en: plant-species photo vascular-plants species-observations testing

en:
Zipped directory containing approximately 5'600 photographs of vascular plants from the Swiss flora, taken at 153 sampling locations. Sampling locations were assigned a TypoCH habitat type.
Folder structure:
 - Each folder represents a plant population, defined by the same species, sampling date, and location.
 - Higher-level directories organize populations observed within the same sampling plot.
Naming convention:
Each image is names img_<index>_<plant organ>.jpg whereby <index> is the number of the photograph within the folder. For each observed population, images have consecutive integers as index. <plant organ> is a single letter indicating the depicted part(s) of the plant:
b = bark
i = inflorescense
f = infructescense
s = several parts
v = vegetative parts

en:
This data set contains roughly 5600 photographs of plants and plant parts from 153 sites recorded in all major biogeographic regions of Switzerland using smartphone cameras. Image names contain a letter referring to depicted plant parts: b = bark, i = inflorescence, f = infructescense, s = several parts, v = vegetative parts.
Photos are grouped in folders by observation (individual or population), i.e., photos in the same folder are of the same species, observed at the same time and sampling plot. At a higher organisational level, these folders are grouped by sampling plot (usually 3×3 m in open areas and 10×10 m in forests). The main folder contains metadata including taxon name, sampling time and location, as well as the TypoCH habitat type encoded as integers.

photo

display_name: photo

plant-species

display_name: plant-species

species-observations

display_name: species-observations

testing

display_name: testing

vascular-plants

display_name: vascular-plants

en: Photos of vegetative and reproductive parts of 560 vascular plant species taken in all biogeographic regions of Switzerland


maintainer: EnviDat Support

en: topsoil-removal soil-biodiversity bacteria biodiversity grassland soil nematodes soil-biology soil-chemistry fungi plants restoration

state: active

type: dataset

en: Fungi data

en:
Sample.data
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)

Taxonomy
OTU.ID	consecutive numbers for operational taxonomic units (OTUe 1-7794)
Taxonomic rank	Domain, Phylum, Class, Order, Family, Genus, Species

Abundance
columns	Plot.ID (see Sample.data)
rows	OTU.ID (see Taxonomy)

format: XLS

en: Vegetation data

en:
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)
column I-LW	Plant abundance per species (in total 327 plant species) expressed as cover percentage estimation according to van der Maarel (vdM 1979; 7 classes: 1-7); nomenclature according to Lauber & Wagner (1996)

en: Distance to target results

en:
Community	biotic community (vegetation, nematoda, fungi, prokaryota)
Treatment	Treatment groups; Y.4-30= restored grasslands (time since restoration); Initial=intensively managed grasslands (I)
Comparison	within- or between-community Bray-Curtis dissimilarity distance comparison
Label	within-community distances of initial/restored grasslands: treatment abbreviation; between-communities distances of initial/restored-to-target grasslands: treatment abbreviation plus Target (T; species-rich grasslands)
mean	Mean distance to Target (6 plots per treatment group)
sd	Standard deviation of distances to Target (6 plots per treatment group)

en: Ecosystem coupling_interactions

en:
Treatment	Treatment groups; Y.4-18 = restored grasslands; Initial = intensively managed grasslands; Target = species-rich semi-wet to semi-dry grasslands
Type	Interactions type; A.A = abiotic.abiotic; A.B = abiotic.biotic; B.B = biotic.biotic
Correlation	non-parametric Spearman rank correlations between biotic and abiotic parameters (ecological interactions)

en: Soil properties

en:
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)
N.pool	Soil nitrogen pool (June 2017); corrected for bulk density and 12 cm sampling depth [kg ha-1 12cm-1]
Corg.pool	Soil organic carbon pool (June 2017); corrected for bulk density and 12 cm sampling depth [kg ha-1 12cm-1]
pH	soil pH [CaCl2]
BD	Bulk density [g cm-3]
FC	Field capacity [%]
Veg.1	principle coordinate axis 1 (vegetation)
Veg.1	principle coordinate axis 2 (vegetation)
Nem.1	principle coordinate axis 1 (nematoda)
Nem.2	principle coordinate axis 2 (nematoda)
Fun.1	principle coordinate axis 1 (fungi)
Fun.2	principle coordinate axis 2 (fungi)
Prok.1	principle coordinate axis 1 (prokaryota)
Prok.2	principle coordinate axis 2 (prokaryota)
Veg.H	Shannon diversity (vegetation)
Nem.H	Shannon diversity (nematoda)
Fun.H	Shannon diversity (fungi)
Prok.H	Shannon diversity (prokaryota)

en: Nematode data

en:
Sample.data
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)

Taxonomy
ASV.ID	unit number.letter code per amplicon sequence variant (in total 904)
Taxonomic rank	Phylum, Class, Order, Family, Genus, Species

Abundance
columns	Plot.ID (see Sample.data)
rows	ASV.ID (see Taxonomy)

en: Networks

en:
Treatment	Treatment groups; Y.4-18 = restored grasslands; Initial = intensively managed grasslands; Target = species-rich semi-wet to semi-dry grasslands
Correlation	non-parametric Spearman rank correlations
Vertex.1+2	Comparison between network components; N.pool = total soil nitrogen pool [kg ha-1 12 cm-1]; Corg.pool = soil organic carbon pool [kg ha-1 12 cm-1], pH = soil pH [CaCl2]; BD = bulk density [g cm-3]; FC = field capacity [%]
	PCoA 1 + 2 biotic communities [Veg = vegetation; Nem = nematoda; Fun = fungi; Prok = prokaryota]
Type	Interactions type; A.A = abiotic.abiotic; A.B = abiotic.biotic; B.B = biotic.biotic

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

Paper Citation:

_Resch, M.C., Schütz, M., Ochoa-Hueso, R., Buchmann, N., Frey, B., Graf, U., van der Putten, W.H., Zimmermann, S., Risch, A.C. (in review). Long-term recovery of above- and belowground interactions in restored grassland after topsoil removal and seed addition. Journal of Applied Ecology_

