maintainer: Sofia Pagliarin

en: milan soil-sealing urban-atlas barcelona imperviousness copernicus

en: Shareable project in Esri ArcGIS format including the HRL IMD data in raster format for each of the territorial boundaries as specified in letter a)-h), see Dataset description

en: Information on data sources

en:
Dataset description

This dataset is a recalculation of the Copernicus 2015 high resolution layer (HRL) of imperviousness density data (IMD) at different spatial/territorial scales for the case studies of Barcelona and Milan. The selected spatial/territorial scales are the following:

 * a)	Barcelona city boundaries
 * b)	Barcelona metropolitan area, Àrea Metropolitana de Barcelona (AMB)
 * c)	Barcelona greater city (Urban Atlas)
 * d)	Barcelona functional urban area (Urban Atlas)
 * e)	Milan city boundaries
 * f)	Milan metropolitan area, Piano Intercomunale Milanese (PIM)
 * g)	Milan greater city (Urban Atlas)
 * h)	Milan functional urban area (Urban Atlas)

In each of the spatial/territorial scales listed above, the number of 20x20mt cells corresponding to each of the 101 values of imperviousness (0-100% soil sealing: 0% means fully non-sealed area; 100% means fully sealed area) is provided, as well as the converted measure into squared kilometres (km2).

 Dataset composition

The dataset is provided in .csv format and is composed of:

_IMD15_BCN_MI_Sources.csv_: Information on data sources

 _IMD15_BCN.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Barcelona:
 * a)	Barcelona city boundaries (label: bcn_city)
 * b)	Barcelona metropolitan area, Àrea metropolitana de Barcelona (AMB) (label: bcn_amb)
 * c)	Barcelona greater city (Urban Atlas) (label: bcn_grc)
 * d)	Barcelona functional urban area (Urban Atlas) (label: bcn_fua)

 _IMD15_MI.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Milan:
 * e)	Milan city boundaries (label: mi_city)
 * f)	Milan metropolitan area, Piano intercomunale milanese (PIM) (label: mi_pim)
 * g)	Milan greater city (Urban Atlas) (label: mi_grc)
 * h)	Milan functional urban area (Urban Atlas) (label: mi_fua)

_IMD15_BCN_MI.mpk_: the shareable project in Esri ArcGIS format including the HRL IMD data in raster format for each of the territorial boundaries as specified in letter a)-h).

Regarding the territorial scale as per letter f), the list of municipalities included in the Milan metropolitan area in 2016 was provided to me in 2016 from a person working at the PIM.

In the IMD15_BCN.csv and IMD15_MI.csv, the following columns are included:

 * Level: the territorial level as defined above (a)-d) for Barcelona and e)-h) for Milan);
 * Value: the 101 values of imperviousness density expressed as a percentage of soil sealing (0-100%: 0% means fully non-sealed area; 100% means fully sealed area);
 * Count: the number of 20x20mt cells corresponding to a certain percentage of soil sealing or imperviousness;
 * Km2: the conversion of the 20x20mt cells into squared kilometres (km2) to facilitate the use of the dataset.

 Further information on the Dataset

This dataset is the result of a combination between different databases of different types and that have been downloaded from different sources. Below, I describe the main steps in data management that resulted in the production of the dataset in an Esri ArcGIS (ArcMap, Version 10.7) project.

 1. The high resolution layer (HRL) of the imperviousness density data (IMD) for 2015 has been downloaded from the official website of Copernicus. At the time of producing the dataset (April/May 2021), the 2018 version of the IMD HRL database was not yet validated, so the 2015 version was chosen instead. The type of this dataset is raster.

 2. For both Barcelona and Milan, shapefiles of their administrative boundaries have been downloaded from official sources, i.e. the ISTAT (Italian National Statistical Institute) and the ICGC (Catalan Institute for Cartography and Geology). These files have been reprojected to match the IMD HRL projection, i.e. ETRS 1989 LAEA.

 3. Urban Atlas (UA) boundaries for the Greater Cities (GRC) and Functional Urban Areas (FUA) of Barcelona and Milan have been checked and reconstructed in Esri ArcGIS from the administrative boundaries files by using a Eurostat correspondence table. This is because at the time of the dataset creation (April/May 2021), the 2018 Urban Atlas shapefiles for these two cities were not fully updated or validated on the Copernicus Urban Atlas website. Therefore, I had to re-create the GRC and FUA boundaries by using the Eurostat correspondence table as an alternative (but still official) data source. The use of the Eurostat correspondence table with the codes and names of municipalities was also useful to detect discrepancies, basically stemming from changes in municipality names and codes and that created inconsistent spatial features. When detected, these discrepancies have been checked with the ISTAT and ICGC offices in charge of producing Urban Atlas data before the final GRC and FUA boundaries were defined.

Steps 2) and 3) were the most time consuming, because they required other tools to be used in Esri ArcGIS, like spatial joins and geoprocessing tools for shapefiles (in particular dissolve and area re-calculator in editing sessions) for each of the spatial/territorial scales as indicated in letters a)-h).

Once the databases for both Barcelona and Milan as described in points 2) and 3) were ready (uploaded in Esri ArcGIS, reprojected and their correctness checked), they have been ‘crossed’ (i.e. clipped) with the IMD HRL as described in point 1) and a specific raster for each territorial level has been calculated. The procedure in Esri ArcGIS was the following:

 * Clipping: Arctoolbox > Data management tools > Raster > Raster Processing > Clip. The ‘input’ file is the HRL IMD raster file as described in point 1) and the ‘output’ file is each of the spatial/territorial files. The option "Use Input Features for Clipping Geometry (optional)” was selected for each of the clipping.
 * Delete and create raster attribute table: Once the clipping has been done, the raster has to be recalculated first through Arctoolbox > Data management tools > Raster > Raster properties > Delete Raster Attribute Table and then through Arctoolbox > Data management tools > Raster > Raster properties > Build Raster Attribute Table; the "overwrite" option has been selected.

 Other tools used for the raster files in Esri ArcGIS have been the spatial analyst tools (in particular, Zonal > Zonal Statistics). As an additional check, the colour scheme of each of the newly created raster for each of the spatial/territorial attributes as per letters a)-h) above has been changed to check the consistency of its overlay with the original HRL IMD file. However, a perfect match between the shapefiles as per letters a)-h) and the raster files could not be achieved since the raster files are composed of 20x20mt cells.

The newly created attribute tables of each of the raster files have been exported and saved as .txt files. These .txt files have then been copied in the excel corresponding to the final published dataset.

barcelona

display_name: barcelona

copernicus

display_name: copernicus

imperviousness

display_name: imperviousness

milan

display_name: milan

soil-sealing

display_name: soil-sealing

urban-atlas

display_name: urban-atlas

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Soil sealing Barcelona and Milan different territorial levels


en: soil transport snow water measurements pore-water

state: active

type: dataset

media_type: application/zip

en: The Hydrus files used for the inverse fitting of the hydraulic conductivity.

format: ZIP

en: Videos of the capillary rise experiments

media_type: text/plain

en: Description of the videos

format: TXT

en: Descriptions of the nomenclature, Hydrus files, and videos.

en: This dataset consists of data related to capillary rise experiments performed with neutron radiography. There are 4 videos of capillary rise experiments as well as the files used to perform the inverse fitting with Hydrus. The videos show the upward flow of water in glass columns filled with sand and snow or sand, gravel, and snow. The videos show the 2D evolution of the unitless optical density with time. The Hydrus files were used to fit the parameter values of the Mualem-van Genuchten model. The experiments were performed at the Paul Scherrer Institute (PSI) in Villigen, Switzerland.

measurements

display_name: measurements

pore-water

display_name: pore-water

snow

display_name: snow

soil

display_name: soil

transport

display_name: transport

water

display_name: water

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

it: Istruzione, cultura e sport

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

it: EnviDat: il portale dei dati ambientali

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Capillary rise rise experiments in snow using neutron radiography


maintainer: Digital Innovation Office

de: ogd104-bfe versorgungssicherheit strom kernkraft erneuerbare-energien wasserkraft

fr: energiedashboard.ch: Producion d'électricité Swissgrid

en: energiedashboard.ch: Electricity production Swissgrid

it: energiedashboard.ch: Produzione di elettricità Swissgrid

format: CSV

fr:
En Suisse, il existe un grand nombre de centrales électriques qui produisent de l'énergie électrique (électricité). En ce qui concerne la production, on distingue les catégories suivantes : centrales nucléaires, centrales thermiques, centrales hydroélectriques fluviales, centrales hydroélectriques à accumulation, centrales éoliennes et centrales photovoltaïques. La production de ces centrales est entraînée par différents facteurs. Alors que les centrales à accumulation et à pompage-turbinage, par exemple, sont fortement guidées par le marché (en fonction des signaux de prix), les centrales nucléaires produisent de l'énergie en bande. En revanche, le photovoltaïque et l'éolien dépendent du soleil et du vent.

La production en Suisse, ajoutée aux importations et aux exportations, donne la consommation nationale, y compris la consommation des centrales de pompage-turbinage.

La tendance indique l'évolution de la production totale suisse par rapport à la moyenne des 10 derniers jours.

