maintainer: Bundesamt für Energie BFE

title_for_slug: Schweizerische Elektrizitätsstatistik: Schweizerische Elektrizitätsbilanz - Monatswerte

fr: ogd35-bfe electricite production importation exportation consommation

de: ogd35-bfe elektrizitat produktion import export verbrauch

en: ogd35-bfe electricity production import export

it: ogd35-bfe elettricita produzione importazione esportazione consumo

fr: Statistique suisse de l'électricité: bilan mensuel suisse de l'électricité

it: Statistica svizzera dell'elettricità: bilancio svizzero dell'elettricità - valori mensili

author: Bundesamt für Energie BFE

fr: Cette statistique recense sur une base mensuelle la production, l'importation, l'exportation et la consommation d'électricité en Suisse. Elle présente la production des centrales hydrauliques, des centrales nucléaires, des centrales thermiques classiques et renouvelables. Elle indique la consommation des installations de pompage d'accumulation et les pertes en ligne. Elle est établie dans le cadre de la Statistique de l'électricité, qui fait partie de la statistique publique de la Suisse (base légale: LSF).

de: Die Schweizerische Elektrizitätsbilanz - Monatswerte gibt Auskunft über Produktion, Import / Export und Verbrauch von Elektrizität in der Schweiz auf monatlicher Basis. Bei der Produktion werden Wasserkraftwerke, Kernkraftwerke, konventionell-thermische und erneuerbare Kraftwerke unterschieden. Der Verbrauch der Speicherpumpen und die Leitungsverluste werden ausgewiesen. Die monatliche Elektrizitätsbilanz wird im Rahmen der Elektrizitätsstatistik erstellt, welche Teil der öffentlichen Statistik der Schweiz ist (gesetzliche Grundlage: BStatG).

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: snow-mechanics x-ray-tomography microstructure slow-snow-compression-experiments ice

state: active

type: dataset

en: slow snow compression experiments

media_type: application/zip

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

format: ZIP

en: README

media_type: text/plain

en: README file gives an overview of the organization of the data

format: TXT

en:
We conducted consecutive loading-relaxation experiments at low strain rates to study the viscoplastic behavior of the intact ice matrix in snow. The experiments were conducted using a micro-compression stage within the X-ray tomography scanner in the SLF cold laboratory.

Next, to evaluate the experiments, a novel, implicit solution of a transient scalar model was developed to estimate the stress exponent and time scales in the effective creep relation (Glen's law). The result reveals that, for the first time, a transition in the exponent in Glen's law depends on geometrical grain size. A cross-over of stress exponent $n=1.9$ for fine grains to $n=4.4$ for coarse grains is interpreted as a transition from grain boundary sliding to dislocation creep.

The dataset includes compression force data from 11 experiments and corresponding 3D image data from tomography scans.

ice

display_name: ice

microstructure

display_name: microstructure

slow-snow-compression-experiments

display_name: slow-snow-compression-experiments

snow-mechanics

display_name: snow-mechanics

x-ray-tomography

display_name: x-ray-tomography

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

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: A grain-size driven transition in the deformation mechanism in slow snow compression


maintainer: Franziska Gerber

en: cryowrf aws antarctica python consistent

en: Scripts to preprocess (and download for some databases) atmospheric weather station data. Furthermore, a list of stations and a map can be created to have an overview.

format: SERVICE

en:
There are many sources providing atmospheric weather station data for the Antarctic continent. However, variable naming, timestamps and data types are highly variable between the different sources. The published python code intends to make processing of different AWS sources from Antarctica easier. For all datasets that are taken into account variables are renamed in a consistent way. Data from different sources can then be handled in one consistent python dictionary.

The following data sources are taken into account:

* AAD: Australian Antarctic Division (https://data.aad.gov.au/aws)
* ACECRC: Antarctic Climate and Ecosystems Cooperative Research Centre by the Australian Antarctic Division
* AMRC: Antarctic Meteorological Research Center (ftp://amrc.ssec.wisc.edu/pub/aws/q1h/)
* BAS: British Antarctic Survey (ftp://ftp.bas.ac.uk/src/ANTARCTIC_METEOROLOGICAL_DATA/AWS/; https://legacy.bas.ac.uk/met/READER/ANTARCTIC_METEOROLOGICAL_DATA/)
* CLIMANTARTIDE: Antarctic Meteo-Climatological Observatory by the italian National Programme of Antarctic Research (https://www.climantartide.it/dataaccess/index.php?lang=en)
* IMAU: Institute for Marine and Atmospheric research Utrecht (Lazzara et al., 2012), https://www.projects.science.uu.nl/iceclimate/aws/antarctica.ph
* JMA: Japan Meteorological Agency (https://www.data.jma.go.jp/antarctic/datareport/index-e.html)
* NOAA: National Oceanic and Atmospheric Administration (https://gml.noaa.gov/aftp/data/meteorology/in-situ/spo/)
* Other/AWS_PE: Princess Elisabeth (PE), KU Leuven, Prof. N. van Lipzig, personal communication
* Other/DDU_transect: Stations D-17 and D-47 (in transect between Dumont d’Urville and Dome C, Amory, 2020)
* PANGAEA: World Data Center (e.g. König-Langlo, 2012)

Important notes

* Information about data sources is available. Some downloading scripts are included in the provided code. However, users should make sure to comply with the data providers terms and conditions.
* Given changing download options of the differnent institutions the above links may not permanently work and data has to be retrieved by the user of this dataset.
* No quality control is applied in the provided preprocessing software - quality control is up to the user of the datasets. Some dataset are quality controlled by the owner.

Acknowledgements
--------------------------
We thank all the data providers for making the data publicly available or providing them upon request. Full acknowledgements can be found in Gerber et al., submitted.

References
---------------
Amory, C. (2020). “Drifting-snow statistics from multiple-year autonomous measurements in Adélie Land, East
Antarctica”. The Cryosphere, 1713–1725. doi: 10.5194/tc-14-1713-2020

Gerber, F., Sharma, V. and Lehning, M.: CRYOWRF - a validation and the effect of blowing snow on the Antarctic SMB, JGR - Atmospheres, submitted.

König-Langlo, G. (2012). “Continuous meteorological observations at Neumayer station (2011-01)”. Alfred Wegener
Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, PANGAEA, doi: 10.1594/PANGAEA.
775173

antarctica

display_name: antarctica

aws

display_name: aws

consistent

display_name: consistent

cryowrf

display_name: cryowrf

python

display_name: python

en: Preprocessing Antarctic Weather Station (AWS) data in python


disdrometer-data-gotschnagrat

maintainer: EnviDat Support

title_for_slug: disdrometer-data-gotschnagrat

en: disdrometer raclets hydrometeors precipitation visibility

en: Disdrometer Data Gotschnagrat

en: The zip folder contains one netCDF file per day for Gotschnagrat from February 11 to March 27 2019 with a temporal resolution of 30 seconds (time in UTC). The naming convention for the files is Gotschna_YYYYMMDD.nc and the files include the following: characteristics of hydrometeors (e.g. diameter, fall velocity, amount per diameter class,...), precipitation rate, radar reflectivity, visibility range, weather codes and instrument performance.

mimetype: application/zip

en: A laser optical disdrometer (Parsivel² , OTT Hydromet) was deployed at Gotschnagrat (LON: 9.849, LAT: 46.859) to measure hydrometeors by extinction when passing a laser beam. The instrument can classify eight different kinds of precipitation, including rain, hail, snow, drizzle, and hybrid forms. The dataset contains information on precipitation amount and type for the period of February 11 to March 27 2019 at Gotschnagrat.

