en:
In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. In this tutorial video, we show how to merge multiple DTM raster tiles into one file, using the QGIS tool ‘Virtual Raster’. This simplifies the digital planning of a cable line using the QGIS plugin ‘Seilaplan’. Please note that the tutorial language is German!

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

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

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. In diesem Tutorialvideo zeigen wir, wie man mit dem QGIS-Plugin Virtuelles Raster mehrere DHM-Kacheln zu einem einzigen Rasterfile zusammenfügen und abspeichern kann. Für die Seillinienplanung mit Seilaplan muss nun nur noch eine Datei, mein neues virtuelles Raster, ausgewählt werden.

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

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

envidat

en: EnviDat: the Environmental Data Portal

en: Seilaplan Tutorial: Merge DTM tiles

en: DHM-Kacheln zusammenfügen

en: View video on youtube!

format: HTML

media_type: text/html

cable-yarding

display_name: cable-yarding

digital-elevation-model

display_name: digital-elevation-model

digital-planning

display_name: digital-planning

geodata-processing

display_name: geodata-processing

geoinformation-system

display_name: geoinformation-system

open-source

display_name: open-source

qgis-plugin

display_name: qgis-plugin

raster-processing

display_name: raster-processing

seilaplan

display_name: seilaplan

swiss-geodata

display_name: swiss-geodata


$oid: OBSOLETE

en:
 Dataset
This dataset is based on terrestrial laser scanning (TLS) data acquired during winter 2020/2021 in leaf-off conditions, with a Leica BLK 360 instrument following a tree-centric scanning pattern.
The data was acquired on two sites (47.42°N 8.49°E and 47.504°N, 7.78°E), both of which were managed mixed temperate forest stands.
Individual trees were semi-automatically segmented from the co-registered TLS point clouds.

 Background
Accurate estimates of individual tree volume or biomass within forest inventories are essential for calibration and validation of biomass mapping products based on Earth observation data.
Terrestrial laser scanning (TLS) enables detailed and non-destructive volume estimation of individual trees, with existing approaches ranging from simple geometrical features to virtual 3D reconstruction of entire trees. Validating such approaches with weight measurements is a key step before the integration of TLS or other close-range technologies into operational applications such as forest inventories.
In this study, we firstly evaluate individual tree volume estimation approaches based on 3D reconstruction through quantitative structure models (QSM) against destructive reference data of 60 trees and compare them to operational allometric scaling models (ASM).
Secondly, we determine the explanatory power of TLS-derived geometric parameters regarding total tree, stem, coarse wood and fine branch volume.

en: biomass forest-inventory lidar single-tree terrestiral-laserscanning tree-volume

envidat

approval_status: approved

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

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

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

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

de: EnviDat: das Umwelt-Datenportal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

state: active

type: organization

wsl

capacity: public

en: Individual tree TLS point clouds for tree volume estimation

type: dataset

key: guid

key: uri

value: Envidat Harvester

educ

de: Bildung, Kultur und Sport

en: Education, culture and sport

fr: Éducation, culture et sport

it: Istruzione, cultura e sport

en:
"ASM" denotes volume estimates calculated by allometric scaling models (as used in Swiss NFI), based on tree height, DBH and D7.
"QSM" denotes volume estimates derived from TLS point clouds by quantitative structure models (QSM).
The remaining variables are geometrical features directly calculated from single tree TLS point clouds.
For more detailed variable descriptions see related publication.

en: Individual tree parameters and volume estimates based on TLS point clouds and allometric models

format: CSV

media_type: text/csv

format: ZIP

media_type: application/zip

biomass

display_name: biomass

forest-inventory

display_name: forest-inventory

lidar

display_name: lidar

single-tree

display_name: single-tree

terrestiral-laserscanning

display_name: terrestiral-laserscanning

tree-volume

display_name: tree-volume


en:
In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. As an alternative to using the ‘Swiss Geo Downloader’ plugin, the DTM can be obtained directly from Swisstopo. In this tutorial we explain step by step how to download the necessary DTM from the Swisstopo Website, and how to use it in QGIS for the digital planning of a cable line using the plugin ‘Seilaplan’. Please note that the tutorial language is German!

Link to the elevation model on the swisstopo website: https://www.swisstopo.admin.ch/de/geodata/height/alti3d.html#technische_details

Link to the rope map website: https://seilaplan.wsl.ch

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

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. Als Alternative zum Swiss Geo Downloader erklären wir in diesem Tutorial Schritt für Schritt, wie man das nötige Höhenmodell von der Swisstopo Webseite herunterladen und in QGIS zur Seillinienplanung verwenden kann.

Link zum Höhenmodell auf der swisstopo Webseite: https://www.swisstopo.admin.ch/de/geodata/height/alti3d.html#technische_details

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

en: cable-yarding digital-elevation-model digital-planning geoinformation-software open-source qgis-plugin seilaplan swiss-geodata swisstopo

en: Seilaplan Tutorial: DTM download from swisstopo website

en: Herunterladen des DHM von swisstopo Website

format: SERVICE

geoinformation-software

display_name: geoinformation-software

swisstopo

display_name: swisstopo


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

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

en: Seilaplan Tutorial: DTM download with SwissGeoDownloader

en: Herunterladen des DHM mit SwissGeoDownloader

cable-yarding

display_name: cable-yarding

digital-elevation-model

display_name: digital-elevation-model

swiss-geo-downloader

display_name: swiss-geo-downloader


en:
In order to digitally plan a cable line using the QGIS plugin ‘Seilaplan’, maps with various background information are helpful. In this tutorial we show you how to obtain maps that are helpful for cable line planning, for example a national map of Switzerland at different scales, the NFI vegetation height model or the NFI forest mix rate. For this we explain what WMS datasets are and how to integrate them into QGIS. No download of large data is needed for this, only a good internet connection. Please note that the tutorial language is German!

Link for the integration of WMS data: https://wms.geo.admin.ch/

Link to the description on the Swisstopo website:
https://www.geo.admin.ch/en/geo-services/geo-services/portrayal-services-web-mapping/web-map-services-wms.html

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

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

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung sind verschiedene Hintergrundkarten hilfreich. In diesem Tutorialvideo zeigen wir, was WMS Daten sind und wie man diese in QGIS einbinden kann. Dafür müssen die Daten nicht heruntergeladen werden. Es braucht lediglich eine gute Internetverbindung. Für die Seillinienplanung hilfreiche Karten sind bspw. die Landeskarte der Schweiz in verschiedenen Massstäben, das Vegetationshöhenmodell LFI oder der Waldmischungsgrad LFI.

Link zur Einbindung der WMS Daten: https://wms.geo.admin.ch/

Link zur Beschreibung auf der Swisstopo Webseite: https://www.geo.admin.ch/de/geo-dienstleistungen/geodienste/darstellungsdienste-webmapping-webgis-anwendungen/web-map-services-wms.html

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

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

envidat

en: EnviDat: the Environmental Data Portal

en: Seilaplan Tutorial: Load WMS layers as background maps

en: WMS-Layer als Hintergrundkarten laden

digital-planning

display_name: digital-planning

geoinformation-system

display_name: geoinformation-system

wms-data

display_name: wms-data


swiss-fluxnet-site-davos

en:
The Swiss FluxNet Site Davos is a managed subalpine evergreen forest, located on the Seehorn mountain near Davos in the Swiss Alps. The site is dominated by Norway spruce. The tower is owned by the Federal Office for the Environment (FOEN).
Ecosystem flux measurements of CO2, H2O (since 1997) as well as CH4 and N2O (since 2016) are performed with the eddy covariance method.
In addition to Swiss FluxNet, the site is part of the National Air Pollution Monitoring Network (NABEL), the Long term Forest Ecosystem Research (LWF), the biological drought and growth indicator network (TreeNet) and of ICOS Switzerland (Integrated Carbon Observation System). Since November 2019, the site is an ICOS Class 1 Ecosystem station.

Measurements

- Ecosystem flux measurements of CO2, H2O vapour (since 1997) as well a CH4 and N2O (since 2016) are performed with the eddy-covariance method. This method is based on measurements of trace gas mixing ratios, using infrared gas analyzers (for CO2, H2O vapor) and laser spectrometers (for CH4 and N2O), combined with wind speed and wind direction measurements, using 3D sonic anemometers. To resolve the short-term turbulent fluctuations in the atmosphere, very fast measurements are needed: we measure at 10-20 Hz, i.e., 10-20 times per second. To assess the energy budget of each ecosystem, also radiation sensors and soil climate profiles are installed at the site.
- Sub-canopy eddy fluxes (CO2, H2O, since 2023 also CH4).
- Continuous profile concentration and forest floor flux measurement of CO2, H2O, CH4, N2O.
- Auxiliary micrometeorology and soil climate measurements.

Data availability

Near real-time flux and meteo data uploaded daily to the ICOS Carbon Portal. Processed flux and meteo data are also available from the European Fluxes Database Cluster and part of Fluxnet2015 dataset.