Disclaimer Données

Les données relatives aux mesures de la production suisse ne sont pas satisfaisantes. Alors que des données de mesure avec une résolution horaire sont disponibles pour les moyennes et grandes centrales, de telles données ne sont pas disponibles pour les plus petites installations ou ne concernent que l'injection dans le réseau au lieu de la production. De plus, toutes les données de mesure des installations décentralisées ne sont mises à disposition par les gestionnaires de réseau pour un enregistrement central dans différentes résolutions qu'avec plusieurs semaines de retard, conformément aux exigences légales en matière de promotion ou d'obligation de garantie d'origine. Il faut donc travailler avec des modèles qui prennent en compte les données météorologiques telles que la température, le rayonnement solaire et les précipitations. En outre, le comportement historique des catégories de production d'électricité est pris en compte. La production affichée dans la résolution journalière comprend à la fois des données mesurées et des données modélisées. Il peut y avoir des différences avec les statistiques fédérales de l'électricité en raison des données et des modélisations.

en:
In Switzerland, there are a large number of power plants that produce electrical energy (electricity). In terms of production, a distinction is made between the categories of nuclear power plants, thermal power plants, river hydropower, storage hydropower, wind power and photovoltaics. The production of these power plants is driven by different factors. For example, while storage and pumped storage power plants are strongly market-driven (based on price signals), nuclear power plants produce band energy. Photovoltaics and wind, on the other hand, are dependent on the sun and wind.

The production in Switzerland together with import and export results in the country's consumption including the consumption of pumped storage.

The trend indicates the development of the total Swiss production compared to the average of the last 10 days.

Disclaimer Data

The data situation regarding measurements of Swiss production is not satisfactory. While measurement data in hourly resolution are available for medium and large power plants, such data are not available for the smaller plants or only feed-in to the grid instead of production. In addition, all the measurement data from the decentralised plants are only made available by the grid operators for central recording in different resolutions with a delay of several weeks, in accordance with the legal requirements for subsidies or the guarantee of origin obligation. Thus, models have to be used which take into account meteorological data such as temperature, solar radiation and precipitation. In addition, the historical behaviour of the electricity generation categories is used. The production shown in the daily resolution includes both measured and modelled data. Due to the data situation and the modelling, there may be deviations from the electricity statistics of the federal government.

de:
In der Schweiz gibt es eine Vielzahl an Kraftwerken, die elektrische Energie (Strom) produzieren. Bei der Produktion werden die Kategorien Kernkraftwerke, thermische Kraftwerke, Flusswasserkraft, Speicherwasserkraft, Windkraft und Photovoltaik unterschieden. Die Produktion dieser Kraftwerke ist getrieben durch unterschiedliche Faktoren. Während beispielsweise Speicher- und Pumpspeicherkraftwerke stark marktgetrieben (anhand von Preissignalen) eingesetzt werden, produzieren die Kernkraftwerke Bandenergie. Photovoltaik und Wind sind hingegen abhängig von Sonne und Wind.

Die Produktion in der Schweiz zusammen mit Import und Export ergibt den Landesverbrauch inklusive des Verbrauchs der Pumpspeicher.

Der Trend gibt die Entwicklung der Schweizer Gesamtproduktion im Vergleich zum Durchschnitt der letzten 10 Tage an.

Disclaimer Daten

Die Datenlage in Bezug auf Messungen zur Schweizer Produktion ist nicht zufriedenstellend. Während für mittlere und grosse Kraftwerke Messdaten in stündlicher Auflösung verfügbar sind, sind solche Daten für die kleineren Anlagen nicht verfügbar oder nur die Einspeisung in das Netz anstelle der Produktion. Zudem werden alle Messdaten der dezentralen Anlagen nur mit mehreren Wochen Verzögerung durch die Netzbetreiber für eine zentrale Erfassung in unterschiedlichen Auflösungen bereitgestellt entsprechend den rechtlichen Anforderungen zu Förderung oder zur Herkunftsnachweispflicht. So muss mit Modellen gearbeitet werden, welche meteorologische Daten wie Temperatur, Sonneneinstrahlung, Niederschlag berücksichtigen. Zusätzlich wird das historische Verhalten der Stromerzeugungskategorien herbeigezogen. Die in der Tagesauflösung angezeigte Produktion umfasst sowohl gemessene als auch modellierte Daten. Es kann aufgrund der Datenlagen und den Modellierungen Abweichungen zur Elektrizitätsstatistik des Bundes geben.

it:
In Svizzera, ci sono un gran numero di centrali elettriche che producono energia elettrica (elettricità). In termini di produzione, si distingue tra le categorie di centrali nucleari, centrali termiche, energia idroelettrica fluviale, energia idroelettrica di accumulo, energia eolica e fotovoltaica. La produzione di queste centrali elettriche è guidata da diversi fattori. Per esempio, mentre le centrali di accumulo e di pompaggio sono fortemente orientate al mercato (basandosi sui segnali di prezzo), le centrali nucleari producono energia di banda. Il fotovoltaico e l'eolico, invece, dipendono dal sole e dal vento.

La produzione in Svizzera, insieme all'importazione e all'esportazione, si traduce nel consumo del Paese, compreso il consumo dell'accumulo con pompaggio.

Il trend indica l'andamento della produzione totale svizzera rispetto alla media degli ultimi 10 giorni.

Disclaimer Dati

La situazione dei dati relativi alle misurazioni della produzione svizzera non è soddisfacente. Mentre i dati di misurazione con risoluzione oraria sono disponibili per le centrali elettriche di medie e grandi dimensioni, tali dati non sono disponibili per le centrali più piccole o che si limitano all'immissione in rete invece che alla produzione. Inoltre, tutti i dati di misurazione degli impianti decentralizzati vengono resi disponibili dai gestori di rete solo per la registrazione centrale in diverse risoluzioni con un ritardo di diverse settimane, in conformità con i requisiti legali per le sovvenzioni o l'obbligo di garanzia di origine. Pertanto, è necessario utilizzare modelli che tengano conto dei dati meteorologici come la temperatura, la radiazione solare e le precipitazioni. Inoltre, viene utilizzato il comportamento storico delle categorie di generazione elettrica. La produzione mostrata nella risoluzione giornaliera comprende sia i dati misurati che quelli modellati. A causa della situazione dei dati e della modellazione, potrebbero esserci delle deviazioni rispetto alle statistiche sull'elettricità del governo federale.

ener

fr: Énergie

de: Energie

en: Energy

it: Energia

gove

fr: Gouvernement et secteur public

de: Regierung und öffentlicher Sektor

en: Government and public sector

it: Governo e settore pubblico

econ

fr: Économie et finances

de: Wirtschaft und Finanzen

en: Economy and finance

it: Economia e finanze

bundesamt-fur-energie-bfe

fr: Office fédéral de l'énergie OFEN

de: Bundesamt für Energie BFE

en: Swiss Federal Office of Energy SFOE

it: Ufficio federale dell'energia UFE

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).

political_level: confederation


en: zurich plant-species uetliberg flora-change occurrence

media_type: text/csv

en:
The list gives the clearly identifiable plant species from Schinz (1775). Die Reise auf den Uetliberg. Verlag des Waysenhauses, Zürich). Several species given in Schinz (1775) are not identifiable to the species level and are not included in the list. Similarly, Vicia pisiformis, Erbsenartige Wicke, given by Schinz (1775) is probably a misidentification and also not included in the list.

Latin plant species name Schinz 1775: Latin plant name (if) given by Schinz (1775); na: not available
German plant species name Schinz 1775: German plant name given by Schinz (1775)
Latin plant species name Info Flora 2024: Latin plant name according to www.infoflora.ch in 2024
German plant species name Info Flora 2024: German plant name according to www.infoflora.ch in 2024
Plant species still occurring in 2020: Plant species given in Schinz (1775) still occurring at the Uetliberg in 2020 (Zürcherische Botanische Gesellschaft. 2020. Flora des Kanton Zürich. Haupt, Bern); occurrence of Thesium alpinum, Alpen-Bergflachs, according to floristic knowledge of Rolf Holderegger and Michèle Büttner; 1: still occurring; 0: no longer occurring / extinct.

en:
The list gives the clearly identifiable plant species from Schinz (1775). Die Reise auf den Uetliberg. Verlag des Waysenhauses, Zürich). Several species given in Schinz (1775) are not identifiable to the species level and are not included in the list. Similarly, *Vicia pisiformis*, Erbsenartige Wicke, given by Schinz (1775) is probably a misidentification and also not included in the list.
Latin plant species name Schinz 1775: Latin plant name (if) given by Schinz (1775); na: not available
German plant species name Schinz 1775: German plant name given by Schinz (1775)
Latin plant species name Info Flora 2024: Latin plant name according to www.infoflora.ch in 2024
German plant species name Info Flora 2024: German plant name according to www.infoflora.ch in 2024
Plant species still occurring in 2020: Plant species given in Schinz (1775) still occurring at the Uetliberg in 2020 (Zürcherische Botanische Gesellschaft. 2020. Flora des Kanton Zürich. Haupt, Bern); occurrence of *Thesium alpinum*, Alpen-Bergflachs, according to floristic knowledge of Rolf Holderegger and Michèle Büttner; 1: still occurring; 0: no longer occurring / extinct.