disdrometer

display_name: disdrometer

hydrometeors

display_name: hydrometeors

precipitation

display_name: precipitation

raclets

display_name: raclets

visibility

display_name: visibility


en: fish-composition edna mediterraneaen teleo marseille marine

en: Data access

en: Please use cyberduck to download for the moment

en:
Description: Fish environmental DNA data set collected in 2022 in the Calanques National Park
The eDNA samples were collected in 2022 in two locations (Moyades, M−FPA and “Impérial du large”, I-LPA), during the winter (January- February), the summer (June to August)and fall (mid-September to November) seasons, with the sampling dates depending on the weather conditions. For the M−FPA, samples were collected between Moyades island and Riou island, while for the I-LPA, they were collected on the south side of the “Impérial du large” island. To account for the existing bathymetry, in the M−FPA the samples were taken at two sampling sites with depths of 20 and 40 m, while in the I-LPA they were taken at two sites with depths of 20 and 80 m.
At each site, in-situ filtration of seawater was performed using a double-head submersible pump (Subspace, Geneva, Switzerland; nominal flow of ca. 1 L/min) strapped to an underwater scooter with 2 VigiDNA 0.20 µm filtration capsules (SPYGEN, le Bourget du Lac, France), along with disposable sterile tubing. The samples were collected along two horizontal transects (up to 400 m in length) during each closed-circuit rebreather dive, enabling the filtration of a water volume of 15 L/filter per depth, as close as possible to the substrate. Two filter replicates were collected by two divers at each sampling site, except in two cases where bad weather conditions or logistical issues meant that only one replicate was sampled.
After the filtration, the remaining seawater was emptied from the capsule back on the boat and replaced by a 80 mL CL1 conservation buffer (SPYGEN, le Bourget du Lac, France). To prevent any contamination, a strict protocol was followed during the entire process, requiring disposable gloves and single-use filtration equipment. Finally, the samples were stored at room temperature. We followed a strict contamination control protocol in both field and laboratory stages. Each water sample processing included the use of disposable gloves and single-use filtration equipment to avoid any risk of contamination. Libraries were prepared with ligation using the MetaFast protocol (Fasteris).
Data content:
	* rawdata/: contains the raw reads for each individual sample. One archive contains the paired-end reads specified by the _R1 or _R2 suffix as well as individually tagged PCR replicates (if available) together with an archive containing all extraction and PCR blank samples of the library. Reads have been demultiplexed using cutadapt but not trimmed, individual demultiplexing tags and primers remain present in the sequences.
	* taxadata/: contains the table with all detected taxonomy for each sample after bioinformatic processing (see Polanco et al. 2020 for details; https://doi.org/10.1002/edn3.140) and associated field metadata.
	* metadata/: contains two metadata files, one related to the data collected in the field for each filter, and the second related to the sequencing process in the lab (including the tag sequence, library name, and marker information for each sample)

edna

display_name: edna

fish-composition

display_name: fish-composition

marine

display_name: marine

marseille

display_name: marseille

mediterraneaen

display_name: mediterraneaen

teleo

display_name: teleo

en: Environmental DNA Marine France Calanques 2022


en: switzerland environmental---history historical-carthography wetlands ecology environmental--policy

en: Wetlands 1850 - 2010 (shapefiles)

en:
 History of wetlands - GIS-Layers
 Location of wetlands based on signatures in topographic maps
* Basis 1850: original measuring table sheets Dufour created between the years 1834 and 1863.
* Basis 1900/ Basis 1950: The Siegfried maps were updated and published at regular intervals between 1870 and 1949. This long period of time made it possible to reconstruct wetlands for two periods based on the Siegfried Maps: the first edition (hereinafter referred to as "1900") and the last edition ("1950").
* Basis 2010: Map sheets from the National Map from 2006 to 2011 were used for the most recent time period of the reconstruction.
 Location of wetlands due to reconstructions
The reconstruction **must not** be interpreted in the sense of a surface-accurate separation, but shows without claim to location accuracy the approximate extent of the areas that would have corresponded to the mapping criteria for wetlands used in today's topographic maps around 1850, 1900 and 1950. The basics can be found in Stuber & Bürgi 2019, chapter 5.4.

en:
Naturally, large parts of the Swiss Plateau are characterised by wetlands
and meandering rivers. That this is no longer the case today is the result
of centuries of efforts to obtain dry land. But how did this process take
place? What were the relevant actors and what were their motivations? And
what can be said about the ecological consequences of this development?

In a research project on the history of wetlands in Switzerland since 1700,
we conducted (a) a historical analysis of the development of land use in
wetlands and the actors involved, (b) a historical-cartographic
reconstruction of wetland extent since 1850 and (c) an evaluation of
ecological effects of changes in wetlands on various organisms groups.

The series of GIS layers on wetland history stem from the second part of
the project. The area reconstruction is based on digitized and homogenized
signatures from national map series, as they have been available since
about 1850. Details about the digitalization process and the homogenization
procedures applied ("Rekonstruktionen") are included in Stuber & Bürgi
2019.

Book Citation:
 > Stuber M, Bürgi M (2019) Vom «eroberten Land» zum Renaturierungsprojekt. Geschichte der Feuchtgebiete in der Schweiz seit 1700. "Bristol Schriftenreihe", Band 59. Haupt Verlag, Bern, Stuttgart, Wien. 262 Seiten.

ecology

display_name: ecology

environmental---history

display_name: environmental---history

environmental--policy

display_name: environmental--policy

historical-carthography

display_name: historical-carthography

switzerland

display_name: switzerland

wetlands

display_name: wetlands

educ

en: Education, culture and sport

en: History of wetlands in Switzerland since 1850


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

en: Sonic temperature

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

en: Sonic wind component w

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

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

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

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

en: Sonic wind component u

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

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

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

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

en: Sonic time

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

en: Sonic wind component v

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

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

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

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

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

anemometer

display_name: anemometer

wind

display_name: wind

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

wind-velocity

display_name: wind-velocity

envidat

en: EnviDat: the Environmental Data Portal

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

en: Snow Drift Station - 3D Ultrasonic


en: water-use-strategy anisohydric water-potential root-distribution stomatal-stringency

en: The data file contains the data shown in the Figures 2-7 in Walthert et al. (2024).

format: XLS

en:
This dataset represents the data shown in the Figures 2 - 7 of Walthert et al. (2024): Coordination between degree of isohydricity and depth of root water uptake in temperate tree species. Science of the Total Environment (https://doi.org/10.1016/j.scitotenv.2024.174346). A detailed methodical description of the data can be found in the Methods section of the paper.

Abstract
In an increasingly dry environment, it is crucial to understand how tree species use soil water and cope with drought. However, there is still a knowledge gap regarding the relationships between species-specific stomatal behaviour, spatial root distribution, and root water uptake (RWU) dynamics.
Our study aimed to investigate above- and below-ground aspects of water use during soil drying periods in four temperate tree species that differ in stomatal behaviour: two isohydric tracheid-bearing conifers, Scots pine and Norway spruce, and two more anisohydric deciduous species, the diffuse-porous European beech, and the ring-porous Downy oak. From 2015 to 2020, soil-tree-atmosphere-continuum parameters were measured for each species in monospecific forests where trees had no access to groundwater. The hourly time series included data on air temperature, vapour pressure deficit, soil water potential, soil hydraulic conductivity, and RWU to a depth of 2 m. Analysis of drought responses included data on stem radius, leaf water potential, estimated osmotically active compounds, and drought damage.
Our study reveals an inherent coordination between stomatal regulation, fine root distribution and water uptake. Compared to conifers, the more anisohydric water use of oak and beech was associated with less strict stomatal closure, greater investment in deep roots, four times higher maximum RWU, a shift of RWU to deeper soil layers as the topsoil dried, and a more pronounced soil drying below 1 m depth. Soil hydraulic conductivity started to limit RWU when values fell below 10-3 to 10-5 cm/d, depending on the soil. As drought progressed, oak and beech may also have benefited from their leaf osmoregulatory capacity, but at the cost of xylem embolism with around 50% loss of hydraulic conductivity when soil water potential dropped below -1.25 MPa. Consideration of species-specific water use is crucial for forest management and vegetation modelling to improve forest resilience to drought.

anisohydric

display_name: anisohydric

root-distribution

display_name: root-distribution

stomatal-stringency

display_name: stomatal-stringency

water-potential

display_name: water-potential

water-use-strategy

display_name: water-use-strategy

educ

en: Education, culture and sport

en: Water-use strategies of temperate tree species


maintainer: Mahdi Jafari

en: vapor-transport numerical-simulation snow-density-profile convection-in-snow openfoam

en: snowpackBuoyantPimpleFoam

en:
This zipped folder includes (1) the source code files, (2) an example case for simulation, and (3) an instruction how to compile and use the new solver.

