maintainer: Elisabeth D. Hafner

en: aerial-imagery avalanche-activity avalanche avalanche-mapping remote-sensing

en: 1999 avalanche data

en:
**for English see below**
Dieser Datensatz enthält die **Umrisse der 11'120 Lawinen** die aus schwarzweiss Luftbildern, welche zwischen dem 25.2.1999 und dem 1.3.1999 aufgenommen wurden, kartiert wurden. Die Lawinenumrisse haben verschiedene Attribute welche im **beigelegten Beispielschlüssel** beschrieben sind (Beispielschluessel_1999_d.pdf).
Es gibt drei Shapefiles:
* **avalanches1999_endversion1.shp**: die kartierten Lawinen (für Attribute siehe Beispielschlüssel!)
* **area_images_1999.shp**: Fläche die durch die Luftbilder abgedeckt wurde
* **clouds_1999.shp**: grobe Umrisse der Wolken in den Bildern
#
 *******************************************************************************
This dataset contains the **outlines of 11'120 avalanches** mapped from panchromatic aerial imagery taken between February 25, 1999 and March 1, 1999. The avalanche outlines have various attributes which are described in the **attached example key** (ExampleKey_AvalMapping_1999_e.pdf).
There are three shapefiles:
* **avalanches1999_endversion1.shp**: the mapped avalanches (for attributes see example key!)
**area_images_1999.shp**: Area covered by the aerial images
**clouds_1999.shp**: rough outlines of the clouds in the images

en:
**for English see below**
*******************************************************************************

 Datenbeschrieb

Dieser Datensatz enthält die **Umrisse der 11'120 Lawinen** die aus schwarzweiss Luftbildern, welche zwischen dem 25.2.1999 und dem 1.3.1999 aufgenommen wurden, kartiert wurden. Die Lawinenumrisse haben verschiedene Attribute welche im **beigelegten Beispielschlüssel** beschrieben sind (Beispielschluessel_1999_d.pdf).

Es gibt drei Shapefiles:
* **avalanches1999_endversion1.shp**: die kartierten Lawinen (für Attribute siehe Beispielschlüssel!)
* **area_images_1999.shp**: Fläche die durch die Luftbilder abgedeckt wurde
* **clouds_1999.shp**: grobe Umrisse der Wolken in den Bildern

 Bildverfügbarkeit:

Die entzerrten Luftbilder können von der [Swisstopo](https://s.geo.admin.ch/rpq3hufnb588) einzeln über den Downloadlink heruntergeladen werden (herzlichen Dank an Holger Heisig für die Prozessierung und ans BAFU für die Finanzierung der Prozessierung!!). Über "Erweiterte Werkzeuge"/ "Datei importieren" können die Bilder nach hereinziehen des Orthofotolinks direkt im map.geo.admin.ch angezeigt werden.

 Weiterführende Litertaur:
Details zu den Luftbildern und zur Kartierung, sowie eine kleine Analyse und Empfehlungen zur Verwendung der Daten finden sich unter

- Hafner, E., Margreth, S., and Bühler, Y.: Die grossflächigen Lawinenkartierungen 1999, 2018 und 2019: ein Überblick für die Praxis, FAN Agenda, 1/2025, 15 - 21, 2025.

- Dal, F. J., Hafner, E. D., Peters, T., Narnhofer, D., Caye Daudt, R., Heisig, H., & Bühler, Y. (2024). Automated snow avalanche mapping with deep learning in aerial imagery from the extreme avalanche winter of 1999. In K. Gisnås, P. Gauer, H. Dahle, M. Eckerstorfer, A. Mannberg, & K. Müller (Eds.), Proceedings of the international Snow Science Workshop 2024 (pp. 1264-1271). Norwegian Geotechnical Institute.

- Hafner, E. D., Techel, F., Heisig, H., Dal, J. F., & Bühler, Y. (2024). Remotely sensed avalanche activity during three extreme avalanche periods in Switzerland. In K. Gisnås, P. Gauer, H. Dahle, M. Eckerstorfer, A. Mannberg, & K. Müller (Eds.), Proceedings of the international Snow Science Workshop 2024 (pp. 1222-1229). Norwegian Geotechnical Institute.

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

 Data description

This dataset contains the **outlines of 11'120 avalanches** mapped from panchromatic aerial imagery taken between February 25, 1999 and March 1, 1999. The avalanche outlines have various attributes which are described in the **attached example key** (ExampleKey_AvalMapping_1999_e.pdf).

There are three shapefiles:
* **avalanches1999_endversion1.shp**: the mapped avalanches (for attributes see example key!)
**area_images_1999.shp**: Area covered by the aerial images
**clouds_1999.shp**: rough outlines of the clouds in the images

 Image availability:
The rectified aerial images can be downloaded individually from [Swisstopo](https://s.geo.admin.ch/bg6beetinfdc) via the download link (many thanks to Holger Heisig for processing and to BAFU for financing the processing!!). The images can be displayed directly in map.geo.admin.ch via “Advanced tools”/“Import file” after dragging in the orthophoto link.

 Further Reading:
Details on the aerial images and mapping, as well as a brief analysis and tips on using the data can be found at

- Hafner, E., Margreth, S., and Bühler, Y.: Die grossflächigen Lawinenkartierungen 1999, 2018 und 2019: ein Überblick für die Praxis, FAN Agenda, 1/2025, 15 - 21, 2025.

- Dal, F. J., Hafner, E. D., Peters, T., Narnhofer, D., Caye Daudt, R., Heisig, H., & Bühler, Y. (2024). Automated snow avalanche mapping with deep learning in aerial imagery from the extreme avalanche winter of 1999. In K. Gisnås, P. Gauer, H. Dahle, M. Eckerstorfer, A. Mannberg, & K. Müller (Eds.), Proceedings of the international Snow Science Workshop 2024 (pp. 1264-1271). Norwegian Geotechnical Institute.

- Hafner, E. D., Techel, F., Heisig, H., Dal, J. F., & Bühler, Y. (2024). Remotely sensed avalanche activity during three extreme avalanche periods in Switzerland. In K. Gisnås, P. Gauer, H. Dahle, M. Eckerstorfer, A. Mannberg, & K. Müller (Eds.), Proceedings of the international Snow Science Workshop 2024 (pp. 1222-1229). Norwegian Geotechnical Institute.

aerial-imagery

display_name: aerial-imagery

avalanche

display_name: avalanche

avalanche-activity

display_name: avalanche-activity

avalanche-mapping

display_name: avalanche-mapping

remote-sensing

display_name: remote-sensing

educ

en: Education, culture and sport

en: Avalanche outlines February and March 1999 from aerial imagery


envidat-supports-open-science

maintainer: Ionut Iosifescu Enescu

title_for_slug: envidat-supports-open-science

en: juypter-notebooks envidat research-data-management open-science wsl

state: active

type: dataset

en: Article as PDF

media_type: application/pdf

format: PDF

mimetype: application/pdf

en:
The article "EnviDat Supports Open Science" originally appeared in WSLintern No. 3 (2020), page 14-15 and it is republished here with permission from the WSLintern editorial team. It contains guidelines for WSL scientists about the main issues behind Open Science and how to pragmatically approach the complexities of doing Open Science with EnviDat’s support.

License: This article is released by WSL and the EnviDat team to the public domain under a Creative Commons 4.0 CC0 "No Rights Reserved" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions.

envidat

display_name: envidat

juypter-notebooks

display_name: juypter-notebooks

open-science

display_name: open-science

research-data-management

display_name: research-data-management

wsl

display_name: wsl

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: EnviDat Supports Open Science


maintainer: Esther R. Frei

en: stillberg picea-abies climate-change forest seedling-recruitment swiss-alps larix-decidua g-tree treeline

media_type: text/plain

en: climate data G-TREE experiment, Davos, Switzerland

format: TXT

mimetype: text/plain

en:
 Background information
Climate change-induced range expansion of treeline populations depends on their successful recruitment, which requires dispersal of viable seeds followed by successful establishment of individual propagules. The Global Treeline Range Expansion Experiment (G-TREE) is a global initiative involving researchers from Europe, North America, Australia and New Zealand (Brown et al., 2013). At 15 alpine and Arctic treeline sites worldwide the mechanisms determining the elevational and latitudinal distribution of tree populations are studied using a standardized experimental approach. In summer 2013, a multifactorial seedling recruitment experiment has been established at the Stillberg ecological treeline research site. The aim of this experiment, is to quantify the effect of multiple abiotic and biotic drivers on emergence, survival, and growth of *Larix decidua* and *Picea abies* seedlings in replicated plots along an elevation gradient with three sites below (1930 m a.s.l.), at (2090 m a.s.l.), and above treeline (2410 m a.s.l.; Frei et al., 2018). All plots have been surveyed annually to count seedlings and to measure their total height. Additional environmental factors, such as soil temperature, have been recorded.

