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

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: 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: 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: 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: 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: 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: 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)

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

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

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


en: chelsa precipitation extreme-events drought multi-year-drought megadrought potential-evapotranspiration

state: active

type: dataset

media_type: application/pdf

en: Technical documentation document including the variable names, units, scaling, offset, and background.

format: PDF

en: Standardized Precipitation Index (SPI)

en: Global monthly layers of the Standardized Precipitation Index (SPI) a 30 arcsec (~1km) spatial resolution

format: SERVICE

en: Standardised Precipitation Evapotranspiration Index (SPEI)

en: Global monthly layers of the Standardized Precipitation Evapotranspiration Index (SPEI) a 30 arcsec (~1km) spatial resolution

en:
Multiyear meteorological droughts (MYMD10) statistics
Statistics of the multiyear meteorological droughts, containing the ID, duration, area, and empirical ranking of the droughts, and the anomalies in kNDVI

format: XLS

en: Statistics of the multiyear meteorological droughts, containing the ID, duration, area, and empirical ranking of the droughts.

en: Multiyear meteorological droughts (MYMD) statistics - Description

en: Description of column names, and units of the fields contained in the dataset "Multiyear meteorological droughts (MYMD) statistics".

en: Global annual layers of the multiyear meteorological droughts (MYMD) at a 1/12° (~10km) spatial resolution

en: Global annual layers of the multiyear meteorological droughts (MYMD) at a 1/24° (~5km) spatial resolution

en: Kernel Normalized Difference Vegetation Index Anomaly (QKNDVI)

en: Global annual layers of the the kernel normalized difference vegetation index anomaly (qkndvi) at a 1/12° (~10km) spatial resolution.

en: Multiyear meteorological droughts (MYMD10) statistics - Description

en: Description of column names, and units of the fields contained in the dataset "Multiyear meteorological droughts (MYMD10) statistics".

en:
The dataset consists of the standardized precipitation (SPI) and the standardized precipitation evapotranspiration index (SPEI) index with a 30 arcsec (~1km) horizontal resolution.
Data for precipitation rates (pr) and potential evapotranspiration (pet) are taken from CHELSA V2.1 (https://doi.org/10.16904/envidat.228.v2.1). Both data sets are monthly time series from 1980 to 2018 at 30 arcsec. and have the unit of kg m-2 month-1. SPEI is a more reliable measure of drought than SPI as it additionally considers the effect of potential evapotranspiration. Potential evapotranspiration (pet) is the amount of water per area per time that can evaporate at the soil surface or be transpired by plants in absence of water supply limitations. SPEI effectively estimates for a given location how the climatic water balance, i.e., the difference between precipitation rate (pr) and potential evapotranspiration (pet), relates to the long-term mean. Here, pet was calculated with the Penman-Montheith approach, assuming surface conductance of a reference crop of 12 cm height following the definition of the Food and Agriculture Organization of the United Nations (FAO), originating from CHELSA-BIOCLIM+ database (https://www.doi.org/10.16904/envidat.332). To obtain global time series of SPEI, in each pixel the frequency distribution of water balance estimates (pr-pet) is approximated with a log-logistic probability distribution function. We calculate monthly SPEI using a 12-month memory and approximate the parameters of the log-logistic probability distribution based on the time series from 1980-2018. Such a 12-month time window is suitable to capture long-term drought events that are long enough to impose substantial impacts on agriculture, hydrology, and ecosystems while ones at shorter time scales would be more appropriate to detect short-term drought. For each month, SPEI is calculated considering the respective water-balance value and the values of the eleven preceding months. Here we use SPEI-12 in December to represent the overall water-balance condition of the respective year. The resulting SPEI data has a horizontal resolution of 130 arcsec (~1km). The calculation of SPI and SPEI is done in R with the package SPEI (https://www.doi.org/10.32614/CRAN.package.SPEI).

chelsa

display_name: chelsa

drought

display_name: drought

extreme-events

display_name: extreme-events

megadrought

display_name: megadrought

multi-year-drought

display_name: multi-year-drought

potential-evapotranspiration

display_name: potential-evapotranspiration

precipitation

display_name: precipitation

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

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: High resolution global standardized drought indices


maintainer: EnviDat Support

en: fractional-snow shading forest shortwave-radiation longwave-radiation

en: Sky-view fraction along the cablecar transect

media_type: text/plain

en: Data is used and described in the following paper: Malle et al. (2019). Shading by Trees and Fractional Snow Cover control the Subcanopy Radiation Budget. Journal of Geophysical Research. DOI:10.1029/2018JD029908.

format: TXT

mimetype: text/plain

en: Radiation Data 18.04.2019

en:
Spatiotemporal incoming/outgoing shortwave and longwave radiation as well as sunlit and shaded-snow view fractions below an evergreen forest stand in the Swiss alps. Data was collected on 18th of April 2018 along a 48m long, heterogeneous forest stand under full snow cover conditions.

The presented data is used and further described in the following paper: Malle et al. (2019). Shading by Trees and Fractional Snow Cover control the Subcanopy Radiation Budget. Journal of Geophysical Research Atmospheres. DOI:10.1029/2018JD029908.

en: Radiation Data 25.4.2018

en:
Spatiotemporal incoming/outgoing shortwave and longwave radiation as well as sunlit and shaded-snow view fractions below an evergreen forest stand in the Swiss alps. Data was collected on 25th of April 2018 along a 48m long, heterogeneous forest stand under partial snow cover conditions.