Data policy

ICOS data license:
[https://www.icos-cp.eu/data-services/about-data-portal/data-license](https://www.icos-cp.eu/data-services/about-data-portal/data-license)

Detailed site info:
[https://www.swissfluxnet.ethz.ch/index.php/sites/ch-dav-davos/site-info-ch-dav/](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-dav-davos/site-info-ch-dav/)

en: eddy-covariance flux forest greenhouse-gas icos meteorological-measurements swiss-fluxnet swiss-forest-lab

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: Swiss FluxNet Site Davos

title_for_slug: swiss-fluxnet-site-davos

en: Site description

en: The flux and meteo data of the Swiss FluxNet site Davos are openly available. Near real-time flux and meteo data are uploaded daily to the ICOS Carbon Portal.

en: Data access

en: Processed flux and meteo data are available from the European Fluxes Database Cluster.

en: Processed data

en: Processed flux and meteo data are also part of the Fluxnet2015 dataset.

en: Fluxnet2015 dataset

eddy-covariance

display_name: eddy-covariance

flux

display_name: flux

forest

display_name: forest

greenhouse-gas

display_name: greenhouse-gas

icos

display_name: icos

meteorological-measurements

display_name: meteorological-measurements

swiss-fluxnet

display_name: swiss-fluxnet

swiss-forest-lab

display_name: swiss-forest-lab


en:
 A spatial dataset and tool to simultaneously assess hydropower potential and ecological potential of the Swiss river network (Version 2016)

 Introduction
The steadily growing demand for energy and the simultaneous pursuit of decarbonisation are increasing interest in the expansion of renewable energies worldwide. In Switzerland, various funding projects have been launched to promote technologies in the field of renewable energies and their application as quickly as possible. With the introduction of a funding instrument in 2009, the number of projects submitted to produce renewable energies increased rapidly. The applications for small hydropower plants (≤ 10 MW) were correspondingly numerous. However, the assessment of the environmental impact and its comparison with hydropower importance is still not standardized.
To provide a basis for decision-making, a methodology was developed to determine the overall hydropower potential of a region. A detailed assessment of each river reach, and the systematic and holistic assessment of small hydropower projects at a regional scale are combined here. The assessment of a river reach is conducted at the river space (i.e., the river and adjacent areas) and at the surrounding landscape level. The HYDROpot_integral methodology was developed as part of Carol Hemund's dissertation (2012) at the University of Bern. It allows the evaluation of river reaches holistically, regarding ecological, social, economic and cultural criteria. As a second part of the overall project, the theoretical hydropower (or hydraulic) potential was calculated for the entire river network, which complemnets the spatial assessment. In particular, it is possible to classify river reaches into those that are more suitable for hydropower production (=”use”) and those that are more suitable for protection.

 Material and method
The HYDROpot_Integral method was developed and tested on the basis of cantonal and national data (Hirschi et al. 2013). The method relies on 73 geodata sets. This holistic assessment is the key element of the entire assessment procedure. Its aim is to quantify the importance of the ecosystem functions in terms of services. The river network (GWN07) is divided into reaches of about 450m and for each reach two study units are defined. The river space (RS) records the ecosystem functions of the water body and the nearby riparian area. The length of the RS is 315 m on average in Switzerland and a maximum of 450 m, whereas the width is based on the FOEN definition (BWG 2001: 18f) and varies between 7-107 m. The surrounding landscape (SLS) is the second survey unit that records the ecosystem functions of the surrounding area over a range of 21 m to 321 m. The SLS is calculated over three times the RS width. The length of the SLS is identical to the length of the RS. The ecosystem functions are divided into three types: regulating (service A), cultural (service B) and provisioning (service C) functions. Accordingly, the assessment of the functions is divided into three parts and three values are assigned to each river reach. The more functions there are and the greater their performance, the higher these values are and the more important the corresponding functions are. Hence, these values quantify the importance of the ecosystem functions and the ecological, cultural and economic ecosystem services of each river reach. The concatenation of ecosystem services results in a value (ABC) that can occur in 125 different versions due to the chosen five-level value scale; i.e. each digit of the three-digit number sequence can be assigned a value between 1 and 5. Each of the 125 combinations, and thus each river reach, has its own characteristics determined by the assessments of the three function types. To record the suitability, the combinations are ranked according to their ecological, cultural and economic ecosystem services. These rules mean that the combination that is most suitable for hydropower production at minimum cost in terms of ecological and cultural ecosystem services and has a high economic potential is ranked first; rank 125 indicates the highest ecological and cultural ecosystem services and the lowest economic services and is therefore most suitable for protection. A river reach that is excluded from hydropower use due to legislation, a so-called priority reach, is given rank 126 from the outset and specially marked. A more detailed description of the methods can be found in Hirschi et al. 2013 [Link]. The dataset presented here presents the latest state of the HYDROpot_integral methodology applied at the national level. Only national data that is easily accessible was used in the preparation of the dataset. The cantonal data, such as renaturation and revitalization, would have to be requested by each canton individually and was excluded here. The nationwide value synthesis was made with R.
A list of data sources can be found here [link to text file]
A list of all parameters can be downloaded here [link to PDF and text files]

 Dataset description
Data is presented as a single shapefile. It contains the river network and all assessment results obtained with HYDROpot_Integral.

 Changes in the methodology compared to the original method (Hirschi et. al 2013)

 * RS_A11 Ecomorphology: recorded for the whole of Switzerland and zero values equated with NA;
individual cantons such as Zug and St. Gallen have no mapped values according to the modular
concept of the federal government, Valais and Graubünden only the main valleys, Ticino and Fribourg not
completely (BAFU 2009).
 * RS_A14 Renaturation and revitalization data: not centrally available at the time of data collection.
centrally available, therefore values in GR were equated with NA.
 * RS_A15 Dilution ratio at wastewater treatment plants (WWTPs) for discharges: Zero values
equal to NA.
 * RS_A20 Water flow: use WASTA (2013) with hydroelectric power plants (> 300 kW) under
Federal control and dams serving hydroelectricity (Dam, as of 2013).
 * RS_C05 Synoptic hazard maps: are cantonally managed at the time of data collection,
Values in GR are marked with a 5 so that the systematics in the decision tree is not affected.
is affected.
 * Water quality (RS_A15, RS_A16, RS_A17, RS_A18, RS_A19): for the evaluation of the function type.
A Nature, it is important whether the median of the five values is less than or equal to 3 in total. This
evaluation is based on the decision tree for evaluating GR (Hirschi et al. 2013:22).
Therefore, an evaluation of the station data is made where critical and possible river segments
with poor quality (median less than 3) exist. Only two longer and one short
sections in Switzerland receive a lower median than 3 for water quality.
 * SLS_B06 Visibility: For 99 percent of the river segments (30,733 of 31,062) in the canton of
Bern (2015 reduced version), the landscape area is considered to be visible. Due to this high number
of sections, a large number of viewpoints in the layer of Swisstopo and
the computation time and computability in ArcGIS, the landscape area is classified as generally viewable
(equal to 1).
16 Method
Additional indicators were added (see Appendix B.2):
 * SLS_A21 Dissection
 * SLS_A22 Forest areas
 * SLS_B03 Hiking trails
 * SLS_B10 Residential and vacation homes
 * SLS_B11 Tourist infrastructure
 * SLS_C01 Landfill
 * SLS_C03 Infrastructure
 * SLS_C05 Industry
 * SLS_C06 Agricultural land
Not to be added, although present to some extent:
 * SLS_B06 Cultural assets of national importance: here, too, the calculability of the visibility analysis is
for the whole of Switzerland is limited
 * SLS_A15 Legally binding protection and land use planning: the individual river sections are not
clearly designated, i.e. no geodata exist
The following data are also not supplemented, as they are cantonal data:
 * SLS_A10 Cantonal nature reserves
 * SLS_A16 Forest reserves
 * SLS_A17 Cantonal inventories and contractually protected areas

en: decision-making ecosystem-protection gis hydropower integral-water-management

educ

en: Education, culture and sport

format: SHP

en: The indicators capture supporting ecosystem functions.

en: Indicators

decision-making

display_name: decision-making

ecosystem-protection

display_name: ecosystem-protection

gis

display_name: gis

hydropower

display_name: hydropower

integral-water-management

display_name: integral-water-management


en:
Meteorological station at Gemstock (3021 m asl) in Canton Uri. The station includes in/out LW/SW and a snow height sensor.

Data from this station is managed by the permos.ch project. More information: https://www.permos.ch/permafrost-monitoring/field-sites

en: gemstock longwave-radiation meteo-station shortwave-radiation snow-height

envidat

en: EnviDat: the Environmental Data Portal

en: GEM2: Meteorological and snow station at Gemsstock (3021 m asl), Canton Uri, Switzerland

en:
Meteo data at Gemsstock [Level 2 dataset](http://models.slf.ch/p/dataset-processing/).

fields      = timestamp TSS HS VW DW VW_MAX OSWR ISWR ILWR RECORD AUX_VOLTAGE LOGGER_VOLTAGE LW_SENSOR_TEMP VENTILATION_FLAG OLWR

en: DATA ACCESS

gemstock

display_name: gemstock

longwave-radiation

display_name: longwave-radiation

meteo-station

display_name: meteo-station

shortwave-radiation

display_name: shortwave-radiation

snow-height

display_name: snow-height


en:
 Overview
The SUNWELL Modelling Environment is a combination of data and code that models electricity production from satellite-derived irradiance data and other spatial data sets for all of Switzerland. This ensemble accompanies the publication "The bright side of PV production in snow-covered mountains", published in the Proceedings of the National Academy of Science and reproduces all results and figures of. Code and resources are in their original form (with documentation). A new version with a more generalized application to PV modelling and with more flexibility in terms of input and output formats will be released in the coming months.

