maintainer: Maria Elvira Murazzi

en: 13c-labelling nanoplastic uptake forest-trees polystyrene

en: Nanoplastic in forests experiment

en:
Data embargoed until August 2022, and it will be provided upon request

-  excel file containing: two working sheets (leaf supply and root supply experiment) with a table with the values 13C-nanopolystyrene in the different tissues of the two forest tree species; graph showing the differences between controls and treatments of 13C-nanopolystyrene enrichment for each tissue of each forest tree species; ANOVA table showing significant values after having exposed the tree species to 13C-nanopolystyrene

en:
The fate of plastic in the environment is of global concern, because its production recently has
increased strongly and it accumulates in terrestrial and aquatic ecosystems. Although some knowledge on
its role in aquatic and terrestrial ecosystems was gained in the recent decade, hitherto very little is known
about the impact of micro and nanoplastics in forest ecosystems.

The aim of this pioneering project was to explore if nanoplastics are taken up by forest trees species
through leaves or roots. In greenhouse experiments, we exposed leaves or roots of seedling of two forest
trees species to solutions with highly 13C-labelled polystyrene nanoparticles (13C-nPS, 99 atom%) and
examined if they were incorporated in different above- and belowground tissues.

Treated part of the trees for both species showed significant 13C-enrichment, indicating that trees take up nanoparticles. However, the overall 13C signal strength in tissues that were not exposed to the 13C label remained low (Δδ13C<1‰) and was confined to a few seedlings, leaving it ambiguous whether nanoplastic transport occurs or not.

We acknowledge that the new method developed might be not sensitive enough to unequivocally detect
mechanisms of nanoplastic uptake and transport at environmentally realistic concentrations.

forest-trees

display_name: forest-trees

nanoplastic

display_name: nanoplastic

polystyrene

display_name: polystyrene

uptake

display_name: uptake

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: Nanoplastics in forests: Exploring the effects of nanoplastics on forest soils and tree physiology (NanoPlast)


maintainer: Robert Pazúr

en: sentinel-2 machine-learning vegetation-indices remote-sensing cropland grassland phenology agriculture habitat switzerland

state: active

type: dataset

en: (current version) Cropland, grassland, and shrubland map of Switzerland (v.1.5)

format: GeoTIFF

en: Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.7 (v1.0)

media_type: image/tiff

en: !!! Please check out the newer version in this repository !!! Assuming that sealed surfaces have little or no phenological response we calculated a mask of unsealed surfaces based on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.7.

format: TIFF

en: Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.6  (v1.0)

en:
!!! Please check out the newer version in the repository !!!
Mask of unsealed surfaces of Switzerland based on 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.6

en: (current version) Cropland and grassland map of Switzerland (v.1.5)

en: Cropland and grassland map of Switzerland  (v.1.0)

en: !!! Please check out the newer version in this repository !!! (1) cropland; (2) grassland; other LC classes are masked out from the map

en: (current version) Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.7 (v1.5)

en: Cropland, grassland, and shrubland map of Switzerland (v1.0)

en: !!! Please check out the newer version in this repository !!! (1) cropland; (2) grassland; (3) shrubland; other LC classes are masked out from the map

en: GEE (Google Earth Engine) script (JavaScript) to derived the output map  (v1.5)

en: The code used in Google Earth Engine to derive the cropland, grassland and shrubland map of Switzerland. Compare to the previous version the newer version includes the shadow masking procedure which might be important in the mountainous areas.

format: SERVICE

en: (current version) Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.6 (v1.5)

en: Assuming that sealed surfaces have little or no phenological response we calculated a mask of unsealed surfaces based on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.6.

en: GEE (Google Earth Engine) script (JavaScript) to derived the output map  (v1.0)

en: !!! Please check out the newer version in the repository !!! The code used in Google Earth Engine to derive the cropland, grassland and shrubland map of Switzerland

en: We developed a map of cropland and grassland allocation for Switzerland based on several indices dominantly derived from Sentinel-2 satellite imagery captured over multiple growing seasons. The classification model was trained based on parcel-based data derived from landholder reporting. The mapping was conducted on Google Earth Engine platform using random forest classifier. Areas of high vegetation, shrubland, sealed surface and non-vegetated areas were masked out from the country-wide map. The resulting map has high accuracy in lowlands as well as mountainous areas.

agriculture

display_name: agriculture

cropland

display_name: cropland

grassland

display_name: grassland

habitat

display_name: habitat

machine-learning

display_name: machine-learning

phenology

display_name: phenology

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

vegetation-indices

display_name: vegetation-indices

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

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.

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Cropland and grassland map of Switzerland based on Sentinel-2 data


maintainer: EnviDat Support

en: water-budget xylem-water forest-ecosystems soil-solution water-isotopes

en: Water isotope signatures of soil solution samples.

format: XLS

media_type: image/png

en: Overview of most xylem water isotopic signatures.

format: PNG

en: Water isotope signatures and gravimetric water content of xylem samples.

en: Water isotope signatures and gravimetric water content of bulk soil samples.

en: Water isotopic signature of deposition samples.

en: Overview of most deposition water isotopic signatures.

en: Overview of most bulk soil water isotopic signatures.

en: Column descriptions for soil solution, deposition, xylem and bulk soil samples.

en: Overview of most soil solution water isotopic signatures.

en:
The isotope composition of different forest water pools was analyzed at 10 sites of the Long-term Forest Ecosystem Research programme in 2021 and 2022. The sampling was done during the vegetation season and included fortnightly xylem water samples taken by stem increment cores and each year 3 bulk soil water sampling campaigns (in April, June, September of 2021 and 2022). Both sample types were cryogenically extracted at WSL and analyzed on a Picarro 2140 with Micro-Combustion-Module. Gravimetric water content in both sample types was computed based on pre-and post-extraction weights. In addition to xylem and bulk soil waters, we analyzed the isotope composition of soil solution from a suction cup lysimeters in three soil depths 0, 15, 50, 80 cm and precipitation from samplers below the canopy and outside of the forest, all collected fortnightly from collection periods of varying length (longest from 2017-2022).
Precipitation samples are further described [here](https://www.doi.org/10.16904/envidat.lwf.15). Lysimeter samples are described further [here](https://www.doi.org/10.16904/envidat.lwf.28).

forest-ecosystems

display_name: forest-ecosystems

soil-solution

display_name: soil-solution

water-budget

display_name: water-budget

water-isotopes

display_name: water-isotopes

xylem-water

display_name: xylem-water

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: Water isotope signatures in Swiss forest ecosystems in 2021 and 2022


maintainer: Martin Schneebeli

en: snow-cast depth-hoar snow weak-layer rounded-snow new-snow 3d-model 3d-print

en: Snow models in STL-format

media_type: application/zip

en: The zipped file contains all data described in the file 3d_snow_models_metadata. The stl-files should be ready for additive manufacturing.

format: ZIP

en: Description of snowtypes, filenames and other metadata

en: This file contains information about the stl-files of the different snow models. Snowtype (Schneeart), International Snow Classification, preview picture, notes about the origin of the snow, filename of the stl-data, and physical size of the model are given.

en: The dataset contains several snow models in the Standard Tesselated Geometry File Format (stl) for 3D visualization, printing and additive manufacturing. Different snow types are available (new snow, rounded snow, depth hoar, buried surface hoar, graupel).

depth-hoar

display_name: depth-hoar

new-snow

display_name: new-snow

rounded-snow

display_name: rounded-snow

snow

display_name: snow

snow-cast

display_name: snow-cast

weak-layer

display_name: weak-layer


maintainer: Michael Nobis

en: computer-vision plants biological-invasions artificial-intelligence invasive-species automatic-detection

en: Detected occurrences of Senecio inaequidens along highways on the Swiss Plateau. The number of image tiles in which the species was recognized is shown (Fig. 25 in the report). Image coordinates of the middle and side strips can be displayed separately in the interactive map.

en: Detected occurrences of Ailanthus altissima along highways on the Swiss Plateau. The number of image tiles in which the species was recognized is shown (Fig. 23 in the report). Image coordinates of the middle and side strips can be displayed separately in the interactive map.

en: Detected occurrences of Senecio inaequidens along highways on the Swiss Plateau. To reduce false-positives, only images in which the species was recognized in at least three of 16 image tiles are considered as occurrences (Fig. 40 in the report). Image coordinates of the middle and side strips can be displayed separately in the interactive map.

en: Detected occurrences of Ailanthus altissima along highways on the Swiss Plateau. To reduce false-positives, only images in which the species was recognized in at least three of 16 image tiles are considered as occurrences (Fig. 38 in the report). Image coordinates of the middle and side strips can be displayed separately in the interactive map.