 Experimental design
The Stillberg research area is located in the Eastern Swiss Alps near Davos, Switzerland. The site has been used for several long-term monitoring as well as experimental studies for the last four decades. Our G-TREE experiment consists of a lowest site located in a subalpine Larch-Spruce forest (*Larici-Picetum*) dominated by *Larix decidua* and *Picea abies* (1930 m a.s.l.), a transition zone site dominated by alpine shrubs (2100 m a.s.l.), and an uppermost site in an alpine meadow with some dwarf shrubs (2390 m a.s.l.). The three experimental sites were set up following the standard protocol of the global G-TREE initiative (Brown et al., 2013). In a split-plot design, 20 plots (224 cm × 45 cm) were established at each site, which were randomly assigned to the 2 × 2 treatment combinations of the main factors seeding and scarification (i.e. seeding and scarification, seeding only, scarification only, and full control), resulting in five replications per main treatment combination. Each plot was divided into 16 split-plots (22.5 cm × 28 cm), to which treatment combinations of four additional two-level factors species (larch and spruce), provenance (low- and high-elevation), herbivore exclosure (with and without exclosure), and seeding year (2013, 2014) were randomly assigned, which resulted in a total of 960 split-plots (Details see Frei et al. 2018).

 Data description
All plots have been surveyed annually to count seedlings and to measure their total height. Seedling height was assessed with a hand ruler as the total length from the original emerging point to the apical meristem (Details see Frei et al. 2018). Additionally, soil temperature at each site, has been continuously recorded since 2013. Here, we present data from eight years (2013–2021).

climate-change

display_name: climate-change

forest

display_name: forest

g-tree

display_name: g-tree

larix-decidua

display_name: larix-decidua

picea-abies

display_name: picea-abies

seedling-recruitment

display_name: seedling-recruitment

stillberg

display_name: stillberg

swiss-alps

display_name: swiss-alps

treeline

display_name: treeline

en: G-TREE: Global Treeline Range Expansion Experiment Davos, Switzerland


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

media_type: application/zip

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.

format: ZIP

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.

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: Acoustic Data from the PMA Bedload Monitoring System


chelsacanaryclim-climatologies

title_for_slug: chelsacanaryclim-climatologies

en: air-temperature precipitation climatologies climate canaries

en: CHELSACanaryClim-climatologies: Daily Minimum Near-Surface Air Temperature

en:
Daily Minimum Near-Surface Air Temperature
Files are provided as Cloud-Optimized GeoTIFFs (COGs) with standardized metadata.
Each COG encodes scale and offset parameters in its metadata. Standards-compliant software will apply these fields automatically and return values in the correct units; however, legacy or non-compliant applications may not. If there is any uncertainty regarding units, please consult the file metadata to verify the applied scale/offset and units.

format: SERVICE

en: CHELSACanaryClim-climatologies: Daily Maximum Near-Surface Air Temperature

en:
Daily Maximum Near-Surface Air Temperature
Files are provided as Cloud-Optimized GeoTIFFs (COGs) with standardized metadata.
Each COG encodes scale and offset parameters in its metadata. Standards-compliant software will apply these fields automatically and return values in the correct units; however, legacy or non-compliant applications may not. If there is any uncertainty regarding units, please consult the file metadata to verify the applied scale/offset and units.

en:
Precipitation
Files are provided as Cloud-Optimized GeoTIFFs (COGs) with standardized metadata.
Each COG encodes scale and offset parameters in its metadata. Standards-compliant software will apply these fields automatically and return values in the correct units; however, legacy or non-compliant applications may not. If there is any uncertainty regarding units, please consult the file metadata to verify the applied scale/offset and units.

en: CHELSACanaryClim-climatologies: Daily Mean Near-Surface Air Temperature

en:
Daily Mean Near-Surface Air Temperature
Files are provided as Cloud-Optimized GeoTIFFs (COGs) with standardized metadata.
Each COG encodes scale and offset parameters in its metadata. Standards-compliant software will apply these fields automatically and return values in the correct units; however, legacy or non-compliant applications may not. If there is any uncertainty regarding units, please consult the file metadata to verify the applied scale/offset and units.

en:
CHELSACanaryClim is a high-resolution, terrain-informed downscaling model developed specifically for the Canary Islands. It builds on the CHELSA v1.2 framework and integrates extensive local meteorological station data to refine temperature and precipitation estimates. The model incorporates high-resolution elevation data (down to 5 m), planetary boundary layer height, and ERA-Interim reanalysis inputs to simulate fine-scale climatic variation in mountainous island terrain.

CHELSACanaryClim-climatologies is a very-high resolution climate dataset generated with the CHELSACanaryClim downscaling model. It consists of monthly climatologies of air-temperatures and precipitation.

air-temperature

display_name: air-temperature

canaries

display_name: canaries

climate

display_name: climate

climatologies

display_name: climatologies

precipitation

display_name: precipitation

en: CHELSACanaryClim-climatologies


maintainer: Marco Moretti

en: wild-bee functional-diversity switzerland urbanization predictive-modelling anthophila community-indices climate elevation-gradient hymenoptera protected-areas beta-diversity resource-availability biodiversity

en: Elevation and waterbodies

en: Zip file containing two rasters: the digital elevation model of Switzerland and the waterbodies of Switzerland.

en: Wild bee trait data

en: Text file containing the scores of the 8 used traits. The data originates from the European trait database (compiler: Stuart Roberts; pollinator loss module of the EU- FP6 ALARM project).

en: Variable importance, model evaluation, partial dependence plots

en: The compress file containes a set of txt files with the variable importance, the model evaluation metrics and the partial dependence plots of each of the responses used (alpha and beta taxonomic and functional diversity metrics; community weighted means of 8 functional traits)

en: Taxonomic and functional metrics in the community plots

en:
Response data used for the modeling.
Lat: latitude community plot
Lon: longitude community plot
Richness: species richness
Shannon: Shannon diversity index
FDis: functional dispersion
TOP: Trait Onion Peeling (functional richness)
TED: Trait Even Distribution (functional evenness)
LCBD_taxo: local community contributions to taxonomic beta diversity
LCBD_fun: local community contributions to functional beta diversity
ITD: community weighted means inter-tegula distance
feeding_specialization: community weighted means feeding specialization
tong_length: community weighted means tongue length
phenostart: community weighted means start of the phenology
phenoduration: community weighted means duration of the phenoloy
cleptoparasite: community weighted means cleptoparasites
belowgound: community weighted means proportion belowground
solitary: community weighted means proportion solitary

en: Protected areas

en: Protected areas considered, divided in three groups and rasters: protected areas sensu stricto, protected areas sensu lato and all protected areas

en: Spatial environmental predictors and responses

en:
The .zip file contains the rasters of the environmental predictors and the predicted responses:
-  **Environmental predictors.**:
  -  The raster on the climate contains the PCA axes on the CHELSA variables.
  -  The raster on resource availability contains the PCA axes on plant communities
  -  The raster on land-use contains the land-use composition at 2500 m.
  -  The raster on beekeeping intensity contains thedensity of hives within 2500 m
-  **Responses**:
- The predicted responses (alpha and beta taxonomic and functional metrics and the traits) without masking the waterbodies, used in the analyses (Diversity_stack_revised_Selected_descriptors.tif). It contains a stack of 20 responses in this order: "belowgound", "cleptoparasite","FDis", "feeding_specialization", "FEve", "FRic", "InvSimpson", "ITD","LCBD_fun" ,"LCBD_taxo","phenoduration", "phenostart", "Rao", "Richness", "Shannon", "Simpson","solitary", "TED", "tong_length", "TOP". "FEve", "FRic", "InvSimpson","Rao", "Simpson" were not included in the linked paper.
- The predicted responses (alpha and beta taxonomic and functional metrics and the traits) masking the waterbodies (masked.stack.all.diversity.tiff). It includes a stack of 15 responses (excludes "FEve", "FRic", "InvSimpson","Rao", "Simpson").

en:
Raw data supporting the paper "Countrywide wild bee taxonomic and functional diversity reveal a spatial mismatch between alpha and beta-diversity facets across multiple ecological gradients". It contains taxonomic and functional metrics in 3343 community-plots distributed across Switzerland. The calculated metrics are:
- Alpha taxonomic community metrics: species richness and Shannon diversity
- Alpha functional community metrics: Functional richness (using the Trait Onion Peeling index, TOP), functional eveness (using the Trait Even Distribution index, TED) and the functional dispersion.
- Community weighted means of 8 functional traits
- The local community contributions on the functional and taxonomic beta diversity (LCBD).