Note that the most updated version of snowpackBuoyantPimpleFoam can be found in the WSL/SLF GitLab repository: https://gitlabext.wsl.ch/snow-models/snowpackbuoyantpimplefoam

en: snowpackBuoyantPimpleFoam is a two-phase solver implemented to model convection of water vapor with phase change in snowpacks. This new solver is based on the standard solver of buoyantPimpleFoam in the open-source fluid dynamics software, OpenFOAM 5.0 (www.openfoam.org).

convection-in-snow

display_name: convection-in-snow

numerical-simulation

display_name: numerical-simulation

openfoam

display_name: openfoam

snow-density-profile

display_name: snow-density-profile

vapor-transport

display_name: vapor-transport

en: snowpackBuoyantPimpleFoam: an OpenFOAM Eulerian–Eulerian two-phase solver for modelling convection of water vapor in snowpacks


maintainer: Digital Innovation Office

title_for_slug: energiedashboard.ch: Füllstände Gasspeicher EU

de: ogd102-bfe versorgungssicherheit gasspeicher fullstand gas

fr: energiedashboard.ch: Niveaux réservoirs gaz UE

en: energiedashboard.ch: Gas storage levels EU

it: energiedashboard.ch: Livelli serbatoi di gas UE

label: Aggregated Gas Storage Inventory

format: CSV

fr:
Le stockage de gaz joue un rôle important dans la sécurité de l'approvisionnement en gaz en hiver, lorsque la consommation de gaz est la plus élevée. Sur une année, le gaz couvre environ 15% des besoins énergétiques de la Suisse. Environ la moitié de cette quantité est utilisée pour le chauffage des bâtiments. La Suisse est presque entièrement dépendante des importations de gaz. Seule une petite quantité de gaz (biogaz) est produite dans le pays. De même, la Suisse ne dispose pas de grandes capacités de stockage sur son territoire. Elle dépend de l'utilisation des stockages de gaz dans les pays voisins.

Le Conseil fédéral a obligé le secteur gazier à garantir des capacités de stockage dans les pays voisins (15% de la consommation annuelle suisse) et des options pour des livraisons de gaz supplémentaires pour l'hiver 2022/23. Fin août 2022, le secteur gazier a annoncé que cette tâche avait été accomplie avec succès.

Les niveaux de remplissage des stockages de gaz en Allemagne, en France, en Italie et en Autriche, ainsi que dans l'ensemble de l'UE, sont indiqués en pourcentage et en térawattheures (TWh). Les niveaux de stockage sont basés sur le pouvoir calorifique supérieur. Les niveaux de remplissage indiquent l'état des stockages de gaz de la veille jusqu'à 23 heures. Les données proviennent de sources publiques de l'UE.

La tendance indique l'évolution du niveau de remplissage. Elle est orientée à la hausse lorsque le niveau de remplissage a augmenté par rapport à la moyenne des 10 derniers jours.

Disclaimer Data

Les données proviennent de Gas Infrastructure Europe (GIE) à partir de l'inventaire agrégé des stockages de gaz (AGSI). Toutes les données sont fournies par les contributeurs sur une base volontaire et gratuitement. Les données fournies par AGSI sont uniquement à titre d'information. Des efforts raisonnables sont déployés pour garantir l'exactitude, l'exhaustivité et l'actualité des informations fournies ici. Les données ont été soigneusement vérifiées, sont régulièrement mises à jour et peuvent être modifiées, supprimées ou complétées sans préavis.

Les données relatives à la part suisse des volumes présents dans les stockages de gaz ne sont pas disponibles.

en:
Gas storage facilities play an important role in the security of gas supply in winter, when most gas is consumed. Over the year, Switzerland covers around 15% of its energy needs with gas. About half of this is consumed for heating buildings. Switzerland is almost entirely dependent on imports for gas. Only a little gas (biogas) is produced domestically. Likewise, Switzerland has no large domestic storage facilities of its own. It is dependent on the use of gas storage facilities in neighbouring countries.

The Federal Council has obliged the gas industry to secure storage capacities in neighbouring countries (15% of Switzerland's annual consumption) and options for additional gas supplies for the winter of 2022/23. At the end of August 2022, the gas industry reported the successful completion of this task.

The gas storage levels in Germany, France, Italy and Austria, as well as the entire EU, are given in percentages and in terawatt hours (TWh). The storage levels are based on the upper calorific value. The levels show the state of the gas storage facilities of the previous day until 11 pm. The data come from EU public sources.

The trend shows the development of the filling level. It points upwards when the level has increased compared to the average of the last 10 days.

Disclaimer Data

The data come from Gas Infrastructure Europe (GIE) from the Aggregated Gas Storage Inventory (AGSI). All data is provided by contributors on a voluntary basis and free of charge. The data provided by AGSI is for information purposes only. Reasonable efforts are made to ensure the accuracy, completeness and timeliness of the information provided herein. The data have been carefully checked, are regularly updated and may be changed, removed or supplemented without prior notice.

Data on the Swiss shares of the volumes available in the gas storage facilities are not available.

de:
Gasspeicher spielen eine wichtige Rolle für die Versorgungssicherheit mit Gas im Winter, wenn am meisten Gas verbraucht wird. Über das Jahr gesehen deckt die Schweiz rund 15% ihres Energiebedarfs mit Gas. Rund die Hälfte davon wird für die Beheizung von Gebäuden verbraucht. Die Schweiz ist beim Gas beinahe vollständig von Importen abhängig. Nur wenig Gas (Biogas) wird im Inland produziert. Ebenso verfügt die Schweiz über keine grossen eigenen Speicher im Inland. Sie ist auf die Nutzung der Gasspeicher in den Nachbarländern angewiesen.

Der Bundesrat hat die Gasbranche verpflichtet, für den Winter 2022/23 Speicherkapazitäten in den Nachbarländern (15% des schweizerischen Jahresverbrauch) und Optionen für zusätzliche Gaslieferungen zu sichern. Ende August 2022 meldete die Gasbranche den erfolgreichen Vollzug dieser Aufgabe.

Die Füllstände der Gasspeicher in Deutschland, Frankreich, Italien und Österreich sowie der gesamten EU sind in Prozent und in Terawattstunden (TWh) angegeben. Die Speicherstände basieren auf dem oberen Heizwert. Die Füllstände zeigen den Zustand der Gasspeicher des Vortags bis 23 Uhr. Die Daten stammen aus öffentlichen Quellen der EU.

Der Trend zeigt die Entwicklung des Füllstandes an. Er zeigt nach oben, wenn der Füllstand verglichen zum Durchschnitt der letzten 10 Tagen zugenommen hat.

Disclaimer Daten

Die Daten stammen von Gas Infrastructure Europe (GIE) aus dem Aggregated Gas Storage Inventory (AGSI). Alle Daten werden von den Mitwirkenden auf freiwilliger Basis und kostenlos zur Verfügung gestellt. Die von AGSI bereitgestellten Daten dienen nur zu Informationszwecken. Es werden angemessene Anstrengungen unternommen, um die Richtigkeit, Vollständigkeit und Aktualität der hier bereitgestellten Informationen sicherzustellen. Die Daten wurden sorgfältig geprüft, werden regelmässig aktualisiert und können ohne vorherige Ankündigung geändert, entfernt oder ergänzt werden.

Daten zu den Schweizer Anteilen der in den Gasspeichern vorhandenen Mengen sind nicht verfügbar.

it:
Gli impianti di stoccaggio del gas svolgono un ruolo importante nella sicurezza della fornitura di gas in inverno, quando si consuma la maggior parte del gas. Nel corso dell'anno, la Svizzera copre circa il 15% del suo fabbisogno energetico con il gas. Circa la metà viene consumata per il riscaldamento degli edifici. La Svizzera dipende quasi interamente dalle importazioni di gas. Solo un po' di gas (biogas) viene prodotto a livello nazionale. Allo stesso modo, la Svizzera non dispone di grandi strutture di stoccaggio nazionali. Il Consiglio federale ha obbligato l'industria del gas a garantire capacità di stoccaggio nei Paesi limitrofi (15% del consumo annuale della Svizzera) e opzioni per ulteriori forniture di gas per l'inverno 2022/23. Alla fine di agosto 2022, l'industria del gas ha riferito di aver completato con successo questo compito.

I livelli di stoccaggio del gas in Germania, Francia, Italia e Austria, così come nell'intera UE, sono indicati in percentuale e in terawattora (TWh). I livelli di stoccaggio si basano sul potere calorifico superiore. I livelli mostrano lo stato degli impianti di stoccaggio del gas del giorno precedente fino alle 23.00. I dati provengono da fonti pubbliche dell'UE.

Il trend mostra lo sviluppo del livello di riempimento. Punta verso l'alto quando il livello è aumentato rispetto alla media degli ultimi 10 giorni.

Dati disclaimer

I dati provengono da Gas Infrastructure Europe (GIE) dall'Aggregated Gas Storage Inventory (AGSI). Tutti i dati sono forniti dai collaboratori su base volontaria e gratuita. I dati forniti da AGSI sono solo a scopo informativo. Vengono compiuti sforzi ragionevoli per garantire l'accuratezza, la completezza e la tempestività delle informazioni qui fornite. I dati sono stati accuratamente controllati, sono regolarmente aggiornati e possono essere modificati, rimossi o integrati senza preavviso.

I dati sulle quote svizzere dei volumi disponibili negli impianti di stoccaggio del gas non sono disponibili.