en:
Transport infrastructure such as **highways** are important corridors for the spread of invasive species. In this project, an automated detection and mapping of **two invasive alien plant species** was developed and applied along highways in the **Swiss Central Plateau**, based on **artificial intelligence (AI, Deep Learning)**. The two target species are **tree of heaven *(Ailanthus altissima)*** and **narrow-leaved ragwort *(Senecio inaequidens)***.
The data is part of the project report and shows the mapping results in **interactive maps**. Technically speaking, these are **self-contained HTML files** that can be displayed with standard web browsers. To reduce the file size, the downloads are provided in zip format.
**Report**: [https://www.mobilityplatform.ch/de/research-data-shop/product/1690](https://www.mobilityplatform.ch/de/research-data-shop/product/1690)
**Project**: [https://www.wsl.ch/en/projects/automated-detection-of-invasive-alien-plants-along-highways/](https://www.wsl.ch/en/projects/automated-detection-of-invasive-alien-plants-along-highways/)

artificial-intelligence

display_name: artificial-intelligence

automatic-detection

display_name: automatic-detection

biological-invasions

display_name: biological-invasions

computer-vision

display_name: computer-vision

invasive-species

display_name: invasive-species

plants

display_name: plants

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Automated detection of invasive alien plants along highways


maintainer: Mountain Hydrology and Mass Movements

en: rockfall-fatalities windstorm-fatalities debris-flow-fatalities landslide-fatalities lightning-fatalities ice-avalanche-fatalities natural-hazard-fatalities earthquake-fatalities flood-fatalities

media_type: text/csv

en: Natural hazard fatalities per year in Swizterland since 1946.

format: CSV

mimetype: text/csv

en:
This dataset contains the number of fatalities due to flood, debris flow, landslide, rockfall, windstorm, lightning, ice avalanche, earthquake and other processes like roof avalanche or lacustrine tsunami for each year since 1946.
The following information is contained (by column and column title):

* year
* total number of hazard fatalities
* number of fatalities by flood (German: Hochwasser, Überschwemmung). Flood includes people drowned in flooded or inundated areas or carried away in streams under high-water conditions.
* number of fatalities by debris flow (German: Murgang).
* number of fatalities by landslide (German: Erdrutsch). Landslide includes people killed by landslides and hillslope debris flows (German: Hangmure).
* number of fatalities by rockfall (German: Steinschlag, Fels- und Bergsturz).
* number of fatalities by windstorm (German: Sturm). Windstorm includes people killed by falling objects or trees during very strong wind conditions and people who drowned in lakes because their boat capsized during such conditions.
* number of fatalities by lightning (German: Blitz).
* number of fatalities by ice avalanche (German: Eislawine).
* number of fatalities by earthquake (German: Erdbeben).
* number of fatalities by other processes like roof avalanche, lacustrine tsunami (German: andere Prozesse wie Dachlawine, Tsunami im See).

The data was collected based on newspaper research. For more information please refer to

_Badoux, A., Andres, N., Techel, F., and Hegg, C.: Natural hazard fatalities in Switzerland from 1946 to 2015, Nat. Hazards Earth Syst. Sci., 16, 2747-2768, https://doi.org/10.5194/nhess-16-2747-2016, 2016._

The data collection is financed by the FOEN (with exception of the collection of the avalanche fatalities). The data contains the official statistics of the FOEN on fatalities due to flood, debris flow, landslide, rock fall and avalanche.

Restrictions: The data set is not complete. Only fatalities in or around settlements and on open transportation routes are included. More precisely, fatalities were not collected, when persons exposed themselves to a great danger on purpose. Or fatalities during leisure activities which are connected to a higher risk were not included (this includes e.g. canoeing or river surfing during flood, canyoning, mountaineering, climbing, walking or driving on a closed road).

Fatalities by avalanches are collected at the WSL Institute for Snow and Avalanche Research SLF. You can download the avalanche fatalities per hydrological year [here](https://www.envidat.ch/dataset/avalanche-fatalities-switzerland-1936) and per calendar year [here](https://www.envidat.ch/dataset/avalanche-fatalities-per-calendar-year-since-1936). For a direct comparison with the fatalities presented here, please download the data set with the calendar years and do not consider fatalities in the backcountry (tour) or in terrain close to ski areas (offpiste).

debris-flow-fatalities

display_name: debris-flow-fatalities

earthquake-fatalities

display_name: earthquake-fatalities

flood-fatalities

display_name: flood-fatalities

ice-avalanche-fatalities

display_name: ice-avalanche-fatalities

landslide-fatalities

display_name: landslide-fatalities

lightning-fatalities

display_name: lightning-fatalities

natural-hazard-fatalities

display_name: natural-hazard-fatalities

rockfall-fatalities

display_name: rockfall-fatalities

windstorm-fatalities

display_name: windstorm-fatalities

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: Number of natural hazard fatalities per year in Switzerland since 1946


restoring-grassland-multifunctionality

title_for_slug: restoring-grassland-multifunctionality

en: fungi plants grassland ecosystem-multifunctionality nematodes mineralization restoration soil-biodiversity biodiversity bacteria

en: Functional diversity above_belowground fauna

en: Please note that EMF, MF.below, MF.abio and Soil.hetero have been corrected.

en: Please note that C.sto and Corg have been corrected.

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

Resch, M. C., Schütz, M., Buchmann, N., Frey, B., Graf, U., van der Putten, W. H., Zimmermann, S., Risch, A. C. 2021. Evaluating long-term success in grassland restoration – an ecosystem multifunctionality approach. Ecological Applications 31, e02271.

 Study area

The study was conducted in the Canton of Zurich, Switzerland, in and around two nature reserves Eigental and Altläufe der Glatt (47°27’ to 47°29’ N, 8°37’ to 8°32’ E, 417 to 572 m a.s.l.). All studied grasslands were located with a radius of approximately 4 km. Average monthly temperatures range from 0.7 ± 2.0 °C (January) to 19.0 ± 1.5 °C (July), and monthly precipitation range from 60 ± 42 mm (January) to 118 ± 46 mm (July [maxima]; 1989-2017; MeteoSchweiz 2018). In our study, we focused on semi-dry and semi-wet oligo- to mesotrophic grasslands characterized by high plant species richness and groundwater fluctuations throughout the year (Delarze et al. 2015, see also Resch et al. 2019).

 Experimental design and sampling

A large-scale restoration experiment to expand and reconnect isolated remnants of species-rich grasslands was initiated in the nature reserve Eigental in 1990. Twenty hectares of adjacent intensive grasslands were chosen for restoration. In 1995, three restoration methods of increasing intervention intensities were implemented. The goal of all three methods was to lower the availability of soil nutrients and hence, facilitate ecosystem development towards the targeted nutrient-poor grasslands. These methods were: Harvest only (hay harvest twice a year), Topsoil (removal of the nutrient-rich topsoil), and Topsoil+Propagules (topsoil removal combined with the application of hay from target vegetation). Plant biomass harvest (once a year in late summer/early autumn) commenced in Topsoil and Topsoil+Propagules five years after the soils were removed and is still ongoing today. We measured restoration success by comparing the three restoration methods with intensively managed (Initial) and semi-natural grasslands (Target) 22 years after restoration. Initial grassland sites share the same agricultural history as the restored sites: mowing and subsequent fertilizing (manure) up to five times a year, as well as different tillage regimes (Resch et al. 2019). Target sites were the sites from which hay for seeding the Topsoil+Propagules sites was collected. Soil conditions (i.e., soil types, soil texture) were comparable to those found in the restored grasslands (Resch et al. 2019). Additionally, Target sites were selected to represent a variety of semi-natural grasslands, including semi-dry to semi-wet conditions. In Target grasslands, biomass is harvested once a year in late summer or early autumn. Eleven 5 m x 5 m (25 m2) plots were randomly established in each of the five treatments (in total 55 plots; for a detailed map see Neff et al. 2020). An additional 2 m x 2 m (4 m2) subplot was randomly established at least 2 m away from each 25 m2 plot for destructive sampling. Data sampling took place between June and September 2017.

Vegetation properties

All plant species were identified within the 25 m2 plots (nomenclature: Lauber and Wagner 1996) in mid-June 2017 (in total 250 species). Vegetation structure and plant biomass were assessed diagonally on a transect of 2 m x 10 cm within the 25 m2 plot in early July 2017. We measured the maximum and mean height of the vegetation at the start, middle and end of the transect and calculated the standard deviation of these measures to describe vegetation structural heterogeneity (Schuldt et al. 2019). Thereafter, biomass was clipped on the entire transect to 1 cm height, sorted into five functional groups (graminoids, forbs, legumes, litter, and woody species), dried at 60 °C for 48 h, and weighed (Meyer et al. 2015).

 Aboveground arthropods

Aboveground arthropods were sampled at two locations in each 25 m2 plot in early July 2017 (see also Neff et al. 2020). Briefly, two cylindrical baskets (50 cm diameter, 67 cm height; woven fabric) were thrown simultaneously from outside the plot into two opposite corners. A closable mosquito mesh sleeve was mounted to the top of the baskets and an integrated metal ring at the bottom was fixed to the ground with metal stakes to assure that insects could not escape. A suction sampler (Vortis, Burkhard Manufacturing Co. Ltd., Hertfordshire, England) was then inserted into one of the baskets through the opening of the sleeve and the plot was “vacuumed" twice for 105 seconds with a 30 seconds break. The collected animals were immediately transferred into 70% ethanol. Arthropods were sorted and assigned to 23 taxonomic groups. Holometabolic larvae were lumped into one category while hemimetabolic larvae were grouped separately from adults in the respective taxonomic rank. We used mean values of individuals per plot for total abundance. Aboveground arthropod richness was defined by the number of different taxa to lowest taxonomic level (in total 23 taxa). All taxa were assigned to one of five trophic levels: 1) primary producers, 2) primary consumers, 3) secondary consumers, 4) tertiary consumers, and 5) quaternary consumers.

