maintainer: Ueli Reber

en: text-corpus environmental--policy switzerland bgb policy-analysis biodiversity policy-documents

en: Replication files

en:
 Introduction

The ZIP file contains all data and code to replicate the analyses reported in the following paper.

Reber, U., Fischer, M., Ingold, K., Kienast, F., Hersperger, A. M., Grütter, R., & Benz, R. (2022). Integrating biodiversity: A longitudinal and cross-sectoral analysis of Swiss politics. *Policy Sciences*. [https://doi.org/10.1007/s11077-022-09456-4](https://doi.org/10.1007/s11077-022-09456-4)

If you use any of the material included in this repository, please refer to the paper. If you use (parts of) the text corpus, please also refer to the sources used for its compilation listed below. The content of the texts may not be changed.

 Data folder

The data folder contains the following files.

* _corpus.parquet_: Text corpus of Swiss policy documents
* _dict_de.csv_: Biodiversity dictionary (German)
* _dict_fr.csv_: Biodiversity dictionary (French)
* _dict_it.csv_: Biodiversity dictionary (Italian)
* _topic_labels.csv_: labels/codes for policy sectors
* _topics.csv_: labels/codes for policy sectors

The corpus and the dictionary were compiled by the authors specifically for this project. The labels/codes for policy sectors are based on the [coding scheme](http://ws-old.parlament.ch/affairs/topics) of the Swiss Parliament.

 Text corpus

The text corpus consists of 439,984 Swiss policy documents in German, French, and Italian from 1999 to 2018. The corpus was compiled from the following source between 2020-10-01 and 2021-01-31.

* Transcripts and parliamentary businesses (e.g. questions, motions, parliamentary initiatives) via the [Web Services (WS)](https://www.parlament.ch/de/%C3%BCber-das-parlament/fakten-und-zahlen/open-data-web-services) provided by the Swiss Parliament
* The official compilation of federal legislation ("Amtliche Sammlung", AS) via [opendata.swiss](https://opendata.swiss/de/dataset/official-compilation-of-federal-legislation-bs-as-1947-2018) provided by the Swiss Federal Archives (SFA)
* The federal gazette ("Bundesblatt") via [fedlex.admin.ch](https://www.fedlex.admin.ch/de/fga/index)
* Decisions of federal courts via [entscheidsuche.ch (ES)](https://entscheidsuche.ch/)

The corpus is stored in a single data frame to use with R saved as [PARQUET](https://parquet.apache.org/) file (corpus.parquet). The data frame has the following structure.

* _text_id_: Unique identifier for each text (source information as prefix, e.g. "t_")
* _doc_type_: Document type (see coding scheme below)
* _branch_: Government branche (1 legislative, 2 executive, 3 judicative)
* _stage_: Stage of policy process (1 drafting, 2 introduction, 3 interpretation)
* _year_: Year of publication
* _topic_: Policy sector (coding scheme in separate file in data folder)
* _lang_: Language (de, fr, it)
* _text_: Text

The following list contains the coding scheme for the doc_type variable.

* 101: Federal gazette // Draft for public consultation ("Vernehmlassungsverfahren")
* 102: Federal gazette // Explanation of draft for parliament ("Botschaft")
* 103: Federal gazette // Strategy, action plan
* 104: Federal gazette // Federal council decree ("Bundesratsbeschluss")
* 105: Federal gazette // (Simple) Federal decree ("(Einfacher) Bundesbeschluss")
* 106: Federal gazette // General decree ("Allgemeinverfügung")
* 107: Federal gazette // Treaty ("Übereinkommen")
* 108: Federal gazette // Treaty ("Abkommen")
* 109: Federal gazette // Draft for parliament ("Entwurf")
* 110: Federal gazette // Report ("Bericht")
* 111: Federal gazette // Report of parliamentary comission ("Bericht")
* 112: Federal gazette // Report of federal council ("Bericht")
* 201: Parl. businesses // Submitted text
* 202: Parl. businesses // Reason text
* 203: Parl. businesses // Federal council response
* 204: Parl. businesses // Initial situation
* 205: Parl. businesses // Proceedings
* 301: Parl. transcripts // Speech of MP
* 302: Parl. transcripts // Speech of federal council
* 401: Federal legislation // Legal text of the official compilation (law, ordinances, etc.)
* 501: Court decisions // Federal Supreme Court
* 502: Court decisions // Federal Criminal Court
* 503: Court decisions // Federal Administrative Court

 Code folder

The code folder contains all R code for the analyses. The files are numbered chronologically.

