thermische-netze-warme-und-kalteangebot

en: Wastewater treatment plants (WWTPs) treat and purify wastewater. Wastewater is water that has been polluted through activities such as cooking, doing laundry or showering and then transported through a sewer system. The average temperature of wastewater is between 10 and 20 °C. A heat pump can recover this heat in order for it to be used as a heat source in a district heating network. The map shows the heat potential of WWTPs in Switzerland. The heat potential is estimated with the number of connected residents. These figures are to be understood as a guideline and should therefore not be used as a definitive basis for planning.

it: Negli impianti di depurazione delle acque (IDA) si trattano e purificano le acque reflue. Le acque reflue sono acque contaminate dall'uso (cucina, igiene personale, bucato) che vengono smaltite attraverso la rete fognaria. Tali acque hanno una temperatura media di 10-20°C. Questo calore può essere recuperato con una pompa di calore e utilizzato come fonte di calore per una rete di teleriscaldamento. La mappa indica il potenziale termico degli IDA in Svizzera, stimato in base al numero di abitanti allacciati. I dati ricavati devono essere intesi come valori indicativi e non possono pertanto essere utilizzati come base per una pianificazione definitiva.

de: In einer Abwasserreinigungsanlage (ARA) wird Abwasser aufbereitet und gereinigt. Abwasser ist Wasser, das durch den Gebrauch (beim Kochen, Waschen oder Duschen) verschmutzt und durch die Kanalisation abgeleitet wird. Abwasser hat eine durchschnittliche Temperatur von 10-20° C. Diese Wärme kann mit einer Wärmepumpe gewonnen und als Wärmequelle für ein Fernwärmenetz verwendet werden. Die Karte zeigt das Wärmepotenzial der ARAs in der Schweiz. Das Wärmepotenzial wird dabei mit der Anzahl angeschlossener Einwohner abgeschätzt. Die Potenziale sind als Richtwert zu verstehen und sollten daher nicht als definitive Planungsgrundlage verwendet werden.

fr: Les stations d’épuration (STEP) servent à traiter et à épurer les eaux usées. Les eaux usées désignent l’eau polluée par la consommation (cuisine, lavage ou douche) et évacuée par les égouts. La température des eaux usées est comprise entre 10 et 20°C. Cette chaleur peut être exploitée grâce à une pompe à chaleur et servir de source de chaleur dans un réseau de chaleur à distance. La carte montre le potentiel thermique des STEP en Suisse. Ce dernier est estimé sur la base du nombre d’habitants raccordés. Les potentiels sont fournis à titre indicatif et ne doivent par conséquent pas servir de base de planification définitive.

bundesamt-fur-energie-bfe

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

de: Bundesamt für Energie BFE

fr: geocat.ch permalien

en: geocat.ch permalink

en: Potential heat recovery from wastewater treatment plants

it: Potenziale degli impianti di depurazione delle acque in termini di recupero termico

de: Potenzial der Abwasserreinigungsanlagen für Wärmenutzung

fr: Le potentiel thermique des stations d’épuration

title_for_slug: thermische-netze-warme-und-kalteangebot

ener

de: Energie

de: Regionen und Städte

envi

it: Ambiente

de: Csv

en: WMS-FSDI service, layer "Potential heat recovery from WWTPs"

it: Servizio WMS-IFDG, strato "Potenziale di recupero termico IDA"

de: WMS-BGDI Dienst, Layer "Potenzial Wärmenutzung ARA"

fr: Service WMS-IFDG, couche "Potentiel thermique STEP"

en: WMTS-FSDI service, layer "Potential heat recovery from WWTPs"

it: Servizio WMTS-IFDG, strato "Potenziale di recupero termico IDA"

de: WMTS-BGDI Dienst, Layer "Potenzial Wärmenutzung ARA"

fr: Service WMTS-IFDG, couche , Layer "Potentiel thermique STEP"

de: Fachportal - map.energie.admin.ch

de: Map (Preview) Fachportal - map.energie.admin.ch

fr: Map (Preview) Géoportail thématique - map.energie.admin.ch

en: Map (Preview) Thematic geoportal - map.energie.admin.ch

it: Map (Preview) Geoportale tematico - map.energie.admin.ch

de: swisstopo REST API


$oid: OBSOLETE

en: A reserved zone serves to keep an area free for future lines. In a reserved zone, no projects that could interfere with the construction of a new line may be undertaken for a maximum of eight years. The national grid company (Swissgrid) can apply to the Swiss Federal Office of Energy for reserved zones. A reserved zone can impose restrictions on landowners. For this reason, reserved zones form part of the cadastre of public-law restrictions on landownership (PLR Cadastre). This dataset contains all the reserved zones currently applicable. It also contains all requested reserved zones if the canton in question uses the PLR Cadastre for the official publication of plans.

it: Una zona riservata ha lo scopo di mantenere libera un'area per future linee. In una zona riservata, per un massimo di otto anni non si può realizzare nulla che possa interferire con la costruzione di una nuova linea. La società nazionale di rete (Swissgrid) può chiedere all'Ufficio federale dell'energia l'attribuzione di zone riservate. Le zone riservate possono comportare restrizioni per i proprietari dei fondi. Per questo motivo, tali zone sono inserite nel Catasto delle restrizioni di diritto pubblico della proprietà (Catasto RDPP). Questo set di dati include tutte le zone riservate valide. Vi si trovano anche tutte le zone riservate richieste, a condizione che il Cantone competente utilizzi il Catasto RDPP per il deposito pubblico.

de: Eine Projektierungszone dient dazu, ein Gebiet für künftige Leitungen freizuhalten. In einer Projektierungszone darf während maximal acht Jahren nichts realisiert werden, was den Bau einer neuen Leitung beeinträchtigen könnte. Die nationale Netzgesellschaft (Swissgrid) kann beim Bundesamt für Energie Projektierungszonen beantragen. Eine Projektierungszone kann Grundeigentümer einschränken. Aus diesem Grund sind Projektierungszonen Teil des Katasters öffentlich-rechtlicher Eigentumsbeschränkungen (ÖREB-Kataster). Dieser Datensatz enthält alle gültigen Projektierungszonen. Darin finden sich auch beantragte Projektierungszonen, falls der zuständige Kanton für die öffentliche Auflage den ÖREB-Kataster verwendet.

fr: Une zone réservée sert à assurer la libre disposition d’un périmètre pour de futures lignes. Dans une zone réservée, pendant une durée maximale de huit ans, rien ne peut être réalisé qui puisse nuire à la construction d’une nouvelle ligne. La Société nationale pour l’exploitation du réseau (Swissgrid) peut demander la définition de zones réservées à l’Office fédéral de l’énergie. Une zone réservée peut constituer une restriction imposée à un propriétaire foncier. C’est pourquoi les zones réservées sont inscrites au Cadastre des restrictions de droit public à la propriété foncière (cadastre RDPPF). Ce jeu de données contient toutes les zones réservées valides. On y retrouve également toutes les zones réservées ayant fait l’objet d’une demande dans le cas où le canton compétent utilise le cadastre RDPPF pour la mise à l’enquête publique.

de: projektierungszone oreb bgdi-bundesgeodaten-infrastruktur energie

bundesamt-fur-energie-bfe

approval_status: approved

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

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

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

en: Swiss Federal Office of Energy SFOE

fr: Office fédéral de l'énergie OFEN

it: Ufficio federale dell'energia UFE

political_level: confederation

state: active

type: organization

en: Swiss Federal Office of Energy

it: Ufficio federale dell'energia

de: Bundesamt für Energie

fr: Office fédéral de l'énergie

de: geocat.ch Permalink

it: geocat.ch link permanente

spatial: Schweiz

en: Projektierungszonen Starkstromanlagen V2.0 OeREB

it: Zone riservate per impianti a corrente forte V2.0 RDPP

fr: Zones réservées des installations électriques à courant fort V2.0 RDPPF

type: dataset

regi

en: Regions and cities

fr: Régions et villes

it: Regioni e città

envi

de: Umwelt

en: Environment

fr: Environnement

de: Interlis

format: INTERLIS

protocol: WWW:DOWNLOAD:INTERLIS 1 (ITF)

en: WMS Service from geo.admin.ch

it: Servizio WMS di geo.admin.ch

de: WMS Dienst von geo.admin.ch

fr: Service WMS de geo.admin.ch

format: WMS

value: BFE - Geocat Harvester


en: The wind energy concept is a concept in accordance with Art. 13 of the Swiss Spatial Planning Act (SPA, SR 700). The concept defines both the principal federal government interests which must be taken into account when planning wind energy plants and the main areas with wind-power potential which must be investigated as part of cantonal structural planning. In this way, the wind energy concept increases planning reliability. The Swiss federal government has identified the main areas with wind-power potential, taking into account the high wind energy yields (basic data from the Wind Atlas of Switzerland, www.windatlas.ch), the principal federal government interests and sufficient spatial concentration. The rough analysis, which is subject to some uncertainty, does not take account of any cantonal or municipal interests and does not permit any assertions to be made about the specific feasibility of wind energy plants. The cartographical representation of the areas with potential should not therefore be seen as a positive plan by the federal government or as competition to structural plan entries that have already been approved; neither does it make any assertions beyond a 1:50,000 scale. The cantons are responsible for identifying suitable areas for wind energy use and the associated balancing of interests appropriate to the relevant levels. The geodata from the main areas with potential are not officially binding but are primarily to be considered as the federal government’s basis for cantons which do not yet have any structural plans in place for wind energy or which want to adapt their structural plans accordingly. In the interests of completeness, the map must be consulted in parallel with the content of the wind energy concept and the associated explanatory report.

it: La Concezione energia eolica è una concezione ai sensi dell'articolo 13 della legge federale sulla pianificazione del territorio (LPT, RS 700). La Concezione definisce, da un lato, i principali interessi federali da considerare nella pianificazione di impianti eolici; dall'altro, i comprensori con un potenziale importante da esaminare nel quadro della pianificazione direttrice cantonale. La Concezione energia eolica serve quindi a migliorare la sicurezza pianificatoria.I comprensori con un potenziale importante vengono identificati dalla Confederazione, tenendo in considerazione i rendimenti energetici elevati (dati base atlante eolico svizzero, www.windatlas.ch), i principali interessi federali nonché una sufficiente concentrazione territoriale. L'analisi sommaria e con un certo grado di incertezza non considera gli interessi cantonali e comunali e, di conseguenza, non permette di formulare indicazioni concrete sulla realizzabilità di impianti eolici. La rappresentazione cartografica dei potenziali comprensori non costituisce perciò una pianificazione «positiva» della Confederazione e non è in concorrenza con le iscrizioni approvate nel piano direttore e fornisce dati al massimo fino alla scala 1:50'000. L'identificazione di comprensori idonei per lo sfruttamento dell'energia eolica con la relativa ponderazione degli interessi a livello di autorità preposta è di competenza dei Cantoni. I geodati dei comprensori con un potenziale importante per l'energia eolica non hanno alcun carattere vincolante, ma servono in primo luogo come fondamento della Confederazione per fornire indicazioni a quei Cantoni che non hanno ancora un capitolo sull'energia eolica nel loro piano direttore oppure sono intenzionati a integrare delle modifiche. L'importanza della carta di sintesi è data solo se si considerano le dovute spiegazioni contenute nella Concezione energia eolica e nel relativo rapporto esplicativo.