 Belowground fauna

Sampling of all belowground fauna took place in mid-July 2017. Earthworms were sampled in two 30 cm x 30 cm x 20 cm soil monoliths at two opposite corners of the 25 m2 plot (opposite to aboveground arthropod sampling). The excavated soil monolith was broken by hand, all earthworms collected and immediately transferred in a 4% formaldehyde solution. Thereafter, earthworm individuals were identified to species level (in total 10 taxa; Christian and Zicsi 1999) and species assigned to three functional groups (Bouché 1977).
To assess soil arthropod communities, we randomly collected one undisturbed soil core (5 cm diameter, 12 cm depth) in each 4 m2 subplot with a slide hammer corer lined with a plastic sleeve (AMS Samplers, American Falls, Idaho, USA). Soil arthropods were extracted using Berlese-Tullgren funnels (3 mm mesh), starting the day of sampling and lasting 14 days. Individuals were stored in 70% ethanol. Soil arthropods were assigned to 41 taxonomic groups and 4 feeding types. Holometabolic and hemimetabolic larvae were treated as previously described for aboveground arthropods. Belowground arthropod richness refers to the 41 taxonomic groups.

For soil nematode sampling, we randomly collected eight soil cores of 2.2 cm diameter (Giddings Machine Company, Windsor, CO, USA) within each 4 m2 subplot to a depth of 12 cm. The eight cores were combined, gently homogenized, placed in coolers, kept at 4 °C and transported to the laboratory at NIOO in Wageningen (NL) within one week after collection. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriator (Oostenbrink 1960) and prepared for morphological identification and quantification as described by Resch et al. (2019). Nematodes were identified to family level (39 taxa) according to Bongers (1988), assigned to 17 functional groups, 5 feeding types and 5 colonizer-persister (C-P) classes (Yeates et al. 1993, Bongers 1990, Resch et al. 2019).

We randomly collected two more soil cores (2.2 cm diameter x 12 cm depth) within each 4 m2 subplot to determine soil microbial communities. Again, the soil cores were combined, homogenized, placed in coolers and transported to the laboratory at WSL in Birmensdorf (Switzerland) where the metagenomic DNA was extracted from 8 g sieved soil (2 mm) using the DNeasy PowerMax Soil Kit (Quiagen, Hilden, NRW, GER) according to the manufacturer`s instructions. PCR amplification of the V3-V4 region of the prokaryotic small-subunit (16S) and the ribosomal internal transcribed spacer region (ITS2) of eukaryotes was performed with 1 ng of template DNA utilizing PCR primers and conditions as previously described (Frey et al., 2016). PCRs were run in triplicates and pooled. The pooled amplicons were sent to the Genome Quebec Innovation Centre (Montreal, QC, Canada) for barcoding using the Fluidigm Access Array technology (Fluidigm) and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, CA, USA). Quality filtering, clustering into operational taxonomic units (OTUs) and taxonomic assignment were performed as described by Frey et al. (2016) and Adamczyk et al. (2019). We used a customised pipeline largely based on UPARSE (Edgar 2013) implemented in USEARCH v. 9.2 (Edgar 2010). After discarding singletons of dereplicated sequences, clustering into OTUs with 97% sequence similarity was performed (Edgar 2013). Quality-filtered reads were mapped on the filtered set of centroid sequences. Taxonomic classification of prokaryotic and fungal sequences was conducted querying against most recent versions of SILVA (v.132, Quast et al. 2013) and UNITE (v.8, Nilsson et al. 2018). Only taxonomic assignments with confidence rankings equal or higher than 0.8 were accepted (assignments below 0.8 set to unclassified). Prokaryotic OTUs assigned to mitochondria or chloroplasts as well as eukaryotic OTUs assigned other than fungi were removed prior to data analysis. In addition, prokaryotic and fungal datasets were filtered to discard singletons and doubletons. Thereafter, OTU abundance matrices were rarefied to the lowest number of sequences per community, to normalize the total number of reads and achieve parity between samples (Prokaryota: 29,843 reads; Fungi: 26,690 reads). Finally, prokaryotic and fungal observed richness (number of OTUs) were estimated (Prokaryota: 14,010 OTUs; Fungi: 5,813 OTUs). For prokaryotes, we distinguished five and for fungi six functional types based on lowest taxonomic resolution (Nguyen et al. 2016, Tedersoo et al. 2014). Belowground taxon richness was defined by the total number of earthworm, arthropod, nematode, fungi, and prokaryote taxa assigned to lowest taxonomic level. Finally, all belowground taxa were assigned to the same five trophic levels as the aboveground arthropods.

 Soil chemical and physical properties, soil nitrogen mineralization

We randomly collected three 5 cm diameter x 12 cm depth soil samples in each 4 m2 subplot with a slide hammer corer (AMS Samplers, American Falls, Idaho, USA), pooled them and then made two subsamples. One was field-fresh and stored at 3 °C until analysis, the other was dried for 48 h at 60 °C and passed through a 4 mm mesh. From the dried sample, we measured soil pH potentiometrically in 0.01 M CaCl2 (soil:solution ratio=1:2; 30 minutes equilibration time). Total and organic carbon content were measured on fine-ground samples (≤ 0.5 mm) by dry combustion using a CN analyzer NC 2500 (CE Instruments, Wigan, United Kingdom). Inorganic carbon of samples with a pH > 6.5 was removed with acid vapor prior to analysis of organic carbon (Walthert et al. 2010). We calculated total soil carbon (C) storage after correcting its content for soil depth, stone content and density of fine earth (see below). Exchangeable cations were determined on another 5 g dry soil sample with 50 mL unbuffered 1 M NH4Cl solution (soil:solution ratio=1:10, end-over-end shaker for 1.5 hours) and measured by an ICP-OES (Optima 7300 DV, Perkin-Elmer, Waltham, Massachusetts, USA). Thereafter, cation exchange capacity (CEC) was calculated as the sum of exchangeable cations and protons (and expressed as mmolc per 1 kg soil) and used to describe nutrient retention capacity in our plots. Concentrations of exchangeable protons were calculated as the difference between total and Al-induced exchangeable acidity as determined by the KCl-method (Thomas 1982).

Ammonium (NH4+) and nitrate (NO3−) were extracted from a 20 g fresh subsample with 80 mL 1M KCl for 1.5 hours on an end-over-end shaker and filtered through ashless folded filter paper (DF 5895 150, ALBET LabScience, Hahnemühle FineArt GmbH, Dassel, Germany). NH4+ concentrations were determined colorimetrically by automated flow injection analysis (FIAS 300, Perkin-Elmer, Waltham, Massachusetts, USA). NO3− concentrations were measured colorimetrically according to Norman and Stucki (1981). Potential soil net nitrogen (N) mineralization was assessed during an 8-week incubation period under controlled moisture (60% of field capacity), temperature (20 °C) and light conditions (dark) in the laboratory. We weighed duplicate samples of fresh soil equivalent to 8 g dry soil (24 h at 104 °C) into 50 mL Falcon tubes. Soil samples were extracted for NH4+ and NO3− at the beginning and after eight weeks as described above. Soil net N mineralization was calculated as the difference between the inorganic nitrogen (NH4+ and NO3−) before and after the incubation (Hart et al. 1994), corrected for the total incubation time and represented per day values expressed as mg N kg-1 soil d-1.

To assess soil physical properties, we randomly collected one undisturbed soil core per 4 m2 subplot (5 cm diameter, 12 cm depth) in a steel cylinder that fit into the slide hammer (AMS Samplers, American Falls, Idaho, USA). The cylinder was capped in the field to avoid disturbance. We then measured field capacity in the laboratory. For this purpose, the cylinder and soil therein were saturated in a water bath and drained on a sand/silt-bed with a suction corresponding to 60 cm hydrostatic head. The moist soil was dried at 105 °C to constant weight. Field capacity was calculated by dividing the mass of water by the total mass of wet soil contained at 60 cm hydrostatic head and used to describe water holding capacity. Thereafter, samples were passed through a 4 mm mesh. Fine-earth and skeleton fractions were weighed separately to assess bulk soil density (fine-earth plus skeleton), density of fine earth, and proportion of skeleton. Particle density was determined with the pycnometer method (Blake and Hartge 1986), and total porosity and proportion of fine pores were calculated (Danielson and Sutherland 1986). Clay, silt, and sand contents were quantified with the sediment method (Gee and Bauder 1986).