* _1_classifier_training.R_: Training of classifiers for classification of policy sectors
* _2_classifier_application.R_: Classification of documents in corpus
* _3_dictionary_application.R_: Biodiversity indexing of documents in corpus
* _4_stm_truncation.R_: Truncation of indexed documents to keep only relevant parts
* _5_stm_translation.R_: Translation of FR and IT documents to DE
* _6_stm_model.R_: Preprocesssing and structural topic model
* _7_plots.R_: Plots and numbers as included in the paper

The code/functions folder contains custom functions used in the scripts, e.g. to support topic model interpretation.

Package versions and setup details are noted in the code files.

 Contact

Please direct any questions to Ueli Reber (ueli.reber@eawag.ch).

bgb

display_name: bgb

biodiversity

display_name: biodiversity

environmental--policy

display_name: environmental--policy

policy-analysis

display_name: policy-analysis

policy-documents

display_name: policy-documents

switzerland

display_name: switzerland

text-corpus

display_name: text-corpus

en: Replication files for "Integrating biodiversity: A longitudinal and cross-sectoral analysis of Swiss politics"


maintainer: EnviDat Support

en: snow deposition atmosphere turbulence particles hill dust

state: active

type: dataset

en: preferential deposition patterns of all simulations performed

media_type: application/zip

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

format: ZIP

mimetype: application/zip

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

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

atmosphere

display_name: atmosphere

deposition

display_name: deposition

dust

display_name: dust

hill

display_name: hill

particles

display_name: particles

snow

display_name: snow

turbulence

display_name: turbulence

education

fr: Education, science

de: Bildung, Wissenschaft

en: Education and science

it: Formazione e scienza

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester


en: drought-2018 soil-moisture precipitation low-flow prevah hydrological-simulations discharge

en: Soil moisture

media_type: text/plain

en: Simulated plant-available soil moisture for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm.

format: TXT

mimetype: text/plain

en: Discharge

en: Simulated discharge for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm/d.

en: Shapefile of 307 catchments in Switzerland

en: Shapefile of 307 catchments for which simulations of precipitation, discharge, soil moisture, and low flow are provided. The OBJECTID entry corresponds to the data columns in the textfiles of the simulated data provided.

en: Precipitation

en: Simulated precipitation for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm/d.

en: Low-flow storage

en: Simulated low-flow storage for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm.

en: The dataset provides simulated 1) precipitation, 2) discharge, 3) soil moisture, and 4) low-flow simulations for 307 medium-sized catchments in Switzerland for the period 1981-2018. The data were simulated using the hydrological model PREVAH in its gridded-version. The simulated time series are provided at daily resolution. A detailed description of the modeling approach can be found in Brunner et al. 2019 submitted to NHESS.

discharge

display_name: discharge

hydrological-simulations

display_name: hydrological-simulations

low-flow

display_name: low-flow

precipitation

display_name: precipitation

prevah

display_name: prevah

soil-moisture

display_name: soil-moisture

en: Hydro-meteorological simulations for the period 1981-2018 for Switzerland


disdrometer-data-gotschnagrat

title_for_slug: disdrometer-data-gotschnagrat

en: visibility raclets precipitation hydrometeors disdrometer

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.

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

raclets

display_name: raclets

visibility

display_name: visibility


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

en: Dataset 3

media_type: .csv

en: Aggregated data on number hives, beekeeping locations and ratios

format: SERVICE

en: Dataset 1

media_type: CSV

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

format: CSV

en: Dataset 2

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

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

honeybees

display_name: honeybees

pollination

display_name: pollination

remote-sensing

display_name: remote-sensing

sustainability

display_name: sustainability

urban-biodiversity

display_name: urban-biodiversity

urbanization

display_name: urbanization

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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

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


snow-drift-station-micro-rain-radar

title_for_slug: snow-drift-station-micro-rain-radar

en: raclets snow radar mountain precipitation rain

en: MRR data of period 3

en: The dataset contains measurements between March 8 and March 13, 2019, with time in UTC. The parameters and specifications are given in MRR_parameters.pdf. The zip folder contains hourly data files in netCDF format and a log file, reporting the instrument status.