 Format
All code is written and has to be executed in Matlab. The input and output data sets are also in the Matlab-specific .mat format. Whenever publicly available, the original data is provided as geotif, .xlsx or other common format.
This is the case for:

- Digital Elevation Model (InputsFromMatlab/MSG/OriginalData/ASTERDEM),
- Landsurface cover type (InputsFromMatlab/MSG/OriginalData/CORINE),
- Population Density (InputsFromMatlab/MSG/OriginalData/popdensRaster,
- Electricity production from three of our validation sites (/Validation/WSL),
- Measured irradiance for two validation sites (/Validation/ASRB)
The ‘Metadata’ documents in the respective folders provide further information about the data sources and processing. Figures are produced either in .pdf or .png format.

 Structure
The central level of the SUNWELL environment holds the 5 Mains, which run the different modelling aspects of the paper; each code is documented separately. Additional code is located in the ‘DataProcessing’ and the ‘functions’ folder. Functions are called in the different Mains.

‘InputsFromMatlab’ contains the radiation and albedo input data sets in separate subfolders (SIS/SISDIR/ALB). The original data is not publicly available, but can be requested for research purposes free of charge. We provide a processed subset of the data set that was used to run the SUNWELL simulations. The MSG subfolder contains additional spatial input data sets.

‘Outputs’ contains the output files from the different mains (matching names, Main_CHallpixels.m  Prod_CHallpixels)

‘Publication_figures’ contains all individual figures from the PNAS publication, as well as the generating code (/code_plot) and the power point figures (/ppts) that provide the combined final figures.

‘Validation’ contains the data sets used in the model validation:

- Electricity production from three of our validation sites (/WSL),
- Measured irradiance for two validation sites (/ASRB)

Electricity production from a validation site at Lac des Toules in Wallis (/LDT), this data set was provided under an NDA and cannot be made publicly available.

Paper Citation:
 > _Annelen Kahl; Jérôme Dujardin; Michael Lehning (2018). Dataset on PV Production in Snow Covered Mountains. PNAS - Proceedings of the National Academy of Sciences. (in press)_

en: albedo energy irradiance mountain photovoltaic reflection snow

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: PV Data

format: XLS

en: Windows users use the 7-zip app to extract the zip-file.

albedo

display_name: albedo

energy

display_name: energy

irradiance

display_name: irradiance

mountain

display_name: mountain

photovoltaic

display_name: photovoltaic

reflection

display_name: reflection

snow

display_name: snow


en:
 DESCRIPTION
The dataset is used to run snowpack simulations with various forcing data at different resolutions with the Flexible Snow Model (FSM2oshd). A dynamical downscaling model (HICAR) and a semi-statistical downscaling approach (COSD) are used to downscale COSMO data to resolutions of 250 m, 100 m and 50 m. Simulations are run with the operational snow cover model and a model extension including wind- and gravity-induced snow redistribution (FSM2trans).

 Input Data
This paragraph describes the input data used to run simulations with FSM2trans

 HICAR
The HICAR input data is created by the High-resolution Intermediate Complexity Atmospheric Research model. The input data is available for different resolutions (250m, 100m and 50m) for a domain covering complex terrain in the Swiss Alps (Dischma)

 COSD
The COSD data was statistically downscaled within the OSHD framework to different resolutions (250m, 100m, 50m) for a domain covering complex terrain in the Swiss Alps (Dischma)

 Simulation files
For the Simulations, a landuse file and a namelist file with the parametrizations need to be supplied to the model

 simulation output
This simulation output that is used for the analysis of the study is provided.

en: dischma fsm2 fsm2trans hicar snow-hydrology snowpack

en: FSM2trans snowpack simulations with HICAR input

en: Available input files for running with FSM2trans

en: input data october 2016 COSD

en: input data november 2016 COSD

en: input data january 2016 COSD

en: Input data January 2017 COSD

en: Input data February 2017 COSD

en: Input data March 2017 COSD

en: Input data April 2017 COSD

en: Input data May 2017 COSD

en: Input data June 2017 COSD

en: Input data July 2017 COSD

en: Input data october 2016 HICAR 50m resolution

en: Input data November 2016 HICAR 50m resolution

en: Input data December 2016 HICAR 50m resolution

en: Input data January 2017 HICAR 50m resolution

en: Input data February 2017 HICAR 50m resolution

en: Input data March 2017 HICAR 50m resolution

en: Input data April 2017 HICAR 50m resolution

en: Input data May 2017 HICAR 50m resolution

en: Input data June 2017 HICAR 50m resolution

en: Input data July 2017 HICAR 50m resolution

en: Input data october 2016 HICAR 100m resolution

en: Input data november 2016 HICAR 100m resolution

en: Input data December 2016 HICAR 100m resolution

en: Input data January 2017 HICAR 100m resolution

en: Input data February 2017 HICAR 100m resolution

en: Input data March 2017 HICAR 100m resolution

en: Input data April 2017 HICAR 100m resolution

en: Input data May 2017 HICAR 100m resolution

en: Input data June 2017 HICAR 100m resolution

en: Input data July 2017 HICAR 100m resolution

en: Input data October 2016 HICAR 250m resolution

en: Input data November 2016 HICAR 250m resolution

en: Input data December 2016 HICAR 250m resolution

en: Input data January 2017 HICAR 250m resolution

en: Input data February 2017 HICAR 250m resolution

en: Input data March 2017 HICAR 250m resolution

en: Input data April 2017 HICAR 250m resolution

en: Input data May 2017 HICAR 250m resolution

en: Input data June 2017 HICAR 250m resolution

en: Input data July 2017 HICAR 250m resolution

en:
time series of daily averages of the whole domain for each simulation run
- rainfall
- snowfall
- runoff
- snowmelt
- temperature

en:
snowheight output of validation days (LiDAR days) for all simulation runs
- 20.03.2027
- 31.03.2017
- 17.05.2017


en: This dataset contains experimental acoustic and vibration signals recorded by the Phased Microphone Arrays (PMA) system during laboratory impact calibration tests. The PMA system is designed for surrogate monitoring of bedload transport. It consists of a stainless-steel plate mounted on elastomer supports and instrumented with an array of microphone elements and an accelerometer fixed to the internal plate. The microphone sensors (sensitivity = 11.2 mV/Pa, frequency response = 10 Hz - 20 kHz) record air-pressure fluctuations caused by particle impacts, while the accelerometer (sensitivity = 50.0 mV/g, frequency response = 0.5 Hz - ~10 kHz) measures vibrations of the internal plate. All channels were synchronously sampled at 10 kHz using a 16-channel, 24-bit data acquisition system. Additional data and details on experimental setup, signal analysis, and interpretation are available in the associated publication.

en: accelerometer acoustic-signals bedload-transport impact-test localization microphone monitoring-techniques natural-hazards particle-size surrogate-measurement

envidat

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

en: EnviDat: the Environmental Data Portal

en: Acoustic Data from the PMA Bedload Monitoring System

educ

en: Education, culture and sport

en: The dataset corresponds to the typical signal shown in Figure 4 of the related publication and represents a centric impact (location 1.4) of a 24.8 mm quartz sphere dropped onto the PMA plate with a velocity of 1.05 m/s. The data include paired recordings from the accelerometer (mounted near the centre of the PMA internal plate) and the microphone element M01, obtained from ten repeated impact experiments. This dataset provides an example of the PMA system’s acoustic and vibration response to a single-particle impact. Additional data and details on experimental setup, signal analysis, and interpretation are available in the associated publication.