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).


en: soil-chemistry topsoil-removal biodiversity nematodes soil-biodiversity fungi plants grassland restoration bacteria soil-biology soil

en: Vegetation data

en:
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)
column I-LW	Plant abundance per species (in total 327 plant species) expressed as cover percentage estimation according to van der Maarel (vdM 1979; 7 classes: 1-7); nomenclature according to Lauber & Wagner (1996)

en: Soil properties

en:
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)
N.pool	Soil nitrogen pool (June 2017); corrected for bulk density and 12 cm sampling depth [kg ha-1 12cm-1]
Corg.pool	Soil organic carbon pool (June 2017); corrected for bulk density and 12 cm sampling depth [kg ha-1 12cm-1]
pH	soil pH [CaCl2]
BD	Bulk density [g cm-3]
FC	Field capacity [%]
Veg.1	principle coordinate axis 1 (vegetation)
Veg.1	principle coordinate axis 2 (vegetation)
Nem.1	principle coordinate axis 1 (nematoda)
Nem.2	principle coordinate axis 2 (nematoda)
Fun.1	principle coordinate axis 1 (fungi)
Fun.2	principle coordinate axis 2 (fungi)
Prok.1	principle coordinate axis 1 (prokaryota)
Prok.2	principle coordinate axis 2 (prokaryota)
Veg.H	Shannon diversity (vegetation)
Nem.H	Shannon diversity (nematoda)
Fun.H	Shannon diversity (fungi)
Prok.H	Shannon diversity (prokaryota)

en: Nematode data

en:
Sample.data
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)

Taxonomy
ASV.ID	unit number.letter code per amplicon sequence variant (in total 904)
Taxonomic rank	Phylum, Class, Order, Family, Genus, Species

Abundance
columns	Plot.ID (see Sample.data)
rows	ASV.ID (see Taxonomy)

en: Ecosystem coupling_interactions

en:
Treatment	Treatment groups; Y.4-18 = restored grasslands; Initial = intensively managed grasslands; Target = species-rich semi-wet to semi-dry grasslands
Type	Interactions type; A.A = abiotic.abiotic; A.B = abiotic.biotic; B.B = biotic.biotic
Correlation	non-parametric Spearman rank correlations between biotic and abiotic parameters (ecological interactions)

en: Networks

en:
Treatment	Treatment groups; Y.4-18 = restored grasslands; Initial = intensively managed grasslands; Target = species-rich semi-wet to semi-dry grasslands
Correlation	non-parametric Spearman rank correlations
Vertex.1+2	Comparison between network components; N.pool = total soil nitrogen pool [kg ha-1 12 cm-1]; Corg.pool = soil organic carbon pool [kg ha-1 12 cm-1], pH = soil pH [CaCl2]; BD = bulk density [g cm-3]; FC = field capacity [%]
	PCoA 1 + 2 biotic communities [Veg = vegetation; Nem = nematoda; Fun = fungi; Prok = prokaryota]
Type	Interactions type; A.A = abiotic.abiotic; A.B = abiotic.biotic; B.B = biotic.biotic

en: Distance to target results

en:
Community	biotic community (vegetation, nematoda, fungi, prokaryota)
Treatment	Treatment groups; Y.4-30= restored grasslands (time since restoration); Initial=intensively managed grasslands (I)
Comparison	within- or between-community Bray-Curtis dissimilarity distance comparison
Label	within-community distances of initial/restored grasslands: treatment abbreviation; between-communities distances of initial/restored-to-target grasslands: treatment abbreviation plus Target (T; species-rich grasslands)
mean	Mean distance to Target (6 plots per treatment group)
sd	Standard deviation of distances to Target (6 plots per treatment group)

en: Fungi data

en:
Sample.data
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)

Taxonomy
OTU.ID	consecutive numbers for operational taxonomic units (OTUe 1-7794)
Taxonomic rank	Domain, Phylum, Class, Order, Family, Genus, Species

Abundance
columns	Plot.ID (see Sample.data)
rows	OTU.ID (see Taxonomy)

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

Paper Citation:

_Resch, M.C., Schütz, M., Ochoa-Hueso, R., Buchmann, N., Frey, B., Graf, U., van der Putten, W.H., Zimmermann, S., Risch, A.C. (in review). Long-term recovery of above- and belowground interactions in restored grassland after topsoil removal and seed addition. Journal of Applied Ecology_

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

Study area and experimental design
The study was conducted in and around two nature reserves, Eigental and Altläufe der Glatt, which were located approximately 5 km apart (47°27´ to 47°29´ N, 8°37´ to 8°32´ E, 417 to 572 m a.s.l., Canton of Zurich, Switzerland; Figure S1 and S2, Table S1). Mean annual temperature and precipitation are 9.8 ± 0.6 °C and 990 ± 168 mm (Kloten climate station 1988-2018; MeteoSchweiz, 2019). TFor this study, we used a space-for-time approach based on eight restoration sites that were between 3 and 32 years old. We measured recovery and restoration success by comparing the restored grasslands with intensively managed and semi-natural grasslands. Using a space-for-time approach requires high similarities in historical properties of the site, such as soil conditions and management regimes, to assure that temporal processes are appropriately represented by spatial patterns (Walker et al., 2010). This was the case in our study. The restored sites had similar soil conditions (i.e., soil type, structure, water availability) as the targeted semi-natural grasslands, while they shared the same agricultural legacy with intensively managed grasslands, i.e., biomass harvest and fertilization (manure and/or slurry) three to five times a year as well as tillage. We randomly established three 5 m x 5 m (25-m2) plots for plant identification and three 2 m x 2 m (4-m2) subplots for soil biotic and abiotic data collection at least 2 m away from the 25-m2 plots in each restoration site. Sites of similar age were grouped into four age classes: Y.4 (3 & 4 years after restoration), Y.18 (17 & 19 years), Y.24 (23 & 25 years), and Y.30 (27 & 32 years). Six intensively managed (Initial) and six semi-natural grassland (Target) sites complemented the experimental set-up, for a total of 36 plots. All plots were sampled under similar conditions, i.e., day of the year, air temperature, soil moisture, and time since last rain event, in June/July 2017 (intensively managed and semi-natural plots) and 2018 (restored plots).

Collection of plants and selected soil biota data
Plant species cover (in %) was visually estimated in each 25-m2 plot in mid-June (Braun-Blanquet, 1964; nomenclature: Lauber & Wagner, 1996). We calculated Shannon diversity and assessed plant community structure. We included soil microbial (fungi, procaryotes) and nematodes in our study as they represent the majority of soil biotic diversity and abundance (Bardgett & van der Putten, 2014), cover various trophic levels of the soil food web (Bongers & Ferris, 1999), and play key roles in soil functioning and ecosystem processes (Bardgett & van der Putten, 2014). In particular, soil nematodes were found to be well suited belowground indicators to evaluate recovery/development after restoration (e.g. Frouz, et al. 2008; Kardol et al., 2009; Resch et al., 2019). We randomly collected ten soil cores (2.2 cm diameter x 12 cm depths; sampler from Giddings Machine Company, Windsor, USA) in the 4-m2 subplots to assess soil nematode and microbial (fungal, prokaryotic) diversities and community structures. For soil nematodes, eight of the soil cores were combined and gently homogenized, placed in coolers and stored at 4 °C and transported to the laboratory (Netherlands Institute of Ecology, NIOO, Wageningen, Netherlands) within three days after collection. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriators (Oostenbrink, 1960). After extraction, each sample was divided into three subsamples, two for molecular identification and one to determine nematode abundance (see Resch et al., 2019). For the molecular work, two subsamples were stored in 70% ethanol (final volume 10 mL each) and transported to the laboratory at the Swiss Federal Research Institute WSL (Birmensdorf, Switzerland). Each subsample was reduced to roughly 200 μL by centrifugation and removal of the supernatant. The remaining ethanol was vaporized (65 °C for 3 h). Thereafter, 180 μL ATL buffer solution (Qiagen, Hilden, Germany) was immediately added and samples were stored at 4 °C until further processing. From these samples, nematode metagenomic DNA was extracted using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) according to the manufacturer`s protocol, except for the incubation step which was run at 56 °C for 4 h. PCR amplification of the V6-V8 region of the eukaryotic small-subunit (18S) was performed with 7.5 μL of genomic DNA template (ca. 1 ng/μL) in 25 μL reactions containing 5 μL PCR reaction buffer, 2.5 mM MgCL2, 0.2 mM dNTPs, 0.8 μM of each primer (NemF: Sapkota & Nicolaisen, 2015; 18Sr2b: Porazinska et al., 2009), 0.5 μL BSA, and 0.25 μL GoTaq G2 Hot Start Polymerase (Promega Corporation, Madison, USA). Amplification was using an initial DNA denaturation step of 95 °C for 2 min, followed by 35 cycles at 94 °C for 40 sec, 58 °C for 40 sec, 72 °C for 1 min, and a final elongation step at 72 °C for 10 min. Filtering, dereplication, sample inference, chimera identification, and merging of paired-end reads was implemented using the DADA2 pipeline (v.1.12; Callahan et al., 2016) to finally assign amplicon sequence variants (ASVs) as taxonomic units.
We combined and homogenized the remaining two soil cores to assess soil microbes, placed them in coolers (4 °C) and transported them to the laboratory at WSL. Metagenomic DNA was extracted from 8 g sieved soil (2 mm) using the DNAeasy PowerMax Soil Kit (Qiagen, Hilden, Germany) according to the manufacturer´s protocol. PCR amplification of the V3-V4 region of the small-subunit (16S) of prokaryotes (i.e., bacteria and archaea) and the ribosomal internal transcribed spacer region (ITS2) of fungi was performed with 1 ng of template DNA using PCR primers and conditions as previously described (Frey et al., 2016). PCRs were run in triplicates, pooled and sent to the Genome Quebec Innovation Centre (Montreal, QC, Canada) for barcoding using the Fluidigm Access Array technology (Fluidigm) and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, USA). Quality filtering, clustering into operational taxonomic units (OTUs, 97% similarity cutoffs) and taxonomic assignment were performed as previously described (Resch et al., 2021).Taxonomic classification of nematode, prokaryotic and fungal sequences was conducted querying against the most recent versions of PR2 (v.4.11.1; Guillou et al., 2013), SILVA (v.132; Quast et al., 2013), and UNITE (v.8; Nilsson et al., 2019) reference sequence databases. Taxonomic assignment cutoffs were set to confidence rankings ≥ 0.8 (below ranked as unclassified). Prokaryotic OTUs assigned to mitochondria or chloroplasts as well as OTUs or ASVs assigned to other than Fungi or Nematoda were manually removed prior to data analysis. The three datasets were filtered to discard singletons and doubletons. Taxonomic abundance matrices were rarefied to the lowest number of sequences per community to achieve parity of the total number of reads between samples (Prokaryotes: 10,929 reads; Fungi: 18,337 reads; Nematodes: 6,662 reads). We calculated Shannon diversity and assessed community structures for soil nematodes, prokaryotes and fungi based on their relative abundances of ASV or OTU at the taxon level.