en: MRR data Period 2

en: The dataset contains measurements between March 5 and March 8, 2019, with time in UTC. The parameters and specifications are given in MRR_parameters.pdf. The zip folder contains hourly data files in netCDF format and a log file, reporting the instrument status.

en: Parameter MRR

media_type: application/pdf

en: The PDF contains additional information on parameters which were set for the certain time periods (time in UTC).

format: PDF

mimetype: application/pdf

en: MMR Data Period 1

en: The dataset contains measurements between March 1 and March 5, 2019, with time in UTC. The parameters and specifications are given in MRR_parameters.pdf. The zip folder contains hourly data files in netCDF format and a log file, reporting the instrument status.

en: MRR data of period 4

en: The dataset contains measurements between March 13 and March 22, 2019, with time in UTC. The parameters and specifications are given in MRR_parameters.pdf. The zip folder contains hourly data files in netCDF format and a log file, reporting the instrument status.

en: The instrument (MRR, Metek) was mounted at Gotschnagrat (LON: 46.859 LAT: 9.849) at a height of 1 m above snow surface (at the beginning of the campaign) with an orientation of 22° with respect to North and a horizontal viewing direction. The sampling time was either 5 s or 10 s, depending on the settings at the specific period. The MRR produces standard outputs like radar reflectivity, doppler velocity, etc., and additional information can be found [here](https://metek.de/de/product/mrr-2/).

mountain

display_name: mountain

radar

display_name: radar

rain

display_name: rain

snow

display_name: snow

en: Snow Drift Station - Micro Rain Radar


maintainer: Christian G. Sommer

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

en: Kinect data

media_type: Link

en: Links to the dataset

en: Processing scripts

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

en: README

en: Describes the dataset in detail

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

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

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

This dataset is the basis of the following publication:

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

kinect

display_name: kinect

wind-crust

display_name: wind-crust

wind-packing

display_name: wind-packing

wind-tunnel

display_name: wind-tunnel

education

en: Education and science

en: Wind crust formation: Microsoft Kinect data


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 B_Transition_Analysis.R
Further Analysis of transition numbers

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

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

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

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

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

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

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

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

 05_Service_Data
Any service calculations will be saved in here

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

en:
Data and scripts of publication:

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

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

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

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

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

land-use

display_name: land-use

land-use-change

display_name: land-use-change

landscape-services

display_name: landscape-services

modeling

display_name: modeling

statistical-model

display_name: statistical-model

switzerland

display_name: switzerland

envidat

en: EnviDat: the Environmental Data Portal

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

en: Land use projections and services for Switzerland


en: lee-side-flow-field lidar preferential-deposition alpine3d tls

en: Dataset

media_type: ZIP

en:
The data presented here corresponds to the publication "A Close-Ridge Small-Scale Atmospheric Flow Field and its Influence on Snow Accumulation" (Gerber et al., 2017), which investigates an eddy-like structure in the vicinity of the Sattelhorn in the Dischma valley (Davos Switzerland) and its influence on snow accumulation. The dataset contains:

* Alpine3D: Alpine3D snow depth grids (25 m resolution) for two simulations with and without snow redistribution.
* ARPS: 10 ARPS simulations (25 m horizontal resolution) with different model setups (wind direction, wind speed, stability).
* LiDAR: Processed LiDAR PPI (D2_PPI_1h) and RHI (D2_cross_1h) across the valley with a hourly resolution for the period 27 October 2015 01:00 - 29 October 2015c 21:00 (spatial resolution: 25 m).
* meteostations-dischma: Meteorological station data of two meteorological stations in the Dischma valley with 10 minute resolution for the period 28 October 2015 - 30 October 2015.
* TLS: Snow depth change data (m) between 28 October 2015 and 30 October 2015 based on terrestrial laser scans.

For more details about the simulation and observation data, see Gerber et al., 2017.