flora-change

display_name: flora-change

occurrence

display_name: occurrence

plant-species

display_name: plant-species

uetliberg

display_name: uetliberg

zurich

display_name: zurich

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Plant species list from 1775 and 2020 for Uetliberg Zürich


photogrammetric-drone-data-schurlialp

maintainer: Yves Bühler

title_for_slug: photogrammetric-drone-data-schurlialp

en: drone-data orthophoto uav dsm photogrammetry wingtra

en: Data Access

en:
- 10 cm DSM
- 10 cm HS
- 3 cm orthophoto
- original RGB images
- Agisoft Metashape Processing Report

All of the products are also available as a cloud optimized geotiff.

format: SERVICE

en: The data was collected on 16.04.2021 and on 28.05.2021 with a Wingtra Gen II and a Sony RX1 II RGB sensor to obtain snow depth and distribution data. Following the data collection, the data was processed with Agisoft Metashape. A 10cm DSM, a 10cm snow depth raster, a 3mm orthophoto and the original drone images are available for download.

drone-data

display_name: drone-data

dsm

display_name: dsm

orthophoto

display_name: orthophoto

photogrammetry

display_name: photogrammetry

uav

display_name: uav

wingtra

display_name: wingtra

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

en: Photogrammetric Drone Data Schürlialp


title_for_slug: energiedashboard.ch: Tägliche Flüsse in die und aus der Schweiz (Strom)

de: ogd107-bfe stromimport stromexport versorgungssicherheit internationale-vernetzung

fr: energiedashboard.ch: Flux quotidiens d'importation et exportation (Électricité)

en: energiedashboard.ch: Daily import and export flows (Electricity)

it: energiedashboard.ch: Flussi giornalieri di import ed export (Elettricità)

fr:
Les réseaux électriques, en tant que lien entre la production et la consommation, sont d'une importance capitale pour garantir la sécurité de l'approvisionnement en électricité. Le réseau électrique de la Suisse ne s'arrête pas simplement à la frontière avec les autres pays européens, mais est étroitement relié aux réseaux électriques voisins. Cela est essentiel pour la sécurité de l'approvisionnement : Si la Suisse a besoin d'électricité supplémentaire, elle peut l'importer.

Le flux physique d'électricité ou échange d'interconnexion désigne la quantité d'énergie en gigawattheures par heure (GWh/h) qui circule sur les lignes électriques transfrontalières entre l'étranger et la Suisse.

Le chiffre relatif à l'importation / exportation est la somme des quantités d'électricité importées et exportées pendant une journée. Une valeur négative signifie que la Suisse exporte en somme de l'électricité.  La flèche de tendance indique la tendance des importations & exportations de la Suisse. Il faut distinguer 4 cas : 1. Si la Suisse importe plus d'électricité que la moyenne des 10 derniers jours, la flèche est orientée à la hausse. 2. si la Suisse importe moins que la moyenne des 10 derniers jours, elle est orientée à la baisse. 3) Dans le cas des exportations, si la Suisse exporte plus que la moyenne des 10 derniers jours, la flèche pointe vers le bas. 4. si la Suisse exporte moins que la moyenne des 10 derniers jours, la flèche pointe vers le haut.

Disclaimer Données

Les données proviennent de l'ENTSO-E. Elles sont disponibles par heure, mais pour des raisons de simplicité, elles sont totalisées sur la journée pour le tableau de bord de l'OFEN et mises à jour quotidiennement.

en:
Electricity grids are of central importance as a link between production and consumption to ensure the security of electricity supply. Switzerland's electricity grid does not simply stop at the border with the rest of Europe, but is closely connected to neighbouring electricity grids. This is central to security of supply: If Switzerland needs additional electricity, it can be imported.

The physical flow of electricity or interconnection exchange is the amount of energy in gigawatt hours per hour (GWh/h) that flows between foreign countries and Switzerland via the cross-border power lines.

The import/export figure is the sum of the imported and exported electricity quantities during one day. A negative value means that Switzerland is exporting electricity in total.  The trend arrow indicates the trend of Switzerland's import & export. A distinction is made between 4 cases: 1. if Switzerland imports more electricity overall than on average over the last 10 days, the arrow points upwards. 2. if Switzerland imports less electricity than on average over the last 10 days, the arrow points downwards. 2. if Switzerland imports less than the average over the last 10 days, the arrow points downwards. 3. in the case of exports, the arrow points downwards if Switzerland exports more than the average over the last 10 days. 4. if Switzerland exports less than average in the last 10 days, the arrow points upwards.

Disclaimer data

The data come from ENTSO-E. They are available per hour, but for simplicity they are summed up over the day for the BFE Dashboard and updated daily.

de:
Stromnetze sind als Bindeglied zwischen Produktion und Verbrauch von zentraler Bedeutung, um die Stromversorgungssicherheit zu gewährleisten. Das Stromnetz der Schweiz hört nicht einfach an der Grenze zum europäischen Ausland auf, sondern ist eng mit den benachbarten Stromnetzen verbunden. Dies ist für die Versorgungssicherheit zentral: Benötigt die Schweiz zusätzlichen Strom, kann dieser importiert werden.

Als physikalischer Stromfluss oder Verbundaustausch wird die Energiemenge in Gigawattstunden pro Stunde (GWh/h) bezeichnet, die über die grenzüberschreitenden Stromleitungen zwischen dem Ausland und der Schweiz fliesst.

Die Zahl zum Import / Export ist die Summe der importierten und exportierten Strommengen während eines Tages. Ein negativer Wert bedeutet, dass die Schweiz in der Summe Strom exportiert.  Der Trendpfeil gibt die Tendenz von Import & Export der Schweiz an. Dabei sind 4 Fälle zu unterscheiden: 1. Importiert die Schweiz insgesamt mehr Strom als durchschnittlich in den letzten 10 Tagen zeigt er nach oben. 2. Importiert die Schweiz weniger als durchschnittlich in den letzten 10 Tagen, zeigt er nach unten. 3. Für den Fall des Exportes gilt, dass der Pfeil nach unten zeigt, wenn die Schweiz mehr exportiert als durchschnittlich in den letzten 10 Tagen. 4. Wenn die Schweiz weniger exportiert als durchschnittlich in den letzten 10 Tagen, dann zeigt der Pfeil nach oben.

Disclaimer Daten

Die Daten stammen von der ENTSO-E. Sie sind pro Stunde verfügbar, werden aber der Einfachheit halber für das BFE Dashboard über den Tag aufsummiert und täglich aktualisiert.

it:
Le reti elettriche sono di importanza centrale come collegamento tra produzione e consumo, per garantire la sicurezza dell'approvvigionamento elettrico. La rete elettrica della Svizzera non si ferma semplicemente al confine con il resto dell'Europa, ma è strettamente collegata alle reti elettriche vicine. Questo è fondamentale per la sicurezza dell'approvvigionamento: Se la Svizzera ha bisogno di energia elettrica aggiuntiva, può importarla.

Il flusso fisico di energia elettrica o scambio di interconnessione è la quantità di energia in gigawattora all'ora (GWh/h) che scorre tra l'estero e la Svizzera attraverso le linee elettriche transfrontaliere.

La cifra di import/export è la somma delle quantità di energia elettrica importate ed esportate durante un giorno. Un valore negativo significa che la Svizzera sta esportando elettricità in totale.  La freccia di tendenza indica l'andamento dell'import-export della Svizzera. Si distingue tra 4 casi: 1. se la Svizzera importa complessivamente più elettricità rispetto alla media degli ultimi 10 giorni, la freccia punta verso l'alto. 2. se la Svizzera importa meno elettricità rispetto alla media degli ultimi 10 giorni, la freccia punta verso il basso. 2. Se la Svizzera importa meno della media degli ultimi 10 giorni, la freccia punta verso il basso. 3. Nel caso delle esportazioni, la freccia punta verso il basso se la Svizzera esporta più della media degli ultimi 10 giorni. 4. Se la Svizzera esporta meno della media negli ultimi 10 giorni, la freccia punta verso l'alto.

Disclaimer Dati

I dati provengono da ENTSO-E. Sono disponibili per ora, ma per semplicità vengono sommati nell'arco della giornata per il cruscotto BFE e aggiornati giornalmente.

ener

en: Energy


en: macronutrients foraging-behaviour lad-use-chages bumblebees zurich bombus-pasquorum urban basel dietary-patterns bees bombus-lapidarius bern nutrient-intake pollen-metabarcoding rural

en:
The dataset contains the fatty acid content and ratios for the two bumblebees. Measures are taken only from pollen from the leg-baskets. SampleID is not the same as in the metabarcoding, several samples from different bumblebees were merged within a Location ,Landscape and Replica. Not for all the samples there is information on both amino acids and fatty acids. The columns are:
- SampleID_chemical: The ID for chemical analyses
- Location: the three Swiss cantons studied. Levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: the two typea of human-dominated ecosystems, nested to the Swiss canton. Levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) nested with the location and the landscape. Levels: A-E.
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- LocationLandscape: combination between location and landscape
- Caprylic, Pelargonic, ..., Lacceroic: Content of individual fatty acids acids.
- sum: total sum of fatty acid content (mg)
- unsaturatedsum: total sum unsaturated fatty acids (mg)
- saturatedsum: total sum saturated fatty acids (mg)
- omega3sum: total sum Omega 3 fatty acids (mg)
- omega6sum: total sum Omega 6 fatty acids (mg)
- omega9sum: total sum Omega 9 fatty acids (mg)
- omega6to3ratio: ratio Omega 6 to Omega3 fatty acids
-saturatedtounsaturatedration: ratio between saturated and unsaturated fatty acids

en:
Processed data. The dataset contains the functional diversity metrics based on the plant composition from the leg-baskets per bumblebee individual. It contains:
- SampleID: Combining bumblebee ID, location, landscape and replica
- Location: canton, levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: landscape type, levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) within location and landscape. Levels: A-E.
- LocationLandscape: combining location and landscape
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- Fric.w: Weighted functional richness
-Fric: functional richness
-FEve.w: Weighted functional evenness
- FEve: functional evenness
- FDiv.w: weighted functional divergence
- FDiv: functional divergence

en:
Processed data. The dataset contains the plant species richness in the leg-baskets per bumblebee individual. It contains:
- SampleID: Combining bumblebee ID, location, landscape and replica
- Location: canton, levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: landscape type, levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) within location and landscape. Levels: A-E.
- LocationLandscape: combining location and landscape
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- Plant species richness: the number of plant species found in the pollen of the leg-baskets.