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

Study area and experimental design
The study was conducted in and around two nature reserves, Eigental and Altläufe der Glatt, which were located approximately 5 km apart (47°27´ to 47°29´ N, 8°37´ to 8°32´ E, 417 to 572 m a.s.l., Canton of Zurich, Switzerland; Figure S1 and S2, Table S1). Mean annual temperature and precipitation are 9.8 ± 0.6 °C and 990 ± 168 mm (Kloten climate station 1988-2018; MeteoSchweiz, 2019). TFor this study, we used a space-for-time approach based on eight restoration sites that were between 3 and 32 years old. We measured recovery and restoration success by comparing the restored grasslands with intensively managed and semi-natural grasslands. Using a space-for-time approach requires high similarities in historical properties of the site, such as soil conditions and management regimes, to assure that temporal processes are appropriately represented by spatial patterns (Walker et al., 2010). This was the case in our study. The restored sites had similar soil conditions (i.e., soil type, structure, water availability) as the targeted semi-natural grasslands, while they shared the same agricultural legacy with intensively managed grasslands, i.e., biomass harvest and fertilization (manure and/or slurry) three to five times a year as well as tillage. We randomly established three 5 m x 5 m (25-m2) plots for plant identification and three 2 m x 2 m (4-m2) subplots for soil biotic and abiotic data collection at least 2 m away from the 25-m2 plots in each restoration site. Sites of similar age were grouped into four age classes: Y.4 (3 & 4 years after restoration), Y.18 (17 & 19 years), Y.24 (23 & 25 years), and Y.30 (27 & 32 years). Six intensively managed (Initial) and six semi-natural grassland (Target) sites complemented the experimental set-up, for a total of 36 plots. All plots were sampled under similar conditions, i.e., day of the year, air temperature, soil moisture, and time since last rain event, in June/July 2017 (intensively managed and semi-natural plots) and 2018 (restored plots).

Collection of plants and selected soil biota data
Plant species cover (in %) was visually estimated in each 25-m2 plot in mid-June (Braun-Blanquet, 1964; nomenclature: Lauber & Wagner, 1996). We calculated Shannon diversity and assessed plant community structure. We included soil microbial (fungi, procaryotes) and nematodes in our study as they represent the majority of soil biotic diversity and abundance (Bardgett & van der Putten, 2014), cover various trophic levels of the soil food web (Bongers & Ferris, 1999), and play key roles in soil functioning and ecosystem processes (Bardgett & van der Putten, 2014). In particular, soil nematodes were found to be well suited belowground indicators to evaluate recovery/development after restoration (e.g. Frouz, et al. 2008; Kardol et al., 2009; Resch et al., 2019). We randomly collected ten soil cores (2.2 cm diameter x 12 cm depths; sampler from Giddings Machine Company, Windsor, USA) in the 4-m2 subplots to assess soil nematode and microbial (fungal, prokaryotic) diversities and community structures. For soil nematodes, eight of the soil cores were combined and gently homogenized, placed in coolers and stored at 4 °C and transported to the laboratory (Netherlands Institute of Ecology, NIOO, Wageningen, Netherlands) within three days after collection. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriators (Oostenbrink, 1960). After extraction, each sample was divided into three subsamples, two for molecular identification and one to determine nematode abundance (see Resch et al., 2019). For the molecular work, two subsamples were stored in 70% ethanol (final volume 10 mL each) and transported to the laboratory at the Swiss Federal Research Institute WSL (Birmensdorf, Switzerland). Each subsample was reduced to roughly 200 μL by centrifugation and removal of the supernatant. The remaining ethanol was vaporized (65 °C for 3 h). Thereafter, 180 μL ATL buffer solution (Qiagen, Hilden, Germany) was immediately added and samples were stored at 4 °C until further processing. From these samples, nematode metagenomic DNA was extracted using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) according to the manufacturer`s protocol, except for the incubation step which was run at 56 °C for 4 h. PCR amplification of the V6-V8 region of the eukaryotic small-subunit (18S) was performed with 7.5 μL of genomic DNA template (ca. 1 ng/μL) in 25 μL reactions containing 5 μL PCR reaction buffer, 2.5 mM MgCL2, 0.2 mM dNTPs, 0.8 μM of each primer (NemF: Sapkota & Nicolaisen, 2015; 18Sr2b: Porazinska et al., 2009), 0.5 μL BSA, and 0.25 μL GoTaq G2 Hot Start Polymerase (Promega Corporation, Madison, USA). Amplification was using an initial DNA denaturation step of 95 °C for 2 min, followed by 35 cycles at 94 °C for 40 sec, 58 °C for 40 sec, 72 °C for 1 min, and a final elongation step at 72 °C for 10 min. Filtering, dereplication, sample inference, chimera identification, and merging of paired-end reads was implemented using the DADA2 pipeline (v.1.12; Callahan et al., 2016) to finally assign amplicon sequence variants (ASVs) as taxonomic units.
We combined and homogenized the remaining two soil cores to assess soil microbes, placed them in coolers (4 °C) and transported them to the laboratory at WSL. Metagenomic DNA was extracted from 8 g sieved soil (2 mm) using the DNAeasy PowerMax Soil Kit (Qiagen, Hilden, Germany) according to the manufacturer´s protocol. PCR amplification of the V3-V4 region of the small-subunit (16S) of prokaryotes (i.e., bacteria and archaea) and the ribosomal internal transcribed spacer region (ITS2) of fungi was performed with 1 ng of template DNA using PCR primers and conditions as previously described (Frey et al., 2016). PCRs were run in triplicates, pooled and 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, USA). Quality filtering, clustering into operational taxonomic units (OTUs, 97% similarity cutoffs) and taxonomic assignment were performed as previously described (Resch et al., 2021).Taxonomic classification of nematode, prokaryotic and fungal sequences was conducted querying against the most recent versions of PR2 (v.4.11.1; Guillou et al., 2013), SILVA (v.132; Quast et al., 2013), and UNITE (v.8; Nilsson et al., 2019) reference sequence databases. Taxonomic assignment cutoffs were set to confidence rankings ≥ 0.8 (below ranked as unclassified). Prokaryotic OTUs assigned to mitochondria or chloroplasts as well as OTUs or ASVs assigned to other than Fungi or Nematoda were manually removed prior to data analysis. The three datasets were filtered to discard singletons and doubletons. Taxonomic abundance matrices were rarefied to the lowest number of sequences per community to achieve parity of the total number of reads between samples (Prokaryotes: 10,929 reads; Fungi: 18,337 reads; Nematodes: 6,662 reads). We calculated Shannon diversity and assessed community structures for soil nematodes, prokaryotes and fungi based on their relative abundances of ASV or OTU at the taxon level.