Collection of soil physical and chemical properties
We randomly collected one undisturbed soil core (5 cm diameter, 12 cm depth) per 4-m2 subplot using a steel cylinder that fit into the soil corer. The cylinders were capped to avoid disturbance during transport and used to measure field capacity, rock content and fine earth density as previously described (Resch et al., 2021). We randomly collected another three soil cores (5 cm diameter, 12 cm depths) in each 4-m2 subplot to determine soil chemical properties. The cores were pooled, dried at 60 °C for 48 h and passed through a 2 mm sieve. We measured soil pH (CaCl2) on dried samples, total nitrogen (N) and organic carbon (C) concentration on dried and fine-ground samples (≤ 0.5 mm; for details see Resch et al., 2021). We calculated total N and organic C pools after correcting its concentration for soil depth, rock content and fine earth density.

bacteria

display_name: bacteria

biodiversity

display_name: biodiversity

fungi

display_name: fungi

grassland

display_name: grassland

nematodes

display_name: nematodes

plants

display_name: plants

restoration

display_name: restoration

soil

display_name: soil

soil-biodiversity

display_name: soil-biodiversity

soil-biology

display_name: soil-biology

soil-chemistry

display_name: soil-chemistry

topsoil-removal

display_name: topsoil-removal

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: Long-term recovery of above-and belowground interactions in restored grasslands


en: time-series switzerland studibach groundwater alptal

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

mimetype: text/plain

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

alptal

display_name: alptal

groundwater

display_name: groundwater

studibach

display_name: studibach

switzerland

display_name: switzerland

time-series

display_name: time-series

educ

en: Education, culture and sport

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


ladestationen-fuer-elektroautos

maintainer: Bundesamt für Energie

title_for_slug: Ladestationen für Elektroautos

author: Bundesamt für Energie

label: Technical documentation

label: API How-to and examples

de: Statische Informationen der Ladestationen (EVSEData) - kontinuierlich aktualisiert

de: Für weitere Informationen siehe https://github.com/SFOE/DIEMO-Documentation

format: JSON

de: Verfügbarkeits-Informationen der Ladestationen (EVSEStatus) - kontinuierlich aktualisiert

fr: La mobilité électrique constitue une technologie clé pour une mobilité plus durable, contribuant à la réalisation des objectifs ambitieux définis en matière de politique énergétique et climatique. Les moteurs électriques sont efficaces et peuvent fonctionner avec de l’électricité issue des énergies renouvelables. Le site www.je-recharge-mon-auto.ch affiche, en temps réel, la disponibilité de stations de recharge pour véhicules électriques. Les stations des principaux réseaux de recharge s’affichent d’ores et déjà. L’OFEN invite les autres fournisseurs proposant des stations de recharge accessibles au public qui le souhaitent à faire figurer leur emplacement sur ce site.

en: Electric mobility is a key technology for more sustainable mobility and is one way of achieving ambitious energy and climate policy goals. Electric engines are efficient, and using electricity means that renewable energies can also be used. recharge-my-car.ch shows where charging points for electric vehicles are available in real time. Currently the website displays the charging points offered by the largest charging networks. The SFOE invites other providers of public charging stations for electric vehicles to participate.

de: Die Elektromobilität ist eine Schlüsseltechnologie für eine nachhaltigere Mobilität und trägt zur Erreichung ambitionierter energie- und klimapolitischer Ziele bei. Elektroantriebe sind effizient und der Einsatz von Strom erlaubt die Nutzung erneuerbarer Energien. ich-tanke-strom.ch zeigt die Verfügbarkeit von Ladestellen für Elektrofahrzeuge in Echtzeit an. Aktuell werden die Ladestationen der grössten Ladenetzwerke angezeigt. Das BFE lädt weitere interessierte Anbietende von öffentlichen Ladestationen für Elektrofahrzeuge ein, sich anzuschliessen.

it: I veicoli elettrici rappresentano una tecnologia chiave per una mobilità più sostenibile e contribuiscono al raggiungimento di ambiziosi obiettivi di politica energetica e climatica. I motori elettrici sono efficienti e possono essere alimentati con energia elettrica prodotta da fonti rinnovabili. pieno-di-elettricita.ch mostra in tempo reale la disponibilità delle stazioni di ricarica elettriche. Attualmente sono visualizzate le stazioni delle maggiori reti di ricarica. L'UFE invita gli altri operatori di stazioni di ricarica pubbliche per veicoli elettrici a unirsi a questa iniziativa.

fr: Stations de recharge pour voitures électriques

de: Ladestationen für Elektroautos

en: Charging points for electric cars

it: Stazioni di ricarica per auto elettriche

soci

fr: Population et société

de: Bevölkerung und Gesellschaft

en: Population and society

it: Popolazione e società

ener

en: Energy

gove

en: Government and public sector

regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

envi

fr: Environnement

de: Umwelt

en: Environment

it: Ambiente

tran

fr: Transports

de: Verkehr

en: Transport

it: Trasporti

econ

en: Economy and finance

name: Bundesamt für Energie & Betreibende von Ladeinfrastruktur

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).