en:
The dataset contains the filtered plant composition1 (removing species with too low relative abundances) and the relative abundance obtained from DNA metabarcoding on pollen samples for both the leg-baskets and the body. The dataset contains the following columns:
- SampleID: Combining bumblebee ID, location, landscape and replica
- Abundance: numeric, relative abundance
- BumblebeeID: numerical ID for each bumblebee individual within a location, landscape and replicate sampling place.
- Location: canton, levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: landscape type, levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) within location and landscape. Levels: A-E.
- LocationLandscape:
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- Transportation_structure: bumblebee pollen transportation structure, levels: B = Body; L = leg-baskets (i.e. corbicula)
- Kingddom: taxonomic
- Phylum: taxonomic
- Order: taxonomic
- Family: taxonomic
- Genus: taxonomic
- Species: plant sicentific name
-Binomial_abundance: Logic, indicating if the plant species is included (1).
The raw metabarcoding data is archived in NCBI project PRJNA1137320

en:
Processed data. The dataset contains the phylogeneitc diversity metrics based on the plant composition from the leg-baskets per bumblebee individual. It contains:
- SampleID: Combining bumblebee ID, location, landscape and replica
- Location: canton, levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: landscape type, levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) within location and landscape. Levels: A-E.
- LocationLandscape: combining location and landscape
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- PVar: phylogenetic variance
- PRic: Phylogenetic richness
- PEve: Phylogenetic evenness
- PClu: Phylogenetic clustering

en:
The dataset contains the information on the 16 study sites:
- Location: the three Swiss cantons studied. Levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: the two typea of human-dominated ecosystems, nested to the Swiss canton. Levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) nested with the location and the landscape. Levels: A-E.
- Site_ID: combination of location, landscape and replicate
- X: X coordinate, WGS84
- Y: Y coordinate WGS84
- Plant S in landscape: Plant species richness within the 800 m calculated using both information from the Global Biodiversity Information. Facility (GBIF) and InfoFlora (www.infoflora.ch).
- % Buildings: the proportion of buildings and other impervious surfaces within 800 m radius.
- % Agricultural: the proportion of agricultural land and other impervious surfaces within 800 m radius.
- % Water: the proportion of water and other impervious surfaces within 800 m radius.
- % Meadows & grasslands: the proportion of meadows and grasslands and other impervious surfaces within 800 m radius.
The raw metabarcoding data is archived in NCBI project PRJNA1137320.

en:
The dataset contains the amino acid content and ratios for the two bumblebees. Measures are taken only from pollen from the leg-baskets. SampleID is not the same as in the metabarcoding, several samples from different bumblebees were merged within a Location ,Landscape and Replica. Not for all the samples there is information on both amino acids and fatty acids. The columns are:
- SampleID_chemical: The ID for chemical analyses
- Location: the three Swiss cantons studied. Levels: Bern =BE; Basel = BS; Zurich =ZH
- Landscape: the two typea of human-dominated ecosystems, nested to the Swiss canton. Levels: Urban = U; Rural = R
- Replicate: sampling place (800-m buffer) nested with the location and the landscape. Levels: A-E.
- Bumblebee_species: the two studied species, levels: B.pasquorum; B. lapidarius
- LocationLandscape: Combining location and landscape
- Asp, Thr, ..., Arg: Content of individual amino acids. Amino acids are written as abbreviated names. FUll names be checked here: https://en.wikipedia.org/wiki/Amino_acid
- sum: total sum of amino acid content (mg)
- sumessential: total sum essential amino acids (mg)
-sumnon-essential: total sum non-essential amino acids (mg)
- proportionnonessentialessential: proportion between essential and non-essential amino acids.

en:
The dataset contains the plant trait values obtined from published sources. It contains:
- Kingddom: taxonomic
- Phylum: taxonomic
- Order: taxonomic
- Family: taxonomic
- Genus: taxonomic
- Species: species name
- plant.species: full scientific name
- native_exotic: origin status. As presented in https://www.doi.org/10.16904/envidat.210
- pollination mode: vector of pollination. As presented in https://www.doi.org/10.16904/envidat.210
- Flowering_months_duration: Number of flowering months. As presented in https://www.doi.org/10.16904/envidat.210
- start_flowering: month of the starting of the flowering. As presented in https://www.doi.org/10.16904/envidat.210
- growth_form_category: plant growth form. As presented in https://www.doi.org/10.16904/envidat.210
- plant-height: plant heght in meters. As presented in https://www.doi.org/10.16904/envidat.210
- inflorescence: Logic, plant with single flowers (0) or inflorescences (1). As presented in https://www.doi.org/10.16904/envidat.210
- structural_blossom_class: depicting the accessibility of the flowers. As presented in https://www.doi.org/10.16904/envidat.210
- symmetry: floral symmetry. As presented in https://www.doi.org/10.16904/envidat.210
- nectar: logic, nectar present. As presented in https://www.doi.org/10.16904/envidat.210
- pollen: logic, pollen rewards present. As presented in https://www.doi.org/10.16904/envidat.210
- oil: logic. oil present. As presented in https://www.doi.org/10.16904/envidat.210
- sugar.concentration: the amount of sugar present in the nectar, in mg.
Additional information can be found in https://doi.org/10.1016/j.dib.2021.107243

en:
Data on the diet of urban and rural bumblebees individuals, Bombus lapidarius and Bombus pascuorum, collected in Switzerland (cantons Bern, Basel and Zürich) obtained using metabarconding and chemical analyses from two pollen transportation structures, that is, the body and the leg-baskets (i.e., corbicula). The bumblebees were collected by Egenberger et al., 2019 (https://doi.org/10.5061/dryad.qt8mr4v)
The data contains:
- The composition and relartive abundance of plant species in the body and the leg-baskets, separately.
- The content and ratios of two groups of macronutrients, that is, amino acids and fatty acids.
The dataset contains the data used in the paper "Urban bumblebees diversify their foraging strategy to maintain nutrient intake" as well as unpublished data.

basel

display_name: basel

bees

display_name: bees

bern

display_name: bern

bombus-lapidarius

display_name: bombus-lapidarius

bombus-pasquorum

display_name: bombus-pasquorum

bumblebees

display_name: bumblebees

dietary-patterns

display_name: dietary-patterns

foraging-behaviour

display_name: foraging-behaviour

lad-use-chages

display_name: lad-use-chages

macronutrients

display_name: macronutrients

nutrient-intake

display_name: nutrient-intake

pollen-metabarcoding

display_name: pollen-metabarcoding

rural

display_name: rural

urban

display_name: urban


maintainer: Nander Wever

en: snow-ice-temperature wind-speed longwave-radiation air-temperature shortwave-radiation humidity precipitation-amount wind-direction snow-depth

en: SNOWPACK ini file (old syntax)

en: This ini-file can be used to run SNOWPACK simulations with the WFJ data set, using version 3.4.0 or higher.

en: SNOWPACK sno file

en: This sno-file can be used to run SNOWPACK simulations with the WFJ data set, using version 3.4.0 or higher.

en: Version 4

en:
Deprecated version. Release notes:

Update: dataset extended to 2016-09-02T00:30

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

en: Version 5

en:
Deprecated version. Release notes:

Update: using the incoming and outgoing short- and longwave radiation data from the new processing chain from 2010 onwards, filling also data gaps in pre-2010 data.

Update: dataset extended to 2016-10-04T00:30

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

en: Version 6

en:
Deprecated version.

Release notes:

- Missing ISWR for 2009-09-30 and 2010-04-30 has been replaced by data from WFJ Gipfel

This dataset is the basis for the Weissfluhjoch dataset in ESM-SnowMIP (doi: http://dx.doi.org/10.16904/16).

en: All versions

en: Version 2

en:
Deprecated version. Release notes:

Bug fix: in v1, incoming radiation components were taken from WFJ_Gipfel station, instead of WFJ Versuchsfeld.

en: Version 7 (current)

en:
Release notes:

Bug fix: in previous versions, snow lysimeter data from September 2010 onwards was erroneously reported for UTC+2, instead of UTC+1 (i.e., reported one hour later than should have been).