Collection of soil physical and chemical properties
We randomly collected one undisturbed soil core (5 cm diameter, 12 cm depth) per 4-m2 subplot using a steel cylinder that fit into the soil corer. The cylinders were capped to avoid disturbance during transport and used to measure field capacity, rock content and fine earth density as previously described (Resch et al., 2021). We randomly collected another three soil cores (5 cm diameter, 12 cm depths) in each 4-m2 subplot to determine soil chemical properties. The cores were pooled, dried at 60 °C for 48 h and passed through a 2 mm sieve. We measured soil pH (CaCl2) on dried samples, total nitrogen (N) and organic carbon (C) concentration on dried and fine-ground samples (≤ 0.5 mm; for details see Resch et al., 2021). We calculated total N and organic C pools after correcting its concentration for soil depth, rock content and fine earth density.

bacteria

display_name: bacteria

biodiversity

display_name: biodiversity

fungi

display_name: fungi

grassland

display_name: grassland

nematodes

display_name: nematodes

plants

display_name: plants

restoration

display_name: restoration

soil

display_name: soil

soil-biodiversity

display_name: soil-biodiversity

soil-biology

display_name: soil-biology

soil-chemistry

display_name: soil-chemistry

topsoil-removal

display_name: topsoil-removal

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Long-term recovery of above-and belowground interactions in restored grasslands


greenland-climate-network-gc-net-data

title_for_slug: greenland-climate-network-gc-net-data

en: greenland automatic-weather-station net-radiation aws climate air-temperature

en: 22-Petermann ELA

format: SERVICE

en: GC Net Config for Stations

media_type: application/json

en: Stations Config from the static bucket

format: JSON

en: GC Net Chart Parameters

en: Stations parameters from static bucket

en: Access the latest L1 data

en:
The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).

Starting October 3, 2022, the access to the latest versions of the "ready to use" L1 data has been migrated to GEUS, (doi:10.22008/FK2/VVXGUT). Future versions will be available from GEUS, while the initial version of L1 data has been archived.

en: Archived GC-Net Data (deprecated)

en:
The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).

Starting October 3, 2022, the access to the latest versions of the "ready to use" L1 data has been migrated to GEUS, (doi:10.22008/FK2/VVXGUT). Future versions will be available from GEUS, while the initial versions of L0 and L1 data have been archived.

en:
In Memory of Dr. Konrad (Koni) Steffen

Update October 2022: The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).
Starting October 3, 2022, the access to the latest versions of the "ready to use" L1 data has been migrated to GEUS. Future data versions will be available at: [https://doi.org/10.22008/FK2/VVXGUT](https://doi.org/10.22008/FK2/VVXGUT)
 Background
Starting with a single station in 1991, the Greenland Climate Network (commonly known as GC-Net) is a set of Automatic Weather Stations (AWS) set up and managed by the late Prof. Dr. Konrad (Koni) Steffen, and spanning the Greenland Ice Sheet (GrIS). This first station was "Swiss Camp" or the "ETH-CU" camp (GC-Net station 01) which was used as a field science and education site by Koni for years. The GC-Net was expanded with multiple NASA, NOAA, and NSF grants throughout the years, and then supported by WSL in the later years. These data (see "C-file" below) were previously hosted by the Cooperative Institute for Research in Environmental Sciences (CIRES) in Boulder, Colorado.

 Overview
Provided in this dataset are the 16 longest running stations in the network, which are spread over a significant area of the GrIS and the majority of the unique climatic zones. From the South Dome high point in the South, to the Western Jakobshavn ablation region in the west, to the Petermann glacier in the North across east of the Northeast Greenland Ice Stream to the east, GC-Net is the longest running climatological record of Greenland.

 The standard GC-Net station consists of:
* Air temperature measurements at 2 heights above the surface
* Temperature and humidity measurements at 2 heights above the surface
* Wind speed and direction measured at 2 heights above the surface
* Sonic distance sounder measurements for 2 snow height and distance of instruments to surface
* Incoming shortwave radiation measurement
* Reflected shortwave radiation measurement
* Net broadband radiation (long- and short-wave) measurement
* Air pressure measurement
Data have often been repatriated in near-real time using one of either the GOES geostationary satellite or the ARGOS polar orbiting satellite transmission system. The stations were visited typically every 1-2 years for maintenance and service, and to download full uncorrupted data directly from the dataloggers. GC-Net stations were visited by Twin Otter equipped with snow skids to land directly on the open-ice at the AWS locations, or by helicopter near the west coast. The AWSs operate on solar and battery power and occasionally lost power during the dark and cold winter months, particularly when the batteries were aging.

 Dataset
This dataset consists of 2 main data levels; Level 0 and Level 1.
Level 0 is the raw data from the dataloggers, historical processing codes, satellite transmissions, and Koni’s personal data archive. Level 0 data (.zip) directories contain subdirectories:
* “C file” - contains the historical processed datafile for each station.
* “Campbell logger files” - contains the raw csv datafiles from the stations’ Campbell Scientific dataloggers since the CR1000 era (~2007-2008 for most stations).
* “Photos” - contains photographs of the station when available marked by year.
Level 1 is the appended, calibrated, cleaned, and quality flagged data. The full processing scheme is open-source and publicly available on the following GitHub repository (please also check GitHub for the latest L1 data):
[GC-Net L1 data on GitHub](https://github.com/GEUS-Glaciology-and-Climate/GC-Net-level-1-data-processing "GC-Net-level-1-data-processing")
Level 1 data is provided in the newly described csv-compatible [NEAD format](https://www.envidat.ch/#/metadata/nead "NEAD format").

 Additional Details
Dataset description publication will be forthcoming.
The Geological Survey of Denmark and Greenland (GEUS) has been imperative in the reprocessing and continuity mission of GC-Net. Multiple GC-Net stations have been replaced with updated and upgraded AWS hardware at the same coordinates by GEUS. This effort will ensure continuity of the GC-Net dataset into the future.

air-temperature

display_name: air-temperature

automatic-weather-station

display_name: automatic-weather-station

aws

display_name: aws

climate

display_name: climate

greenland

display_name: greenland

net-radiation

display_name: net-radiation

en: Greenland Climate Network (GC-Net) Data


maintainer: Jana von Freyberg

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

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

media_type: application/pdf

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

format: PDF

mimetype: application/pdf

en: Data set of MEE-20-04-264 (CSV)

media_type: text/csv

format: CSV

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

ecohydrology

display_name: ecohydrology

endmember-mixing-models

display_name: endmember-mixing-models

plant-water-uptake

display_name: plant-water-uptake

review

display_name: review

root-zone

display_name: root-zone

stable-water-isotopes

display_name: stable-water-isotopes

uncertainties

display_name: uncertainties

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


en: measurements soil transport pore-water snow water

media_type: application/zip

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

format: ZIP

media_type: text/plain

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

format: TXT

en: Videos of the capillary rise experiments

en: Description of the 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

snow

display_name: snow

soil

display_name: soil

transport

display_name: transport

water

display_name: water

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: Capillary rise rise experiments in snow using neutron radiography


en: avalanche climate-change dry-snow-avalanche wet-snow-avalanche climate-projections snow-stratigraphy snow snow-instability

en: Climate scenarios .smet

en: contains one .smet file per combination of station, climate model chain and RCP scenario. Each .smet file contains the downscaled meteorological data (time span 1.9.1981-31.8.2099) that was used to drive the SNOWPACK simulations.