The presented data is used and further described in the following paper: Malle et al. (2019). Shading by Trees and Fractional Snow Cover control the Subcanopy Radiation Budget. Journal of Geophysical Research Atmospheres. DOI:10.1029/2018JD029908.

en: Seasonal sub-canopy radiation data

en:
Seasonal, spatially aggregated incoming and outgoing shortwave radiation below an evergreen forest stand collected with a cable car system.

The presented data is used and further described in the following paper: Malle et al. (2019). Shading by Trees and Fractional Snow Cover Control the Subcanopy Radiation Budget. Journal of Geophysical Research Atmospheres. DOI:10.1029/2018JD029908.

en: This data set consists of incoming and outgoing short- and longwave radiation as well as sunlit-snow-view-fraction as described in the JGR-Atmospheres paper "Shading by trees and fractional snow cover control the sub-canopy radiation budget", by Malle et al. (2019). Data was collected along a 48m long, heterogeneous forest transect between January and June 2018 close to Davos, Switzerland.

forest

display_name: forest

fractional-snow

display_name: fractional-snow

longwave-radiation

display_name: longwave-radiation

shading

display_name: shading

shortwave-radiation

display_name: shortwave-radiation

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.


photogrammetric-drone-data-schurlialp

maintainer: Yves Bühler

title_for_slug: photogrammetric-drone-data-schurlialp

en: dsm wingtra orthophoto drone-data uav photogrammetry

en: Data Access

en:
- 10 cm DSM
- 10 cm HS
- 3 cm orthophoto
- original RGB images
- Agisoft Metashape Processing Report

All of the products are also available as a cloud optimized geotiff.

en: The data was collected on 16.04.2021 and on 28.05.2021 with a Wingtra Gen II and a Sony RX1 II RGB sensor to obtain snow depth and distribution data. Following the data collection, the data was processed with Agisoft Metashape. A 10cm DSM, a 10cm snow depth raster, a 3mm orthophoto and the original drone images are available for download.

drone-data

display_name: drone-data

dsm

display_name: dsm

orthophoto

display_name: orthophoto

photogrammetry

display_name: photogrammetry

uav

display_name: uav

wingtra

display_name: wingtra

en: Photogrammetric Drone Data Schürlialp


maintainer: Athanasios Nenes

en: raclets sigma-w cloud-droplets supersaturation cloud-modeling wolfgangpass

en: Predicted cloud droplet numbers (kappa=0.1) Davos Wolfgang

media_type: text/csv

en:
In-situ measurements of aerosol size distributions along with updraft velocities collected from a wind Doppler Lidar are coupled with a state-of-the art droplet activation parameterization (Morales and Nenes, 2014) to determine the cloud droplet number and maximum in-cloud supersaturation that can potentially form over the Davos Wolfgang station between February 24 and March 8 2019. For this, as a sensitivity test, a hygroscopicity parameter of 0.1 is assumed, which is the lower limit determined from the analysis carried out for Weissfluhjoch (https://www.envidat.ch/dataset/ccn-hygroscopicity-predicted-cloud-droplet-numbers-weissfluhjoch). The data file contains:

* Date and time (DD/MM/YYYY hh:mm:ss) in UTC
* The wind-lidar derived standard deviation of updraft velocities (sigma-w) in m/s, used to apply the parameterization employing a single characteristic velocity (Morales and Nenes, 2010)
* The maximum predicted cloud parcel supersaturation (Smax%)
* The total in-situ aerosol number concentration (Naer) in cmˉ³, measured by the SMPS
* The predicted cloud droplet numbers (Nd) in cmˉ³
* The activation fraction (%)

format: CSV

mimetype: text/csv

en: Predicted cloud droplet numbers (kappa=0.25) Weissfluhjoch

en:
In-situ measurements of aerosol size distributions along with updraft velocities collected from a wind Doppler Lidar are coupled with a state-of-the art droplet activation parameterization (Morales and Nenes, 2014) to determine the cloud droplet number and maximum in-cloud supersaturation that can potentially form over the Davos Wolfgang station between February 24 and March 8 2019. For this, as a sensitivity test, a hygroscopicity parameter of 0.25 is assumed, which is the upper limit determined from the analysis carried out for Weissfluhjoch (https://www.envidat.ch/dataset/ccn-hygroscopicity-predicted-cloud-droplet-numbers-weissfluhjoch). The data file contains:

* Date and time (DD/MM/YYYY hh:mm:ss) in UTC
* The wind-lidar derived standard deviation of updraft velocities (sigma-w) in m/s, used to apply the parameterization employing a single characteristic velocity (Morales and Nenes, 2010)
* The maximum predicted cloud parcel supersaturation (Smax%)
* The total in-situ aerosol number concentration (Naer) in cmˉ³, measured by the SMPS
* The predicted cloud droplet numbers (Nd) in cmˉ³
* The activation fraction (%)

en: Cloud droplet properties were predicted between February 24 and March 8 2019 for the measurement site Davos Wolfgang (1630 m a.s.l., LON: 9.853594, LAT: 46.835577). Droplet calculations are carried out with the physically based aerosol activation parameterization of Morales and Nenes (2014), employing the “characteristic velocity” approach of Morales and Nenes (2010). Aerosol size distribution observations required to predict the cloud droplet numbers and maximum in-cloud supersaturation are obtained from a Scanning Mobility Particle Size Spectrometer (SMPS) instrument deployed at Davos Wolfgang (https://www.envidat.ch/dataset/aerosol-data-davos-wolfgang). The required vertical velocity measurements are derived from the wind Doppler Lidar (https://www.envidat.ch/dataset/lidar-wind-profiler-data) deployed at the same station and are extracted from the first bin of the instrument, being 200 m above ground level. The hygroscopic properties of the particles measured at Davos Wolfgang could not be estimated, owing to a lack of concurrent CCN measurements. As a sensitivity test, droplet calculations are performed assuming two different values of the aerosol hygroscopicity parameter, 0.1 and 0.25, based on the analysis carried out for Weissfluhjoch. Additional information can be found in Section 2.3 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).

