stage operated in the X-ray tomography scanner in the SLF cold laboratory. The dataset comprises the compression force data of 17 compression experiments, the 3D image data from 4 X-ray tomography scans and the results of numerical simulations.

compression-experiments

display_name: compression-experiments

microstructure

display_name: microstructure

snow

display_name: snow

snow-mechanics

display_name: snow-mechanics

x-ray-tomography

display_name: x-ray-tomography

en: Compressive stick slip and snow-micro-quakes


radiosondes

maintainer: Jörg Wieder

title_for_slug: radiosondes

en: wind radiosonde raclets holoballoon pressure temperature wind-direction relative-humidity

en: Radiosond Sounding Files

en:
The "soundings" radiosond data contains information on ambient pressure (mbar), temperature (C), relative humidity (%), wind speed (m s‾ ¹) and wind direction (true deg), and are ordered with respect to the height above ground (m AGL) with a step size of 20 m. The first line of each file idicates the geographic location and the start time (in UTC) of the ascent. If the wind speed was zero, no wind direction is given.
The zip files contain ascents on the following days:
February 10 (morning and afternoon),
February 11 (morning),
February 16 (afternoon),
February 22 (morning),
March 1 (morning),
March 5 (morning),
March 11 (morning),
March 12 (morning),
March 13 (morning),
March 14 (morning),
March 17 (afternoon), and
March 18.
The data was managed with Windsond Receiver Application Demo Version 2.92, (C) Sparv Embedded AB 2016.

en: Radiosond Template Files

en:
The "template" files contain standard parameters, i.e. ambient pressure (mbar and Pa), temperature (°C), relative humidity (%), wind speed (m s‾ ¹) and wind direction (true deg), as well as additional information, e.g. time (UTC and local time), dew point (°C), mixing ration (g/kg), density (kg m³), altitude (m MSL), height above ground (ft and m AGL), and geographic coordinates (longitude, latitude). If the wind speed was zero, no wind direction is given.
The zip files contain ascents on the following days:
February 10 (morning and afternoon),
February 11 (morning),
February 16 (afternoon),
February 22 (morning),
March 1 (morning),
March 5 (morning),
March 11 (morning),
March 12 (morning),
March 13 (morning),
March 14 (morning),
March 17 (afternoon), and
March 18.
The data was managed with Windsond Receiver Application Demo Version 2.92, (C) Sparv Embedded AB 2016.

en: Radiosondes (Windsond, Sparv Embedded AB) were started in Davos Wolfgang (LON: 9.853594, LAT: 46.835577) to report height profiles of pressure, relative humidity and temperature at specific days. In addition to regular launches of radiosondes, sondes were attached to [HoloBalloon](https://www.envidat.ch/group/clouds-in-situ-raclets) to report the ambient conditions of the in-situ measurements. Further profiles of meteorological measures were recorded at [HoloGondel](https://www.envidat.ch/group/clouds-in-situ-raclets) which was installed at the gondola moving between Gotschnaboden and Gotschnagrat at 2285 m a.s.l.

holoballoon

display_name: holoballoon

pressure

display_name: pressure

raclets

display_name: raclets

radiosonde

display_name: radiosonde

relative-humidity

display_name: relative-humidity

temperature

display_name: temperature

wind

display_name: wind

wind-direction

display_name: wind-direction

en: Radiosondes