The dataset also includes the following:
- The used predictors to model the spatial distribution of the community metrics (climate PCA, vegetation PCA, land-use metrics, beekeeping intensity).
-The three types of protected areas, defined according to the protective measures.
- The model evaluation, variable importance and partial dependece data.

anthophila

display_name: anthophila

beta-diversity

display_name: beta-diversity

biodiversity

display_name: biodiversity

community-indices

display_name: community-indices

elevation-gradient

display_name: elevation-gradient

functional-diversity

display_name: functional-diversity

hymenoptera

display_name: hymenoptera

predictive-modelling

display_name: predictive-modelling

protected-areas

display_name: protected-areas

resource-availability

display_name: resource-availability

switzerland

display_name: switzerland

urbanization

display_name: urbanization

wild-bee

display_name: wild-bee

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Data on wild bee taxonomic and functional diversity in Switzerland


maintainer: EnviDat Support

en: landscape-perception survey traffic-noise outdoor-restoration ppgis

media_type: text/csv

en: Processed survey data for window view for article in Landscape and Urban Planning [https://doi.org/10.1016/j.landurbplan.2025.105339](https://doi.org/10.1016/j.landurbplan.2025.105339)

format: CSV

en: Cleaned raw data from survey for article in Landscape and Urban Planning [https://doi.org/10.1016/j.landurbplan.2025.105339](https://doi.org/10.1016/j.landurbplan.2025.105339)

en: Variable list for article in Urban Forestry and Urban Greening [https://doi.org/10.1016/j.ufug.2025.128746](https://doi.org/10.1016/j.ufug.2025.128746)

en: Processed data for article in Urban Forestry and Urban Greening [https://doi.org/10.1016/j.ufug.2025.128746](https://doi.org/10.1016/j.ufug.2025.128746)

en: Coding analysis third round for article in Urban Forestry and Urban Greening [https://doi.org/10.1016/j.ufug.2025.128746](https://doi.org/10.1016/j.ufug.2025.128746)

format: XLS

en: Description of the sample stratification process for the invitation to participate in the survey

en: RESTORE WP4_Questionnaire_Italian.pdf

en: Questionnaire in Italian

en: RESTORE WP4_Questionnaire_German.pdf

en: Questionnaire in German

en: Variable list for statistical analyses for article in Landscape and Urban Planning [https://doi.org/10.1016/j.landurbplan.2025.105339](https://doi.org/10.1016/j.landurbplan.2025.105339)

en: Processed survey data of restorative locations for article in Landscape and Urban Planning [https://doi.org/10.1016/j.landurbplan.2025.105339](https://doi.org/10.1016/j.landurbplan.2025.105339)

en: Raw data for article in Urban Forestry and Urban Greening [https://doi.org/10.1016/j.ufug.2025.128746](https://doi.org/10.1016/j.ufug.2025.128746)

en: Coding analysis second round for article in Urban Forestry and Urban Greening [https://doi.org/10.1016/j.ufug.2025.128746](https://doi.org/10.1016/j.ufug.2025.128746)

en: Coding analysis first round for article in Urban Forestry and Urban Greening [https://doi.org/10.1016/j.ufug.2025.128746](https://doi.org/10.1016/j.ufug.2025.128746)

en: Study protocol of the survey

en: Processed data for coding analysis for article in Urban Forestry and Urban Greening [https://doi.org/10.1016/j.ufug.2025.128746](https://doi.org/10.1016/j.ufug.2025.128746)

en: RESTORE WP4_Questionnaire_French.pdf

en: Questionnaire in French

en:
This repository contains data related to the Swiss-wide survey of the RESTORE project. This project is a collaboration between the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) and the Swiss Federal Laboratories for Materials Science & Technology (EMPA). It has received funding from the Swiss National Science Foundation. The objective of the RESTORE project was to assess the effects of green spaces as facilitators and noise as impediment to recover from stress in people's daily environments across Switzerland.
We conducted a nation-wide participatory mapping survey among Swiss residents to explore:
1.	The effects of greenness and noise exposure on psychological restoration when looking out of the window at home or when visiting nearby green spaces. It adopted a landscape approach to examine the interplay between environmental factors, individual perceptions, and personal traits – Scientific article 1
2.	The multiple aspects that constraint restoration when visiting nearby green spaces – Scientific article 2
Accordingly, this repository includes data and documentation for the following 2 scientific articles:
1.	Scientific article 1. “The role of greenness and road traffic noise for psychological restoration in everyday environments. A participatory mapping approach”. Landscape and Urban Planning, 2025
2.	Scientific article 2. “Perceived constraints for psychological restoration in nearby greenspaces. An exploratory and multi-dimensional approach”. Urban Forestry and Urban Greening, 2025

landscape-perception

display_name: landscape-perception

outdoor-restoration

display_name: outdoor-restoration

ppgis

display_name: ppgis

survey

display_name: survey

traffic-noise

display_name: traffic-noise

en: Swiss-wide survey on restorative places and road traffic noise


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

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

indian-ocean

display_name: indian-ocean

landscape

display_name: landscape

landscape-evolution

display_name: landscape-evolution

landscape-genetics

display_name: landscape-genetics

morphology

display_name: morphology

reef-fish

display_name: reef-fish

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

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


en: photogrammetry snow-depth-distribution dischma snow-hydrology avalanches davos

en: Snowdepth maps

format: GeoTIFF

en:
The available datasets are snow depth maps with a spatial resolution of 0.5 m derived from images of the survey camera Vexcel Ultracam mounted on a piloted airplane. Image acquisition was carried out during the approximately peak of winter (time when the thickest snowpack is expected) in spring. The snow depth maps are calculated by the subtraction of a summer-DTM from the processed winter- DSM of the corresponding date. The summer-DTM used was derived from a point cloud of an airborne laser scanner from 2020.

Due to the occurrence of inaccuracies of the calculated snow depth values caused by the photogrammetric method, we applied different masks to significantly increase the reliability of the snow depth maps. We masked out settled areas, high-frequented streets and technical constructions, pixels with high vegetation (height > 0.5 m) , outliers and unrealistic snow depth values. In addition, we modified the snow depth values of snow-free pixels to 0.
The information on buildings and infrastructure comes from the exactly classified ALS point cloud and the TLM dataset from Swisstopo (https://www.swisstopo.admin.ch/de/geodata/landscape/tlm3d.html#links). High vegetation is also derived from the classification and the calculated object height from the point cloud.
Outliers and unrealistic snow depth values are defined as negative snow depth values and snow depths exceeding 10 m. The classification of each pixel of the corresponding orthophoto into snow-covered or snow-free is based on the application of a threshold of the NDSI or manually determined ratios of the RGB values.

An extensive accuracy assessment proves the high accuracy of the snow depth maps with a root mean square error of 0.25 m for the year 2017 and 0.15 m for the following snow depth maps.