cloud-droplets

display_name: cloud-droplets

cloud-modeling

display_name: cloud-modeling

raclets

display_name: raclets

sigma-w

display_name: sigma-w

supersaturation

display_name: supersaturation

wolfgangpass

display_name: wolfgangpass

educ

en: Education, culture and sport

en: Predicted cloud droplet numbers Davos Wolfgang


en: plants soil-mapping switzerland soil-moisture soil-ph landolt ecological-indicator-values ecological-maps

en: Predictors

media_type: application/zip

en: Predictors generated and used in Descombes et al. (2020)

format: ZIP

mimetype: application/zip

en: Description

en: Description of the predictors provided in this dataset

en:
Ecologically meaningful predictors are often neglected in plant distribution studies, resulting in incomplete niche quantification and low predictive power of species distribution models (SDMs). Because environmental data are rare and expensive to collect, and because their relationship with local climatic and topographic conditions are complex, mapping them over large geographic extents and at high spatial resolution remains a major challenge. Here, we derived environmental data layers by mapping ecological indicator values (EIVs) in space by using a large set of environmental predictors in Switzerland.

This dataset contains the predictors (raster layers) generated and used in the following publication (Descombes et al. 2020). Only predictors for which we have the rights to share them are provided. Other datasets and predictors can be accessed via the original data provider. Details on the predictors and sources are fully described in the publication. The predictors are provided as GeoTIFF files, at 93 m spatial resolution and Mercator projection ("+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"). The excel file (xlsx) provides a short description of the raster layers.

Paper Citation:

Descombes, P. et al. (2020). Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes. Ecography. (accepted)

ecological-indicator-values

display_name: ecological-indicator-values

ecological-maps

display_name: ecological-maps

landolt

display_name: landolt

plants

display_name: plants

soil-mapping

display_name: soil-mapping

soil-moisture

display_name: soil-moisture

soil-ph

display_name: soil-ph

switzerland

display_name: switzerland


maintainer: Tessa Hegetschweiler

en: teenagers childhood recreational-activities forest adolescents

en: Questionnaire school survey

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

mimetype: application/pdf

en: Survey at schools 2017

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

en: Dataset survey at schools

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

en: Dataset comments

en: The codebook to the dataset

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

adolescents

display_name: adolescents

childhood

display_name: childhood

forest

display_name: forest

recreational-activities

display_name: recreational-activities

teenagers

display_name: teenagers

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: Factors influencing teenagers' forest visit frequency


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

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.

catchment

display_name: catchment

catchment-data

display_name: catchment-data

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

educ

en: Education, culture and sport

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


soil-property-maps-for-the-swiss-forest

title_for_slug: soil-property-maps-for-the-swiss-forest

en: soc soil-properties soil-texture soil-ph switzerland soil-depth forest soil dsm

en:
We used 2071 forest soil profiles to model a wide range of soil properties for the forested area of Switzerland. The spatial prediction is based on the principle of «digital soil mapping». This involves linking soil profiles with soil forming factors using statistical or machine learning methods.

A quantile regression forest (QRF) approach was applied to predict the following soil properties at six depth ranges: clay, gravel, sand, fine earth density, SOC. The depth ranges correspond to the standard depths of the [GlobalSoilMap.Net](https://www.isric.org/) specification: 0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm. In addition, the total soil depth down to a non-root-permeable layer or solid rock soil thick was predicted. To quantify the uncertainty for each predicted pixel, the upper and lower limit of the 90% prediction interval derived from QRF was calculated. More details on the methods and results are described in Baltensweiler et al. 2021 and Baltensweiler et al 2022.

The soil property maps, and the uncertainty maps are provided as a GeoTIFF files at 25 m resolution. The excel file (xlsx) provides a short description of the raster layers.

**The soil and the uncertainty maps can be viewed in a simple web-GIS application available at:**
[www.wsl.ch/soilmaps](https://www.wsl.ch/soilmaps).

dsm

display_name: dsm

soc

display_name: soc

soil

display_name: soil

soil-depth

display_name: soil-depth

soil-properties

display_name: soil-properties

soil-texture

display_name: soil-texture

en: Soil property maps for the Swiss forest


maintainer: Stephan Zimmermann

en: magnesium fagus-orientalis-lipsky potassium calcium biogeochemical-cycling

en: Concentrations of Ca, Mg, K, and Na in throughfall, litterflow and soil solution of a Hyrcanian forst in Northern Iran

en:
Throughfall, litterflow and soil solution were sampled during one whole year under five Oriental beech trees in a mixed Hyrcanian beech forest. The amounts of Ca2+, Mg2+, K+ and Na+ in these fluxes were calculated based on their concentrations and the sampled volumes, and subsequently compared with the respective fluxes in the rainfall and soil solution of an adjacent forest gap.
In addition six soil profiles, one close to every single tree and one in the forest gap, were analyzed for pH, CaCO3, organic matter and texture.

biogeochemical-cycling

display_name: biogeochemical-cycling

calcium

display_name: calcium

fagus-orientalis-lipsky

display_name: fagus-orientalis-lipsky

magnesium

display_name: magnesium

potassium

display_name: potassium

en: Base cation dynamics in an Oriental beech forest


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

en: (a) Metadata

en: General and specific metadata for all files.