Publication: Gerber et al., 2017: A Close-Ridge Small-Scale Atmospheric Flow Field and its Influence on Snow Accumulation, submitted to JGR - Atmospheres.

lee-side-flow-field

display_name: lee-side-flow-field

lidar

display_name: lidar

preferential-deposition

display_name: preferential-deposition

tls

display_name: tls

en: DISCHMEX - Observations and simulations of the close-ridge small-scale atmospheric flow field and snow accumulation at Sattelhorn, Dischma valley, Davos, Switzerland.


maintainer: Stephan Zimmermann

en: magnesium biogeochemical-cycling calcium potassium fagus-orientalis-lipsky

en: Concentrations of Ca, Mg, K, and Na in throughfall, litterflow and soil solution of a Hyrcanian forst in Northern Iran

format: XLS

en:
Throughfall, litterflow and soil solution were sampled during one whole year under five Oriental beech trees in a mixed Hyrcanian beech forest. The amounts of Ca2+, Mg2+, K+ and Na+ in these fluxes were calculated based on their concentrations and the sampled volumes, and subsequently compared with the respective fluxes in the rainfall and soil solution of an adjacent forest gap.
In addition six soil profiles, one close to every single tree and one in the forest gap, were analyzed for pH, CaCO3, organic matter and texture.

biogeochemical-cycling

display_name: biogeochemical-cycling

calcium

display_name: calcium

fagus-orientalis-lipsky

display_name: fagus-orientalis-lipsky

magnesium

display_name: magnesium

potassium

display_name: potassium

en: Base cation dynamics in an Oriental beech forest


en: surface-mass-balance cryowrf antarctica reproducibility blowing-snow

en: Processed CRYOWRF output used for the analysis and the figures in the paper.

en: Python scripts to reproduces figures in Gerber et al., 2022, submitted.

media_type: application/x-zip-compressed

en: Python scripts to reproduces figures in Gerber et al., 2022, revised

mimetype: application/x-zip-compressed

en: Processed WRF output (run with Noah-MP land surface model) used for the analysis and the figures in the paper.

en: Static input data from the WRF simulations (used in the python scripts in Scripts_Reproducibility).

en: Namelist and input files and information to run the WRF and CRYOWRF simulations used in the paper.

en:
This dataset contains data and scripts for "CRYOWRF - a validation and the effect of blowing snow on the Antarctic SMB" (Gerber et al., submitted).

* Simulation_setup: Namelists and input information to run the simulation. Some input files need to be downloaded from Sharma et a., 2021.
* Static_input: Static topography input file of WRF (geo_em.d01).
* WRF_27km_NoahMP: Preprocessed WRF output of the simulation run with the WRF using the surface parameterization Noah-MP to reproduce the figures and results in the paper.
* WRF_27km_CRYOWRF: Preprocessed WRF output of the simulation run with CRYOWRF to reproduce the figures and results in the paper.
* Scripts_Reproducibility: Python scripts to reproduce the figures and results in the paper.

Note:

* To run some of the scripts Atmospheric Weather station data needs to be prepared using Gerber and Lehning, 2022.
* AWS data is not provided and needs to be downloaded from the corresponding databases. Please make sure to comply with the respective terms and conditions.

antarctica

display_name: antarctica

blowing-snow

display_name: blowing-snow

cryowrf

display_name: cryowrf

reproducibility

display_name: reproducibility

surface-mass-balance

display_name: surface-mass-balance

education

en: Education and science

en: Reproducibility dataset for CRYOWRF validation


maintainer: Andrin Caviezel

en: rockfall rockfall-runout dem natural-hazards rockfall-experiments stonenode induced-rockfall sensor-stream stonenodedata deposition-points

en: Induced Rockfall Data, Tschamut, GR

en:
 Induced Rockfall Experimental Data, Tschamut, GR, Switzerland

The archive contains site specific geographical data as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 30–80 kg of mass. Additionally availabe are the StoneNode v1.0 sensor streams for the equipped rocks.

The _OverviewFigures_ file serves as quick representation of the included data. It also contains the overview table for the rock masses in order to set the dimensions accordingly in RAMMS::ROCKFALL.

The archive contains the following folders:
<\ul>
* DEM: ArcGIS compatible DEM Tschamut_DEM_2m (grid resolution of 2 m) as well as the .asc grid used in RAMMS::ROCKFALL simulations
* _DepositionPoints_: containing two folders _RF Tschamut Frozen_ and _RF Tschamut WET_ containing each a shapefile with the complete deposition data of each series as well as deposition shapfiles for each rock shape.
* _DigitizedRocks_: Digitized point clouds of the used rocks as used for RAMMS::ROCKFALL simulations (.pts files) (dimensions have to be checked).
* _Orthofoto_: Orthophoto of the experimental site obtained via UAV with 5 cm resolution.

en: StoneNode v1.0 Data for equipped experimental Runs.

en: Corresponding StoneNode v1.0 data streams belonging to the deposition points.

en:
 Dataset of an experimental campaign of induced rockfall in Tschamut, Grisons, Switzerland.