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

en: Post Processed TLS's

en: Compressed dat files

en: Data processing documentation

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

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

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


title_for_slug: energiedashboard.ch: Stromverbrauch Prognose SDSC

de: strom endverbrauch ogd110-bfe data-science machine-learning prognose

fr: energiedashboard.ch : Prévisions de consommation d'électricité SDSC

en: energiedashboard.ch: Power consumption forecast SDSC

it: energiedashboard.ch: Previsione del consumo di energia SDSC

de: Stromverbrauch Prognose SDSC

fr: Le modèle de consommation d'électricité produit une estimation de la consommation d'électricité à l'échelle nationale suisse. Le modèle est entraîné sur des données provenant des sources suivantes : consommation nationale horaire fournie par Swissgrid (aperçu de l'énergie), données météorologiques de MétéoSuisse (y compris les prévisions météorologiques), et informations calendaires telles que les jours fériés fournis par le paquet de jours fériés python et les jours fériés acquis manuellement. Le modèle appartient à la catégorie des modèles additifs généralisés (GAM) et produit une prédiction sur une base horaire et quotidienne avec un intervalle de confiance inférieur et supérieur de 2,5 et 97,5 respectivement.

en: The electricity consumption model outputs an estimate of electricity consumption at the Swiss national scale. The model is trained on data from the following sources: hourly national consumption provided by Swissgrid (Energy overview), meteorological data from MeteoSwiss (including weather forecast), and calendar information such as public holidays as provided by the python holidays package and manually acquired holidays. The model belongs to the category of Generalized Additive Models (GAMs) and output is a prediction at an hourly and daily basis with a lower and upper confidence interval of 2.5 and 97.5 respectively.

de: Das Stromverbrauchsmodell liefert eine Schätzung des Stromverbrauchs auf nationaler Ebene in der Schweiz. Das Modell wird mit Daten aus den folgenden Quellen trainiert: stündlicher nationaler Verbrauch von Swissgrid (Energieübersicht), meteorologische Daten von MeteoSchweiz (einschliesslich Wettervorhersage) und Kalenderinformationen wie Feiertage, die vom python Holiday Package bereitgestellt werden, sowie manuell erfasste Feiertage. Das Modell gehört zur Kategorie der verallgemeinerten additiven Modelle (GAMs) und liefert eine Vorhersage auf stündlicher und täglicher Basis mit einem unteren und oberen Konfidenzintervall von 2,5 bzw. 97,5.

it: Il modello di consumo di elettricità produce una stima del consumo di elettricità su scala nazionale svizzera. Il modello è addestrato sui dati provenienti dalle seguenti fonti: consumo nazionale orario fornito da Swissgrid (panoramica dell'energia), dati meteorologici di MeteoSvizzera (comprese le previsioni del tempo) e informazioni sul calendario, come i giorni festivi forniti dal pacchetto vacanze di python e i giorni festivi acquisiti manualmente. Il modello appartiene alla categoria dei modelli additivi generalizzati (GAM) e il risultato è una previsione su base oraria e giornaliera con un intervallo di confidenza inferiore e superiore di rispettivamente 2,5 e 97,5 .

data-science

display_name: data-science

endverbrauch

display_name: endverbrauch

machine-learning

display_name: machine-learning

prognose

display_name: prognose

strom

display_name: strom

ener

de: Energie

en: Energy

gove

de: Regierung und öffentlicher Sektor

econ

de: Wirtschaft und Finanzen


maintainer: Matthias Haeni

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

en: Site aerial photo (Isone)

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

mimetype: image/jpeg

en: Data plot of the field station (ISF)

media_type: application/pdf

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

format: PDF

mimetype: application/pdf

en: Field station data (ISF)

media_type: text/csv

en: Data file.

mimetype: text/csv

en: Data plot of the forest station (ISB)

en: Site photo of the field station (ISF)

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

en: Site photo of the forest station (ISB)

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

en: Forest station data (ISB)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Isone in Switzerland where one station is located within a natural broad-leaved forest stand (ISB) with European beech (_Fagus sylvatica_; 70-100 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, ISF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Isone is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

educ

de: Bildung, Kultur und Sport

en: Education, culture and sport

envidat

de: EnviDat: das Umwelt-Datenportal

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.

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


maintainer: Elisabeth Hafner

en: avalanche-mapping avalanche avalanche-size-estimation remote-sensing reliability

en: AvalancheReliabilityData

en: The .zip contains all data used to conduct the reliability study as well as the results, a detailed description of the included files is contained in the included pdf. All geospatial data is provided in the Swiss Coordinate System: CH1903+ / LV95 (EPSG 2056). For any use of the data the proper citation provided is obligatory!

mimetype: application/x-zip-compressed

en:
This dataset contains the data used and collected to investigate the reliability of avalanche size estimates and avalanche outlines (see related publications). Namely this is

 Avalanche size estimates

* the 10 pictures depicting avalanches
* the survey results from the 170 participants

 Avalanche outlines mapped from oblique photographs
* the pictures of the six avalanches
* the avalanche outlines mapped by the 10 participants
* the reference mapped from the georeferenced photographs

 Avalanche outlines from remotely-sensed imagery
* the orthophotos in the four different resolutions (2m, 1m, 50cm, 25cm)
* the area of interest (AoI) delimiting the research area
* the avalanche mappings in each resolution for all five participants
* the shade masks used to separate illuminated and shaded areas

The acquisition of the orthophotos has been partially supported by the Swiss National Science Foundation (SNF; Grant N°200021_172800).

avalanche

display_name: avalanche

avalanche-mapping

display_name: avalanche-mapping

avalanche-size-estimation

display_name: avalanche-size-estimation

reliability

display_name: reliability

remote-sensing

display_name: remote-sensing

en: EnviDat: the Environmental Data Portal

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

en: Data reliability study: avalanche size estimates and outlines


en: avalanche-release-probability machine-learning avalanche-forecast forecast-models spatial-modelling

en: Model predictions (interpolated)

en: The zip file contains csv-files the interpolated model predictions for three models, in forecast and nowcast-mode, for the three different data sets, as described in the publication.

en: Human-triggered avalanches 2022/2023

en: The file contains the reported human-triggered avalanches. The variables are as follows: id, release date (date), name, Swiss coordinates (x,y), elevation (ele), remark, reported avalanche size expressed using avalanche activity index (sizeAAI), aspect, event type (type), event (yes = 1, no = 0)

en: Randomly sampled subset of grid points from 1 km DEM

en: Contains the randomly sampled subset of grid points from 1 km DEM. Three variables: coordinates (x, y) and elevation (elev).

en: Natural avalanches 2023/2024

en: Natural avalanche observation (dry avalanches). 2023/2024 forecasting season.

en: GPS data and human-triggered avalanches

en: Contains (1) the subset of points extracted from GPS tracks recorded during backcountry touring and uploaded to the website skitourenguru.ch and (2) the corresponding subset of human-triggered avalanches. Data (1) was provided by Skitourenguru GmbH and data (2) was collected by SLF. - Variables contained are date, Swiss coordinates (x, y), aspect (N = 0, NE = 45, ...), elevation (ele), and event (event = 1, non-event = 0).

en: Avalanche forecast

en: Data contains forecasts relating to dry-snow avalanche conditions only. Forecast issued at 17.00 local time. Contains variables for the warning region (sector_id), publication date (date), danger level - sub-level combination expressed using numeric values (integer value of danger level -0.33 for sub-level minus, -0 for sub-level neutral, +0.33 for sub-level plus, altitude as indicated in the bulletin, and whether aspect (North = 0, Northeast = 45, ...) was indicated in the bulletin (no: 0, yes: 1).

en: Model predictions (raw data)

en: Model predictions (raw data) for three models, in nowcast and forecast mode. Not all models are available for both seasons. - These data are the basis for the interpolated predictions, which can be found in the other zip-folder

en: Human-triggered avalanches 2023/2024

en: Snowline estimates 2023/2024

en: Daily snowline estimates provided by observers for North (aspect = 0) and South (aspect = 180) slopes for the 2023/2024 season. For aspects in between North and South, values are interpolated. Contains observation date (date), coordinates (Swiss coordinates x, y), snowline elevation (snowline), and aspect.

en: Snowline estimates 2022/2023

en: Daily snowline estimates provided by observers for North (aspect = 0) and South (aspect = 180) slopes for the 2022/2023 season. For aspects in between North and South, values are interpolated. Contains observation date (date), coordinates (Swiss coordinates x, y), snowline elevation (snowline), and aspect.

en: Natural avalanches 2022/2023

en: Natural avalanche observation (dry avalanches). 2022-2023 forecasting season.

en:
This data set was used in the analysis by Techel et al. *Forecasting avalanche danger: human-made forecasts vs. fully automated model-driven predictions*, submitted to *Natural Hazards Earth System Sciences* on 20 Aug 2024.
The repository contains data from two avalanche forecasting seasons (2022/2023, 2023/2024) in Switzerland.
**Interpolated predictions**
- The .zip file contains the interpolated predictions for the three models in nowcast- and forecast- mode. This data is needed to reproduce the figures and tables in the submitted preprint.
The other data are the **raw data** underlying the interpolations:
- Avalanche forecast by WSL Institute for Snow and Avalanche Research SLF, published at 17.00 local time, valid for the following 24 hours and relating to dry snow avalanche conditions.
- Model predictions in *nowcast*- and *forecast*-mode for three models (*danger level*, *instability*, *natural avalanche*), valid for 12.00 local time
- Subset of points extracted from GPS tracks (courtesy of Skitourenguru GmbH)
- Avalanche observations - natural avalanches and human-triggered avalanches
- Estimates of the snowline
- Randomly chosen subset of grid points used for generating reference distributions
For details regarding the data sets refer to the publication.