en: (h) Sonic data 5

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

en: (f) Sonic data 3

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

en: (g) Sonic data 4

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

en: (i) Sonic data 6

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

en: (e) Sonic data 2

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

en: (l) AWS data

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

en: (b) SPC time series

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

en: (m) Weather observations

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

en: (j) Spike percentage

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

en: (d) Sonic data 1

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

en: (k) Wind speed profiles

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

en: (c) SPC average profiles

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

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

antarctica

display_name: antarctica

atmospheric-turbulence

display_name: atmospheric-turbulence

blowing-snow

display_name: blowing-snow

drifting-snow

display_name: drifting-snow

meteorological-measurements

display_name: meteorological-measurements

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


figures-perspective-urban-beekeeping

title_for_slug: figures-perspective-urban-beekeeping

en: urban-ecology diversity-distributions hymenoptera urban-beekeeping insects wildbees

en: Proportion honeybees and wildbees

en: Outdated, use the new resource "Propotion urban honeybees and wildbees"

en: Local overheating values within 3 buffers in hives in Zurich

en:
The dataset contains the average overheating values around beehives in Zurich at three spatial scales (250, 500 and 1000 m). It contains 4 columns:
- buffer: indicating the spatial scale
-ID: hive ID (annonymised)
- mean.overwarming: mean overheating value, expressed in degrees Kelvin
-mena.overwarming.cor = transformed values do degrees Celsius

en: Coordinates studied cities

en: Coordinates (LatLon format) of the cities where data from urban bee ecology studies was used.

en: DEPRECATED. Distirbution beehives among beekeepers

en: The dataset contains the number of beehives per registered beekeeper ID (annonymised) in four Swiss cities (Bern, Lausanne, Geneve, Zurich).

en: Proportion urban honeybees and wildbees

en:
The dataset contains the proportion of wildbees and honeybees in the different studies obtained from the literature review.
Article_ID: ID for each article
DOI
Authors
Year: publication year
Duration sampling (years): numerical
Start_sampling: numerical, date
End_sampling: numerical, date
Continent
Country
N. cities: number of cities sampled in the article. Numerical.
City
Sampling type: categorical. Levels: active (e.g., Hand-netting); passive (e.g., pantraps. trap-nests); both (combination of passive and active methods).
Sampling methodology: categorical. Levels: direct observations, hand-netting, pantraps, interception traps, vane traps.
Number sites: total number of study sites.
Abundance honeybees: number of honeybees recorded
Abundance wild bees: number of wildbees recorded
Total bees: total number of bees recorded.
Proportion honeybees: Abundance of honeybees / Total number of bees
Proportion wildbees: Abundance of wildbees / Total number of bees

en: R script for the figures V2

en: R script for the figures

format: OCTET

en: Data growth urban beekeeping

en:
The dataset contains the data of the growth of urban beekeeping in different cities.
For each city in a given year, it contains the following:
Continent.
Country.
City: the name of the city
Year: the year when the information was collected
Area: of the city
Total N. beekeepers: when available, the estimated number of beekeepers in that city for that given year
Total N. hives: when available, the estimated number of beehives in that city for that given year
Density: the total number of hives divided by the city area
Reference: the source of the information.

en: Distribution beehives among beekeepers

en: The dataset contains the number of beehives per registered beekeeper ID (annonymised) in four Swiss cities (Bern, Lausanne, Geneve, Zurich) and Paris.

en: NDVI values within 3 buffers  in hives in Zurich

en:
The dataset contains the average NDVI values around beehives in Zurich at three spatial scales (250, 500 and 1000 m). It contains 3 columns_
- buffer: indicating the spatial scale
- ID: hive ID (annonymised)
- mean.NDVI: mean NDVI value

en: Data rank-abundance distribution urban bees

en: Outdated, use the new resource

en: R script for the figures V1

en: Deprecated version of the R script

en: Species-abundance data

en:
Abundance data at the species level per used dataset for the rank abundance distribution (RAD).
40 datasets used.

en: DEPRECATED. Data growth urban beekeeping

en: Results literature review

en:
 The file shows the results from the literature review. Briefly, we searched papers using google scholar search engine using the following query: (“WILD BEE OR WILDBEE”)(“URBAN” OR “CITY”), and limited the time span from 2010-2023. The search query returned 200 candidate papers. In addition, we complemented the candidate papers from the literature research performed by Ferrari & Polidori (2022), adding 22 additional candidate papers. To select papers, we checked that (1) were conducted in urban areas; (2) sampled the whole bee community; (3) provided data on abundance of wildbees; (4) provided data on honeybees; and (5) provided individual abundance data of each wild bee species.
The dataset contains the following:
-Article_ID: ID for the specific article in the form "A1", "A2"..."An"
- Title: of the article
- Year: of publication
- Journal: of publication
- Publisher:
- Link: link to the publication
- DOI: when applicable
- Total abundance wild bees reported: was the total number of collected bees reported? Logical (Y/N)
- Honey bee abundance reported: was thenumber of honeybees collected reported? 	Logical (Y/N)
- Abundance per wild bee species: was the number of wildbee species sampled provided? Logical (Y/N)
- Select proportion: paper selected to calculate the proportion of honeybees and wildbees.
- Select RAD: paper selected to compute the rank-abundance distribution diagram
Comment: comments on the papers, data availability, etc.