The data archive contains site specific geographical data such as DEM and orthophoto as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 30–80 kg of mass. Additionally available are all the StoneNode data streams for rocks equipped with a sensor. The data set consists of

* Deposition points from two series (wet (27/10/2016) and frozen (08/12/2016) ground)
* Digital Elevation Model (grid resolution 2 m) obtained via UAV
* Orthophoto (5 cm resolution) obtained via UAV
* Digitized rock point clouds (.pts input files for RAMMS::ROCKFALL)
* StoneNode v1.0 raw data stream for equipped rocks.

Further information is found in

* A. Caviezel et al., _Design and Evaluation of a Low-Power Sensor Device for Induced Rockfall Experiments_, IEEE Transactions on Instrumentation and Measurement, 2018, 67, 767-779, http://ieeexplore.ieee.org/document/8122020/
* P. Niklaus et al., _StoneNode: A low-power sensor device for induced rockfall experiments_, 2017 IEEE Sensors Applications Symposium (SAS), 2017, 1-6, http://ieeexplore.ieee.org/document/7894081/

dem

display_name: dem

deposition-points

display_name: deposition-points

induced-rockfall

display_name: induced-rockfall

natural-hazards

display_name: natural-hazards

rockfall

display_name: rockfall

rockfall-experiments

display_name: rockfall-experiments

rockfall-runout

display_name: rockfall-runout

sensor-stream

display_name: sensor-stream

stonenode

display_name: stonenode

stonenodedata

display_name: stonenodedata

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: Induced Rockfall Dataset (Small Rock Experimental Campaign), Tschamut, Grisons, Switzerland


maintainer: Vanessa Burg

en: bioenergy switzerland manure biogas review

en: Biogas from animal manure in Switzerland: energy potential, technology development and resource mobilization

media_type: PDF

en: Aim of this white paper is to provide decision-makers, administrations and stakeholders with the most current research findings in order to promote the optimal use of bioenergy from manure in the Swiss energy transition. For this purpose, the results of the Swiss competence center for bioenergy research - SCCER BIOSWEET - are summarized and presented in a broader context. If nothing else is mentioned, the results refer to Switzerland and in case of the feedstock to the domestic biomass potentials.

bioenergy

display_name: bioenergy

biogas

display_name: biogas

manure

display_name: manure

review

display_name: review

switzerland

display_name: switzerland


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

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.

national-economy

fr: Economie nationale

de: Volkswirtschaft

en: National economy

it: Economia

energy

fr: Energie

de: Energie

en: Energy

it: Energia

politics

fr: Politique

de: Politik

en: Politics

it: Politica

prices

fr: Prix

de: Preise

en: Prices

it: Prezzi

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: avalanche-forecast avalanche-danger-level avalanche-forecasting avalanche-bulletin verification

en: D_QC for forecasting seasons 2001/2002 until 2019/2020.

en: D_QC for forecasting seasons 2020/2021 - 2022/2023.

en:
The data set contains the re-analyzed (or quality-checked) regional avalanche danger levels (D_QC) for Switzerland.

D_QC relates to dry-snow avalanche conditions only. Measuring the avalanche danger level D is not possible; forecast, nowcast, and hindcast assessments of D are judgments by humans interpreting data. However, combining several pieces of information indicating the same D, it can be expected that it is more likely that D_QC represents the avalanche conditions well.

For the **forecasting seasons 2001/2002 until 2019/2020**, the approach to obtain D_QC is described in detail in Appendix A of [Pérez-Guillén et. al. (2022)](https://nhess.copernicus.org/articles/22/2031/2022/nhess-22-2031-2022.html).