avalanche-forecast

display_name: avalanche-forecast

avalanche-release-probability

display_name: avalanche-release-probability

forecast-models

display_name: forecast-models

machine-learning

display_name: machine-learning

spatial-modelling

display_name: spatial-modelling

en: Evaluating the predictive performance of human avalanche forecasts and model predictions in Switzerland


en: sentinel-2 raster-processing force remote-sensing switzerland

media_type: image/tiff

en: An example of the index EVI for one of the generated 30 by 30 km tiles located around the city of Zürich. The values are multiplied by 10.000. The date of the image is 24.07.2018.

format: TIFF

en: Parameter file that defined all the parameters for processing the Sentinel-2 Level 2 imagery

format: OCTET

en:
We processed Sentinel-2 images from 2017 to 2023 for Switzerland with the Software FORCE (Frantz 2019). The respective parameter files can be found here: [Github](https://github.com/TLKoch/Sentinel-2_CH). All the images consist of several TB and therefore, access will be granted upon request.
The available bands (in spatial reference system EPSG 3035) are the following:
*Red, Green, Blue, NIR, Red-Edge-1, Red-Edge-2, Red-Edge-3, SWIR-1, SWIR-2*
The available indices (in spatial reference system EPSG 3035) are the following:
*CCI, CIRE, NDWI/NDMI, NDVI, EVI*
Further indices might be provided upon request.

 Processing
After downloading Level 1 data for the 14 Sentinel-2 tiles covering Switzerland (T31TGN, T32TLT, T32TMT, T32UMU, T32TNT, T32TPT, T31TGM, T32TLS, T32TMS, T32TNS, T32TPS, T32TLR, T32TMR, T32TNR) with an estimated cloud cover of less than 80 \%, we processed the data further with FORCE v. 3.7.8-12.
FORCE was used for radiometric correction, including atmospheric and topographic corrections using the digital elevation model from Copernicus Land Monitoring Service (EU-DEM v. 1.1), with a 25 m spatial resolution, a bidirectional reflectance distribution function (BRDF) correction, and an adjacency effect correction. For cloud masking, the improved Fmask algorithm with buffers of 300 m for clouds, 90 m for shadows, and 30 m for snow were used (Frantz et al. 2018; Zhu et al. 2015). All bands were resampled using cubic convolution to 10 m spatial resolution. Shadows, low and poor illumination as well as saturation were masked out. The co-registration of all images was made with monthly composites of near-infrared Landsat Collection 2 images from 2014 to 2021 (Rengarajan et al. 2020;Rufin et al. 2021). With this the geometric consistency improved. The processed images are available in tiles of 30 by 30 km.

 Example images
Uploaded is an example of the index EVI for one of the generated 30 by 30 km tiles located around the city of Zürich. The values are multiplied by 10.000. The date of the image is 24.07.2018.

force

display_name: force

raster-processing

display_name: raster-processing

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: Sentinel-2 imagery of Switzerland


en: sense-of-place urban-ecology landscape place-attachment electrodermal-activity

en: 02. Questionnaire Data

en: 05. Visuals

en: 01. EDA Processed Data

en: 04. Scripts

en: 00. EDA Raw Data

en: 03. Publication Ready Data

en: This repository contains data on EDA measurements of visitors with different cultural backgrounds in virtual urban park settings. The parks are a Persian garden (Shiraz, Iran) and a historical park in Zurich, Switzerland. The cultural background of the visitors is Persian and Central European. The repository contains raw data from EDA, processed time series and statistical procedures.

electrodermal-activity

display_name: electrodermal-activity

landscape

display_name: landscape

place-attachment

display_name: place-attachment

sense-of-place

display_name: sense-of-place

urban-ecology

display_name: urban-ecology

en: Electrodermal Activity (EDA) of Bi-cultural Visitors In Virtual Park Settings


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

urbanization

display_name: urbanization

en: Land use projections and services for Switzerland


en: climate-projections avalanche snow-instability dry-snow-avalanche wet-snow-avalanche snow climate-change snow-stratigraphy

en: SNOWPACK settings

en: contains .ini and .sno file indicating the settings used to drive SNOWPACK with the climate scenarios

en: Projections of avalanche activity

en: contains one file per IMIS station that indicates the daily probability for the occurrence of dry- or wet-snow avalanches in the vicinity of the respective station for each day during the time period 1.12.1981-31.05.2099 (months December to May) and all climate model chains and RCP scenarios.

en: Climate scenarios .smet

en: contains one .smet file per combination of station, climate model chain and RCP scenario. Each .smet file contains the downscaled meteorological data (time span 1.9.1981-31.8.2099) that was used to drive the SNOWPACK simulations.

en:
This data set includes 21st century projections of dry- and wet-snow avalanche activity for seven sites in the Swiss Alps as well as the downscaled climate scenarios and SNOWPACK configuration files used to produce these projections. The methods used to produce these data are described in the research article:
Mayer, S., Hendrick, M., Michel, A., Richter, B., Schweizer, J., Wernli, H., and van Herwijnen, A.: Changes in snow avalanche activity in response to climate warming in the Swiss Alps, EGUsphere, 2024, 1-32, https://doi.org/10.5194/egusphere-2024-1026, 2024.
We used downscaled climate projections to drive the snow cover model SNOWPACK and evaluated future dry- and wet-snow avalanche occurrences in the vicinity of seven automatic weather stations from the IMIS network using machine learning models trained with observed records of avalanche activity. Projections are available for three different emission scenarios (Representative Concentration Pathways, RCP2.6, RCP4.5 and RCP8.5) and eight climate model chains from the CH2018 ensemble. The output of the model chain is given by a daily probability for dry- or wet-snow avalanche occurrences. These probabilities can then be used to classify a day as a dry- and wet-snow avalanche day (AvD) or non-avalanche day.

avalanche

display_name: avalanche

climate-change

display_name: climate-change

climate-projections

display_name: climate-projections

dry-snow-avalanche

display_name: dry-snow-avalanche

snow

display_name: snow

snow-instability

display_name: snow-instability

snow-stratigraphy

display_name: snow-stratigraphy

wet-snow-avalanche

display_name: wet-snow-avalanche

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: alpine3d-simulations switzerland snow climate-change graubunden

en: Configuration file

en: Simulation data

en: Reference data as well as the 2050 and 2099 scenarios for ARPEGE and ETHZ other RCMs available on demand)

en:
This is the simulation dataset from _"Response of snow cover and runoff to climate change in high Alpine catchments of Eastern Switzerland"_, M. Bavay, T. Grünewald, M. Lehning, Advances in Water Resources 55, 4-16, 2013

A model study on the impact of climate change on snow cover and runoff has been conducted for the Swiss Canton of Graubünden. The model Alpine3D has been forced with the data from 35 Automatic Weather Stations in order to investigate snow and runoff dynamics for the current climate. The data set has then been modified to reflect climate change as predicted for the 2021-2050 and 2070-2095 periods from an ensemble of regional climate models.

The predicted changes in snow cover will be moderate for 2021-2050 and become drastic in the second half of the century. Towards the end of the century the snow cover changes will roughly be equivalent to an elevation shift of 800 m. Seasonal snow water equivalents will decrease by one to two thirds and snow seasons will be shortened by five to nine weeks in 2095.

Small, higher elevation catchments will show more winter runoff, earlier spring melt peaks and reduced summer runoff. Where glacierized areas exist, the transitional increase in glacier melt will initially offset losses from snow melt. Larger catchments, which reach lower elevations will show much smaller changes since they are already dominated by summer precipitation.

graubunden

display_name: graubunden

switzerland

display_name: switzerland

en: Alpine3D simulations of future climate scenarios for Graubunden


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

de: ogd101-bfe versorgungssicherheit gas nettoimport gasverbrauch

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

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

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

fr:
L'importation nette est la différence quotidienne entre le total des flux de gaz vers la Suisse et les flux de gaz de la Suisse vers les pays voisins.

Le gaz est importé mais aussi exporté vers la Suisse via douze points d'entrée en provenance des pays voisins : Allemagne, France, Italie et Autriche. Le point d'entrée le plus important ces dernières années est Wallbach, sur le gazoduc de transit, par lequel transitent environ 80% des importations de gaz naturel.

