en: snow-temperature wet-snow radar snow-roughness snow-lwc surface-roughness snow-ssa satellite snow-melt snow-profiles sentinel-1 snow-density

en: Snow water equivalent time series automatically recorded from the snow scale (2022-2024).

en: Manually measured time series of surface roughness (2022-2023).

en: Snow profiles collected on the snow season 2023-2024.

en: Runoff time series automatically recorded from the lysimeter (2022-2024).

en: Manually measured time series of snow water equivalent (2022-2023).

en: Manually measured time series of snow water equivalent (2023-2024).

en: Manually measured time series of surface roughness (2023-2024).

en: Snow profiles collected on the snow season 2022-2023.

en: General metadata and detailed data description.

en:
*Currently, this dataset is intended solely for article review purposes and may not be used for any other purposes.*
This dataset consists of a series of full snow profiles collected at the field site of Weissfluhjoch, Davos, Switzerland over the two snow seasons of 2022-2023 and 2023-2024. This series of snow profiles was collected with a high vertical and temporal resolution, with a specific focus on the melting seasons, during which up to 3 snow profiles per week were sampled. This dataset is part of the measurement campaign carried out within the SnowTinel project, whose aim is to explore the Sentinel-1 SAR backscattering response to melting Alpine snowpacks. Therefore, this dataset contains manual measurements of the main properties of snow which are responsible for scattering, i.e. temperature, density, specific surface area (SSA), liquid water content (LWC), surface roughness and snow water equivalent (SWE).
The dataset is composed of a total of 85 snow profiles, 38 carried out in 2022-2023 and 47 in 2023-2024.
 - Profiles of snow **temperature** were sampled at a vertical resolution of 10 cm (2022-2023) and 5 cm (2023-2024) using HI98501 Checktemp from Hanna Instruments;
  - Profiles of snow **density** were sampled at a vertical resolution of 3 cm using a box density cutter and a digital scale;
  - Profiles of snow **specific surface area** were sampled at a vertical resolution of 4 cm using the InfraSnow sensor from FPGA;
  - Profiles of snow **liquid water content** were sampled at a vertical resolution of 2 cm using the Denothmeter (2022-2023) and the New Capacitive Sensor from FPGA (2023-2024). In conditions of ripe snow, dielectric measurements were backed up by melting calorimetry measurements following a recently revised field protocol;
  - Snow **surface roughness** is expressed by the Root Mean Square of the Heights (RMSH) and the correlation length (CL), both computed from a digitized snow transect obtained by a digital photograph of a panel vertically inserted into the snow.
- **Snow water equivalent** was sampled in sections from the surface to the bottom of the snowpack with a cylinder cutter.

Additionally, **runoff** was automatically measured in the close proximity to the measurement field at a sub-hourly resolution by a lysimeter. The instrument was unfortunately discovered to be clogged at the start of the runoff in 2023 and only repaired in late May 2023. Therefore, the time series is not explanatory for the runoff start in 2023. The instrument was inspected timely and assessed as fully functional for the following snow season. The manually measured values of **SWE** are complemented by an automatically recorded time series at sub-hourly time intervals with a SSG1000 snow scale manufactured by Sommer Messtechnik, Austria.

 ---

- References:
 - HI98501 Checktemp: [https://www.hannainstruments.co.uk/modules/teapotknowledgehub/uploads/ist98501_06_18-60d496ca2cd31.pdf](https://www.hannainstruments.co.uk/modules/teapotknowledgehub/uploads/ist98501_06_18-60d496ca2cd31.pdf)
 - InfraSnow sensor: Wolfsperger, Fabian & Ziegler, Silvio & Schneebeli, Martin & Löwe, Henning. (2022). Evaluation of the InfraSnow: a handheld device to measure snow specific surface (SSA). 10.13140/RG.2.2.31566.95047; [https://snow-sen.com/infrasnow-ssa-sensor-2/](https://snow-sen.com/infrasnow-ssa-sensor-2/)
 - Denothmeter: Denoth, A. “An Electronic Device for Long-Term Snow Wetness Recording.” Annals of Glaciology 19 (1994): 104–6. [https://doi.org/10.3189/S0260305500011058](https://doi.org/10.3189/S0260305500011058)
 - New Capacitive Sensor: Wolfsperger, Fabian & Geisser, Michel & Ziegler, Silvio & Löwe, Henning. (2023). A NEW HANDHELD CAPACITIVE SENSOR TO MEASURE SNOW DENSITY AND LIQUID WATER CONTENT; [https://snow-sen.com/slf-snowpro-40/](https://snow-sen.com/slf-snowpro-40/)
 - Melting calorimetry: Barella, R., Bavay, M., Carletti, F., Ciapponi, N., Premier, V., and Marin, C.: Unlocking the potential of melting calorimetry: a field protocol for liquid water content measurement in snow, The Cryosphere, 18, 5323–5345, [https://doi.org/10.5194/tc-18-5323-2024](https://doi.org/10.5194/tc-18-5323-2024), 2024.
 - Surface roughness: B. Riccardo, C. Marin, M. Callegari, M. Gianinetto, T. Moranduzzo and C. Notarnicola, "A Low-Cost Portable Automatic System for Snow Surface Roughness Measurements Based on Digital Photography," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021, pp. 5562-5565, doi: [https://doi.org/10.1109/IGARSS47720.2021.9553989](https://doi.org/10.1109/IGARSS47720.2021.9553989).
 - SSG: [https://www.sommer.at/de/produkte/schnee-eis/schneewaage-ssg-2](https://www.sommer.at/de/produkte/schnee-eis/schneewaage-ssg-2)

radar

display_name: radar

satellite

display_name: satellite

snow-density

display_name: snow-density

snow-lwc

display_name: snow-lwc

snow-melt

display_name: snow-melt

snow-profiles

display_name: snow-profiles

snow-roughness

display_name: snow-roughness

snow-ssa

display_name: snow-ssa

snow-temperature

display_name: snow-temperature

surface-roughness

display_name: surface-roughness

wet-snow

display_name: wet-snow

en: SnowTinel high-temporal-resolution ground truth dataset for SAR remote sensing of snow


data-set-on-snow-instability

maintainer: EnviDat Support

title_for_slug: data-set-on-snow-instability

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

state: active

type: dataset

en: data set on snow instability

media_type: text/csv

format: CSV

mimetype: text/csv

en: Read-me file on snow instability data

media_type: application/pdf

format: PDF

mimetype: application/pdf

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

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

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

avalanche-forcasting

display_name: avalanche-forcasting

crack-propagation

display_name: crack-propagation

failure-initiation

display_name: failure-initiation

propgatoin-saw-test

display_name: propgatoin-saw-test

rutschblock

display_name: rutschblock

slab

display_name: slab

slab-tensile-support

display_name: slab-tensile-support

snow-avalanche

display_name: snow-avalanche

snow-stability-evaluation

display_name: snow-stability-evaluation

weak-layer

display_name: weak-layer

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Data set on snow instability


en: biodiversity statistical-interpolation ecology climate-change radiation-transfer microclimate forest

media_type: application/zip

en: Predicted microclimate air temperatures at 100 cm above ground. 10 m spatial resolution raster files are provided for mean daily minimum (tmin), mean (tmean) and maximum (tmax) temperatures during each month from April (04) to October (10) over the period between 2012 to 2021.

format: ZIP

en: Predicted microclimate air temperatures at 5 cm above ground. 10 m spatial resolution raster files are provided for mean daily minimum (tmin), mean (tmean) and maximum (tmax) temperatures during each month from April (04) to October (10) over the period between 2012 to 2021.

en: Predicted microclimate topsoil temperatures at 5 cm below ground. 10 m spatial resolution raster files are provided for mean daily minimum (tmin), mean (tmean) and maximum (tmax) temperatures during each month from April (04) to October (10) over the period between 2012 to 2021.

en: Climate data matching the scales at which organisms experience climatic conditions are often missing. Yet, such data on microclimatic conditions are required to better understand climate-change impacts on biodiversity and ecosystem functioning. Here we combine a national network of microclimate temperature measurements with a novel radiative transfer model to map monthly minimum, mean and maximum temperatures during the vegetation period at a 10 meter spatial resolution across Switzerland. The temperature measurements took place in 107 sampling plots distributed across different habitat types, with 62 plots in forests, 22 below trees outside forests, and 23 in open grasslands. In each plot we measured temperature in the topsoil (-5cm), as well as in the air at 5cm and 100cm height above ground. Spatial interpolation was achieved by using a hybrid approach based on linear mixed effects models with input from detailed radiation estimates that account for topographic and vegetation shading, as well as other predictor variables related to the macroclimate, topography and vegetation height. Our data reveals strong horizontal and vertical variability in microclimate temperature, particularly for maximum temperatures at 5 cm above the ground and within the topsoil. Compared to macroclimate conditions as measured by weather stations outside forests, diurnal air and topsoil temperature ranges inside forests were reduced by up to 3.0 and 7.8 °C, respectively, while below trees outside forests, e.g. in hedges and below solitary trees, this buffering effect was 1.8 and 7.2 °C. We also found that in open grasslands, maximum temperatures at 5 cm above ground are on average 3.4 °C warmer than that of macroclimate, suggesting that in such habitats heat exposure close to the ground is often underestimated when using macroclimatic data. After accounting for macroclimate effects, microclimate patterns were primarily driven by radiation, with particularly strong effects on maximum temperatures. Results from spatial block cross-validation revealed predictive accuracies as measured by RSME’s ranging from 1.18 to 3.43 °C, with minimum temperatures generally being predicted more accurately than maximum temperatures. The microclimate maps presented here enable a more biologically relevant perspective when analysing climate-species interactions, which is expected to lead to a better understanding of biotic and ecosystem responses to climate and land use change.

biodiversity

display_name: biodiversity

climate-change

display_name: climate-change

ecology

display_name: ecology

forest

display_name: forest

microclimate

display_name: microclimate

radiation-transfer

display_name: radiation-transfer

statistical-interpolation

display_name: statistical-interpolation

en: Monthly topsoil and near surface microclimate temperature data for Switzerland


maintainer: Matthias Haeni

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

en: Site aerial photo (Beatenberg)

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

format: SERVICE

mimetype: image/jpeg

en: Forest station data (BAB)

en: Data file.

en: Site photo of the forest station (BAB)

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

en: Site photo of the field station (BAF)

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

en: Field station data (BAF)

en: Data plot of the forest station (BAB)

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

en: Data plot of the field station (BAF)

en:
High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Beatenberg in Switzerland where one station is located within a natural coniferous forest (BAB) with Norway spruce (_Picea abies_; 190-210 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, BAF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Beatenberg is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the
Swiss Federal Research Institute WSL.