en:
Data and code from the perspective paper "When honeybees comes to town"
The .r file provides the code to generate the figures.
In addition, this repository contains the data for the figures 1-3.
For Figure 4, part of the data is confidential, Please, refer to the contacts provided:
- For the distribution of hives in Zurich (2018): https://www.zh.ch/de/gesundheitsdirektion/veterinaeramt.html
- For the overheating map of Zurich: Prof. Dr. Eberhard Parlow / Stadt Zurich (https://www.zh.ch/de/umwelt-tiere/umweltschutz/umweltpraxis/definitionsseite/2012/68/zup068_2012_a0030_klimaanalyse-pdf.html)

diversity-distributions

display_name: diversity-distributions

hymenoptera

display_name: hymenoptera

insects

display_name: insects

urban-beekeeping

display_name: urban-beekeeping

urban-ecology

display_name: urban-ecology

wildbees

display_name: wildbees

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: mediterraneaen teleo marine edna marseille fish-composition

en: Data access

en:
Description: Fish environmental DNA data set collected in 2021 in the Calanques National Park
The eDNA samples were collected in 2021 in two locations (Moyades, M−FPA and “Impérial du large”, I-LPA), during 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 take into account 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

en: Environmental DNA Marine France Calanques 2021


en: biodiversity-monitoring threatened-species crispr-dx crispr-cas african-manatee edna environmental-dna qpcr field-based-protocol

en: Description of project and folders and files

en: File containing overview of site names and extract number (field-based or lab-based extraction). The extract_number_field_based corresponds to the identically called column in CRISPR_field_based_classifications.csv file in the data folder, as the extract_number_lab_based to the column in the file CRISPR_lab_based_classifications.csv file and the qPCR_Cp_2nd_derivative.csv file (also in the data folder). The file includes the GPS coordinates of the sampling location (transect start point).

en:
Folder containing the data files with Cp values (qPCR) and the CRISPR-Dx classifications. CRISPR_field_based_classifications.csv file contains the classifications per pool per extract (0: no detection, 1: detection) for the African manatee (Trichechus senegalensis).
CRISPR_lab_based_classifications.csv file contains the classifications per pool per extract (0: no detection, 1: detection).
qPCR_Cp_2nd_derivative.csv file contains the Cp value of the 8 replicates of each extract.

en:
Data associated to the publication "Rapid field-based detection of a threatened and elusive species with environmental DNA and CRISPR-Dx"

 Authors:

Flurin Leugger, Martina Lüthi, Michel Schmidlin, Zacharias Kontarakis and Loïc Pellissier

 Contact:

flurinleugger@gmail.com
loic.pellissier@usys.ethz.ch

 Project description:

We collected eDNA samples in the Conkouati-Douli National Park in the Republic of Congo in 2023 in collaboration with Beauval Nature and HELP Congo. We analyzed the samples on site in the national park using CRISPR-Dx assays to detect the African manatee (*Trichechus senegalensis*). Additionally, we transported the remaining samples (buffer) to Switzerland and used a high-sensitivity protocol with CRISPR-Dx assays and a qPCR assay to verify the field-based detections. The qPCR of Hunter et al. (2018) detects the genus manatee (Trichechus). See publication for more details (doi will be added after publication).

 Folders:

 lateral_flow_tests_images\:

Images of the lateral flow tests used to analyze the eDNA extracts in the national park. File names correspond to the site names. At each site, we took two filter replicates. We ran 8 PCRs per filter/extract and pooled two PCRs together, resulting in 4 CRISPR-Dx assays per extract. The EVE number on top of the tests indicates the environmental extract number we use to track the extracts in our data base. It corresponds to the extract_number_field_based in the metadata and data folder. The negative extraction control with the strongest "band" was placed right to the eDNA extract on each image (labelled hand written with NC x.y).

 metadata\:

File containing overview of site names and extract number (field-based or lab-based extraction). The extract_number_field_based corresponds to the identically called column in CRISPR_field_based_classifications.csv file, as the extract_number_lab_based to the column in the file CRISPR_lab_based_classifications.csv file and the qPCR_Cp_2nd_derivative.csv file. The file includes the GPS coordinates of the sampling location (transect start point).

 data\:

Folder containing the files with Cp values (qPCR) and the CRISPR-Dx classifications. CRISPR_field_based_classifications.csv file contains the classifications per pool per extract (0: no detection, 1: detection) for the African manatee (Trichechus senegalensis).
CRISPR_lab_based_classifications.csv file contains the classifications per pool per extract (0: no detection, 1: detection).
qPCR_Cp_2nd_derivative.csv file contains the Cp value of the 8 replicates of each extract.

african-manatee

display_name: african-manatee

biodiversity-monitoring

display_name: biodiversity-monitoring

crispr-cas

display_name: crispr-cas

crispr-dx

display_name: crispr-dx

environmental-dna

display_name: environmental-dna

field-based-protocol

display_name: field-based-protocol

qpcr

display_name: qpcr

threatened-species

display_name: threatened-species

en: Field-based eDNA analysis to detect the threatened and elusive African manatee


en: albedo energy mountain irradiance snow photovoltaic reflection

en: PV Data

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

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

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

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

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

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

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

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

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

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

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

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

albedo

display_name: albedo

energy

display_name: energy

irradiance

display_name: irradiance

mountain

display_name: mountain

photovoltaic

display_name: photovoltaic

reflection

display_name: reflection

snow

display_name: snow


maintainer: Achille Capelli

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

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

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

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

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

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

gnss

display_name: gnss

gps

display_name: gps

liquid-water-content

display_name: liquid-water-content

lwc

display_name: lwc

snow-water-equivalent

display_name: snow-water-equivalent

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


maintainer: Elisabeth Hafner

en: snow validation satellite avalanche avalanche-hazard-mapping

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

validation

display_name: validation

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: Satellite avalanche mapping validation data


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

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: 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:
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.