Update: dataset extended to 2017-07-11T00:30.

en: Undercatch correction

media_type: application/pdf

en: This document describes how the undercatch correction for the Weissfluhjoch measurement site was determined.

format: PDF

mimetype: application/pdf

en: Version 3

en:
Deprecated version. Release notes:

Bug fix: in version 2, the undercatch correction was not applied correctly

Bug fix: in version 2, snow lysimeter data was missing for extended periods of time

Update: dataset extended to 2016-01-02T00:30

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

en: SNOWPACK ini file (new syntax)

en: This ini-file can be used to run SNOWPACK simulations with the WFJ data set, using the new MeteoIO Filter, Generator and Interpolation syntax.

en: Version 1

en:
Deprecated version. Release notes:

First release of dataset.

en:
Dataset of meteorological and snowpack measurements from the automatic weather station at Weissfluhjoch, Davos, Switzerland, suitable for driving snowpack models. The dataset contains standard meteorological measurements, and additionally snowpack runoff data from a snow lysimeter. Where possible, data is quality checked and missing data are replaced from backup sensors from the measurement site itself, or (in only a few cases) from the MeteoSwiss weather station at 470 m distance and 150 m above the measurement site.

Publication
Wever, N., Schmid, L., Heilig, A., Eisen, O., Fierz, C., and Lehning, M. Verification of the multi-layer SNOWPACK model with different water transport schemes. 2015. The Cryosphere. Volume 9. 2271-2293. http://dx.doi.org/10.5194/tc-9-2271-2015.

air-temperature

display_name: air-temperature

humidity

display_name: humidity

longwave-radiation

display_name: longwave-radiation

precipitation-amount

display_name: precipitation-amount

shortwave-radiation

display_name: shortwave-radiation

snow-depth

display_name: snow-depth

snow-ice-temperature

display_name: snow-ice-temperature

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: WFJ_MOD: Meteorological and snowpack measurements from Weissfluhjoch, Davos, Switzerland


en: beech swiss-plateau forest-in forest-reserves temperate-forests

en: Results habitat trees

en: The file results_habitat_trees.csv contains results for the number of habitat trees.

format: XLS

en: Results TreMs

en: The file results_trems.csv contains results for the density of TreMs per ha.

en: Data description

en: This document briefly describes the datasets.

en: Results lying deadwood

en: The file results_lying_deadwood.csv contains results of the lying deadwood survey.

en: Lookup

en: The file lookup.csv contains description for the values shown in the 5 result tables.

en: Results trees

en:
The file results_trees.csv contains results of living, standing and lying and dead standing
trees as well as for stumps (trees found on the plot but broken / cut below 1.3 m).

en: Results regeneration

en: The file results_regeneration.csv contains results of the regeneration survey.

en:
The Jorat is one of the largest continously forested areas in the Swiss Plateau. On a forested area of 778 ha, the Parc Naturel du Jorat (PNJ), a periurban parc, has been established in 2022. To document the initial state of the forest within the perimeter of the PNJ, a sample plot inventory (SPI) was carried out on 132 sample plots (SP) in winter 2021/22.
This dataset contains results from this sample plot inventory. It consists of the following files:
- results_trees.csv: Results for or living and dead trees.
- results_regeneration.csv: Results for trees with DBH < 7.0 cm, assessed in three height classes.
- results_lying_deadwood.csv: Results for lying deadwood, assessed no three line transects
- results_trems.csv: Results for occurence of tree related microhabitats (TreMs)
- results_habitat_trees.csv: Results for occurence / densities of trees carrying at least one TreM or with a DBH >= 80 for living trees or >= 36 cm for dead trees respectively.
- lookup.csv: Contains lookup tables which describe the respective results in-depth.
- data_description.pdf: Briefly describes the datasets mentioned above.

beech

display_name: beech

forest-in

display_name: forest-in

forest-reserves

display_name: forest-reserves

swiss-plateau

display_name: swiss-plateau

temperate-forests

display_name: temperate-forests

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: Sample plot inventory data form the Parc Naturel du Jorat


en: land-use snow clm5 forest-productivity oshd snow-simulation evapotranspiration land-cover high-resolution

en:
12 gridded CLM5 simulations over the spatial extent of Switzerland:
- 1km, 0.25deg, 0.5deg
- 3 different meteorological forcings (Clim_CRU, CLIM_CRU*, CLIM_OSHD)
- 2 different land use forcings
each with the following outputs:
- daily snow depth (file SNOW_DEPTH.nc)
- monthly summed gross primary production (file FPSN_MONTH_SUM.nc)
- monthly summed evapotranspiration (file QFLX_EVAP_TOT_MONTH_SUM.nc).
Temporal extent of all simulations: 01/01/2015 - 31/12/2020.

en: Forcing land surface datasets used for the CLM5 simulations at 1km, 0.25° and 0.5° resolution. For the 1km resolution both the standard global (LU_Gl) and the high-resolution dataset (LU_HR) are provided.

en: Point-scale CLM5 simulation output of snow depth and snow water equivalent for 9 different simulation scenarios, each performed at 36 snow station locations across Switzerland. Please have a look at table A1 in the appendix of the linked publication for coordinates of the stations. Additional information on the setup, and the differences between simulation setups can also be found in the linked publication.

en:
This dataset contains Community Land Model 5 (CLM5) simulation output over the spatial extent of Switzerland at different resolutions and based on a range of input datasets. It further contains land-use surface data used for the CLM5-simulations.

**Detailed description of the CLM5 simulation setup and the various input datasets can be found in the accompanying publication: https://doi.org/10.5194/egusphere-2023-1832.**

 CLM5 simulation output

This dataset includes gridded CLM5 simulations of snow depth, gross primary productivity (GPP) and evapotranspiration at different resolutions ( 1km, 0.25° and 0.5°) and based on a range of input datasets over the spatial extent of Switzerland (see folder *gridded_CLM5_simulations*). Additionally, point-scale CLM5 simulations of snow depth and snow-water-equivalent at 36 snow-station locations (see folder *point_scale_CLM5_simulations*) are included. Latitude, longitude and elevation for these station locations can be found in table A1 of the above-mentioned publication. All simulation output spans from 01/01/2015 - 31/12/2019.

Included CLM5 simulation results are based on 3 different meteorological forcing datasets:
* Clim_CRU: standard global dataset, we used the recent state-of-the-art standrd global dataset CRU-JRA (https://catalogue.ceda.ac.uk/uuid/aed8e269513f446fb1b5d2512bb387ad)

* Clim_CRU*: ClimCRU upraded by downscaling temperature data using a temperature lapse rate of -6.5K/1000m and a high-resolution DEM

* Clim_OSHD: highest level of detail, meteorological forcing generated according to methods developed by the Operational Snow Hydrological Service (OSHD), at 1km spatial and 1hour temporal resolution</li>

 Land-use surface data

This dataset further includes forcing land surface datasets used for the CLM5 simulations at 1km, 0.25° and 0.5° resolution (see folder *surface_landuse_datasets*). For the 1km resolution both the standard global (LU_Gl) and the high-resolution dataset (LU_HR), which includes a higher level of detail and is based on a more up-to-date land use datase, are provided. More details on these two datasets can be found in the above-mentioned publication.

evapotranspiration

display_name: evapotranspiration

forest-productivity

display_name: forest-productivity

high-resolution

display_name: high-resolution

land-cover

display_name: land-cover

land-use

display_name: land-use

oshd

display_name: oshd

snow

display_name: snow

snow-simulation

display_name: snow-simulation

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Multi-resolution CLM5 simulations across Switzerland


maintainer: Franziska Gerber

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

en: Static data for WRF - Soiltype bottom

en: Static data for WRF - Topography

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

en: Static data for WRF - Soiltype top

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

en: Documentation

en: Documentation of the dataset

en: Static data for WRF - Landuse

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

cosmo-wrf

display_name: cosmo-wrf

cryowrf

display_name: cryowrf

landuse

display_name: landuse

static-data

display_name: static-data

topography

display_name: topography

wrf

display_name: wrf

en: High resolution static data for WRF over Switzerland


maintainer: Bundesamt für Energie BFE

de: ogd103-bfe versorgungssicherheit landesverbrauch-strom endverbrauch-strom

fr: energiedashboard.ch: Consommation en électricité Swissgrid

en: energiedashboard.ch: Electricity consumption Swissgrid

it: energiedashboard.ch: Consumo di elettricità Swissgrid

fr: Consommation estimée modélisée

de: Modellbasiert geschätzter Verbrauch

en: Model-based estimated consumption

it: Consumo stimato modellizzato

fr: Les données actuelles ne sont pas disponibles, c'est pourquoi un modèle doit être utilisé pour estimer la consommation sur la base du passé.

en: Real-time data is not available, so a model must be used to estimate consumption based on the past.

de: Zeitnahe Daten sind nicht verfügbar, weshalb mit einem Modell der Verbrauch auf Basis der Vergangenheit geschätzt werden muss.

it: I dati in tempo reale non sono disponibili, quindi è necessario utilizzare un modello per stimare il consumo in base al passato.

fr: Consommation nationale et finale

de: Landesverbrauch und Endverbrauch

en: Nation and final consumtion

it: Consumo nazionale e finale

fr:
La consommation nationale, y compris la consommation des pompes de stockage, est un indicateur important en ce qui concerne l'approvisionnement en électricité. L'évolution est d'abord présentée sur la base des données communiquées à Swissgrid par les gestionnaires de réseau de distribution. Ces données peuvent être incomplètes ou erronées et sont corrigées ultérieurement.