For the **forecasting seasons 2020/2021 and later**, D_QC is derived using the following approach:
1. *Combination of forecast (D_forecast) and nowcast (D_nowcast)*: If there was only one assessment available by an observer after a day in the field for a region, and if D_forecast = D_nowcast --> D_QC = D_forecast.
2. *Combination of several nowcast assessments (D_nowcast)*: If two (or more) observers agreed (or majority opinion) in their (independent) assessments of D_nowcast after a day in the field in the same warning region. --> D_QC = D_nowcast.
3. *Hindcast analysis (D_hindcast)*: In Switzerland, avalanche forecasters re-evaluate all situations when D = 4 (high) or D = 5 (very high) were either forecast, should have been forecast, or when forecasters discussed given one of these two levels but had not given them. Generally, two forecasters assess each situation. In these cases, D_QC = D_hindcast. The hindcast analysis, only available since the forecasting season 2020/2021, replaces what was step (2) in Appendix A of [Pérez-Guillén et. al. (2022)](https://nhess.copernicus.org/articles/22/2031/2022/nhess-22-2031-2022.html).

All other cases - ties in case of (1) or (2), no new information from the warning region in question, or if no D_hindcast was available - are not considered quality-checked, and are, thus, not contained in the data set.

In addition to D_QC, the file contains information on the elevation and aspect, where D_QC likely prevails.
- The indicated elevation is the mean of the respective elevations in (1), (2), or (3). At danger level 1 (low), when no elevation is indicated in the Swiss forecast, a value of 1500 m is set.
- For the four cardinal aspects N, E, S, and W, a value of 1 means that there was agreement that D was reached in this aspect and a value of 0 means that there was agreement that D was not reached in this aspect. Intermediate values correspondingly mark disagreements in the assessments.

avalanche-bulletin

display_name: avalanche-bulletin

avalanche-danger-level

display_name: avalanche-danger-level

avalanche-forecast

display_name: avalanche-forecast

avalanche-forecasting

display_name: avalanche-forecasting

verification

display_name: verification

education

en: Education and science

en: Re-analyzed regional avalanche danger levels in Switzerland


en: landscape conservation amphibians wetlands occupancy-models

en: Model code

media_type: Rmd

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

en: Covariate data 2

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

en: Original raw data

media_type: XLSX

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

en: Readme

en: Description of methods and datasets.

en: Covariate data 1

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

en: Model code, description

media_type: HTML

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

format: HTML

en: Observation data

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

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

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

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

amphibians

display_name: amphibians

conservation

display_name: conservation

landscape

display_name: landscape

occupancy-models

display_name: occupancy-models

wetlands

display_name: wetlands

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: Amphibian observation and pond data (Aargau, Switzerland)


maintainer: Franziska Gerber

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

media_type: .zip

en: Static data for WRF - Topography

en: Static data for WRF - Soiltype top

media_type: .txt

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

en: Documentation

media_type: .pdf

en: Documentation of the dataset

en: Static data for WRF - Soiltype bottom

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

en: Static data for WRF - Landuse

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

cosmo-wrf

display_name: cosmo-wrf

cryowrf

display_name: cryowrf

landuse

display_name: landuse

static-data

display_name: static-data

topography

display_name: topography

wrf

display_name: wrf

education

en: Education and science

en: High resolution static data for WRF over Switzerland


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

national-economy

en: National economy

energy

en: Energy

politics

en: Politics

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).


ladestationen-fuer-elektroautos

maintainer: Bundesamt für Energie

title_for_slug: Ladestationen für Elektroautos

fr: Stations de recharge pour voitures électriques

de: Ladestationen für Elektroautos

en: Charging points for electric cars

it: Stazioni di ricarica per auto elettriche

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.

statistical-basis

fr: Bases statistiques

de: Statistische Grundlagen

en: Statistical basis

it: Basi statistiche

construction

fr: Construction, logement

de: Bau- und Wohnungswesen

en: Construction and housing

it: Costruzione e abitazione

culture

fr: Culture, médias, société de l'information, sport

de: Kultur, Medien, Informationsgesellschaft, Sport

en: Culture, media, information society, sport

it: Cultura, media, società dell'informazione, sport

geography

fr: Géographie

de: Geographie

en: Geography

it: Geografia

industry

fr: Industrie, services

de: Industrie, Dienstleistungen

en: Industry and services

it: Industria, servizi

mobility

fr: Mobilité et transports

de: Mobilität und Verkehr

en: Mobility and Transport

it: Mobilità e trasporti

population

fr: Population

de: Bevölkerung

en: Population

it: Popolazione

territory

fr: Territoire et environnement

de: Raum und Umwelt

en: Territory and environment

it: Territorio e ambiente

tourism

fr: Tourisme

de: Tourismus

en: Tourism

it: Turismo

name: Bundesamt für Energie & Betreibende von Ladeinfrastruktur


en: avalanche-activity glide-snow-avalanche snowpack-simulation time-lapse-photography avalanche

en: SNOWPACK simulations

media_type: .pro

en: SNOWPACK simulations (.pro and .smet) covering season 2008/09 to 2021/22 at 10 virtual locations on Dorfberg. The locations correspond to the most common glide-snow avalanche paths.