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

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


automatic-classification-of-avalanches

maintainer: Alec van Herwijnen

title_for_slug: automatic-classification-of-avalanches

en: machine-learning localization seismic monitoring avalanche

en:
This tar file contains classification results for two seismic geophone arrays deployed above Davos, Switzerland. The of the first array located at the Dischma Valley was used to automatically identify all seismic events produced by avalanches, whereas the classification perfomed at the second array located at the Wannengrat field site was used to identify airplanes and earthquakes.
Furthermore, this tar file also contains the median back-azimuth of all classified events at the Dischma field site.

mimetype: application/x-tar

en: This dataset contains the classification and localization results obtained during the automatic classification of avalanches during the winter season 2017.

avalanche

display_name: avalanche

localization

display_name: localization

machine-learning

display_name: machine-learning

monitoring

display_name: monitoring

seismic

display_name: seismic

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Automatic Classification of Avalanches


maintainer: Tessa Hegetschweiler

en: childhood forest adolescents recreational-activities teenagers

en: Dataset survey at schools

en: The dataset of the questionnaire survey at schools in excel format.

format: XLS

en: Dataset comments

en: The codebook to the dataset

en: Questionnaire school survey

en: The questionnaire used in the survey at 8 schools aimed at teenagers in German.

en: Survey at schools 2017

en: The dataset of the questionnaire survey at schools in SPSS-format.

en: The data results from a questionnaire survey conducted at 8 schools in the cantons Zurich, Aargau and St. Gallen. Respondents aged 13-22 years. The aim of the survey was to gain insight into teenagers' relationship to the forest, reasons for visiting or not visiting the forest and activities in the forest.

adolescents

display_name: adolescents

childhood

display_name: childhood

forest

display_name: forest

recreational-activities

display_name: recreational-activities

teenagers

display_name: teenagers

en: Factors influencing teenagers' forest visit frequency


modeling-snow-failure-with-dem

title_for_slug: modeling-snow-failure-with-dem

en: snow-avalanche snow-micro-mechanics snow-failure snow discrete-element-modelling

en: Read-me

en: The read-me file contains a description of all the data provided in the zip file.

en: Dataset

en: The zip file contains the data underlying the figures presented in Bobillier et al. (2019). The figures can be reproduced with the python file, which is also provided.

mimetype: application/zip

en: Python file

en: Python file to reproduce the figures in Bobillier et al. (2019)

mimetype: text/x-python

en: This data set includes the modeling results described in the research article by Bobiller et al. (2020). All the figures in the article can be reproduced with the data provided.

discrete-element-modelling

display_name: discrete-element-modelling

snow

display_name: snow

snow-avalanche

display_name: snow-avalanche

snow-failure

display_name: snow-failure

snow-micro-mechanics

display_name: snow-micro-mechanics

en: Modeling snow failure with DEM


maintainer: Frank Techel

en: avalanche avalanche-size machine-learning natural-avalanches snowpack-model

en: Data set AV3 was used for validation of the avalanche-day classifier (Sections 2.1.3 and 3.3(1) in Mayer et al.).

en: Data set AV2 used for training the avalanche-size estimator (Sections 2.1.2 and 3.2 in Mayer et al.)

en: Data set DL was used for validation of the avalanche-day classifier and the avalanche-size estimator. The data set contains the quality-checked danger levels merged with the respective SNOWPACK simulations (Sections 2.3 and 3.3(2) in Mayer et al.).

en: Data set AV1 was used for training the avalanche-day classifier (Sections 2.1.1, 2.2, and 3.1 in Mayer et al.).

en:
The data set contained in this repository was used in the analysis by Mayer et al. (2023):
Mayer, S. I., Techel, F., Schweizer, J., and van Herwijnen, A.: Prediction of natural dry-snow avalanche activity using physics-based snowpack simulations, EGUsphere, https://doi.org/10.5194/egusphere-2023-646, 2023.

avalanche-size

display_name: avalanche-size

natural-avalanches

display_name: natural-avalanches

snowpack-model

display_name: snowpack-model

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Data-set for prediction of natural dry-snow avalanche activity and avalanche size using physics-based snowpack simulations


maintainer: Nander Wever

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

en: SNOWPACK ini file (new syntax)

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

en: Version 2

en:
Deprecated version. Release notes:

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

en: Version 7 (current)

en:
Release notes:

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

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

en: SNOWPACK ini file (old syntax)

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

en: Undercatch correction

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

en: Version 1

en:
Deprecated version. Release notes:

First release of dataset.

en: All versions

en: Version 6

en:
Deprecated version.

Release notes:

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

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

en: SNOWPACK sno file

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

en: Version 3

en:
Deprecated version. Release notes:

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

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

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

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

en: Version 5

en:
Deprecated version. Release notes:

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

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

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

en: Version 4

en:
Deprecated version. Release notes:

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

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

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

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

air-temperature

display_name: air-temperature

humidity

display_name: humidity

longwave-radiation

display_name: longwave-radiation

precipitation-amount

display_name: precipitation-amount

shortwave-radiation

display_name: shortwave-radiation

snow-depth

display_name: snow-depth

snow-ice-temperature

display_name: snow-ice-temperature

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

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


en: snow particles deposition atmosphere turbulence dust hill

en: preferential deposition patterns of all simulations performed

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

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

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

atmosphere

display_name: atmosphere

deposition

display_name: deposition

dust

display_name: dust

hill

display_name: hill

particles

display_name: particles

turbulence

display_name: turbulence

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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


en: spatial-variability drone-data polar snow sea-ice antarctica

en:
Data of snow and sea ice in the McMurdo Sound, October-December 2022. The data was collected as part of the New Zealand Marsden Fund Research Grant 21-VUW-103 "Can Snow Change the Fate of Antarctic Sea Ice?"
The dataset includes raw data of the manual snow and sea ice measurements from snow pits and ice cores (temperature, density, salinity, dO18), measurements of snow water equivalent (SWE), spatial information of snow height (MagnaProbe) and sea ice thickness (EM-31), AWS (air temperature, wind speed, wind direction, relative humidity, pressure), radiations stations (shortwave, longwave, thermal IR, spectral shortwave), differential GPS data (3 fixed stations on different sea ice thicknesses, + 1 rover station for georeferencing UAV measurements), SIMBA buoy temperature (+heated temperature) data (3 buoys during November, 1 buoy for 15 months), UAV data: RGB, thermal IR, broadband albedo, spectral albedo, Chlorophyll-a from ice cores (bottom 10 cm), NIR reflectivity data of snow at 850 nm, and 940 nm (snow surface, profile, ice surface), photographs (1. overview of field sites, 2. for Structure from Motion for surface roughness, 3. macrophotos of snow) surface impurity concentrations, microCT data of snow microstructure, Denoth probe (density) and InfraSnow (specific surface area - SSA).
See the README file in each dataset for detailed information.

antarctica

display_name: antarctica

drone-data

display_name: drone-data

polar

display_name: polar

sea-ice

display_name: sea-ice

snow

display_name: snow

spatial-variability

display_name: spatial-variability

en: Snow on Antarctic Sea Ice - McMurdo Sound 2022


maintainer: Armin Sigmund

en: meteorological-measurements atmospheric-turbulence drifting-snow blowing-snow antarctica

en: (c) SPC average profiles

en: Average vertical profiles of the horizontal snow mass flux, particle number size distribution, and mean and standard deviation of the particle diameter.

en: (e) Sonic data 2

en: Ultrasonic anemometer data in the period from 11 to 21 October 2000.

en: (f) Sonic data 3

en: Ultrasonic anemometer data in the period from 21 to 30 October 2000.

en: (g) Sonic data 4

en: Ultrasonic anemometer data in the period from 30 October to 6 November 2000.

en: (d) Sonic data 1

en: Ultrasonic anemometer data in the period from 30 September to 11 October 2000.

en: (a) Metadata

en: General and specific metadata for all files.

en: (i) Sonic data 6

en: Ultrasonic anemometer data in the period from 13 to 18 November 2000.

en: (l) AWS data

en: Automatic weather station data with 10-min intervals.

en: (b) SPC time series

en: Data from the snow particle counters with 1-s intervals.

en: (h) Sonic data 5

en: Ultrasonic anemometer data in the period from 6 to 13 November 2000.

en: (m) Weather observations

en: Weather elements and phenomena observed by the Japanese Meteorological Agency.

en: (j) Spike percentage

en: Percentage of spikes removed from the ultrasonic anemometer data per 5-min interval.

en: (k) Wind speed profiles

en: Average vertical profiles of horizontal wind speed based on the ultrasonic anemometer data.