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: protective-effect compound-events forest-fire protection-forest natural-hazards drought extreme-events bark-beetles

en: Conclusions and recommendations of the project (in German).

en:
This file contains the anonymous answers to the survey for the case study in the protective forest, Val Mesolcina, GR, Switzerland within the BAFU Project "Wenn Risiken kumulieren - Analyse und Umgang mit unerwarteten Ereignissen und kombinierten Risiken".
The survey consists of two parts, based on different concepts: "top-down" and "bottom-up". Answers for both concepts are available in two different worksheets within the Excel file.

en: Factsheet summarising the key results of the study (in Italian).

en: Description of the study site, extreme event (trigger event) and informations about the survey.

en: Factsheet summarising the key results of the study (in French).

en: Factsheet summarising the key results of the study (in German).

en: Case study website

media_type: text/html

en: Page of the case study on the project website. It includes figures fo all questions of the survey and additional synthesis graphs.

format: HTML

en: Factsheet summarising the methods used in this project (in German).

en:
This dataset contains the results of a case study analysing the potential impacts of a compound meteorological extreme event on the protective forest in the Val Mesolcina. These impacts were assessed in an experts survey. 29 Experts from practice, administration and science estimated the probabilities for a loss of the protective function due to a drought-related compound extreme event based on different hazard scenarios. The experts not only assessed these probabilities but also provided estimates about the reliability of the scenarios, other socio-economic consequences and options for adaptation.
This dataset contains the following information:
- Table with all (anonymised) answers from the survey
- Factsheets summarising the results and methods in German, French and Italian
- A detailed description of the study site and survey instructions
- Link to the project website wit illustrations for each individual question of the survey

bark-beetles

display_name: bark-beetles

compound-events

display_name: compound-events

drought

display_name: drought

extreme-events

display_name: extreme-events

forest-fire

display_name: forest-fire

natural-hazards

display_name: natural-hazards

protection-forest

display_name: protection-forest

protective-effect

display_name: protective-effect

en: Experts survey protective forest and combined extremes in Val Mesolcina


global-cryosphere-watch-data-survey

maintainer: Mathias Bavay

title_for_slug: global-cryosphere-watch-data-survey

en: data-usage data-exchange data-portal gcw automatic-publication

en: Data interoperability survey

en: Third party data usage

en: Two surveys on the topic of data usage where conducted for the Global Cryosphere Watch data portal. The first one focused on the data provider point of view while the second one focused on the data user point of view. 37 data providers (ie institutions) worldwide provided their answers for the first survey (from fall 2017 until summer 2018) while 54 users (contacted through various mailing list such as the Cryolist) answered the questions on their third party data usage (fall 2019 until January 2020).

automatic-publication

display_name: automatic-publication

data-exchange

display_name: data-exchange

data-portal

display_name: data-portal

data-usage

display_name: data-usage

gcw

display_name: gcw

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: Global Cryosphere Watch data survey


en: water-potential root-distribution water-use-strategy stomatal-stringency anisohydric

en: The data file contains the data shown in the Figures 2-7 in Walthert et al. (2024).

en:
This dataset represents the data shown in the Figures 2 - 7 of Walthert et al. (2024): Coordination between degree of isohydricity and depth of root water uptake in temperate tree species. Science of the Total Environment (https://doi.org/10.1016/j.scitotenv.2024.174346). A detailed methodical description of the data can be found in the Methods section of the paper.

Abstract
In an increasingly dry environment, it is crucial to understand how tree species use soil water and cope with drought. However, there is still a knowledge gap regarding the relationships between species-specific stomatal behaviour, spatial root distribution, and root water uptake (RWU) dynamics.
Our study aimed to investigate above- and below-ground aspects of water use during soil drying periods in four temperate tree species that differ in stomatal behaviour: two isohydric tracheid-bearing conifers, Scots pine and Norway spruce, and two more anisohydric deciduous species, the diffuse-porous European beech, and the ring-porous Downy oak. From 2015 to 2020, soil-tree-atmosphere-continuum parameters were measured for each species in monospecific forests where trees had no access to groundwater. The hourly time series included data on air temperature, vapour pressure deficit, soil water potential, soil hydraulic conductivity, and RWU to a depth of 2 m. Analysis of drought responses included data on stem radius, leaf water potential, estimated osmotically active compounds, and drought damage.
Our study reveals an inherent coordination between stomatal regulation, fine root distribution and water uptake. Compared to conifers, the more anisohydric water use of oak and beech was associated with less strict stomatal closure, greater investment in deep roots, four times higher maximum RWU, a shift of RWU to deeper soil layers as the topsoil dried, and a more pronounced soil drying below 1 m depth. Soil hydraulic conductivity started to limit RWU when values fell below 10-3 to 10-5 cm/d, depending on the soil. As drought progressed, oak and beech may also have benefited from their leaf osmoregulatory capacity, but at the cost of xylem embolism with around 50% loss of hydraulic conductivity when soil water potential dropped below -1.25 MPa. Consideration of species-specific water use is crucial for forest management and vegetation modelling to improve forest resilience to drought.