de: Das Konzept Windenergie ist ein Konzept nach Artikel 13 des Bundesgesetzes über die Raumplanung (RPG, SR 700). Das Konzept definiert einerseits die wichtigsten Bundesinteressen, welche bei der Planung von Windenergieanlagen zu berücksichtigen sind, und andererseits die Hauptpotenzialgebiete, welche im Rahmen der kantonalen Richtplanung abzuklären sind. Dadurch erhöht das Konzept Windenergie die Planungssicherheit.Die Hauptpotenzialgebiete sind durch den Bund unter Berücksichtigung hoher Windenergieerträge (Basisdaten Windatlas Schweiz, www.windatlas.ch), der wichtigsten Bundesinteressen und einer ausreichenden räumlichen Konzentration ermittelt worden. Die grobe, mit Unsicherheit behaftete Analyse berücksichtigt dabei keine kantonalen und kommunalen Interessen und erlaubt keine Aussagen zur konkreten Realisierbarkeit von Windenergieanlagen. Die kartografische Darstellung der Potenzialgebiete stellt somit keine Positivplanung des Bundes dar und konkurriert keine genehmigten Richtplaneinträge und macht nur Aussagen bis zum Massstab 1:50‘000. Die Bezeichnung von geeigneten Gebieten für die Windenergienutzung mit der damit verbundenen stufengerechten Interessenabwägung liegt in der Kompetenz der Kantone. Die Geodaten der Hauptpotenzialgebiete haben keinen behördenverbindlichen Charakter, sondern sind primär als eine Grundlage des Bundes für Kantone aufzufassen, welche noch kein Richtplankapitel zur Windenergie haben oder ein entsprechendes Richtplankapitel anpassen wollen. Die Karte entfaltet ihre volle Aussagekraft nur unter Einbezug der Ausführungen im Konzept Windenergie sowie des dazugehörigen Erläuterungsberichts.

fr: Le concept d'énergie éolienne se fonde sur l'art. 13 de la loi fédérale sur l'aménagement du territoire (LAT; RS 700). Il définit d'une part les principaux intérêts de la Confédération qui doivent être pris en compte lors de la planification des éoliennes et d'autre part les principales régions à potentiel qui doivent être établies dans le cadre des plans directeurs cantonaux. La planification des éoliennes gagne en sécurité grâce à ce concept.Les principales régions à potentiel ont été définies par la Confédération en tenant compte de rendements énergétiques élevés (données de base: atlas des vents de la Suisse, www.windatlas.ch ), des principaux intérêts de la Confédération et d'une concentration spatiale suffisante. L'analyse grossière, entachée d'incertitudes, ne tient pas compte des intérêts cantonaux ou communaux et ne permet pas d'émettre des affirmations sur la réalisation concrète des éoliennes. La présentation cartographique des régions à potentiel ne constitue ainsi pas une planification positive de la part de la Confédération et ne fait pas concurrence à des contributions au plan directeur dûment approuvées. Les informations sont disponibles pour une échelle allant jusqu'à 1:50 000. La désignation de certaines régions pour une exploitation de l'énergie éolienne, avec la pondération des intérêts à tous les niveaux qui en découle, est du ressort des cantons. Les géodonnées des principales régions à potentiel ne revêtent aucun caractère impératif pour les autorités mais constituent bien plus une base proposée par la Confédération aux cantons qui ne disposent pas encore dans leur plan directeur d'un chapitre consacré à l'énergie éolienne ou qui souhaitent adapter celui-ci. La carte ne prend tout son sens qu'en tenant compte des explications contenues dans le concept d'énergie éolienne et dans le rapport explicatif correspondant.

de: wind planung windenergie erneuerbare-energiequelle bgdi-bundesgeodaten-infrastruktur

bundesamt-fur-energie-bfe

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

de: Bundesamt für Energie BFE

en: Wind energy concept – Swiss federal government basic map of main areas with wind-power potential

it: Concezione energia eolica – carta di base della Confederazione concernente le principali zone a potenziale eolico

de: Konzept Windenergie – Grundlagenkarte des Bundes betreffend die hauptsächlichen Windpotenzialgebiete

fr: Conception énergie éolienne – carte de base de la Confédération concernant les principales zones à potentiel éolien

educ

de: Bildung, Kultur und Sport

en: Education, culture and sport

fr: Éducation, culture et sport

it: Istruzione, cultura e sport

ener

en: Energy

fr: Énergie

it: Energia

en: WMS-FSDI service, layer "Areas with wind-power potential"

it: Servizio WMS-IFDG, strato "Zone a potenziale eolico"

de: WMS-BGDI Dienst, Layer "Windpotenzialgebiete"

fr: Service WMS-IFDG, couche "Zones à potentiel éolien"

de: GeoTIFF

format: GeoTIFF

protocol: WWW:DOWNLOAD:GeoTIFF (GEOTIFF)

en: Thematic geoportal - map.geo.admin.ch

it: Geoportale tematico - map.geo.admin.ch

de: Fachportal - map.geo.admin.ch

fr: Géoportail thématique - map.geo.admin.ch

de: Map (Preview)

fr: Map (Preview)

en: Map (Preview)

it: Map (Preview)

format: SERVICE


en: The demand for heat and cooling is a key element in the strategic planning of thermal networks. Building a thermal network is only viable if sufficient sales turnover can be generated from heat and/or cooling. In strategic planning, heat demand is used to identify large connected areas that may be appropriate for a thermal network. Areas with a heat density of at least 700 MWh/a per hectare are considered suitable. As well as heat density, heat must be supplied at a specific temperature. Some industries require process heat at a very high temperature (sometimes 1,000°C or more), and this cannot be generated by a thermal network. Heat demand from industry has therefore been recorded in a separate data set. Of most interest for thermal networks is the heat demand from residential and commercial buildings, since the vast majority of such buildings can be served at low temperatures ranging from around 12°C to maximum 90°C. The data are based on the Federal Statistical Office's 2014 Buildings and Dwellings Statistics and 2013 Statistics on Company Structures (not available in English), and the Federal Office of Energy's figures in the annual report ‘Energy consumption in the industry and services sector'. On the hectare grid, heat demand is represented as heat density shown on a colour scale. The information is provided for the general public and is not official or legally binding.

it: La domanda di calore e di freddo è di importanza fondamentale per la pianificazione strategica delle reti termiche. Vale la pena costruire una rete termica in un'area soltanto se vi è un sufficiente volume di vendita di calore e/o freddo. Nella pianificazione strategica la domanda di calore serve a identificare aree contigue di dimensioni maggiori che possono essere idonee a una rete termica. Un'area è considerata idonea a partire da una densità di calore di 700 MWh/a per ettaro. Oltre alla densità di calore, per la fornitura di calore è importante anche il livello di temperatura necessario. L'industria e l'artigianato necessitano in parte di un livello di calore molto elevato (in certi casi 1000 °C e oltre), che una rete termica non è in grado di fornire. Pertanto la domanda di calore dell'industria è stata rilevata in un set di dati a sé stante. La domanda di calore degli edifici abitativi e del terziario è particolarmente interessante per le reti termiche, visto che la maggior parte degli edifici di queste categorie può essere approvvigionata con temperatura basse, comprese tra circa 12°C e un massimo di 90°C. La base di dati è costituita dalla statistica degli edifici e delle abitazioni (SEA) del 2014 e dalla statistica strutturale delle imprese (STATENT) dell'UST del 2013, integrate dalle cifre del rapporto annuale dell'UFE sul consumo energetico nell'industria e nel terziario (rapporto «Energieverbrauch in der Industrie und im Dienstleistungssektor»). La domanda di calore è rappresentata come densità di calore nella griglia ettometrica. La densità di calore è indicata tramite una scala cromatica. I dati sono da intendersi come materiale informativo per il pubblico, non hanno carattere ufficiale e non sono vincolanti sotto il profilo giuridico.

de: Die Wärme- und Kältenachfrage ist für die strategische Planung von thermischen Netzen von zentraler Bedeutung. Der Bau eines thermischen Netzes in einem Gebiet lohnt sich erst bei genügendem Wärme- und/oder Kälteabsatz. Die Wärmenachfrage dient in der strategischen Planung zur Identifizierung von grösseren zusammenhängenden Gebieten, die sich für ein thermisches Netz eignen können. Ab einer Wärmedichte von 700 MWh/a pro Hektare gilt ein Gebiet als geeignet. Neben der Wärmedichte ist auch das erforderliche Temperaturniveau für die Wärmeversorgung wichtig. In der Industrie und dem Gewerbe wird zum Teil Prozesswärme auf sehr hohem Niveau (z.T. 1'000 °C und mehr) benötigt, die mit einem thermischen Netz nicht bereitgestellt werden kann. Deshalb wurde die Wärmenachfrage von Industrie in einem eigenen Datensatz erfasst. Die Wärmenachfrage von Wohn- und Dienstleistungsgebäuden ist für thermische Netze am interessantesten, da die allermeisten Gebäude dieser Kategorien mit tiefen Temperaturen von ca. 12°C bis max. 90°C versorgt werden können. Die Datengrundlage bildet die Gebäude- und Wohnungsstatistik (GWS) aus dem Jahr 2014 und die Statistik der Unternehmensstruktur (STATENT) des BFS aus dem Jahr 2013, ergänzt mit Zahlen aus dem jährlichen Bericht «Energieverbrauch in der Industrie und im Dienstleistungssektor» des BFE. Die Wärmenachfrage wird als Wärmedichte im Hektarraster dargestellt. Die Wärmedichte ist anhand einer Farbskala ersichtlich. Die Angaben dienen als Informationsmaterial für die Öffentlichkeit und stellen keine amtliche Auskunft oder rechtsverbindliche Aussage dar.