en: README

en: README.txt explaining the dataset and columns of tables in more detail.

en: Glide distances for opening glide-cracks

media_type: zip-compressed folder of csv

en: This folder contains all glide-cracks where glide-distances were extracted from time-lapse photographs. Glide-distances were calculated in x-direction (width) and y-direction (downslope, length) by georeferencing the images.

en: Glide-snow avalanche activity

en: Table that contains all glide-snow avalanche events that were detected in time-lapse photographs (season 2008/09 to 2021/22). The columns are explained in the README.txt

en:
This dataset includes the processed data of the glide-snow avalanche activity and dynamics on Dorfberg (Davos, Switzerland) covering seasons 2008/09 to 2021/22. This dataset was described in the research article:

Fees, A., van Herwijnen A., Altenbach, M., Lombardo, M., Schweizer, J.: Glide-snow avalanche characteristics at different time-scales extracted from time-lapse photography, Annals of Glaciology, 91

We extracted the dynamics of opening glide-cracks and the glide-snow avalanche activity from time-lapse photographs. Glide-snow avalanches were separated into surface and interface events using the liquid water content which was simulated with SNOWPACK at 10 virtual stations on Dorfberg.

avalanche

display_name: avalanche

avalanche-activity

display_name: avalanche-activity

glide-snow-avalanche

display_name: glide-snow-avalanche

snowpack-simulation

display_name: snowpack-simulation

time-lapse-photography

display_name: time-lapse-photography

education

fr: Education, science

de: Bildung, Wissenschaft

en: Education and science

it: Formazione e scienza

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

en: Glide-snow avalanche activity on Dorfberg, Davos, Switzerland


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).

national-economy

fr: Economie nationale

de: Volkswirtschaft

en: National economy

it: Economia

energy

fr: Energie

de: Energie

en: Energy

it: Energia

politics

fr: Politique

de: Politik

en: Politics

it: Politica

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).


maintainer: Yves Bühler

en: hazard-indication-mapping hazard-mapping avalanche release snow ramms

en: Potential Release  Area  NHESS

media_type: application/zip

en: This folder contains the output of the different investigated algorithms plus the reference dataset, used for training and validation.

en: RAMMS simulations NHESS

en: Simulated maximum pressures for the scenarios frequent and extreme taking the forest layer into account

en:
This dataset contains the output and reference data published in the paper "Automated snow avalanche release area delineation - validation of existing algorithms and proposition of a new object-based approach for large scale hazard indication mapping" Yves Bühler, Daniel von Rickenbach, Andreas Stoffel, Stefan Margreth, Lukas Stoffel, Marc Christen (2018) Natural Hazards And Earth System Sciences.

Abstract:
Snow avalanche hazard is threatening people and infrastructure in all alpine regions with seasonal or permanent snow cover around the globe. Coping with this hazard is a big challenge and during the past centuries, different strategies were developed. Today, in Switzerland, experienced avalanche engineers produce hazard maps with a very high reliability based on avalanche cadastre information, terrain analysis, climatological datasets and numerical modelling of the flow dynamics for selected avalanche tracks that might affect settlements. However, for regions outside the considered settlement areas such area-wide hazard maps are not available mainly because of the too high cost, in Switzerland and in most mountain regions around the world. Therefore, hazard indication maps, even though they are less reliable and less detailed, are often the only spatial planning tool available. To produce meaningful and cost-effective avalanche hazard indication maps over large regions (regional to national scale), automated release area delineation has to be combined with volume estimations and state-of-the-art numerical avalanche simulations.