La consommation finale indique la quantité d'électricité consommée par les ménages, le commerce, l'agriculture, les services, l'industrie et les transports. C'est le paramètre le plus important pour les efforts d'économie des Suisses. L'évolution est d'abord présentée sur la base des données communiquées à Swissgrid par les gestionnaires de réseau de distribution. Ces données peuvent être incomplètes ou erronées et sont corrigées.

en:
National consumption, including the consumption of storage pumps, is an important indicator in relation to the electricity supply. The trend is initially presented on the basis of data reported to Swissgrid by distribution grid operators. This data may be incomplete or incorrect and is subsequently corrected.

Final consumption indicates how much electricity is consumed by households, commerce, agriculture, services, industry and transport. It is the decisive factor when it comes to the Swiss' efforts to save energy. The trend is initially presented on the basis of data reported to Swissgrid by distribution grid operators. These data may be incomplete or incorrect and are corrected.

de:
Der Landesverbrauch inklusive des Verbrauchs der Speicherpumpen ist ein wichtiger Indikator in Bezug auf die Stromversorgung. Der Verlauf wird zunächst auf Basis von durch Verteilnetzbetreiber an Swissgrid gemeldeten Daten dargestellt. Diese Daten können unvollständig oder fehlerhaft sein und werden nachkorrigiert.

Der Endverbrauch gibt an, wieviel Strom durch Haushalte, Gewerbe, Landwirtschaft, Dienstleistungen, Industrie und Verkehr verbraucht wird. Wenn es um Sparbemühungen der Schweizer geht ist er die massgebliche Grösse. Der Verlauf wird zunächst auf Basis von durch Verteilnetzbetreiber an Swissgrid gemeldeten Daten dargestellt. Diese Daten können unvollständig oder fehlerhaft sein und werden nachkorrigiert.

it:
Il consumo nazionale, compreso il consumo delle pompe di accumulo, è un indicatore importante in relazione alla fornitura di elettricità. Il trend viene inizialmente presentato sulla base dei dati riferiti a Swissgrid dai gestori delle reti di distribuzione. Questi dati possono essere incompleti o errati e vengono successivamente corretti.

Il consumo finale indica la quantità di elettricità consumata dalle famiglie, dal commercio, dall'agricoltura, dai servizi, dall'industria e dai trasporti. È il fattore decisivo quando si tratta degli sforzi degli svizzeri per risparmiare energia. Il trend viene inizialmente presentato sulla base dei dati riferiti a Swissgrid dai gestori delle reti di distribuzione. Questi dati possono essere incompleti o errati e vengono corretti.

fr:
La consommation nationale de la Suisse, y compris la consommation des pompes d'accumulation des centrales de pompage-turbinage, est un indicateur important en ce qui concerne l'approvisionnement en électricité. Il est important de différencier les chiffres de la consommation. La consommation nationale comprend la consommation totale des ménages, de l'artisanat, de l'agriculture, des services, de l'industrie et des transports (ce que l'on appelle la consommation finale), plus les pertes de transport et de distribution (pertes de réseau). La consommation actuellement affichée comprend, outre la consommation nationale, la consommation d'électricité pour le fonctionnement des pompes de stockage.

La tendance indique l'évolution de la consommation nationale, y compris la consommation des pompes de stockage, par rapport à la consommation moyenne des 10 derniers jours. La valeur prévisionnelle indique la consommation nationale, y compris la consommation des pompes de stockage, estimée sur la base d'un modèle dans 5 jours.

Consommation nationale, y compris la consommation des pompes de stockage, sur les dernières semaines

La consommation d'électricité dépend également de l'utilisation des pompes de stockage. Plus les chiffres sont éloignés dans le temps, plus ils sont robustes. Les données proviennent des gestionnaires de réseau de distribution, qui les communiquent chaque mois à Swissgrid. Ces données peuvent être incomplètes ou erronées et sont corrigées ultérieurement. Ces données de Swissgrid remontent au mieux à 1 mois. Les données de consommation mesurées en temps réel ne sont pas disponibles. Pour déterminer la consommation nationale actuelle, y compris la consommation des pompes de stockage, des modèles sont utilisés. Ces modèles ne tiennent pas compte de l'évolution des températures en Suisse, mais des changements de comportement de la population, par exemple des réactions aux appels à l'économie d'énergie. De tels changements de comportement ne peuvent être détectés que par des données de consommation mesurées.

Consommation finale actuelle sur les dernières semaines

La consommation finale est l'énergie électrique consommée par l'économie, les transports et les ménages privés. Les éventuels efforts d'économie d'électricité peuvent certes être interprétés à partir de ce graphique, mais doivent être considérés comme purement indicatifs en raison de la disponibilité des données. Le graphique se base avant tout sur les données communiquées par le passé à Swissgrid par les gestionnaires de réseau de distribution et corrigées ultérieurement. En raison des incertitudes concernant les données relatives à la consommation finale, l'OFEN a spécialement fait développer un modèle de consommation finale à l'aide des méthodes de science des données les plus récentes, telles que l'apprentissage automatique.

Graphique d'expert comparant les sources de données relatives à la consommation nationale, y compris la consommation des pompes de stockage.

Les analyses montrent qu'il existe différentes différences entre les principales sources de données relatives à la consommation. Ces différences sont illustrées afin de permettre une évaluation des informations qui ont circulé jusqu'à présent. Ces différences s'expliquent par différentes raisons, telles que des approches méthodologiques et des délimitations différentes. Mais les différences sont également dues à des corrections et à des informations complémentaires fournies par le secteur.  Pour la comparaison sur une base mensuelle, les statistiques de l'OFEN sont utilisées comme base et critère de mesure.

Disclaimer Données

Les données relatives à la consommation ne sont pas satisfaisantes. Différentes sources existent, avec des approches méthodologiques différentes. La source la plus fiable est la statistique de l'OFEN, qui ne fournit cependant que des valeurs sur une base mensuelle et qui est actuellement publiée avec un retard d'environ 2 mois.  Il existe également des données de Swissgrid, basées sur les déclarations des gestionnaires de réseau de distribution. Ces données basées sur des valeurs mesurées sont entachées d'incertitudes, car les gestionnaires de réseau de distribution font régulièrement des déclarations erronées, des déclarations complémentaires et des corrections, parfois jusqu'à 6 mois après la publication. Pour que des valeurs actuelles puissent être indiquées, il faut travailler avec des modèles. Ceux-ci tiennent compte des données du passé et notamment des statistiques de l'OFEN. Pour une estimation actuelle de la consommation finale, l'OFEN a fait développer un modèle de science des données utilisant l'apprentissage automatique moderne. Ce modèle prévoit la consommation finale, mais ne peut reproduire que partiellement les changements de comportement importants au sein de la population. L'OFEN travaille actuellement sur de meilleures approches avec le secteur de l'électricité.

en:
The national consumption of Switzerland, including the consumption of the storage pumps of the pumped storage power plants, is an important indicator with regard to the electricity supply. When it comes to consumption, it is essential to differentiate between various figures. National consumption includes the total consumption of households, commerce, agriculture, services, industry and transport (the so-called final consumption) plus transmission and distribution losses (grid losses). In addition to the national consumption, the currently reported consumption also includes the electricity consumption for the operation of the storage pumps.

The trend shows the development of the national consumption including the consumption of the storage pumps compared to the average consumption of the last 10 days. The forecast value indicates the model-based estimated country consumption including the consumption of the storage pumps in 5 days.

Country consumption including consumption of storage pumps over the last few weeks

The electricity consumption also depends on the use of the storage pumps. The further back the figures go, the more reliable they are. The data comes from distribution grid operators, who report the data to Swissgrid on a monthly basis. This data can be incomplete or incorrect and is subsequently corrected. At best, this data from Swissgrid goes back 1 month. Timely measured consumption data are not available. Models are used to determine the current national consumption, including the consumption of the storage pumps. These models do not take into account the temperature development in Switzerland, but they do not take into account changes in the behaviour of the population, for example the reaction to calls to save energy. Such behavioural changes can only be recorded using measured consumption data.

Current final consumption over the last few weeks

The final consumption is the electrical energy consumed by the economy, transport and private households. Any efforts to save electricity can be interpreted from this graph, but must be considered purely indicative due to the data situation. The graph is based primarily on the data reported to Swissgrid in the past by distribution grid operators and subsequently corrected. Due to the uncertainties in the data on final consumption, the SFOE has had a model of final consumption specially developed using the latest data science methods such as machine learning.

Expert graph comparing data sources on national consumption including consumption of storage pumps.

Analyses show that there are various differences between the most important data sources on consumption. These differences are illustrated to enable an assessment of the information that has been circulating so far. The differences have various reasons, such as different methodological approaches and delimitations. However, differences also occur due to corrections and follow-up reports from the industry.  For comparison on a monthly basis, the SFOE statistics are used as a basis and yardstick.