en:
This data was collected at Mizuho Station (44.315°E, 70.71°S, 2230 m a.s.l.), East Antarctica, from **30 September to 22 November 2000** during the 41st Japanese Antarctic research expedition. Vertical profiles of the horizontal mass flux and particle number size distribution of drifting and blowing snow were measured in the lowest 10 m of the atmosphere using four **snow particle counters** (SPCs). Three 3-dimensional **ultrasonic anemometers** were deployed in the lowest 25 m of the atmosphere to record the wind velocity components and sonic temperature at a high frequency of 100 Hz. Additionally, an **automatic weather station** provided air temperature, relative humidity, air pressure, shortwave and longwave downward and upward radiation fluxes, surface temperature, snow temperatures at depths of 0.1 and 0.4 m below the surface, wind speed, and wind direction. The data is complemented by **weather observations** performed twice to eleven times per day by the Japanese Meteorological Agency.
Most of the time, the lowest SPC was situated at a height of 0.05 or 0.1 m above the snow surface. During certain periods called profile runs, the height of the lowest SPC was systematically varied between 0.02 and 0.2 m to increase the vertical resolution of the measured profiles. The other SPC's were installed at fixed heights of 1.1, 3.1, and 9.6 m. During each profile run, the lowest SPC was kept at a specific height for approximately 10 min before changing the height. If the wind speed remained approximately constant throughout a profile run, the data was used to compute the average snow-transport profile in that period. In total, 24 of such average profiles were obtained. A part of these SPC measurements are discussed in Nishimura and Nemoto (2005).
The ultrasonic anemometers were placed at heights of 0.3, 1, and 25 m. However, the height of the lowest ultrasonic anemometer was changed several times in the measurement period and ranged from 0.05 to 0.32 m. On top of that, 19 short periods (each lasting 15 to 160 min) were used to systematically vary the height of the lowest ultrasonic anemometer and thus increase the vertical resolution of the measured wind speed profile. Occasionally, drifting and blowing snow particles perturbed the ultrasonic measurement signal and electrically-charged particles caused an electric charge of the anemometers, leading to artifacts such as spikes and dropouts in the measured time series. These artifacts were largely removed and replaced by NaN, using the statistical spike removal algorithm of Mauder et al. (2013). However, the artifact removal was sometimes incomplete for the lowest anemometer because of very intense snow transport close to the surface. While temporal averages are barely affected by the artifacts, the high-frequency data of the lowest anemometer should be used carefully when computing turbulent fluxes using the eddy-covariance method. Further important explanations are provided in the file Readme.pdf in data resource '(a) Metadata'.
References
- Mauder, M., Cuntz, M., Drüe, C., Graf, A., Rebmann, C., Schmid, H.P., Schmidt, M., Steinbrecher, R. (2013). A strategy for quality and uncertainty assessment of long-term eddy-covariance measurements. Agric For Meteorol 169:122–135. https://doi.org/10.1016/j.agrformet.2012.09.006
- Nishimura, K., Nemoto M. (2005). Blowing snow at Mizuho station, Antarctica. Phil. Trans. R. Soc. A. 363: 1647–1662. https://doi.org/10.1098/rsta.2005.1599

atmospheric-turbulence

display_name: atmospheric-turbulence

blowing-snow

display_name: blowing-snow

drifting-snow

display_name: drifting-snow

meteorological-measurements

display_name: meteorological-measurements

en: Profile measurements of snow transport and micrometeorology at Mizuho Station in Antarctica


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

media_type: image/tiff

format: TIFF

en: Parameter file that defined all the parameters for processing the Sentinel-2 time series

format: OCTET

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

 Processing
On the basis of processed Sentinel-2 images for the 14 Sentinel-2 tiles covering Switzerland (T31TGN, T32TLT, T32TMT, T32UMU, T32TNT, T32TPT, T31TGM, T32TLS, T32TMS, T32TNS, T32TPS, T32TLR, T32TMR, T32TNR), we processed the image time series further with FORCE v. 3.7.8-12.
We generated interpolated Sentinel-2 time series with a 5-day interval, corresponding to the theoretical revisit time of the Sentinel-2 satellites. It's important to note that the 5-day time series consist of interpolated and smoothed composites, not the original images. We used the radial basis convolutional filtering (RBF) available in the FORCE time series analysis (TSA) submodule (Schwieder et al. 2016). The RBF is similar to a spatial moving window average approach over time (Schwieder et al. 2016). We applied kernel width values of 10, 20, 30, and 50 days. We spectrally adjusted all the images to match Sentinel-2A, and we removed curve outliers and pixels that failed the quality checks for clouds and their shadows, snow, saturation, and limited illumination. The processed image time series are available in tiles of 30 by 30 km.

 Example images
Uploaded is an example of the index EVI for one of the generated 30 by 30 km tiles located around the city of Zürich. The values are multiplied by 10.000. The time series spans the month of July from 2018.

force

display_name: force

raster-processing

display_name: raster-processing

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

time-series

display_name: time-series

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Sentinel-2 time series of Switzerland


en: gridded-data antarctica terrestrial-laser-scans terrestiral-laserscanning depth-of-snowfall

en: Scan from 2 January 2019, 10 cm resolution

en: Scan from 21 December 2018, 10 cm resolution

en: Scan from 11 January 2019, 1 mm resolution

en: Scan from 27 December 2018, 1 mm resolution

en: Scan from 27 December 2018, 10 cm resolution

en: Scan from 2 January 2019, 1 mm resolution

en: Scan from 4 January 2019, 10 cm resolution

en: Scan from 11 January 2019, 10 cm resolution, version 2

en: Deprecated: Scan from 11 January 2019, 10 cm resolution, version 1

en: This resource is deprecated. It contains erroneous data.

en: Scan from 21 December 2018, 1 mm resolution

en: Scan from 4 January 2019, 1 mm resolution

en:
Surface topography maps (spatial extent: 400 m x 400 m) obtained at approximately 300 m from the top of the Hammarryggen Ice Rise in Dronning Maud Land, East Antarctica, using a Riegl VZ-6000 Terrestrial Laser Scanner (TLS). Scans were obtained on 5 days in the 2018-2019 Austral summer: on December 21 and 27, and January 2, 4 and 11. By using reflectors installed on bamboo poles, scans were registered with respect to the reflectors, such that the difference between two successive scans reveals the spatial patterns of erosion and deposition of snow. On each scan day, we used multiple scan positions to create one combined point cloud. After applying an octree filter on the point cloud, a 3D surface was obtained. For each day, the dataset contains a 1 mm and a 10 cm octree filter resolution file, only including points in a 400 m x 400 m area centered around the scan positions.

Notes:

* All files in the dataset are in the same coordinate system. However, this coordinate system is arbitrary (i.e., not related to any global coordinate system).

* From the installed reflectors, 4 reflectors could be used over the full period. The scan accuracy is generally higher within the area enclosed by the reflectors.

* The scans from January 2 were found to have exhibited small tilt during the scan and are of lesser accuracy.

* By walking along fixed corridors, disturbance of the snow was limited.

depth-of-snowfall

display_name: depth-of-snowfall

gridded-data

display_name: gridded-data

terrestiral-laserscanning

display_name: terrestiral-laserscanning

terrestrial-laser-scans

display_name: terrestrial-laser-scans

en: Terrestrial laser scans on Hammarryggen Ice Rise, Dronning Maud Land, East Antarctica


maintainer: Leo Gallus Bont

en: digital-planning open-source cable-yarding qgis-plugin digital-elevation-model geoinformation-system swiss-geo-downloader seilaplan swiss-geodata

en: Herunterladen des DHM mit SwissGeoDownloader

en: View video on youtube!

en:
In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. The plugin ‘Swiss Geo Downloader’, which is available for the open source geoinformation software QGIS, allows freely available Swiss geodata to be downloaded and displayed directly within QGIS. It was developed in 2021 by Patricia Moll in collaboration with the Swiss Federal Institute for Forest, Snow and Landscape Research WSL. In this tutorial we describe how to download the high accuracy elevation model ‘swissALTI3D’ with the help of the ‘Swiss Geo Downloader’ and how to use it for digital planning of a cable line with the plugin ‘Seilaplan’. Please note that the tutorial language is German!

Link to the Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader

Link to Seilaplan website: https://seilaplan.wsl.ch

*********************

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. Das Plugin Swiss Geo Downloader, welches für das Open Source Geoinformationssystem QGIS zur Verfügung steht, ermöglicht frei verfügbare Schweizer Geodaten direkt innerhalb von QGIS herunterzuladen und anzuzeigen. Es wurde 2021 von Patricia Moll in Zusammenarbeit mit der eidgenössischen Forschungsanstalt Wald, Schnee und Landschaft WSL entwickelt. In diesem Tutorial beschreiben wir, wie man mit Hilfe des Swiss Geo Downloaders das hochgenaue Höhenmodell swissALTI3D herunterladen und für die Seillinienplanung mit dem Plugin Seilaplan verwenden kann.

Link zum Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader

Link zur Seilaplan-Webseite: https://seilaplan.wsl.ch

cable-yarding

display_name: cable-yarding

digital-elevation-model

display_name: digital-elevation-model

digital-planning

display_name: digital-planning

geoinformation-system

display_name: geoinformation-system

open-source

display_name: open-source

qgis-plugin

display_name: qgis-plugin

seilaplan

display_name: seilaplan

swiss-geo-downloader

display_name: swiss-geo-downloader

swiss-geodata

display_name: swiss-geodata

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Seilaplan Tutorial: DTM download with SwissGeoDownloader


maintainer: Valentina Vitali

en: dendrochronology pfynwald isotopes tree-rings scots-pine pinus-sylvestris tree-ring-cellulose

en: Read me file.docx

en: Isotope measurements of tree-level tissues for the vegetative season 2019.

en:
Data from a 17-year-long irrigation experiment (Pfynwald, Switzerland) in a naturally dry forest dominated by 100-year-old pine trees (Pinus sylvestris). This dataset includes measure tree-ring width and tree-ring isotope chronologies (stable isotope ratios of non-exchangeable hydrogen δ2H, oxygen δ18O, and carbon δ13C), for the Pfynwald experiment, including control trees, irrigation and irrigation-stop treatments until the year 2019.