fr: Il est primordial de connaître la demande de chaleur ou de froid pour procéder à la planification stratégique des réseaux thermiques. En effet, ce n'est que si les ventes de chaleur et/ou de froid sont suffisantes dans une région donnée qu'il vaut la peine d'y construire un tel réseau. La demande de chaleur sert à identifier les grandes zones contiguës qui pourraient s'avérer appropriées à la création d'un réseau thermique. Une région présentant une densité thermique égale ou supérieure à 700 MWh/a par hectare est considérée comme appropriée. Autre élément dont il faut tenir compte dans la planification stratégique: la température à atteindre dans l'approvisionnement en chaleur. Dans l'industrie ou l'artisanat, il est des domaines où les températures requises sont si élevées (1000°C, voire davantage) qu'elles ne peuvent pas être obtenues au moyen d'un réseau thermique. La demande de chaleur pour l'industrie a donc été enregistrée dans un jeu de données spécifique. Les bâtiments d'habitation ou administratifs sont ceux qui se prêtent le mieux à un réseau thermique, car les températures nécessaires pour la plupart de ces édifices se situent dans une fourchette allant de 12°C à 90°C au maximum. Les données de base proviennent de la Statistique des bâtiments et des logements (StatBL) pour 2014 et de la Statistique structurelle des entreprises (STATENT) pour 2013, toutes deux établies par l'Office fédéral de la statistique. Elles sont étoffées par des chiffres ressortant du rapport annuel de l'Office fédéral de l'énergie portant sur la consommation d'énergie dans l'industrie et les services. La demande de chaleur est représentée en tant que densité thermique par hectare et mise en évidence selon une palette de couleurs. Les indications servent à informer le grand public et ne constituent pas des informations officielles ni des déclarations juridiquement contraignantes.

fr: geocat.ch permalien

en: geocat.ch permalink

en: Thermal networks: demand from residential and commercial buildings

it: Reti termiche: domanda degli edifici abitativi e del terziario

de: Thermische Netze: Nachfrage Wohn- und Dienstleistungsgebäude

fr: Réseaux thermiques: demande concernant les bâtiments d'habitation ou administratifs

de: Energie

regi

de: Regionen und Städte

envi

it: Ambiente

de: Geopackage

format: GPKG

protocol: WWW:DOWNLOAD:OGC Geopackage (GPKG)

en: WMS-FSDI service, layer "Heat/cooling demand: resid./comm."

it: Servizio WMS-IFDG, strato "Domanda calore/freddo abit./servizi"

de: WMS-BGDI Dienst, Layer "Wärme-/Kältenachfrage Wohnen und DL"

fr: Service WMS-IFDG, couche "Demande de chaleur/froid: bâtiments"

en: WMTS-FSDI service, layer "Heat/cooling demand: resid./comm."

it: Servizio WMTS-IFDG, strato "Domanda calore/freddo abit./servizi"

de: WMTS-BGDI Dienst, Layer "Wärme-/Kältenachfrage Wohnen und DL"

fr: Service WMTS-IFDG, couche , Layer "Demande de chaleur/froid: bâtiments"

format: WMTS

en: Thematic geoportal - map.energie.admin.ch

it: Geoportale tematico - map.energie.admin.ch

de: Fachportal - map.energie.admin.ch

fr: Géoportail thématique - map.energie.admin.ch

de: Map (Preview) Fachportal - map.energie.admin.ch

de: swisstopo REST API

format: API


en: A ‘renewable heating' advice service is available to building owners who have oil, gas or electric heating and who wish to find out which renewable low-carbon heat source is most appropriate to heat their building. An advisor visits the client (property owner/property agent/condominium owners) to discuss the building's energy requirements, location-specific factors and the owner's wishes. The advisor takes these factors into account and gives the owner/ condominium owners a checklist setting out recommendations for optimising the current heating system. On the basis of the checklist, the client can compare options, get appropriate quotes for a new system and set about switching to renewables. This database contains all the trained advisors in the ‘renewable heating' programme who meet the quality criteria.

it: Nel quadro della prima consulenza «calore rinnovabile», le proprietarie e i proprietari di immobili e appartamenti con un sistema di riscaldamento a gasolio, a gas o elettrico vengono informati sulle possibili fonti di calore rinnovabile intelligenti e con un basso contenuto di CO2. Nell'ambito della consulenza un/a esperto/a si reca dal cliente (proprietario/amministratore/comunità di proprietà per piani). Durante il colloquio di consulenza vengono discussi il fabbisogno energetico dell'edificio, i fattori specifici legati al luogo e i desideri dei proprietari. L'esperto/a verifica questi fattori e consegna al proprietario/amministratore/comunità di proprietà per piani una checklist, che riporta informazioni sulla situazione dell'immobile e raccomandazioni sulla sostituzione del sistema di riscaldamento. Questa checklist rappresenta una buona base per commissionare un confronto delle varianti, richiedere offerte e iniziare un progetto di costruzione di successo. Questa banca dati contiene tutti gli esperti/e «prima consulenza» formati nell'ambito del programma «calore rinnovabile» e che soddisfano i criteri di qualità.

de: Gebäude- und Wohnungsbesitzerinnen und -besitzer mit einer Öl-, Gas- oder Elektroheizung erfahren mit der Impulsberatung «erneuerbar heizen», mit welchen erneuerbaren Wärmequellen ihr Gebäude sinnvoll und CO2-arm beheizt werden kann. Bei der Impulsberatung erneuerbar heizen kommt ein Berater oder eine Beraterin zum Kunden (Hauseigentümer/Verwalter/ Stockwerkeigentümerschaften) nach Hause. Im Beratungsgespräch wird der Energiebedarf des Gebäudes, die standortspezifischen Faktoren und die Wünsche der Eigentümer diskutiert. Der Berater prüft diese Faktoren und überliefert dem Hauseigentümer / der Hauseigentümerin oder Stockwerkeigentümerschaft eine Checkliste mit der Ausgangslage und den Empfehlungen zum Heizsystemwechsel. Die Checkliste ist eine gute Basis, um allenfalls einen Variantenvergleich in Auftrag zu geben, sich die richtigen Offerten einzuholen und ein erfolgreiches Bauprojekt zu starten. Dieser Datenbestand enthält alle im Rahmen des Programms «erneuerbar heizen» geschulten Impulsberater und Impulsberaterinnen, welche die Qualitätskriterien erfüllen.

fr: Dans le cadre du conseil incitatif « chauffez renouvelable », les propriétaires de bâtiments et d'appartements utilisant un système de chauffage au mazout, au gaz ou à l'électricité sont informés sur les sources de chaleur renouvelables permettant de chauffer de manière optimale leur bâtiment tout en minimisant les émissions de CO2. Un conseiller rend visite au client (propriétaire/administrateur/copropriétaires d'étage) à domicile et discute, dans le cadre de l'entretien de conseil, des besoins en énergie du bâtiment, des facteurs propres au site ainsi que des souhaits des propriétaires. Le conseiller examine ces éléments et établit une check-list contenant des informations sur la situation actuelle du bâtiment et des recommandations sur le remplacement du système de chauffage. Cette liste de contrôle fournit au propriétaire ou au copropriétaire d'étage une bonne base pour commander éventuellement une comparaison de variantes, obtenir les offres adéquates et ainsi initier un projet de rénovation avec succès. Cette base de données contient tous les prestataires de conseil incitatif formés dans le cadre du programme « chauffez renouvelable » et répondant aux critères de qualité requis.

bundesamt-fur-energie-bfe

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

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

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

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

de: Bundesamt für Energie BFE

en: Swiss Federal Office of Energy SFOE

fr: Office fédéral de l'énergie OFEN

it: Ufficio federale dell'energia UFE

en: Swiss Federal Office of Energy

it: Ufficio federale dell'energia

de: Bundesamt für Energie

fr: Office fédéral de l'énergie

de: geocat.ch Permalink

it: geocat.ch link permanente

en: Heating with renewables: Apartment blocks with more than 6 apartments

it: Prima consulenza calore rinnovabile: case plurifamiliari con più di 6 unità abitative

de: Impulsberatung erneuerbar heizen: MFH mit mehr als 6 Wohneinheiten

fr: Conseil incitatif chauffez renouvelable: immeubles locatifs avec plus de 6 unités d'habitation

de: Umwelt

en: WMS-FSDI service, layer "Heating advice large apartments"

it: Servizio WMS-IFDG, strato "Prima consulenza calore rinnovabile"

de: WMS-BGDI Dienst, Layer "Impulsberatung grosse MFH"

fr: Service WMS-IFDG, couche "Conseil incitatif grands immeubles"

de: Csv

format: CSV

protocol: WWW:DOWNLOAD:TEXT, comma separated (CSV)

de: Interlis


en:
The dataset contains a time series of countrywide Sentinel-1 synthetic aperture radar (SAR) composites for Switzerland and Liechtenstein starting in 2015. The temporal sampling step of the time series is 6 days and the compositing period 12 days resulting in 60 generated composites per year. Each composite contains the amplitude information of C-band backscatter and features a grid spacing of 10 m. The composites were generated according to the local resolution weighting (LRW) methodology described in Small et al. (2022, https://doi.org/10.1109/TGRS.2021.3055562). For the generation of each composite, backscatter information from all Sentinel-1 acquisitions of Switzerland and Liechtenstein during the compositing period was obtained.
The naming of each composite file follows the logic “S1_LRW_PD\-LRW compositing period in days\-\_SP\-Temporal sampling step in days\-_\-Start date and time for LRW composite generation\--\-End date and time for LRW composite generation\-.tif”, e.g. “S1_LRW_PD12_SP6_20150101000000-20150112235959.tif” for the first file. Each file contains four raster layers: 1) Vertically submitted and horizontally received (VH) and 2) vertically submitted and vertically received (VV) polarisation backscatter in the logarithmic dB scale, 3) composite local resolution, which is a measure for the quality of the pixel (for details, see Small et al. (2022, https://doi.org/10.1109/TGRS.2021.3055562), and 4) the number of contributing observations.
Contains modified Copernicus Sentinel data.

en: sentinel-1 c-band time-series earth-observation radar remote-sensing switzerland sar

envidat

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.