anisohydric

display_name: anisohydric

root-distribution

display_name: root-distribution

stomatal-stringency

display_name: stomatal-stringency

water-potential

display_name: water-potential

water-use-strategy

display_name: water-use-strategy

en: Water-use strategies of temperate tree species


maintainer: Esther R. Frei

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

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

en: GTREE Website

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

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

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

climate-change

display_name: climate-change

forest

display_name: forest

g-tree

display_name: g-tree

larix-decidua

display_name: larix-decidua

picea-abies

display_name: picea-abies

seedling-recruitment

display_name: seedling-recruitment

stillberg

display_name: stillberg

swiss-alps

display_name: swiss-alps

treeline

display_name: treeline

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


en: meteorological-data climate europe mountains climate-indicators forest-fire climate-change european-alps indicator-values fire

en: Data access

en: This dataset contains the Fire Weather Index for Hydrological Bavaria from 1980 - 2099 as stated in the paper "Climate change impacts on regional fire weather in heterogeneous landscapes of Central Europe" published in Natural Hazards and Earth System Sciens (NHESS) 2023. The dataset contains daily Fire Weather Index values for all 50 members (subfolders of the dataset) of the CRCM5-LE (11 km spatial resolution) from 1980 to 2099 over the domain of Hydrological Bavaria. Please cite this dataset as the publication: Miller, J., Böhnisch, A., Ludwig, R., & Brunner, M. I. (2023). Climate change impacts on regional fire weather in heterogeneous landscapes of Central Europe. Natural Hazards and Earth System Sciences Discussions, 1-25. doi: 10.5194/nhess-2023-51.

climate

display_name: climate

climate-indicators

display_name: climate-indicators

europe

display_name: europe

european-alps

display_name: european-alps

fire

display_name: fire

forest-fire

display_name: forest-fire

indicator-values

display_name: indicator-values

meteorological-data

display_name: meteorological-data

mountains

display_name: mountains

en: Fire Weather Index for Hydrological Bavaria from 1980-2099 derived from the 50 member CRCM5-LE


en: reliability avalanche-mapping avalanche avalanche-size-estimation remote-sensing

en: AvalancheReliabilityData

en: The .zip contains all data used to conduct the reliability study as well as the results, a detailed description of the included files is contained in the included pdf. All geospatial data is provided in the Swiss Coordinate System: CH1903+ / LV95 (EPSG 2056). For any use of the data the proper citation provided is obligatory!

mimetype: application/x-zip-compressed

en:
This dataset contains the data used and collected to investigate the reliability of avalanche size estimates and avalanche outlines (see related publications). Namely this is

 Avalanche size estimates

* the 10 pictures depicting avalanches
* the survey results from the 170 participants

 Avalanche outlines mapped from oblique photographs
* the pictures of the six avalanches
* the avalanche outlines mapped by the 10 participants
* the reference mapped from the georeferenced photographs

 Avalanche outlines from remotely-sensed imagery
* the orthophotos in the four different resolutions (2m, 1m, 50cm, 25cm)
* the area of interest (AoI) delimiting the research area
* the avalanche mappings in each resolution for all five participants
* the shade masks used to separate illuminated and shaded areas

The acquisition of the orthophotos has been partially supported by the Swiss National Science Foundation (SNF; Grant N°200021_172800).

avalanche

display_name: avalanche

avalanche-mapping

display_name: avalanche-mapping

avalanche-size-estimation

display_name: avalanche-size-estimation

reliability

display_name: reliability

remote-sensing

display_name: remote-sensing

educ

en: Education, culture and sport

en: Data reliability study: avalanche size estimates and outlines


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

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

media_type: image/tiff

format: TIFF

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

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

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

force

display_name: force

raster-processing

display_name: raster-processing

switzerland

display_name: switzerland

time-series

display_name: time-series

envidat

en: EnviDat: the Environmental Data Portal

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

en: Sentinel-2 time series of Switzerland


en: spermatophytes vert01 vertebrates environmental-dna freshwater edna switzerland sper01

en:
Catchment-based sampling of river eDNA integrates terrestrial and aquatic biodiversity of alpine landscapes

From 22-Jun-2020 to 26-Jun-2020, we sampled five sites comprising one low, two intermediate and two high-elevation sites per catchment (Fig. 1a). We visited two catchments per day—one in the morning and one in the afternoon—for a total of ten catchments. All samples per catchment were collected within a maximum four-hour period by three groups of samplers. The intermediate and high-elevation sites were situated along two tributaries leading into the low-elevation site of the river. We used three filters for each relative elevation class and filtered 60 L per relative elevation class. We sampled 30 L per tributary for a combined volume of 60 L at the intermediate and high-elevation sites. In total, 180 L were sampled in total per catchment. A filtration device composed of either the Athena® peristaltic pump (Proactive Environmental Products LLC; 1 L/min nominal flow) or the Subspace® underwater peristaltic pump (Subspace Pictures; 1 L/min nominal flow), combined with a VigiDNA® 0.2 µM cross-flow filtration capsule (VigiDNA, SPYGEN) was used in order to filter a large water volume. We used a finer mesh than the recommended VigiDNA® 0.45 µM cross-flow filtration capsule to maximise the capture of biological material since mountain water does not transport high quantities of sediments. For each filtration capsule, we used disposable sterile tubing. At the end of each filtration, we emptied the water inside the capsules, replaced it with 80 ml of CL1 conservation buffer (SPYGEN), and stored it at room temperature. We followed a strict contamination control protocol in both field and laboratory stages (Goldberg et al. 2016; Valentini et al. 2016). Each water sample was processed using disposable gloves and single-use filtration equipment. We used two primer sets targeting vertebrates (Vert01, forward: − TTAGATACCCCACTATGC, reverse: − TAGAACAGGCTCCTCTAG, mean marker length: 97 bp) and spermatophytes (g-h/Sper01, forward: − GGGCAATCCTGAGCCAA, reverse: CCATTGAGTCTCTGCACCTATC, mean marker length: 48 bp). Though both primers are relatively broad with low species-level resolution, we selected them as the goal was to minimise cost and effort and maximise the identification of a broad range of taxa which can represent the species assemblages of the region. Libraries were prepared with ligation using the MetaFast protocol (Fasteris).