accelerometer

display_name: accelerometer

acoustic-signals

display_name: acoustic-signals

bedload-transport

display_name: bedload-transport

impact-test

display_name: impact-test

localization

display_name: localization

microphone

display_name: microphone

monitoring-techniques

display_name: monitoring-techniques

natural-hazards

display_name: natural-hazards

particle-size

display_name: particle-size

surrogate-measurement

display_name: surrogate-measurement


en:
Data and code for the paper published in Ecography: Community structure and range shifts in Arctic marine fish under climate change
Abstract:
Arctic marine ecosystems are rapidly transforming due to climate change. Warming temperatures and shrinking sea ice are enabling boreal fish to expand northward, possibly disturbing cold-adapted Arctic species assemblages. Species range shifts have been documented in the Bering and Barents Seas, raising concerns about ecosystem restructuring. Range shifts are especially difficult to detect in the Arctic due to sparse and inconsistent data. Here, we studied fish composition from eDNA water samples taken in East Greenland, Svalbard, the Barents Sea, and the Kara Sea during the TOPtoTOP and Arctic Century expeditions. We examined the environmental drivers of fish community structure using global dissimilarity models. We calculated the decadal rate of temperature change to identify the fastest-changing areas. We compared fish detections from eDNA with published historical records for the Kara Sea to assess possible range expansions. We found that temperature was the main factor influencing the taxa turnover of fish communities, with Gadidae and Liparis sp. driving the greatest compositional differences. Over the past 30 years, temperatures increased by 0.2 to 0.6°C per decade at our study sites, with the highest increases in western Svalbard and the lowest in the eastern Kara Sea. Despite the apparent dependence on temperature, we identified only one species detected outside its known latitudinal range, and five species in the Kara Sea with recent occurrences or representing an extended distribution. Our study suggests that temperature, the main driver of fish community assembly, is increasing rapidly in the Arctic, and a few species have likely already shifted recently, or at least their detections are new in some areas. While these detections cannot be definitively linked to range shifts, our results highlight the need to improve monitoring of high-latitude fish communities to detect and predict future ecosystem changes.
Article:
Marques, V., Fopp, F., Jaquier, M., Ellingsen, K. E., Yoccoz, N., Jucker, M., ...Pellissier, L. (2025). Community structure and range shifts in Arctic marine fish under climate change. Ecography, e8014. doi: 10.1002/ecog.08014
Data:
The resource contains a zip file with the entire project structure.
Data README:
 Intro
This repo presents data and code associated with the paper "Community structure and range shifts in Arctic marine fish under climate change" published in Ecography
 Usage
Launch the `main.R` script to reproduce the entire analysis.
It executes code blocks to create the data necessary for the analysis and then creates the figures.
💣 attention, total size is expected ~20 Go and the script will query the CMEMS database to fetch environmental data
 Tools
To make the scripts run, you need R, R packages (see session info below), python3, and the copernicusmarine python tool (https://pypi.org/project/copernicusmarine/).

 SessionInfo
```
sessionInfo()
R version 4.4.0 (2024-04-24)
Platform: aarch64-apple-darwin20
Running under: macOS 15.5
Matrix products: default
BLAS:  /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Zurich
tzcode source: internal
attached base packages:
[1] stats   graphics grDevices
[4] utils   datasets methods
[7] base
other attached packages:
 [1] lwgeom_0.2-14
 [2] metR_0.15.0
 [3] ggtext_0.1.2
 [4] scico_1.5.0
 [5] ggnewscale_0.5.0
 [6] colorspace_2.1-1
 [7] climetrics_1.0-15
 [8] rts_1.1-14
 [9] xts_0.14.0
[10] zoo_1.8-12
[11] ggpubr_0.6.0
[12] adespatial_0.3-23
[13] ggspatial_1.1.9
[14] colorplaner_0.1.4
[15] ape_5.8
[16] factoextra_1.0.7
[17] corrgram_1.14
[18] FactoMineR_2.11
[19] betapart_1.6
[20] gdm_1.5.0-9.1
[21] terra_1.7-78
[22] rnaturalearth_1.0.1
[23] ggrepel_0.9.5
[24] vegan_2.6-6.1
[25] lattice_0.22-6
[26] permute_0.9-7
[27] cowplot_1.1.3
[28] tidyterra_0.6.1
[29] sf_1.0-16
[30] egg_0.4.5
[31] gridExtra_2.3
[32] patchwork_1.2.0
[33] viridis_0.6.5
[34] viridisLite_0.4.2
[35] mgcv_1.9-1
[36] nlme_3.1-165
[37] MBA_0.1-0
[38] reshape2_1.4.4
[39] conflicted_1.2.0
[40] lubridate_1.9.3
[41] forcats_1.0.0
[42] stringr_1.5.1
[43] dplyr_1.1.4
[44] purrr_1.0.2
[45] readr_2.1.5
[46] tidyr_1.3.1
[47] tibble_3.2.1
[48] ggplot2_3.5.1
[49] tidyverse_2.0.0
[50] devtools_2.4.5
[51] usethis_2.2.3
loaded via a namespace (and not attached):
 [1] splines_4.4.0
 [2] later_1.3.2
 [3] bitops_1.0-7
 [4] minpack.lm_1.2-4
 [5] XML_3.99-0.16.1
 [6] lifecycle_1.0.4
 [7] rstatix_0.7.2
 [8] doParallel_1.0.17
 [9] MASS_7.3-60.2
 [10] flashClust_1.01-2
 [11] backports_1.5.0
 [12] magrittr_2.0.3
 [13] rmarkdown_2.27
 [14] yaml_2.3.8
 [15] remotes_2.5.0
 [16] httpuv_1.6.15
 [17] sp_2.1-4
 [18] sessioninfo_1.2.2
 [19] pkgbuild_1.4.4
 [20] mapproj_1.2.11
 [21] pbapply_1.7-2
 [22] DBI_1.2.3
 [23] RColorBrewer_1.1-3
 [24] ade4_1.7-22
 [25] maps_3.4.2
 [26] abind_1.4-5
 [27] pkgload_1.3.4
 [28] RCurl_1.98-1.14
 [29] itertools_0.1-3
 [30] yaImpute_1.0-34
 [31] rcdd_1.6
 [32] units_0.8-5
 [33] adegenet_2.1.10
 [34] codetools_0.2-20
 [35] xml2_1.3.6
 [36] adephylo_1.1-16
 [37] DT_0.33
 [38] tidyselect_1.2.1
 [39] RNeXML_2.4.11
 [40] raster_3.6-26
 [41] farver_2.1.2
 [42] jsonlite_1.8.9
 [43] e1071_1.7-14
 [44] phylobase_0.8.12
 [45] ellipsis_0.3.2
 [46] iterators_1.0.14
 [47] emmeans_1.10.4
 [48] systemfonts_1.1.0
 [49] foreach_1.5.2
 [50] progress_1.2.3
 [51] tools_4.4.0
 [52] ragg_1.3.2
 [53] snow_0.4-4
 [54] Rcpp_1.0.13
 [55] glue_1.8.0
 [56] xfun_0.44
 [57] withr_3.0.0
 [58] fastmap_1.2.0
 [59] boot_1.3-30
 [60] latticeExtra_0.6-30
 [61] fansi_1.0.6
 [62] spData_2.3.1
 [63] digest_0.6.35
 [64] timechange_0.3.0
 [65] R6_2.5.1
 [66] mime_0.12
 [67] estimability_1.5.1
 [68] wk_0.9.1
 [69] textshaping_0.4.0
 [70] jpeg_0.1-10
 [71] utf8_1.2.4
 [72] generics_0.1.3
 [73] data.table_1.15.4
 [74] class_7.3-22
 [75] prettyunits_1.2.0
 [76] httr_1.4.7
 [77] htmlwidgets_1.6.4
 [78] scatterplot3d_0.3-44
 [79] spdep_1.3-5
 [80] pkgconfig_2.0.3
 [81] gtable_0.3.5
 [82] picante_1.8.2
 [83] adegraphics_1.0-21
 [84] htmltools_0.5.8.1
 [85] carData_3.0-5
 [86] profvis_0.3.8
 [87] multcompView_0.1-10
 [88] scales_1.3.0
 [89] leaps_3.2
 [90] png_0.1-8
 [91] doSNOW_1.0.20
 [92] geometry_0.4.7
 [93] rnaturalearthhires_1.0.0.9000
 [94] knitr_1.47
 [95] rstudioapi_0.16.0
 [96] rncl_0.8.7
 [97] uuid_1.2-0
 [98] tzdb_0.4.0
 [99] checkmate_2.3.1
[100] coda_0.19-4.1
[101] magic_1.6-1
[102] proxy_0.4-27
[103] cachem_1.1.0
[104] KernSmooth_2.23-24
[105] parallel_4.4.0
[106] miniUI_0.1.1.1
[107] s2_1.1.6
[108] pillar_1.9.0
[109] grid_4.4.0
[110] vctrs_0.6.5
[111] urlchecker_1.0.1
[112] promises_1.3.0
[113] car_3.1-2
[114] xtable_1.8-4
[115] cluster_2.1.6
[116] evaluate_0.24.0
[117] mvtnorm_1.2-5
[118] cli_3.6.3
[119] compiler_4.4.0
[120] rlang_1.1.4
[121] crayon_1.5.2
[122] ggsignif_0.6.4
[123] labeling_0.4.3
[124] interp_1.1-6
[125] classInt_0.4-10
[126] plyr_1.8.9
[127] fs_1.6.4
[128] stringi_1.8.4
[129] deldir_2.0-4
[130] munsell_0.5.1
[131] Matrix_1.7-0
[132] hms_1.1.3
[133] seqinr_4.2-36
[134] shiny_1.8.1.1
[135] gridtext_0.1.5
[136] igraph_2.0.3
[137] broom_1.0.6
[138] memoise_2.0.1
[139] fastmatch_1.1-4
```
 Data content and reproducibility
Uncleaned raw table out of the bioinformatics pipeline can be found concatenated in `outputs/table_raw_before_cleaning.csv`, yet we caution readers to properly read the cleaning scripts should they wish to reproduce our analysis and this file is presently uncleaned.