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

wsl

capacity: public

de: EnviDat

en: Sentinel-1 Synthetic Aperture Radar Composite Backscatter Time Series of Switzerland

key: guid

key: uri

value: Envidat Harvester

envi

en: Environment

fr: Environnement

c-band

display_name: c-band

earth-observation

display_name: earth-observation

radar

display_name: radar

remote-sensing

display_name: remote-sensing

sar

display_name: sar

switzerland

display_name: switzerland

time-series

display_name: time-series


en:
**Schweizweite Hinweiskarten des lichtbedingten Verjüngungspotentials (Stand LFI VHM 2024)**
This is a dataset for Swiss forestry, for a description in English please see below.
Il s'agit d'un ensemble de données relatives à la sylviculture suisse. Pour une description en français, voir ci-dessous.
Si tratta di una serie di dati relativi alla silvicoltura svizzera. Per una descrizione in francese, vedere sotto.
Die Karte zeigt, wo die Lichtbedingungen für genügend Höhenzuwachs der Verjüngung (-= 300 cm) der jeweiligen Baumartengruppe ausreichen. Die Karte basiert auf gutachtlich erhobenen Feldaufnahmen in 39 Forstrevieren, verteilt über die ganze Schweiz und verschiedene Höhenlagen, sowie auf dem Kronenöffnungsgrad, der aus LiDAR Daten abgeleitet wurde. Die berücksichtigten Baumarten wurden anhand ihrer Lichtansprüche in die unten aufgeführten Klassen eingeteilt. Indikativ kann die Karte auch für andere Baumarten der jeweiligen Klasse verwendet werden:
- Lärche, Birke, Stiel- und Traubeneiche, Waldföhre und Kirschbaum: Lichtbaumarten (Klasse 1)
- Berg- und Spitzahorn: Halblichtbaumarten (Klasse 2)
- Fichte, Sommer- und Winterlinde: Halbschattenbaumarten (Klasse 3):
- Buche und Tanne: Schattenbaumarten (Klasse 4)
- Zu dunkel für das Aufwachsen sämtlicher Baumarten (Klasse 5)
Zweck: Die Karte zeigt, ob die lokal vorhandenen Lichtverhältnisse ausreichen, damit Jungbäume ohne zusätzlichen Eingriff vital ins Stangenholz aufwachsen können, und macht damit potenziell nötige waldbauliche Massnahmen frühzeitig erkennbar.
Hinweise zur Verwendung: Unterstützung von Försterinnen und Waldbewirtschaftern bei der Planung von Verjüngungseingriffen, der Jungwaldpflege, der Baumartenwahl und beim zeitlichen und räumlichen Monitoring, da die Karte bei neuen LiDAR-Befliegungen regelmässig aktualisiert wird.
Bemerkungen: Das Verjüngungspotential wurde ausschliesslich aufgrund des Kronenöffnungsgrads berechnet, der die Abschattung durch die umliegenden Baumkronen und dem Gelände berücksichtigt. Die direkte Sonneneinstrahlung wurde bisher nicht berücksichtigt, da damit statistisch weniger gute Ergebnisse erreicht werden. Somit kann auch die Hangexposition oder die Ausrichtung der Lücken bisher nicht berücksichtig werden. Ebenso können bisher keine Höhenstufen-spezifischen Unterteilungen erfolgen, da das verwendete nationale Modell in den statistischen Tests am besten abschneidet. In Beständen mit Bäumen und Sträuchern von ca. 3-7 Höhe werden die Lichtbedingungen in der Unterschicht überschätzt, d.h. die Karte zeigt in solchen Fällen ein zu hohes Verjüngungspotential. Die Karte wurde auf die Waldfläche gemäss LFI reduziert und Waldstrassen wurden so gut wie möglich ausmaskiert.
**Cartes nationales indiquant le potentiel de régénération lié à la lumière (état IFN VHM 2024)**
La carte montre les endroits où les conditions de luminosité sont suffisantes pour permettre une croissance en hauteur suffisante (-= 300 cm) de la régénération pour chaque groupe d'essences. La carte est basée sur des relevés effectués sur le terrain dans 39 districts forestiers répartis sur toute la Suisse et à différentes altitudes, ainsi que sur le degré d'ouverture de la canopée, dérivé des données LiDAR. Les essences prises en compte ont été classées dans les catégories ci-dessous en fonction de leurs besoins en lumière. La carte peut également être utilisée à titre indicatif pour d'autres essences de la catégorie correspondante :
Mélèze, bouleau, chêne pédonculé et chêne rouvre, pin sylvestre et cerisier : essences photophiles (classe 1)
Érable sycomore et érable plane : essences semi-photophiles (classe 2)
Épicéa, tilleul à petites feuilles et tilleul à grandes feuilles : essences semi-ombelibles (classe 3)
Hêtre et sapin : essences ombéliques (classe 4)
Trop sombre pour la croissance de toutes les essences (classe 5)
Objectif : la carte indique si les conditions de luminosité locales sont suffisantes pour que les jeunes arbres puissent atteindre le stade de perchis sans intervention supplémentaire et permet ainsi d'identifier à un stade précoce les mesures sylvicoles potentiellement nécessaires.
Conseils d'utilisation : aide les forestiers et les gestionnaires forestiers à planifier les mesures de rajeunissement, l'entretien des jeunes forêts, le choix des essences et le suivi temporel et spatial, car la carte est régulièrement mise à jour lors de nouveaux survols LiDAR.
Remarques : le potentiel de régénération a été calculé exclusivement sur la base du degré d'ouverture de la canopée, qui tient compte de l'ombrage causé par les cimes des arbres environnants et le terrain. L'ensoleillement direct n'a pas été pris en compte jusqu'à présent, car il donne des résultats statistiquement moins bons. De même, l'exposition des pentes ou l'orientation des clairières ne peuvent pas être prises en compte pour l'instant. De même, aucune subdivision spécifique à l'altitude ne peut être effectuée jusqu'à présent, car le modèle national utilisé obtient les meilleurs résultats dans les tests statistiques. Dans les peuplements composés d'arbres et d'arbustes d'une hauteur d'environ 3 à 7 mètres, les conditions d'ensoleillement dans la strate inférieure sont surestimées, c'est-à-dire que la carte indique dans ces cas un potentiel de régénération trop élevé. La carte a été réduite à la surface forestière selon l'IFN et les routes forestières ont été masquées autant que possible.
**Mappe indicative a livello nazionale del potenziale di ringiovanimento legato alla luce (stato LFI VHM 2024)**
La mappa mostra dove le condizioni di luce sono sufficienti per garantire un'adeguata crescita in altezza del rimboschimento (-= 300 cm) del rispettivo gruppo di specie arboree. La mappa si basa su rilevamenti sul campo effettuati da esperti in 39 distretti forestali distribuiti su tutto il territorio svizzero e a diverse altitudini, nonché sul grado di apertura della chioma ricavato dai dati LiDAR. Le specie arboree prese in considerazione sono state classificate in base alle loro esigenze di luce nelle classi elencate di seguito. La mappa può essere utilizzata in modo indicativo anche per altre specie arboree della rispettiva classe:
- Larice, betulla, farnia e faggio, pino silvestre e ciliegio: specie arboree che necessitano di luce (classe 1)
- Acero di montagna e acero riccio: specie arboree che necessitano di luce moderata (classe 2)
- Abete rosso, tiglio estivo e tiglio invernale: specie arboree di mezz'ombra (classe 3):
- Faggio e abete: specie arboree di ombra (classe 4)
- Troppo buio per la crescita di tutte le specie arboree (classe 5)
Scopo: la mappa mostra se le condizioni di luce locali sono sufficienti affinché i giovani alberi possano crescere vitalmente senza ulteriori interventi e rende così riconoscibili tempestivamente le misure selvicolturali potenzialmente necessarie.
Indicazioni per l'uso: supporto ai forestali e ai gestori forestali nella pianificazione degli interventi di ringiovanimento, nella cura dei boschi giovani, nella scelta delle specie arboree e nel monitoraggio temporale e spaziale, poiché la mappa viene aggiornata regolarmente con nuove rilevazioni LiDAR.
Osservazioni: il potenziale di rigenerazione è stato calcolato esclusivamente sulla base del grado di apertura della chioma, che tiene conto dell'ombreggiamento causato dalle chiome degli alberi circostanti e dal terreno. Finora non è stata presa in considerazione l'esposizione diretta al sole, poiché ciò comporta risultati statisticamente meno attendibili. Pertanto, non è stato possibile tenere conto dell'esposizione del pendio o dell'orientamento delle radure. Allo stesso modo, non è ancora possibile effettuare suddivisioni specifiche per altitudine, poiché il modello nazionale utilizzato ottiene i migliori risultati nei test statistici. Nei popolamenti con alberi e arbusti di circa 3-7 metri di altezza, le condizioni di luce nello strato inferiore sono sovrastimate, ovvero in questi casi la mappa mostra un potenziale di rigenerazione troppo elevato. La mappa è stata ridotta alla superficie forestale secondo l'IFN e le strade forestali sono state mascherate il più possibile.
**Switzerland-wide maps showing light-induced regeneration potential (as of NFI VHM 2024)**
The map shows where light conditions are sufficient for sufficient height growth of regeneration (-= 300 cm) for each tree species group. The map is based on expert field surveys in 39 forest districts spread across Switzerland and at various altitudes, as well as on the degree of canopy opening derived from LiDAR data. The tree species considered were divided into the classes listed below based on their light requirements. The map can also be used as a guide for other tree species in the respective class:
- Larch, birch, English oak, sessile oak, Scots pine, and cherry: light-demanding tree species (class 1)
- Sycamore maple and Norway maple: semi-light-demanding tree species (class 2)
- Spruce, small-leaved and large-leaved lime: semi-shade tree species (class 3):
- Beech and fir: shade tree species (class 4)
- Too dark for all tree species to grow (class 5)
Purpose: The map shows whether the local light conditions are sufficient for young trees to grow vigorously into pole wood without additional intervention, thus identifying potentially necessary silvicultural measures at an early stage.
Notes on use: Support for foresters and forest managers in planning regeneration measures, young forest maintenance, tree species selection, and temporal and spatial monitoring, as the map is regularly updated with new LiDAR flights.
Comments: The regeneration potential was calculated solely on the basis of the degree of canopy opening, which takes into account the shading caused by the surrounding tree crowns and the terrain. Direct sunlight has not been taken into account so far, as this leads to statistically less reliable results. This means that slope exposure or the orientation of gaps cannot be taken into account at this stage. Similarly, no altitude-specific subdivisions can be made at present, as the national model used performs best in statistical tests. In stands with trees and shrubs of approx. 3-7 m in height, the light conditions in the understorey are overestimated, i.e. in such cases the map shows an excessively high regeneration potential. The map has been reduced to the forest area according to the NFI, and forest roads have been masked out as much as possible.

en: forest-management rejuvenation light-availability lidar protection-forest

en: Switzerland-wide maps of light-induced regeneration potential (as of NFI VHM 2024)

de:
Resource Title: LiDAR_Befliegungsjahre_VHM_LFI_2024.png
    Size: 201083 bytes
    Format: image/png
    URL: https://www.envidat.ch/dataset/c9b1019d-78cf-49b5-b065-25d8e2dd2700/resource/75048199-3e98-4715-9d2e-8e27cb265b12/download/lidar_befliegungsjahre_18122025.png
    Description: Karte mit den Befliegungsjahren der LiDAR Daten, welche für die Berechnung des Verjüngungspotential verwendet wurden.