This dataset contains all data on which the following publication below is based.
Please cite this paper together with the citation for the datafile.

dendrochronology

display_name: dendrochronology

isotopes

display_name: isotopes

pfynwald

display_name: pfynwald

pinus-sylvestris

display_name: pinus-sylvestris

scots-pine

display_name: scots-pine

tree-ring-cellulose

display_name: tree-ring-cellulose

tree-rings

display_name: tree-rings

en: Pfynwald 2019 - Dendrochronological and tree-ring isotope dataset


maintainer: Jana von Freyberg

en: stable-water-isotope catchment precipitation alptal hydrology stream-water

en:
Version 2 of the dataset contains additional information on:
- line-conditioned (lc) excess
- precipitation sample volumes
- isotope data quality

media_type: text/plain

en: Single comma-separated txt-file with daily data of stable water isotopes measured in streamwater and precipitation in the Swiss Alptal catchment and two of its tributaries (Erlenbach and Vogelbach), as well as daily data of key hydrometeorological variables from the Alptal valley

format: TXT

en: Data Set Documentation: alptal_Isotopes_daily_2015-2019_v2

en: Documentations contains meta data of the data columns and information about the study catchments.

en: This dataset contains four years of daily measurements of the natural isotopic composition (2H, 18O) of precipitation and stream water at the Alp catchment (area 47 km2) in Central Switzerland and two of its tributaries (0.73 km2 and 1.55 km2). In addition, the dataset contains daily measurements of key hydrometeorological variables.

alptal

display_name: alptal

catchment

display_name: catchment

hydrology

display_name: hydrology

precipitation

display_name: precipitation

stable-water-isotope

display_name: stable-water-isotope

stream-water

display_name: stream-water

en: Four years of daily stable water isotope data in stream water and precipitation from three Swiss catchments


maintainer: Luca Bütikofer

en: modelling landscape land-cover landscape-change machine-learning land-use

en: Compressed (.zip) file containing six georeferenced raster files in the GeoTIFF format with embedded coordinate reference system, and a table (comma separated values, .csv) encoding a colour palette for plotting (HEX colour codes for each class). GeoTIFF file names include the time-step and the RCP (e.g. “LULC_2020_2049_RCP45.tiff”). Each raster file has 3484x2207 square pixels (i.e. 7.7 megapixels) and is projected in the Swiss coordinate system CH1903+/LV95 (EPSG:2056).

en:
Code used to produce the Land-use/land-cover forecasts for Switzerland across the 21st century at 100-m resolution.
The script is based on input data which are not included as they are not freely available, therefore it will not run as it is. These input data are described in the accompanying publication on Data Science access to them can be requested to their owners. Alternatively the code can be modified to run on other data.

en: We present forecasts of land-use/land-cover (LULC) change for Switzerland for three time-steps in the 21st century under the representative concentration pathways 4.5 and 8.5, and at 100-m spatial and 14-class thematic resolution. We modelled the spatial suitability for each LULC class with a neural network (NN) using >200 predictors and accounting for climate and policy changes. We used data augmentation to increase performance for underrepresented classes, resulting in an overall quantity disagreement of 0.053 and allocation disagreement of 0.15, which indicate good model performance. These class-specific spatial suitability maps outputted by the NN were then merged in a single LULC map per time-step using the CLUE-S algorithm, accounting for LULC demand for the future and a set of LULC transition rules. As the first LULC forecast for Switzerland at a thematic resolution comparable to available LULC maps for the past, this product lends itself to applications in land-use planning, resource management, ecological and hydraulic modelling, habitat restoration and conservation.

land-cover

display_name: land-cover

land-use

display_name: land-use

landscape

display_name: landscape

landscape-change

display_name: landscape-change

machine-learning

display_name: machine-learning

modelling

display_name: modelling

en: High resolution land use forecasts for Switzerland in the 21st century


en: forest-persistence spatial-modelling urban-morphology basic-services built-up-classification forest-gain land-use-and-cover-change topography planning-outcome nature-conservation urban-compactness urban-sprawl

en: Master plan Ubatuba (zones)

en: Variables description

en: Table of content. Variables description, source, and other important information

en: Topographic Index Position (TPI)

format: GeoTIFF

en:
Basic education in % per census sector (change per
year)

en: Increase in Normalized Difference Vegetation Index

en: Permanent housing density increase

en: Master plan Ihabela (zones)

format: SHP

en: Axial

en: New built-up pixels classified as axial

en: Population density per pixel (change per year)

en: Ecological-Economic Zoning (EEZ)

en: Human Development Index (HDI) per municipality (change per year)

en: slope

en: Slope °

en: Increase in sanitation service

en: Urban persistence: Built-up 2005 = Built-up 2015

en: Descricao dos dados em portiuguês

en: Portugues description

en: Mean income in Reais (R$) per census sector (change per year)

en: Increase in clean water provision service

en: isolated

en: New built-up pixels classified as isolated

en: Native forest cover persistence from 2005 to 2015

en: infill

en: New built-up pixels classified as infill

en: Increase in waste collection service

en: Native forest cover gains from 2005 to 2015

en:
The present dataset is part of the published scientific paper entitled “The role of spatial planning in land change: An assessment of urban planning and nature conservation efficiency at the southeastern coast of Brazil” (Pierri Daunt, Inostroza and Hersperger, 2021). In this work, we evaluated the conformance of stated spatial planning goals and the outcomes in terms of urban compactness, basic services and housing provision, and nature conservation for different land-use strategies. We evaluate the 2005 Ecological-Economic Zoning (EEZ) and two municipal master plans from 2006 in a coastal region in São Paulo State, Brazil. We used Partial Least Squares Path Modelling (PLS-PM) to explain the relationship between the plan strategies and land-use change ten years after implementation in terms of urban compactness, basic services and housing increase, and nature conservation.

We acquired the data for the explanatory variables from different sources listed on Table 1. Since the model is spatially explicit, all input data were transformed to a 30 m resolution raster. Regarding the evaluated spatial plans, we acquired the zones limits from the São Paulo State Environmental Planning Division (CPLA-SP), Ilhabela and Ubatuba municipality.

1)	Land use and cover data: Urban persistence, Urban axial, Urban infill, Urban Isolates, Forest cover persistence, Forest cover gain, NDVI increase

We acquired two Landsat Collection 1 Higher-Level Surface Reflectance images distributed by the U.S. Geological Survey (USGS), covering the entire study area (paths 76 and 77, row 220, WRS-2 reference system, https://earthexplorer.usgs.gov/). We classified one image acquired by the Landsat 5 Thematic Mapper (TM) sensor on 2005-05-150, and one image from the Landsat 8 Operational Land Imager (OLI) sensor from 2015-08-15. We collected 100 samples for forest cover, 100 samples for built-up cover and 100 samples for other classes. We then classified these three classes of land cover at each image date using the Support Vector Machine (SVM) supervised algorithm (Hsu et al., 2003), using ENVI 5.0 software.

Land-use and land-cover changes from 2005 to 2015 were quantified using map algebra, by mathematically adding them together in pairs (10*LULC2015 + LULC2005). We reclassified the LULC data into forest gain (conversion of any 2005 LULC to forest cover in 2015); forest persistence (2005 forested pixels that remained forested in 2015); new built-up area (conversion of any 2005 LULC to built-up in 2015); and urban maintenance (2005 built-up pixels that remained built-up in 2015).

To describe the spatial configuration of the urban expansion, we classified the new built-up areas into axial, infill and isolated, following Inostroza et al. (2013) (For details, please refer to Supplementary Material I at the original publication).

The NDVI was obtained from the same source used for the LULC data. With the Google Engine platform, we used an annual average for the best pixels (without clouds) for 2005 and 2015, and we calculated the changes between dates. We used increases of > 0.2 NDVI to represent an improvement in forest quality.

2)	Federal Census data organization: Urban Basic Services and Housing indicator, socioeconomic and population:

The data used to infer the values of basic services provision, socioeconomic and population drivers was derived from the Brazilian National Census data (IBGE, 2000 and 2010). Population density, permanent housing unit density, mean income, basic education, and the percentage of houses receiving waste collection, sanitation and water provision services, called basic services in the context of this study, were calculated per 30 m pixel. The Human Development Index is only available at the municipality level. We attributed the HDI for the vector file with the municipality border, and we rasterized (30 m resolution) this file in QGIS. Annual rates of change were then calculated to allow comparability between LULC periods. To infer the BSH, we used only areas with an increase in permanent housing density and basic services provision (See Supplementary Material I at the original publication).