en: arctic climate-change community-structure environmental-dna fish

en: Data and code for Community structure and range shifts in Arctic marine fish under climate change

en: Zip file with data and code

en: Data and code to reproduce the analysis of "Community structure and range shifts in Arctic marine fish under climate change"

arctic

display_name: arctic

climate-change

display_name: climate-change

community-structure

display_name: community-structure

environmental-dna

display_name: environmental-dna

fish

display_name: fish


en:
This dataset contains the experimental results described in Bergfeld et al. (2025). It includes three Propagation Saw Test (PST) experiments, each approximately 9 m long, performed side-by-side on a 37° slope. For each PST, we provide the full field of view along the crack-propagation direction. For the second and third PSTs, we additionally provide close-up recordings focused on the weak layer where cracking occurred.
All data are supplied as netCDF files containing displacement and strain measurements derived from Digital Image Correlation (DIC) analysis. Metadata describing dimensions and units are stored directly within the netCDF files. We recommend using the xarray package in Python to read and work with these datasets.
All figures presented in Bergfeld et al. (2025) can be reproduced using the included Python scripts.
Information about the snowpack is provided in PDF, pickle, and CAAML file formats.

en: crack-propagation crack-speed dry-snow-avalanche snow weak-layer

en: Sub-Rayleigh to supershear fracture transition in long Propagation Saw Tests

en: reference frame used for Digital image correlation of the video of PST1

format: TIFF

media_type: image/tiff

en: netCDF file of dispalcement and strain measurement of PST1 in full view perspective.

format: OCTET

en: netCDF file of dispalcement and strain measurement of PST2 in full view perspective.

en: netCDF file of dispalcement and strain measurement of PST3 in full view perspective.

en: netCDF file of dispalcement and strain measurement of PST2 in closeup view.

en: netCDF file of displacement and strain measurement of PST3 in closeup view.

en: manual profile taken next to the PSTs. Open the caaml-file using niviz.org

en: pickled version of the manual profile. read into python with pickle. E.g. given the density is used in plotting scripts.

en: pdf Version of the manual profile taken next to the PSTs

format: PDF

media_type: application/pdf

en: Emodul parametrizations for snow. Some functions from this script are imported from the plotting scripts.

en: Jupyter script to load the netcdf files and reproduce Figure 2 from the related publication.

en: Jupyter script to load the netcdf files and reproduce Figure 3 from the related publication.

en: Jupyter script to load the netcdf files and reproduce Figure 4 from the related publication.

crack-propagation

display_name: crack-propagation

crack-speed

display_name: crack-speed

dry-snow-avalanche

display_name: dry-snow-avalanche

weak-layer

display_name: weak-layer


en:
This dataset originates from a two-year transplant experiment conducted across a temperature gradient in Europe (mean annual temperatures: 8.4–21.8 °C). It includes physiological measurements of one invasive palm species (Trachycarpus fortunei) and two co-occurring native species (Ilex aquifolium and Tilia cordata). The dataset captures plant acclimation responses to air temperature, focusing on photosynthetic and respiratory traits that influence carbon balance.
Variables that can be extracted:
- Optimal temperature of photosynthesis (Topt)
- Photosynthetic rate at optimal temperature (Aopt)
- Thermal breadth of photosynthesis (T80)
- Respiration rate at 25 °C (R25)
- Temperature sensitivity of respiration (Q10)
Raw data are reported for all species across multiple sites and time points, enabling assessment of their acclimation capacity to warming. The dataset supports comparisons of functional plasticity between an invasive species and native taxa, providing insights into climate responses and invasion ecology.

en: acclimation invasive-species leaf photosynthesis respiration spac-model

envidat

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

en: EnviDat: the Environmental Data Portal

en: Thermal acclimation fails to confer a carbon budget advantage to invasive species over natives

educ

en: Education, culture and sport

en: Thermal tolerance of T. fortunei.zip

acclimation

display_name: acclimation

invasive-species

display_name: invasive-species

leaf

display_name: leaf

photosynthesis

display_name: photosynthesis

respiration

display_name: respiration

spac-model

display_name: spac-model


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.

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

en: Snow on Antarctic Sea Ice - McMurdo Sound 2022

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:
Acknowledgements: The CORE format was proudly inspired by the Cloud Optimized GeoTIFF ([COG](https://www.cogeo.org/)) format, by considering how to leverage the ability of clients issuing HTTP GET range requests for a time-series of remote sensing and aerial imagery (instead of just one image).

License: The Cloud Optimized Raster Encoding (CORE) specifications are released to the public domain under a Creative Commons 1.0 CC0 "No Rights Reserved" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions.

-----------------------

Summary:
The Cloud Optimized Raster Encoding (CORE) format is being developed for the efficient storage and management of gridded data by applying video encoding algorithms. It is mainly designed for the exchange and preservation of large time series data in environmental data repositories, while in the same time enabling more efficient workflows on the cloud. It can be applied to any large number of similar (in pixel size and image dimensions) raster data layers. CORE is not designed to replace COG but to work together with COG for a collection of many layers (e.g. by offering a fast preview of layers when switching between layers of a time series).

WARNING: Currently only applicable to RGB/Byte imagery. The final CORE specifications may probably be very different from what is written herein or CORE may not ever become productive due to a myriad of reasons (see also 'Major issues to be solved').

With this early public sharing of the format we explicitly support the Open Science agenda, which implies "shifting from the standard practices of publishing research results in scientific publications towards sharing and using all available knowledge at an earlier stage in the research process" (quote from: European Commission, Directorate General for Research and Innovation, 2016. Open innovation, open science, open to the world).

CORE Specifications:

1) a MP4 or WebM video digital multimedia container format (or any future video container playable as HTML video in major browsers)

2) a free to use or open video compression codec such as H.264, VP9, or AV1 (or any future video codec that is open sourced or free to use for end users)

Note: H.264 is currently recommended because of the wide usage with support in all major browsers, fast encoding due to acceleration in hardware (which is currently not the case for AV1 or VP9) and the fact that MPEG LA has allowed the free use for streaming video that is free to the end users. However, please note that H.264 is restricted by patents and its use in proprietary or commercial software requires the payment of royalties to [MPEG LA](https://www.mpegla.com/programs/avc-h-264/). However, when AV1 matures and accelerated hardware encoding becomes available, AV1 is expected to offer 30% to 50% smaller file size in comparison with H.264, while retaining the [same quality](https://trac.ffmpeg.org/wiki/Encode/AV1).

3) the encoding frame rate should be of one frame per second (fps) with each layer segmented in internal tiles, similar to COG, ordered by the main use case when accessing the data: either layer contiguous or tile contiguous;

Note: The internal tile arrangement should support an easy navigation inside the CORE video format, depending on the use case.

4) a CORE file is optimised for streaming with the moov atom at the beginning of the file (e.g. with -movflags faststart) and optional additional optimisations depending on the codec used (e.g. -tune fastdecode -tune zerolatency for H.264)

5) metadata tags inside the moov atom for describing and using geographic image data (that are preferably compatible with the [OGC GeoTIFF standard](https://www.ogc.org/standards/geotiff) or any future standard accepted by the geospatial community) as well as list of original file names corresponding to each CORE layer

6) it needs to encode similar source rasters (such as time series of rasters with the same extent and resolution, or different tiles of the same product; each input raster should be having the same image and pixel size)

7) it provides a mechanism for addressing and requesting overviews (lower resolution data) for a fast display in web browser depending on the map scale (currently external overviews)

Major issues to be solved:

- Internal overviews (similar to COG), by chaining lower resolution videos in the same MP4 container for fast access to overviews first); Currently, overviews are kept as separate files, as external overviews.

- Metadata encoding (how to best encode spatial extent, layer names, and so on, for each of the layer inside the series, which may have a different geographical extent, etc...; Known issues: adding too many tags with FFmpeg which are not part of the standard MP4 moov atom; metadata tags have a limited string length.

- Applicability beyond RGB/Byte datasets; defining a standard way of converting cell values from Int16/UInt16/UInt32/Int32/Float32/Float64/ data types into multi-band Byte values (and reconstructing them back to the original data type within acceptable thresholds)

Example

Notice: The provided CORE (.mp4) examples contain modified Copernicus Sentinel data [2018-2021].

For generating the CORE examples provided, 50 original Sentinel 2 (S-2) TCI data images from an area located inside Switzerland were downloaded from www.copernicus.eu, and then transformed into CORE format using ffmpeg with H.264 encoding using the [x264 library](https://www.videolan.org/developers/x264.html).

DISCLAIMER: Basic scripts are provided for the Geomatics peer review (in 2021) and kept as additional information for the dataset. Nevertheless, please note that software dependencies and libraries, as well as cloud storage paths, may quickly become deprecated over time (after 2021). For compatibility, stable dependencies and libraries released around 2020 should be used.

en: core geodata gridded-data raster research-data-management sentinel-2 time-series video-encoding

en: CORE Dataset (80 GB)

en: View publication in the Digital Object Repository at WSL (DORA)

en: Disclaimer: provided for the Geomatics peer review in 2021 and left as additional information for the dataset. Software dependencies and libraries, as well as cloud storage paths, may change over time (after 2021).

core

display_name: core

geodata

display_name: geodata

gridded-data

display_name: gridded-data

raster

display_name: raster

research-data-management

display_name: research-data-management

time-series

display_name: time-series

video-encoding

display_name: video-encoding


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

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

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

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

en: alpine3d-simulations climate-change graubunden snow switzerland

en: Alpine3D simulations of future climate scenarios for Graubunden

en: Configuration file

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

en: Simulation data

climate-change

display_name: climate-change

graubunden

display_name: graubunden

switzerland

display_name: switzerland


en:
 Overview
We present GROUNDEYE, a new model of radiative transfer over mountainous terrain, which considers for the first time the forward scattering properties of snow.
Embedded in the surface process model Alpine3D, the new terrain radiation model GROUNDEYE receives interpolated real weather data with diffuse and direct broadband shortwave radiation for each pixel as well as a spatially variable plane albedo from the module SNOWPACK.