format: PNG

media_type: image/png

de:
Resource Title: Verjüngungspotential_LFI_2024.tif
    Size: 163577856 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/c9b1019d-78cf-49b5-b065-25d8e2dd2700/resource/2a3adf57-5d17-4c04-897b-13c2c94149b9/download/verjpot_ch_18122025.tif
    Description:

format: TIFF

media_type: image/tiff

de:
Resource Title: Verjüngungspotentialkarte_Kurzbeschrieb.pdf
    Size: 364954 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/c9b1019d-78cf-49b5-b065-25d8e2dd2700/resource/1f901079-7a5a-46a5-8246-a685d71e0c8f/download/verjungungspotentialkarte_kurzbeschrieb_17022026.pdf
    Description: Kurzbeschrieb inkl. Legende und Limitierungen

format: PDF

media_type: application/pdf

forest-management

display_name: forest-management

lidar

display_name: lidar

light-availability

display_name: light-availability

protection-forest

display_name: protection-forest

rejuvenation

display_name: rejuvenation


en: This dataset comprises long-term daily 1 km gridded data of snow water equivalent and snow depth for Switzerland spanning the hydrological years 1962 until 2023. The SWE data were generated using the OSHD temperature index model and subsequent quantile mapping (Michel et al. 2024). The snow depth data were derived from the SWE data after applying the SWE2HS conversion algorithm. Details on theses datasets can be found in doi.org/10.5194/tc-19-4391-2025. The products were developed as part of the SPAtial Snow climatology for Switzerland (SPASS) project, a collaboration between MeteoSwiss and SLF.

en: swe 1km-resolution gridded-data snow-depth switzerland snow-climatology

en: SPASS - new gridded climatological snow datasets for Switzerland

de:
Resource Title: SWECLQMD_ch01h.swiss.lv95_WY_1962_2023.zip
    Size: 1717986918 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/da0c9ee4-ff02-4848-bc65-cbcaae343636/resource/a5db7556-7a91-413a-8d90-4c02aa2e9e8c/download/sweclqmd_ch01h.swiss.lv95_wy_1962_2023.zip
    Description: Long-term daily 1 km gridded data of snow water equivalent for Switzerland. The SWE data were generated using the OSHD temperature index model and subsequent quantile mapping. Details can be found in Michel et al. (2024), see doi.org/10.5194/gmd-17-8969-2024. A first version of this SWE data was publised at https://zenodo.org/records/7886773

format: ZIP

de:
Resource Title: HSCLQMD_ch01h.swiss.lv95_WY_1962_2023.zip
    Size: 1814623683 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/da0c9ee4-ff02-4848-bc65-cbcaae343636/resource/688d1caf-ed63-4cb8-8579-8c017bb7458c/download/hsclqmd_ch01h.swiss.lv95_wy_1962_2023.zip
    Description: Long-term daily 1 km gridded data of snow depth for Switzerland. The data were produced by applying the SWE2HS algorithm of Aschauer et al. (2023) to the SWE dataset, which can also be found in this directory. Details can be found in (doi.org/10.5194/gmd-16-4063-2023).

gridded-data

display_name: gridded-data

snow-climatology

display_name: snow-climatology

snow-depth

display_name: snow-depth

swe

display_name: swe


en:
This dataset contains several useful components for **Swiss Electricity system modeling**:
1. **High-voltage Swiss grid**: 2019 snapshot of the high-voltage (-150kV) grid composed of :
- Nodes: names, voltage, position.
- Lines: ends nodes, resistance, power capacity.
- Transformers: ends nodes, power capacity, resistance, tap ratio.
2. **Hydropower topology** : Representative description of Swiss reservoirs and plants (2019) composed of:
- Plants: Lower reservoir, Upper reservoir, pump/turbine, debit, power capacity.
- Reservoirs: Volume, altitude, position, canton.
3. **Inflows** into all the reservoirs: hourly time series post-processed from PREVAH model outputs from 2009 to 2019.
4. **Wind production per node**: COSMO-1 data processed and scaled to match SFOE annual production.
5. **Solar irradiance maps projected onto tilted surfaces**: hourly time series for 35° and 65° tilts, azimuth 180°. (same method as [Frischholz, Y. (2025)](https://www.doi.org/10.16904/envidat.632))
6. **Processed ERA5 (historical), ECMWF SEAS5 (seasonal forecast) and ECMWF TIGGE (short-term forecast)**:
- ERA5 Land: For inflow forecast model: historical runoff, snow depth, temperature and total precipitation per catchment (hourly, 2009-2019). For residual demand forecast model: historical temperature, u-v wind, ssrd per country (CH, AT, DE, FR, IT, hourly 2015-2019)
- ECMWF SEAS5: For inflow forecast model: same variables as ERA5 (yearly forecast starting from every 1st day of every month in 2019). For residual demand forecast model: same variables as ERA5 (yearly forecast starting from every 1st day of every month in 2019).
- ECMWF TIGGE: For inflow forecast model: same variables as ERA5 (two weeks forecast starting from every 1st day of every week in 2019). For residual demand forecast model: same variables as ERA5 (two weeks forecast starting from every 1st day of every week in 2019).
7. **Run-of-river production per electricity node**: 15-min resolution run-of-river production time-series (15min, 2009-2019). Raw data and documentation can be found on [https://data.sccer-jasm.ch/runofriver_production/2017-10-16/].
\
More details can be found on *README.txt*.

en: reservoir-inflow electricity-grid renewable-energy meteorological-data forecast-models hydropower solar-irradiance

en: Swiss electricity system modeling -- HV grid - hydropower topology and inflows - renewable productions - past and forecast meteorological data

de:
Resource Title: data_IFM_RDFM.zip
    Size: 1202590843 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/97e11e0b-8146-422d-8b04-33c9337eaba6/resource/7f514513-cb54-44a9-8ea4-8f8087048247/download/data_ifm_rdfm.zip
    Description: ERA5 Land, Seasonal forecast and short-term forecast meteorological data to train inflow and residual demand forecast models.

media_type: application/zip

de:
Resource Title: node_RunOfRiver_15min.ftr
    Size: 527832187 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/97e11e0b-8146-422d-8b04-33c9337eaba6/resource/9923c621-7238-4b16-b20d-6736da165a5a/download/node_runofriver_15min.ftr
    Description: Run-of-river production on 15min resolution from 2009 to 2019 in Switzerland, aggregated by electrical nodes.

format: OCTET

de:
Resource Title: data_IFM_RDFM.zip
    Size: 1191853425 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/97e11e0b-8146-422d-8b04-33c9337eaba6/resource/965d0b4b-a87f-4023-8bb8-a33dd2d86c5a/download/data_ifm_rdfm.zip
    Description:

de:
Resource Title: SwissGrid_data.zip
    Size: 17664 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/97e11e0b-8146-422d-8b04-33c9337eaba6/resource/a1224789-8bb8-46cd-a36d-4cb81d6d47ec/download/swissgrid_data.zip
    Description: Description of Swissgrid high-voltage (>150kV) grid, including lines, transformers and nodes (snapshot of the grid in 2019) .

de:
Resource Title: PV_data.zip
    Size: 2072321720 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/97e11e0b-8146-422d-8b04-33c9337eaba6/resource/e0e501b9-1041-4ff9-a3d9-485cce4aadac/download/pv_data.zip
    Description: Solar irradiance maps projected onto tilted surfaces: hourly time series for 35° and 65° tilts, azimuth 180°. (same method as [Frischholz, Y. (2025)](https://www.doi.org/10.16904/envidat.632))

de:
Resource Title: hydropower.zip
    Size: 21349007 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/97e11e0b-8146-422d-8b04-33c9337eaba6/resource/66f009b8-0cef-4f4e-bc12-c3a7d684f229/download/hydropower.zip
    Description: Plants and reservoirs description, with associated hourly inflows (2009 - 2019).

de:
Resource Title: README.txt
    Size: 6216 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/97e11e0b-8146-422d-8b04-33c9337eaba6/resource/0dc23d95-7563-4f21-a416-ca46643d9b69/download/readme.txt
    Description: Data structure and description.

format: TXT

media_type: text/plain

electricity-grid

display_name: electricity-grid

forecast-models

display_name: forecast-models

hydropower

display_name: hydropower

meteorological-data

display_name: meteorological-data

renewable-energy

display_name: renewable-energy

reservoir-inflow

display_name: reservoir-inflow

solar-irradiance

display_name: solar-irradiance


en: This dataset contains code and data to reproduce the figures and analyses in the article 'Environmental DNA metabarcoding reveals biodiversity recovery in tropical forest restoration across multiple landscapes' in Ecosphere journal. In this study, we analysed environmental DNA data from water and soil samples collected across gradients of forest regrowth at six sites in Colombia to assess the recovery of mammal and soil biodiversity associated with forest restoration.

en: soil-biodiversity colombia environmental-dna mammals forest-restoration biodiversity-assessment

en: Data and code underlying 'Environmental DNA metabarcoding reveals biodiversity recovery in tropical forest restoration across multiple landscapes'

envi

de: Umwelt

de:
Resource Title: Raw Data.zip
    Size: 1610612736 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/18ce28d1-0fa3-4f97-9690-78697dd39683/resource/b97d2608-f0d8-4544-a9a6-024f43b1d55d/download/raw-data.zip
    Description: Paired-end reads (specified by the R1 and R2 suffixes) for each individual sample. The reads have been demultiplexed using cutadapt but primers remain present in the sequences.

de:
Resource Title: Analysis Data and Code.zip
    Size: 15005123 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/18ce28d1-0fa3-4f97-9690-78697dd39683/resource/275e7eac-1898-48ed-8c91-e3d2ee840782/download/analysis-data-and-code.zip
    Description: A zipped folder containing the scripts and data necessary to reproduce the analyses described in the study. Further instructions are provided in scripts/ReadMe.txt

biodiversity-assessment

display_name: biodiversity-assessment

colombia

display_name: colombia

environmental-dna

display_name: environmental-dna

forest-restoration

display_name: forest-restoration

mammals

display_name: mammals

soil-biodiversity

display_name: soil-biodiversity


en:
This repository contains the data necessary to reproduce Fig. 5 of the publication: L. Mewes, H. Löwe, M. Schneebeli & B. Walter, *Optical determination of snow density via sub-surface scattering*, Commun Phys, doi: [10.1038/s42005-026-02490-1](https://doi.org/10.1038/s42005-026-02490-1).
The source code generating Fig. 5 for the above mentioned publication is available at [10.16904/envidat.726](https://doi.org/10.16904/envidat.726).
 Files
`SI01_Reflectance_Coldlab.h5`: Reflectance and partial reflectance data of a snow profile using the [SnowImager](https://www.snowimager.ch/).
`uCT_reference_data.csv`: Reference data extracted from a micro-computed tomography measurement.
Please refer to the `README` of the analysis code at [10.16904/envidat.726](https://doi.org/10.16904/envidat.726) for further information.