3)	Topographic drivers

To infer the values of the topographic driver, we used the slope data and the Topographic Index Position (TPI) based on the digital elevation model from SRTM (30 m resolution) produced by ALOS (freely available at eorc.jaxa.jp/ALOS/en/about/about_index.htm), and both variables were considered constant from 2005 to 2015 (See Supplementary Material I at the original publication).

basic-services

display_name: basic-services

built-up-classification

display_name: built-up-classification

forest-gain

display_name: forest-gain

forest-persistence

display_name: forest-persistence

land-use-and-cover-change

display_name: land-use-and-cover-change

nature-conservation

display_name: nature-conservation

planning-outcome

display_name: planning-outcome

spatial-modelling

display_name: spatial-modelling

topography

display_name: topography

urban-compactness

display_name: urban-compactness

urban-morphology

display_name: urban-morphology

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: Spatially explicit data to evaluate spatial planning outcomes in a coastal region in São Paulo State, Brazil


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

en: Field station data (BTF)

en: Data file.

en: Data plot of the field station (BTF)

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

en: Site photo of the forest station (BTB)

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

en: Site photo of the field station (BTF)

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

en: Site aerial photo (Bettlachstock)

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

en: Data plot of the forest station (BTB)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Bettlachstock in Switzerland where one station is located within a natural mixed forest stand (BTB) with European beech (_Fagus sylvatica_; 170-190 yrs), European silver fir (_Abies alba_; 190 yrs) and Norway spruce (_Picea abies_; 200 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, BTF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Bettlachstock is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

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


en: landscape-genetics reef-fish morphology indian-ocean landscape-evolution landscape

en:
Tropical reef fishes exhibit a large disparity of organismal morphologies contributing to their astonishing biodiversity. Morphological disparity, scaling from differences among individuals within populations to differences among species, is governed by ecological and evolutionary processes.
Here, we examined the relationship between intra- and interspecific disparity in 1111 individuals from 17 tropical reef fish species, representing 10 families with different dispersal abilities, across four Indian Ocean regions. We compared intraspecific measurements with species-level measures from a database of 1061 reef fish species. Species with high morphological disparity among individuals from distinct regions are found to be nested in families that display a high disparity among their genera. We show an association between the morphological disparity at the intra- and interspecific levels for several morphological ratios such as the caudal peduncle elongation. We evaluated the link between morphological disparity and genetic diversity with species dispersal ability. A structural equation model indicates that dispersal ability correlates positively with species genetic diversity, which is associated with morphological disparity.
Our results suggest that traits associated with dispersal may foster gene flow and morphological evolution. However, future works combining genomic, morphological and environmental data across more species is necessary to generalize these findings to other regions.

indian-ocean

display_name: indian-ocean

landscape

display_name: landscape

landscape-evolution

display_name: landscape-evolution

landscape-genetics

display_name: landscape-genetics

morphology

display_name: morphology

reef-fish

display_name: reef-fish

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: Continuity in morphological disparity in tropical reef fishes across evolutionary scales


maintainer: LUSTAT Statistik Luzern

de: funktion statistische-grundlagen gemeinden artenkonto verwaltung gemeindefinanzen sachkonto gemeindebudgets offentliche-finanzen volkswirtschaft

fr: Statistique des finances communales canton de Lucerne: budgets annuels

en: Municipal Finance Statistics Canton Lucerne: annual budgets

de: Gemeindefinanzstatistik Kanton Luzern: Budgets

it: Statistica finanziaria comunale cantone di Lucerna: bilanci annuali

de: Das Datenset beinhaltet die Budgetdaten der  Gemeindefinanzstatistik des Kantons Luzern seit 2020 gemäss dem HRM2-Standard. Es werden für jede Gemeinde die Funktionen, die Kontoarten und die entsprechenden Saldi ausgewiesen. Es ist der Gebietsstand des jeweiligen Jahres aufgeführt. Die Daten wurden direkt bei den Gemeinden erhoben und bilden einen Auszug aus deren Buchhaltungssystem ab. Statistische Korrekturen zwecks Gewährleistung der Konformität mit dem kantonalen Kontenrahmen wurden in Einzelfällen vorgenommen.

en: The dataset contains the budget data of the Municipal Finance Statistics of the Canton Lucerne since 2020 in accordance with the HRM2 standard. The table shows the cost centers or the cost units, the account types and the corresponding account balances for each municipality. The territorial status of the respective year is disclosed. The data was collected directly from the municipalities and represents an extract of their accounting system. Statistical corrections were made in exceptional cases to ensure conformity with the cantonal chart of accounts.

artenkonto

display_name: artenkonto

funktion

display_name: funktion

gemeindebudgets

display_name: gemeindebudgets

gemeindefinanzen

display_name: gemeindefinanzen

gemeinden

display_name: gemeinden

offentliche-finanzen

display_name: offentliche-finanzen

sachkonto

display_name: sachkonto

statistische-grundlagen

display_name: statistische-grundlagen

verwaltung

display_name: verwaltung

volkswirtschaft

display_name: volkswirtschaft

spatial: Kanton Luzern

gove

fr: Gouvernement et secteur public

de: Regierung und öffentlicher Sektor

en: Government and public sector

it: Governo e settore pubblico

econ

fr: Économie et finances

de: Wirtschaft und Finanzen

en: Economy and finance

it: Economia e finanze

fr: LUSTAT Statistik Luzern

de: LUSTAT Statistik Luzern

en: LUSTAT Statistik Luzern

it: LUSTAT Statistik Luzern

lustat

fr: LUSTAT statistique Lucerne

en: LUSTAT Statistics Lucerne

it: LUSTAT statistica Lucerna

fr:
LUSTAT statistique Lucerne est le centre de compétences et de services pour la statistique publique du canton de Lucerne. Avec ses informations statistiques objectives, LUSTAT accompagne les processus de formation d'opinion et de prise de décision dans la politique, l'administration, la société et les affaires.
LUSTAT statistique Lucerne publie ici progressivement des jeux de données appropriées de la statistique cantonale sous forme de données OGD.

en:
LUSTAT Statistics Lucerne is the competence and service center for public statistics in the Canton of Lucerne. With its objective statistical information, LUSTAT accompanies opinion-forming and decision-making processes in politics, administration, society and business.
LUSTAT Statistics Lucerne is gradually publishing here suitable datasets from the cantonal statistics as OGD data.

de:
LUSTAT Statistik Luzern ist das Kompetenz- und Dienstleistungszentrum der öffentlichen Statistik im Kanton Luzern. Mit ihren objektiven statistischen Informationen begleitet LUSTAT Meinungsbildungs- und Entscheidungsprozesse in Politik, Verwaltung, Gesellschaft und Wirtschaft.
LUSTAT Statistik Luzern veröffentlicht hier schrittweise geeignete Datensammlungen der kantonalen Statistik als OGD-Daten.

it:
LUSTAT statistica Lucerna è il centro di competenza e servizi per la statistica pubblica nel cantone di Lucerna. Con le sue informazioni statistiche oggettive, LUSTAT accompagna i processi di formazione delle opinioni e decisionali in politica, amministrazione, società e affari.
LUSTAT statistica Lucerna sta gradualmente pubblicando qui oportuni set di dati della statistica cantonale come dati OGD.

political_level: canton

value: I14Y DCAT Harvester LUSTAT


de: gemeinderatswahlen politik wahlen kommunale-wahlen kandidierende gewahlte

fr: Élections communales canton de Lucerne de 2020

de: Gemeinderatswahlen Kanton Luzern seit 2020

en: Municipal council elections Canton Lucerne since 2020

it: Elezioni del consiglio comunale cantone di Lucerna dal 2020

de: Kandidierende und Gewählte bei den Gemeinderatswahlen seit 2020 im Kanton Luzern

identifier: lustat-grwahlen-lu

gemeinderatswahlen

display_name: gemeinderatswahlen

gewahlte

display_name: gewahlte

kandidierende

display_name: kandidierende

kommunale-wahlen

display_name: kommunale-wahlen

politik

display_name: politik

wahlen

display_name: wahlen


de: subjektive-lebensbedingungen lebensqualitat lebensbedingungen bevolkerung befragung

en: living-conditions quality-of-life population subjective-living-conditions survey

de: Grundgesamtheit

en: Universe

identifier: bbf-lu-grundgesamtheit

de: Antworten zu den Fragen

en: Answers to the questions

identifier: bbf-lu-antworten

de:
Die Datensammlung zur Bevölkerungsbefragung Kanton Luzern 2023 besteht aus zwei Distributionen/Ressourcen:
- Individualdaten mit den Antworten zu den Fragen (bbf-lu-antworten.csv)
- Angaben zur Grundgesamtheit der gewichtungsrelevanten Variablen für die Endlichkeitskorrektur (bbf-lu-grundgesamtheit.csv)

en:
The data set for the population survey Canton of Lucerne 2023 consists of two distributions/resources:
- Individual data with the answers to the questions (bbf-lu-antworten.csv)
- Information on the population of the weighting-relevant variables for the finiteness correction (bbf-lu-grundgesamtheit.csv)

befragung

display_name: befragung

bevolkerung

display_name: bevolkerung

lebensbedingungen

display_name: lebensbedingungen

lebensqualitat

display_name: lebensqualitat

living-conditions

display_name: living-conditions

population

display_name: population

quality-of-life

display_name: quality-of-life

subjective-living-conditions

display_name: subjective-living-conditions

subjektive-lebensbedingungen

display_name: subjektive-lebensbedingungen

survey

display_name: survey

soci

fr: Population et société

de: Bevölkerung und Gesellschaft

en: Population and society

it: Popolazione e società

en: Population survey Canton of Lucerne 2023


de: bevolkerungsentwicklung gemeinden bevolkerungswachstum bevolkerung bevolkerungsszenarien wachstum

fr: Scénario bas 2023 - 2050

de: Tiefes Szenario 2023 - 2050

en: Low scenario 2023 - 2050

it: Scenario basso 2023 - 2050

fr: Le scénario bas repose sur des hypothèses qui orientent moins le niveau d'instruction et la croissance économique, réduisant ainsi la croissance démographique par rapport au scénario de référence.

en: The low scenario is based on hypotheses that drive educational attainment and economic growth less, thus reducing population growth compared to the reference scenario.

de: Das tiefe Szenario basiert auf Hypothesen, welche das Bildungsniveau und das Wirtschaftswachstum weniger vorantreiben und somit das Bevölkerungswachstum im Vergleich zum Referenzszenario mindern.

it: Lo scenario basso si basa su ipotesi che spingono meno il livello di istruzione e la crescita economica, riducendo così la crescita della popolazione rispetto allo scenario di riferimento.

fr: Scénario haut 2023 - 2050

de: Hohes Szenario 2023 - 2050

en: High scenario 2023 - 2050

it: Scenario alto 2023 - 2050

fr: Le scénario haut combine des hypothèses favorables à la croissance démographique et économique. La croissance démographique est donc estimée plus élevée que dans le scénario de référence.

en: The high scenario combines hypotheses that favor population and economic growth. Population growth is therefore estimated to be higher than in the reference scenario.

de: Beim hohen Szenario werden Hypothesen kombiniert, die das Bevölkerungs- und Wirtschaftswachstum begünstigen.