 Format
The GROUNDEYE model is written in c++, as is the entire environment of Alpine3d. The input and output data sets are .xlsx or .txt format, pre- and postprocessing including the generation of all figures is in .R format.

 Structure
In Data_Forward_Scattering.zip you will find all necessary data and model details to
reproduce the results of the JGR publication "How forward-scattering snow and terrain change the Alpine radiation balance with application to solar panels"

 - 	Model Input Data contains the meteorological and topographic input data sets, the BRDF, and preprocessing scripts.
 - Model Code contains the full model Alpine3d including the radiative transfer module GROUNDEYE.
 - Model Output Data contains the results of the simulation of terrain irradiance and irradiance of solar panels; hourly resolution, 1. Sptember 2017 - 31. August 2018.
 - Measurements Solar Testsite contains information and measurements of the solar testsite at the Totalp near Davos, Switzerland.
 - Postprocessing contains all R-Scripts used for the analysis and
plotting of the corresponding data.

In each of these folders you will find detailed information in
the file 'About this Folder.txt'.

en: albedo photovoltaic shortwave-radiation snow winter-energy-production

envidat

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

en: EnviDat: the Environmental Data Portal

en: Data, Code Link and Metadata on Forward Scattering of Snow at Totalp

en: GROUNDEYE model for multiple forward scattering in snow-covered terrain. Meteorological and topographical input data set for the Meierhofertäli near Davos, Switzerland, September 2017 - August 2018. Output data for September 2017 - August 2018 at hourly resolution and every pixel. Post-processing scripts for plotting and analysis.

en: Data Forward Scattering

albedo

display_name: albedo

photovoltaic

display_name: photovoltaic

shortwave-radiation

display_name: shortwave-radiation

winter-energy-production

display_name: winter-energy-production


en:
This work presents the first extensive study of climate change impacts on rivers temperature in Switzerland. Results show that even for low emissions scenarios, water temperature increase will lead to adverse effect for both ecosystems and socioeconomic sectors (such as nuclear plant cooling) throughout the 21st century. For high emissions scenarios, the effect will be worsen. This study also shows that water warming in summer will be more important in Alpine regions than in lowlands.
This material is distributed under CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/legalcode).

en: climate-change-impact discharge hydrology model water-temperature

en: Dataset for: Future water temperature of rivers in Switzerland under climate change investigated with physics-based models

educ

en: Education, culture and sport

en: Data access

climate-change-impact

display_name: climate-change-impact

discharge

display_name: discharge

hydrology

display_name: hydrology

model

display_name: model

water-temperature

display_name: water-temperature


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

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

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

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

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

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

en: Dataset

lee-side-flow-field

display_name: lee-side-flow-field

lidar

display_name: lidar

preferential-deposition

display_name: preferential-deposition

tls

display_name: tls


en:
This dataset contains the data acquired during the expedition to Princess Elisabeth Antarctica Station in December 2016 and January 2017. The dataset consits of meterorological data, drifting snow mass flux data, SnowMicroPen data and Terrestrial Laser Scanning data.

Please refer to the README for more information about the data.

This dataset is the basis of the following publication:

Sommer, C. G., Wever, N., Fierz, C., and Lehning, M.: Wind-packing of snow in Antarctica, The Cryosphere Discuss., https://doi.org/10.5194/tc-2018-36, in review, 2018.

en: antarctica drifting-snow mass-flux meteorological-data snowmicropen terrestrial-laser-scanning

en: Expedition to Princess Elisabeth Antarctica Station, 2016/2017

en: Describes the dataset in detail

en: README

format: TXT

media_type: text/plain

antarctica

display_name: antarctica

drifting-snow

display_name: drifting-snow

mass-flux

display_name: mass-flux

meteorological-data

display_name: meteorological-data

snowmicropen

display_name: snowmicropen

terrestrial-laser-scanning

display_name: terrestrial-laser-scanning


wind-crust-formation-snowmicropen-data

en:
This dataset contains the SnowMicroPen (SMP) data from 38 wind tunnel experiments on wind-packing / wind crust formation. These experiments were performed in the winters 2015/16 and 2016/17 and include more than 1000 SMP measurements.

The SMPs are organized per experiment. Each experiment subfolder contains the processed SMP profiles and some additional files.

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

This dataset is the basis of the following publication:

Sommer C.G., Lehning M., & Fierz C. (2017). Wind tunnel experiments: Saltation is necessary for wind-packing. Journal of Glaciology, 63(242), 950-958. doi:10.1017/jog.2017.53

en: smp snow snowmicropen wind-crust wind-packing

envidat

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

en: EnviDat: the Environmental Data Portal

en: Wind crust formation: SnowMicroPen data

title_for_slug: wind-crust-formation-snowmicropen-data

en: SMP data

en: Contains the R-scripts used to process the SMPs

en: Processing scripts

smp

display_name: smp

snow

display_name: snow

wind-crust

display_name: wind-crust

wind-packing

display_name: wind-packing


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

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

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

This dataset is the basis of the following publication:

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

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

en: Wind crust formation: Microsoft Kinect data

educ

en: Education, culture and sport

en: Kinect data

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

kinect

display_name: kinect

wind-tunnel

display_name: wind-tunnel


en:
This dataset contains measurement and simulation data. The measurements characterize the standard meteorology, turbulence, and snow transport at the S17 site near Syowa Station in East Antarctica during an expedition in austral summer 2018/2019. Large-eddy simulations with sublimating particles provide additional insight into the latent and sensible heat exchange between snow and air in two example situations observed at the S17 site.

A part of the measurement data was recorded by an automatic measurement station from 10th January 2019 to 26th January 2019. This measurement station was equipped with standard meteorological sensors, a three-dimensional ultrasonic anemometer, an open-path infrared gas analyzer, a snow particle counter, an infrared radiometer for measurements of the surface temperature, and a sonic ranging sensor measuring changes in snow surface elevation.

At a horizontal distance of approximately 500 m, a Micro Rain Radar (MRR) was installed in a tilted configuration with an elevation angle of 7° for remote sensing of blowing snow between 25th December 2018 and 24th January 2019. In addition, near-surface in-situ measurements of snow transport were performed at the location of the MRR by deploying a snow particle counter from 27th December 2018 to 24th January 2019.

The simulations cover a 18 x 18 x 6 m³ domain and reproduce the steady-state conditions during a 10-min interval with significant snow transport and another 10-min interval with negligible snow transport. We provide the model source code and the post-processed simulation data, i.e., horizontally averaged quantities as a function of height and time.

en: antarctica blowing-snow drifting-snow eddy-covariance-measurements large-eddy-simulations mass-flux meteorological-data micro-rain-radar turbulence

en: Meteorology and snow transport at S17 near Syowa, Antarctica, in austral summer 2018/2019

en: Meta data

en: Readme

en: Automatic in-situ measurements of micro-meteorology and snow transport

en: Measurement data from micro-meteorological station

en: Remote sensing data of blowing snow from a Micro Rain Radar (MRR).

en: Measurement data from Micro Rain Radar (MRR)

en: Near-surface observations of drifting snow from a Snow Particle Counter (SPC) installed at the location of the Micro Rain Radar.

en: Measurement data from snow particle counter at MRR site

en: Source code of the large-eddy simulations coupled with a Lagrangian stochastic model (LES-LSM), available on GitLab.

en: Data from two LES-LSM simulations called Drift_1 and Drift_2, which reproduce the conditions observed at the S17 site on 11th January 2019, 00:40 to 00:50 UTC.

en: Simulation data for a situation with significant snow transport

en: Results from two LES-LSM simulations called NoDrift_1 and NoDrift_2, which reproduce the conditions observed at the S17 site on 12th January 2019, 05:40 to 05:50 UTC.

en: Simulation data for a situation with negligible snow transport

blowing-snow

display_name: blowing-snow

eddy-covariance-measurements

display_name: eddy-covariance-measurements

large-eddy-simulations

display_name: large-eddy-simulations

micro-rain-radar

display_name: micro-rain-radar

turbulence

display_name: turbulence


en:
 Overview

The CH2014-Impacts initiative is a concerted national effort to describe impacts of climate change in Switzerland quantitatively, drawing on the scientific resources available in Switzerland today. The initiative links the recently developed Swiss Climate Change Scenarios CH2011 with an evolving base of quantitative impact models. The use of a common climate data set across disciplines and research groups sets a high standard of consistency and comparability of results. Impact studies explore the wide range of climatic changes in temperature and precipitation projected in CH2011 for the 21st century, which vary with the assumed global level of greenhouse gases, the time horizon, the underlying climate model, and the geographical region within Switzerland. The differences among climate projections are considered using three greenhouse gas scenarios, three future time periods in the 21st century, and three climate uncertainty levels (Figure 1). Impacts are shown with respect to the reference period 1980-2009 of CH2011, and add to any impacts that have already emerged as a result of earlier climate change.