en: reflection snow-microstructure snowimager snow-density snow-stratigraphy snow-profile laboratory-study

envidat

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.

de: EnviDat: das Umwelt-Datenportal

en: Data for the publication Optical determination of snow density via sub-surface scattering

laboratory-study

display_name: laboratory-study

reflection

display_name: reflection

snow-density

display_name: snow-density

snow-microstructure

display_name: snow-microstructure

snow-profile

display_name: snow-profile

snow-stratigraphy

display_name: snow-stratigraphy

snowimager

display_name: snowimager


en:
This is the source code for modeling preferential snowfall deposition and snowfall downscaling as described in:
Helbig N., Mott R., Bühler Y., Le Toumelin L., Lehning M. (2024) Snowfall deposition in mountainous terrain: a statistical downscaling scheme from high-resolution model data on simulated topographies. Front. Earth Sci. 11, 1308269 (19 pp.). https://doi.org/10.3389/feart.2023.1308269
The code reads wind speed, wind direction, and snowfall values for each time step from the example file meteo.txt, as well as terrain elevations (DEM) from dem.txt. The snowfall value is then downscaled to the fine grid defined by the DEM and written to pref_dep.txt.
The current version can be found in the WSL/SLF Gitlab repository:
https://gitlabext.wsl.ch/snow-models/statistical-preferential-deposition-model

en: downscaling snowfall preferential-deposition statistical-model mountains

envidat

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

en: EnviDat: the Environmental Data Portal

de: EnviDat

en: Source code for a preferential snowfall deposition model

downscaling

display_name: downscaling

mountains

display_name: mountains

preferential-deposition

display_name: preferential-deposition

snowfall

display_name: snowfall

statistical-model

display_name: statistical-model


en:
The repository contains the source code necessary to reproduce Figs. 2-5 of the publication: L. Mewes, H. Löwe, M. Schneebeli & B. Walter, *Optical determination of snow density via sub-surface scattering*, Commun Phys, doi: [10.1038/s42005-026-02490-1](https://doi.org/10.1038/s42005-026-02490-1).
The data for Fig. 5 of the above mentioned publication is available at [10.16904/envidat.724](https://doi.org/10.16904/envidat.724).
Please refer to the `README` in this repository for additional information.
- **License:** The scripts in this repository are licensed under the MIT License; see LICENSE.txt for the full text.
 Files
`generate_figures.py`: Main script to generate the figures of the publication.
`snow_optics.py`: Core radiative-transfer functions.
`snow_image.py`: Utilities for loading and processing SnowImager ([www.snowimager.ch](https://www.snowimager.ch)) HDF5 data.
`helper.py`: Miscellaneous helper functions.
`lambertw.py`: Lambert W function implementation (John Burkardt, 2023, MIT license, -https://people.sc.fsu.edu/~jburkardt/py_src/lambert_w/lambert_w.py-).
 Third party resources
- Refractive indices of ice are taken from 'Warren, S. G., and R. E. Brandt (2008), Optical constants of ice from the ultraviolet to the microwave: A revised compilation, J. Geophys. Res., 113, D14220, doi:[10.1029/2007JD009744](https://doi.org/10.1029/2007JD009744).' available at: https://refractiveindex.info/database/data/main/H2O/nk/Warren-2008.yml
- The Lambert *W* function is implemented using the code of John Burkardt, modified 20 June 2023 and distributed under the MIT license (-https://people.sc.fsu.edu/~jburkardt/py_src/lambert_w/lambert_w.py-).
- The source code in this repository is part of a larger project for post-processing [SnowImager](https://www.snowimager.ch) data hosted at [https://code.wsl.ch/snow-physics/snowimagerpro](https://code.wsl.ch/snow-physics/snowimagerpro).

en: snow-profile laboratory-study snowimager snow-microstructure reflection snow-density snow-stratigraphy

en: Source code for the publication Optical determination of snow density via sub-surface scattering

envi

en: Environment

de:
Resource Title: Source code
    Size: 22825 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/5ef9984d-7fd8-4b95-87c4-f479b2e5ac13/resource/9af1ca7b-8780-46ec-abbc-e4b6890eead6/download/src.zip
    Description: Source code to generate the figures for Mewes et al., Commun Phys, 2026.

The scripts in this repository are licensed under the MIT License; see LICENSE.txt for the full text.

de:
Resource Title: README.md
    Size: 5181 bytes
    Format: text/markdown
    URL: https://www.envidat.ch/dataset/5ef9984d-7fd8-4b95-87c4-f479b2e5ac13/resource/97d46f1d-9321-4b1a-9a72-d79978da633f/download/readme.md
    Description: Instructions to generate figures for Mewes et al., Commun Phys, 2026.

format: markdown


en: This dataset accompanies the publication “Operational snow hydrological modeling for Switzerland” (Mott et al., 2023; https://doi.org/10.3389/feart.2023.1228158). It provides a climatological record of gridded snow water equivalent and snowmelt runoff for the period 1998–2022, resolved at 1 km spatial and daily temporal scales. All data were generated using the conceptual OSHD Temperature Index Model.

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

en: Climatological snow data since 1998, OSHD

operational-snow-hydrological-service

display_name: operational-snow-hydrological-service

snow-climatology

display_name: snow-climatology

snow-cover-fraction

display_name: snow-cover-fraction

snow-melt

display_name: snow-melt

snow-water-equivalent

display_name: snow-water-equivalent


en:
High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth’s land surface areas) data of downscaled temperature and precipitation to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction.

CHELSA data published in EnviDat includes the deprecated version 1.2 (originally published under 10.5061/dryad.kd1d4). Please use the current 2.1 version.
Paper Citation:
 - _Karger DN. et al. Climatologies at high resolution for the earth’s land surface areas, Scientific Data, 4, 170122 (2017)  [doi: 10.1038/sdata.2017.122](https://doi.org/10.1038/sdata.2017.122)._

en: temperature climate-layers climate climate-change precipitation bioclim climatology free-climate-data

de:
Resource Title: CHELSA V1.2 (deprecated)
    Size: 1319413953331 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fchelsav1%2F
    Description: CHELSA Version 1.2 based on ERA-Interim forcings and CMIP5

de:
Resource Title: CHELSA V2.1 (current)
    Size: 2308974418330 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.unil.cloud.switch.ch%2Fchelsa02%2F&prefix=chelsa%2Fglobal%2Fclimatologies%2F
    Description: CHELSA Version 2.1 based on ERA5 forcings and CMIP6

de:
Resource Title: CHELSA Technical Documentation
    Size: 285413 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/2adb0c83-4653-4337-af28-f75c63ab7c74/resource/61fff0a7-6abb-45f7-a5f6-48f6e2c24851/download/chelsa_file_specification.pdf
    Description: Technical documentation document including the variable names, units, scaling, offset, and background.

bioclim

display_name: bioclim

climate

display_name: climate

climate-change

display_name: climate-change

climate-layers

display_name: climate-layers

climatology

display_name: climatology

free-climate-data

display_name: free-climate-data

precipitation

display_name: precipitation

temperature

display_name: temperature


en:
Carabid beetle and wild bee occurrences in the city of Zurich, Switzerland.
Dataset available upon request (An agreement between the data provider and the data recipient is necessary).

en: ground-beetle species community-assembly environmental-heterogeneity bettergarden wild-bees niche-breadth wild-bee bettergardens ecology allotments functional urban-ecology niche allotment diversity functional-traits ground-beetles gardens urbanization composition species-composition functional-diversity garden trait

en: The origin of urban communities: from the regional species pool to community assemblages in city

de:
Resource Title: Arthropod-databank query used to access data.
    Size: 7751 bytes
    Format: application/sql
    URL: https://www.envidat.ch/dataset/33eb0e1a-fa8b-4b57-8db9-7a929feda811/resource/040d9b89-6455-4af9-8092-47e4fdd487d8/download/fido_global_extract-2019-10-30_13-31-00-urban-carabidae-apidae.sql
    Description:

format: sql

de:
Resource Title: Urban carabid and bee species city of Zurich Switzerland
    Size: 12151612 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/33eb0e1a-fa8b-4b57-8db9-7a929feda811/resource/0d32e239-6d49-4d69-a1fd-81a3cd854591/download/fido_global_extract-2019-10-30_13-31-00-urban-carabidae-apidae.txt
    Description: Carabid beetle and wild bee occurrences in the city of Zurich, Switzerland, from the years (project) 2009 (BiodiverCity), 2011 (ENHANCE) and 2016 (BetterGardens).

allotment

display_name: allotment

allotments

display_name: allotments

bettergarden

display_name: bettergarden

bettergardens

display_name: bettergardens

community-assembly

display_name: community-assembly

composition

display_name: composition

diversity

display_name: diversity

ecology

display_name: ecology

environmental-heterogeneity

display_name: environmental-heterogeneity

functional

display_name: functional

functional-diversity

display_name: functional-diversity

functional-traits

display_name: functional-traits

garden

display_name: garden

gardens

display_name: gardens

ground-beetle

display_name: ground-beetle

ground-beetles

display_name: ground-beetles

niche

display_name: niche

niche-breadth

display_name: niche-breadth

species

display_name: species

species-composition

display_name: species-composition

trait

display_name: trait

urban-ecology

display_name: urban-ecology

urbanization

display_name: urbanization

wild-bee

display_name: wild-bee

wild-bees

display_name: wild-bees


en:
This is the source code for computing the seasonal fractional snow-covered area. It is written in Fortran 90. The code reads snow depth (HS) and snow water equivalent (SWE) data from the provided example file HS_SWE.txt and writes the computed fractional snow-covered area (fSCA) to a file fSCA.txt.
The current version can be found in the WSL/SLF Gitlab repository:
https://gitlabext.wsl.ch/snow-models/fractional-snow-covered-area

en: snow-depth snow-water-equivalent subgrid-topography snow-cover-modeling source-code hysteresis

en: Seasonal fractional snow-covered area algorithm

hysteresis

display_name: hysteresis

snow-cover-modeling

display_name: snow-cover-modeling

snow-depth

display_name: snow-depth

snow-water-equivalent

display_name: snow-water-equivalent

source-code

display_name: source-code

subgrid-topography

display_name: subgrid-topography


en: This is an R script written to calculate the relative terrain aspect parameter, which is utilized in the preferential snowfall downscaling scheme for mountainous terrain. The code reads a terrain aspect angle grid from file 'terrain_aspect_grid.txt' and  writes the computed relative terrain aspect angle grid to file 'delta_psi_grid.txt'.