The work is published in:

avalanches

display_name: avalanches

davos

display_name: davos

dischma

display_name: dischma

photogrammetry

display_name: photogrammetry

snow-depth-distribution

display_name: snow-depth-distribution

snow-hydrology

display_name: snow-hydrology

en: Snow depth mapping by airplane photogrammetry (2017 -  ongoing)


maintainer: Jokin Idoate-Lacasia

en: forest-reserves forest-inventory biomass-dynamics environmental-gradients carbon-storage

en: R markdown document used to download the data files, reproduce the analysis and plot generation.

format: OCTET

en: Climatic space covered by the Swiss National Forest Inventory (NFI) that will be used in Figure 5.

en:
This dataset documents the analyses and the plot generation for the manuscript **Long-term biomass dynamics in temperate forests in Europe after cessation of management**.
We analyse biomass development in an extensive network of 224 plots in 37 natural forest reserves (NFR) covering a wide environmental gradient with mean annual temperatures ranging from 1 to 10.4 °C and mean annual precipitation ranging from 901 to 2317 mm. Forest inventories have been conducted approximately every 10 years during the last 60 years.
It consists of the following files:
+ **Long_term_biomass_script.Rmd**: R markdown document to reproduce the analysis and the plot generation.
+ **dat_biomass.RDS**: Biomass values for each permanent plot at each inventory. This dataset is used for data exploration and for the Biomass model, and to create Figure 2 and Figure 3.
+ **dat_biomass_change.RDS**: Biomass change values between inventories for each permanent plot. It includes the explanatory variables as well. This dataset is used for the Biomass change model and to create Figure 4 and Figure 5.
+ **NFI_climspace_netCDF.RDS**: Climatic space covered by the Swiss National Forest Inventory (NFI) that is used in Figure 5.

biomass-dynamics

display_name: biomass-dynamics

carbon-storage

display_name: carbon-storage

environmental-gradients

display_name: environmental-gradients

forest-inventory

display_name: forest-inventory

forest-reserves

display_name: forest-reserves

en: Long-term biomass dynamics of temperate forests in Europe after cessation of management


en: studibach rainfall time-series overland-flow plot runoff alptal topsoil-interflow land

en:
Time series of overland flow and topsoil interflow discharge at 14 runoff plot locations in the Studibach catchment over 27 rain events in 2022.
Format: NEAD
Metadata included in the file: latitude, longitude, data units, timezone.
Source for reading and writing NEAD format: https://www.doi.org/10.16904/envidat.187.

en:
The dataset comprises the time series of overland flow (OF) and topsoil interflow (TIF) discharges from 14 small runoff plots (1 x 3 m) over 27 rainfall events in 2022 across the Studibach in the Alptal, a typical pre-Alpine catchment in Switzerland.
The runoff plots were installed on different topographic indices and vegetation covers in order to study the spatio-temporal variability of OF and TIF.
The runoff was collected into self-made "Upwelling Bernoulli Tubes" and the water level inside these tubes was measured by pressure sensors (DCX-22-CTD, Keller Druck, Switzerland) at a 5-min resolution. The discharge was derived from the water level.
The precipitation was measured with a tipping bucket at the Erlenhöhe meteorological station and has a 10-min resolution.

alptal

display_name: alptal

land

display_name: land

overland-flow

display_name: overland-flow

plot

display_name: plot

rainfall

display_name: rainfall

runoff

display_name: runoff

studibach

display_name: studibach

time-series

display_name: time-series

topsoil-interflow

display_name: topsoil-interflow

en: Overland and topsoil interflow time series from the Studibach - summer 2022


maintainer: Nander Wever

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

en: Version 1

en:
Deprecated version. Release notes:

First release of dataset.

en: SNOWPACK ini file (new syntax)

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

en: SNOWPACK ini file (old syntax)

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

en: Version 6

en:
Deprecated version.

Release notes:

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

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

en: Version 7 (current)

en:
Release notes:

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

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

en: SNOWPACK sno file

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

en: Undercatch correction

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

en: All versions

en: Version 5

en:
Deprecated version. Release notes:

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

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

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

en: Version 3

en:
Deprecated version. Release notes:

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

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

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

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

en: Version 4

en:
Deprecated version. Release notes:

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

Update: refreshing data originating from MeteoSwiss (particularly precipitation)

en: Version 2

en:
Deprecated version. Release notes:

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

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

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

air-temperature

display_name: air-temperature

humidity

display_name: humidity

longwave-radiation

display_name: longwave-radiation

precipitation-amount

display_name: precipitation-amount

shortwave-radiation

display_name: shortwave-radiation

snow-depth

display_name: snow-depth

snow-ice-temperature

display_name: snow-ice-temperature

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

educ

en: Education, culture and sport

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


maintainer: Brandon Vanschaik

en: atmospheric-turbulence atmospheric-dynamics wind-energy lidar wind remote-sensing

en:
This data include wind speeds, wind directions, turbulence, temperature, pressure and humidity data of 10 LiDAR locations along a transect in the Gotthard wind park taken in during the summer of 2023.
The data is quality controlled and annotated according to CF-1.8 data principles. The methods behind this data collection are explained in an article under review called "Resolving three-dimensional wind velocity fields with sequential wind-Doppler LiDAR for wind energy in the complex terrain - Gotthard Pass, Switzerland".

atmospheric-dynamics

display_name: atmospheric-dynamics

atmospheric-turbulence

display_name: atmospheric-turbulence

lidar

display_name: lidar

remote-sensing

display_name: remote-sensing

wind

display_name: wind

wind-energy

display_name: wind-energy

envidat

en: EnviDat: the Environmental Data Portal

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

en: Sequential Wind-Doppler LiDAR wind profile measurements on the Gotthard pass in Switzerland - Summer 2023


en: large-eddy-simulation parameterization turbulence snow-sublimation blowing-snow antarctica drifting-snow

en: (h) LES-LM_particles_case_3b.Rdata

en: Properties of individual drifting and blowing snow particles in the LES-LM simulation of Case 3b.

en: (b) Data_of_figures.zip

en: Folder containing the data (csv files) visualized in figures of the related publication. The filenames specify the respective figure(s). However, data shown in Figures S5 and S6 are included in data resource (d).

en: (c) LES-LM_code_settings.zip

en: Simulation-specific settings (part of the LES-LM source code).

en: (e) LES-LM_particles_case_1.Rdata

en: Properties of individual drifting and blowing snow particles in the LES-LM simulation of Case 1.

en: (d) LES-LM_main_results.zip

en: Main results of the LES-LM simulations. We do not provide the complete raw output of the simulations because of the large amount of data. For example, the three-dimensional fields of wind velocity components, air temperature and specific humidity are only provided in horizontally averaged form.

en: (f) LES-LM_particles_case_2.Rdata

en: Properties of individual drifting and blowing snow particles in the LES-LM simulation of Case 2.

en: (i) 1D_model_code.zip

en: Source code of the 1D model (R scripts).

en: (k) Postprocessing_code.zip

en: R scripts used to post-process the results of the LES-LM model and 1D model and produce the figures.

en: (j) Turbulence_measurements_case_1.txt

en: Post-processed high-frequency measurements used to plot turbulence cospectra (data underlying Figure S2 of the related publication). Further measurements are published in a related dataset (Sigmund et al., 2021).

en: (g) LES-LM_particles_case_3a.Rdata

en: Properties of individual drifting and blowing snow particles in the LES-LM simulation of Case 3a.

en: (a) Readme.pdf

en: Meta data for the whole dataset including a link to the LES-LM source code.

en:
This dataset supports a related publication (Sigmund et al., 2025) and contains
- results of four **large-eddy simulations** (LES) coupled to a Lagrangian model (LM) for snow transport, aiming to reproduce field measurements, especially latent heat fluxes, at the S17 site near Syowa Station, East Antarctica, in conditions of drifting and blowing snow,
- references to the **source code** of the LES-LM model,
- the source code and results of a simple **one-dimensional (1D) model** inspired by the large-scale model CRYOWRF (Sharma et al., 2023), aiming to reproduce the LES-LM results,
- post-processed **measurements** shown in the figures of the related publication,
- **programming code** for post-processing and plotting the results.

The LES-LM simulations solve the Navier-Stokes equations for incompressible flows and advection-diffusion equations for air temperature and specific humidity in a domain of 19.2 x 18.32 x 18.32 m³. In these simulations, the airflow is driven by a prescribed large-scale pressure gradient. We assume that the air density is constant (1.18 kg/m³) and the snow particles have a spherical shape and a density of 918.4 kg/m³. Further methodological details are described in the related publication. The 1D model and post-processing code was created using the R software version 4.3.2 (R Core Team, 2024).