 Experimental Setup

Future snow cover changes are simulated with the physics-based model Alpine3D (Lehning et al., 2006). It is applied to two regions: The canton of Graubünden and the Aare catchment. These domains are modeled with a Digital Elevation Model (DEM) with a resolution of 200 m × 200 m. This defines the simulation grid that has to be filled with land cover data and downscaled meteorological input data for each cell for the time period of interest at hourly resolution. The reference data set consists of automatic weather station data. All meteorological input parameters are spatially interpolated to the simulation grid. The reference period comprises only thirteen years (1999–2012), because the number of available high elevation weather stations for earlier times is not sufficient to achieve unbiased distribution of the observations with elevation. The model uses projected temperature and precipitation changes for all greenhouse gas scenarios (A1B, A2, and RCP3PD) and CH2011 time periods (2035, 2060, and 2085).

 Data

Snow cover changes are projected to be relatively small in the near term (2035) (Figure 5.1 top), in particular at higher elevations above 2000 m asl. As shown by Bavay et al. (2013) the spread in projected snow cover for this period is greater between different climate model chains (Chapter 3) than between the reference period and the model chain exhibiting the most moderate change. In the 2085 period much larger changes with the potential to fundamentally transform the snow dominated alpine area become apparent (Figure 5.1 bottom). These changes include a shortening of the snow season by 5–9 weeks for the A1B scenario. This is roughly equivalent to an elevation shift of 400–800 m. The slight increase of winter precipitation and therefore snow fall projected in the CH2011 scenarios (with high associated uncertainty) can no longer compensate for the effect of increasing winter temperatures even at high elevations. In terms of Snow Water Equivalents (SWE), the projected reduction is up to two thirds toward the end of the century (2085). A continuous snow cover will be restricted to a shorter time period and/or to regions at increasingly high elevation. In Bern, for example, the number of days per year with at least 5 cm snow depth will decrease by 90% from now 20 days to only 2 days on average.

envidat

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

en: EnviDat: the Environmental Data Portal

en: Alpine3D simulations of future climate scenarios CH2014

educ

en: Education, culture and sport

en:
CH2014-Impacts: Toward Quantitative Scenarios of Climate Change Impacts in Switzerland

The CH2014-Impacts report presents the results of a concerted effort of 15 Swiss research institutions to describe impacts of climate change in Switzerland quantitatively. This dataset contributes to the Crosphere/Snowcover section.

en: CH2014 REPORT

en: Datasets are grouped under experiment "Aare" and "Graubunden".Data is also made available in netCDF format. A range of tools are available to [read netCDF](http://www.unidata.ucar.edu/software/netcdf/software.html).

en: Dataset

en: Grid input data for the 'Graubunden' CH2014 simulation.

en: Graubunden input

en: Grid input data for the 'Aare' CH2014 simulation.

en: Aare input

climate-change

display_name: climate-change

snow-depth

display_name: snow-depth

snow-water-equivalent

display_name: snow-water-equivalent


snow-drift-station-micro-rain-radar

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

en: mountain precipitation raclets radar rain snow

en: Snow Drift Station - Micro Rain Radar

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

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

en: Parameter MRR

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

en: MMR Data Period 1

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

en: MRR data Period 2

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

en: MRR data of period 3

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

en: MRR data of period 4

mountain

display_name: mountain

precipitation

display_name: precipitation

raclets

display_name: raclets

radar

display_name: radar

rain

display_name: rain


en:
This dataset contains long-term snow water equivalent and corresponding snow depth data 11 observer sites in Switzerland between 1200 and 2500 m a.s.l. compiled for the Global Climate Observing System (GCOS) and supported by MeteoSwiss. Snow depth (cm) and snow water equivalent (mm) are manually recorded every 2 weeks since the 1947 (depending on station). The attached metadata file gives details for each station.
The measurement series agree with GCOS objectives according to the GCOS Implementation Plan: This inlcudes:
• Raw data are archived in the snow and avalanche database at SLF.
• Measuring techniques are traceable and documented as snow depth and snow water equivalent have in general remained the same since beginning up to now. When planning new systems or changes of existing systems in the future, their impact will be assessed prior to implementation.
• Historical data of these 11 stations have been digitized and all data have been quality controlled.
• Detailed metadata (location of measurements) are available.
• Public availability of the data has been ensured by publishing the data on the Envidat portal (https://www.envidat.ch/dataset/gcos-swe-data).

en: gcos snow snow-height snow-profile snow-water-equivalent

envidat

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

en: EnviDat: the Environmental Data Portal

en: GCOS SWE data from 11 stations in Switzerland

educ

en: Education, culture and sport

en: STATION METADATA

en: DATA ACCESS

gcos

display_name: gcos

snow

display_name: snow

snow-height

display_name: snow-height

snow-profile

display_name: snow-profile


irkis-soil-moisture-measurements-davos

en:
Meteorological and soil moisture measurements from soil moisture stations installed from October 2010 - October 2013 in the area surrounding Davos, in particular in the Dischma catchment.

There are in total 7 stations: 1202, 1203, 1204, 1205, 222, 333 and SLF2. For each of the stations, there is a:

* vwc_[stn].smet: containing the soil moisture measurements

* station_[stn].smet: in-situ measured meteorlogical parameters. Note, the quality of these measurements for stations 1202, 1203, 1204 and 1205 is very low, with data gaps. Use this data with care. For stations 222, 333 and SLF2, data quality is high and only the default cautiousness should be applied.

* interpolatedmeteo_[stn].smet contains per stations a dataset derived by interpolating from several stations in the Davos area to the stations location. This dataset was generated from the output of the Alpine3D model, of which simulations are presented in the Wever et al. (2017) manuscript.

At the soil moisture measurement sites, Decagon 10HS sensors were installed, at 10, 30, 50, 80 and 120 cm depth. Per depth 2 sensors were installed, labelled A and B in the datafiles. Note that at stations 1203, 1204 and 1205, sensors were only installed at 10, 30 and 50 cm depth.

The files follow the SMET format: https://models.slf.ch/docserver/meteoio/SMET_specifications.pdf and metadata for the stations can be found in the header of the smet files.

Please cite the Wever et al. (2017) reference when using this data in publications.

For a more detailed description, please refer to: Wever, N., Comola, F., Bavay, M., and Lehning, M.: Simulating the influence of snow surface processes on soil moisture dynamics and streamflow generation in an alpine catchment, Hydrol. Earth Syst. Sci., 21, 4053-4071, https://doi.org/10.5194/hess-21-4053-2017, 2017.

en: davos dischma-catchment meteo-station meteorological-measurements soil-moisture

en: IRKIS Soil moisture measurements Davos

title_for_slug: irkis-soil-moisture-measurements-davos

en: Compressed archive (zip) containing soil moisture measurements, as well as meteorological measurements and interpolated meteorological time series.

en: IRKIS soil moisture data (zip file)

davos

display_name: davos

dischma-catchment

display_name: dischma-catchment

meteo-station

display_name: meteo-station

meteorological-measurements

display_name: meteorological-measurements

soil-moisture

display_name: soil-moisture


bats-in-beaver-systems

en:
This dataset contains processed bat acoustic data collected to evaluate the ecological impacts of beaver-engineered ponds on bat species richness, activity, and feeding activity. It includes nightly activity and feeding call counts identified to species or species group level, site and treatment information (beaver pond vs. control areas along the same stream), and associated environmental covariates such as deadwood volume, arthropod abundance, and vegetation structure.

Methods: Bats were recorded using passive acoustic detectors placed at paired beaver-influenced and control sites. The resulting data was processed in the software Batscope. Arthropod abundance was estimated using flight-interception traps. The amount of deadwood and vegetation cover was measured in the field. Canopy cover and canopy heterogeneity were modelled from GIS models.

Temporal and Spatial Coverage:
Data were collected from eight sites in Switzerland between June and July 2022, each site sampled for 16 nights.

en: beaver castor chiroptera deadwood ecosystem-engineer flying-arthropods habitat-heterogeneity nature-conservation wetland

en: Bats in beaver systems

title_for_slug: bats-in-beaver-systems

en: README text file providing context for the data. Please do not hesitate to contact us if you have any questions

en:
For the time information about the research data, created (2022-09-23 to 2022-09-23) refers to the GIS data, collected (2022-06-13 to 2022-07-31) to the field sampling. Details can be found in the csv “sampling_dates”.