en: complex-topography downscaling snowfall preferential-deposition r-script

envidat

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

en: EnviDat: the Environmental Data Portal

en: Snowfall  downscaling Source code to calculate the relative terrain aspect parameter

complex-topography

display_name: complex-topography

downscaling

display_name: downscaling

preferential-deposition

display_name: preferential-deposition

r-script

display_name: r-script

snowfall

display_name: snowfall


en:
Base data which were used for the methodology projecting future regional snow depths based on temperature scenarios. This study was supported by Seilbahnen Schweiz, Schweiz Tourismus, Seco and Speed2Zero.
The data is split in three folders:
- Literature-Fit dataset
- Literature-Validation dataset
- Projections
The Literature-Fit dataset is the data which was used to retrieve the fit parameters a, Delta b and Delta c, for different elevations and temperature scenarios, which can be found in data/literature_fit/fitfactor.csv. Those fit factors were used to project future snow depths from climatological snow depths. Futhermore, this dataset was used to link decreases in mean snow depths for different seasonal periods to the NDJFMA period (November until April, in total six months), those differences in reduction values can be found in data/literature_fit/difference_decrease_4differentperiods.csv.  The raw reference and future snow depths of different studies and different elevation and temperature scenarios (dt) can be found in data/literature_fit/literature_data/* where each file is named by data_author_station_dt.csv.
The Literature-Validation dataset can be found in data/literature_validation/literature_data.csv contains the reference, RCP scenario, reference period, projected period and corresponding temperature scenarios. Furthermore, it contains region, elevation and relative decreases with corresponding seasonal periods, and the number of days with a certain amount of snow during the reference period and the projected period, if literature reported these values.
The folder projections contains mean snow depths, 5th and 95th percentile snow depth for each day of the year for the reference period and a temperature scenario of +2°C for all four stations Engelberg, Maloja, Saanenmöser and Weissfluhjoch.

en: future snow-depth snow-climatology alps climate-change

en: Literature based approach to estimate future snow

envi

en: Environment

alps

display_name: alps

climate-change

display_name: climate-change

future

display_name: future

snow-climatology

display_name: snow-climatology


en:
This dataset forms the base of the paper 'Contrasting canopy air temperatures across complex terrain during the snow season' submitted to JGR Atmospheres. It aims to ensure full reproducibility of the figures by providing all relevant scripts (MATLAB R2024a), functions, processed data, and primary data. The primary data consists of three January-April periods in two Alpine valleys (Flüela, 2023; Monstein; 2024, 2025) of:
i) Ventilated canopy air temperatures and humidity (60s intervals) measured at 2.5 m above ground by automatic weather stations (AWS) at opposing north- and south-facing slopes.
ii) Concurrent AWS observations (15s intervals) at a non-forested site in the Monstein valley (2024, 2025) of incoming LW, SW at approximately 0.5 m above ground. At approximately 2.5 m above ground, RH (ventilated), air temperature (ventilated), wind speed, and wind direction were measured.
iii) Passive-ventilated HOBO loggers measuring air temperature at 0.5 m, 1.0 m, 2.5 m, 4.5 m above ground along vertical profiles installed on stems at 30-minute (2023, 2024) or 15-minute (2025) intervals at multiple sites across at opposing north- and south-facing slopes.
iv) In addition, information on when snowfall occurred and when snow cover was present below the canopy or within the canopy is available for the 2023 and 2024 observation periods.
Full details are available in the corresponding publication (citation reference to be found under 'Related Research' once accepted).

en: canopy-cover snow forest-canopy forest-snow subalpine

en: Canopy air temperatures in subalpine forests during three snow seasons

canopy-cover

display_name: canopy-cover

forest-canopy

display_name: forest-canopy

forest-snow

display_name: forest-snow

snow

display_name: snow

subalpine

display_name: subalpine


en:
This repository contains all WRF model outputs and observational data sets used for the paper:
Georgakaki, P., Sotiropoulou, G., Vignon, É., Billault-Roux, A.-C., Berne, A., and Nenes, A.: Secondary ice production processes in wintertime alpine mixed-phase clouds, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-760, in review, 2021.

en: ice-water-content liquid-water-content blowing-snow wrf secondary-ice-production swiss-alps ice-crystal-number-concentration jungfraujoch

en: Data for the publication "Secondary ice production processes in wintertime alpine mixed-phase clouds"

de:
Resource Title: WRF outputs
    Size: 741751014 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/a014f39f-34c3-499f-9627-0efde504d2e1/resource/45b96d72-d1ef-4208-8b22-6c6ab546208b/download/wrf-outputs.zip
    Description: The .zip file contains all the NetCDF outputs produced by the mesoscale Weather Research and Forecasting (WRF) model (version: 4.0.1). Two case studies were simulated in this study, between 25-28 January 2014 (00:00 UTC) and 29 January - 1 February 2014 (00:00 UTC), respectively. The former is denoted as "NW" and the latter as "SE", owing to the prevailing wind conditions at the Jungfraujoch (JFJ) station.

The outputs come from the innermost domain of the simulations with a spatial resolution of 1 km and are extracted from the nearest to JFJ model grid point (46.55°N 7.98°E). The dimensions of each model variable is [145,100], which corresponds to time and height, respectively. The frequency of the outputs is 30 min. Note that the first 24 hours of each simulation (i.e., the first 48 outputs) were considered spin up time.

The following sets of sensitivity simulations were presented in this study:

* CNTRL_NW.nc, CNTRL_SE.nc : Sensitivity simulations performed with the default version of WRF
* DS_PHILL_NW.nc, DS_PHILL_SE.nc : Sensitivity simulations including a description of the droplet shattering process following Phillips et al. (2018)
* DS_SULL_NW.nc, DS_SULL_SE.nc : Sensitivity simulations including a description of the droplet shattering process following Sullivan et al. (2018)
* PHILL_NW.nc, PHILL_SE.nc : Sensitivity simulations including a description of the ice-ice collisional break-up process following Phillips et al. (2017)
* TAKAH_NW.nc, TAKAH_SE.nc : Sensitivity simulations including a description of the ice-ice collisional break-up process following Sullivan et al. (2018) based on Takahashi et al. (1995)
* TAKAHrim_NW.nc, TAKAHrim_SE.nc : Same set-up as before but applied only to collisions between rimed ice particles
* BIPS_Xk0_DY_NW.nc, BIPS_Xk0_DY_SE.nc : Sensitivity simulations including a source of Xk0 (either 10.0 or 100.0 L⁻¹) ice crystals with sizes of DY (either 10 or 100 μm) in the first atmospheric level of the model
* BIPS100_PHILL_NW.nc, BIPS100_PHILL_SE.nc : Sensitivity simulations where the combined effect of blowing snow and ice-ice collisional break-up is examined

de:
Resource Title: Observational data
    Size: 4448 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/a014f39f-34c3-499f-9627-0efde504d2e1/resource/4ea9d0c6-106e-424a-9d61-9fc1dc99f576/download/observations.zip
    Description: The .zip file contains 6 .csv files including all microphysical measurements taken at the Jungfraujoch station during the two periods of interest:

* 25-28 January 2014 : northwesterly-winds (NW)
* 29 January - 1 February 2014 : southeasterly-winds (SE)

The total Ice Crystal Number Concentration (ICNC; reported in L⁻¹) is measured by the two-dimensional stereo hydrometeor spectrometer (2D-S; Lawson et al., 2006) over the size range 10-1280 μm. The total ice water content (IWC; reported in gm⁻³) is also derived by the 2D-S data using the Brown and Francis (1995) mass-diameter relationship. Finally, the quantification of the liquid water content (LWC; reported in gm⁻³) is based on the liquid droplet size distribution data derived from a DMT cloud droplet probe (CDP; Lance et al., 2010) over the size range 2-50 μm. The frequency of the measurements in the .csv files is 30 min, to match the output frequency of the model. Datetime is also reported in the files (DD/MM/YYYY h:mm) in UTC.

__Acknowledgment__: Collection and pre-processing of the microphysical measurements has been performed by Gary Lloyd (gary.lloyd@manchester.ac.uk). More details about the instrumentation during the CLACE2014 campaign can be found in Lloyd et al. (2015).