edna

display_name: edna

environmental-dna

display_name: environmental-dna

freshwater

display_name: freshwater

spermatophytes

display_name: spermatophytes

vertebrates

display_name: vertebrates

en: Environmental DNA Freshwater Switzerland VaudCatchment Modipl 2020


en: ecosystem-functions microbial-respiration beta-diversity soil-respiration microbial-biodiversity

en: Bacterial amplicon sequencing

en: Bacterial ASVs and taxonomic tables

en: Microbial functions and metadata

en: Extracellular enzyme activities, respiration and sites metadata

en: Fungal amplicon sequencing

en: Fungal ASVs and taxonomic tables

en: Structural and functional changes of microbial communities in ephemeral stream sediments and adjacent soils between was assessed in an artificial newly created catchment at 3 and 13 years after catchment construction. Bacterial and fungal communities, respiration and extracellular enzyme activities were assessed.

beta-diversity

display_name: beta-diversity

ecosystem-functions

display_name: ecosystem-functions

microbial-biodiversity

display_name: microbial-biodiversity

microbial-respiration

display_name: microbial-respiration

soil-respiration

display_name: soil-respiration

en: Chicken Creek 10 years


maintainer: Zhao

en: land-dynamics austin urban-land-use driving-factors planning

en: Potential driving factors of urban transformations of Austin over 25 years

en: In this study, the Austin metropolitan area, Texas, U.S., one of the fastest urban transformations and transformations regions, is selected to test the hypothesis that spatial planning and policies are important factors of urban transformations. Despite ample previous work in understanding the interactions between human and urban form transformation at specific areas, the actual interventions and outcomes of planning and policies (e.g., ‘smart growth’) on urban forms have been poorly measured. In this study, the potential influencing factors of urban transformations of Austin over 25 years were selected and collected.

austin

display_name: austin

driving-factors

display_name: driving-factors

land-dynamics

display_name: land-dynamics

planning

display_name: planning

urban-land-use

display_name: urban-land-use

educ

en: Education, culture and sport


en: 18o stable-water-isotope snow metamorphism advection

en: Read-me

en: A short description of the data contained in the zipped-file. Recommended for reading before downloading the dataset.

en: Data files and read-me

en: raw data and micro-CT data available from contacts

en:
Stable water isotopes (δ18O) obtained from snow and ice samples of polar regions are used to reconstruct past climate variability, but heat and mass transport processes can affect the isotopic composition. Here we present an experimental study on the effect on the snow isotopic composition by airflow through a snow pack in controlled laboratory conditions. The influence of isothermal and controlled temperature gradient conditions on the δ18O content in the snow and interstitial water vapor is elucidated. The observed disequilibrium between snow and vapor isotopes led to exchange of isotopes between snow and vapor under non-equilibrium processes, significantly changing the δ18O content of the snow. The type of metamorphism of the snow had a significant influence on this process.
Ebner, P. P., Steen-Larsen, H. C., Stenni, B., Schneebeli, M., and Steinfeld, A.: Experimental observation of transient δ18O interaction between snow and advective airflow under various temperature gradient conditions, The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-16, accepted, 2017.

advection

display_name: advection

metamorphism

display_name: metamorphism

snow

display_name: snow

stable-water-isotope

display_name: stable-water-isotope

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: Experiments on stable water isotopes,  snow metamorphism, and advection


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

en: Snowdepth maps

format: GeoTIFF

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

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

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

The work is published in:

avalanches

display_name: avalanches

davos

display_name: davos

dischma

display_name: dischma

photogrammetry

display_name: photogrammetry

snow-depth-distribution

display_name: snow-depth-distribution

snow-hydrology

display_name: snow-hydrology

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


maintainer: Frank Techel

en: avalanche-accident-statistics avalanche-fatalities avalanche-statistics avalanche-accident long-term-trends

en: Data avalanche fatalities 1969/1970 - 2014/2015

en: The data comprises avalanche fatality data from the Alpine regions in the seven countries Austria, France, Germany, Italy, Liechtenstein, Slovenia, and Switzerland.

en:
During the last 45 years, about 100 people lost their lives in avalanches in the European Alps each year. Avalanche fatalities in settlements and on transportation corridors have considerably decreased since the 1970s. In contrast, the number of avalanche fatalities during recreational activities away from avalanche-secured terrain doubled between the 1960s and 1980s and has remained relatively stable since, despite a continuing strong increase in winter backcountry recreational activities.

Data complementing Figure 2 in:
_"Avalanche fatalities in the European Alps: long-term trends and statistics"_, by Techel, F., Jarry, F., Kronthaler, G., Mitterer, S., Nairz, P., Pavšek, M., Valt, M., and Darms, G.

Data description:
please refer to section 2 (Data and Methods) in the mentioned publication

avalanche-accident

display_name: avalanche-accident

avalanche-accident-statistics

display_name: avalanche-accident-statistics

avalanche-fatalities

display_name: avalanche-fatalities

avalanche-statistics

display_name: avalanche-statistics

long-term-trends

display_name: long-term-trends

en: Avalanche fatalities in the European Alps (1969/1970 - 2014/2015)