Surface and soil temperature (12 cm depth, water-resistant digital pocket thermometer; IP65, H-B Instrument, Trappe, Pennsylvania, USA) as well as volumetric soil moisture content (12 cm depth, time domain reflectometry; Field-Scout TDR 300, Spectrum Technologies, Aurora, Illinois, USA) were measured at five random locations within the 4 m2 subplots every month from June to September. We calculated the standard deviation of each temperature and moisture measure over four months to describe seasonal variations. Slope inclination was determined at plot-level via GPS measurements (GPS 1200, Leica Geosystem, Heerbrugg, Switzerland) and categorized into slope gradient classes according to FAO standards (1990). Thickness of the topsoil horizon (equivalent to Ah or Aa horizon) was determined at one soil monolith (30 x 30 x 30 cm3) per 4 m2 subplot in cm and rounded to next integer.

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Blake, G.R., and K. H. Hartge. 1986. Particle Density. Pages 377-382 in A. Klute, editor. Methods of soil analysis: Part 1—Physical and mineralogical methods. Soil Science Society of America (SSSA) Inc., Madison.

Bongers, T. 1988. De nematoden van Nederland. Stichting Uitgeverij van de Koniklijke Nederlandse Natuurhistorische Verenigung (KNNV), Utrecht.

Bongers, T. 1990. The maturity index: an ecological measure of environmental disturbance based on nematode species composition. Oecologia 83:14-19.

Bouché, M. B. 1977. Strategies lombriciennes. Ecological Bulletins 25:122-132.

Christian, E., and A. Zicsi. 1999. Ein synoptischer Bestimmungsschlüssel der Regenwürmer Österreichs (Oligochaeta: Lumbricidae). Die Bodenkultur 50:121-131.

Danielson, R. E., and P. L. Sutherland. 1986. Porosity. Pages 443-461 in A. Klute, editor. Methods of soil analysis: Part 1—Physical and mineralogical methods. Soil Science Society of America (SSSA) Inc., Madison.

Delarze, R., Y. Gonseth, S. Eggenberg, and M. Vust. 2015. Lebensräume der Schweiz: Ökologie ‐ Gefährdung ‐ Kennarten, Ott Verlag, Bern.

Edgar, R. C. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460–2461.

Edgar, R. C. 2013. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods 10:996–998.

FAO. 1990. Guidelines for soil description, third ed. Land and Water Development Division at the Food and Agriculture Organization of the United Nations (FAO), Rome.

Frey, B., T. Rime, M. Phillips, B. Stierli, I. Hajdas, F. Widmer, and M. Hartmann. 2016. Microbial diversity in European alpine permafrost and active layers. FEMS Microbiology Ecology 92:fiw018.

Gee, G.W., and J. W. Bauder. 1986. Particle-size analysis. Pages 383-411 in A. Klute, editor. Methods of soil analysis: Part 1—Physical and mineralogical methods. Soil Science Society of America (SSSA) Inc., Madison.

Hart, S. C, J. M. Stark, E. A. Davidson, and M. K. Firestone. 1994. Nitrogen mineralization, immobilization, and nitrification. Pages 985-1016 in R. W. Weaver, S. Angle, P. Bottomley, D. Bezdicek, S. Smith, A. Tabatabai, and A. Wollum, editors. Methods of soil analysis: Part 2—Microbiological and biochemical properties. Soil Science Society of America (SSSA) Inc., Madison.

Lauber, K., Wagner, G., 1996. Flora Helvetica. Flora der Schweiz. Haupt Verlag, Bern.

MeteoSchweiz, 2018. Klimabulletin Jahr 2017. MeteoSchweiz, Zürich.

Meyer, S. T., C. Koch, and W. W. Weisser. 2015. Towards a standardized rapid ecosystem function assessment (REFA). Trends in Ecology and Evolution 30:390-397.

Neff, F., M. C. Resch, A. Marty, J. Rolley, M. Schütz, A. C. Risch, and M. M. Gossner. 2020. Long-term restoration success of insect herbivore communities in semi-natural grasslands – a functional approach. Ecological Applications 0:e02133

Nguyen, N.H., Z. W. Song, S. T. Bates, S. Branco, L. Tedersoo, J. Menke, J. S. Schilling, and P. G. Kennedy. 2016. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecology 20:241–248.

Nilsson, R. H., K.-H. Larsson, A. F. S. Taylor, J. Bengtsson-Palme, T. S. Jeppesen, D. Schigel, P. G. Kennedy, K. Picard, F. O. Glöckner, L. Tedersoo, I. Saar, U. Kõljalg, and K. Abarenkov. 2018. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Research 47:259–264.

Norman, R. J., and J. W. Stucki. 1981. The Determination of Nitrate and Nitrite in Soil Extracts by Ultraviolet Spectrophotometry. Soil Science Society of America Journal 45:347-353.

Oostenbrink, M. 1960. Estimating nematode populations by some selected methods. Pages 81-101 in J. J. Sasser, and W. R. Jenkins, editors. Nematology. Univ. of North Carolina Press, Chapel Hill.

Quast, C., E. Pruesse, P. Yilmaz, J. Gerken, T. Schweer, P. Yarza, J. Peplies, and F. O. Glöckner. 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research 41:590-596.

Resch, M. C., M. Schütz, U. Graf, R. Wagenaar, W.H. van der Putten, and A. C. Risch. 2019. Does topsoil removal in grassland restoration benefit both soil nematode and plant communities? Journal of Applied Ecology 56:1782-1793.

Schuldt, A., A. Ebeling, M. Kunz, M. Staab, C. Guimarães-Steinicke, D. Bachmann, N. Buchmann, W. Durka, A. Fichtner, F. Fornoff, W. Härdtle, L. R. Hertzog, A-N. Klein, C. Roscher, J. Schaller, von G. Oheimb, A. Weigelt, W. Weisser, C.Wirth, J. Zhang, H. Bruelheide, and N. Eisenhauer. 2019. Multiple plant diversity components drive consumer communities across ecosystems. Nature Communications 10:1460.

Tedersoo, L., M. Bahram, S. Põlme, U. Kõljalg, N. S. Yorou, R. Wijesundera, L. Villarreal Ruiz, A. M. Vasco-Palacios, P. Q. Thu, A. Suija, M. E. Smith, C. Sharp, E. Saluveer, A. Saitta, M. Rosas, T. Riit, D. Ratkowsky, K. Pritsch, K. Põldmaa, M. Piepenbring, C. Phosri, M. Peterson, K. Parts, K. Pärtel, E. Otsing, E. Nouhra, A. L. Njouonkou, R. H. Nilsson, L. N. Morgado, J. Mayor, T. W. May, L. Majuakim, D. J. Lodge, S. See Lee, K.-H. Larsson, P. Kohout, K. Hosaka, I. Hiiesalu, T. W. Henkel, H. Harend, L.-D. Guo, A. Greslebin, G. Grelet, J. Geml, G. Gates, W. Dunstan, C. Dunk, R. Frenkhan, L. Dearnaley, A. De Kesel, T. Dang, X. Chen, F. Buegger, F. Q. Brearley, G. Bonito, S. Anslan, S. Abell, and K. Abarenkov. 2014. Global diversity and geography of soil fungi. Science 346:1256688.

Thomas, G.W. 1982. Exchangeable cations. Pages 159-165 in A. L. Page, R. H. Miller, and D. R. Keeney, editors. Methods of Soil Analysis: Part 2—Chemical and microbiological properties. Soil Science Society of America (SSSA) Inc., Madison.

Walthert, L., U. Graf, A. Kammer, J. Luster, D. Pezzotta, S. Zimmermann, and F. Hagedorn. 2010. Determination of organic and inorganic carbon, δ13C, and nitrogen in soils containing carbonates after acid fumigation with HCl. Journal of Plant Nutrition and Soil Sciences 173:207-216.

Yeates, G. W., T. Bongers, R. G. M. de Goede, D. W. Freckman, and S. S. Georgieva. 1993. Feeding habits in soil nematode families and genera – an outline for soil ecologists. Journal of Nematology 25:315-331.

bacteria

display_name: bacteria

biodiversity

display_name: biodiversity

ecosystem-multifunctionality

display_name: ecosystem-multifunctionality

fungi

display_name: fungi

grassland

display_name: grassland

mineralization

display_name: mineralization

nematodes

display_name: nematodes

restoration

display_name: restoration

soil-biodiversity

display_name: soil-biodiversity

en: Restoring grassland multifunctionality


en: fem x-ray-tomography geometrical crystallographic elasticity microstructure anisotropy

en:
The study aims to determine the effective elastic properties of snow, firn, and bubbly ice based on microstructural quantities. Anisotropy, one of these quantities (the other being ice volume fraction) in snow and ice, has two types: geometrical and crystallographic, resulting in snow's macroscopic anisotropic elastic behavior. The research focuses on the impact of geometrical anisotropy on potential ice volume fractions in snow and ice. 391 micro-CT images from various locations, including laboratories, the Alps, the Arctic, and Antarctica, were analyzed to achieve this.
 The analysis involved microstructure-based finite element simulations, which inherently consider microstructure and calculate the elasticity tensor. Hashin-Shtrikman bounds were utilized to predict the elastic properties of the microstructure samples. These bounds effectively captured the nonlinear interplay between geometrical anisotropy, captured by the Eshelby tensor and density. HS bounds have the advantage of the correct limiting behavior for low to high-ice volume fractions.
 We derived parameterization for five transversely isotropic elasticity tensor components, requiring only two free parameters. This parameterization was valid for ice volume fractions ranging from 0.06 to 0.93. The analysis employing the Thomsen parameter highlighted the dominance of geometrical anisotropy until an ice volume fraction of 0.7. However, to fully comprehend the elasticity of bubbly ice, a comprehensive approach is necessary to integrate coupled elastic theories that account for both geometrical and crystallographic anisotropy.
This dataset includes a Jupyter notebook with all the necessary functions required to predict the elasticity tensor of snow for the given ice volume fraction and anisotropy. Also, the code contains the least squares optimization function to compute the elasticity tensor for the six components of stress and strain. For example, we consider our dataset to calculate the samples' elasticity tensor and reproduce Fig. 7 from the paper. We take the stress and strain values obtained from load states as input for this example.
Also, a .csv file contains all the microstructural information: ice volume fraction, anisotropy, correlation functions, voxels size, and no. of voxels of the samples and the elasticity tensor obtained from finite element simulations and from present work parameterization.