it: Lo scenario alto combina ipotesi che favoriscono la popolazione e la crescita economica. La crescita della popolazione è quindi stimata superiore a quella dello scenario di riferimento.

fr: Scénario moyen (référence) 2023 - 2050

en: Medium scenario (reference) 2023 - 2050

de: Mittleres Szenario (Referenz) 2023 - 2050

it: Scenario medio (riferimento) 2023 - 2050

fr: Le scénario moyen (scénario de référence) tient compte des hypothèses du modèle qui extrapolent les évolutions démographiques des dernières décennies. Dans la perspective actuelle, il représente un cours probable.

en: The medium scenario (reference scenario) takes into account model assumptions that extrapolate the demographic developments of the last decades. It represents a probable course – from the current perspective.

de: Das mittlere Szenario (Referenzszenario) berücksichtigt Modellannahmen, welche die demografischen Entwicklungen der letzten Jahrzehnte fortschreiben. Es stellt dabei einen – aus gegenwärtiger Sicht – wahrscheinlichen Verlauf dar.

it: Lo scenario medio (scenario di riferimento) tiene conto delle ipotesi del modello che estrapolano gli sviluppi demografici degli ultimi decenni. Dal punto di vista attuale, rappresenta un corso probabile.

fr: Les scénarios de population LUSTAT pour le canton de Lucerne sont des modèles de calcul qui extrapolent l'évolution de la population résidente permanente au niveau communal. Trois variantes ont été calculées pour chaque scénario, qui diffèrent par les hypothèses sous-jacentes. Ils tiennent compte des hypothèses des modèles qui sont basées sur les tendances démographiques et socio-économiques des dernières décennies. L'année de calcul des chiffres clés sur lesquels se basent les calculs correspond à l'année 2023. Les scénarios partent de la population résidente permanente au 31 décembre 2022 et, en outre, aux personnes bénéficiant d'un statut de protection S.

de: Die LUSTAT-Bevölkerungsszenarien für den Kanton Luzern sind Modellberechnungen, welche die Entwicklung der ständigen Wohnbevölkerung auf kommunaler Ebene fortschreiben. Für die Szenarien wurden jeweils drei Varianten berechnet, welche sich in den zugrundeliegenden Hypothesen unterscheiden. Sie berücksichtigen Modellannahmen, welche auf demografischen und sozioökonomischen Trends der letzten Jahrzehnte beruhen. Das Berechnungsjahr der, bei den Berechnungen zugrundeliegenden, Kennziffern entspricht dem Jahr 2023. Als Anfangsbestand dient den Szenarien die ständige Wohnbevölkerung per 31.12.2022 plus die Personen mit Schutzstatus S.

en: The LUSTAT population scenarios for the canton of Lucerne are model calculations that extrapolate the development of the permanent resident population at the municipal level. Three variants were calculated for each scenario, which differ in the underlying hypotheses. They take into account model assumptions based on demographic and socioeconomic trends of recent decades. The year of calculation of the key figures on which the calculations are based on corresponds to the year 2023. The permanent resident population as of 31.12.2022 and additionally persons with protection status S is used as the initial stock for the scenarios.

it: Gli scenari demografici LUSTAT per il Cantone di Lucerna sono modelli di calcolo che estrapolano l'evoluzione della popolazione residente permanente a livello comunale. Per ogni scenario sono state calcolate tre varianti, che si differenziano per le ipotesi sottostanti. Prendono in considerazione le ipotesi del modello che si basano sulle tendenze demografiche e socioeconomiche degli ultimi decenni. L'anno di calcolo per le cifre chiave su cui si basano i calcoli corrisponde all'anno 2023. Gli scenari iniziano con la popolazione residente permanente al 31 dicembre 2022 e inoltre le persone con status di protezione S.

bevolkerungsentwicklung

display_name: bevolkerungsentwicklung

bevolkerungsszenarien

display_name: bevolkerungsszenarien

bevolkerungswachstum

display_name: bevolkerungswachstum

gemeinden

display_name: gemeinden

wachstum

display_name: wachstum

fr: Scénarios d’évolution démographique des communes du canton de lucerne

de: Szenarien zur Bevölkerungsentwicklung der Luzerner Gemeinden

en: Scenarios for population development of the communes of Canton Lucerne

it: Scenari per l'evoluzione demografica per comune di cantone di Lucerna


de: verwaltung funktion gemeinderechnungen statistische-grundlagen gemeindefinanzen volkswirtschaft offentliche-finanzen finanzkennzahlen sachkonto gemeinden artenkonto

de: Jahresrechnungen Erfolgsrechnung: Aufwand und Ertrag (Artengliederung)

de:
Erfolgsrechnungsdaten der Jahresrechnungen der Gemeindefinanzstatistik vom Kanton Luzern nach ein- bis vierstufiger Kontoart. Datensammlung umfasst folgende Arbeitsblätter:
- Aufwand nach einstufiger Art
- Ertrag nach einstufiger Art
- Aufwand nach zweistufiger Art
- Ertrag nach zweistufiger Art
- Aufwand nach dreistufiger Art
- Ertrag nach dreistufiger Art
- Aufwand nach vierstufiger Art
- Ertrag nach vierstufiger Art

identifier: gefis-lu-jr-er-art

de: Jahresrechnungen: Bilanz, Erfolgsrechnung und Investitionsrechnung (Kontofunktion und Kontoart)

de: Daten der Jahresrechnungen der Gemeindefinanzstatistik vom Kanton Luzern im höchsten Detaillierungsgrad der Kontofunktion und Kontoart (aggregiert auf Minimalvorgabe vom Kanton: Stufen 3 oder 4 bei Kontofunktion und Stufen 4, 5 oder 6 bei Kontoart)

identifier: lustat-gefis-lu-jr

de: Jahresrechnungen Investitionsrechnung: Investitionsausgaben und Investitionseinnahmen (Kontofuntktion und Kontoart)

de:
Investitionsrechnungsdaten der Jahresrechnungen der Gemeindefinanzstatistik vom Kanton Luzern nach ein- bis dreistufiger Kontofunktion kombiniert mit zwei- bis vierstufiger Kontoart. Datensammlung umfasst folgende Arbeitsblätter:
- Investitionsausgaben nach einstufiger Kontofunktion und zweistufiger Kontoart
- Investitionseinnahmen nach einstufiger Kontofunktion und zweistufiger Kontoart
- Investitionsausgaben nach einstufiger Kontofunktion und dreistufiger Kontoart
- Investitionseinnahmen nach einstufiger Kontofunktion und dreistufiger Kontoart
- Investitionsausgaben nach einstufiger Kontofunktion und vierstufiger Kontoart
- Investitionseinnahmen nach einstufiger Kontofunktion und vierstufiger Kontoart
- Investitionsausgaben nach zweistufiger Kontofunktion und zweistufiger Kontoart
- Investitionseinnahmen nach zweistufiger Kontofunktion und zweistufiger Kontoart
- Investitionsausgaben nach zweistufiger Kontofunktion und dreistufiger Kontoart
- Investitionseinnahmen nach zweistufiger Kontofunktion und dreistufiger Kontoart
- Investitionsausgaben nach zweistufiger Kontofunktion und vierstufiger Kontoart
- Investitionseinnahmen nach zweistufiger Kontofunktion und vierstufiger Kontoart
- Investitionsausgaben nach dreistufiger Kontofunktion und zweistufiger Kontoart
- Investitionseinnahmen nach dreistufiger Kontofunktion und zweistufiger Kontoart
- Investitionsausgaben nach dreistufiger Kontofunktion und dreistufiger Kontoart
- Investitionseinnahmen nach dreistufiger Kontofunktion und dreistufiger Kontoart
- Investitionsausgaben nach dreistufiger Kontofunktion und vierstufiger Kontoart
- Investitionseinnahmen nach dreistufiger Kontofunktion und vierstufiger Kontoart

identifier: gefis-lu-jr-ir-fkt-art

de: Jahresrechnungen Investitionsrechnung: Investitionsausgaben, Investitionseinnahmen und Nettoinvestitionen (funktionelle Gliederung)

de:
Investitionsrechnungsdaten  der Jahresrechnungen der Gemeindefinanzstatistik vom Kanton Luzern nach ein- bis dreistufiger Kontofunktion. Datensammlung umfasst folgende Arbeitsblätter:
- Investitionsausgaben nach einstufiger Kontofunktion
- Investitionseinnahmen nach einstufiger Kontofunktion
- Nettoinvestitionen nach einstufiger Kontofunktion
- Investitionsausgaben nach zweistufiger Kontofunktion
- Investitionseinnahmen nach zweistufiger Kontofunktion
- Nettoinvestitionen nach zweistufiger Kontofunktion
- Investitionsausgaben nach dreistufiger Kontofunktion
- Investitionseinnahmen nach dreistufiger Kontofunktion
- Nettoinvestitionen nach dreistufiger Kontofunktion

identifier: gefis-lu-jr-ir-fkt

de: Jahresrechnungen Bilanz: Aktiven und Passiven (Artengliederung)

de:
Bilanzdaten der Jahresrechnungen der Gemeindefinanzstatistik vom Kanton Luzern nach ein- bis vierstufiger Kontoart. Datensammlung umfasst folgende Arbeitsblätter:
- Aktiven nach einstufiger Art
- Passiven nach einstufiger Art
- Aktiven nach zweistufiger Art
- Passiven nach zweistufiger Art
- Aktiven nach dreistufiger Art
- Passiven nach dreistufiger Art
- Aktiven nach vierstufiger Art
- Passiven nach vierstufiger Art

identifier: lustat-gefis-lu-jr-bl-art

de: Jahresrechnungen Erfolgsrechnung: Aufwand und Ertrag (Kontofuntktion und Kontoart)