sampling_dates with columns
"site" (sampling site),
"bats_1" (first period of bat survey in June),
"bats_2" (second period of bat survey in June),
"bats_3" (first period of bat survey in July),
"bats_4" (second period of bat survey in July),
"flying_arthropods_1" (arthropod sampling in June),
"flying_arthropods_2" (arthropod sampling in July),
"deadwood survey" (date of deadwood survey),
"vegetation survey" (date of vegetation survey)

en: sampling dates.xlsx

en:
vegetation_characteristics data on vegetation_cover and cumulative_cover with columns "laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"Vegetation_cover" (estimated vegetation cover over 5x1 m plot in %),
"Cumulative cover" (summed up cover over tree, shrub and herb layer in %),
"Cumulative cover trees" (tree cover level, higher than 3 metres in %),

en:
gewichtungsfaktor with columns
"Project" (sampling site),
"Treatment" (sampling location within site, with levels ctrl for Control and pool for Pool),
"date" (sampling date),
"gew" (weighting factor to standardize night length)

en:
dead_wood for deadwood with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"type" (deadwood type),
"height_length" (the height or lenghth of a piece of deadwood in m),
"DBH_diamlarge" (diameter at breast height or diameter of larger end if lying in m),
"diamsmall (diameter of smaller end if lying in m",
"volume" (calculated volume in m3)

en:
bats_biodiv.day.guilds with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"community" (species group),
"date" (sampling date),
"month" (sampling month),
"guild" (which feeding guild information),
"feeding" (number of feeding buzzes summed per 5 minutes and identification level), "abundance" (sequences per 5 minutes and identification level),
"richness" (species richness),
"richness_feeding" (species richness of feeding bats)

en:
bats_biodiv.day with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"community" (species group),
"date" (sampling date),
"month" (sampling month),
"feeding" (number of feeding buzzes summed per 5 minutes and identification level), "abundance" (sequences per 5 minutes and identification level),
"richness" (species richness),
"richness_feeding" (species richness of feeding bats)

en:
bats.environment with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"canopy" (modelled tree canopy cover, averaged across radius of 100 m, unitless),
"structure_ruggedness" (mean ruggedness of the total vertical structure > 3 m, averaged across radius of 100 m, unitless), "vegetation_ruggedness" (mean ruggedness of the canopy > 3 m, later called canopy_heterogenneity, averaged across radius of 100 m, unitless),
"pool_size" (maximum width of beaver pool in m),
"area_deadwood_monitoring" (total area of deadwood moonitoring in m3),
"age" (beaver presence in years)

en:
bat_guilds with columns
"SP" (identification level),
"EndGuilde" (information on feeding guild)

en:
arthropods_flying the arthropod abundance with columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"community" (species group),
"month" (sampling month),
"abundance" (number of flying arthropods)

en:
site_info contains columns
"laufnummer" (unique identifier per sampling point),
"site" (sampling site),
"sample" (sampling location within site),
"location" (sampling location row within site),
"sample_nr" (sampling number within location)

en:
Raw data data_bats with columns:
“Categories” (empty or "feeding", indicating that call sequence contains feeding buzz)
“Sequence” (sequence number)
“Timestamp” (recording time)
“Temperature” (temperature recorded by batlogger in degree celcius)
“Serial” (serial number of bat logger)
“Duration” (duration of call sequence in seconds)
“Call.Count” (call count number, how many calls could be detected)
“Verification.1” (bat sequence verification)
“Notes” (remarks for identification)
“treatment” (treatment ctrl = Control and Pool)
“slot” (slot number, 4 slots, two in June, two in July)
“Laufnummer” (unique identifier per sampling point)

en: R script bats_age to test the beaver pool age effect on bat richness, activity and feeding activity

en: R script bats_data_cleaning to clean the raw data

en: R script bats_data_exploration_Figure_nighly to explore the data and display nightly species richness, activity and feeding activity

en: R script bats_glmm to test the influence of beavers on bat richness, activity and feeding activity

en: R script bats_pool_size to test the beaver pool size on bat richness, activity and feeding activity

en: bats_raw_values_overview to produce raw value summaries and tables

en: R script bats_SEM to test the influence of the beaver on bat richness, activity and feeding activity through feeding and habitat heterogeneity

beaver

display_name: beaver

castor

display_name: castor

chiroptera

display_name: chiroptera

deadwood

display_name: deadwood

ecosystem-engineer

display_name: ecosystem-engineer

flying-arthropods

display_name: flying-arthropods

habitat-heterogeneity

display_name: habitat-heterogeneity

nature-conservation

display_name: nature-conservation

wetland

display_name: wetland


en:
This dataset contains the population evolution of a pest and its biocontrol agent in terms of presence proportion at gall level and absolute number of insects.

The study area extends from the Cuneo region (Piedmont, Italy) to southern Switzerland. In order to provide a complete range of data covering the entire process from the pest arrival to complete biological control by its natural enemy T. sinensis, a space-for-time substitution approach has been adopted so as to create a temporal gradient of the epidemic stages over the whole study area.

The southernmost Swiss sites roughly represent the arrival and establishment of the pest without the presence of the natural enemy, the central ones the early epidemic stage and the epidemic peak, whereas the northern ones the end of the epidemic with the beginning of the biocontrol. The Italian ones represent the beginning of the equilibrium between the two population as well as the situation with stable T. sinensis populations on the long term.

These data are used in the paper entitled: Torymus sinensis local and regional early population dynamics in the Insubrian and Piedmont regions

en: biological-control castanea-sativa dryocosmus-kuriphilus pest-invasion population-evolution southern-switzerland torymus-sinensis

envidat

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

en: EnviDat: the Environmental Data Portal

educ

en: Education, culture and sport

en:
Data has been collected from summer 2011 to 2016.

Main reasons for NA values:

-	D. kuriphilus still not arrived in the site.
-	No galls were found during the field sampling.
-	Site has not been sampled in the correspondent year because of time, weather or money constrains.
-    T. sinensis not found in the galls

Variable description:

Year = sampling year.

Coordinate, elevation, slope, aspect = average site characteristics.

ts_arrival = T. sinensis arrival year.

dk_arrival = D. kuriphilus arrival year.

dk_parasitism_by_ts = D. kuriphilus parasitism rate by T. sinensis. Formula: (no. of T. sinensis/ no. of D. kuriphilus chambers) * 100 at gall level.

dk_infestation = Percentage of attacked buds. Formula: no. of attacked buds / no. of total developed buds*100 (as presented in Kotobuki et al. 1985) at shoot level.

ts_per_bud = number of alive T. sinensis individual calculated at bud level.

dk_per_bud = number of alive D. kuriphilus individual calculated at bud level.

dk_pot_per_bud = overall number (dead and alive) D. kuriphilus individual calculated at bud level.

total_gall_nr_per_bud = average gall number per bud.

chamber_per_galls = average D. kuriphilus chamber number per gall.

For details see the submitted publication with the same name

References
Kotobuki, K., Mori, K. and Sato, Y. (1985) ‘2 methods to estimate the tree damage by chestnut gall wasp Dryocosmus-kuriphilus’, Bulletin of the fruit tree research station A (Yatabe), 2(12), pp. 29–36.

biological-control

display_name: biological-control

castanea-sativa

display_name: castanea-sativa

dryocosmus-kuriphilus

display_name: dryocosmus-kuriphilus

pest-invasion

display_name: pest-invasion

population-evolution

display_name: population-evolution

southern-switzerland

display_name: southern-switzerland

torymus-sinensis

display_name: torymus-sinensis


en:
This report was prepared as one of the synthesis report chapters of the Hydro-CH2018 project of the Federal Office for the Environment (FOEN). An important feature of snow cover is the fact that its volume and duration is subject to large year-to-year fluctuations. As frozen precipitation, snow cover is nothing other than a natural water reservoir that delays precipitation to runoff and is thus of outstanding importance for the seasonal water balance in Switzerland. Over a whole year, approximately 40% (22 km3) of the annual runoff currently comes from snow melting and only 1% from glacier melting. Typically, the snow cover in the Alpine region builds up over the autumn and winter months, reaches its maximum between February and May, depending on the altitude, and dominates the runoff processes during melting in the following spring and summer months.
Due to the great dependence on minus temperatures and precipitation, the snow cover reacts sensitively to temperatures above 0° Celsius and more or less precipitation. Due to climate change and the associated warming, the proportion of precipitation that falls as snow decreases measurably. In addition to this reduction in snowfall, the warmer temperatures also cause the snow cover to melt more quickly. The decline in snowfall has so far mainly affected lower altitudes, where winter temperatures often reach positive levels.
As climate change progresses, this trend is likely to continue and above all affect higher zones. Even at higher altitudes, the snow cover will then start later, melt away earlier and is increasingly no longer permanently present. This development will also have an effect on the water bodies. Today nival regimes, i.e. regimes shaped by snow, are shifting towards pluvial regimes, i.e. regimes dominated by rain. Overall, winter runoff increases, summer runoff decreases. By the end of the century, the proportion of runoff from snowmelt will decrease throughout Switzerland, albeit to a lesser extent than the proportion from glacier melt.

en: climate-change-impacts hydrology snow snowfall water-cycle

en: Snow and the water cycle in a changing climate

en: Synthesis report chapter

en: DORA Persistent URL

climate-change-impacts

display_name: climate-change-impacts

hydrology

display_name: hydrology

snow

display_name: snow

snowfall

display_name: snowfall

water-cycle

display_name: water-cycle


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

en: atmospheric-conditions precipitation radiation temperature-profiles wind

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

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

en: DATA ACCESS

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

en: README

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

en: Overview plot

format: PNG

media_type: image/png

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

en: Site photo 1

en: Site photo 2

en: Site photo 3

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

en: Latest data from Stillberg climate station

en: STB2 CSV

atmospheric-conditions

display_name: atmospheric-conditions

precipitation

display_name: precipitation

radiation

display_name: radiation

temperature-profiles

display_name: temperature-profiles

wind

display_name: wind