anisotropy

display_name: anisotropy

crystallographic

display_name: crystallographic

elasticity

display_name: elasticity

fem

display_name: fem

geometrical

display_name: geometrical

microstructure

display_name: microstructure

x-ray-tomography

display_name: x-ray-tomography

en: Effective, anisotropic elasticity tensor of snow, firn, and bubbly ice


en: arctic conservation collections biodiversity plastisphere permafrost biobank fungi glaciers swiss-alps bacteria active-layer extreme-habitat

en: Complete collection of bacterial and fungal isolates from Permafrost, active layer and glaciers in Switzerland, Svalbard, northern Greenland and Russia.

en:
The cold habitats of the Swiss Alps and the Arctic are undergoing change and are at risk of disappearing completely or partially in the future. Along with them, a poorly known diversity of microorganisms that have adapted to life in these supposedly hostile places. By cryopreserving microorganisms from these habitats, some of today's biodiversity can be preserved in biobanks.
The collection contains cryopreserved bacteria and fungi isolated from permafrost and active layer of Muot da Barba Peider and Val Lavirun, from the glacier forefield and glacier toe of the Damma glacier, from active layer in northern Greenland and from plastic waste in Svalbard, as well as isolates from plastic incubations in Muot da Barba Peider, northern Greenland and arctic Russian soils.
Project overview: https://www.wsl.ch/en/projects/life-at-chilly-temperatures-a-collection-of-microorganisms-from-extreme-habitats/

active-layer

display_name: active-layer

arctic

display_name: arctic

biobank

display_name: biobank

collections

display_name: collections

conservation

display_name: conservation

extreme-habitat

display_name: extreme-habitat

glaciers

display_name: glaciers

permafrost

display_name: permafrost

plastisphere

display_name: plastisphere

swiss-alps

display_name: swiss-alps

educ

en: Education, culture and sport

en: Life at chilly temperatures - A collection of microorganisms from extreme habitats


backward-trajectories

maintainer: Michael Sprenger

title_for_slug: backward-trajectories

en: backward-trajectories alps atmospheric-dynamics complex-topopgraphy cosmo lagrangian-analysis raclets

en: Backward Trajectories

en: The trajectories are started from surface up to 5000 m altitude, with an equidistant vertical spacing of 200 m, and calculated 120 h backward in time. As long as the trajectories stay in the COSMO1 domain (essentially the Alpine region), as run operationally at MeteoSwiss, the COSMO analysis wind fields are used. When the trajectories leave this domain, they are extended further backward in time based on ECMWF analysis data. The following fields are traced along the trajectories: longitude, latitude, geopotential height, pressure, specific humidity, temperature and surface topography. Furthermore, it is indicated for each trajectory time instance on which model data, COSMO or ECMWF, the trajectory time is based. Finally, the trajectories are started every hour in the specified time period, and positions and fields are available every 10 min. Each day is saved as a separate file. The last 8 digits of the file names indicate the starting date of the trajectories.

mimetype: application/zip

en: Backward trajectories were calculated from two positions: Davos Wolfgang (LON: 9.85361, LAT: 46.83551) and Weissfluhjoch (LON: 9.80646 LAT: 46.83304) for the time period February 2 until March 27 2019 using COSMO or ECMWF, respectively.

alps

display_name: alps

atmospheric-dynamics

display_name: atmospheric-dynamics

backward-trajectories

display_name: backward-trajectories

complex-topopgraphy

display_name: complex-topopgraphy

cosmo

display_name: cosmo

lagrangian-analysis

display_name: lagrangian-analysis

raclets

display_name: raclets

envidat

en: EnviDat: the Environmental Data Portal

en: Backward  Trajectories


maintainer: Michael Eisenring

en: alan light-pollution european-beech phytochemistry insect-herbivores

en: Raw_data_Ceppi et al..xlsx

en: Raw data for the Publication Ceppi et al. 2025 in Journal of Applied Ecology: ARTIFICAL NIGHT LIGHT INTENSITY MODULATES HERBIVORY AND PHYTOCHEMISTRY IN EUROPEAN BEECH

en: Raw data on plant morphological traits, phytochemistry and herbivory that were analyzed in the study entitled: Artificial night light intensity modulates herbivory and phytochemistry in European beech

alan

display_name: alan

european-beech

display_name: european-beech

insect-herbivores

display_name: insect-herbivores

light-pollution

display_name: light-pollution

phytochemistry

display_name: phytochemistry

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: Raw data - Artificial night light intensity modulates herbivory and phytochemistry in European beech


en: snow-water-equivalent meteodat-gmbh wagital snow gcos

media_type: text/plain

format: TXT

en:
Total water reserves of the snow cover [mio m3] for Wägital catchment, Switzerland, for reference date April 1. Data is separated in 2 elevation zones 900m-1500m asl and 1500m-2300m asl. Time period 1943-2024, status 2024-04-22.

Funded currently or in the past by:

- Federal Office of Meteorology and Climatology MeteoSwiss in the context of GCOS Switzerland
- Meteodat GmbH
- Institute of Geography, University of Zurich
- WSL Institute for Snow and Avalanche Research SLF
- Institute of Geography, ETH Zurich (IAC ETH Zurich)
- AG Kraftwerk Wägital (AXPO and EWZ)

See also https://www.meteodat.ch/waegital.html

gcos

display_name: gcos

meteodat-gmbh

display_name: meteodat-gmbh

snow

display_name: snow

snow-water-equivalent

display_name: snow-water-equivalent

wagital

display_name: wagital

en: Snow water equivalent for reference date April 1 for Wägital catchment, starting 1943


weissfluhjoch-dataset-for-esm-snowmip

maintainer: Nander Wever

title_for_slug: weissfluhjoch-dataset-for-esm-snowmip

en: snow snow-depth esm-snowmip weissfluhjoch snow-models

en: Conversion script

en:
Use this script to convert WFJ_optimaldataset_v6.smet to wfj.smet, the file that is used for the ESM-SnowMIP project. It also requires the file: WFJ_AirPressure_Data.zip

Use: bash create_wfj_dataset.sh > input/wfj.smet

mimetype: text/x-sh

en: Dataset Weissfluhjoch for ESM-SnowMIP

en: ini file

en: This ini file is required by data-converter from MeteoIO version 1887 (http://models.slf.ch/p/meteoio/)

en: This Weissfluhjoch dataset is a processed version of the Weissfluhjoch dataset version 6 from https://doi.org/10.16904/6. This dataset was specially created for the ESM-SnowMIP project. Here it is documented how this dataset has been created.

esm-snowmip

display_name: esm-snowmip

snow-depth

display_name: snow-depth

snow-models

display_name: snow-models

weissfluhjoch

display_name: weissfluhjoch

educ

en: Education, culture and sport

en: Weissfluhjoch dataset for ESM-SnowMIP


maintainer: Achille Capelli

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

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

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: 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: 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 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 from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 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 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: 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 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: 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: 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 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: 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.

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: 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

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


mosaic-snow-on-sea-ice-data

title_for_slug: mosaic-snow-on-sea-ice-data

en: surface-mass-balance spatial-variability drifting-and-blowing-snow arctic-sea-ice snow-distribution

en: SNOWPACK setup for MOSAiC

en:
 The zip file contains both input and output data as well as the model configuration as used in the submitted manuscript "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model".

The zip file contains 2 main folders:
* SNOWPACK_JGR_ALPHA1
* SNOWPACK_JGR_ALPHA3

In the SNOWPACK_JGR_ALPHA1 (reference "SP_R" simulation) and SNOWPACK_JGR_ALPHA3 (comparison "SP_C" scenario) folders you find the corresponding inputs, outputs and configuration as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each SNOWPACK_JGR_ALPHA/setup/io.ini file. The input data is already linked accordingly in each io.ini file.