de:
Erfolgsrechnungsdaten der Jahresrechnungen der Gemeindefinanzstatistik vom Kanton Luzern nach ein- bis dreistufiger Kontofunktion kombiniert mit zwei- bis vierstufiger Kontoart. Datensammlung umfasst folgende Arbeitsblätter:
- Aufwand nach einstufiger Kontofunktion und zweistufiger Kontoart
- Ertrag nach einstufiger Kontofunktion und zweistufiger Kontoart
- Aufwand nach einstufiger Kontofunktion und dreistufiger Kontoart
- Ertrag nach einstufiger Kontofunktion und dreistufiger Kontoart
- Aufwand nach einstufiger Kontofunktion und vierstufiger Kontoart
- Ertrag nach einstufiger Kontofunktion und vierstufiger Kontoart
- Aufwand nach zweistufiger Kontofunktion und zweistufiger Kontoart
- Ertrag nach zweistufiger Kontofunktion und zweistufiger Kontoart
- Aufwand nach zweistufiger Kontofunktion und dreistufiger Kontoart
- Ertrag nach zweistufiger Kontofunktion und dreistufiger Kontoart
- Aufwand nach zweistufiger Kontofunktion und vierstufiger Kontoart
- Ertrag nach zweistufiger Kontofunktion und vierstufiger Kontoart
- Aufwand nach dreistufiger Kontofunktion und zweistufiger Kontoart
- Ertrag nach dreistufiger Kontofunktion und zweistufiger Kontoart
- Aufwand nach dreistufiger Kontofunktion und dreistufiger Kontoart
- Ertrag nach dreistufiger Kontofunktion und dreistufiger Kontoart
- Aufwand nach dreistufiger Kontofunktion und vierstufiger Kontoart
- Ertrag nach dreistufiger Kontofunktion und vierstufiger Kontoart

identifier: gefis-lu-jr-er-fkt-art

de: Jahresrechnungen Investitionsrechnung: Investitionsausgaben und Investitionseinnahmen (Artengliederung)

de:
Investitionsrechnungsdaten der Gemeindefinanzstatistik vom Kanton Luzern nach ein- bis vierstufiger Kontoart. Datensammlung umfasst folgende Arbeitsblätter:
- Investitionsausgaben nach einstufiger Art
- Investitionseinnahmen nach einstufiger Art
- Investitionsausgaben nach zweistufiger Art
- Investitionseinnahmen nach zweistufiger Art
- Investitionsausgaben nach dreistufiger Art
- Investitionseinnahmen nach dreistufiger Art
- Investitionsausgaben nach vierstufiger Art
- Investitionseinnahmen nach vierstufiger Art

identifier: gefis-lu-jr-ir-art

de: Jahresrechnungen Erfolgsrechnung: Aufwand, Ertrag und Nettoaufwand (funktionelle Gliederung)

de:
Erfolgsrechnungsdaten der Jahresrechnungen der Gemeindefinanzstatistik vom Kanton Luzern nach ein- bis dreistufiger Kontofunktion. Datensammlung umfasst folgende Arbeitsblätter:
- Aufwand nach einstufiger Kontofunktion
- Ertrag nach einstufiger Kontofunktion
- Nettoaufwand nach einstufiger Kontofunktion
- Aufwand nach zweistufiger Kontofunktion
- Ertrag nach zweistufiger Kontofunktion
- Nettoaufwand nach zweistufiger Kontofunktion
- Aufwand nach dreistufiger Kontofunktion
- Ertrag nach dreistufiger Kontofunktion
- Nettoaufwand nach dreistufiger Kontofunktion

identifier: gefis-lu-jr-er-fkt

de: Das Datenset beinhaltet die Jahresrechnungen der  Gemeindefinanzstatistik des Kantons Luzern seit 2019 gemäss dem HRM2-Standard. Es werden für jede Gemeinde die Funktionen, die Kontoarten und die entsprechenden Saldi ausgewiesen. Es ist der Gebietsstand des jeweiligen Jahres aufgeführt. Die Daten wurden direkt bei den Gemeinden erhoben und bilden einen Auszug aus deren Buchhaltungssystem ab. Statistische Korrekturen zwecks Gewährleistung der Konformität mit dem kantonalen Kontenrahmen wurden in Einzelfällen vorgenommen.

en: The dataset contains the annual accounts of the Municipal Finance Statistics of the Canton Lucerne since 2019 in accordance with the HRM2 standard. The table shows the cost centers or the cost units, the account types and the corresponding account balances for each municipality. The territorial status of the respective year is disclosed. The data was collected directly from the municipalities and represents an extract of their accounting system. Statistical corrections were made in exceptional cases to ensure conformity with the cantonal chart of accounts.

artenkonto

display_name: artenkonto

finanzkennzahlen

display_name: finanzkennzahlen

funktion

display_name: funktion

gemeindefinanzen

display_name: gemeindefinanzen

gemeinderechnungen

display_name: gemeinderechnungen

offentliche-finanzen

display_name: offentliche-finanzen

sachkonto

display_name: sachkonto

statistische-grundlagen

display_name: statistische-grundlagen

verwaltung

display_name: verwaltung

volkswirtschaft

display_name: volkswirtschaft

gove

de: Regierung und öffentlicher Sektor

en: Government and public sector

econ

de: Wirtschaft und Finanzen

en: Economy and finance

de: LUSTAT Statistik Luzern

en: LUSTAT Statistik Luzern

lustat

en: LUSTAT Statistics Lucerne

de:
LUSTAT Statistik Luzern ist das Kompetenz- und Dienstleistungszentrum der öffentlichen Statistik im Kanton Luzern. Mit ihren objektiven statistischen Informationen begleitet LUSTAT Meinungsbildungs- und Entscheidungsprozesse in Politik, Verwaltung, Gesellschaft und Wirtschaft.
LUSTAT Statistik Luzern veröffentlicht hier schrittweise geeignete Datensammlungen der kantonalen Statistik als OGD-Daten.

fr: Statistique des finances communales canton de Lucerne: comptes annuels

de: Gemeindefinanzstatistik Kanton Luzern: Jahresrechnungen

en: Municipal Finance Statistics Canton Lucerne: annual accounts

it: Statistica finanziaria comunale cantone di Lucerna: conti annuali


maintainer: Bundesamt für Gesundheit, Abteilung Übertragbare Krankheiten

fr: BAG-OFSP-UFSP-FOPH Website

de: BAG-OFSP-UFSP-FOPH Website

en: BAG-OFSP-UFSP-FOPH Website

it: BAG-OFSP-UFSP-FOPH Website

fr: Tularémie - Système de déclaration obligatoire

de: Tularämie - Obligatorisches Meldesystem

en: Tularaemia - mandatory reporting system

it: Tularemia - Sistema di dichiarazione obbligatoria

fr: Les données se fondent sur les informations que les laboratoires, les médecins et les hôpitaux ont transmises à l’OFSP dans le cadre de la déclaration obligatoire.

en: The data are based on information that laboratories, doctors and hospitals have submitted to the FOPH under the mandatory reporting requirement.

de: Die Daten basieren auf Informationen, die Laboratorien, Ärztinnen und Ärzte sowie Spitäler dem BAG im Rahmen der Meldepflicht übermittelt haben.

it: I dati si basano su informazioni comunicate dai laboratori, dai medici e dagli ospedali all’Ufficio federale della sanità pubblica (UFSP) nel quadro della dichiarazione obbligatoria.

fr: Données concernant la propagation et l’évolution temporelle des cas de tularémie en Suisse et dans la Principauté de Liechtenstein. Vous trouverez des donnés sur les cas provenant du système de déclaration obligatoire.

de: Daten zur Verbreitung und zeitlichen Entwicklung von Tularämie Infektionen Erkrankungsfällen in der Schweiz und im Fürstentum Liechtenstein. Sie finden Daten zu laborbestätigten Fällen aus dem obligatorischen Meldesystem.

en: Data on the spread and development of tularemia infections and cases in Switzerland and the Principality of Liechtenstein. You will find data on  cases from the mandatory reporting system.

it: Dati della diffusione e dell’evoluzione temporale dei casi di tularemia in Svizzera e nel Principato del Liechtenstein. Qui trovate dati dei casi basate sul sistema di dichiarazione obbligatoria.

tularaemia-foph

display_name: tularaemia-foph

tularamie-bag

display_name: tularamie-bag

tularemia-ufsp

display_name: tularemia-ufsp

tularemie-ofsp

display_name: tularemie-ofsp

heal

fr: Santé

de: Gesundheit

en: Health

it: Salute

abteilung-uebertragbare-krankheiten

fr: Division Maladies transmissibles

de: Abteilung Übertragbare Krankheiten

en: Communicable Diseases Division

it: Divisione Malattie trasmissibili

fr: Les maladies transmissibles constituent une menace pour la santé publique. La division les surveille, publie régulièrement des rapports sur la situation épidémiologique, définit des stratégies de prévention et de contrôle, et les met en œuvre.

en: Communicable diseases pose a risk to public health. The division monitors these diseases, publishing regular reports on the epidemiological situation, laying down and implementing prevention and control strategies.

de: Übertragbare Krankheiten gefährden die öffentliche Gesundheit. Die Abteilung überwacht diese Krankheiten, publiziert regelmässig Berichte zur epidemiologischen Situation, legt Präventions- und Kontrollstrategien fest und setzt sie um.

it: Le malattie trasmissibili mettono in pericolo la salute pubblica. La divisione Malattie trasmissibili le monitora, pubblica regolarmente rapporti sulla situazione epidemiologica, stabilisce strategie di prevenzione e di controllo e le attua.

political_level: confederation

value: DCAT-BAG-IDD Harvester

fr: Tularémie

en: Tularemia

de: Tularämie

it: Tularämie


name: Bundesamt für Gesundheit, Abteilung Übertragbare Krankheiten

fr: Shigellose - système de déclaration obligatoire

en: Shigellosis - mandatory reporting system

de: Shigellose - Obligatorisches Meldesystem

it: Shigellosi - sistema di dichiarazione obbligatoria

fr: Données concernant la propagation et l’évolution temporelle des cas de shigellose en Suisse et dans la Principauté de Liechtenstein. Vous trouverez des donnés sur les cas provenant du système de déclaration obligatoire.