Disclaimer Data

The data situation on consumption is not satisfactory. Different sources exist that follow different methodological approaches. Of these, the most reliable source is the SFOE statistics, which, however, only offer values on a monthly basis and are currently published with a delay of about 2 months.  In addition, there is data from Swissgrid, which is based on reports from the distribution grid operators. These data are based on measured values and are subject to uncertainties, as there are always false reports, subsequent reports and corrections by the distribution grid operators, sometimes up to 6 months after publication. In order to be able to provide current values, models must be used. These take into account past data and, in particular, SFOE statistics. For a current estimate of end consumption, the SFOE has had a data science model developed using modern machine learning. This model forecasts final consumption, but is only able to partially reproduce major changes in the population's behaviour. The SFOE is currently working on better approaches with the electricity industry.

de:
Der Landesverbrauch der Schweiz inklusive des Verbrauchs der Speicherumpen der Pumpspeicherkraftwerke ist ein wichtiger Indikator in Bezug auf die Stromversorgung. Beim Verbrauch gilt es unbedingt zwischen verschiedenen Zahlen zu differenzieren. Der Landesverbrauch enthält den gesamten Verbrauch von Haushalten, Gewerbe, Landwirtschaft, Dienstleistungen, Industrie und Verkehr (der sogenannte Endverbrauch) zuzüglich Übertragungs- und Verteilverluste (Netzverluste). Im aktuell ausgewiesenen Verbrauch ist neben dem Landesverbrauch auch der Stromverbrauch für den Betrieb der Speicherpumpen enthalten.

Der Trend zeigt die Entwicklung des Landesverbrauches inklusive des Verbrauches der Speicherpumpen im Vergleich zum durchschnittlichen Verbrauch der letzten 10 Tage an. Der prognostizierte Wert gibt den modellbasiert geschätzten Landesverbrauch inklusive des Verbrauches der Speicherpumpen in 5 Tagen an.

Landesverbrauch inklusive Verbrauch Speicherpumpen über die letzten Wochen

Der Stromverbrauch hängt auch vom Einsatz der Speicherpumpen ab. Je weiter die Zahlen zurückliegen, desto belastbarer sind sie. Die Daten stammen von Verteilnetzbetreibern, die die Daten monatlich an Swissgrid melden. Diese Daten können unvollständig oder fehlerhaft sein und werden nachkorrigiert. Diese Daten der Swissgrid reichen bestenfalls 1 Monat zurück. Zeitnahe gemessenen Verbrauchsdaten sind nicht verfügbar. Um den aktuellen Landesverbrauch inklusive des Verbrauchs der Speicherpumpen zu ermitteln, werden Modelle herangezogen. Diese Modelle berücksichtigen die Temperaturentwicklung in der Schweiz nicht aber Verhaltensänderungen der Bevölkerung, also beispielsweise Reaktion auf Aufrufe zum Energiesparen. Solche Verhaltensänderungen können nur über gemessene Verbrauchsdaten erfasst werden.

Aktueller Endverbrauch über die letzten Wochen

Der Endverbrauch ist die durch Wirtschaft, Verkehr und Privathaushalte verbrauchte elektrische Energie. Allfällige Anstrengungen zu Stromeinsparungen können aus dieser Grafik zwar interpretiert werden, müssen aber aufgrund der Datenlage als rein indikativ gelten. Die Grafik basiert vor allem auf den in der Vergangenheit durch Verteilnetzbetreiber an Swissgrid gemeldete und nachkorrigierte Daten. Aufgrund der Unsicherheiten in der Datenlage zum Endverbrauch hat das BFE eigens ein Modell des Endverbrauchs mit neuesten Data Science Methoden wie dem maschinellen Lernen entwickeln lassen.

Expertengrafik zum Vergleich der Datenquellen zum Landesverbrauch inklusive Verbrauch der Speicherpumpen.

Analysen zeigen, dass es verschiedene Unterschiede zwischen den wichtigsten Datenquellen zum Thema Verbrauch gibt. Diese Unterschiede werden veranschaulicht, damit eine Einschätzung der bisher kursierenden Information möglich wird. Die Unterschiede haben verschiedene Gründe, wie etwa unterschiedliche methodologische Ansätze und Abgrenzungen. Unterschiede kommen aber auch durch Korrekturen und Nachmeldungen aus der Branche zustande.  Zum Vergleich auf Monatsbasis wird die BFE Statistik als Grundlage und Messlatte herangezogen.

Disclaimer Daten

Die Datenlage zum Verbrauch ist nicht zufriedenstellend. Es existieren unterschiedliche Quellen, die unterschiedliche methodologische Ansätze verfolgen. Davon ist die belastbarste Quelle die BFE Statistik, welche jedoch nur Werte auf Monatsbasis bietet und derzeit mit Verzug von etwa 2 Monaten erscheint.  Daneben gibt es Daten der Swissgrid, die auf Meldungen der Verteilnetzbetreiber basieren. Diese Daten basieren auf Messwerten sind mit Unsicherheiten behaftet, da es immer wieder zu Fehlmeldungen, Nachmeldungen und Korrekturen seitens der Verteilnetzbetreiber kommt, teilweise bis zu 6 Monate nach der Veröffentlichung. Damit aktuelle Werte angegeben werden können muss mit Modellen gearbeitet werden. Diese berücksichtigen Daten der Vergangenheit und insbesondere der BFE Statistik. Für eine aktuelle Schätzung des Endverbrauchs hat das BFE ein Data Science Modell unter Nutzung modernen Maschinenlernens entwickeln lassen. Es prognostiziert den Endverbrauch, vermag aber grosse Verhaltensänderungen in der Bevölkerung nur ansatzweise nachzubilden. Das BFE arbeitet derzeit an besseren Ansätzen mit der Strombranche.

it:
Il consumo nazionale della Svizzera, compreso il consumo delle pompe di accumulo delle centrali elettriche con sistema di pompaggio, è un indicatore importante per quanto riguarda l'approvvigionamento elettrico. Quando si parla di consumi, è fondamentale distinguere tra varie cifre. Il consumo nazionale comprende il consumo totale delle famiglie, del commercio, dell'agricoltura, dei servizi, dell'industria e dei trasporti (il cosiddetto consumo finale) più le perdite di trasmissione e distribuzione (perdite di rete). Oltre al consumo nazionale, il consumo attualmente riportato include anche il consumo di elettricità per il funzionamento delle pompe di accumulo.

Il trend mostra l'andamento del consumo nazionale, incluso il consumo delle pompe di accumulo, rispetto al consumo medio degli ultimi 10 giorni. Il valore di previsione indica il consumo stimato del Paese basato sul modello, incluso il consumo delle pompe di accumulo in 5 giorni.

Consumo del Paese incluso il consumo delle pompe di accumulo nelle ultime settimane

Il consumo di elettricità dipende anche dall'uso delle pompe di accumulo. Più le cifre vanno indietro, più sono affidabili. I dati provengono dagli operatori delle reti di distribuzione, che li riportano a Swissgrid su base mensile. Questi dati possono essere incompleti o errati e vengono successivamente corretti. Questi dati di Swissgrid risalgono al massimo a 1 mese fa. Non sono disponibili dati di consumo misurati tempestivamente. I modelli vengono utilizzati per determinare il consumo nazionale attuale, compreso il consumo delle pompe di accumulo. Questi modelli non tengono conto dell'andamento della temperatura in Svizzera, ma non tengono conto dei cambiamenti nel comportamento della popolazione, ad esempio la reazione alle richieste di risparmio energetico. Tali cambiamenti comportamentali possono essere registrati solo utilizzando dati di consumo misurati.

Consumo finale attuale nelle ultime settimane

Il consumo finale è l'energia elettrica consumata dall'economia, dai trasporti e dalle famiglie. Qualsiasi sforzo per risparmiare energia elettrica può essere interpretato da questo grafico, ma deve essere considerato puramente indicativo a causa della situazione dei dati. Il grafico si basa principalmente sui dati riferiti a Swissgrid in passato dai gestori delle reti di distribuzione e successivamente corretti. A causa delle incertezze dei dati sul consumo finale, l'UFE ha fatto sviluppare appositamente un modello di consumo finale utilizzando i più recenti metodi di scienza dei dati come il machine learning.

Grafico di esperti che confronta le fonti di dati sul consumo nazionale, compreso il consumo di pompe di accumulo.

Le analisi mostrano che esistono varie differenze tra le fonti di dati più importanti sul consumo. Queste differenze sono illustrate per consentire una valutazione delle informazioni che sono circolate finora. Le differenze hanno varie ragioni, come approcci metodologici e delimitazioni diverse. Tuttavia, le differenze si verificano anche a causa delle correzioni e dei rapporti di follow-up del settore.  Per il confronto su base mensile, si utilizzano le statistiche dell'UFE come base e metro di paragone.