L'importation nette est un indicateur de la consommation en Suisse, étant donné que la Suisse ne dispose pas de réserves propres notables. Il convient toutefois de faire la distinction entre l'importation nette, la consommation nationale et la consommation finale. L'importation nette comprend la consommation nationale moins l'injection de biogaz dans le réseau de gaz naturel (production suisse). La consommation finale correspond à la consommation nationale moins l'utilisation du gaz pour la production d'électricité et de chauffage urbain (transformation) et la consommation propre du secteur gazier ainsi que les pertes de transport et de réseau. Contrairement à la consommation finale de gaz, les pertes de transport, la consommation propre du secteur gazier ou les injections de biogaz sont incluses dans les importations nettes. Pour ces postes, aucune donnée n'est actuellement disponible sur une base mensuelle/journalière et la consommation finale ne peut donc pas être estimée.

Disclaimer Données

Les données proviennent de différentes sources. Les données actualisées quotidiennement sont disponibles à partir des sources publiques de l'UE. Elles sont affichées avec un retard de 3 jours, car elles sont alors complètes. Ces sources présentent toutefois certains problèmes de qualité pour la Suisse, dans la mesure où des flux parfois physiques, parfois uniquement commerciaux, sont disponibles. D'une pertinence manifestement variable, elles peuvent être utilisées de manière transitoire pour une approximation des importations nettes. Cela ne change pas grand-chose aux tendances générales des importations nettes. Des données quotidiennes fiables sur les importations nettes, la consommation nationale et la consommation finale ne sont pas disponibles actuellement. L'OFAE, en collaboration avec l'OFEN, travaille sur des solutions avec le VSG. Les données sur les importations nettes mensuelles proviennent de l'Association Suisse de l'Industrie Gazière (ASIG).

en:
Net import is the daily difference between the total gas flows into Switzerland and the gas flows from Switzerland to neighbouring countries.

Gas is imported into Switzerland via twelve entry points from the surrounding countries of Germany, France, Italy and Austria, but it is also exported. The most important entry point in recent years has been Wallbach on the transit gas pipeline, which handles around 80% of natural gas imports.

Net imports are an indicator of consumption in Switzerland, as Switzerland has no significant storage facilities of its own. However, a distinction must be made between net imports, national consumption and final consumption. Net import includes national consumption minus biogas injection into the natural gas grid (Swiss production). Final consumption results from national consumption minus gas input for the production of electricity and district heating (conversion) and the gas sector's own consumption as well as transport and network losses. In contrast to the final consumption of gas, transport losses, own consumption of the gas sector or biogas feed-ins are included in net imports. For these items, no data are currently available on a monthly/daily basis, which is why final consumption cannot be estimated.

Disclaimer Data

Data are obtained from different sources. Daily updated data are available from EU public sources. They are shown with a delay of 3 days, as they are then complete. However, these sources have certain quality problems for Switzerland in that sometimes physical flows are available, sometimes only commercial flows. Obviously of varying significance, they can be used transitionally for an approximation of net imports. This does not change much in the rough trends of net imports. Reliable daily data on net imports, national consumption and final consumption are currently not available. The FONES is working on solutions with the Swiss Federal Office of Energy (SFOE) and the Swiss Association of Energy Producers (VSG). The data on monthly net imports come from the Swiss Gas Industry Association (VSG).

de:
Als Nettoimport versteht man den täglichen Unterschied zwischen den gesamten Gasflüssen in die Schweiz und den Gasflüssen aus der Schweiz in die Nachbarländer.

Gas wird über zwölf Einspeisepunkte aus den umliegenden Ländern Deutschland, Frankreich, Italien und Österreich in die Schweiz importiert aber auch exportiert. Der wichtigste Einspeisepunkt in den letzten Jahren war Wallbach auf der Transitgasleitung, über den rund 80% der Erdgasimporte abgewickelt werden.

Der Nettoimport ist ein Indikator für den Verbrauch in der Schweiz, da die Schweiz über keine namhaften eigenen Speicher verfügt. Es muss jedoch zwischen Nettoimport, Landesverbrauch und Endverbrauch unterschieden werden. Der Nettoimport umfasst den Landesverbrauch abzüglich der Biogaseinspeisung ins Erdgasnetz (Schweizer Produktion). Der Endverbrauch ergibt sich aus dem Landesverbrauch abzüglich dem Gaseinsatz für die Erzeugung von Elektrizität und Fernwärme (Umwandlung) und dem Eigenverbrauch des Gassektors sowie Transport- und Netzverlusten. Im Unterschied zum Endverbrauch von Gas sind Transportverluste, Eigenverbrauch des Gassektors oder Biogaseinspeisungen im Nettoimport enthalten. Für diese Posten sind aktuell keine Daten auf Monats-/Tagesbasis verfügbar, weshalb der Endverbrauch nicht abgeschätzt werden kann.

Disclaimer Daten

Die Daten werden aus unterschiedlichen Quellen bezogen. Tagesaktuelle Daten sind aus den öffentlichen Quellen der EU verfügbar. Sie werden mit einem Verzug von 3 Tagen angezeigt, da sie dann vollständig sind. Diese Quellen weisen aber für die Schweiz gewisse Qualitätsprobleme auf, indem manchmal physische, manchmal nur kommerzielle Flüsse verfügbar sind. Offensichtlich unterschiedlich aussagekräftig können sie übergangsweise für eine Näherung der Nettoimporte verwendet werden. An den groben Tendenzen des Nettoimportes ändert dies nicht viel. Belastbare tägliche Daten zu Nettoimporten, Landesverbrauch und Endverbräuch sind aktuell nicht verfügbar. Das BWL arbeitet unter Einbezug des BFE mit dem VSG an Lösungen. Die Daten zu den monatlichen Nettoimporten stammen vom Verband der Schweizerischen Gasindustrie (VSG).

it:
L'importazione netta è la differenza giornaliera tra i flussi totali di gas in Svizzera e i flussi di gas dalla Svizzera verso i Paesi limitrofi.

Il gas viene importato in Svizzera attraverso dodici punti di ingresso dai Paesi limitrofi di Germania, Francia, Italia e Austria, ma viene anche esportato. Il punto di ingresso più importante negli ultimi anni è stato Wallbach sul gasdotto di transito, che gestisce circa l'80% delle importazioni di gas naturale.

Le importazioni nette sono un indicatore del consumo in Svizzera, in quanto la Svizzera non dispone di strutture di stoccaggio significative proprie. Tuttavia, occorre distinguere tra importazioni nette, consumo nazionale e consumo finale. L'importazione netta comprende il consumo nazionale meno l'iniezione di biogas nella rete del gas naturale (produzione svizzera). Il consumo finale deriva dal consumo nazionale meno l'apporto di gas per la generazione di elettricità e il teleriscaldamento (conversione) e il consumo proprio del settore del gas, nonché il trasporto e le perdite di rete. A differenza del consumo finale di gas, le perdite di trasporto, il consumo proprio del settore del gas o le immissioni di biogas sono incluse nelle importazioni nette. Per queste voci, attualmente non sono disponibili dati su base mensile/giornaliera, motivo per cui non è possibile stimare il consumo finale.

Disclaimer Data

I dati sono ottenuti da diverse fonti. I dati aggiornati quotidianamente sono disponibili presso le fonti pubbliche dell'UE. Vengono mostrati con un ritardo di 3 giorni, perché poi sono completi. Tuttavia, queste fonti presentano alcuni problemi di qualità per la Svizzera, in quanto a volte sono disponibili flussi fisici, a volte solo flussi commerciali. Ovviamente di importanza variabile, possono essere utilizzati transitoriamente per un'approssimazione delle importazioni nette. Questo non cambia molto nelle tendenze approssimative delle importazioni nette. Attualmente non sono disponibili dati giornalieri affidabili sulle importazioni nette, sul consumo nazionale e sul consumo finale. Il FONES sta lavorando a soluzioni con l'Ufficio federale dell'energia (UFE) e l'Associazione svizzera dei produttori di energia (VSG). I dati sulle importazioni nette mensili provengono dall'Associazione svizzera dell'industria del gas (VSG).

ener

fr: Énergie

en: Energy

it: Energia

gove

fr: Gouvernement et secteur public

en: Government and public sector

it: Governo e settore pubblico

econ

en: Economy and finance

bundesamt-fur-energie-bfe

de: Bundesamt für Energie BFE

en: Swiss Federal Office of Energy SFOE

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).


photogrammetric-drone-data-gruenbodeli

title_for_slug: photogrammetric-drone-data-gruenbodeli

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

en: Data Access

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

All products are also available as a cloud optimized geotiff.

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

drone-data

display_name: drone-data

dsm

display_name: dsm

orthophoto

display_name: orthophoto

photogrammetry

display_name: photogrammetry

snow-depth-mapping

display_name: snow-depth-mapping

uav

display_name: uav

wingtra

display_name: wingtra

educ

fr: Éducation, culture et sport

it: Istruzione, cultura e sport

en: Photogrammetric Drone Data Grüenbödeli