de: Daten zur Verbreitung und zeitlichen Entwicklung von Shigellose Erkrankungsfällen in der Schweiz und im Fürstentum Liechtenstein. Sie finden Daten zu Fällen aus dem obligatorischen Meldesystem.

en: Data on the spread and development of shigellosis infections and cases in Switzerland and the Principality of Liechtenstein. You will find data on cases from the mandatory reporting system.

it: Dati della diffusione e dell’evoluzione temporale dei casi di shigellosi in Svizzera e nel Principato del Liechtenstein. Qui trovate dati dei casi basate sul sistema di dichiarazione obbligatoria.

shigellose-bag

display_name: shigellose-bag

shigellose-ofsp

display_name: shigellose-ofsp

shigellosi-ufsp

display_name: shigellosi-ufsp

shigellosis-foph

display_name: shigellosis-foph

fr: Shigellose

en: Shigellosis

de: Shigellose

it: Shigellosi


fr: Covid-19 - Sentinella

en: Covid-19 - sentinella

de: Covid-19 - Sentinella

it: Covid-19 - Sentinella

fr: Les données se fondent sur les informations recueillies par le système Sentinella. Dans le cadre de ce système de déclaration, les médecins transmettent depuis mars 2020 des informations sur les premières consultations de patients présentant une infection respiratoire aiguë dans leur cabinet ou lors de visites à domicile.

de: Die Daten basieren auf Informationen aus dem Sentinella-Meldesystem. Im Rahmen dieses Meldesystems übermitteln ambulant tätige Ärztinnen und Ärzte seit März 2020 Informationen zu Erstkonsultationen von Patientinnen und Patienten mit akuter respiratorischer Infektion (ARI) in Arztpraxen und bei Hausbesuchen.

en: The data are based on information from the Swiss Sentinel Surveillance Network (SSSN, Sentinella) monitoring system. Since March 2020, the system has been used by doctors working on an outpatient basis to submit information on initial consultations in medical practices and during home visits for patients with acute respiratory infections (ARIs).

it: I dati si basano sulle informazioni ricavate dal sistema di dichiarazione Sentinella. Nel quadro di questo sistema di dichiarazione, dal marzo 2020 i medici che esercitano nel settore ambulatoriale trasmettono informazioni relative alle prime consultazioni svolte negli studi medici o durante le visite domiciliari in cui i pazienti presentano un’infezione respiratoria acuta (acute respiratory infection, ARI).

fr: SARS-CoV-2 (Covid-19) - monitorage des variants dans les eaux usées

de: SARS-CoV-2 (Covid-19) - Virusvarianten im Abwasser

en: SARS-CoV-2 (Covid-19) -  monitoring of variants in wastewater

it: SARS-CoV-2 (Covid-19) - monitoraggio delle varianti nelle acque reflue

fr: Les données se fondent sur les informations issues du monitorage des variants dans les eaux usées de la Suisse. Ce monitorage enregistre la répartition des variants celle du SARS-CoV-2 dans des échantillons des stations d’épuration des eaux usées (STEP) participantes depuis février 2021.

de: Die Daten basieren auf Informationen aus dem Variantenmonitoring im Abwasser der Schweiz . Dieses Monitoring erfasst seit Februar 2021 die Verteilung der Virusvarianten von SARS-CoV-2 in Abwasserproben der teilnehmenden Abwasserreinigungsanlagen (ARA).

en: The data are based on information from the monitoring programme of variants in wastewater of Switzerland. This monitoring programme has been recording the distribution of variants of SARS-CoV-2 since February 2021 in wastewater samples from participating wastewater treatment plants (WWTPs).

it: I dati pubblicati di seguito si basano sulle informazioni ricavate dalmonitoraggio delle varianti nelle acque reflue della Svizzera. Questo monitoraggio registra la distribuzione delle varianti del SARS-CoV-2 dal febbraio 2021 nei campioni di acque reflue degli impianti di depurazione delle acque reflue (IDA) partecipanti.

fr: Covid-19 - surveillance génomique

en: Covid-19 - genomic surveillance

de: Covid-19 - Genomische Überwachung

it: Covid-19 - monitoraggio genomico

fr: Les données se fondent sur les informations tirées du Programme national de surveillance génomique du SARS-CoV-2, auquel participent, à l’heure actuelle, des laboratoires des hôpitaux universitaires et de grands hôpitaux régionaux de Suisse, ainsi qu’une plateforme de séquençage.

de: Die Daten basieren auf Informationen aus dem nationalen genomischen SARS-CoV-2 Überwachungsprogramm. An diesem beteiligen sich aktuell Laboratorien der Universitätsspitäler und grösserer Regionalspitäler der Schweiz sowie eine Sequenzierungsplattform.

en: The data are based on information from the national genomic SARS-CoV-2 surveillance programme. Currently participating in the programme are laboratories of the university hospitals and larger regional hospitals in Switzerland, as well as a sequencing platform.

it: I dati si basano sulle informazioni ricavate dal programma nazionale di sorveglianza genomica del SARS-CoV-2, al quale attualmente aderiscono i laboratori degli ospedali universitari e dei maggiori ospedali regionali della Svizzera, nonché una piattaforma di sequenziamento.

fr: Virus respiratoire - Sentinella

de: Respiratorische Viren - Sentinella

en: Respiratory viruses - sentinella

it: Virus respiratori - Sentinella

fr: Les données se fondent sur les informations recueillies par le système de déclaration Sentinella. Dans le cadre de ce système, les médecins transmettent des informations sur les premières consultations en raison d’une infection respiratoire aiguë (ARI) ou d’une affection grippale (ILI) dans leurs cabinets ou lors de visites à domicile.

de: Die Daten basieren auf Informationen aus dem Sentinella-Meldesystem. Im Rahmen dieses Meldesystems übermitteln ambulant tätige Ärztinnen und Ärzte Informationen zu Erstkonsultationen aufgrund akuter respiratorischer Infektion (ARI) oder grippeähnlicher Erkrankung (ILI) in Arztpraxen und bei Hausbesuchen.

en: The data are based on information from the Swiss Sentinel Surveillance Network (SSSN, Sentinella) monitoring system. Within the framework of this reporting system, outpatient doctors send information on initial consultations due to acute respiratory infection (ARI) or influenza-like illness (ILI) in medical practices and during home visits.

it: I dati si basano sulle informazioni ricavate dal sistema di dichiarazione Sentinella. Nel quadro di questo sistema di dichiarazione, i medici che esercitano nel settore ambulatoriale trasmettono informazioni relative alle prime consultazioni svolte negli studi medici o durante le visite domiciliari in cui i pazienti presentano un’infezione respiratoria acuta (acute respiratory infection, ARI) o una sindrome simil-influenzale (influenza-like illness, ILI).

fr: Covid-19 - Système de déclaration obligatoire

en: Covid-19 - mandatory reporting system

de: Covid-19 - Obligatorisches Meldesystem

it: Covid-19 - Sistema di dichiarazione obbligatoria

fr: Virus respiratoire - Monitorage des eaux usées

de: Respiratorische Viren - Abwassermonitoring

en: Respiratory viruses - wastewater monitoring

it: Virus respiratori - Monitoraggio delle acque reflue

fr: Die Daten basieren auf Informationen aus dem nationalen Abwassermonitoring der Schweiz und des Fürstentums Liechtenstein. Dieses Monitoring erfasst seit Januar 2022 die Viruslast von SARS-CoV-2 und seit Juli 2023 die Viruslast von Influenza und RSV in Abwasserproben der teilnehmenden Abwasserreinigungsanlagen (ARA).

fr: Données concernant la propagation et l’évolution temporelle des infections au SARS-CoV-2 et des cas de COVID-19 en Suisse et dans la Principauté de Liechtenstein. Vous trouverez des donnés sur la charge virale dans les eaux usées provenant du monitorage correspondant, sur les consultations ambulatoires provenant du système de déclaration Sentinella, sur les variantes de virus provenant de la surveillance génomique et sur sur les cas, hospitalisations, décès et tests provenant du système de déclaration obligatoire.

de: Daten zur Verbreitung und zeitlichen Entwicklung von SARS-CoV-2 Infektionen und Covid-19 Erkrankungsfällen in der Schweiz und im Fürstentum Liechtenstein. Sie finden Daten zur Viruslast im Abwasser aus dem Abwassermonitoring, zu ambulanten Konsultationen aus dem Sentinella-Meldesystem, zu Virusvarianten aus der genomischen Überwachung und zu Fällen, Hospitalisationen, Todesfällen und Tests aus dem obligatorischen Meldesystem.

en: Data on the spread and development of SARS-CoV-2 infections and Covid-19 cases in Switzerland and the Principality of Liechtenstein. You will find data of the viral load in wastewater from the wastewater monitoring system, of outpatient consultations from the Sentinella reporting system, of virus variants from the genomic surveillance system and of cases, hospitalisations, deaths and tests from the obligatory reporting system.

it: Dati della diffusione e dell’evoluzione temporale delle infezioni da SARS-CoV-2 e dei casi di COVID-19 in Svizzera e nel Principato del Liechtenstein. Qui trovate dati della carica virale nelle acque reflue basate sul sistema di monitoraggio delle acque reflue, dati delle consultazioni ambulatoriali basate sul sistema di dichiarazione Sentinella, dati delle varianti del virus basate sul sistema di sorveglianza genomica e dati di casi, ospedalizzazioni, decessi e test basate sul sistema di dichiarazione obbligatoria.

fr: SARS-CoV-2 (Covid-19)

de: SARS-CoV-2 (Covid-19)

en: SARS-CoV-2 (Covid-19)

it: SARS-CoV-2 (Covid-19)