References
- Sharma, V., Gerber, F., & Lehning, M. (2023): Introducing CRYOWRF v1.0: multiscale atmospheric flow simulations with advanced snow cover modelling. Geosci. Model Dev. 16, 719–749. [https://doi.org/10.5194/gmd-16-719-2023](https://doi.org/10.5194/gmd-16-719-2023)
- Sigmund, A., Melo, D. B., Dujardin, J., Nishimura, K., & Lehning, M. (2025). Parameterizing snow sublimation in conditions of drifting and blowing snow. Journal of Advances in Modeling Earth Systems, 17, e2024MS004332. [https://doi.org/10.1029/2024MS004332](https://doi.org/10.1029/2024MS004332)

antarctica

display_name: antarctica

blowing-snow

display_name: blowing-snow

drifting-snow

display_name: drifting-snow

large-eddy-simulation

display_name: large-eddy-simulation

parameterization

display_name: parameterization

snow-sublimation

display_name: snow-sublimation

turbulence

display_name: turbulence

en: Modeled and measured fluxes of latent and sensible heat and snow transport at S17 near Syowa in Antarctica


en: end-member-mixing-analysis eemma tracers mixing-models stable-isotopes emma

en: eemma user's guide

en: A brief user's guide to EEMMA, with illustrations using a simple data set

en: An R script that performs ensemble end-member mixing analysis

en: eemma vignette script

en: An R script for the vignettes (simple data analysis demonstrations) shown in the user's guide

mimetype: application/octet-stream

en: vignette data

en: A simple data set for the vignette script

en:
The R script eemma.R, which implements Ensemble End-Member Mixing Analysis (EEMMA) to estimate source fractions in mixtures, exploiting information contained in time-series correlations among tracer time series.

A brief user's guide, a demonstration script, and a demonstration data set are also provided, to accompany Kirchner, J.W., Mixing models with multiple, overlapping, or incomplete end-members, quantified using time series of a single tracer, Geophysical Research Letters, 2023.

The user's guide is available for public use under Creative Commons CC-BY-SA. Public use of the scripts is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/

eemma

display_name: eemma

emma

display_name: emma

end-member-mixing-analysis

display_name: end-member-mixing-analysis

mixing-models

display_name: mixing-models

stable-isotopes

display_name: stable-isotopes

tracers

display_name: tracers

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: eemma.R, an R script for Ensemble End-Member Mixing Analysis


en: traffic-noise urban-landscape forest field-data restoration

en: 03a_Studyprotocol_Data on evaluation of acoustic impression of the test settings_Studyprotocol.sav

en: This data file includes information for an additional post hoc classification of the sound environment of the test sites. This evaluation was conducted in the afternoons of the same week in 2023 at two locations within each test site by a civil servant who was not familiar with the study's research questions.

en: 11_P4_S1_Instructions for individual mindful walking.pdf

en: This document contains the booklet with instructions for mindful walking, which participants in the mindfulness condition were encouraged to take with them and use during their individual walks over the 10 days following on-site participation.

en: This document includes the questionnaire for Papers 2 and 3, administered at the second measurement time point (after the walk).

en: 03b_Studyprotocol_Script_Analysis stationary noise evaluation.sps.sps

en: This SPSS syntax file contains a script for analyzing data related to the stationary evaluation of the acoustic impressions at the test sites, which is numbered 03a in this repository.

en: 06_Studyprotocol_GPS tracks of the walking routes.zip

en: This folder contains the GPS tracks of the walking routes in the test sites.

en: 13_P4_Data mindful walking.sav

en: This file contains data for the manuscript "Effects of a mindful walking intervention in urban forests for healthy adults".

en: 02_Studyprotocol_Data on Laeq from noise recordings of walking routes_Studyprotocol.sav

en: This data file contains information on the LAeq levels recorded along the final walking routes between September 2021 and March 2022, prior to the commencement of data collection.

en: 01_Studyprotocol_Locations of the starting points of the walking routes in the test settings.pdf

en: This document includes GPS coordinates of the starting points of the walking routes in the test sites.

en: This document includes the questionnaire for Papers 2 and 3, administered at the first measurement time point (before the walk began).

en: This file contains the informed consent document that participants signed to participate in the study.

en: 09_P2_Fotos Noise ROS paper.zip

en: This folder included the example pictures of the conditions included in paper 2 ("Psychophysiological effects of walking in forests and urban built environments with disparate road traffic noise exposure: A randomized controlled trial“).

en: This file includes the information sheet provided to participants before they consented to take part in the study.

en: This SPSS data file includes the data used for analysis in paper 2 (Psychophysiological effects of walking in forests and urban built environments with disparate road traffic noise exposure: A randomized controlled trial) and paper 3 ("The effects of walking in urban forests and urban built environments with disparate road traffic noise exposure on repetitive negative thinking and connectedness with the non-human world: A randomized controlled trial").

en: 12_P4_S2_Description of mindful walking intervention on site.pdf

en: This file includes the guided mindful walking instructions provided to participants in the mindfulness condition during the on-site walks.

en: 07_Studyprotocol_Test site pictures.zip

en: This folder includes two pictures of each test site. The locations for the two pictures on each route were chosen to be as characteristic as possible in terms of the visual and acoustical impressions of the walk.

en: This document includes the questionnaire for Papers 2 and 3, administered at the third measurement time point (10 days after participation on site).

en:
This repository contains data related to the field experiments of the RESTORE project. This project is a collaboration between the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) and the Swiss Federal Laboratories for Materials Science & Technology (EMPA). It has received funding from the Swiss National Science Foundation. The overall objective of the RESTORE project was to assess the effects of green spaces as facilitators and noise as impediment to recover from stress in people's daily environments across Switzerland.
We conducted a randomized, controlled field study to compare the psychophysiological benefits of exposure to forests and urban built environments with different levels of road traffic noise in healthy adults during 30-minute walks in Zürich, Switzerland to explore:
1) The psychophysiological effects of walking in forests and urban built environments with disparate road traffic noise exposure
2) The effects of walking in urban forests and urban built environments with disparate road traffic noise exposure on repetitive negative thinking and connectedness with the non-human world
3) The effects of a mindful walking intervention in urban forests for healthy adults

Accordingly, this repository includes data and documentation for the 3 scientific articles:

Scientific article 1. “Psychophysiological effects of walking in forests and urban built environments with disparate road traffic noise exposure: study protocol of a randomized controlled trial", BMC Psychology, 2024. https://doi.org/10.1186/s40359-024-01720-x

Scientific article 2. "Psychophysiological effects of walking in forests and urban built environments with disparate road traffic noise exposure: A randomized controlled trial“. Journal of Environmental Psychology, 2025.

Scientific article 3. “The effects of walking in urban forests and urban built environments with disparate road traffic noise exposure on repetitive negative thinking and connectedness with the non-human world: A randomized controlled trial". To be submitted to PLOS One, 2025.

Scientific article 4: "Effects of a mindful walking intervention in urban forests for healthy adults". To be submitted to Current Psychology, 2025.

field-data

display_name: field-data

forest

display_name: forest

restoration

display_name: restoration

traffic-noise

display_name: traffic-noise

urban-landscape

display_name: urban-landscape


maintainer: Yves Bühler

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

en: Potential Release  Area  NHESS

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

mimetype: application/zip

en: RAMMS simulations NHESS

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

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

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

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

avalanche

display_name: avalanche

hazard-indication-mapping

display_name: hazard-indication-mapping

hazard-mapping

display_name: hazard-mapping

ramms

display_name: ramms

release

display_name: release

snow

display_name: snow

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Automated Avalanche Release Area (PRA) Delineation Davos


maintainer: Kunfeng Gao

en: inp-parameterization aerosol-source-apportionment biological-particles ice-nucleation aerosol-cloud-interactions

en: This dataset includes all processed and raw data in the paper titled "Biological and dust aerosol as sources of ice nucleating particles in the Eastern Mediterranean: source apportionment, atmospheric processing and parameterization"

en:
This repository contains all observational data sets during CALISHTO campaign used for the paper:
Gao, K., Vogel, F., Foskinis, R., Vratolis, S., Gini, M. I., Granakis, K., Billault-Roux, A.-C., Georgakaki, P., Zografou, O., Fetfatzis, P., Berne, A., Papagiannis, A., Eleftheridadis, K., Möhler, O., and Nenes, A.: Biological and dust aerosol as sources of ice nucleating particles in the Eastern Mediterranean: source apportionment, atmospheric processing and parameterization, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-511, 2024.