en: SNOWPACK settings

en: contains .ini and .sno file indicating the settings used to drive SNOWPACK with the climate scenarios

en: Projections of avalanche activity

en: contains one file per IMIS station that indicates the daily probability for the occurrence of dry- or wet-snow avalanches in the vicinity of the respective station for each day during the time period 1.12.1981-31.05.2099 (months December to May) and all climate model chains and RCP scenarios.

en:
This data set includes 21st century projections of dry- and wet-snow avalanche activity for seven sites in the Swiss Alps as well as the downscaled climate scenarios and SNOWPACK configuration files used to produce these projections. The methods used to produce these data are described in the research article:
Mayer, S., Hendrick, M., Michel, A., Richter, B., Schweizer, J., Wernli, H., and van Herwijnen, A.: Changes in snow avalanche activity in response to climate warming in the Swiss Alps, EGUsphere, 2024, 1-32, https://doi.org/10.5194/egusphere-2024-1026, 2024.
We used downscaled climate projections to drive the snow cover model SNOWPACK and evaluated future dry- and wet-snow avalanche occurrences in the vicinity of seven automatic weather stations from the IMIS network using machine learning models trained with observed records of avalanche activity. Projections are available for three different emission scenarios (Representative Concentration Pathways, RCP2.6, RCP4.5 and RCP8.5) and eight climate model chains from the CH2018 ensemble. The output of the model chain is given by a daily probability for dry- or wet-snow avalanche occurrences. These probabilities can then be used to classify a day as a dry- and wet-snow avalanche day (AvD) or non-avalanche day.

avalanche

display_name: avalanche

climate-change

display_name: climate-change

climate-projections

display_name: climate-projections

dry-snow-avalanche

display_name: dry-snow-avalanche

snow-instability

display_name: snow-instability

snow-stratigraphy

display_name: snow-stratigraphy

wet-snow-avalanche

display_name: wet-snow-avalanche


en: precipitation geochemical-solutes stream-water stable-water-isotopes spatially-distributed-sampling

en: The dataset contains daily measurements of volumes, isotopes and solutes of stream water as well as precipitation measured several locations; meteorological variables are added. Date: 18.07.2019

en:
This dataset contain measurements of solute and stable water isotopes in stream water and precipitation in the Alp catchment and two of its tributaries (between 2015 -2018) . The river Alp is a snow-dominated catchment situated in Central Switzerland characterized by an elevation range from 840 to 1898 m a.s.l. The dataset provides solutes (major anions and cations, trace metals) and stable water isotopes and water fluxes (precipitation rates, discharge) at daily intervals from several sampling locations.
An updated version of the isotope dataset is available here: https://www.doi.org/10.16904/envidat.242

geochemical-solutes

display_name: geochemical-solutes

precipitation

display_name: precipitation

spatially-distributed-sampling

display_name: spatially-distributed-sampling

stream-water

display_name: stream-water

en: Daily data of solute and stable water isotopes in stream water and precipitation in the Alp catchment, Central Switzerland


maintainer: Francesco Comola

en: snow sand wind sediment saltation cohesion

en: Output of the model simulations

en: Model results on granular splash and saltation process of sand and snow particles

mimetype: application/zip

en:
The wind-driven saltation of sand and snow shapes dunes and ripples, generates dust emission, and erodes the surface of the Antarctic ice sheet. Here, we use a model based on the discrete element method to simulate grain-flow interactions and study the effect of particle cohesion on saltation dynamics.

The data contains the model output of granular splash simulations and saltation simulations. Granular splash, the main particle entrainment process in saltation, occurs upon impact of saltating particles with the granular bed. We performed Monte Carlo simulations of granular splash for loose sand grains and for cohesive ice grains. The analysis indicate that different values of cohesion have significant effects not on the number of splashed grains, on the ejection velocity, and the rebound velocity.

In our saltation simulations, we trigger particle movement with a single splash event at the inlet section section and let the system evolve until steady state. Our results show that saltation over cohesive surfaces is difficult to initiate but easy to sustain at low wind speed. The occurrence of transport thus depends on the history of the wind speed, a phenomenon known as hysteresis. We also show that saltation over cohesive surfaces presents higher mass fluxes but requires longer distances to saturate, which increases the size of the smallest stable surface ripples. Our model results have implications for large-scale aeolian processes on Earth and Titan, where sand grains are thought to be very cohesive.

cohesion

display_name: cohesion

saltation

display_name: saltation

sand

display_name: sand

sediment

display_name: sediment

wind

display_name: wind

en: Saltation of cohesive granular materials


data-interactive-avalanche-segmentation

title_for_slug: data-interactive-avalanche-segmentation

en: snow deep-learning segmentation interactive avalanche

en:
Images and corresponding avalanche annotations used for interactive avalanche segmentation:
SLF train, vali and test
WebNew
GroundPic
UserPic
UIBK dataset: List of images used
Pictures are from SLF (mostly from the Dischma Cams) if not otherwise indicated. If used, the data needs to be properly cited!

deep-learning

display_name: deep-learning

interactive

display_name: interactive

segmentation

display_name: segmentation

snow

display_name: snow

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

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

en: Data interactive avalanche segmentation


en: landscape-planning urban-planning urban-landscape land-change concur

en:
Book of abstracts from the virtual conference "From Plans to Land Change: Dynamics of Urban Regions"

Cities and urban regions are among the most dynamic land-use systems in the world, with dramatic consequences for the provision of ecosystem services and the livelihood of people. Planning is a multifaceted activity with extensive experience in the management of these urbanization processes. However, our understanding of planning’s contribution to shaping urban land use, form and structure is still incomplete, with serious consequences for the efficacy of urban planning and land change models.

This international conference aims to bring together the community of scholars working on planning evaluation and urban modelling. The participants are offered the opportunity to present their current research and to discuss how theoretical developments, data sources, comparative studies and modelling approaches might advance the field.