blowing-snow

display_name: blowing-snow

ice-crystal-number-concentration

display_name: ice-crystal-number-concentration

ice-water-content

display_name: ice-water-content

jungfraujoch

display_name: jungfraujoch

liquid-water-content

display_name: liquid-water-content

secondary-ice-production

display_name: secondary-ice-production

swiss-alps

display_name: swiss-alps

wrf

display_name: wrf


en:
Data associated to the publication "Environmental DNA sequences from aquatic insects indicate freshwater ecosystem state"
 Authors:
Flurin Leugger, Martina Lüthi, Meret Jucker, Virginie Marques, Sarah Thurnheer, Zacharias Kontarakis and Loïc Pellissier
 Contact:
flurinleugger@gmail.com
loic.pellissier@usys.ethz.ch
 Project description:
In this project, we aimed to compare kick-sampling-derived taxa lists and ecosystem state classifications (IBCH: Indece biologique Suisse) with eDNA metabarcoding and CRISPR-Dx. Additionally, we calculated the association with environmental variables and ecosystem state.
We retrieved the kick-sampling-derived data from Haberthür et al. (2021) and Keck et al. (2023). We collected eDNA samples in 36 sites in the Rhine catchment in Switzerland (lowland) which are used for the national monitoring of freshwater in Summer 2024. We extracted the samples in the clean lab at ETH using the protocol described in Leugger et al. (2025). We used the 16S Ins primer (Elbrecht et al. 2016) for metabarcoding and for CRISPR analysis. To select indicator eDNA metabarcoding sequences, we applied a machine learning approach combining LASSO and rpart decision trees. For CRISPR-Dx, we used the rules of Leski et al. (2023) to in silicon predict detections based on the metabarcoding sequences and used the same machine learning approach as for the indicator metabarcoding eDNA sequences (see publication for more details). The data for the environmental variables is from the SWECO25 database (https://zenodo.org/communities/sweco25/records?q=&l=list&p=1&s=10&sort=newest; Külling et al. 2024).
 Folders:
 catchments\:
Catchments in Switzerland, downloaded from https://data.geo.admin.ch/browser/index.html/collections/ch.bafu.wasser-einzugsgebietsgliederung/items/wasser-einzugsgebietsgliederung?.language=en&.asset=asset-wasser-einzugsgebietsgliederung_2056-gpkg-zip
The subfolder upstream_catchments includes the catchment for each buffer size tested.
comparison_classification_methods\:
File with the ecosystem state classification based on kick-sampling, indicator eDNA metabarcoding sequences (using the rpart decision tree) and CRISPR-Dx (using predicted, lab and retrained rpart trees).
 eDNA\:
 Folder metabarcoding\:
Raw and cleaned metabarcoding data (ASVs) per environmental extract (EVE), including taxa information from Ecotag reference data base. Additionally, the rpart trees for indicator sequences only from Ephemeroptera and all EPT are provided.
Columns which might not be self explanatory
taxon: identified taxon
rank: rank of identified taxon
nb_reads: read numbers
EVS and metabarcoding: key columns to link with metadata files
ASV_cleaned_by_site.csv: Read count by site (after cleaning)
ASV_raw_by_site.csv: Read count by site (before cleaning), column 'NC' is for the negative controls
sequence_count_per_site.csv: Comparison of reads per site before and after cleaning/filtering. 'NC' refers again to Negative Controls.
 Folder CRISPR\:
crispr_by_site.csv: Lab-based detection of the guide per site. Maximum number of detections is 6, as each site had two environmental extracts (EVE), and the PCR replicates were combined in 3 pools each. GRN refers to lab internal numbering of CRISPR-Dx assays.
detection_matrix_Ephemeroptera_28_site.csv: Predicted detection based on eDNA metabarcoding sequences and the model of Leski et al 2023 per site.
rpart_model_Ephemeroptera.rds: best performing rpart model for ecosystem state classification using the predicted CRISPR-Dx detections
rpart_model_retrained.rds: best performing rpart model for ecosystem state classification using the actual lab-based CRISPR-Dx detections (thus "retrained" based on actual detections).
 IBCH\:
IBCH_classification_filtered_and_extended.csv: includes IBCH_cat (ecosystem category from kick-sampling with IBCH 2019 categorization).
IBCH_taxa_kick-sampling.csv: contains taxa detect per site (site_code as column name to link with metadata or eDNA data)
kicknet_Trend_IBCH_sites.csv: taxa detected by site, raw data
 metadata\:
Folder containing files with overview of site names, extract number and coordinates. Columns named "metabarcoding" (and "EVS") are keys to link rows from the different tables.
 SWECO25\:
Folder containing raster stack from SWECO25 data (Külling et al. 2024) used in the article and a table with the extracted variable value per site/catchment with the different upstream buffer sizes tested.
site_code: Code for the sampling site, see also metadata to link to EVS
Temperature_avg: annual mean temperature in upstream catchment [°C]
Precip_annual: annual precipitation [mm]
Population_density: mean population density in 25m cells
Traffic_noise: noise levels
EVI, NDVI and LAI: each standard deviation (sd) and average (avg) value per upstream catchment
Natural rivers: proportion of upstream river length which is classified as natural
Little-disturbed_rivers: proportion of upstream river length which is classified as little disturbed
Heavily-disturbed_rivers: proportion of upstream river length which is classified as heavily disturbed
unnatural_rivers: proportion of upstream river length which is classified as unnatural
culverted_rivers: proportion of upstream river length which is culverted
Forest_edges: proportion of forest edges in upstream catchment
Forest: proportion of forest in upstream catchment
Crops: proportion of crops in upstream catchment
Built_environment: proportion of built environment in upstream catchment
buffer: buffer width for upstream catchment area [m]
catchment_area_m2: upstream catchment area in square meters
 References:
Elbrecht, V., Taberlet, P., Dejean, T., Valentini, A., Usseglio-Polatera, P., Beisel, J. N., Coissac, E., Boyer, F., & Leese, F. (2016). Testing the potential of a ribosomal 16S marker for DNA metabarcoding of insects. PeerJ, 2016(4). https://doi.org/10.7717/peerj.1966
Haberthür, M. (2021). Ergebnisse der 3. Erhebung NAWA-Trend Los 2, Makrozoobenthos. https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://www.bafu.admin.ch/dam/bafu/de/dokumente/wasser/externe-studien-berichte/nawa_trend_biologie_2019-teil_makrozoobenthos.pdf.download.pdf/nawa_trend_biologie_2019-teil_makrozoobenthos.pdf&ved=2ahUKEwjI5crG-IaPAxUF_gIHHQisBZEQFnoECBgQAQ&usg=AOvVaw1QL3GRZdTllPsHLQ6hQtlP
Külling, N., Adde, A., Fopp, F., Schweiger, A. K., Broennimann, O., Rey, P. L., Giuliani, G., Goicolea, T., Petitpierre, B., Zimmermann, N. E., Pellissier, L., Altermatt, F., Lehmann, A., & Guisan, A. (2024). SWECO25: a cross-thematic raster database for ecological research in Switzerland. Scientific Data, 11(1). https://doi.org/10.1038/s41597-023-02899-1
Leski, T. A., Spangler, J. R., Wang, Z., Schultzhaus, Z., Taitt, C. R., Dean, S. N., & Stenger, D. A. (2023). Machine learning for design of degenerate Cas13a crRNAs using lassa virus as a model of highly variable RNA target. Scientific Reports, 13(1), 6506. https://doi.org/10.1038/s41598-023-33494-4
Leugger, F., Lüthi, M., Schmidlin, M., Kontarakis, Z., & Pellissier, L. (2025). Rapid field-based detection of a threatened and elusive species with environmental DNA and CRISPR-Dx. Global Ecology and Conservation, 59, e03518. https://doi.org/10.1016/j.gecco.2025.e03518

en: landuse conservation biodiversity-monitoring river-restoration biomonitoring edna aquatic-invertebrates dna-metabarcoding crispr-dx

en: Assessing ecosystem state with environmental DNA of aquatic insects

de:
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    Description:

de:
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de:
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    Size: 1165 bytes
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    Description:

de:
Resource Title: eDNA.zip
    Size: 885782 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/3c77b20b-7b1f-4ccc-9ece-aca9b4d680b8/resource/d1e0c8df-b744-44f4-9721-e95e144bd7cc/download/edna.zip
    Description:

aquatic-invertebrates

display_name: aquatic-invertebrates

biodiversity-monitoring

display_name: biodiversity-monitoring

biomonitoring

display_name: biomonitoring

conservation

display_name: conservation

crispr-dx

display_name: crispr-dx

dna-metabarcoding

display_name: dna-metabarcoding

edna

display_name: edna

landuse

display_name: landuse

river-restoration

display_name: river-restoration


en:
The SwissRad10 dataset contains light availability maps at 10 m and hourly resolution across an entire annual cycle with wall-to-wall coverage across the whole of Switzerland.
Areas below and within 25 m of forests include light availability calculated for both leaf-on and leaf-off canopy structure, and terrain only data is available across the full domain.
The variables sky-view factor and direct-beam transmissivity are both included, allowing estimation of both direct and diffuse light at the ground surface.
The dataset was calculated using the detailed synthetic hemispheric image-based ray-tracing Canopy Radiation Model (CanRad) with the latest airborne lidar and terrain surface data from the swissSURFACE3D dataset.

en: terrain solar-energy light-availability shortwave-radiation transmissivity lidar solar-irradiance radiation-transfer sky-view-fraction forests

envidat

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.

de: EnviDat: das Umwelt-Datenportal

en: SwissRad10 Hourly light availability maps at 10 m resolution over Switzerland

envi

de: Umwelt

forests

display_name: forests

lidar

display_name: lidar

light-availability

display_name: light-availability

radiation-transfer

display_name: radiation-transfer

shortwave-radiation

display_name: shortwave-radiation

sky-view-fraction

display_name: sky-view-fraction

solar-energy

display_name: solar-energy

solar-irradiance

display_name: solar-irradiance

terrain

display_name: terrain

transmissivity

display_name: transmissivity


en: Countrywide vegetation height models (VHM) were generated for Armenia based on Copernicus Sentinel-2 imagery within the framework of the FORACCA project. “The Forest Restoration and Climate Change in Armenia” (FORACCA) project is funded by the Swiss Agency for Development and Cooperation (SDC). The project is implemented by the Swiss Federal Institute for Forest, Snow, and Landscape Research (WSL) in collaboration with Zoï Environment Network as well as the Forest Alliance of Armenia. A Convolutional Neural Network (CNN) model was trained in Switzerland to estimate the maximum vegetation height at the spatial resolution of the Sentinel-2 pixel of 10 m. Vegetation heights from the spatially higher-resolved VHM Lidar NFI were used as reference data for the CNN training. Then, the model was spatially transferred and VHMs for Armenia were generated annually based on available Sentinel-2 imagery from May – September of the respective year. Further details on the dataset and model to create the Sentinel-2 VHMs can be found in the paper Jiang et al. (2023, https://doi.org/10.1016/j.srs.2023.100099). Contains modified Copernicus Sentinel data.

en: remote-sensing forest armenia sentinel-2 cnn foracca vegetation-height

envidat

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

en: EnviDat: the Environmental Data Portal

de: EnviDat

en: Sentinel-2 Vegetation Height Model Armenia

de:
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de:
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de:
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de:
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de:
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de:
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    Description:

de:
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    Format: image/tiff
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    Description:

de:
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    Description:

de:
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    Description:

armenia

display_name: armenia

cnn

display_name: cnn

foracca

display_name: foracca

forest

display_name: forest

remote-sensing

display_name: remote-sensing

vegetation-height

display_name: vegetation-height


en:
The available datasets are snow depth maps with a spatial resolution of 0.5 m derived from images of the survey camera Vexcel Ultracam mounted on a piloted airplane. Image acquisition was carried out during the approximately peak of winter (time when the thickest snowpack is expected) in spring. The snow depth maps are calculated by the subtraction of a summer-DTM from the processed winter- DSM of the corresponding date. The summer-DTM used was derived from a point cloud of an airborne laser scanner from 2020.
Due to the occurrence of inaccuracies of the calculated snow depth values caused by the photogrammetric method, we applied different masks to significantly increase the reliability of the snow depth maps. We masked out settled areas, high-frequented streets and technical constructions, pixels with high vegetation (height - 0.5 m) , outliers and unrealistic snow depth values. In addition, we modified the snow depth values of snow-free pixels to 0.
The information on buildings and infrastructure comes from the exactly classified ALS point cloud and the TLM dataset from Swisstopo (https://www.swisstopo.admin.ch/de/geodata/landscape/tlm3d.htmllinks). High vegetation is also derived from the classification and the calculated object height from the point cloud.
Outliers and unrealistic snow depth values are defined as negative snow depth values and snow depths exceeding 10 m. The classification of each pixel of the corresponding orthophoto into snow-covered or snow-free is based on the application of a threshold of the NDSI or manually determined ratios of the RGB values.
An extensive accuracy assessment proves the high accuracy of the snow depth maps with a root mean square error of 0.25 m for the year 2017 and 0.15 m for the following snow depth maps.
The work is published in:

en: avalanches photogrammetry davos snow-hydrology dischma snow-depth-distribution

en: Snow depth mapping by airplane photogrammetry (2017 -  ongoing)

envi

en: Environment

avalanches

display_name: avalanches

davos

display_name: davos

dischma

display_name: dischma

photogrammetry

display_name: photogrammetry

snow-depth-distribution

display_name: snow-depth-distribution

snow-hydrology

display_name: snow-hydrology