aerosol-cloud-interactions

display_name: aerosol-cloud-interactions

aerosol-source-apportionment

display_name: aerosol-source-apportionment

biological-particles

display_name: biological-particles

ice-nucleation

display_name: ice-nucleation

inp-parameterization

display_name: inp-parameterization

en: CALISHTO campaign dataset for the publication Biological and Dust Aerosols as Sources of Ice-nucleating Particles in the Eastern Mediterranean


en: celerity studibach velocity rainfall-simulation tracers runoff-plot alptal topsoil-interflow preferential-flow overland-flow

en:
Description:
This dataset comprises the time series of overland flow (OF) and topsoil interflow (TIF) discharge, and tracer concentrations during artificial rainfall simulation experiments at two large (>80 m2) trenched runoff plots in the Studibach catchment in the Alptal, a typical pre-Alpine headwater catchment in Switzerland.
One plot is located in a natural clearing in an open mixed forest and the other in a grassland. Together, they represent the dominant land cover types in the region.
We applied streamwater to the surface of the plots using sprinklers and added tracers after OF and TIF had reached steady state. Deuterium enriched water was applied to the surface of the plots via the sprinklers, while Uranine and NaCl were applied as a line tracer at multiple distances from the trench. NaBr was injected into the topsoil at ~20 cm depth. Samples of overland flow and topsoil interflow were collected for several hours after tracer application, while the sprinklers continued to apply water to the surface. The runoff was collected into self-made "Upwelling Bernoulli Tubes" and the water level inside these tubes was measured using pressure sensors (DCX-22-CTD, Keller Druck, Switzerland) at a 1-min resolution during the day and 5-min resolution during the night. Uranine concentrations and electrical conductivity (EC) were recorded at a 1-minute interval. Samples for deuterium and Bromide were analysed in the lab.
The celerity of overland flow and topsoil interflow was determined during another experiment by temporarily adding more water to the surface of the plots at different distances from the trench after steady state conditions had been reached. The overland flow and topsoil interflow discharge were again measured using the Upwelling Bernoulli Tubes and pressure transducers.

alptal

display_name: alptal

celerity

display_name: celerity

overland-flow

display_name: overland-flow

preferential-flow

display_name: preferential-flow

rainfall-simulation

display_name: rainfall-simulation

runoff-plot

display_name: runoff-plot

studibach

display_name: studibach

topsoil-interflow

display_name: topsoil-interflow

tracers

display_name: tracers

velocity

display_name: velocity

en: Flow and tracer data for overland flow and topsoil interflow during rainfall simulation experiments in the Studibach catchment - Alptal - Switzerland


maintainer: Ariel Bergamini

en: biodiversity-experiment bryophytes pandiv nitrogen fungicide grassland moss functional-traits biomass species-richness

en: main data as used in Lin et al. 2025

en:
This dataset was used to analyse effects of different biodiversity and functional group treatments, fertilizer and fungicide applications as well as weeding of vascular plants on the species richness and species composition of bryophytes for the following publication: Lin M., Bergamini A., Pichon N.A., Allan E, Boch S. 2025. Nitrogen enrichment and vascular plant richness loss reduce bryophyte richness. Scientific Reports.
In this study we surveyed 96 of 216 2 m x 2 m experimental plots of the PaNDiv experiment, a large grassland field experiment in the Swiss lowlands (Pichon et al. 2020). In the PaNDiv experiment four experimental treatments were applied in a full-factorial design manipulating: (1) the nitrogen availability (through fertilization), (2) the presence of fungal pathogens (through fungicide application), (3) the functional composition of the vascular plant community (represented here by community-weighted mean (CWM) SLA), and (4) the vascular plant richness (sowing of either 1, 4, 8 or 20 species). To maintain the original sown vascular plant species richness and composition, one part of each plot (1.5 m × 2 m) was weeded, while the other part has been left unweeded since the beginning of the experiment (0.5 m × 2 m).
On each plot we surveyed two subplots, the unweeded subplot and a weeded subplot of the same area (0.5 m × 2 m). On each subplot we established a complete list of all bryophyte species together with an estimate of their cover.
The provided excel file contains three sheets:
1.	A sheet with raw species lists of all plots surveyed together with information on all treatments and an estimation of the percentage cover of vascular plants in a summer campaign in August 2022. Cover of bryophytes was estimated for each species in percent.
2.	A sheet with a list of all bryophyte species with its growth form (acroarpous/pleurocarpous)
3.	A sheet with some explantations
By means of the provided R code and the data in the excel file, all analyses can be repeated.
More details to the data sampled can be found in Lin et al (2025).

biodiversity-experiment

display_name: biodiversity-experiment

biomass

display_name: biomass

bryophytes

display_name: bryophytes

functional-traits

display_name: functional-traits

fungicide

display_name: fungicide

grassland

display_name: grassland

moss

display_name: moss

nitrogen

display_name: nitrogen

pandiv

display_name: pandiv

species-richness

display_name: species-richness

en: Data on bryophyte diversity and treatment factors in the PaNDiv experiment


maintainer: Achille Capelli

en: gnss liquid-water-content snow-water-equivalent snow swe lwc gps

en: Snow and meteo data from automatic sensors from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020.

mimetype: text/csv

en: Snow and meteo data from automatic sensors for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

en: Daily data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en: Snow and meteo data from automatic sensors from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020.

en: Profile data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

en: GNSS derived SWE, LWC and HS from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-20

en: Snow profiles data from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020.

en: GNSS derived SWE, HS, and LWC for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en: Daily data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en: GNSS derived data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

en: Webcam images

en: Webcam images showing some typical differences in the snow conditions at the four sites (Küblis, Klosters, Laret and Weissfluhjoch) for two time periods (14-18.3.2019 and 27.1-11.2.2020). Both time periods included a precipitation event with partial rain-on–snow occurring at the lower locations.

en: Daily observer data from automatic sensors from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020.

en: Daily observer data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en: Snow profiles data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en: Snow and meteo data from automatic sensors for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en: Daily data from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en: Daily data from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en: Daily observer data from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020.

en: Snow profiles data from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020.

en: GNSS derived SWE, LWC and HS from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-20

en: Manual profiles

en: Data of the traditional manual profiles for the locations Weissfluhjoch, Klosters and Küblis. For the location of Küblis there are no traditional manual profile data. We provided the data as both PDF and .CAAML format (See https://niviz.org/ or http://caaml.org/Schemas/V5.0/Profiles/SnowProfileIACS/CAAMLv5_SnowProfileIACS.pdf). Moreover, we added LWC and density profiles when available (all locations).

en: This database contains GNSS derived snow water equivalent (SWE), liquid water content (LWC), and snow height (HS) and reference data collected during the two winter 2018-2020 at 4 sites Weissfluhjoch (2540 m asl, 46°49’47’’ N, 9°48’34’’E), Laret (1515 m asl, . 46°50’2’’N, 9°52’17’’E), Klosters (1200 m asl, 46°51’49’’N, 9°53’17’’E), and Küblis (815 m asl, 46°54’48’’N, 9°46’54’’E).

gnss

display_name: gnss

gps

display_name: gps

liquid-water-content

display_name: liquid-water-content

lwc

display_name: lwc

snow

display_name: snow

snow-water-equivalent

display_name: snow-water-equivalent

en: Snow water equivalent measurements with low-cost GNSS receivers along a steep elevation gradient in the East-ern Swiss Alps


maintainer: Jana von Freyberg

en: plant-water-uptake endmember-mixing-models review stable-water-isotopes uncertainties ecohydrology root-zone

en: Data set of MEE-20-04-264

en: The following two tables contain information about the data sources of the values reported in Table 1 and 2 in the paper “Plant and root-zone water isotopes are difficult to measure, explain, and predict: some practical recommendations for determining plant water sources” published in the journal ‘Methods in Ecology and Evolution’.

en: The following two tables contain information about the data sources of the values reported in Table 1 and 2 in the paper “Plant and root-zone water isotopes are difficult to measure, explain, and predict: some practical recommendations for determining plant water sources” published in the journal 'Methods in Ecology and Evolution'.