The conference was financially supported by the CONCUR project and sustained by Swiss Federal Research Institute WSL.

concur

display_name: concur

land-change

display_name: land-change

landscape-planning

display_name: landscape-planning

urban-landscape

display_name: urban-landscape

urban-planning

display_name: urban-planning

en: From Plans to Land Change: Dynamics of Urban Regions. Book of Abstracts


photogrammetric-drone-data-dorfberg

maintainer: Yves Bühler

title_for_slug: photogrammetric-drone-data-dorfberg

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

en: Data Access

en:
- 3cm-resolution Orthophoto
- 10cm-resolution DSM
- 10cm-resolution snow depth raster
- original RGB images
- Agisoft Metashape Report

All products are also available as a cloud optimized geotiff.

en: The data was collected with a Wingtra Gen II drone and a Sony RX1R II sensor. In total, 10 flights were conducted at different dates, both in summer and winter. A DSM, an orthophoto, a snow depth raster and the original drone images from every flight are available at a high resolution (10cm and 3cm, respectively).

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

snow-distribution

display_name: snow-distribution

uav

display_name: uav

en: Photogrammetric Drone Data Dorfberg


en: snow-depth snow swe remote-sensing uas

en: HS map Brämabül, 14. April 2015

media_type: image/tiff

en:
10 cm resolution
15 cm accuracy

format: TIFF

mimetype: image/tiff

en: .tfw file

en: coordinates for the .tif raster

en:
This snow depth map was generated 14 January 2015, close to peak of winter accumulation, applying Unmanned Aerial System digital surface models with a spatial resolution of 10 cm. The covered area is 285'000 m2 at the top of Brämabüel, 2490 m a.s.l. covering all expositions. Coordinate system: CH1903LV03.

A detailed description is given here:

Bühler, Y., Adams, M. S., Bösch, R., and Stoffel, A.: Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations, The Cryosphere, 10, 1075-1088, 10.5194/tc-10-1075-2016, 2016.

Abstract:
Detailed information on the spatial and temporal distribution, and variability of snow depth (HS) is a crucial input for numerous applications in hydrology, climatology, ecology and avalanche research. Nowadays, snow depth distribution is usually estimated by combining point measurements from weather stations or observers in the field with spatial interpolation algorithms. However, even a dense measurement network is not able to capture the large spatial variability of snow depth in alpine terrain.
Remote sensing methods, such as laser scanning or digital photogrammetry, have recently been successfully applied to map snow depth variability at local and regional scales. However, such data acquisition is costly, if manned airplanes are involved. The effectiveness of ground-based measurements on the other hand, is often hindered by occlusions, due to the complex terrain or acute viewing angles. In this paper, we investigate the application of unmanned aerial systems (UAS), in combination with structure-from-motion photogrammetry, to map snow depth distribution. Such systems have the advantage that they are comparatively cost-effective and can be applied very flexibly to cover also otherwise inaccessible terrain. In this study we map snow depth at two different locations: a) a sheltered location at the bottom of the Flüela valley (1900 m a.s.l.) and b) an exposed location (2500 m a.s.l.) on a peak in the ski resort Jakobshorn, both in the vicinity of Davos, Switzerland. At the first test site, we monitor the ablation on three different dates. We validate the photogrammetric snow depth maps using simultaneously acquired manual snow depth measurements. The resulting snow depth values have a root mean square error (RMSE) better than 0.07 to 0.15 m on meadows and rocks and a RMSE better than 0.30 m on sections covered by bushes or tall grass. This new measurement technology opens the door for efficient, flexible, repeatable and cost effective snow depth monitoring for various applications, investigating the worlds cryosphere.

remote-sensing

display_name: remote-sensing

snow-depth

display_name: snow-depth

swe

display_name: swe

uas

display_name: uas

en: UAS based snow depth maps Brämabüel, Davos, CH


en: teleo edna marseille mediterraneaen fish-composition marine

en: Data access

en: Please use cyberduck to download for the moment

en:
Description: Fish environmental DNA data set collected in 2022 in the Calanques National Park
The eDNA samples were collected in 2022 in two locations (Moyades, M−FPA and “Impérial du large”, I-LPA), during the winter (January- February), the summer (June to August)and fall (mid-September to November) seasons, with the sampling dates depending on the weather conditions. For the M−FPA, samples were collected between Moyades island and Riou island, while for the I-LPA, they were collected on the south side of the “Impérial du large” island. To account for the existing bathymetry, in the M−FPA the samples were taken at two sampling sites with depths of 20 and 40 m, while in the I-LPA they were taken at two sites with depths of 20 and 80 m.
At each site, in-situ filtration of seawater was performed using a double-head submersible pump (Subspace, Geneva, Switzerland; nominal flow of ca. 1 L/min) strapped to an underwater scooter with 2 VigiDNA 0.20 µm filtration capsules (SPYGEN, le Bourget du Lac, France), along with disposable sterile tubing. The samples were collected along two horizontal transects (up to 400 m in length) during each closed-circuit rebreather dive, enabling the filtration of a water volume of 15 L/filter per depth, as close as possible to the substrate. Two filter replicates were collected by two divers at each sampling site, except in two cases where bad weather conditions or logistical issues meant that only one replicate was sampled.
After the filtration, the remaining seawater was emptied from the capsule back on the boat and replaced by a 80 mL CL1 conservation buffer (SPYGEN, le Bourget du Lac, France). To prevent any contamination, a strict protocol was followed during the entire process, requiring disposable gloves and single-use filtration equipment. Finally, the samples were stored at room temperature. We followed a strict contamination control protocol in both field and laboratory stages. Each water sample processing included the use of disposable gloves and single-use filtration equipment to avoid any risk of contamination. Libraries were prepared with ligation using the MetaFast protocol (Fasteris).
Data content:
	* rawdata/: contains the raw reads for each individual sample. One archive contains the paired-end reads specified by the _R1 or _R2 suffix as well as individually tagged PCR replicates (if available) together with an archive containing all extraction and PCR blank samples of the library. Reads have been demultiplexed using cutadapt but not trimmed, individual demultiplexing tags and primers remain present in the sequences.
	* taxadata/: contains the table with all detected taxonomy for each sample after bioinformatic processing (see Polanco et al. 2020 for details; https://doi.org/10.1002/edn3.140) and associated field metadata.
	* metadata/: contains two metadata files, one related to the data collected in the field for each filter, and the second related to the sequencing process in the lab (including the tag sequence, library name, and marker information for each sample)

edna

display_name: edna

fish-composition

display_name: fish-composition

marine

display_name: marine

marseille

display_name: marseille

mediterraneaen

display_name: mediterraneaen

teleo

display_name: teleo

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: Environmental DNA Marine France Calanques 2022


en: metamorphism numerical-simulations snow fem microstructure

en: Simulation parameters and outputs for "Towards a physically and microstructure-based equation for the evolution of the specific surface area in snow"