To reproduce the results, download and compile the source code for the adjusted SNOWPACK model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/snowpack.git under the "snowpack_mosaic" branch. After installing, you can run the provided model setup uploaded here.

en: ALPINE3D setup for MOSAiC

en:
The zip file contains a folder structure that must be kept as it is in order to run the simulation in the current configuration.
The setup contains both input and output data as well as the model configuration as used in the submitted manuscript
"Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model".

The zip file contains 3 main folders:
* base_setup_files
* a3d_jgr_alpha1
* a3d_jgr_alpha3

The "base_setup_files" contains all input files that are necessary to run the reference (R) simulation ("a3d_jgr_alpha1" folder) and the comparison "C" scenario ("a3d_jgr_alpha3") folder. In the a3d_jgr_alpha1 and a3d_jgr_alpha3 folders you find the corresponding outputs as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each a3d_jgr_alphax/setup/io.ini file. The input data is already linked accordingly in each io.ini file.

a3d_jgr_alpha1 also contains the detailed snow profiles for each point along the transects.

To reproduce the results, download and compile the source code for the adjusted ALPINE3D model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/alpine3d.git under the "alpine3d_mosaic" branch. After installing, you can run the provided model setup uploaded here.

en:
Data accompanying David Wagners' Dissertation.
Covers model results and various input from ALPINE3D and SNOWPACK adjusted for sea ice during MOSAiC.

arctic-sea-ice

display_name: arctic-sea-ice

drifting-and-blowing-snow

display_name: drifting-and-blowing-snow

snow-distribution

display_name: snow-distribution

spatial-variability

display_name: spatial-variability

surface-mass-balance

display_name: surface-mass-balance

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: MOSAIC Snow on Sea Ice Data


maintainer: Michael Lombardo

en: measurements transport snow water pore-water soil

en: Videos of the capillary rise experiments

en: Description of the videos

en: The Hydrus files used for the inverse fitting of the hydraulic conductivity.

en: Descriptions of the nomenclature, Hydrus files, and videos.

en: This dataset consists of data related to capillary rise experiments performed with neutron radiography. There are 4 videos of capillary rise experiments as well as the files used to perform the inverse fitting with Hydrus. The videos show the upward flow of water in glass columns filled with sand and snow or sand, gravel, and snow. The videos show the 2D evolution of the unitless optical density with time. The Hydrus files were used to fit the parameter values of the Mualem-van Genuchten model. The experiments were performed at the Paul Scherrer Institute (PSI) in Villigen, Switzerland.

measurements

display_name: measurements

pore-water

display_name: pore-water

soil

display_name: soil

transport

display_name: transport

water

display_name: water

en: Capillary rise rise experiments in snow using neutron radiography


en: norway-spruce climate-change drought european-beech douglas-fir

en: This file contains the growth data of the 8 plant species used in the Doch-WO experiment.

en: This data is related to the publication "Non-native Douglas fir seedlings outcompete native Norway spruce, silver fir and Scots pine under dry and nutrient-poor conditions". It contains growth and biomass data on Douglas fir seedlings in competition with three native conifers and four native broadleaf trees over three years und varying environmental conditions (i.e., water, nutrients and light availabilities).

climate-change

display_name: climate-change

douglas-fir

display_name: douglas-fir

drought

display_name: drought

european-beech

display_name: european-beech

norway-spruce

display_name: norway-spruce

en: Data on Douglas fir seedlings from the DOCH-WO experiment


snow-drift-station-micro-rain-radar

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

en: precipitation rain snow mountain radar raclets

en: MRR data of period 3

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

en: MRR data of period 4

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

en: MRR data Period 2

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

en: Parameter MRR

media_type: application/pdf

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

format: PDF

mimetype: application/pdf

en: MMR Data Period 1

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

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

mountain

display_name: mountain

precipitation

display_name: precipitation

raclets

display_name: raclets

radar

display_name: radar

rain

display_name: rain

snow

display_name: snow

en: Snow Drift Station - Micro Rain Radar


maintainer: Izabela Bujak-Ozga

en: drainage-network precipitation channel catchment water-level drainage catchment-data dynamics

en: The dataset consists of time series of measured surface and groundwater levels, calculated flowing drainage network lengths, and nearby precipitation data for two sub-catchments of the Haute-Mentue catchment, Switzerland (Summer-Autumn 2022). The details of the dataset are described in the provided documentation (short-term_drainage_density_dynamics_dataset_documentation_for_the_haute-mentue_catchment.pdf)

en: Documentation for the Short-term Drainage Density Dynamics Dataset for the Haute-Mentue Catchment (dataset_of_flowing_drainage_network_lengths_water_levels_and_precipitation_for_the_haute-mentue_catchment.csv). This document provides a detailed description of the collected data, the locations where it was recorded, the data format, and the curation process applied.

en: The dataset contains time series of water levels, precipitation measured in the two sub-catchments of the Haute-Mentue catchment and its vicinity during summer and autumn 2022, as well as flowing drainage network lengths calculated for these areas using the CEASE method developed by the authors. Detailed description of the dataset is provided in the documentation.

catchment

display_name: catchment

catchment-data

display_name: catchment-data

channel

display_name: channel

drainage

display_name: drainage

drainage-network

display_name: drainage-network

dynamics

display_name: dynamics

water-level

display_name: water-level

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: imis ultrasonic alpine snow monitoring snowmelt phenology grassland climate long-term

en: Is the readme that documents all data and code in this repository

format: OCTET

en: Contains IMIS station location data

en: Contains daily climate station data including daily median temperatures, snow depth, plant height, and annual melt-out timings.

en: Contains filtered annual phenological data from 40 IMIS stations suitable and used for phenological analysis

en: Snow3.png and grass.png are needed for plotting the methods plot in 5_methods_plots.Rmd

en: Temporary data. Contains annual unfiltered output of phenofit (i.e. the variable names are in line with the phenofit documentation), including all curve parametrization variables for plotting and additional variables, which are not necessarily used in the analysis.

en: Documents and reproduces the statistical models used in the analysis.

en: implements the phenofit method. Note: This script is computationally intensive and may take a while to run. We recommend using the temporary data created from it (i.e. starting from 2_temperature.R). For a better description of the phenofit R package and the different methods to determine the SOG, see Kong et al. (2022): https://doi.org/10.1111/2041-210X.13870

en: Contains unfiltered annual phenological, temperature and snow-climatic data for all IMIS stations.

en: Generates the methods-related figures used in the publication.

en: Contains annual phenological variables and phenofit outputs, sorted, ordered and renamed according with the readme in this repository

en: Contains custom functions required for the scripts; this file must be sourced before running other scripts.

en:
Long-term phenological data in alpine regions are often limited to a few locations and thus, little is known about climate-change induced plant phenological shifts above the treeline. Because plant growth initiation in seasonally snow-covered regions is largely driven by snowmelt timing and local temperature, it is essential to simultaneously track phenological shifts, snowmelt, and near-ground temperatures. In this study, we make use of ultrasonic snow height sensors installed at climate stations in the Swiss Alps to reveal phenological advance of grassland ecosystems and relate them to climatic changes over 25 years (1998 – 2023). When snow is absent, these snow height sensors additionally provide information on plant growth at a uniquely fine temporal scale. Daily temperatures and snow- / plant height values, annual phenological- and melt-out dates were derived from IMIS climate station data (doi.org/10.16904/envidat.406). We observed an advance of green-up by -2.4 days/decade coinciding with strong warming of up to +0.8°C/decade. Although the timing of melt-out has not changed significantly over the study period in this focal region, phenological responses to early melt-out years varied due to differing influences of photoperiodic and thermal constraints, which were not equally important across elevations and communities. Phenological shifts of alpine grasslands are thus likely to become even more pronounced if melt-out timing advances in the future as predicted.
This repository relates to the publication Zehnder et al. (2025). Snow height sensors reveal phenological advance in alpine grasslands. Global Change Biology. accepted. The model data and code that support our findings are deposited in this repository.

alpine

display_name: alpine

climate

display_name: climate

grassland

display_name: grassland

imis

display_name: imis

long-term

display_name: long-term

monitoring

display_name: monitoring

phenology

display_name: phenology

snowmelt

display_name: snowmelt

ultrasonic

display_name: ultrasonic

en: IMIS Phenology Data Since 1998


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

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: 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: SNOWPACK ini file (new syntax)

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

en: Version 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: Undercatch correction

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

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: SNOWPACK sno file

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

en: Version 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: Version 2

en:
Deprecated version. Release notes:

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

en: Version 1

en:
Deprecated version. Release notes:

First release of dataset.