ecohydrology

display_name: ecohydrology

endmember-mixing-models

display_name: endmember-mixing-models

plant-water-uptake

display_name: plant-water-uptake

review

display_name: review

root-zone

display_name: root-zone

stable-water-isotopes

display_name: stable-water-isotopes

uncertainties

display_name: uncertainties

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Data set of: Plant and root-zone water isotopes are difficult to measure, explain, and predict: some practical recommendations for determining plant water sources


en: hengduan-mountains hypervolume model-residuals habitat-heterogeneity phytodiversity

en: Eurasia plant family richness map

en: This is the plant family level richness map at the Eurasia scale calculated from https://www.nature.com/articles/s41597-021-00997-6

en: Hengduan plant family richness map

en: This is the family level plant richness map in the Hengduan mountain scale at 1km resoution

en: HDM heterogeneity scales

en: The Heterogeneity matrics in the Hengduan mountain scale

en: Plant range map at Hengduan scale

en: This is the plant range map at the Hengduan mountain scale based on species distribution model, elevation, and county level distribution information

en: Eurasia SR and heteorgeneity

en: This table is used for the Eurasia scale analyses, including both family level species richness map and different heterogeneity predictors

en: This research data is used to re includes 1 km resolution seed plant species range maps for the Hengduan mountain region (southwest China), elevation information for each species, family-level plant richness data, and habitat heterogeneity data at both Eurasia and Hengduan mountain regions. This research data includes 1 km resolution seed plant species range maps for the Hengduan mountain region (southwest China), elevation information for each species, family-level plant richness data, and habitat heterogeneity data at both Eurasia and Hengduan mountain regions.

habitat-heterogeneity

display_name: habitat-heterogeneity

hengduan-mountains

display_name: hengduan-mountains

hypervolume

display_name: hypervolume

model-residuals

display_name: model-residuals

phytodiversity

display_name: phytodiversity

en: Phytodiversity is associated with habitat heterogeneity from Eurasia to Hengduan


maintainer: Camille Albouy

en: actinopterygii edna fish-composition atlantic-region chondrichthyes teleo bay-biscay dna-metabarcoding

en: Data access

en:
Environmental DNA complements scientific trawling in surveys of marine fish biodiversity (Dataset 2020)

In October 2020 we chose 16 sites from the 2020 EVHOE survey for eDNA sampling. The French international EVHOE bottom trawl survey is carried out annually during autumn in the BoB to monitor demersal fish resources. All sites were located on the continental shelf (26–170 m depth), except one on the upper slope with a depth of 1045 m. To perform eDNA sampling, we collected water samples at 15 sites (Figure 1a). At each site, we sampled seawater using Niskin bottles deployed with a circular rosette. There were nine bottles on the rosette, each of them able to hold ∼5 l of water. At each site, we first cleaned the circular rosette and bottles with freshwater, then lowered the rosette (with bottles open) to 5 m above the sea bottom, and finally closed the bottles remotely from the boat. The 45 l of sampled water was transferred to four disposable and sterilized plastic bags of 11.25 l each to perform the filtration on-board in a laboratory dedicated to the processing of eDNA samples. To speed up the filtration process, we used two identical filtration devices, each composed of an Athena® peristaltic pump (Proactive Environmental Products LLC, Bradenton, Florida, USA; nominal flow of 1.0 l min–1 ), a VigiDNA 0.20 μm filtration capsule (SPYGEN, le Bourget du Lac, France), and disposable sterile tubing. Each filtration device filtered the water contained in two plastic bags (22.5 l), which represent two replicates per sampling site. We followed a rigorous protocol to avoid contamination during fieldwork, using disposable gloves and single-use filtration equipment and plastic bags to process each water sample. At the end of each filtration, we emptied the water inside the capsule that we replaced by 80 ml of CL1 conservation buffer and stored the samples at room temperature following the specifications of the manufacturer (SPYGEN, Le Bourget du Lac, France). We processed the eDNA capsules at SPYGEN, following the protocol proposed by Polanco-Fernández et al., (2020). We performed library preparation and sequencing at Fasteris (Geneva, Switzerland). Specifically, we prepared four libraries using the MetaFast protocol (a ligation-based method) and sequenced them separately. We carried out paired-end sequencing using a MiSeq sequencer (2 × 125 bp, Illumina, San Diego, CA, USA) on two MiSeq Flow Cell Kits (v3; Illumina), following the manufacturer’s instructions. We analysed the sequence reads using the OBITools package (http:
//metabarcoding.org/obitools; Boyer et al., 2016), following the protocol described by Valentini et al. (2016).

Data content:
	* rawdata/: contains the raw reads for each individual sample. One archive contains the paired-end reads specified by the _R1 or _R2 suffix as well as individually tagged PCR replicates (if available) together with an archive containing all extraction and PCR blank samples of the library. Reads have been demultiplexed using cutadapt but not trimmed, individual demultiplexing tags and primers remain present in the sequences.
	* taxadata/: contains the table with all detected taxonomy for each sample after bioinformatic processing (see Polanco et al. 2020 for details; https://doi.org/10.1002/edn3.140) and associated field metadata.
	* metadata/: contains two metadata files, one related to the data collected in the field for each filter, and the second related to the sequencing process in the lab (including the tag sequence, library name, and marker information for each sample)

actinopterygii

display_name: actinopterygii

atlantic-region

display_name: atlantic-region

bay-biscay

display_name: bay-biscay

chondrichthyes

display_name: chondrichthyes

dna-metabarcoding

display_name: dna-metabarcoding

edna

display_name: edna

fish-composition

display_name: fish-composition

teleo

display_name: teleo

educ

en: Education, culture and sport

en: Environmental DNA Marine France Evhoe 2020


maintainer: Armin Sigmund

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

en: (j) Spike percentage

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

en: (f) Sonic data 3

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

en: (i) Sonic data 6

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

en: (g) Sonic data 4

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

en: (l) AWS data

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

en: (e) Sonic data 2

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

en: (h) Sonic data 5

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

en: (m) Weather observations

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

en: (b) SPC time series

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

en: (a) Metadata

en: General and specific metadata for all files.

en: (k) Wind speed profiles

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

en: (c) SPC average profiles

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

en: (d) Sonic data 1

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

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

antarctica

display_name: antarctica

atmospheric-turbulence

display_name: atmospheric-turbulence

blowing-snow

display_name: blowing-snow

drifting-snow

display_name: drifting-snow

meteorological-measurements

display_name: meteorological-measurements

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


en: forest-vitality defoliation forest-heath icp-forests crown-condition

en: Climate and air pollution data.csv

en:
Linked plot level climate and air pollution data:
Climate data were obtained from two primary sources: SPEIbase v2.9, provided by the Spanish National Research Council (CSIC) ( https://spei.csic.es/) and the ERA5-Land dataset, produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) (https://cds.climate.copernicus.eu/dataset).
Air pollution data were sourced from the European Monitoring and Evaluation Programme (EMEP)(https://emep.int/).

en: Country level forest cabon dataset.csv

en: Country-level forest carbon data extracted from the common reporting format (CRF) tables (Table 4a therein, relating to forests) based on the National Inventory Reports provided by all countries signatory to the Conference of Parties (https://unfccc.int/ghg-inventories-annex-i-parties/2024).

en: Data Visulaization of the ICP Forest Defoliation 1990-2022

en:
This data set has been obtained after processing original data from the ICP Forests data infrastructure. It includes "clean" defoliation and symptoms data from 19 countries over the period 1990-2022 after removing dubious cases of e.g. species attribution, dubious coding, long-standing dead trees. It is based on ICP Forests Level I plots.
Defoliation is the relative loss (shed or not formed) of tree needles / leaves in relation to a hypothetical fully foliated optimum and is visually assessed using a sliding scale recorded in 5% steps (from 0%= no defoliation to 100%=standing dead tree).
Occurrence of s^Symptoms attributable to damaging agents (e.g., insects, fungi, drought, hail, fire, direct action of men…) on each tree and associated to defoliation are also included in this data set.
This data set includes a total of 2’688’512 observations from 219’854 trees in 12’104 plots.
In addition there are two associated datasets included here relating to Forest Carbon data at the national level and Plot level Climate and Air Pollution variables which were used in publications based on the primary dataset. The Forest Cabon data was extracted from the common reporting format (CRF) tables provided by all countries signatory to the Conference of Parties (https://unfccc.int/ghg-inventories-annex-i-parties/2024). Climate data were obtained from two primary sources: SPEIbase v2.9, provided by the Spanish National Research Council (CSIC) ( https://spei.csic.es/) and the ERA5-Land dataset, produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) (https://cds.climate.copernicus.eu/dataset). Air pollution data were sourced from the European Monitoring and Evaluation Programme (EMEP)(https://emep.int/).

crown-condition

display_name: crown-condition

defoliation

display_name: defoliation

forest-heath

display_name: forest-heath

forest-vitality

display_name: forest-vitality

icp-forests

display_name: icp-forests

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

en: ICP Forests Defoliation and Symptoms Data Set


maintainer: Beat Frey

en: soil-biodiversity lichens soil-quality eco-fauna database soil-dependency fauna conservation soil-organisms info-fauna red-list ecosystem-protection fungi

en: Proportion of endangered species grouped by the four soil dependency classes.

en: Raw dataset of species and their soil dependency as well as endangerment status.

en: Overview over the soil dependency and endangerment status of different groups of organisms.