format: ODS

en:
In the associated study [1], evolution of randomly generated bi-continuous microstructures that resemble real snow microstructures has been investigated. The control of the ambient conditions (temperature and temperature gradient) and of the physical processes at play on the rate of change of the specific surface area have been evaluated. Based on the evaluation, a new physically-informed governing law for the coarsening of microstructures has been proposed. This law accurately predicts the simulation outputs under various conditions. Furthermore, a potential parametrization of newly introduced material properties has been proposed and evaluated on snow coarsening data.
This dataset contains the parameters used for the mesh generation and the finite-element simulations. The output parameters of simulations, such as the ice fraction, surface of the ice-air interface per total volume, mean curvature and growth rate are provided.
[1]: A.Braun, K. Fourteau, S. Frei, M. Lehning, H. Löwe: Towards a physically and microstructure-based equation for the evolution of the specific surface area in snow, Journal of Glaciology, accepted for publication in 2024

fem

display_name: fem

metamorphism

display_name: metamorphism

microstructure

display_name: microstructure

numerical-simulations

display_name: numerical-simulations

snow

display_name: snow

en: Simulation parameters and outputs for towards a physically and microstructure-based equation for the evolution of the specific surface area in snow


snow-drift-station-snow-and-air-data

maintainer: Benjamin Walter

title_for_slug: snow-drift-station-snow-and-air-data

en: snow-height temperature snow raclets snow-temperature snow-depth

en: Air Temperature

en: The air temperature (°C) was measured with a Campbell CS-215 which was mounted at a height of 2.2 m above ground. The time is given in UTC+1.

en: Snow Surface Temperature

en: The snow surface temperature (°C) was measured with a Campbell SI-111 at 0.7 m above ground. The time is given in UTC+1.

en: Relative Humidity

en: The relative humidity (%) was measured with a Campbell CS-215 at a height of 2.2 m above ground. The time is given in UTC+1.

en: Snow Depth

en: The snow depth (cm) was measured with a Lufft SHM-31 which was mounted at 1.7 m above ground. The time is given in UTC+1.

en: Snow and air data was monitored at Gotschnagrat (LON: 46.859 LAT: 9.849) by an infrarot radiometer (Campbell SI-111) for snow temperature (°C), a snow height sensor (Lufft SHM-31) for snow height change (cm) and a temperature and humidity sensor (Campbell CS-215) for air temperature (°C) and relative humidity (%). No filter was applied to the sensors and the smapling frequency was 1 Hz.

raclets

display_name: raclets

snow-height

display_name: snow-height

snow-temperature

display_name: snow-temperature

temperature

display_name: temperature

en: Snow Drift Station - Snow and Air Data


en: vertebrates food-webs metaweb invertebarates switzerland vascular-plants trophic-networks

en: trophiCH

en: We provide seven datasets: 1) the metaweb, 2) the taxa checklist, 3) the data source meta-dataset, 4) the list of generalist basal and predator families and polylectic species with citations and 5) a dataset with citations for the inferences of missing predators, 6) a dataset with citations for the parallel inference of diets from similar species and 7) a list of other existing metawebs. The empirical dataset is 00_empirical_metaweb.csv. To reproduce the final metaweb (01_final_metaweb.csv), start the R Project file: trophich.Rproj. We provide five scripts, accompanying functions, and the raw data required to run these scripts to reproduce the taxonomic expansion and validation of the datasets. In the first script (01_inferring_interactions.R), we infer interactions using genus and family level interactions and for basal feeding groups (see Methods: Taxonomic expansion). In the second script (02_inferring_interactions_special_cases.R), we infer further interactions for a few special cases with detailed explanations. In the third script (03_metaweb_comparisons.Rmd), we provide the statistical comparisons between our metaweb and other empirical metawebs as an R Markdown document. We additionally provide a a Python Jupyter Notebook document, outlining the error validation of the data extraction process (04_error_validation.ipynb and an accompanying .html file). Finally, we provide a script to reproduce figure 1 from the data paper (05_metaweb_summary_figure_1.R).

en:
Environmental pressures on species can cascade within food webs and even extend beyond individual ecosystems to interconnected systems at large spatial scales. To facilitate the exploration of these dynamics, we construct a data-based national trophic meta-food web (henceforth metaweb), that includes well-documented vertebrates, invertebrates, and vascular plants within Switzerland's national boundaries, and compiles 160 years of ecological knowledge. We additionally use a combination of taxonomic and geographic information to infer further species-level interactions. Our comprehensive dataset catalogues 1,112,073 trophic interactions involving 23,151 species and 125 feeding guilds (e.g. detritivores, fungivores, etc). While explorations of large-scale food webs in space and time have often relied on modelling approaches due to limited data availability, this empirically based metaweb paves the way for data-driven large-scale studies of real-world food webs. By integrating the metaweb with knowledge of local species assemblages, future studies can gain insights into the impact of global change drivers, including climate change and land-use intensification, on food web structures across spatial scales.
**Please note that this dataset is under embargo until publication of the paper.**

food-webs

display_name: food-webs

invertebarates

display_name: invertebarates

metaweb

display_name: metaweb

switzerland

display_name: switzerland

trophic-networks

display_name: trophic-networks

vascular-plants

display_name: vascular-plants

vertebrates

display_name: vertebrates

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: trophiCH v1 - a food web for Switzerland


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

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

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

biodiversity-experiment

display_name: biodiversity-experiment

biomass

display_name: biomass

bryophytes

display_name: bryophytes

functional-traits

display_name: functional-traits

fungicide

display_name: fungicide

grassland

display_name: grassland

moss

display_name: moss

nitrogen

display_name: nitrogen

pandiv

display_name: pandiv

species-richness

display_name: species-richness

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


maintainer: Matthias Haeni

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

en: Site photo of the forest station (ISB)

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

mimetype: image/jpeg

en: Site aerial photo (Isone)

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

en: Data plot of the field station (ISF)

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

en: Forest station data (ISB)

en: Data file.

mimetype: text/csv

en: Data plot of the forest station (ISB)

en: Field station data (ISF)

en: Site photo of the field station (ISF)

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

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Isone in Switzerland where one station is located within a natural broad-leaved forest stand (ISB) with European beech (_Fagus sylvatica_; 70-100 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, ISF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Isone is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

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


aerosol-data-weissfluhjoch

title_for_slug: aerosol-data-weissfluhjoch

en: particles ice-nucleation raclets aerosol inp linda

en: OPC-N3 Dataset Weissfluhjoch

en: Particle concentrations in a size range of 0.35 μm to 40 μm were measured with an OPC-N3 (Alphasense, Inc.) in a resolution of 20 seconds (time in UTC). PM₁, PM₂,₅ and PM₁₀ were directly derived.