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

envidat

en: EnviDat: the Environmental Data Portal

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


maintainer: Chunhong Zhao

en: land-dynamics urban-morphology austin landscape land-use

en: Land use dynamics in Austin since 1992

en: The present dataset is part of the published scientific paper Zhao C, Weng Q, Hersperger A M. Characterizing the 3-D urban morphology transformation to understand urban-form dynamics: a case study of Austin, Texas, USA. Landscape and urban planning, 2020, 203:103881. The overall objective of this paper is to understand urban form dynamics in the Austin metropolitan area for the periods 2006–2011 and 2011–2016. The study also aims to understand to what extent the changes in the built environment (in terms of ‘efficient growth’ versus ‘inefficient growth’) from the 1990s to 2016 in the Austin metropolitan area corresponded with ‘compact and efficient growth’ planning policy documents. The UMT distribution can be found in the paper. The area of transitioning UMT was provided in Table 2 and Table 3 can be found in the Appendix of the paper. A protocol was developed to perform the content analysis of the strategic plans and gather the data. The detailed list of protocol items can be found in Appendix B of the paper. This study demonstrates the advantage of applying Lidar data to characterize 3-D urban morphology type (UMT) transition and understand its dynamics, which helps develop a comprehensive understanding of the urbanization process and provides a tool for planning intentions and policies evaluation on urban development over time. The UMT maps can be found in Appendix A of the paper. The Lidar point datasets and the 30 × 30 m National Land Cover Database (NLCD) are the two main data sources of UMT mapping. Lidar datasets were gathered from different projects that had been conducted and collected by state agencies and other organizations between 2007 and 2017. Table A1 in the appendix in the paper shows the accuracies and acquisition parameters of the Lidar projects from 2007 to 2017. Land use/cover dynamics in Austin metropolitan area dataset provides Land use/cover patterns in the years 1992, 2001, 2004, 2006, 2008, 2011, 2013, 2016 with a spatial resolution of 30 meters. Since NLCD 1992 used a different classification system for the urban land classes, we first reclassified the NLCD 1992 using a customized Arcpy package.

austin

display_name: austin

land-dynamics

display_name: land-dynamics

land-use

display_name: land-use

landscape

display_name: landscape

urban-morphology

display_name: urban-morphology

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

en: Land use/cover dynamics in Austin metropolitan area since 1992


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

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)

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

en: RESTORE WP4_Questionnaire_French.pdf

en: Questionnaire in French

en: Study protocol of the survey

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: 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: 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 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)

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: RESTORE WP4_Questionnaire_Italian.pdf

en: Questionnaire in Italian

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: 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: 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:
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


maintainer: Brandon Vanschaik

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

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

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


maintainer: Frank Hagedorn

en: treeline-ecosystems climate-change soil-nutrients biomass-dynamics plant-traits plant-community

en: Data on soils, tree biomass, plant nutrients, rock mineralogy, climate of treeline ecosystems in Khibiny mountains, Kola Penninsula, Russia

en: Data on soil nutrients and charcteristics, tree and understory biomass, foliage nutrients, mineralogy, soil temperature and moisture along an elevation gradient from boreal forest to mountain tundra in teh Khibiny mountains (Kola Penninsula, Russia). Samples were taken along two gradients at 7 elevation level either under tree canopy (tree) and in open areas (open).

biomass-dynamics

display_name: biomass-dynamics

climate-change

display_name: climate-change

plant-community

display_name: plant-community

plant-traits

display_name: plant-traits

soil-nutrients

display_name: soil-nutrients

treeline-ecosystems

display_name: treeline-ecosystems


photogrammetric-drone-data-gruenbodeli

title_for_slug: photogrammetric-drone-data-gruenbodeli

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

en: Data Access

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

All products are also available as a cloud optimized geotiff.

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

drone-data

display_name: drone-data

dsm

display_name: dsm

orthophoto

display_name: orthophoto

photogrammetry

display_name: photogrammetry

snow-depth-mapping

display_name: snow-depth-mapping

uav

display_name: uav

wingtra

display_name: wingtra

en: Photogrammetric Drone Data Grüenbödeli


maintainer: Stephan Zimmermann

en: soil beech nutrient sustainability mass-balance

en: With this study, our aim was to estimate the nutrient fluxes relevant for assessing nutrient sustainability as accurately as possible and to calculate nutrient balances for alternative forest management scenarios. Furthermore, we tested whether mapping units from existing geologic maps can serve as a basis for forest practitioners to estimate nutrient sustainability or whether more detailed data is needed. Positive fluxes include deposition and weathering, while negative fluxes include losses due to leaching and nutrient removal through timber harvesting in the balance. Weathering and leachate losses were modeled with a geochemical model. The SwissStandSim model was used to simulate the biomass growth under different harvesting and silvicultural strategies, allowing sustainability to be assessed for each nutrient at a given intensity of use. This assessment was made per rotation period of 100 years based on two criteria: i) nutrient supply; and ii) total stocking volume.

beech

display_name: beech

mass-balance

display_name: mass-balance

nutrient

display_name: nutrient

soil

display_name: soil

sustainability

display_name: sustainability

en: Nutrient sustainability in beech forests


en: sea-ice snow-temperature snow-roughness drone antarctica irra snow uav

en:
This dataset accompanies the publication "How Flat is Flat? Investigating the spatial variability of snow surface temperature and roughness on landfast sea ice using UAVs in McMurdo Sound, Antarctica".
The dataset consists of 1) processed orthomosaic of RGB values, snow topography (digital elevation model), and surface temperature, 2) topography-dependent irradiance map, and 3) point data of measured radiation and atmospheric parameters in the McMurdo Sound, October-December 2022. The data was collected as part of the New Zealand Marsden Fund Research Grant 21-VUW-103 "Can Snow Change the Fate of Antarctic Sea Ice?". Unprocessed data associated with this field campaign are published in 10.16904/envidat.633.
For the airborne measurements, we used a DJI Matrice 30T, a multi-rotor (quadcopter) UAV with self-heating batteries, suitable for operations in polar conditions (temperature range from -20 to +50 °C). The UAV has a wide-angle camera (12 megapixels) for RGB images and a thermal camera (Uncooled Vox Microbolometer, long-wave infrared spectrum 8-14 µm, 1.31 megapixels) for TIR images with an accuracy of ±2 °C or ± 2 %.
GNSS data includes high-precision location information (latitude, longitude, ellipsoidal height, receiver clock offset, and timestamps formatted in decimal hours and day-of-year notation).
WE calculated the distribution of received solar irradiance over the sea-ice surface using a terrain-corrected radiative transfer model and measured shortwave radiation components, accounting for surface roughness, aspect, slope, and solar position during the flight, while applying terrain shading and measured diffuse radiation.
The broadband radiation station data consists of shortwave (Sw) and longwave (Lw) radiation measurements (Kipp & Zonen CMP22 pyranometers and CGR4 pyrgeometers).
Surface temperature readings from cold, sediment, and hot targets were recorded using Apogee SI-121-SS sensors.
This dataset also contains automated weather station (AWS) data (air and snow surface temperatures, relative humidity, wind direction, and wind speed).
For detailed information, please use the README file.

antarctica

display_name: antarctica

drone

display_name: drone

irra

display_name: irra

sea-ice

display_name: sea-ice

snow

display_name: snow

snow-roughness

display_name: snow-roughness

snow-temperature

display_name: snow-temperature

en: Snow On Antarctic Sea Ice - McMurdoSound 2022 - UAV Retrievals of Snow Topography and Snow Surface Temperature


maintainer: Sofia Pagliarin

en: urban-atlas copernicus milan imperviousness soil-sealing barcelona

en: Shareable project in Esri ArcGIS format including the HRL IMD data in raster format for each of the territorial boundaries as specified in letter a)-h), see Dataset description

en: Information on data sources

en:
Dataset description

This dataset is a recalculation of the Copernicus 2015 high resolution layer (HRL) of imperviousness density data (IMD) at different spatial/territorial scales for the case studies of Barcelona and Milan. The selected spatial/territorial scales are the following:

 * a)	Barcelona city boundaries
 * b)	Barcelona metropolitan area, Àrea Metropolitana de Barcelona (AMB)
 * c)	Barcelona greater city (Urban Atlas)
 * d)	Barcelona functional urban area (Urban Atlas)
 * e)	Milan city boundaries
 * f)	Milan metropolitan area, Piano Intercomunale Milanese (PIM)
 * g)	Milan greater city (Urban Atlas)
 * h)	Milan functional urban area (Urban Atlas)

In each of the spatial/territorial scales listed above, the number of 20x20mt cells corresponding to each of the 101 values of imperviousness (0-100% soil sealing: 0% means fully non-sealed area; 100% means fully sealed area) is provided, as well as the converted measure into squared kilometres (km2).

 Dataset composition

The dataset is provided in .csv format and is composed of:

_IMD15_BCN_MI_Sources.csv_: Information on data sources

 _IMD15_BCN.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Barcelona:
 * a)	Barcelona city boundaries (label: bcn_city)
 * b)	Barcelona metropolitan area, Àrea metropolitana de Barcelona (AMB) (label: bcn_amb)
 * c)	Barcelona greater city (Urban Atlas) (label: bcn_grc)
 * d)	Barcelona functional urban area (Urban Atlas) (label: bcn_fua)

 _IMD15_MI.csv_: This file refers to the 2015 high resolution layer of imperviousness density (IMD) for the selected territorial/spatial scales in Milan:
 * e)	Milan city boundaries (label: mi_city)
 * f)	Milan metropolitan area, Piano intercomunale milanese (PIM) (label: mi_pim)
 * g)	Milan greater city (Urban Atlas) (label: mi_grc)
 * h)	Milan functional urban area (Urban Atlas) (label: mi_fua)

_IMD15_BCN_MI.mpk_: the shareable project in Esri ArcGIS format including the HRL IMD data in raster format for each of the territorial boundaries as specified in letter a)-h).