In this paper we validate existing potential release area (PRA) delineation algorithms, published in peer-reviewed journals, that are based on digital terrain models and their derivatives such as slope angle, aspect, roughness and curvature. For validation, we apply avalanche cadastre data from three different ski resorts in the vicinity of Davos, Switzerland, where experienced ski-patrol staff mapped most avalanches in detail since many years. After calculating the best fit input parameters for every tested algorithm, we compare their performance based on the reference datasets. Because all tested algorithms do not provide meaningful delineation between individual potential release areas (PRA), we propose a new algorithm based on object-based image analysis (OBIA). In combination with an automatic procedure to estimate the average release depth (d0), defining the avalanche release volume, this algorithm enables the numerical simulation of thousands of avalanches over large regions applying the well-established avalanche dynamics model RAMMS. We demonstrate this for the region of Davos for two hazard scenarios, frequent (10 – 30 years return period) and extreme (100 – 300 years return period). This approach opens the door for large scale avalanche hazard indication mapping in all regions where high quality and resolution digital terrain models and snow data are available.

hazard-indication-mapping

display_name: hazard-indication-mapping

hazard-mapping

display_name: hazard-mapping

ramms

display_name: ramms

release

display_name: release

snow

display_name: snow

en: Automated Avalanche Release Area (PRA) Delineation Davos


wind-lidar-davos-wolfgang

title_for_slug: wind-lidar-davos-wolfgang

en: profiles raclets wolfgangpass wind lidar

en: Wind LIDAR data Wolfgang

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

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

lidar

display_name: lidar

profiles

display_name: profiles

raclets

display_name: raclets

wind

display_name: wind

wolfgangpass

display_name: wolfgangpass

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

en: Wind LIDAR Davos Wolfgang


en: atmospheric-conditions precipitation wind radiation temperature-profiles

en: Site photo 2

media_type: JPEG

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

en: README

media_type: TXT

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

en: Site photo 1

en: Site photo 3

en: Latest data from Stillberg climate station

media_type: smet

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

en: STB2 CSV

media_type: text/csv

mimetype: text/csv

en: DATA ACCESS

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

en: Overview plot

media_type: PNG

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

format: PNG

en:
 Important

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

atmospheric-conditions

display_name: atmospheric-conditions

precipitation

display_name: precipitation

radiation

display_name: radiation

temperature-profiles

display_name: temperature-profiles

envidat

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

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


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

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

media_type: ZIP

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

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

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

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

fagales

display_name: fagales

pinales

display_name: pinales

species-distribution-model

display_name: species-distribution-model

species-occurrence

display_name: species-occurrence

species-range-map

display_name: species-range-map

species-richness

display_name: species-richness

education

en: Education and science

en: EnviDat: the Environmental Data Portal

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


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

en: Data Access

media_type: geotiff

format: GeoTIFF

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

beta-diversity

display_name: beta-diversity

conservation

display_name: conservation

european-alps

display_name: european-alps

functional-diversity

display_name: functional-diversity

global-change

display_name: global-change

land-use-change

display_name: land-use-change

migclim

display_name: migclim

phylogenetic-diversity

display_name: phylogenetic-diversity

plants

display_name: plants

species-distribution-models

display_name: species-distribution-models

taxonomic-diversity

display_name: taxonomic-diversity

zonation

display_name: zonation

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


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

fr: energiedashboard.ch: Producion d'électricité Swissgrid

en: energiedashboard.ch: Electricity production Swissgrid

it: energiedashboard.ch: Produzione di elettricità Swissgrid

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

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

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

Disclaimer Données

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

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

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

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

Disclaimer Data

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

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

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

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

Disclaimer Daten

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

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

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

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

Disclaimer Dati

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

trade

fr: Commerce

de: Handel

en: Trade

it: Commercio

national-economy

en: National economy

energy

en: Energy

politics

en: Politics

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).


ceilometer-klosters

title_for_slug: ceilometer-klosters

en: klosters raclets ceilometer vertical-visibility cloud-base-height

en: Ceilometer data Klosters

media_type: mat

en: Ceilometer installed at Klosters measuring cloud base height, cloud cover and backscatter profile. The time resolution is 30 seconds, with time given in number of days from January 0, 0000, UTC. More information on the variables in the manufacturer manual.

en: Cloud base height (m) and vertical visibility (m) were measured with the VAISALA Ceilometer CL31 in Klosters (LON: 9.880413, LAT: 46.869019). The CL31 is an instrument with constant reliability for all weather conditions and simultaneous detection of three cloud layers in heights up to 7.6 km.

ceilometer

display_name: ceilometer

cloud-base-height

display_name: cloud-base-height

klosters

display_name: klosters

raclets

display_name: raclets

vertical-visibility

display_name: vertical-visibility

envidat

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

en: Ceilometer Klosters