en: SMPS Dataset Weissfluhjoch

en: The SMPS file contains aerosol data (particle concentration, total number concentration, median, mean, geometric mean, mode and geometric standard deviation of the size distribution) in a size range between 12 and 460 nm (electrical mobility diameter) as well as instrument characteristics (sample temperature, pressure, relative humidity, mean free path and gas viscosity) in a temporal resolution of 2 minutes, with time in UTC, measured at Weissfluhjoch.

mimetype: application/x-netcdf

en: APS Dataset Weissfluhjoch

en: The APS file contains aerosol data (total number concentration, median, mean, geometric mean, mode and geometric standard deviation of the size distribution) for aerodynamic diameters between 0.5 μm and ~20 μm as well as instrument characteristics, in a temporal resolution of 20 seconds, with time in UTC, measured at Weissfluhjoch.

en: OPC-GT526S Dataset Weissfluhjoch

en: Particle counts were recorded in six size channels (0.3, 0.5, 0.8, 1.5, 2.5 and 5 μm) in a resolution of 20 seconds (time in UTC) by a particle counter (GT-526S, Met One Instruments, Inc.).

en: LINDA Dataset Weissfluhjoch

en: The LINDA file contains information on temperature (in °C) dependent frozen fractions, number of Ice Nuleating Particles (INP, per standard liter) and differential INP concentrations (per standard liter and Kelvin).

en:
Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room.

Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0).

Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³).

Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³).

Coriolis μ and LINDA: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l min‾¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles.

aerosol

display_name: aerosol

ice-nucleation

display_name: ice-nucleation

inp

display_name: inp

linda

display_name: linda

particles

display_name: particles

raclets

display_name: raclets

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: Aerosol Data Weissfluhjoch


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

en: Low-flow storage

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

mimetype: text/plain

en: Shapefile of 307 catchments in Switzerland

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

en: Soil moisture

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

en: Discharge

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

en: Precipitation

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

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

discharge

display_name: discharge

hydrological-simulations

display_name: hydrological-simulations

low-flow

display_name: low-flow

prevah

display_name: prevah

soil-moisture

display_name: soil-moisture

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


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

en: The dataset contains time series of water levels, precipitation measured in the two sub-catchments of the Erlenbach catchment and its vicinity during summer and autumn 2021, 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

data

display_name: data

dataset

display_name: dataset

drainage

display_name: drainage

drainage-network

display_name: drainage-network

dynamics

display_name: dynamics

erlenbach

display_name: erlenbach

water-level

display_name: water-level

educ

en: Education, culture and sport

en: Short-term Drainage Density Dynamics Dataset for the Erlenbach Catchment


en: hydrogen-isotopes catchment-data solutes catchment oxygen-isotopes groundwater stream-water

en: This dataset includes mean concentrations of solutes (Calcium, Iron, Magnesium, Sodium, Potassium, Boron, Nitrate, Sulfate, and Chloride) and stable isotope values (hydrogen and oxygen) in water molecules. These were measured in precipitation, groundwater, soil water, and surface water samples collected from the Haute-Mentue catchment in Switzerland during Spring and Fall 2022. Detailed information is available in the accompanying documentation (documentation_for_the_hydrochemical_data_collected_during_spring_fall_2022_in_the_haute-mentue_catchment.pdf).

en: This document provides a detailed description of the hydrochemical dataset, including, among other things, an overview of the dataset content, an explanation of the column headers, a description of the locations where samples were collected, and an explanation of the abbreviations used in the dataset.

en: The dataset contains hydrochemical data collected from spring to fall 2022 in the Haute-Mentue catchment, Switzerland. This hydrochemical data includes solute concentrations and stable isotopes of oxygen and hydrogen in water molecules, measured in streamwater, soil water, groundwater, and rainfall samples. Samples were collected during eight rainfall events with higher temporal resolution (hourly) as well as at lower resolution between events. A detailed description of the dataset is provided in the documentation.

groundwater

display_name: groundwater

hydrogen-isotopes

display_name: hydrogen-isotopes

oxygen-isotopes

display_name: oxygen-isotopes

solutes

display_name: solutes

stream-water

display_name: stream-water

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: Hydrochemical Data Collected during Spring-Fall 2022 in the Haute-Mentue Catchment


maintainer: Elisabeth Hafner

en: avalanche-hazard-mapping avalanche satellite snow validation

en: Satellite Avalanche Mapping Validation data and data description

en: 14 shapefiles to be used only together with the data description also provided as a pdf.

en:
Validation points, validation area, ground truth coverage, SPOT 6 avalanche outlines, Sentinel-1 avalanche outlines, Sentinel-2 avalanche outlines, Davos avalanche mapping (DAvalMap) avalanche outlines as shapefiles and a detailed attribute description (DataDescription_EvalSatMappingMethods.pdf).

Coordinate system: CH1903+_LV95

The generation of this dataset is described in detail in:

Hafner, E. D., Techel, F., Leinss, S., and Bühler, Y.: Mapping avalanches with satellites – evaluation of performance and completeness, The Cryosphere, https://doi.org/10.5194/tc-2020-272, 2021.

avalanche

display_name: avalanche

avalanche-hazard-mapping

display_name: avalanche-hazard-mapping

satellite

display_name: satellite

snow

display_name: snow

validation

display_name: validation

en: Satellite avalanche mapping validation data


en: engineering hydrology decision-making estimation-methods flood

en:
(English version below)
Ziel dieser Faktenblätter ist es Informationen zu den Schätzmethoden, Hinweise zur Parametrisierung und eine grobe Einschätzung der Güte der Resultate in übersichtlicher Form bereitzustellen. Ein zeitaufwändiges Zusammensuchen dieser Informationen aus unterschiedlichen Quellen entfällt daher weitgehend. Die Bearbeitenden sollen damit in der Lage sein, sich innerhalb weniger Minuten die wichtigsten Hintergrundinformationen zu jeder der vorgestellten Methoden in Erinnerung zu rufen. Der Leitfaden unterstützt bei der Plausibilisierung und bei der Mittelung der Ergebnisse der einzelnen Schätzmethoden zu einem repräsentativen Endergebnis. Vorausgesetzt wird Grundwissen und Erfahrung in der Thematik.
Der Leitfaden führt die Fachperson anhand eines Entscheidungsbaums in acht Schritten durch den Arbeitsablauf und unterstützt mit einem Fragenkatalog bei der Plausibilisierung der Ergebnisse.
Auf den Faktenblättern sind für jede Schätzmethode alle relevanten Informationen stichwortartig und grafisch übersichtlich auf einer DIN-A4 Seite zusammengefasst. Jedes Faktenblatt ist einheitlich gegliedert.
The aim of these fact sheets is to provide information on the flood estimation methods, notes on their parameterization and a rough assessment of the quality of the results in a clear form. A time-consuming research for this information from different sources is therefore largely eliminated. The users should be able to recall the most important background information on each of the methods presented within a few minutes. The guide provides support in checking plausibility and averaging the results of the individual estimation methods to produce a representative final result. Basic knowledge and experience in the subject are assumed.
The guide leads the expert through the workflow in eight steps using a decision tree and supports the plausibility check of the results with a list of questions.
On the fact sheets, all relevant information for each estimation method is summarized with key words and graphics on one DIN A4 page. Each fact sheet has a standardized structure.