Regarding the territorial scale as per letter f), the list of municipalities included in the Milan metropolitan area in 2016 was provided to me in 2016 from a person working at the PIM.

In the IMD15_BCN.csv and IMD15_MI.csv, the following columns are included:

 * Level: the territorial level as defined above (a)-d) for Barcelona and e)-h) for Milan);
 * Value: the 101 values of imperviousness density expressed as a percentage of soil sealing (0-100%: 0% means fully non-sealed area; 100% means fully sealed area);
 * Count: the number of 20x20mt cells corresponding to a certain percentage of soil sealing or imperviousness;
 * Km2: the conversion of the 20x20mt cells into squared kilometres (km2) to facilitate the use of the dataset.

 Further information on the Dataset

This dataset is the result of a combination between different databases of different types and that have been downloaded from different sources. Below, I describe the main steps in data management that resulted in the production of the dataset in an Esri ArcGIS (ArcMap, Version 10.7) project.

 1. The high resolution layer (HRL) of the imperviousness density data (IMD) for 2015 has been downloaded from the official website of Copernicus. At the time of producing the dataset (April/May 2021), the 2018 version of the IMD HRL database was not yet validated, so the 2015 version was chosen instead. The type of this dataset is raster.

 2. For both Barcelona and Milan, shapefiles of their administrative boundaries have been downloaded from official sources, i.e. the ISTAT (Italian National Statistical Institute) and the ICGC (Catalan Institute for Cartography and Geology). These files have been reprojected to match the IMD HRL projection, i.e. ETRS 1989 LAEA.

 3. Urban Atlas (UA) boundaries for the Greater Cities (GRC) and Functional Urban Areas (FUA) of Barcelona and Milan have been checked and reconstructed in Esri ArcGIS from the administrative boundaries files by using a Eurostat correspondence table. This is because at the time of the dataset creation (April/May 2021), the 2018 Urban Atlas shapefiles for these two cities were not fully updated or validated on the Copernicus Urban Atlas website. Therefore, I had to re-create the GRC and FUA boundaries by using the Eurostat correspondence table as an alternative (but still official) data source. The use of the Eurostat correspondence table with the codes and names of municipalities was also useful to detect discrepancies, basically stemming from changes in municipality names and codes and that created inconsistent spatial features. When detected, these discrepancies have been checked with the ISTAT and ICGC offices in charge of producing Urban Atlas data before the final GRC and FUA boundaries were defined.

Steps 2) and 3) were the most time consuming, because they required other tools to be used in Esri ArcGIS, like spatial joins and geoprocessing tools for shapefiles (in particular dissolve and area re-calculator in editing sessions) for each of the spatial/territorial scales as indicated in letters a)-h).

Once the databases for both Barcelona and Milan as described in points 2) and 3) were ready (uploaded in Esri ArcGIS, reprojected and their correctness checked), they have been ‘crossed’ (i.e. clipped) with the IMD HRL as described in point 1) and a specific raster for each territorial level has been calculated. The procedure in Esri ArcGIS was the following:

 * Clipping: Arctoolbox > Data management tools > Raster > Raster Processing > Clip. The ‘input’ file is the HRL IMD raster file as described in point 1) and the ‘output’ file is each of the spatial/territorial files. The option "Use Input Features for Clipping Geometry (optional)” was selected for each of the clipping.
 * Delete and create raster attribute table: Once the clipping has been done, the raster has to be recalculated first through Arctoolbox > Data management tools > Raster > Raster properties > Delete Raster Attribute Table and then through Arctoolbox > Data management tools > Raster > Raster properties > Build Raster Attribute Table; the "overwrite" option has been selected.

 Other tools used for the raster files in Esri ArcGIS have been the spatial analyst tools (in particular, Zonal > Zonal Statistics). As an additional check, the colour scheme of each of the newly created raster for each of the spatial/territorial attributes as per letters a)-h) above has been changed to check the consistency of its overlay with the original HRL IMD file. However, a perfect match between the shapefiles as per letters a)-h) and the raster files could not be achieved since the raster files are composed of 20x20mt cells.

The newly created attribute tables of each of the raster files have been exported and saved as .txt files. These .txt files have then been copied in the excel corresponding to the final published dataset.

barcelona

display_name: barcelona

copernicus

display_name: copernicus

imperviousness

display_name: imperviousness

milan

display_name: milan

soil-sealing

display_name: soil-sealing

urban-atlas

display_name: urban-atlas

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Soil sealing Barcelona and Milan different territorial levels


en: switzerland nuclear-microsatellite-markers trichopria-drosophilae simple-sequence-repeats biological-control multilocus-genotypes population-genetics drosophila-suzukii

en: Multilocus genotypes (biallelic coding for 21 nuclear microsatellites) of Trichopria drosophilae described in Gugerli et al. Agrarforschung Schweiz 2019. Missing data denoted as '999'. Population codes (PopNo) are the following: 1, Vezia; 3, Contone ; 5, Davesco; 6, Corteglia; 7, Mezzana; 8, Stabio; 9, Gordola; 10, Sementina; 12, Brusata di Novazzano; 14, Monteggio; 16, Giubiasco; 17, ComoBP; 18, VD; 19, Weil_D; 20, Dossenheim; 21, BioPlanet_Como. Coordinates (CoordCH_E, CoordCH_N) refer to the Swiss coordinates system CH1903.

en: Table comprising all pertinent information for multiplex PCRs of species-specific nuclear microsatellite makers for Trichopria drosophilae, comprising all 21 newly developed markers, details on full sequences with GenBank accession numbers, primer sequences (including labeling and pig-tails), and repeat motives. PCRs with respective annealing temperatures (AnnealingTemp) refer to Type-It Multiplex PCR Kit (Qiagen, Hilden, Germany).

en:
 Nuclear microsatellite markers and genotype data for _Trichopria ddrosophilae_

This data set comprises (i) the characteristics of a set of 21 species-specific nuclear microsatellites for PCR amplification in _Trichopria drosophilae_ (ii) and genotype data for samples collected in southern Switzerland (Canton of Ticino), with few reference samples from Canton of Vaud, southern Germany, and northern Italy (lab-reared population).
Markers were developed by Ecogenics GmbH, Balgach (Switzerland), using MiSeq Nano 2x250 v2 format (on a mix of 10 individuals). Multiplex PCR assays for multilocus genotyping were established by Ecological Genetics (WSL Birmensdorf), and population genetic analyses are found in Gugerli et al., Agrarforschung Schweiz 2019.

biological-control

display_name: biological-control

drosophila-suzukii

display_name: drosophila-suzukii

multilocus-genotypes

display_name: multilocus-genotypes

nuclear-microsatellite-markers

display_name: nuclear-microsatellite-markers

population-genetics

display_name: population-genetics

simple-sequence-repeats

display_name: simple-sequence-repeats

switzerland

display_name: switzerland

trichopria-drosophilae

display_name: trichopria-drosophilae

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: Nuclear microsatellite markers for Trichopria drosophilae, parasitoid wasp on Drosophila suzukii


en: avalanche-fatalities avalanche-accidents number-of-caught-persons avalanche-accident-statistics number-of-dead-persons

en: Fatal avalanche accidents in Switzerland since 1995-1996

en:
Attention: this data is not updated after 2022 anymore.
This data collection contains information concerning all accidents by snow avalanches causing at least one fatality in Switzerland. The data set commences on 01/10/1995. After the completion of a hydrological year, the new data is added.
The following information is provided:
 * avalanche identifier
 * date of the accident
 * accuracy of the date in range of days before and after
 * canton
 * name of the locality
 * start zone of the avalanche
  * coordinates (Swiss coordinate system, approximately in middle of start zone)
  * accuracy of the coordinates in meters
 * elevation (in meteres above sea level, app. in middle of start zone)
 * slope aspect (main orientation of start zone)
 * slope inclination (in degree, steepest point within start zone)
 * number of dead persons
 * number of caught persons
 * number of fully buried persons
 * forecasted avalanche danger level
 * activity/location of the accident party at the time of the incident

avalanche-accident-statistics

display_name: avalanche-accident-statistics

avalanche-accidents

display_name: avalanche-accidents

avalanche-fatalities

display_name: avalanche-fatalities

number-of-caught-persons

display_name: number-of-caught-persons

number-of-dead-persons

display_name: number-of-dead-persons


en: isotopes subalpine picea-abies belowground-biomass afforestation

en: This data set provides soil organic carbon, total nitrogen, and stable carbon isotope (13C) values of mineral soil (0-45cm), organic horizons (Oi, Oe, Oa), and root biomass of an afforestation sequence (0-130years) on a subalpine pasture in Jaun, Switzerland.

afforestation

display_name: afforestation

belowground-biomass

display_name: belowground-biomass

isotopes

display_name: isotopes

picea-abies

display_name: picea-abies

subalpine

display_name: subalpine

en: Soil organic carbon stock of an afforestation sequence on a subalpine pasture