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

Paper Citation:
Frey B., Maurer C., Schneider K. (2023) Informationen zum Boden anhand der Gebundenheit der Rote-Liste Arten BAFU. Bundesamt für Umwelt BAFU. 26 p.

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

Soil is the habitat and basis of life for countless creatures. The great importance of soil organisms for the fertility of our soils is widely recognized, but information is sparse on their distribution and possible endangerment in Switzerland. In the present project, target species, i.e. species that are closely linked to the soil, were identified, and for the the first time, information was gathered on the degree of endangerment of the soil ecosystem in Switzerland on the basis of Red List species. For eleven Red List groups, the species' soil dependency was determined, i.e. whether their life cycle includes stages in or on the soil. The assessment of soil dependency was made using four classes, which enabled the classification not only of insects but also of vertebrates, land snails, lichens and fungi.

conservation

display_name: conservation

database

display_name: database

eco-fauna

display_name: eco-fauna

ecosystem-protection

display_name: ecosystem-protection

fauna

display_name: fauna

fungi

display_name: fungi

info-fauna

display_name: info-fauna

lichens

display_name: lichens

red-list

display_name: red-list

soil-biodiversity

display_name: soil-biodiversity

soil-dependency

display_name: soil-dependency

soil-organisms

display_name: soil-organisms

soil-quality

display_name: soil-quality

en: Information on soil based on the soil dependency of the FOEN red list species


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

en: Shapefile of 307 catchments in Switzerland

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

en: Discharge

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

en: Soil moisture

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

en: Precipitation

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

en: Low-flow storage

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

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

discharge

display_name: discharge

hydrological-simulations

display_name: hydrological-simulations

low-flow

display_name: low-flow

precipitation

display_name: precipitation

prevah

display_name: prevah

soil-moisture

display_name: soil-moisture

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


defoliation-growth-data

title_for_slug: defoliation-growth-data

en: tree-growth defoliation forest-health literature-review relative-growth

en:
Using a literature review extracted data from 26 published papers which had data on individual tree defoliation and growth relationships In Europe.
This data set includes 415 defoliation-growth data points from 16 different tree species covering 15+ countries in Europe. Growth metrics used in individual papers were different, and included increments in terms of volume, tree rings width, radial growth, basal area and biomass. All growth metrics were converted into relative growth (presented in this dataset), expressed as a percentage of the growth of progressively defoliated trees in relation to the growth of those trees adopted as reference (control) defoliation level (i.e. the growth of the lowest defoliation level in each study was used as the reference growth point).

defoliation

display_name: defoliation

forest-health

display_name: forest-health

literature-review

display_name: literature-review

relative-growth

display_name: relative-growth

tree-growth

display_name: tree-growth

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

en: Defoliation-Growth data


en: smouldering database holdover forest-fire lightning-location-system zenodo

en: This database contains open, harmonized, and ready-to-use global data on holdover time. Holdover time is defined as the time between lightning-induced fire ignition and fire detection. The first version of the database is composed of three data files (censored data, non-censored data, ancillary data) and three metadata files (description of database variables, list of references, reproducible examples). These data were collected through a literature review of LIW studies and some datasets were assembled by authors of the original studies, covering more than 150,000 LIW from 13 countries in five continents and a time span of a century from 1921 to 2020. Censored data are the core of the database and consist of frequency data reporting the number or relative frequency of LIW per interval of holdover time. Ancillary data provide additional information on the methods and contexts in which the data were generated in the original studies. Potential contributors to the database are encouraged to contact the corresponding author in the readme file.

database

display_name: database

forest-fire

display_name: forest-fire

holdover

display_name: holdover

lightning-location-system

display_name: lightning-location-system

smouldering

display_name: smouldering

zenodo

display_name: zenodo

en: Database on holdover time of lightning-ignited wildfires


en: snow arctic ice antarctica sea-salt-aerosol

en: Fig 1A

en: Data Figure 1A

en: Fig 1F_1

en: Individual data to mean shown in Figure 1F

en: Fig 1B_2

en: Individual data to mean shown in Figure 1B

en: Fig 1F_2

en: Fig 1E

en: Data to Figure 1E

en: Code

en: Matlab code to read and plot the data

en: Fig 1F

en: Data to Figure 1F

en: Fig 1B_1

en: Table 2

en: This table lists all NEXAFS data files in chronological order with PE carbon to oxygen signal intensity ratios -from which the atomic ratio were calculated in the paper - before ("preCtoO") and after ("postCtoO") each NEXAFS measurement. Also listed is the change in Na ("lossSodi"), O ("lossOxy"), Cl ("lossChlor"), and C ("lossCarb") signal intensity after the NEXAFS measurement compared to prior to the NEXAFS measurement.

en: Read Me

en: Description of the data tables

en: Fib 1B_3

en: Fig 1B

en: Data in Figure 1B

en: Fig 1C

en: Data to Figure 1C

en: This table lists all data files in chronological order. "SampleTemp" refers to the reading of the PT-1000 at the side of the sample holder. "cellPressure" is the water vapour pressure during the measurements. From this, in presence of ice, the temperature of the ice sample can be calculated which is given in the column "iceTemp". "dT" is the difference, or temperature offset, between sensor's reading and the ice samples temperature. The offset is due to the sensor being mounted away from the cooled spot of the sample were it experiences heating from warmer parts of the sample holder. The off-set is used to calibrate the temperature sensor and the resulting temperature during the experiments is given in the column "temperature". "relHum" is the relative humidity above (supercooled) liquid and "concBrine" is the concentration of a NaCl solution in mol/l in equilibrium with ice at the experimental temperature. "Area" is the integrated PE signal.

en: Laboratory experiments are presented on the phase change at the surface of sodium chloride – water mixtures at temperatures between 259 K and 240 K. High selectivity to the upper few nanometres of the frozen solution – air interface is achieved by using electron yield near-edge X-ray absorption fine structure (NEXAFS) spectroscopy. We present the NEXAFS spectrum of the hydrohalite.

en: Data set on interfacial supercooling and the precipitation of hydrohalite in frozen NaCl solutions by X-ray absorption spectroscopy


en: soil soil-classification lwc glide-snow-avalanche alpine measurements soil-texture matric-potential

en: Coordinates of each soil location, location of the snow profiles, and location of the snow liquid water content measurements

en: Hydrus files used for the inverse fitting

en: Complete list of the van Genuchten parameter values for each soil and method

en: Daily averaged values used for the direct fitting

en: Detailed information of each file in the dataset

en: Texture data of the soil locations

en: The dataset contains measured values of soil liquid water content, matric potential, and soil texture of 40 soil samples in Davos, Switzerland. The measurements were used for determining van Genuchten parameter values through fitting of water retention curves, pedotransfer functions, and inverse fitting with Hydrus-1D.

alpine

display_name: alpine

glide-snow-avalanche

display_name: glide-snow-avalanche

lwc

display_name: lwc

matric-potential

display_name: matric-potential

measurements

display_name: measurements

soil

display_name: soil

soil-classification

display_name: soil-classification

soil-texture

display_name: soil-texture

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Soil measurements of Seewer Berg and Davos