Dati disclaimer

La situazione dei dati sul consumo non è soddisfacente. Esistono diverse fonti che seguono approcci metodologici differenti. Di queste, la fonte più affidabile è la statistica dell'UFE, che però offre solo valori su base mensile e attualmente viene pubblicata con un ritardo di circa 2 mesi.  Inoltre, ci sono i dati di Swissgrid, che si basano sui rapporti degli operatori della rete di distribuzione. Questi dati si basano su valori misurati e sono soggetti a incertezze, poiché ci sono sempre segnalazioni false, segnalazioni successive e correzioni da parte degli operatori della rete di distribuzione, a volte fino a 6 mesi dopo la pubblicazione. Per poter fornire i valori attuali, è necessario utilizzare dei modelli. Questi tengono conto dei dati passati e, in particolare, delle statistiche dell'OFE. Per una stima attuale del consumo finale, l'UFE ha fatto sviluppare un modello di scienza dei dati utilizzando il moderno apprendimento automatico. Questo modello prevede il consumo finale, ma è in grado di riprodurre solo parzialmente i principali cambiamenti nel comportamento della popolazione. L'UFE sta attualmente lavorando per migliorare gli approcci con l'industria elettrica.

ener

en: Energy

gove

en: Government and public sector

econ

en: Economy and finance

bundesamt-fur-energie-bfe

en: Swiss Federal Office of Energy SFOE

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).


maintainer: EnviDat Support

en: canopy-height snow-distribution snow-depth forest-structure airborne-laser-scanning

en: Snow depth maps

en: Snow depth maps from two snow-on flights at each of the three field areas Davos (March 20th and 31st 2017), Engadien (March 20th and 31st, 2017) and Grand Mesa (February 8th and 16th, 2017)

mimetype: application/zip

en: Readme

mimetype: text/plain

en: Terrain elevation maps

en: Digital elevation model computed from snow-off flights for each of the field areas Davos, Engadine and Grand Mesa

en: Canopy height maps

en: Canopy height maps of each of the three field areas Davos, Engadine and Grand Mesa

en:
This dataset includes snow depth, canopy height and terrain elevation maps of forest stands in the Grisons (CH) and at Grand Mesa (CO,USA) derived from airborne lidar. Data were acquired i) within a pilot mission of NASA's Airborne Snow Observatory in the Swiss Alps in March 2017 and ii) during NASA’s SnowEx campaign at Grand Mesa in February 2017. Snow depth maps are available for two dates separated by approx.1 week, and include an area of ca. 0.5km2 for each of the three sites Davos, Engadine and Grand Mesa.
All data were presented and analyzed in the publication 'Revisiting Snow Cover Variability and Canopy Structure within Forest Stands: Insights from Airborne Lidar Data' (Mazzotti et al., 2019, WRR, doi: 10.1029/2019WR024898). This publication must be cited when using this dataset.

Paper Citation:
 > _Giulia Mazzotti; William Ryan Currier; Jeffrey S. Deems; Justin M. Pflug; Jessica D. Lundquist; Tobias Jonas (2019). Revisiting Snow Cover Variability and Canopy Structure Within Forest Stands: Insights From Airborne Lidar Data. Water Resources Research, 55, 6198– 6216, [doi: 10.1029/2019WR024898](https://doi.org/10.1029/2019WR024898)._

airborne-laser-scanning

display_name: airborne-laser-scanning

canopy-height

display_name: canopy-height

forest-structure

display_name: forest-structure

snow-depth

display_name: snow-depth

snow-distribution

display_name: snow-distribution

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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: ALS-based snow depth and canopy height maps from flights in 2017 (Grisons, CH and Grand Mesa, CO)


en: sea-ice tls sea-ice-elevation terrestrial-laser-scans polar

en: Link to post-processed TLS’s of Antarctic sea ice

en: Post Processed TLS's

en: Compressed dat files

en: Data processing documentation

en:
This dataset comprises of a post-processed set of terrestrial laser scans (TLS’s) of Antarctic sea ice obtained during the Sea Ice Physics and Ecosystem Experiment-2 (SIPEX-2, http://seaice.acecrc.org.au/sipex2012/) in September-November 2012. The post-processing steps include the registration of the individual scans into a single 3-dimensional point cloud, the removal of unwanted noise caused by particles in the air (i.e., snow crystals), and the final generation of surface grids based on the cleaned individual point returns. The final product includes the ‘xyz’ coordinates of the individual point measurements, and gridded surfaces covering study areas of 100m x 100 m, and at resolutions of 0.01 m, 0.1 m, 0.25 m, 0.5 m and 1 m for each of the survey dates. Additionally, subgrid statistics that include the mean elevation, standard deviation, minimum and maximum elevations, range, and number of point returns in each gridcell are generated. The final product is provided in space-delimited text files, with the surface grids provided in Digital Terrain Model (DTM) format ready for visualization in any GIS software.

How to cite:
Please also cite the original publication when using this data set.: Trujillo, E., K. Leonard, T. Maksym, and M. Lehning (2016), Changes in snow distribution and surface topography following a snowstorm on Antarctic sea ice, J. Geophys. Res. Earth Surf., 121, doi:[10.1002/2016JF003893](https://dx.doi.org/10.1002/2016JF003893).

polar

display_name: polar

sea-ice

display_name: sea-ice

sea-ice-elevation

display_name: sea-ice-elevation

terrestrial-laser-scans

display_name: terrestrial-laser-scans

tls

display_name: tls

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.

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.

en: High resolution sea ice surface topography from the SIPEX-2 expedition, East Antarctica, 2012


en: urbanization spiders species-richness-models snails insects species-abundance-distributions sdms diversity birds arthropods urban-ecology

en: contains the raw data to perform the species richness models of the rare species

en: contains the raw data to perform the species richness models of the hypercommon species

en: abundance and occurrence data of the 1446 species studied, used to investigate species-abundance distributions and species-occurrence distributions

en: contains the raw data to perform the species richness models of all species

en: contains the raw data to perform species distribution models, only for common species

en:
Richness, site occurrence and abundance data of bees, beetles, birds, hoverflies, net-wingeds, true bugs, snails, spiders, milipides, wasps collected in the city of Zurich using different sampling techniques, and the environmental variables for each sampling site.
Data are provided on request to contact person against bilateral agreement.

arthropods

display_name: arthropods

birds

display_name: birds

diversity

display_name: diversity

insects

display_name: insects

sdms

display_name: sdms

snails

display_name: snails

species-abundance-distributions

display_name: species-abundance-distributions

species-richness-models

display_name: species-richness-models

spiders

display_name: spiders

urban-ecology

display_name: urban-ecology

urbanization

display_name: urbanization

en: Ecological properties of urban ecosystems. Biodiversity dataset of Zurich


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 B_Transition_Analysis.R
Further Analysis of transition numbers

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

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

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

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

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

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

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

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

 05_Service_Data
Any service calculations will be saved in here

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

en:
Data and scripts of publication:

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

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

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

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

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

land-use

display_name: land-use

land-use-change

display_name: land-use-change

landscape-services

display_name: landscape-services

modeling

display_name: modeling

statistical-model

display_name: statistical-model

switzerland

display_name: switzerland

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Land use projections and services for Switzerland


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

en: CHELSA-BIOCLIM+ Technical Documentation

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

en: CHELSA-BIOCLIM+

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

format: GeoTIFF

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

growing-degree-days

display_name: growing-degree-days

growing-season-length

display_name: growing-season-length

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

soil-water-balance

display_name: soil-water-balance

solar-radiation

display_name: solar-radiation

temperature

display_name: temperature

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


present-weather-sensor-klosters

maintainer: Maxime Hervo

title_for_slug: present-weather-sensor-klosters

en: precipitation weather raclets sensor visibility

en: Present Weather Sensor Klosters

en: Present Weather sensor measuring visibility and weather type installed at Klosters.

en: A present weather sensor (Vaisala PWD22) was deployed in Klosters (LON: 9.880413, LAT: 46.869019) for weather observation, combining the functions of a forwardscatter visibility meter and a present weather sensor. Besides measuring ambient light, it detects the intensity as well as the amount of both liquid and solid precipitation. More information can be found in the [User's Manual](ftp://ftp.cmdl.noaa.gov/aerosol/doc/manuals/PWD22_Manual.pdf).

raclets

display_name: raclets

sensor

display_name: sensor

visibility

display_name: visibility

weather

display_name: weather


maintainer: Marco Moretti

en: urban-biodiversity sustainability remote-sensing honeybees urbanization pollination

en: Dataset 1

en: Dataset 1: Processed data for urban beekeeping 2012 & 2018

en: Dataset 2

en: Dataset 2: Processed data for modelling balance urban greenspace

en: Dataset 3

en: Dataset on the the number of beehives and apiaries on the years 2012, 2013, 2014, 2015, 2016, 2017, 2018 in several Swiss cities.

en: Not used:

en: Not used for the analyses and not link to the paper.

en: Data on: (1) (Dataset 1) spatial distribution of urban beekeeping (number of hives and number of beekeeping locations) in 14 Swiss cities (Geneva, Lausanne, Biel, Neuchatel, Basel, Zurich, Chur, Luzern, St. Gallen, Winterthur, Bern, Lugano, Bellinzona, Thun) for the period 2012-2018; (2) (Dataset 2) aggregated data to model the sustainability of urban beekeeping.

honeybees

display_name: honeybees

pollination

display_name: pollination

remote-sensing

display_name: remote-sensing

sustainability

display_name: sustainability

urban-biodiversity

display_name: urban-biodiversity

urbanization

display_name: urbanization

en: Challenging the sustainability of urban beekeeping: evidence from Swiss cities


maintainer: James W. Kirchner

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

en: Weekly stable water isotopes

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

en: 7-hourly stable water istopes

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

en: Data Set Documentation

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

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

catchment

display_name: catchment

chloride

display_name: chloride

deuterium

display_name: deuterium

plynlimon

display_name: plynlimon

stable-water-isotopes

display_name: stable-water-isotopes

streamflow

display_name: streamflow

tracer

display_name: tracer

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

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