decision-making

display_name: decision-making

engineering

display_name: engineering

estimation-methods

display_name: estimation-methods

flood

display_name: flood

hydrology

display_name: hydrology

en: Faktenblätter und Leitfaden für Hochwasserschätzmethoden in der Praxis


en: mars sediment-transport model erosion snow

en: Datasets of Figure 3b

en: Datasets of Figure 3a

en: Datasets of Figure 4a

en: Datasets of Figure 4b

en: Datasets of Figure 2

en:
The files contain the datasets used to produce Figures 2, 3, and 4 of the manuscript ([doi: 10.1002/2016GL071822](http://dx.doi.org/10.1002/2016GL071822)).

 Manuscript Abstract:
Despite being the main sediment entrainment mechanism in aeolian transport, granular splash is still poorly understood. We provide a deeper insight into the dynamics of sand and snow ejection with a stochastic model derived from the energy and momentum conservation laws. Our analysis highlights that the ejection regime of uniform sand is inherently different from that of heterogeneous sand. Moreover, we show that cohesive snow presents a mixed ejection regime, statistically controlled either by energy or momentum conservation depending on the impact velocity. The proposed formulation can provide a solid base for granular splash simulations in saltation models, leading to more reliable assessments of aeolian transport on Earth and Mars.

erosion

display_name: erosion

mars

display_name: mars

model

display_name: model

sediment-transport

display_name: sediment-transport

educ

en: Education, culture and sport

en: Energy- and momentum-conserving model of splash entrainment in sand and snow saltation


maintainer: Sebastian Schlögl

en: snow atmosphere atmospheric-turbulence turbulent-sensible-heat-fluxes stable-boundary-layer

en: Turbulence data WFJ11

en: Turbulence data PM07

en: SEE

en: Turbulence data AA00

en: sensor in 25 m height

en: Turbulence data GR00

en: sensor in 2 m height

en: sensor in 1 m height

en: sensor in 0.2 m height

en: Turbulence data WFJ07

en: lower sensor

en: NWW

en: upper sensor

en:
We used five different atmospheric turbulence datasets from four test sites, with these sites showing differences in their topographical characteristics. We chose one typical alpine test site with high topographical complexity (Weissfluhjoch, Davos, Switzerland) and three test sites consisting of one glacier site (Plaine Morte, Crans-Montana, Switzerland) and two polar sites (Greenland and Antarctica) representing a quasi-ideal site with homogeneous surface and quasi infinite fetch in all directions.
The turbulent sensible heat flux was calculated using the eddy-covariance method. Note that the sonic temperature fluctuations have been converted into virtual temperature fluctuations.
Three-dimensional wind velocity and air temperature were processed using a linear detrending (Rannik and Vesala, 1999) and a planar fit approach (Massmann and Lee, 2002) to rotate the coordinate system. Air temperature, relative humidity and air pressure from weather stations were used to calculate air properties, which are required for the data processing. The weather stations are located in the immediate vicinity of the turbulence tower and are affected by the same air masses. Turbulence data were averaged to 30-min intervals, whilst changing to a 15-min time interval marginally affects the heat fluxes at the Weissfluhjoch test site (Mott et al., 2011). Note that we define a negative sensible heat flux as being directed towards the snow surface and a positive sensible heat flux as being directed upwards.
The selected datasets and corresponding test sites are briefly introduced below:

Weissfluhjoch 2007 (WFJ07): A vertical set-up of two three-dimensional ultrasonic anemometers (CSAT3, Campbell Scientific, Inc.) was used at the traditional field site Weissfluhjoch (2540 m asl.) to measure three-dimensional wind velocity and air temperature at a frequency of 20 Hz. The sensors were mounted 3 m and 5 m above the ground and provided reliable data for 50 days between 11 February 2007 and 24 April 2007. Further information on the field campaign can be found in Stössel et al. (2010) and Mott et al. (2011).

Weissfluhjoch 2011-13 (WFJ11): Three-dimensional wind velocity and air temperature were recorded at 5 m above the ground at a frequency of 10 Hz with a three-dimensional ultrasonic anemometer (CSAT3). The analysis was conducted for data obtained between February and March in the years 2011-13.

Plaine Morte 2007 (PM07): Two three-dimensional ultrasonic anemometers (CSAT3) were installed on a horizontal boom facing opposite directions (west-north-west vs. east-south-east) at 3.75 m above the ground to measure air temperature and three-dimensional wind velocity at 20 Hz. The data were collected at the almost flat field site on the Plaine Morte glacier (2750 m asl.) near Crans-Montana, Switzerland from February to April 2007. High quality meteorological data were additionally recorded and used to force the model. A detailed description about the set-up at the Plaine Morte glacier can be found in Huwald et al. (2009) and Bou-Zeid et al. (2010).

Greenland 2000 (GR00): High-frequency three-dimensional ultrasonic anemometer measurements (CSAT3) were recorded at 50 Hz at the Summit Camp (72.3 °N, 38.8 °W, 3208 m asl.) located on the northern dome of the Greenland ice sheet. Data were collected at 1 m and 2 m above the snow surface during summer in 2000 and 2001. Additionally, meteorological measurements were obtained for the post processing and used to force the model. More information about the field campaign can be found in Cullen et al. (2007, 2014).

Antarctica 2000 (AA00): A set-up of three vertical three-dimensional ultrasonic anemometers (DA-600, Kaijo Denki) were installed at Mizuho Station (70°42' S, 44°20' E, 2230 m asl.) in Eastern Antarctica at 0.2, 1 and 25 m and recorded turbulence data at a frequency of 100 Hz from October to November 2000. Longwave and shortwave radiation, relative humidity, air and snow surface temperature were additionally measured and used to force the model. More information about the field campaign can be found in Nishimura and Nemoto (2005).

atmosphere

display_name: atmosphere

atmospheric-turbulence

display_name: atmospheric-turbulence

stable-boundary-layer

display_name: stable-boundary-layer

turbulent-sensible-heat-fluxes

display_name: turbulent-sensible-heat-fluxes

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: How do stability corrections perform in the stable boundary layer over snow?